Optimization of the Internal Logistics Kinnarps Production AB › smash › get › diva2:24471 ›...

Postal Address: Visiting Address: Telephone: Box 1026 Gjuterigatan 5 036-10 10 00 551 11 Jönköping

Optimization of the Internal Logistics Served by an AGV System

A case study at Kinnarps Production AB

Grigor Mishev

Omid Shahidi

THESIS WORK 2008

Industrial Engineering and Management

Postal Address: Visiting Address: Telephone: Box 1026 Gjuterigatan 5 036-10 10 00 551 11 Jönköping

Optimization of the Internal Logistics Served by an AGV System

A case study at Kinnarps Production AB

Grigor Mishev

Omid Shahidi

This thesis work is performed at Jönköping Institute of Technology within the subject area of Industrial Engineering and Management. The work is part of the university’s two-year master’s degree. The authors are responsible for the given opinions, conclusions and results. Supervisor: Mikael Thulin Points: 30 points (D-level) Datum: 2008-05-28 Archive number:

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Abstract

Logistics plays a crucial role in companies’ ability to sustain competitive on the market. Time is regarded as one of the important metric in terms of logistics, since time influences the lead time of the products, which results in the vital advantage of speed. The recent business environment forces Kinnarps Production AB, a manufacturer of furniture, to optimize their inventory material flow in a way of increasing their capability and capacity to load more distribution containers with goods daily. The present circumstances of the material flow are associated with number of negative factors. An automated guided vehicle (AGV) system is operating and handling the distribution of goods. Wrong sequence of activities, long transportations, high transportation delays, waiting time, varying numbers of AGVs, wastes, bottlenecks are among the important issues to be considered.

The purpose of this thesis is to analyze the material flow in Kinnarps Production AB’s inventory seven and to improve the AGV system activities within.

Concepts associated with identifying waste were used by separating value-adding activities from the non-value added ones. The usage of ideal reference systems techniques were the base for identifying problems. Concrete literature regarding AGV’s design system was applied in establishing different statements about the material flow and identification of problems. Different methods and techniques were used in approaching the research, but most above others is the case study approach. Numerous amounts of secondary data have been employed in verifying the collection of other data carried out by primary data collection as well as verifying the qualitative and quantitative analysis.

A mapping of the current system was established with respect to orders, times and distances. Numbers of congestion points were identified, non-value adding activities were eliminated or decreased, rearrangement of sequencing of different activities was considered and different pallets arrangement system was established. A stochastic model was used in identifying the AGV’s time variables in the system and estimating appropriate amount of AGVs within the inventory.

All the findings from different calculations and estimations were associated with Kinnarps Production’s future increased demand. Different issues and considerations were analyzed and appropriate suggestions were given for elimination of the negative factors in the inventory or their decrease. Among the most influencing results was the proper amount of AGVs in the inventory area, pallets rearrangement, different stocking policy, rearrangement in the sequence of activities, increase of the buffer for pallets used for loading containers, new conveyor system for the incoming goods in the inventory, transportation layout changes resulting in blocking free routines, separate control system, decrease of the effect of stoppages. All these findings are considered to optimize the material flow and increase the system capacity contributing to an increase of the daily containers outgoing from Kinnarps Production AB.

Keywords: AGV system, Improvement, Lean Principles, Logistics, Material flow, Material handling, Orders, Pallets, Transportation, Value adding activity, Waiting time

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Acknowledgement

This thesis of 30 points is a concluding part of a master program in Industrial Engineering and Management, which was carried out in Kinnarps Production AB. The project was carried out in the field of Logistics and gave the authors of this thesis a good picture of the processes in the field and Kinnarps as a company.

We would like to express our deep gratitude to Kinnarps Production AB and all the people being involved in our research. The warmth and interest about our research from Kinnarps Production’s side gave us a confidence and stimulation in our work. The devotion and the response from the people being involved in the processes we investigated helped us in carrying out our work in a good structured manner, with a clear picture about the field of research. We would like to express our gratitude to Leif Ericson providing us the possibility to carry out this research. We would like deeply to greet Johan Hassel who was our supervisor in the company, for his guidance and valuable help. The outcome of this research wouldn’t be possible without the deep support of Kinnarps Production AB.

We would like to acknowledge Mikael Thulin, our supervisor in Jönköping University, for his guidance and help during our work. The discussions and advices given by him helped us in carrying out this research. We are also grateful to the department of Industrial Engineering and Management at Jönköping University for helping us.

Jönköping may 2008

Omid Shahidi and Grigor Mishev

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List of Abbreviations

• AGV Automated Guided Vehicle

• A, B, C and D Classification of pallet places according to their dimensions

• O Places for oversized products

• P Loading places

• LF Loading gate

• E Empty pallets places

• R, L Right and left position at the loading gate

• PL Automatic storage place

• 20-30 Numeration of the available lines among the rack system

• L27 Inventory place at line 27

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Table of Content

1 Introduction ............................................................................. 1

1.1 BACKGROUND PROBLEM........................................................................................................2 1.2 PURPOSE AND GOALS.............................................................................................................3 1.3 LIMITATIONS ..........................................................................................................................4 1.4 OUTLINE ................................................................................................................................4

2 Theoretical background .......................................................... 5

2.1 LOGISTICS FLEXIBILITY..........................................................................................................5 2.2 MATERIAL FLOW ....................................................................................................................6 2.3 MATERIAL HANDLING ............................................................................................................7 2.4 LEAN THINKING AND CONCEPTS.............................................................................................8

2.4.1 Waste Categories..............................................................................................................8 2.5 PROCESS ANALYSIS AND MAPPING.........................................................................................9

2.5.1 Mapping guidelines and tools......................................................................................... 11 2.6 AGV SYSTEM....................................................................................................................... 15

2.6.1 AGV design systems........................................................................................................ 16 2.6.2 Methods for estimating the required number of AGVS................................................... 18 2.6.3 Simulation versus Analysis ............................................................................................. 20

3 Methodology........................................................................... 21

3.1 RESEARCH PROCESS............................................................................................................. 21 3.2 CASE STUDY......................................................................................................................... 22

3.2.1 Explanatory (analytical) survey...................................................................................... 23 3.2.2 Longitudinal studies (cohort/panel studies).................................................................... 23

3.3 DATA ANALYSIS ................................................................................................................... 24 3.3.1 Qualitative and quantitative analysis ............................................................................. 25 3.3.2 Primary data................................................................................................................... 26 3.3.3 Secondary data ............................................................................................................... 29

3.4 TRIANGULATION .................................................................................................................. 29 3.5 VALIDITY AND RELIABILITY ................................................................................................. 30

4 Results..................................................................................... 31

4.1 CURRENT STATE OF THE INVENTORY.................................................................................... 31 4.1.1 Transportation activities................................................................................................. 31 4.1.2 Pallets and rack system .................................................................................................. 33 4.1.3 Loading area................................................................................................................... 34 4.1.4 Empty pallets area .......................................................................................................... 36 4.1.5 Goods arriving in the inventory...................................................................................... 36 4.1.6 Distribution of goods from inventory 7 to the loading area ........................................... 37 4.1.7 Stocking policy................................................................................................................ 37 4.1.8 AGV routing, lines and transportation control ............................................................... 38 4.1.9 Planning ......................................................................................................................... 40 4.1.10 Stoppages and Errors ................................................................................................ 40 4.1.11 Process mapping activity ........................................................................................... 41 4.1.12 Waiting time and transportation delay ...................................................................... 42

4.2 COMPARISON BETWEEN THE PRESENT AND PAST STATE OF THE INVENTORY ........................ 44

5 Future state system suggestion............................................ 48

5.1.1 Transportation activities................................................................................................. 51

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5.1.2 Loading area................................................................................................................... 52 5.1.3 Empty pallets area .......................................................................................................... 53 5.1.4 Goods arriving in the inventory...................................................................................... 53 5.1.5 Distribution of goods from inventory 7 to the loading area ........................................... 53 5.1.6 Stocking policy................................................................................................................ 54 5.1.7 AGV routing, lines and transportation control ............................................................... 54 5.1.8 Future state demands...................................................................................................... 58 5.1.9 Process mapping activity ................................................................................................ 60

6 Analysis ................................................................................... 62

6.1 TRANSPORTATIONS AND DISTRIBUTION OF GOODS............................................................... 62 6.2 PALLETS AND RACK SYSTEM................................................................................................ 64 6.3 TRANSPORTATION LAYOUT.................................................................................................. 65

6.3.1 Transportation control and routing ................................................................................ 66 6.3.2 Number of AGVs............................................................................................................. 67

6.4 LOADING AREA .................................................................................................................... 69 6.5 EMPTY PALLETS AREA.......................................................................................................... 72 6.6 STOCKING POLICY................................................................................................................ 72

6.6.1 Stoppages and Errors ..................................................................................................... 75 6.7 PROCESS MAPPING ACTIVITY, TRANSPORTATION DELAY AND WAITING TIME....................... 76

7 Conclusions ............................................................................ 83

7.1 DISCUSSION.......................................................................................................................... 84 7.2 FUTURE RESEARCH............................................................................................................... 84 7.3 CRITICISM OF THE THESIS..................................................................................................... 84

8 References .............................................................................. 86

9 Appendix List ......................................................................... 90

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List of Figures

FIGURE 2-1. EVALUATION METHODOLOGY AIMING TO IMPROVEMENTS IN THE MATERIAL FLOW (JOHANSSON

AND ÖJMERTZ, 1996)...................................................................................................................... 13 FIGURE 2-2. PROCEDURE FOR EVALUATING THE MATERIALS FLOW EFFICIENCY OF A STUDIED SYSTEM WITH

RESPECT TO A REFERENCE ONE, (ÖJMERTZ, 1998)........................................................................... 14 FIGURE 2-3. FACTORS INFLUENCING THE REQUIRED NUMBER OF AGVS (MANTEL AND LANDEWEERD (1995);

UJVARI AND HILMOLA, (2006)).......................................................................................................16 FIGURE 2-4. PART WAITING TIME AND VEHICLE TRAVEL TIME, (KOO, JANG AND SUH, 2005)....................... 18 FIGURE 2-5. FLEET SIZING ESTIMATION PROCEDURE, (KOO, JANG AND SUH, 2005)................................... 19 FIGURE 4-1. LAYOUT OF INVENTORY 7 ......................................................................................................32 FIGURE 4-2. AVERAGE ORDERS PER HOUR FROM THE LOADING AREA......................................................... 37 FIGURE 4-3. A MATERIAL FLOW MAP BETWEEN STATIONS, WITH RESPECT TO TRANSPORTATION ACTIVITIES

CARRIED OUT BY THE AGV SYSTEM.................................................................................................. 41 FIGURE 4-4. PERCENTAGE OF THE WAITING TIME OF ORDERS IN THE RACK SYSTEM CORRESPONDING TO THE

LINES WITHIN IT............................................................................................................................... 42 FIGURE 4-5. AVERAGE WAITING TIME PER ORDER...................................................................................... 43 FIGURE 4-6. COMPARISON OF VARIABLES FROM THE PAST AND PRESENT SYSTEM........................................ 45 FIGURE 4-7. IMPROVEMENTS RANGE OF THE VARIABLES, IN TERMS OF HIGHER ORDER OUTPUT................... 45 FIGURE 4-8. CONNECTION OF THE WAITING TIME ERRORS WITH RESPECT TO AREA IN THE INVENTORY......... 46 FIGURE 4-9. CONNECTION OF THE BUMPER ERRORS WITH RESPECT TO AREA IN THE INVENTORY................. 47 FIGURE 5-1. CAUSE AND EFFECT DIAGRAM FOR KINNARPS’ INVENTORY..................................................... 48 FIGURE 5-2. FUTURE STATE LAYOUT OF KINNARPS’ INVENTORY................................................................. 50 FIGURE 5-3. AVERAGE ORDERS PER HOUR FROM THE RACK SYSTEM TO THE LOADING AREA, FUTURE DEMAND

...................................................................................................................................................... 59 FIGURE 5-4. A MATERIAL FLOW MAP BETWEEN STATIONS, WITH RESPECT TO TRANSPORTATION ACTIVITIES

CARRIED OUT BY THE AGV SYSTEM IN THE FUTURE STATE MODEL..................................................... 61 FIGURE 6-1. COMPARISON OF VARIABLES FROM THE PRESENT AND THE PAST STATE, WHEN THE EMPTY

PALLETS ARE NOT SERVED BY THE AGV SYSTEM................................................................................ 70 FIGURE 6-2. IMPROVEMENTS RANGE OF THE VARIABLES, IN TERM OF HIGHER OUTPUT ORDER, WHEN THE

EMPTY PALLETS ARE REMOVED BY THE AGV SYSTEM IN COMPARISON TO 2007.................................. 71 FIGURE 6-3. IMPROVEMENTS RANGE OF VARIABLES, IN TERMS OF HIGHER OUTPUT ORDER, WHEN THE EMPTY

PALLETS ARE REMOVED BY THE AGV SYSTEM (2008)........................................................................ 71 FIGURE 6-4. COMPARISON OF THE PRESENT AND THE FUTURE MATERIAL FLOW MAP.................................. 80

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List of Tables

TABLE 4-1. AVAILABLE PLACES WITHIN THE INVENTORY ACCORDING TO THEIR CLASSIFICATION................. 33 TABLE 4-2. AVAILABLE PLACES FOR LOADING AT THE CORRESPONDING GATES FOR LOADING..................... 34 TABLE 4-3. AVERAGE PALLETS ORDERS FROM THE DIFFERENT LINES IN THE RACK SYSTEM......................... 38 TABLE 4-4. AVERAGE DELAY TIME IN ACCORDANCE TO THE POSITIONS IN THE INVENTORY.......................... 44 TABLE 5-1. PALLET PLACES AVAILABLE IN THE NEW SYSTEM...................................................................... 52 TABLE 5-2. NUMBER OF LOADING PLACES WITH RESPECT TO DIFFERENT LOADING GATES AND POSSIBLE

ALTERNATIVE METHODS FOR RETRIEVING EMPTY PALLETS................................................................. 53 TABLE 5-3. AVERAGE OVERALL NUMBER OF ORDERS FROM THE INVENTORY SPREAD AMONG THE RACK

SYSTEM........................................................................................................................................... 54 TABLE 6-1. AVERAGE TIME NECESSARY FOR DELIVERING GOODS FROM THE DIFFERENT LINES IN THE RACK

SYSTEM TO THE LOADING AREA........................................................................................................ 73 TABLE 6-2. AVERAGE TIME NECESSARY FOR DELIVERING GOODS FROM THE DISTRIBUTION CONVEYORS TO

THE DIFFERENT LINES IN THE RACK SYSTEM...................................................................................... 74 TABLE 6-3. AVERAGE TIME NECESSARY FOR A MATERIAL HANDLING ACTIVITY RESPECTFULLY BOTH TO THE

RACK SYSTEM AND THE LOADING AREA. ............................................................................................ 74

Introduction

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1 Introduction

This chapter defines a background description in the area of the research, Kinnarps Production AB and its problem area and the purpose and goal of this thesis.

Logistics is regarded as the process that combines all the activities that are associated with the movement and positioning of the inventory to fulfill the customer requirements (Bowersox, 1999). Logistic includes plenty of functions among which the transportation, warehousing, and inventory management are considered important. The definitions of logistics involve the management of the inventory, with respect to materials and information (Goldsby, 2005; Harrison, 2002). Today companies are facing a severe competition in the recent global market and this is expressed in shorter product life cycle, increased expectations of the customers, service level, customized products and various services (Simchi-Levi, 2003).

From the market value perspective the logistic effectiveness is measured by the customer satisfaction. The successful evaluation of the logistics is measured by the service performance goals with respect to on-time deliveries, availability and accessibility of the delivery (Bowersox, 1999). In today’s market, which is becoming more customer-driven the measure of the customer satisfaction is more orienting towards the impression of the external services and the customer value (Simchi-Levi, 2003).

Logistics is mainly oriented in managing inventories and the point where the products are sold, by having the right inventory with the right amount of goods results in satisfying the final customers (Goldsby, 2005).The external measurements of a successful logistical integration are significantly related to the internal integration of the processes (Bowersox, 1999), by that it is significant to integrate the operations that are associated with reduction of duplication, waste and redundancy.

According to Goldsby (2005) transportation is an important aspect of activities in the logistics with respect to fast and efficient transportations. Big consideration is emphasized on the time the goods spend in transition stage. Time plays an important issue in logistics which is a subject to the lead time of the deliveries of goods. The management of the materials is carried out by the inbound and outbound logistics, where the inbound logistics deals with the deliveries materials necessary for the production and the outbound logistics is managing with the physical distribution of finished goods (Harrison, 2002).

The material flow is an essential part of the logistics and it tracks the flow from the source to the end customer (Harrison, 2002). According to Wagner (2006) material flow management corresponds to the analysis and the optimization of materials of products and services, where the internal flow of material refers to the flow of materials within an organization. All the negative aspects within the material flow result in building up inventories and slow response to the customers (Harrison, 2002).

Introduction

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Material handling activities are important aspect of the logistics and play a major role (Coyle et al., 1996). The material handling activities should be looked upon as value-adding activities, but deeper analysis of the material flow presents the non-value added ones within the overall flow (Öjmertz, 1998; Liker, 2004). Material handling corresponds to the context of transportation. All the factors within this activity should be considered and investigate those parts associated with wasted time and transportations. Optimizing the motions and activities in a way of achieving optimal value-adding activity is ultimate goal (Shingo, 1981).

Important category according to Tompkins (1991) in the material handling methods is the consideration of the material handling equipment in a way of optimizing and utilizing the automated guided vehicles (AGV) in achieving smoother flow in the material handling. In order to improve the effectiveness of the material handling in more value-adding manner there is a need for analysis which can present the understanding of the materials handling contribution to the value-adding aspect of the material flow. Activity analysis has been considered as important for understanding the non-value activities that create unnecessary time, considered as waste (Shingo, 1981; Monden, 1998).

The design of an AGV transportation system is regarded as a complex one, since it involves many parameters influencing the material flow and handling activities. By effective analysis and optimization techniques, which considers all the variables affecting the flow a fast smooth undisturbed flow, able to serve the needed demand of orders, can be established (Koo, Jang and Suh, 2005; Ujvari and Hilmola, 2006).

1.1 Background Problem This thesis work is carried out by two students from Jönköping University in the field of production development and management. The thesis is a master level work and is an examination for completing the program of Industrial Engineering and Management. The project is carried out in accordance with Kinnarps Production AB, which is an office furniture manufacturer. The company has its market mainly in Europe and is considered to be the third furniture manufacturer in Europe. There are three factories that are fulfilling the production demand of the company located in Sweden; Kinnarp, Skillingaryd and Jönköping. Continual improvement of our production processes is one of the main foundations of the company’s strategy. Speeding up the production and shortening delivery lead times is not the only issue Kinnarps in aiming for, but also to improve the quality of the products. The investigated manufacturing plant that is accounted in this work is situated in Kinnarp. The recent business environment demands an increase of the company’s capabilities and capacity. There is an increase of the orders and the products needed to be produced, due to the increase of sales and the desire of the company to reach higher goals in a long run being able to sustain competitive in the ever changing market. All the stated issues are driving Kinnarps Production AB to look after constant improvements. The distribution centre for goods within the territory of Sweden and other neighboring countries in Europe is located in inventory 7, which serves as an inventory and as a loading area.

Introduction

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The focus of this work is oriented in analyzing and investigating the flow of goods within inventory 7. The current condition of the inventory measures that the number of containers loaded daily is less than it is expected to be and due to the constant increase in sales there is a necessity for improvements. The improvements are aiming to increase the loading capabilities in inventory 7 by speeding the material flow. In order to analyze the flow of materials within the inventory 7 and the dispatching area there are several aspects regarded to be important for the current condition of the area. They can be listed as follows:

• The AGV system, which is the only material handling tool used for making deliveries of materials and goods where they are necessary. The AGV’s system capacity, speed and the time during which they are not bringing any value for the company due to number of factors is a subject of analysis. These factors are associated with different breakdowns, unnecessary movements and waiting time;

• The pallets being used for distributing and storing goods within the factory. The variance of the pallets that are used and their utilization with respect to the number of stored goods on them. The improper utilization of the pallets increases the number of necessary transportations of goods;

• The inventory itself, in accordance to its layout and the material distribution. The material distribution can be considered with respect to the way the goods are delivered and stored within the inventory, and the paths on which the AGVs are making all the necessary deliveries;

• The planning of the orders in the production and the difficulties arising from the long term planning with respect to matching of the order’s batches in the inventory 7. That results in the inability to have smooth planning abilities in loading the containers.

1.2 Purpose and goals

The main purpose of this thesis is to analyze the material flow in Kinnarps Production AB’s inventory 7 and to improve the AGV system activities within. This analysis will be carried out by considering all the negative factors that are influencing the flow, which results in the slow distribution of the goods.

The main goal for this thesis is to identify the negative factors that are preventing the ability to load distribution containers at the desirable level the company is aiming for, and propose solutions for their reduction or elimination. In order to fulfill this goal number of areas will be investigated:

• Mapping the current physical transportation of finished goods within the investigated inventory 7;

• Investigating the material flow with respect to times, number of transportations, the timing of goods being delivered, the number of automated guided vehicles being involved in the process (AGVs), which perform all the transportation of goods;

• Comparing the present state of the physical flow of goods to other conditions within the inventory, where certain parameters are changed;

Introduction

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• Presenting the negative factors that are preventing the proper flow within the inventory;

• Proposing solutions and suggestions aiming to improving the present state by eliminating and decreasing all the impediments of the material flow.

The follow-up of all the steps being identified is fulfilling the attempt to create a holistic view of the inventory and the physical distribution of goods. According to the picture being created there is a convenient possibility to identify all the draw backs in the system. And by that mean managing to give an opportunity for improvements and suitable solutions that will reach the goal of the thesis.

1.3 Limitations This thesis work will be narrowed in a way that some of the problem areas that have been identified will not be considered. The pallets will be investigated with respect to their type and position in inventory 7. The planning will not be investigated and taken into consideration in a way how it influences the distribution of materials. The data generation during that research will cover certain periods of time in achieving the necessary picture. By sampling the data hourly during a day, some of the variables would not be registered as they are excluded, but affecting the system conditions in the sample. In this way certain connections between the variables would be lost and presumably certain data will not be used. The suggested improvements will not be considered from a financial perspective and the eventual cost associated with their implementation. All the suggestions and proposals are conceptual and arise from the analysis and their suitability in achieving the overall goal.

1.4 Outline

This thesis is consisted of six main chapters. In their sequential order there is a theoretical part, which refers to the area of this research and presents techniques and tools for understanding and analyzing the topic. The methodology part includes the methods according to which the research has been carried out, as well as the different techniques for data collection being used. The result section presents the findings of the investigation with respect to mapping of the current system conditions. The future state system suggestion model presents the conceptual ideas and suggestions for improvements being given. The analytical section is aiming to analyze the findings from the current system and discusses the implementation of the new suggestions being given. Special emphasize has been given on the effect of the outcome from the suggestions being given. The concluding part section of the thesis is aiming to point out the importance of the findings in the research being carried out. It points out the contribution of the suggestions being given, suggestions for future research and critics associated with the work done.

Theoretical background

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2 Theoretical background

This chapter includes theoretical background referring to the research, logistic concepts, AGVs design systems, analyzing techniques and tools according to which the investigation proceeds.

2.1 Logistics flexibility

Logistics is oriented in managing inventory, as companies are aiming to selling goods and respectfully possessing products to fulfill this demand. Often it is emphasized on the importance of logistics as the ability to deliver the right products on the right place at the right needed time and the right amount needed for the right cost (Goldsby, 2005).

According to Zhang (2005) and Harrison (2002) for an enterprise to stay strongly competitive and to succeed in an uncertain environment, firms must respond to changing customer needs.

Because flexibility is multidimensional, complex and hard-to-capture concept, confusion and uncertainty are inhibited in an effective implementation (Sethi, 1990; Upton, 1995). The distinctions between internal competences that the organisations control but customers cannot see, and external competences that customers in other hand see and value are often unclear and makes the understanding of logistic flexibility difficult (Cunningham, 1996). The physical distribution flexibility is a capability that the customers value, because they see and feel the direct impact. On the other hand the physical supply flexibility is not visible to the customers since it is buffered within the company’s inventory and production process.

According to van Hoek (2001) logistic flexibility enables a higher customer service by synchronizing the delivery of the finished goods with customer demands. This is accomplished by accurate planning and controlling the flow of the goods and the related information. By eliminating non-added-value activities and reducing response time enhancement it is possible to achieve the company’s ability to respond to market‘s changes (Fung, 1998).

According to Day (1994) logistic flexibility can be divided into four components: physical supply flexibility, purchasing flexibility, physical flexibility and demand flexibility. When talking about physical distribution flexibility it should be emphasized on the firm’s ability to adjust the inventory, warehousing, packaging and transportation of physical products in order to meet customer needs at the right time and in the correct quantity (Zhang, 2002). It involves material and information handling as well as requires quickness in activities such as packaging, warehousing and outgoing transportation. These capabilities are very important since they impact the customer directly through delivery and speed, also they are visible to customers and they experience firstly the performance of the distribution system.


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Improving customer services and satisfaction depends on both efficient material handling and fast information processing. The physical logistics flows are part of converting materials quickly to products, therefore creates value for customers. The logistics information system converts data into information so important decision can be made hence strengthen the effectiveness, efficiency and flexibility (Damen, 2001).

2.2 Material flow

Material flow is the term that describes the physical flow of materials and its path. It is a general term that refers to the process of the activities in terms of equipment, labor, different methods and equipments possessing the purpose of fulfilling specific objectives on a certain part of the flow of materials (Harrison, 2002).

Material flow within the logistic area is the physical movement of materials from sources to the final destination points as parts are important to be moved as quickly as possible from one place to another. The synchronization of the movements and their coordination results in an undisturbed flow and preventing the building up of unnecessary inventories and bottlenecks (Harrison, 2002).

The material flow is usually consisted of activities associated with material handling reaching the final customer and transportation needed in order to reach the orders placed. If the system is ideal there would be activities only regarded as perfect. Material handling and transportation are those activities that are connecting different parts of the supply chain, and these activities are commonly performed in indirect time periods, since the goods are placed in storages and buffers (Öjmertz, 1998b).

As the manufacturing companies are growing in size and complexity the material flow control have become an important issue for the system efficiency and manufacturing output (Koo, Jang and Suh, 2005). The main objective when dealing with material flow is to provide an improved understanding of the purpose of the material handling and at the same time ensure principles and certain procedures in utilizing the knowledge and evaluating and improving the performance with respect to efficiency and manufacturing output (Bowersox, 1999; Harrison, 2002).

Identifying the value and the stream of goods is followed by the focus on design, the orders and products and tracking the goods from the start point to the destination when the tasks are completed. The next step is to go beyond the traditional boundaries of jobs, careers and functions in order to create a flow by removing all the impediments. The last step is the evaluation of the work practices and tools to eliminate backflows, scrap and stoppages of any sort which prevents the stable and continuous flow (Harrison, 2002; Sekine, 1992).

Essential part of the supply chain is the physical flow, which includes all material handling activities, taking place at different areas in the system. A physical flow characterized as one that has short throughput times, low resource consumption and safe goods is necessary in achieving an efficient supply chain. The physical flow in a company must be designed in a manner in which constitutes a base of efficiency and effectiveness. There is a connection between the efficiency of one system and its effectiveness, since the first corresponds to the consumption of resources needed and the effectiveness is referring to the utility and the benefit the user is receiving from the product and the system itself (Öjmertz and Johansson, 1997).


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2.3 Material handling

Material handling term refers to the movements associated with the manipulation of the materials within a facility. The material handling is the system that combines the methods, facilities, labor and the equipment for the movement and storing of materials in order to meet certain objectives (Harrison, 2002).

The material handling is considered as the inclusive in the internal transportations, storage and the manufacturing, and material handling activities are stated as part of the material flow (Öjmertz, 1998). Material handling plays a crucial role in warehousing since the labor and the investments in the necessary equipment is reflecting in the overall logistics costs (Bowersox, 1999; Arifin and Egbelu, 2000).

Authors classify that material handling should be minimized (Bowersox, 1999; Coyle et al., 1996), and according to others it is regarded as a waste and it shouldn’t exist. In the cases when it is impossible to eliminate all the material handling activities the efficiency of the tasks within the material handling can be improved. If it is understood why the material handling activities are performed it is possible to focus the attention in eliminating of these activities or defining the necessary requirements. According to Coyle et al. (1996) there is a system principle where there are integrated as many handling activities as the outcome of a practical nature is in a coordinated system. That is further developed in a material flow principle in providing a sequence and a layout of the equipment that optimizes the material flow and at the same time simplifying the handling by reducing, eliminating or at the end combining certain unnecessary movements or equipment. Material handling can be divided into general work simplification check where the steps are included in a way of:

• Eliminating operations by considering them as necessary or potential candidates for elimination;

• The ability for combining different operations; • At the same time the sequence and the ability for rearranging the place and

the people, in a way of changing or rearranging; • Improving the details in a way of changing the method or the equipment to

be improved.

All the activities associated with creation, ordering and good services can be created as a flow, and lining up of all the essential steps needed to accomplish the needed tasks in an uniform continuous flow, with no wasted motions and interruptions or queues is reflecting in an value-adding flow of goods (Womack, 1996).

The evaluation of the material handling activities is important in understanding the physical flow and this includes the design of the activities and the handling equipment, which results in the efficiency of the system. Material handling can add value to the activities with respect to positioning, orienting and sequencing the orders. In order to affect the improvement of the effectiveness in the material handling the proper execution of the activities is important in accomplishing their tasks in a manner that fulfills the required functions in the chain. In other words it is the execution of the material handling activities by contributing with adding of value (Öjmertz, 1998b).


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2.4 Lean thinking and concepts “The right processes will produce the right result” (Liker, 2004), this sentence can describe clearly what the main objectives and aims are of lean manufacturing, within the principles used there are tools for improvement of manufacturing processes as well as that truly reflect in the lean production and ideas behind it. By trying to remove the waste as much as possible within the company you can achieve high productivity and clear product flow. The aim is to process only value adding activities, and remove the unnecessary non-value-adding ones (Monden, 1998).

Shingo (1981) and Monden (1994) emphasize on the importance of eliminating waste, regarding the importance of eliminating all the non-value adding activities and the usage enough resources in achieving the necessary activity. Considering a picking system the fulfillment of a task is a complete execution of a picking order, which is regarded as a product of a complete logistic value arising from a material handling activity. An activity regarded as a value-adding character is the one that accomplish the desired position in a desirable state in the investigated supply chain.

The understanding and the improvement of the material handling functions and their efficiency evaluation is reflecting in achieving value-adding activities. Improvements are seen and able to be engaged by separating the value-added activities from the non-value adding ones, which results in overall efficiency advance. By decreasing and eliminating the non-value activities the overall material handling activities would be improved. The union between the material handling activities and their relation to the material flow system design presents the ability to identify the functions in deep (Harrison, 2002).

The order according to which the material handling activities are carried out with respect to the position and orientation of their existence according to other existing systems is expressing the dimensions of the state of the system. The degree of disorder can be used as a measurement and as distinguishing between the value-adding and non-value adding activities (Öjmertz, 1998a).

2.4.1 Waste Categories

The main objective is to design and sustain a functional system, which possess high flexibility and is designed in a way that can be constantly improved and eliminate all the wastes along the production process, (Monden, 1998). According to J. K. Liker et al, (2004) and Hines and Rich (1997), there are also seven major non-value-adding wastes in the business or manufacturing processes. They are listed as follows:

• Overproduction; • Waiting; • Unnecessary transport or conveyance; • Over processing or incorrectly designed processing; • Excess inventory; • Unnecessary movement; • Defects.

The overproduction concept has been discussed and turned to be an important issue and overly affecting the whole process in the system. The waiting is associated with


9

the time items need in order to be handled. Unnecessary transport is due to the long distances in the factory, inappropriate layout, inefficient transport of parts and products into storage rooms and so. Incorrect processing is a big issue, which is regarding the unneeded steps necessary to process the parts. The incorrect processing lead to unnecessary motion and producing defects, and in that way waste occurs when there is a poor or inefficient design of tools and products. Excessive inventory hides problems which can be described as production imbalances, defective goods, equipment downtimes, and long setup times. The unnecessary movements are regarding the movements made by the workers, such as looking for necessary things, stocking of parts and tools like set-up times and so.

Besides the accepted wasted in the manufacturing environment, there are presented numerous of wastes in logistics as well despite the fact that there is not so much mentioned about them. The wastes in logistics are as dominant as in any other functional area of the company even though they are not as visible as in the manufacturing itself. There are several latent wastes found in logistics and they can be listed as follows (Goldsby, 2005):

• Inventory; • Transportation; • Space and facilities; • Time; • Packaging; • Administration; • Knowledge.

Transportation is the necessary activity within the logistics concept (Goldsby, 2005). A big cost consideration is associated with the transportation, but at the same time with the time goods spend in a transition and that results in order lead time and affects the variance in order cycle time. One of the goal initiatives in transportation is to minimize the average time to move goods and to decrease that average.

Time is regarded as one of the most important metric encountered in terms of logistics. The measurement is associated with measuring the lead times and with respect to deliveries ultimately on time. These aspects are important when considering the competitive advantages in terms of speed, which is crucial (Goldsby, 2005).

2.5 Process analysis and mapping

When an existing automated system is being improved one of the essential things is to recognize the whole system with respect to task analysis, which corresponds to the fact that the environment is changing due to demand and responsibilities, which is most of the time unexpected and unplanned (Sheridan and Wickens, 2000). The analysis of an existing system in terms of the processes involved is of a great importance as well as the transparency of the variables in a way of dependency and not, among them.


10

Regardless of the difficulties associated with the visualization of the weaknesses of one system there are usually techniques used related to mathematical analysis, block diagrams representation, arrow influence, flow charts, statistical analysis, timelines and so forth. The useful information is synthesized, the decision made and the one expected to be, and the control of the plant process as well as the ideas generated so far. After the analytical step being carried out the decision making process is an important step as well as the feedback of the work done so far.

The analysis should be separated from the existing real conditions or distinguished to an appropriate level in order to have a better visualization of the processes. There are number of techniques existing for task analysis, but still there is not a common accepted one in a way of making allocation of the processes. There is always existing connection between the different variables even if the processes are separated, since the parameters are rarely independent and the lever of interaction differs (Sheridan and Wickens, 2000).

The supply chain mapping is used in order to generate visibility of the processes within the supply chain. And as the visibility is being achieved it is possible to estimate the processes. The processes being considered are the actual ones that are taking place. An actual picture of the processes has been established and the investigation is focusing on. The basic principle is to track certain orders within the process, and the generated map is regarded as the picture being given for the process. The workload of demands and orders can be varying from time to time depending on different factors. The mapping is carried out in terms of recording different variables that are used for the estimation of the average of the collected data (Harrison, 2002).

According to Hines and Rich (1997) in order to fully understand the different value streams it is important to map the value adding processes. The value adding activities are giving to the final product more value than it would be. The value stream term refers to a specific part of a company that adds value to a specific product or service which is considered.

Waste removal inside companies is using different techniques and tools aiming to helping identifying different kinds of wastes in individual streams and finding appropriate ways in removing them or reduction of the wastes. The usage of such waste removal techniques drives competitive advantages of the companies and it arises from the studies of Toyota production system.

According to Monden (1993) there are three types of operations that are undertaken regarded to:

• Non-value adding; • Necessary but non-value adding activities; • Value-adding.

The first one is regarded as pure waste and has to be eliminated, which includes waiting time, stacking intermediate products and double handling.

The necessary but non-value adding activities are regarded as waste, but at the same time are necessary to be carried out. These activities are associated with long distances, unpacking deliveries and so. These changes are connected with making changes in the operation system and layout reconfigurations.


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The value-adding activities are those ones which contribute to the processing of materials and goods that bring value for the customer (Liker, 2004).

2.5.1 Mapping guidelines and tools

Initially it will be difficult to map the whole processes within the supply chain, but taking an overview of the core process under investigation and deciding the priorities under investigation gives a possibility for a detailed mapping. By selecting the process there is the customer that serves the process itself and the clear start and end of the process is important to be considered (Harrison, 2002).

When the data is collected the items are being followed along the process and record their behavior in terms of different values, the term used is usually walking the process. Actual orders are followed through all the stages of the process (Harrison, 2002).

The next step is the separation of the tasks with respect to value-adding and non-value-adding activities (Harrison, 2002).

The time based process map is representing the data collected clearly and briefly so that the investigated aspects of the network can be managed in an easier manner. The final goal is to represent the process so all the issues can be easily understood. At the end of the time based process map the creation of a flow diagram is effective in presenting the linkages and dependences between the steps of the process. The flow diagram is useful in approximating the time that the process takes in order to be fully covered (Harrison, 2002).

The solution generation stage is accomplished by investigating the picture of the process being described and presenting areas of improvements. The picture gives ideas and categorizes the non-value adding activities (Harrison, 2002).

2.5.1.1 Process activity mapping

Process activity mapping is a tool that has its origins in the industrial engineering practices and its main reflection is to provide high quality goods and services quickly and inexpensively. There are five different stages in fulfilling this approach stated as (Hines and Rich, 1997):

• Study of the flow processes; • Identification of waste; • Consideration of the processes in a way whether they can be rearranged in a

more efficient manner; • The consideration of better flow pattern, where different flow layouts or

transport routing are involved; • Consideration of the activities being done at any stage is necessary and what

will be the result if certain tasks are removed.


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The mapping is involving certain steps as preliminary analysis of the process, detailed recording of the items and tools used in each process. The machines and the area are recorded for the activities as well as the distances associated with movement, necessary time and number of units involved in that process with respect to times and machines. All the distances, necessary time and people involved can be recorded and calculated. The generated picture can be used for further analysis and suitable improvements (Hines and Rich, 1997).

The cause and effect diagram provides a structured and qualitative approach to problem solving. One of the main advantages it has is the generation of discussion that is oriented in the root of the cause of a focal problem. The diagram provides structure to usual analysis brainstorming and serves as a starting point for a deeper analysis. Its function is to narrow the scope of subsequent analysis. The commonly accepted categories for looking into as potential sources of the causes are people, process, technology, equipment, material and the environment. As these areas are having application in the logistics environment as well (Goldsby, 2005).

Techniques like 5whys (Why?, Who?, Which?, Where?, When, and How?) and cause and effect diagram are used in identifying the possibility for removing certain activities regarded as unnecessary and simplify others, combination of others and sequencing certain activities in achieving reduction of waste (Hines and Rich, 1997; Sekine and Arai, 1998). A certain diagram including all the data can be used as a basis for further analysis and eventual improvements.

2.5.1.2 Demand amplification mapping

Another tool is the demand amplification mapping, which corresponds to system dynamics. It is associated with the investigation of the information and material flow. The tool is effective in estimating the demand change in time and quantity along the supply chain. The information generated can be used on a basis for a decision making for further analysis in trying to redesign the value stream configuration, managing the fluctuations and reducing them as well as estimating a regular and smooth flow (Hines and Rich, 1997).

2.5.1.3 Material handling activity analysis

Analyzing the material handling activities can be carried out in several steps, which investigate the character of the flow with respect to transportations, storages and buffers (Öjmertz and Johansson, 1997).

The first step in the analysis is focused in investigating the nodes within the line, with respect to whether the node is needed and what kind of functions it has. As well as determining whether it can be moved in a different sequential order with respect to the entire material flow (Öjmertz and Johansson, 1997).

The next step is identifying the activities within the node and their importance and value-adding character to the material flow (Öjmertz and Johansson, 1997).


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The next step is the identification of the localizations, the reasons for division in time and space that restrict and interrupt the proper flow. At this moment there is an investigation of the effect of fewer nodes that add value. The aim is less divisions and breaks in the flow that result in less time, space and as a result less material handling activities (Öjmertz and Johansson, 1997).

The next step is focused in identifying the possibility of reducing the steps that are necessary to be done in achieving the final destination (Öjmertz and Johansson, 1997).

The last step is the investigation of the connections that are needed with respect for the orders to reach the final destination. These activities can be divided in time or space, or the combination of both (Öjmertz and Johansson, 1997).

The importance of understanding the functions and performance of the activities in a system are important to identify how resources are being used and the linkages between them. This understanding is reflecting in all types of activities in companies, embracing the materials flows of production, comprising inbound and outbound logistics materials flows. It is important to analyze the material handling activities as a separate function, which is not considered as a waste, but influencing the value of the products. The following figure 2-1 recommends a certain methodology in analyzing and evaluating a materials flow system (Johansson and Öjmertz, 1996).

Figure 2-1. Evaluation methodology aiming to improvements in the material flow (Johansson and Öjmertz, 1996)

Physical data can be used in connection to the studied field of the material handling activities, and the data used gives more stable generated picture. The resources needed for a material handling task is a measurement associated with the material handling effort. In order to estimate the material handling effort it is important to consider the time consumption as well as the number of activities associated with that. A handling operation is regarded as the taking and placing a certain unit, in case of when the unit are transported and manipulated by a fork-lift truck. A material handling activity is regarded as the procedure when each time materials are investigated along the flow, and it can contain several handling operations. It is more relevant to investigate the actual time consumptions for different material handling activities as well as the number of activities needed for executing the tasks (Öjmertz and Johansson, 1997). According to Womack and Jones (1996) if the final goal of the study is known and all the data is existing for the needed analysis there is no need for benchmarking.

Identifying the materials flow system

Identifying sub-processes and

material handling activities

Estimating the effectiveness by

comparison to ideal system and

analyzing the disorder

Identifying the causes of material handling activities

Finding restrictions on improvements

Make changes according to the results obtained


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When determining the value-adding characteristic of one material handling activity in a material flow a certain reference system has to be defined. The analysis starts from defining the system boundaries, which gives the possibility to estimate the initial condition of the materials in the beginning of the material flow as well as the final conditions. The reference system is the ideal system, where there are no non-value adding activities (Öjmertz and Johansson, 1997).

When the necessary measurements are acquired for analyzing the material flow system with respect to its efficiency a reference system is used in order to present the material handling effort required. A systematic procedure for evaluating a material flow system is presented in the following figure 2-2.

Figure 2-2. Procedure for evaluating the materials flow efficiency of a studied system with respect to a reference one, (Öjmertz, 1998).

The material handling is associated with the need for connection between sub-processes, which are separated in time and space. And by understanding of these sub-processes and having the necessary knowledge to all the included activities along the material flow relevant suggestions can be given (Öjmertz and Johansson, 1997).

Definition of system

boundaries

Mapping of the studied system with respect to:

- Process localization -Material handling

operations

Definition of reference system with respect to:

-Only value-adding materials handling

-Minimum number of handling operations

Handling effort in the studied system

Handling effort in the reference

system

Evaluation of efficiency


15

When the looses are estimated in a material flow system it is necessary to grade the magnitude of the different categories of losses in the system and how much they influence the flow relevant to another system. This gives a picture of the magnitude of the losses existing in the system and estimating them as high and low. The knowledge is used in forming a statement of what changes are necessary in the system design that are relevant for being effective in the increase of the material flow efficiency. At the same time the classification of the losses itself can be useful itself without the necessity of comparison to other systems. This can be seen when a studied material flow system is suffering from problems in meeting certain demands with respect to number of orders needed to be fulfilled. The need for clear connection between the losses and the material flow is important in suggestion for improvements of that system depending on the magnitude with which these losses affect the system. If the losses are able to be measured and how they impact the system it can be reliable to find the cause of these losses and how they can be prevented by changing and improving the system. Losses are classified as value-reducing activities with their influence on the material flow. Another useful categorization is to relate these losses to certain activities within the material flow and in that manner estimate their influence. This can be the case for storing, transporting and material handling in reaching the final destination of the activity (Öjmertz, 1998b).

2.6 AGV system

Since the material handling activities are considered as non-value adding activities, the long term perspective is to be eliminated; hence in the real environment it is hard to be accomplished (Arifin and Egbelu, 2000). The usage of techniques and concepts aiming to decreasing the material handling activities and needs, by designing an efficient flexible system satisfying the transportation request of goods between work stations in an economical manner.

Today an AGV system can be found in many industries as serving a key role in the material handling activities (Ujvari and Hilmola, 2006). According to Arifin and Egbelu (2000) and Ujvari and Hilmola (2006) the application of an AGV system in the transportation capabilities and material handling has a significant impact on the flexibility of the manufacturing systems. The AGV system is appropriate to be used where different materials are moved from different loading points with respect to different unloading points (Groover, 2001). There have been recognized different benefits coming from the usage of an AGV system as follows:

• Flexible paths for material flow; • Flexible usage of the ground space; • Increased flexibility in manufacturing; • Computer integration and control of the functions with respect to material

handling; • The AGVs are available 24 hours and they depend on battery changes; • The labour cost is decreased; • The changeover time is decreased.

In most of the cases when an AGV system has to be optimized there is a need for improvements of the lead time performance of the system by applying different alternatives with the considerations of all the factors affecting the material flow Mantel and Landeweerd (1995).


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2.6.1 AGV design systems

According to Mantel and Landeweerd (1995), there are three important factors that affect an AGV system, which are track layout, proper number of AGVs required and operational transportation control. According to Koo, Jang and Suh (2005) these factors are the transportation guide path layout, the traffic flow pattern, the number of vehicles required, the capacity of the vehicles, the location of the pickups and delivery stations Among all these factors the required number of the AGVs serving this system is among the fundamental decisions after the traffic network is considered (Koo, Jang and Suh, 2005; Ujvari and Hilmola, 2006, Arifin and Egbelu, 2000).

The factors and their relations that significantly influence the required number of AGVs are represented in the following figure 2-3. As can be seen the control system and its work directly affects the empty travel and the blocking effect of the AGVs in the system. The demand variability and the rate according to which the orders are made affect the capacity need and the number of AGVs in the system. As well as the complexity of the guide-paths the travel distance would be decreased between different points and decrease the number of AGVs (Ujvari and Hilmola, 2006).

Figure 2-3. Factors influencing the required number of AGVs (Mantel and Landeweerd (1995); Ujvari and Hilmola, (2006)).

2.6.1.1 Transportation control system

The direction of the flow path is important consideration in the AGVs’ system modelling, but special emphasize is made on the computational time required to fulfil the tasks should reasonably be estimated (Ujvari and Hilmola, 2006). Transportation systems are system-based ones and unanticipated performance downtimes affect the stability of the material flow, since that even minor change in the system modelling can have major effect on the final result. In the case of AGVs transportations it delivers goods to a destination point and blocks the guide-path during the time needed to fulfil the task.

The transportation control is either regarded as centralized or decentralized one. In the centralized system the transportation tasks are concurrently considered as the vehicles are routed and scheduled in the system. The decentralized system is when the AGVs

Control system sophistication

Arrival rate of loads Layout flexibility

Empty travel distance Travel distance

Blocking Number of AGVs


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are constantly looking for the first transportation task to encounter (Mantel and Landeweerd, 1995). There are two different situations distinguished as one of them is the think ahead control without time windows. In this case the number of orders is known and time doesn’t play a role as only the proper routing of the vehicles is taken into consideration, as the empty travel distance is minimized. One of the cases is that all the present transportation tasks are listed in a routing scheme and it is executed until new task arrives. The rates with which the tasks arrive determine the nervousness of the system. The other case is the periodic control, where all the arriving transportation tasks are listed and executed until the next tasks arrive. In both of these cases there is a delay in the transportations, and a prioritizing sequence should be adopted (Mantel and Landeweerd, 1995). The other situation is the think ahead with time windows considerations, where for a certain period of time the orders are known, which are also connected with the production. The transportation times are already taken into a consideration, and it plays an important role. In this way since the lead time is established for a certain period of time and that affects the possibility to estimate the waiting time and empty travel time. In this way a proper routing can be established (Mantel and Landeweerd, 1995). In all the cases the production scheduling and planning affects the nervousness of the transportation system. A better production planning will influence the easiness with which the scheduling of the transportation activities of the AGVs would be carried out (Mantel and Landeweerd, 1995; Ujvari and Hilmola, 2006).

2.6.1.2 Guideline path of the AGVs

Mantel and Landeweerd (1995) pointed out the importance that the proper design of the guide-paths of the AGV system will result in a shorter distances. As well as less blocking possibilities, which contributes to minimal waiting times at the stations, where the orders are given.

If the given track layout is existing it can be predicted, where the AGV interfere the most, which results in congestion (Mantel and Landeweerd, 1995). In the cases where the traffic is too high there is a need for finding blocking-free routes. This results in the advantage of travel time variance reductions, and this corresponds to more deterministic character of the routing.

According to Arifin and Egbelu (2000) by decreasing the number of conflicting nodes in the AGV system blocking is reduced. That results in decreasing the time variables in the AGV system, which results in ability of the AGV system to deal with more orders.

2.6.1.3 Required numbers of AGVs

Ujvari and Hilmola (2006) summarized that the driving factor of the output of the AGV system is the number of the AGVs serving the material handling transportation system. Unrealistic increase in the number of vehicles serving the system would lead to overcrowding.


18

Determining the proper number of AGVs required serving the system minimizes the total travel of loaded vehicles and as well the overall travel, which includes the unloaded travel. If the number of vehicles exceeds the amount in the system, then the empty travel time decreases, but the loaded travel time increases and idle conditions of the AGVs increase as well (Ujvari and Hilmola, 2006).

2.6.2 Methods for estimating the required number of AGVS

According to Mantel and Landeweerd (1995) a way for estimating the number of AGVs required to serve the system is the sum of the total loaded time and empty travel time, and the waiting time with respect to busy period of the system. That sum is divided by the time an AGV is available during that period.

When designing the required number of AGVs in a transportation system the queuing system perspective has to be taken into consideration (Koo, Jang and Suh, 2005). The AGVs should be considered as resources and the delivery requests as the customer orders arrive, which is measured from the parts needed to be moved from one station to another. The estimation of the servers needed is dependant on the number of requests and service time. The following figure 2-4 represents the part waiting time, which consists of assigning waiting and empty vehicle travel time. The assignment waiting time is the delayed time that certain order has to wait until a vehicle becomes available to take that order. The mean and the variance of the travel time are the two parameters, which are used to approximate the expected assignment waiting time. The empty travel time is estimated by different dispatching rules. The empty travel time is a subject of different dispatching rules.

Figure 2-4. Part waiting time and vehicle travel time, (Koo, Jang and Suh, 2005).

The following figure 2-5 represents the overall fleet sizing procedure in determining the required amount of AGVs in a transportation system.


19

Proper

Waiting

Time

Figure 2-5. Fleet sizing estimation procedure, (Koo, Jang and Suh, 2005).

The stochastic system is being considered when the delivery requests are being issued at random periods of time and being executed by vehicles assigned in real time, in this way there is a possibility for estimating the fleet size of the AGV system (Koo, Jang and Suh, 2005). According to the model proposed by Koo, Jang and Suh (2005) the following assumptions have been made:

• The travel time between pickups and delivery station is unique and deterministic;

• The average delivery request between the locations is known, but the certain time is known probabilistically;

• When there is no delivery request waiting for a vehicle the vehicle stays idle; • One vehicle serves only one delivery order at a time; • If multiple idle vehicles are waiting for a delivery request, a certain AGV is

selected by a predefined policy, and if multiple requests are waiting for an idle vehicle, the requests are served by the order in which the request arrived.

The mean and the variance of the empty travel time and loaded travel time are estimated, and the expected part waiting time is calculated as well, probabilistically by the usage of the following formulas:

{ }1 1

( ) ( / )( )n n

l ij ij ui j

E t f F t l= =

= +

∑ ∑ , (1), is the expected loaded travel time

{ }2 2

1 1

( ) ( / )( ) ( )n n

l ij ij u li j

V t f F t l E t= =

= + −

∑ ∑ , (2), is the variance of the loaded time

1 1

( ) ( )n n

e si k kii k

E t f fd t= =

=

∑ ∑ , (3), is the expected empty travel time

2 2

1 1

( ) ( ) ( )n n

e si k ki ei k

V t f fd t E t= =

= −

∑ ∑ , (4), is the variance of the empty travel time

The following variables are being considered as follows:

• Number of pick-ups and drop-off locations; • Number of vehicles;


20

• Vehicle utilization, which is the total travel time of an AGV/ total vehicle travel time available;

• Delivery requests rate; • The vehicle travel time; • Sum of loading and unloading time; • The delivery request rate between the locations.

The models proposed should be designed in a way that the variables being considered, should be reflecting real demand and times, and the time when the demand is not high should be avoided where there is a slow down in the activities. The reason for that is that this data will lead to bias and unreasonable conclusion not reflecting the real circumstances (Arifin and Egbelu, 2000; Koo, Jang and Suh, 2005).

The model proposed by Koo, Jang and Suh (2005) represents with high closeness in the estimation of the required number of AGVs serving the system with comparison with other existing techniques. It can be stated that its accuracy is close to well-known simulation techniques, and it is suitable to be used for its purpose.

2.6.3 Simulation versus Analysis

Transportation simulation has been recognized as a challenging research problem, where the difficulties for estimating the system boundaries, time spend and its complexity makes it difficult to be implemented. Even though, that simulation tools have been developed recently the researchers in the area state that their usage in real time environment has been limited. This is especially the case when there is an existing system being considered (Ujvari and Hilmola, 2006).

On the other hand Lainema and Hilmota (2005) point out that simulation can be used for improvement techniques after an AGV system has been already established. In this way it serves like an optimization tool in a way of detecting bottlenecks and apply different control methods. At the same time more convenient option being pointed out for the usage of a simulation tool is the case when manufacturing facilities are being designed.

According to Arifin and Egbelu (2000) the usage of an analytical model where there is a mathematical dependence between the variables affecting the required number of AGVs in a material handling system. The usage of a real data collected from the factory is suggested to be used in the analysis, since when existing system is being investigated the data comes originally from a simulation analysis being used, when the system was designed.

Methodology

21

3 Methodology

This chapter includes the methods according to which the research has been carried out, as well as the different techniques for data collection being used.

A formal research is the persistency and the systematic discipline with which it is carried out in achieving high reasoning (Williamsons, 2002). According to Burns (1990) a research is the systematic investigation of a phenomenon in finding answers to a problem. The term methodology is used in a more specific sense as a synonym for research design (Oliver, 2004).

3.1 Research process

The research at Kinnarps Production AB was carried out from January until June 2008. The initial contacts with the company were made in the end of December 2007, and a meeting was established in the beginning of January 2008. A company presentation was made for the participants and two different proposal for a master thesis were discussed both in the area of logistics. One of the suggested areas was chosen as the desired one for the thesis after discussions regarding the importance of the problems within the areas for the company directed the choice. In the next couple of weeks Kinnarps Production AB were gathering the necessary resources needed for the research to be launched. A meeting was held in the end of January with the supervisor of the research and the chief logistics manager. The main areas of the thesis were decided and the necessary goal was established. During the meeting the company provided all the necessary resources for the research. There was a pre-study that took place for the period of three weeks where observations and unstructured interviews were made mainly aiming in achieving a holistic picture of the processes within the area of the research. Literature review was used in order to focus the participants in problems associated with the area of the research. Statistical data was taken from the company aiming in identifying the variables and the extent of the possible data that can be used in the research. A meeting was held with the supervisor of the project and the logistics manager among with other important people involved. At the meeting the research was narrowed and at the same time the free will in the approach and the outcome was given. The follow of literature reviews and results from the research was the main focus. Since the area of the research was complex and new for the investigators the main center of attention was in understanding the environment. The usage of structured interviews and focused observations in gaining the necessary knowledge was the following step in identifying and managing the issues in the task. Statistical data and a simulation tool that records the activity was gathered and used in documenting and mathematically present the processes. There were interviews with key personnel familiar with the IT system and the software in the AGV environment.

Methodology

22

The follow-up of all the research methods being used the researchers were able to identify and realize the problems associated with the material flow in inventory 7. Calculations and different findings were the core for analyzing the circumstances and line the outcome of the research, as they were oriented in the AGVs time variables and the number of vehicles in the area as well as the number of orders in the inventory 7. The researchers linked the findings from the tools used for gathering the primary data and the secondary data with the theory being used and made the necessary connections in providing the findings with scientific character. The analysis was conducted in understanding the findings and suggestions given based as on the theories as well as on the appropriateness for Kinnaprs Production AB. The usage of the different system states in the inventory gave the chance to value the outcome of the suggestions and their benefits to the company. The suggestions given were analyzed as well with the supervisor of the research and his feedback was important for their outcome and realization. At the end of the research process the work done so far was concluded and discussed. A presentation was held in Kinnarps Production AB in the end of May 2008 where the suggestions and their outcome were presented.

3.2 Case study

The term “case” descends from the Latin casus (case) and emphasises the importance of the individual case (Jacobsen, 2002). According to Yin (1994), a case study can be defined as: “An empirical enquiry that investigates a contemporary phenomenon within its real-life context, especially when boundaries between phenomenon and context are not clearly evident.” Case study research is mainly appropriate for situations in which the investigator has little or no understanding in the field and answers the question why and how processes or phenomena occur (Williamson, 2002). Case study is also appropriate not only when the examiners are aiming to deeper understanding of a situation or an occurrence but also when the phenomenon is not dynamic or the terminology is not yet defined. Multiple sources of data collection are used where interviews, observation, questionnaires and documents are the typical techniques (Williamson, 2002; Yin, 2003). Although qualitative and quantitative data collection and analysis methods can be used, case study research is often concerned with qualitative data (Williamson, 2002). The most challenging phase in a case study is to define the unit of analysis, as it may be an individual, an organisation, a process or some other events. The unit of analysis is closely linked to the problem specification and questions raised, hence the selection of the candidates which are relevant to the case can be studied with the significant data to be collected (Yin, 2003). Concerning the generalisations which the researcher is intended to do at the conclusion of the research it is also useful to determine the unit of analysis, meaning

Methodology

23

that the results will be useful to other individuals, organisations, processes or events (Williamson, 2002).

The case study is the main tool being used in this research and it will give deeper understanding in the field, since there is no clear picture of the problem. This will provide the case of Kinnarps in a broader perspective, by which this paper will manage with the identification of the problem areas. The problems being identified are within inventory 7, which serves as a loading area and all the processes involved within it would be a subject for investigation. Due to the fact that this research is carried out by two participants the analysis and conclusions will hopefully be unbiased and realistic, as well as the combination of qualitative and quantitative data used will support the validity of this report. These problems being identified would be the foundation for carrying out the work for achieving the stated goal in this paper and reflect in improvements within Kinnarps Production AB.

3.2.1 Explanatory (analytical) survey

Within the research at least six kinds of case studies can be identified, as one of them is explanatory (causal) which is suitable for designing and doing causal case studies. Hence explanatory case study presents data bearing on cause-effect relationships and it attempts to explain “how” and “why” events happened (Yin, 2003; Williamson, 2002).

According to Williamson (2002) explanatory survey doesn’t firmly create causal relationships, but rather correlations which identify the variables. Hence may be interpreted as giving a clue of causality, unless appropriate statistical techniques are applied to present that the correlations are false relationships.

The usage of the explanatory survey in the Kinnarps Production AB case will present the cause and effect correlations between variables within the field of this research. Within the AGV’s system variables the usage of explanatory survey analysis presented the important dependences between the time measures, as well as the number of AGVs serving the system and their relation to the output of the system.

3.2.2 Longitudinal studies (cohort/panel studies)

The term longitudinal describes a variety of studies that are conducted over a period of time, as the data is gathered over an extended period of time. It can be a short-term investigation (weeks or months) or a long-term study which can extend over many years (Williamson, 2002). Measurements are being taken at different points in time from the same selected group, this corresponds to both cohort and panel study. A cohort study can differ from a panel study in a way that selective sampling can occur within the sample meaning some units of the sample may not be included each time. By contrast in a panel study each same unit are selected over time (Williamson, 2002).

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The Strengths and weaknesses of longitudinal studies according to Williamson (2002) are: Strengths: • Useful for establishing causal relationships and for making reliable conclusions; • Separates real trends from accidental occurrence; • Enables the active change to be caught; • The sampling error decrease as the study remains with the same sample over time; • Gives clear recommendations for intervention to be made. Weaknesses: • Time-consuming as it takes long time for the studies to be conducted and the

result to come out; • Data is too rich, which makes it complex to analyse. Cohort study was used in inventory 7 in order to obtain a holistic view over the related variables and their interactions. These variables that were identified in the matter for contributing to the problems have been generated through secondary data, observations and interviews. The sample used in the cohort study is covering one month period for year 2007 and two months within 2008. Within these time perspective certain days will be used, which give the realistic picture of the process, in other words days of holidays and inactive days will be excluded. The period being selected during 2007 hopefully will give a clear picture of the flow of material, when certain different variables were present in case of the method according to which the empty travel transportation is presented. These variables are excluded in the sample taken during year 2008, and therefore there is a possibility to track the “change” of the flow and in this way to map what variables have changed and why. Therefore it can be seen how these changes have affected the current flow of materials. From the cohort study we can clearly identify the main variables that are connected to the problems being identified. In order to break down the important variables separately for further investigation the panel study was implemented. Here it was possible to be seen how each variable behaved during the same period of time parallel to each other. By the consideration of a certain variable and its behavior within the panel study there is a possibility to reflect this information to other variables within the cohort one. Therefore it is possible to conduct reliable correlations between the variables connected to the problems.

3.3 Data analysis The term data originate from the Latin dare which means “to give”. Data are those pieces of information that any particular situation that are given to the observer in order to discover the truth. It is important to remember that data is ever changing and not absolute reality, rather demonstration of reality. Data is referred to either primary or secondary one (Leedy and Ormrod, 2001). The main goal of analysis in interpretive studies is to produce an understanding of the contexts of the phenomena and the interactions between them and their contexts. The

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strength of analysis obtains from the strength of the explanation the researcher has done which is based on the interpretation of data (Williamson, 2002). The biases in the researcher’s way to collect the data are another crucial aspect which needs to be considered (Williamson, 2002). It is unavoidable for biases to occur since the researcher’s own beliefs, values and interests shape the investigation. However, the outcome of the biases can be counteracted by using multiple sources of evidence to provide multiple illustrations from different sources (Williamson, 2002; Yin, 1994, p.92). All the data was conducted through the usage of both primary and secondary data categorization within the problem areas. The collected data was aiming to identify the factors affecting the AGVs performance in the whole area within inventory 7. In order to reduce the biases in the outcome, different methods of approach were used and the results were compared and investigated.

3.3.1 Qualitative and quantitative analysis

Quantitative analysis research method has been implemented by the post positivistic approach perspective. It is oriented in the cause and effect thinking, avoiding certain variables, usage of materials and observations and the testing of existing theories (Creswell, 2003). The method employs the usage of experiments, surveys and the data collection that yields statistical data. Analyzing the data can be a hard thing to be achieved, since the entry is quite complex. According to Williamson (2003) the establishment of categories is important to regard the data to, as well as there is a need for separation of the data in a way to exclude the effect on each other. It is important to know that the generalizations arising from the data are based on random sample and appropriate statistical test, even if it is really difficult to involve the separation.

The qualitative analysis approach is oriented in gaining knowledge coming primarily from the constructivist perspective, arising from the research of grounded theory and case study (Creswell, 2003). The main objective is to collect data, which is an open, arising data that continues in further developments. The qualitative analysis is the structure, in which the researchers are making sense of the collected data, and it is valuable to refer the findings to other sources like reports, books and related articles (Williamson, 2003). There are many forms in which the qualitative research stimulates the researchers to analyze the data simultaneously as it is collected (Strauss, 1987, p.8). One important issue being pointed out by Williamson (2003) is that certain techniques should be used for making sense of the data generation and interprets it in the right way and at the same time the generation of ideas can be carried out at any stage. According to Grbich (2007) the procedure of comparison aims to clarifying the differences and the concepts, which create the understanding and prepositions in establishing discussions from the generated results. The most important thing is the appropriateness of the sample in the data generation in a way that the sample is purposeful (Grbich, 2007).

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The mixture of the two methods approach tends to be the one in which the researchers base generated knowledge. It combines the simultaneous or sequential generation of data in order to fully understand the research problem. The data collection involves the generation of numeric information as well as text information and as a final result the study consists of both quantitative and qualitative information Creswell (2003). In improving the validity of the findings generated there is a need for more in deep data in increasing the capacity of the findings by comparison of the data in a way of setting concepts against each other (Grbich, 2007). According to Bradley and Sutton (1993), Williamson (2003) the mixture of the two approaches of viewing the reality and the methods favors each other and according to others it doesn’t.

Within the scope of this project the authors initiated the research with a qualitative approach with the aim to gain knowledge about AGVs behavior and their impact on the inventory. Books, reports and related articles were reviewed deeply so this could be the foundation for future detailed investigation. For the data collection a quantitative approach was used with respect to observations, measurements, interviews, and statistics from the company. Due to the high extend of the statistical data it was hard to establish a clear connection between the variables. Although this obstacle could be reduced since the mixture of the two methods were applied and variables were able to be compared. This enabled a deeper understanding in the research problem and also increased the reliability of the data which is used further in this project.

3.3.2 Primary data

Primary data is collected with the aim to being a foundation for the analysis in research investigation (Jacobsen, 2002). The primary data is consisted of measurements, interviews, questionnaires and observations.

3.3.2.1 Measurement

The main reason of measurement is to obtain “objectivity and simplicity” through relating the observed event to the theory. Measurement shouldn’t start without the observer has first clarified the objectives with the measurement. “When you can measure what you are speaking about, and can express it in numbers, you know something about it, but when you cannot measure it, when you cannot express it in numbers, then your knowledge is of a meagre and unsatisfactory kind, Lord Kelvin (1883)”, (Sarle, 1997). In order not only to rely on the secondary data from the company, the authors of this report decided to make measurements by clocking the AGVs activities. The measurements being done used both the real time environment and at the same time the secondary data obtained by Kinnarps Production AB. The usage of the secondary data was based on a simulation, which records the AGVs’ behaviour in the area of research. An important information which was necessary to be received, but Kinnarps data base couldn’t provide was; “The time needed for the AGVs to regain their normal flow

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after they had been disturbed due to different stoppages of the AGVs. Hence this time could only be measured by clocking and it also gave a clear picture how this time could differ due to the location and amount of the AGVs being involved.

3.3.2.2 Observations

Observation is a method which the researcher uses to gather live data from live situations, to observe means to register what actually happens rather then regarding verbal explanations (Hansson, 2002). According to Hansson (2002) the perceptions about reality are based on observations with our sense and it is always inadequate, hence needs to be supported by different techniques. Patton (1990) suggests that the observed data should enable the researcher to understand the event that is being described, hence action can be implemented. Observation can be distinguished into two types: non-participant observation or participant observation. The last one mentioned is the most common approach in a research work (Walliman, 2001). Glesne and Peshkin (1992) describe the participant observation as ranging a continuum from extreme observation to extreme participation. At the end of the continuum is the observer with very fewer activities within the settings. The observer as participant is mainly an observer, but has some limited participation in the activities and settings. The participant observer is highly interacting and participates with the environment. Finally the full participant is a full member of the group and also a researcher, the full participant can also join the group after the research has started. These different characters have different ways of working structures regarded as highly structured, semi-structured and unstructured observations. A highly structured observation will know in advance what it is looking for. The researchers use a more structured way of doing their observation, then kind of a table can be quite useful in order to decrease the complexity. A semi-structured may have an outline of the issues but has to collect data to highlight these issues in a pre-systematic manner. An unstructured observation is not clear in what it is looking for and has to make brainstorming through the observations before deciding the significant for the research (Cohen, 2000). Observation of events or objects can be quick and efficient method of gaining preliminary knowledge but on the other hand observation can be very time-consuming and difficult when there is too much to observe and the activity is not constant. Hence instrumentation can be devised to overcome these problems such as; cameras, watches, photographs etc. (Walliman, 2001). Observations were made with the intention to map the flow of activities in the current process that is conducted along the logistics line. Before starting mapping the process, the activities within it were identified. A combination of highly structured, semi-structured and unstructured observations was used in gaining the holistic picture of the process.

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The authors in this report took the role as participant observer in the beginning and the worked in an unstructured manner. This approach was used in a short time and the main reason was to gain a holistic view of the activities among the AGVs in the dispatching area, where the AGVs are controlled and coordinated. As the project carried on a more structured way of working was initiated and the areas of interest within the inventory could be identified. Observations were taken with the intention to guide the path which the AGVs were taking for pick up/delivery of the orders. Certain areas and activities were a subject of observations: • AGVs entering the inventory from outside the company into the rack system; • AGVs delivering goods from the conveyors into the rack system; • The stocking policy; • Orders being delivered from rack system to the loading area; • Empty pallets being processed from the loading area to the empty pallets area; • The errors occurring within the areas and their influence on the whole chain.

3.3.2.3 Interviews

Interview is another technique which can be used for collecting primary data and it is mostly seen as qualitative data but also useful for quantitative data. It is suitable to use in range different methods and is frequently used in case studies such as a supplement to a survey (Williamson, 2002). There are two main methods of conduction interviews; face-to-face and telephone. Face-to-face method can be used in variety situations such as; at work, outdoors can be used to question members of the general public, specific segments of the society (Walliman, 2001). According to Cohen (2000) when designing an interview, the researcher should translate the objectives of the research into questions. Hence the questions should adequately reflect what the researcher is trying to find out. According Williamson (2002) there are both advantages and disadvantages with interviews; the interviewer can control the context of the interviews and make sure that the respondent concentrate on the relevant issues. Face-to-face interviews result in establishing a higher level of motivation among the respondents than words on paper. Interviews are time consuming, the personal characters (such as age, sex educational level and experience at interviewing) of the interviewer affects the outcome. The face-to-face interviews performed within the line of this project have been both structured and unstructured. At the initial state of this project more unstructured questions were asked to the personnel within the loading area and the control room in order to collect relevant data for the problem area. The unstructured interviews were aiming in identifying problems and issues, which can give directions to our research. As the project has proceeded more structured and focused questions have been asked within the same areas to gain more knowledge regarding the underling problems within the system performance, appendix 25.

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3.3.3 Secondary data

According to Jacobsen (2002), the secondary data is consisted of literature review. It serves as a foundation of the work of the researchers by considering other sources of information corresponding to the field of research, in a way how things are done and seen by others. The sources of information are books, journals, articles and other relevant sources (Williamson, 2002). The data represents the literature and its connection to the review of the topic of the research (Williamson, 2002). According to Hansson (2002) the sources of information should be criticized in a way that there are authentic, independence, possessing a tendency, related to time and have the competence of the observer.

Secondary data is classified according to Zikmund (2000) as the fact finding procedure, where it is considered a statistics previously being collected and edited and pointing out the connection between the variables in the data. The company database and previously recorded data is considered as an internal data from a firm, based on that data there is a high possibility for future decision making processes (Zikmund, 2000).

The secondary data that was generated from the databases at Kinnarps had a crucial relevance for the outcome of the investigation. The statistic from the system revealed and tracked important variables of the AGVs such as; the transportation times of the AGVs within the areas, the amount of the different errors in the system, the daily peaks of the orders being processed by the AGVs, the path of the AGVs etc.

3.4 Triangulation

Triangulation is a selected model when the researchers use two or more methods in endeavoring the confirmation, cross-validation, and integrate the findings within one study. The method triangulation is the principle for checking of the consistency of the findings. The source triangulation at the same time is checking for the information deriving during different times and sources about the same event (Williamson, 2002).

The main advantage by using triangulation is that the conclusions being made are relatively reliable if data is generated by more then one method and several sources. The triangulation gives both narrowed and broad perspective of the problem and off-set the weaknesses presented in one method and at the same time the strengths in others (Williamson, 2002; Creswell, 2003). The dependability of the findings both in validity and reliability is measured with the triangulation approach.

In order to define and combine the different aspects of the problems the usage of several methods and sources was necessary. The problem areas gained from the different methods were compared to each other and their interdependence could be understood. The study wouldn’t be able to be achieved without the mixture of different data collection methods, techniques and analytical tools.

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3.5 Validity and reliability When a research is carried out there is a need for considering the possibility for wrong results and conclusions. The selected sample has to be scientifically selected and the principles of validity and reliability must be considered. Both of the terms are complex and difficult to be explained and investigated (Williamson, 2002). Validity is a measurement tool that identifies the extent to which the research measures what is supposed to be. Validity refers to the depth of access to which the researchers receive the knowledge and meanings of the topic being investigated (Easterby-Smith et al., 1991). The experimental researchers need to identify threats to the internal validity of the experiment in progress and relate them to type of design in the research. The internal validity threats are associated with experimental issues, which are regarding that the participants in the research are affected by the ability to draw wrong conclusions in the experiment. The external validity is associated with the possibility for intersecting the data from different sources, as this can be regarded as the problem for generalizations among other situations (Williamson, 2002). Another important characterization of validity according to Yin (2003) is the construct validity. It emphasizes on the importance of correct operational measures for the concept being investigated. In order to meet the specifications of the test of construct validity certain types of changes should be selected and related to the original objectives within the study. And at the end showing that the selected measurements reflect the types of changes being selected. In order to prove the construct validity with solid evidences multiple sources of information should be taken, and arrange the evidences in a chain relevant to each other. The reliability principle is concerned with obtaining stable and compatible results from the research with higher replication that comes from the repetition of the study (Williamson, 2002). The importance of similar results from the repetitive research contributes to reliable conclusions and authenticity of the research. To be able to provide validity and reliability in this report different tools of conducting data have been implemented. By using the triangulation method the researchers wanted to ensure adequacy of the measurements. The measured times were also made with the intention to compare the results with the company’s secondary data and see the validity in the presented statistics. In order not only to rely on the interviews carried out as the only method the observations were used to confirm the reasoning. Hence observations enclose the actual events and eliminate the assumptions of the events that presumably could be the outcome of conducting only interviews. At the same time all the collected data and the calculations made based on this data were tested from several perspectives. Different sources of information were used and the estimations from the calculations were repeated by using the same variables’ values from different sources in the database such as times and number of orders, which proved the construct validity of the achieved results. As well as this method provided the possibility to estimate the strong connections between the variables, this makes them reliable in other applications.

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4 Results

This part of the thesis presents the findings of the investigation by mapping of the current system conditions.

4.1 Current state of the inventory

4.1.1 Transportation activities

All the transportation activities of goods in the inventory 7 are carried out by the AGV system. The AGVs are in charge of picking up pallets with goods from different places within the inventory and distributing them to the right places needed to be delivered. Within the distribution orders in the system one order corresponds to distributing one pallet. The transportation activities within the inventory can be divided into two different categories:

• The first one is the delivery of goods to the inventory racks from different places within the company. The supply of these goods can be divided as products arriving from different workshop areas from the production and products arriving from the conveyor distribution system used in the company. The products arriving from the conveyor system are placed on a certain pick-up place, which is positioned within the inventory. All the deliveries are made with accordance to the planning, which places the orders for the goods.

• The second transportation activities are associated with the distribution of goods to the loading area. The goods are picked up from a rack system as well as from the places where pallets are being stored, considered as D places, these places are situated among the loading area and part of the rack system, figure 4-1.

The average of 568 pallets going out from the inventory 7 to the loading area, 610 pallets incoming in the inventory, 284 pallets coming from the conveyors and 325 pallets incoming from other places of the company was established. The sample is for 27 days and covers 18 hours of activity per day, as the days are randomly selected with the intentions to cover realistic constant activity. The reason for the difference between incoming and outgoing goods is due to the fact that most of the time during the weekdays the incoming goods are more then the outgoing ones. And from Fridays to Mondays is vice versa. It can be seen that 47 % of the goods are coming from the conveyors and 54 % are coming from the AGV’s transportations system, entering the inventory 7, appendix 1.

Through the investigation of the data gathered from the company it was found out that the transportation of goods occurs in a way that pallets of goods are stored in inventory 7, and the transportation system is delivering them outside of the inventory, to other loading places in the factory, excluded from the scope of this project, an average of 29 pallets per day for a period of 27 days was estimated, appendix 2.

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Figure 4-1. Layout of inventory 7

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4.1.2 Pallets and rack system

Within the distribution of goods in Kinnarps different kinds of pallets are being used. In particular the pallets within inventory 7 are classified in accordance to their dimensions. There are four different types of pallets places being classified with respect to their dimensions and for easiness to be described, the authors of this thesis will classify them by type A, B, C and D, see figure 4-1. A detailed explanation regarding the types of pallets is given as follows:

• Type A are those places within the inventory, which can fit pallets with definite dimension ranges, these dimensions are regarded as middle ranges. These places occupy the first level within the rack system and the estimated available places for that type are 229.

• Type B are those places within the inventory, which can fit pallets with definite dimensions and that places are fitting the smallest sizes of pallets. The pallets are stored on the second level in the rack system and the places available are 226.

• Type C are occupying the third level in the rack system, and their dimensions are varying in height, since then the third level of the rack system is open in a way that there are no boundaries for the height. The places estimated for this type are 210.

• Type D are those places, which are designed for only one type of pallet. These pallets are high and have a metal frame, as well as they are the heaviest type of pallets used in the company. Due to security reasons coming from the characteristic of the pallets, they are not placed on the rack system, but occupy only the floor space, and they are not part of the rack system. These places occupy three rows from the rack system which are served by line 27 and 25, and the places marked as D on figure 4-1.The control system, which classifies the pallets according to their dimensions, will determine different kind of pallets differing from the one described to be stored on the places meant for type D. Due to that fact the places are sometimes occupied by other kinds of pallets. The places determined for type D are 109.

All the types A, B and C places are part of the rack system used in the inventory. Due to security reasons the line on the third floor of the rack system, closest to the loading area is not occupied with type C, pallets. The following table 4-1 summarizes the places available for the different kinds of pallets being stored in accordance to their dimensions. The total number of available places in the inventory in accordance of all the types is 774.

Type A B C D Available places 229 226 210 109

Table 4-1. Available places within the inventory according to their classification

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Through the investigation of the data from the company it was found out that the usage of pallets classified as A, B, C is 85% of all the orders coming for delivery and the ones classified as D is 15 %. The sample taken is regarding 27 days for 18 hours of activity, appendix 3.

From the interviews and the observations made it was found out that most of the time the number of free places for D type are scarce in comparison to the other type of pallets used. Most of the time there are less free available places for D type in comparison to the others.

According to the observations carried out for studying the transportation system, it was noticed that there are a lot of cases in which pallets are not fully utilized and used. In other words there are single products occupying a whole pallet, that results in transportation of pallets with a single product as well as occupying space in the inventory. The reasons for that were found out due to the reason that the company is custom oriented and some of the batches are consisting of single products. The other reason for that is that the goods are coming from different workshop places and there is not a merging point for the different batches.

4.1.3 Loading area

The loading area in the inventory 7 consists of four gates used for loading distribution containers. The loading of the containers is executed by three workers and most of the time two of them are managing with the loading of the container and one of them is associated with the delivering of pallets from the buffer area, where the pallets are placed for loading, these places can be seen on figure 4-1 marked as P. The worker bringing the pallets to the containers are using manual forklifts and place the pallet at the entrance of the container. The buffer area for loading consists of different places; this difference is seen on the different loading gates, see figure 4-1. At loading area LF 29 the whole buffer is used for loading and that sums up to 18 places for loading. At loading area LF 31 there are 16 loading places, the same number has loading area LF 33, LF 35 is having 18 places. The AGVs are dealing with placing the pallets with goods on the mentioned places. The table 4-2 summarizes the places for loading in the loading area.

Gate LF29 LF31 LF33 LF35 Places 18 16 16 18

Table 4-2. Available places for loading at the corresponding gates for loading

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There are two places from the buffer area on loading area LF 31, LF 33, LF 35 that are used for positioning the empty pallets that are taken away from the AGV transportation system, they are marked as E on figure 4-1. The average amount of empty pallets orders from all of the loading places using these two places is 149 pallets; the sample taken regards 27 days and 18 hours of activity in the inventory, appendix 4. At the same time 3 of the loading areas, which are further from the place used for manipulation of all the empty pallets has a system for storing empty pallets. Only one type of pallets, the one that is used the most is stored there. That place occupies one place from the loading places marked as PL on figure 4-1. When a certain level of these pallets is reached a manual fork lifter is taking away the empty pallets from these areas.

The places used for placing pallets for loading the containers are connected to the computer system in a way that the workers know exactly on which place what kind of goods are being placed. When the pallet is removed from the buffer spot and loaded in the container, i.e. when the scanner is used for marking the products loaded, a signal is sent to the system, which places an order for delivering a new pallet that is supposed to be loaded in the same container. By the observations made and the interviewed carried out with the workers at the loading area it was found out that often occurs that there are no pallets being delivered on time. That creates waiting for the right pallets to be supplied at the loading area.

The time necessary for loading a container varies, but averagely it is from 3 to 4 hours needed for loading a container, but sometimes it is less and more, depending on many factors. Some of these factors are the level of the workers’ involvement as well as the type of goods being loaded. Some of the goods are big in size and there are not so many needed activities to load one container and others are small in size and many in numbers, so there is a need for more activities, which corresponds to more time needed to load a full container.

The number of pallets needed for loading a full container with goods differs, but it is estimated that 90% of the time there are between minimum of 35 and maximum of 50 pallets needed for one full container to be loaded. That corresponds to average of 43 pallets needed for loading a container. These estimated numbers are varying from the estimated mean of average, and the information was taken from the interviews made.

The containers are loaded in a special sequence, as the products are loaded in a sequence of first in last out, with respect to the delivery sequence to the customers. Another issue being identified from the observation and the interviews made is the fact that even there are pallets being delivered, due to the sequential order followed for loading the containers the workers are not loading the incoming pallets, but wait for the right one to come, that has to be loaded. There are circumstances when pallets are delivered to the loading area, but they are not meant to be loaded at that moment.

The different loading areas are not following any certain logical order in which they are used for loading distribution containers, they are picked up randomly.

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4.1.4 Empty pallets area

The empty pallets after the loading of the containers are delivered to an area where they are arranged and then moved in upstream direction to the workshop areas, where they are loaded with new goods preparing for distribution in the company. The area contains 14 places where the empty pallets are left after being transported by the usage of the AGV transportation system. The places are designed in such a way that all the pallets that have been used in the loading area were transported by the AGV system to these 14 places. Since the system is not functioning in this way and the places designed for that activity are not entirely used, the area can be seen on figure 4-1.

In the empty pallets area there are also two conveyors, which are used for storing and arranging two kinds of pallets used in the distribution system in Kinnarps. When a signal is sent to the system the AGVs are in charge of delivering them in the upstream direction of the supply chain. The average of 54 orders per day has been identified as 27 days are considered with 18 hours of activity per day, appendix 23.

4.1.5 Goods arriving in the inventory

There are two different ways in which the goods are arriving in inventory 7. All the incoming goods are stored in the rack system in the inventory or the places that are regarded as type D. The two different systems used for delivering goods to the inventory 7 are as follows, figure 4-1:

• The distribution conveyors are playing the role of pick-up places. There are 3 conveyors in total and the AGVs are responsible for picking up the pallets with orders and distributing them to the inventory 7 only. The goods that are picked up from there are coming from the other inventory in the company. The reason according to which they are stored there arises from the fact that there weren’t enough space in inventory 7. The other place the goods are arriving is from production within the company, these areas are excluded from this thesis. According to the secondary data, the statistics of the company it has been found out that the average of 284 pallets per day are arriving in the inventory, the sample considers 27 days and it is for 18 hours per day, appendix 1.

• The products are delivered to the inventory 7 from other places in the company by the usage of the AGV’s transportation system. An average of 325 pallets per day has been established for 27 days and 18 hours per day activity. A detailed data representation can be seen in appendix 1.

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4.1.6 Distribution of goods from inventory 7 to the loading area

According to the planning, the pallets are distributed to the loading area from the rack system and from the inventory places classified as type D places. It is seen that number of pallets are being transported from the inventory to other places outside of the inventory 7, where other loading areas are being used for distribution, as being explained earlier. The withdrawing of goods from the inventory starts when an order for loading a container is activated, and the pallets are dispatched to the loading area. The sample used considers 27 days and the days are selected due to the fact that they represent realistically the activities in the inventory. An average of 633 pallets per day delivered from the rack system to the loading area has been calculated from the secondary data obtained from the company, appendix 2.

Figure 4-2 represents the average of orders per hour from the loading area, with respect to 27 days. It can be seen that the orders are having two peaks, which are from 6 to 7 and from 19 to 20, see appendix 5.

average orders per hour

05

1015202530354045

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24hours

ord

ers

orders

Figure 4-2. Average orders per hour from the loading area

4.1.7 Stocking policy

All the goods arriving in inventory 7 are stored in the rack system, or the places that are serving the D type. Some products are oversized and there is a special place where can be stored marked with letter O on the figure 4-1. All the goods entering inventory 7 are passing through a control station that as shown on the figure 4-1, there they are dimensionally measured and stored at different places in inventory 7 depending on the size. The stocking in the rack system follows a certain predefined order. The storing of the goods starts from the line 21, where all the pallets places are aiming to be filled up and then it is normally distributed along the rest of the rack rows. Through the interviews being made it was identified that the reason why the system is designed like that is due to the fact that from line 21 it predefines the goods to be on equal distances from the loading areas. The following table 4-3 summarizes the average orders from the rack system for 27 days and 18 hours of activity in the inventory, appendix 4, with respect to lines in the rack system.

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line 27 25 23 21 20 22 24 26 28 30 Average for 27 days, in terms of pallets 22 27 57 87 79 72 60 56 42 29

Table 4-3. Average pallets orders from the different lines in the rack system

The collected data confirms that the stocking of pallets in the rack system starts from the line 21. As seen line 27 and 25 are with few orders and that is due to the fact that there are stored pallets of type D, which are placed on the ground and there are no racks being used. The other reasoning for that is the average small orders, as being explained earlier the orders for these pallets is much less in a comparison with the others.

Through the observation being made and the secondary data from the company it has been found out that there is a lot of AGV activity in the area around the line 21 in the rack system. Since the stocking policy directs the AGVs to be situated in this area the most that overcrowds the area.

4.1.8 AGV routing, lines and transportation control

The AGV activity within the inventory follows certain routing, which is determined by the control system. Through the observation and the secondary data it has been found out that the number of AGVs within the area is not fixed, but varies. This variation is difficult to be estimated, since during the times when there is less activity there are few AGVs in the inventory being active and during the busy times the number of the AGVs varies between 13 to 21.

According to the AGV routing and the control system, due to the division of the inventory in zones and due to the fact that the control system of the AGVs is for the whole company the AGVs are entering and leaving the inventory periodically. These movements are associated with many factors. One of them is the fact that AGVs are entering the inventory 7 fulfilling deliveries. As well as there are AGVs picking up orders from the inventory 7, but they are positioned outside of it. Another reason is the fact that when the AGVs are idle they leave the inventory and fulfill other tasks outside the area of the inventory. When the pallets are stored in inventory 7 and they are not supposed to be loaded in the same area the AGVs are fulfilling transportation activities to other loading areas, outside of the area. The last reason is the fact that the empty pallets stored on the conveyors are manipulated by the AGV system and they are taken out from the inventory.

Through the observations and the secondary data from the company it was obtained a clear picture that the AGVs aiming to leave the inventory are creating waiting time with respect to the AGVs looping in the rack system and picking up orders from the conveyor system. Since the AGVs that are leaving the inventory are using line 27, which gathers number of AGVs at one place.

Results

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The lines that the AGVs are following when execute tasks are unidirectional. The flow according to which the AGVs are moving can be seen on figure 4-1, the arrows shown are representing the flow of materials. The lines that are serving the rack system are designed in a way that the AGVs should make loops in order to pick up goods from different location. There is one line that is used for delivering goods from the conveyor system to the rack system, regardless of the area the AGVs are ordered to go. The separation between the rack system and the loading area is made only by the existence of one line. It is used for both the AGVs that are looping in the racking system issuing orders and for the ones that are delivering goods to the loading area, as well as for those AGVs leaving the loading area issuing other orders.

The control system dispatches the AGVs in selecting the shortest possible distance when the deliveries are carried out to the destination point. Through the secondary data obtained it was found out that the distribution of goods from the rack system to the loading area from line 27 and 25 is not following that rule. The AGVs are not following the shortest line, but instead making unnecessary movement which creates longer distance and results in more transportation time. Particularly the AGVs picking up orders from line 25 and 27 are not using line 21 to fulfill the orders to loading area LF29 L and LF31 R, but use line 22 instead.

The transportation control is designed in a manner that the AGVs are stopping and waiting for other AGVs involved in a pick up or drop off actions. This occurs when the activities are beside the transportation line, which is used by other AGVs, if there are any occupying the same line at that moment. The AGVs are fulfilling one order at the same time and they are ready to execute the next one, which is waiting in queue, but if they are close to other order, they are taking it and the one that was in queue system is given to another AGV closer to it. In other words the AGVs are following the nearest vehicle selection rule.

When the AGV activity is more in a certain areas they stop and wait for each other until they read each others tasks and go the way they are supposed to. Also when the AGVs are programmed in a way that there should be certain security distance and due to that reason they stop and wait in order to keep the distance.

From the observation being made and the secondary data generated from the company it has been found that the waiting time for the AGVs is noticeable in two lines. Respectfully to the line used by the AGVs to deliver goods to the rack system and the central line in the inventory. The line which is used for delivering goods from the conveyors to the rack system is used also for the AGVs looping around the racks. Due to the fact that the AGVs are aiming to the line 21 due to the stocking policy of the system the number of the AGVs in this area creates waiting time for those AGVs that use the lines in this precise region. The fact that the AGVs are making deliveries mainly around line 21 reflects in occupying this line the most and makes the AGVs first waiting for each other and secondly creates waiting for those AGVs that pick up and drop off goods.

The fact that the central line is used for both the AGVs looping among the rack system and the ones making deliveries to the loading area, the high number of AGVs using the line at the same time creates waiting time. Form the secondary data and the observations being made it was found out that there are occasions in which the AGVs are registered in the system as inactive due to the long waiting time created.

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4.1.9 Planning

The planning in Kinnarps is weekly based according to the interviews carried out. There is a schedule being made for the week with respect to the production in the company. The planning in the inventory is associated with scheduling the loading of the containers. It is based on the estimation of the availability if the produced goods from the production and the possibility of the pallets to be delivered at the right place on time. The planners estimate the possibility, whether they can be able to provide the goods in a certain time period for loading a distribution container.

4.1.10 Stoppages and Errors

There are number of stoppages being recognized in the inventory, due to AGV different failures. They are associated with failure of the system, emergency stop, stoppages due to AGVs going out of the line, error due to the load, bumper stops and error due to long waiting time. All the errors are valuable for the analysis of the material flow, but the bumper stops and errors due to long waiting time would only be considered. This is due to the fact that the bumpers are statistically the higher percentage of errors based on the secondary data obtained. The error due to high waiting time is considered due to the fact that it represents the degree of involvement of the AGVs in a stoppage as well as the excessive waiting time in the system.

• Bumper stops occur when the sensors on the front or back of the forklift are compressed and this arises when the AGVs are picking up or dropping off pallets within the inventory. This stoppage happen where the activities of the AGVs are presented among the rack system, loading area and the distribution conveyors. For the present time from the secondary data from the company there is an average of 84 bumpers per day, where 37 % of them occur in the rack system, 54 % in the loading area and 10 % at the conveyor system, appendix 6.

From the observations made and the secondary data from the company it has been found that bumper stops are happening all around the inventory, but it was highly recognized that the effect of these errors is affecting the system the most when number of AGVs have been involved. Due to that reason in the areas where the activity of the AGVs is high the effect of the stoppages is high. Another important consideration being recognized is the fact that when the stoppages occurred and numbers of AGVs have been involved there is a disorder created, and it takes time until the smooth flow is reestablished, this disorder is proportional to the number of AGVs being involved in the stoppage.

• The errors due to high waiting time occur due to bumper stop and other errors

and that register the AGVs being involved in the queue, but not all the AGVs being involved in the queue are marked with this error. The consideration of this error indicates the extent of the involvement of the AGVs in an error. From the secondary data for the present time there is an average of 81 errors

Results

41

per day, and the sample taken considers 20 days of activity, the same days as being considered for the bumper stops. From this number 58% of these errors occurred in the rack system, 32% are situated in the loading area and 10 % are at the conveyor area, appendix 7.

4.1.11 Process mapping activity

The process activity mapping of the inventory is done with respect to the activities carried out by the AGV system in terms of times and distances. In appendix 8 can be found a detailed map regarding all times and distances between the different points in the inventory, where the orders are placed. The times are ideal with respect to points of pick ups and drop offs, the distances and the times are measured from the middle of the lines, which is at equal distances from the edges of the rack system to the middle of the drop point area. The times are acquired from the secondary data obtained from the company and correspond to transportations, when there are no stoppages or AGVs occupying the lines. That gives the possibility for estimating the ideal time for a transportation activity without registering the effect of stoppages and the interdependence effect AGVs occupying the transportation lines, which results in a transportation delay varying in terms of different circumstances.

Figure 4-3 represents the material flows system, where the different nodes of the system have been pointed out as well as the connection between the processes and the sequence according to which the activities occur. It also represents the transportations that take place in the inventory, and the possible scenarios and directions according to which the activities are carried out.

Figure 4-3. A material flow map between stations, with respect to transportation activities carried out by the AGV system

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4.1.12 Waiting time and transportation delay

The waiting time in the transportation control system is a measurement that corresponds to the waiting time needed for the AGV system to take an order from a pick up place, which is the sum of the assignment waiting time and the empty travel time. From the secondary data it was obtained that the average empty travel time with respect to the lines in the rack system, the distribution conveyors and the empty pallets places is 3.3 minutes. The sample was taken for 10 days and 18 hours of activity, with high demand of orders, appendix 24. Considering the waiting time that the orders have in order to be picked up from the rack system a generated picture is obtained on figure 4-4. The measurement corresponds to 27 days and 18 hours of activity in the system showing a realistic picture in the inventory, the data is represented in appendix 9. The values are taken from the secondary data obtained from the company and it corresponds to the lines in the rack system. It is seen that the waiting time in some lines is much higher with respect to others.

Figure 4-4. Percentage of the waiting time of orders in the rack system corresponding to the lines within it

According to the picture obtained it can be seen that line 21 has the highest waiting time for orders, and that is proportional to the amount of orders coming from those lines, which is connected to the stocking policy used in the system. The amount of orders for those selected days is presented in appendix 4. In order to connect the variables the sum of the waiting time corresponding to the lines is divided by the amount of orders for the same period of time corresponding to the same lines in the rack system,

Sum of the waiting time Average waiting time=

Amount of orders , see figure 4-5.

waiting time

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

27 25 23 21 20 22 24 26 28 30

Lines

%waiting time

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proportion of waiting time over number of orders

0:00:000:01:260:02:530:04:190:05:460:07:120:08:380:10:050:11:31

27 25 23 21 20 22 24 26 28 30

lines

aver

age

wai

tin

g ti

me

average waiting time

Figure 4-5. Average waiting time per order

The delay time is considered as the added time that is necessary to deliver the orders at the destination points. The base according to which the delay is estimated comes from the ideal times obtained in the process mapping in appendix 8. The delay time is a measurement that provides the research with the statement of the presence of obstacles in the transportation system, disturbing the proper material flow. The delay according to the observations being carried out is due to different circumstances. These circumstances are associated with the case when high involvement of AGVs is noticed in a certain area. That creates bottle necks and after effect of disorder when all the AGVs waiting on each other until reestablishing the proper flow. According to the secondary data and connected to the observations made it was noticed that there are cases when AGVs are registered with the state of error due to excessive waiting time. The other reason for that delay and waiting is the case when there is a stoppage of AGVs on a line that the other AGVs are using. According to the secondary data obtained from the company and comparing this data with the ideal transportation times generated in appendix 8 a comparison was conducted between the times. The measured transportation times are based on the usage of the company’s secondary data in the face of the simulation recording the AGV behavior. The dates are chosen in a manner of conducting the possibility of the system to manage with high amount of orders, which is the strategic goal for this paper. The following table 4-4 generates the results with respect to different lines from the rack system, places for empty pallets and the conveyor system used for delivering goods in the inventory. Detailed information and the dates of that result can be seen in appendix 10.

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Pick up points

Ideal transportation times

Real transportation times

Delayed time

Lane 27 0:03:17 0:04:24 0:01:07 Lane 25 0:03:51 0:05:28 0:01:37 Lane 23 0:02:45 0:03:44 0:00:59 Lane 21 0:02:53 0:03:59 0:01:06 Lane 20 0:02:37 0:03:54 0:01:16 Lane 22 0:02:33 0:03:19 0:00:46 Lane 24 0:02:44 0:03:47 0:01:03 Lane 26 0:02:12 0:03:09 0:00:57 Lane 28 0:03:02 0:04:19 0:01:17 Lane 30 0:02:20 0:03:03 0:00:43

LF31 empty pallets 0:01:59 0:02:42 0:00:43 LF33 empty pallets 0:02:19 0:03:26 0:01:07 LF35 empty pallets 0:02:41 0:04:27 0:01:46 Conveyor 0:02:31 0:03:49 0:01:19

Table 4-4. Average delay time in accordance to the positions in the inventory

4.2 Comparison between the present and past state of the inventory

In order to compare the present state with the past state of the activities being carried out in the inventory the past state map of the system should be clarified. In that manner the only difference between the past system and the present one is oriented only in manipulating the empty pallets from the loading area. In the past system despite the way that the empty pallets are manipulated in the current one is the fact that all the pallets were carried away by the AGV system. According to the interviews made the usage of the storage system for pallets was limited. The two places that are presenting in the current system for carrying out the empty pallets weren’t only used, but instead all the places for loading had codes that give an order to the AGV system to take away the used pallet. In other words when the workers were done with loading the goods from a certain pallet they placed the pallet on the spots and place an order for the AGV system to carry out the empty pallet.

The sample taken for this investigation is considering a high season within the company. There are three variable considered in the sample and compared to the present state with respect to number of order from the rack system to the loading area, number of orders from the distribution conveyor to the rack system and number of empty pallets orders. The sample taken for the past system is from 2007 during august, and it considers 15 days for 24 hours per day, and the one taken from the present state is from 2008, which regards 15 days and 18 hours of activity. The calculations are made in a way of involving the number of orders with respect to the variables chosen in a way of affecting each other. Figure 4-6 summarizes the percentage of involvement of each of the three selected variables and the influence of them on each other.

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Figure 4-6. Comparison of variables from the past and present system

From the figure obtained it is clear that the involvement of the variable with respect to the number of empty pallet orders is affecting the possibility for the system to deliver orders. Since there are fewer orders of empty pallets there is higher involvement of the orders for delivering. The data used and the sample taken is found in appendix 11. The result also shows the percentage of improvement from the past system with respect to the current one figure 4-7. In other words decrease of 31.3% of activities of manipulating empty pallets results is 7.8 % of improvement of the possibility for delivering more pallets to the loading area. The change in the conveyor capability is 1.5 %, the data used is found in appendix 11.

-40.00

-30.00

-20.00

-10.00

0.00

10.00

%

improvements

improvements 7.82 1.52 -31.31

1 2 3

Figure 4-7. Improvements range of the variables, in terms of higher order output

0.00

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%

delivery orders 2007 52.26

conveyor 2007 27.37

empty pallets 2007 20.37


conveyor 2008 27.80

empty palltes 2008 15.51

1

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0

10

20

30

40

50

60

% of errors

Waiting time error-areas

rack system 2007 48

rack system 2008 58

loading area 2007 45


conveyor 2007 8

conveyor 2008 11

1

Another issue that is taken into consideration is the amount of stoppages in the past and the present system. As being explained, only two kinds of stoppages will be taken into consideration as the ones affecting the system the most. Different areas in the inventory were investigated in a way of presenting where the errors occur and the dependency between the error and the AGVs involvement. From the secondary data obtained from the company figures 4-8 and 4-9 were generated. The figures present the areas where errors occur and their interrelation among each other. The sample taken from the past system covers 20 days of activity, and the one from the current system corresponds to 16 days of activity. Since the sum of the errors is not used, but their connection with respect to the areas of occurrence, the difference in the dates is not leading to bias, see appendix 6 and appendix 7.

Figure 4-8. Connection of the waiting time errors with respect to area in the inventory

According to figure 4-8 it is seen that the number of waiting time errors percentage in 2007 is less then the one 2008 in the rack system. As well as the waiting time error percentage in the loading area in 2007 is more then 2008. With respect to the conveyors the percentage is less in 2007 compared with 2008.

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0

10

20

30

40

50

60

% of errors

Bumper stops-areas

rack system 2007 36

rack system 2008 37



conveyor 2007 14

conveyor 2008 10

1

Figure 4-9. Connection of the bumper errors with respect to area in the inventory

The bumper stop errors according to the figure 4-9 obtained show that in 2007 in the rack system the error percentage is slightly less then the one in 2008. And at the same time the bumper stop percentage in the loading area in 2007 is less then the one in 2008. With respect to the conveyors the percentage is more in 2007 compared with 2008.

Future state system suggestion

48

5 Future state system suggestion

In this chapter the future state system suggestion model has been presented with the conceptual ideas and suggestions for improvements being given.

Through the research carried out there were number of factors recognized, which according to the authors of this thesis have negative influence on the material flow system. Based on the research assumptions, figure 5-1 presents the problem areas being identified. The generated diagram is a foundation for deeper analysis and the causes of these problem areas. In accordance with the reasons for these issued problems the future state map of the system is generated, where they are removed or decreased their negative influence on the AGVs distribution system.

Figure 5-1. Cause and effect diagram for Kinnarps’ inventory

What obstacles are preventing the ability

for loading more distribution containers?

People Process Technology

Equipment Material Environment

People react in certain time when an error occurs

The time consuming needed for loading a container differs

The AGVs are old and the chance for errors is higher

Sometimes material occupies small space on the pallets

Pallets used are damaged

Lack of spaces in the inventory

Stoppages

Daily peaks

Control system

Transportation layout

Sequencing of activities

Stocking policy

Excessive number of AGVs

Pallets distribution, empty pallets transportation

Planning

The goods arrives late

Lack of spaces in the loading area


49

In accordance with the research carried out and the investigations being made in the field of this thesis certain areas were identified. These issues were selected and pointed out since they influence the most the distribution of and loading of goods in inventory 7 at Kinnarps Production AB. The following areas were identified as candidates for changes and eliminations, where the interrelation among these variables was recognized. Based on their consideration the future model of the AGV system in Kinnarps’ inventory 7 was developed. The areas can be listed as follows:

• Stocking policy • Excessive number of AGVs • Sequencing of activities • Transportation layout • The goods arrive late to the loading area • Control system • Lack of spaces

Figure 5-2 represented the conceptual idea for the future state map of the layout in Kinnarps’ inventory. The map is based on the ideal implementation of the effect arising from the areas listed above and through the necessary calculations and considerations being made. Some of the changes being made are not presented with the precise estimation and the dimensions, but rather consider the effect of the changes being made and the output of the new system with respect to the goals of this thesis.


50

Figure 5-2. Future state layout of Kinnarps’ inventory


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5.1.1 Transportation activities

The transportation activities in the inventory in the new model of the system are carried out by the AGV system. The functionality of the system has the same dimensions and reasoning as the one in the old system.

The distribution of goods to the inventory will be done by the usage of the old conveyor system as it is in the old model of the system. The goods arriving in the inventory would enter inventory 7 from the same conveyor was used for a pick up place. The method how the products are arriving from the production in company would be changed. The pallets with goods would not enter the inventory by the usage of the AGV system, but they would be transported from a new conveyor system. From where the AGVs would pick them and distribute them in inventory 7, figure 5-2.

The orders placed for delivering of pallets to the loading area would come only from the rack system, where all the different type of pallets is stored. The orders for pallets that are currently stored in inventory 7 but are not supposed to be loaded there are going to be taken out by placed on a conveyor system next to the new one described in the future model. In this was the AGVs dealing in the inventory 7 are not going to leave the area for carrying out deliveries outside. After being placed on the conveyor system the AGVs from outside of the company are in charge of distributing the orders to the other loading places in the company. Figure 5-2 visualizes the stated issues above.

5.1.1.1 Pallets and rack system

As being explained in the current system with respect to number of different pallets used and the used classification according to the available places in this thesis, the new way of dealing with them would be explained further on. As being mentioned the number of different places being used for storing pallets is classified as A, B, C and D.

In the new model all the pallets would be stored in the rack system in the inventory as shown on figure 5-2. In this way the inventory would be a mixture of all the pallets being used in the distribution system. The arrangement would be made in a way that pallets type places A, B and C would occupy the rack system and would keep its current arranging system in level perspective. Though, the arrangement would be among the whole rack system and in comparison with the old system lines 27 and 25 would be used for delivering and retrieving of A, B and C types of pallets places as well. The arrangement of D type pallets would be in a way that they would occupy the first two row of every rack on the side of the loading area. And on the other side of the rack system they will occupy the first three rows of the rack system. The only place where it would be occupied only by type D pallets would be on the side of line 27 marked as L27, where there is a single line and on line 30 there is single line as well.


52

As in difference with the past system the new system would be designed in a way that on the top of D pallets places would be an open area where there could be stored all kinds of the other pallets A, B and C. The place where that activity wouldn’t be carried out would be the line 27 marked as L27. This difference is made in accordance to security issues. The new arrangement can be seen on figure 5-2.

According to the new system for storing pallets there would be 172 fixed places for type A, B and C pallets places and 113 for D. The second level over the D pallets places would generate 103 places, which are flexible in a way that if there is a fluctuating demand for types A, B and C it would fulfill it. Otherwise if these places are used evenly that would generate 34 places for each of the kind A, B and C. Table 5-1 summarizes the new available places. According to the new pallets arrangement there would be generate 732 places in the inventory for all kinds of pallets.

Type A,B,C A B C D fixed 172 172 172 113 flexible 103 34 34 34

Table 5-1. Pallet places available in the new system

5.1.2 Loading area

The loading area would be changed in a way that since all the type D pallets places would be stored in the rack system there would more places for loading goods in the containers. Later on in the project would be presented the changes in the layout by adding additional line in the middle in the inventory. Though for that moment it would be commented that the second line built would eliminate the first line of loading places in comparison of the old system as well as the first line of the places that were used for type D pallets places.

According to the new model the loading places in the loading area would be in total 112, respectively 23 to loading gate LF29, 28 to LF31, 28 to LF 33 and 33 to LF35. The places, which are meant for positioning empty pallets, would be changed as visualized on figure 5-2. Instead of being on side where the entrance in the loading area is from the central line they would be at the exit from the loading area looping lines.

If the system is changed to an extent, which is not considered in the future state map on figure 5-2, where the empty pallets transportation is not carried out by the AGV system. But rather be designed a transportation system, which is retrieving the empty pallets to the empty pallets area by the usage of a conveyor system, there would be more places for loading and a lot of activities saved from the AGVs activity. This would result in 124 places for pallets used for loading the containers, in this case the empty pallets places are used for loading places. And an average of 158 orders for empty pallets per day, estimated in the current state. Respectfully to 24 places to loading gate LF29, 32 to LF31, 32 to LF33 and 36 to LF35.The following table 5-2 summarizes the places for both of the cases:


53

method\gate LF29 LF31 LF33 LF35 AGV 23 28 28 33 Another transportation system of empty pallets 24 32 32 36

Table 5-2. Number of loading places with respect to different loading gates and possible alternative methods for retrieving empty pallets

5.1.3 Empty pallets area

After considering the empty pallets area during the investigation of the area several changes have been considered to be made. In the future model the empty pallets transportation instead of being performed by the AGV system within the inventory would be carried out from the AGVs outside of the inventory. The conveyors would change their current direction and would deliver the empty pallets outside of the inventory as shown on figure 5-2. That would result in average of 54 orders being saved from the AGV system within the inventory, the sample corresponds to the current system estimation.

The other change made is the building of the new distribution conveyors, which will serve as distribution conveyors as being explained earlier and for the goods that are going out from the inventory 7.

According to the new model the places where the empty pallets are dropped down from the AGVs dealing with these actions will be reduced, but since they are not used all of them that will not disturb the system to such a level. And by implementing another system retrieving the empty pallets instead of the AGVs, the places for dropping empty pallets in the area would not be used.

5.1.4 Goods arriving in the inventory

In comparison to the old model, in the new one all the goods arriving in the inventory are stored in the rack system. As this will be the only place for storing goods of all the different types of pallets places A, B, C and D. There will be two conveyor systems that work in a similar manner. As the newly one designed will gather all the goods that are arriving from the production in comparison to the old system where the AGV system was used for distributing goods.

5.1.5 Distribution of goods from inventory 7 to the loading area

According to the new model of the system all the goods that are ordered to be delivered to the loading area are going to be picked up from the rack system. In this way every different type of pallets is going to be stored in the same area. That results in the fact that the AGVs are going to pick up pallets only from the rack system for deliveries to the loading area.


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5.1.6 Stocking policy

The suggestion for the new stocking policy is oriented in arranging all the incoming goods equally among the rack system. Despite the old policy where the staring stocking point is in line 21. By referring to the secondary data in appendix 4, a new average representation of the orders can be found out in table 5-3. In this case the average orders coming from the pallet type D places are mixed equally with the average orders from the rack system.

Line 27 25 23 21 20 22 24 26 28 30 Average of all the orders for delivering pallets to the loading area for 27 days for 18 hours 57 57 57 57 57 57 57 57 57 57

Table 5-3. Average overall number of orders from the inventory spread among the rack system

5.1.7 AGV routing, lines and transportation control

The suggestion regarding the new model of the system is oriented in implying the routing activity of the AGVs in the area of the inventory 7. The AGVs serving the orders in the inventory 7 would be routed only within the area and would have the possibility to leave the area in some certain cases. One of the cases can be found out when the AGVs have to recharge their batteries.

5.1.7.1 Lines and transportation control

The suggestions corresponding to the layout changes are associated with creating a second transportation line parallel to the central line as shown on figure 5-2. The implementation of this change is done in a way of estimating the allowable distance between the two lines. From the interviews carried out it was estimated that the distance between the lines should be 2.3 meters. Based on that distance and the safety distance from the side of the loading area the conceptual layout showed on figure 5-2 was developed. In this way the AGVs would have 2 lines as corresponding to one of them used for making deliveries and the other for looping among the rack system.

The flow remains its current direction in the future suggestion of the transportation layout. The other changes in the lines would be associated with the creation of a connection between the line 25 and the line that serves the loading area LF 29 L and LF 31 R, in this way the new connection would save travel distance and respectfully the time necessary to carry out the actions.

Since it was found out in the present state that the dispatching policy from lines 25 and 27 are not following the shortest way of choosing the delivery by going through line 21, but use 22 instead. With establishing the connection as explained earlier, the AGV dispatch would choose the newly build line. If the connection is not established as proposed, then there is a need for a change in the dispatching policy in a way of routing the AGVs to choose the shortest way through line 21.


55

According to the proposals given above the AGV activity in the inventory would be spread in the area, since the AGVs wouldn’t occupy only certain lines and areas. That would result in less waiting on each other. The same rule applies to the area around line 21.

The transportation control within the inventory area would be based on a separate control system. In this way the AGV activity wouldn’t be affected by the company’s one. In a similar manner the transportation control can be optimized to a high extend since there would be certain number of AGVs serving all the orders. In this way the sequencing of the activities can be done in more efficient manner, by saving more empty travel time of the AGVs.

5.1.7.2 Number of AGVs within the inventory

The determination of the required AGVs necessary to fulfill the system demands would start form the investigation of the present system requirements. That would include the usage of the secondary data acquired from the company, with respect to the real times acquired from the data. Since the inability to identify the source from where the orders are coming from the different places of the company, the estimation would use the present conveyor system as the place from where these orders are taken. The secondary data used corresponds to time measurement with respect to empty travel time, loaded time and waiting time. These values and their interrelation can be seen on figure 2-4.

According to the model proposed by Mantel and Landeweerd (1995) the required number of AGVs that serves the system can be found out by summing up all the total loaded time, empty travel time and waiting time in case of a busy period of the system and the sum is divided by the total time the AGVs are available. The model is applied by taking into consideration one day of activity with respect to 18 hours per day. The day was taken from the secondary data and it was chosen, because it includes occurrences of stoppages, high orders from the rack system and approximately equal usage of the loading gates in the inventory. As well as high activity of the AGV’s system during that time period that would give a realistic picture of the calculations. By applying the model and carrying out the necessary calculations it was estimated that the required number of AGVs necessary to serve the system is 22, see appendix 12.

The usage of the model proposed by (Koo, Jang and Suh, 2005) gives the possibility for more precise estimation since the usage of average times is not leading to biases and integrates the times and the variables more realistically. The model uses stochastic analysis where the usage of times and number of orders estimate the necessary parameters within the model.

The times being used within the model are coming from the ideal process map developed in appendix 8 and uses the average delay of the transportation times, which was acquired from the calculations found in appendix 10. Base on these calculations the new process map was developed presenting the realistic delay times of the transportation activities, corresponding to the areas in the inventory, see appendix 13.


56

The sample used in this model covers the same date used as in the model proposed by Mantel and Landeweerd (1995) and it assumes that the AGVs are staying only in the area of the inventory, as well as the fact that all the incoming goods are coming from the conveyor system. Firstly the model will be used on the real case when the goods are spread averagely according to the present stocking policy that is found out in appendix 4. And secondly it will present the case when the orders are coming equally from the rack system. The D pallets places are assumed to be in the first calculation on line 27, 25 where they are presently stored and in the second case they are equally spread among the rack system.

By using the model in the first case the tables presented in appendix 14 and 15 were used. Respectfully appendix 14 represents the travel times between the different locations and appendix 15 is covering the requested rate for deliveries. Based on these tables it was estimated the average loaded travel time. In order to do that equation (1) was used.

{ }1 1

( ) ( / )( )n n

l ij ij ui j

E t f F t l= =

= +

∑ ∑ , (1)

For estimating the variance of the loaded travel time equation (2) was used.

{ }2 2

1 1

( ) ( / )( ) ( )n n

l ij ij u li j

V t f F t l E t= =

= + −

∑ ∑ , (2)

Where the sum (tij+lu) stands for the travel time between the locations in the inventory and lu is the time needed for pick up and drop off of the pallets, this sum corresponds to the travel time values in listed in appendix 14, where the pick up and drop off times have been included. The values of fij are taken from appendix 15 and correspond to request rate from the different locations. F is the total number of delivery requests in the inventory, which in this case is F=1608.

After the calculations being made the estimated values were as follows: E(tl) = 4.04 minutes and the V(tl) = 0.58 minutes.

In order to estimate the mean and the variance of the empty travel time the following equations were used. Respectfully in order to calculate the estimated empty travel time equation (3) was used.

1 1

( ) ( )n n

e si k kii k

E t f fd t= =

=

∑ ∑ , (3)

For estimating the average time for the variance of empty travel time equation (4) was used.

2 2

1 1

( ) ( ) ( )n n

e si k ki ei k

V t f fd t E t= =

= −

∑ ∑ , (4)


57

In order to use the equations (3) and (4) the table in the appendix 16 was developed, which corresponds to the proportion of the delivery requirements on the different locations in the inventory, which in the equations (3) and (4) corresponds to the values of tki. In order to estimate the times corresponding to these locations the table in appendix 17 was developed.

In the equations (3) and (4) used 1

1 n

k iki

fd fF =

= ∑ and1

1 n

i iji

fs fF =

= ∑ , where fdk is found

in appendix 17 and fsi is found in appendix 15. After using equations (3) and (4) the estimated values for average empty travel time are E (te) = 1.11 minutes and the variance is V (te) = 2.22 minutes.

The calculations being made corresponds to random vehicle selection rule, if the method is repeated iteratively for the case of nearest vehicle selection the new values are as follows E (te) = 0.53 minutes and V (te) = 2.11 minutes.

The next step carried out was to calculate the estimated average total travel time and the variance. The results are as follows respectfully as ( ) ( ) ( )v l eE t E t E t= + = 4.35

minutes and the variance is ( ) ( ) ( )v l eV t V t V t= + = 3.2 minutes. And the same

procedure for the nearest vehicles selection rules gives the results as E (tv) = 4.17 minutes, and V (tv) = 3.09 minutes.

Based on the calculations made it was possible to estimate the number of the AGVs

required to serve the system. The formula used was l

a

T

T

+

(5) and it includes the Tl

total sum of time required by the vehicles for loaded travel and Ta is the total time the vehicle is available, the “+” is used to round up the number to the positive value of the answer. According to the sample used it covers 18 hours of activity. The Tl value is the product of E (tv) and F, so after the calculations it was established that the number of AGVs needed is 6. This number corresponds to Markovian chain or Poisson distribution of arrival rate, since the case in Kinnarps is not stable in way of incoming orders, another method was used to determine the fleet size.

Since there are picks during the days and especially for the chosen date when the data deals only for 18 hours the estimation was based on several assumptions. The peak of order would correspond to the percentage of all the orders during the selected time period. In other words by the usage of the data in appendix 18 it was determined that the peak of orders is between 12 and 13 o’clock and the number of orders are 58, which corresponds to 8.9 % of all the orders in the inventory for the selected period of time, which are 1608. The estimated 8.9 % corresponds to 143 of all the activities in the inventory. So, for the time period of one hour there are 143 orders needed to be carried out by the AGV system, which corresponds to Tl= 621.4 minutes of loaded travel time. And by the usage of equation (5) it was estimated that during that period of time the system would require 11 AGVs. This case is considering the worst case scenario during the day of the investigation.


58

The same iterative procedure was carried out in estimating the required number of AGVs if the all incoming orders are evenly coming from the rack system. In other words for the same day that was selected the procedure proposed by (Koo, Jang and Suh, 2005) was repeated assuming that there is an average of 68 orders coming from every line in the rack system. By that assumption this will result in an equal average of pallets stored in the rack system, since statistically the taken goods would provide space for the goods needed to be stored.

The same procedure for estimating the required number of AGVs was repeated and the data is taken from appendixes 14,16,19,20, it regards the case when the orders are coming equally from the rack system. The result obtained after the calculations made are respectfully E(tl) = 4.041, V(tl) =1.02, E(te) = 1.13, V(te)= 2.37 minutes and the estimated total travel time ( ) ( ) ( )v l eE t E t E t= + = 4.37 minutes and the total variance

travel time is ( ) ( ) ( )v l eV t V t V t= + = 3.39 minutes. The estimated values are according

to the random selection rule and according to the nearest vehicle selection rule they are E(te)= 0.54, V(te)= 2.26 minutes and the estimated total travel time

( ) ( ) ( )v l eE t E t E t= + = 4.18 and the variance is ( ) ( ) ( )v l eV t V t V t= + = 3.27 minutes.

According to this scenario the estimated number of AGVs required during the busiest time is 11 AGVs.

5.1.8 Future state demands

There is a tendency for an increase of the demanded daily loaded containers leaving Kinnarps Production AB. According to the secondary data obtained from the company and with respect to the interviews carried out it was estimated that there is a need for 43 pallets needed to load one container. Through the daily number of orders to the loading area it was found out that there is an average of 633 pallets delivered to the loading area. That corresponds to an average of 14 loaded containers per day. If there is a demand for 24 containers leaving the factory daily then the average number of pallets orders for delivering to the loading area would correspond to 1032 pallets.

From the secondary data gathered from the company in appendix 2, figure 4-2 was created where the daily demand per hour was presented. In accordance to statistically the same sequence of distributing goods and the new demand obtained per hour figure 5-3 is generated. This figure corresponds to an increase in 63% in the demand orders to the loading area in comparison to the present state, the new demand per hour is found in appendix 21.


59

future demand for average order per hour

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

hours

ord

ers

orders

Figure 5-3. Average orders per hour from the rack system to the loading area, future demand

5.1.8.1 Required number of AGVs

According to the future state demand considering 24 containers being loaded and the estimated increase of 63 % of the orders necessary to be delivered to the loading area certain assumptions were made. The first assumption is that if the data in appendix 5 is used for calculations the average order for a day is 633 pallets to the loading area. Since then if the increase in the demand is 63% then the future state demand should be able to cover 1032 orders to the loading area. The second assumptions use the fact that if the system fulfills 633 orders proportionally all the orders in the inventory including the orders coming from the conveyors and the orders for the empty pallets would be 1581 orders. This proportionality is based on the data from appendix 15 where it is estimated that for 644 orders the system has in total 1608 orders.

After considering the results obtained it has been predicted that for 1032 orders from the rack system to the loading area the overall amount of orders necessary to be processed in the inventory would be 2586 orders. By using the approximation from the model given by Koo, Jang and Suh (2005) as shown in appendix 18 a new percentage of activities for certain periods during the days was defined corresponding to 1032 orders, appendix 22.

According to the model used for estimating the proper number of AGVs serving the system and by considering the data from appendix 22, it is possible to estimate the proper number of AGVs in the system. Several scenarios will be considered.

• If the time when there is a maximum is considered with respect to 67.33 orders per hour and the number of orders in the system is 2586, then it is possible to estimate that during that period of time the system would have 168 orders. That is the product of the percentage of 67.33 orders and the total number of orders in the system during that time.


60

o If the E (tv) = 4.35 minutes is used, which represents the current state of the system, then the required number of AGVs for 60 minutes

would be ( ) 168 4.35 168

1360 60

vE tm

× ×= = = AGVs

o In the case when the pallets are equally spread around the rack system and the time in this circumstance E (tv) =4.37 minutes is taken then

m( ) 168 4.37 168

1360 60

vE t × ×= = = AGVs

o Under the nearest selection rule the travel time is E (tv) =4.18 minutes averagely for both of the cases of arrangement of the pallets, which corresponds to 12 AGVs in the system.

• If iteratively the procedure is repeated for the minimum value from the appendix 22 where the number of orders is 14.85 orders, then :

o For E (tv) = 4.35 minutes the number of AGVs is 3;

o For E (tv) = 4.37 minutes the number of AGVs is 3;

o And the same number of AGVs=3 is found out in the nearest vehicle selection rule case when E (tv) =4.18 minutes.

• If the mode is taken from all the listed values in appendix 22, which corresponds to 61 orders per hour and repeating the same steps as above the results are listed as follows:

o For that period of time the AGVs=11, when E (tv) = 4.35 minutes;

o For the next case is 12 AGVs, when the E (tv) =4.37 minutes;

o And in the case of nearest vehicle selection the number is 11 AGVs, when the E (tv) =4.18 minutes.

The different scenarios considering different number of orders are giving different picture of the proper number of AGVs. In the case of the maximum orders the system would be able to manage with the demand, but be idle in the rest of the time. In the minimum case it would be manage with the orders. And in the case when the mode is considered the time with the peak would result in higher waiting time of the orders, but be less idle.

5.1.9 Process mapping activity

In the future state model the process activity map developed in accordance to the present state model, presented in appendix 8, would differ in some ways. Since the same AGVs are used and there are not so many changes being made in a manner of the transportation layout these changes would slightly differ from the future state model.


61

The variables that would be changed from the present state model map are the ones highly influenced by the changes made in the transportation layout, the new pick up place for the incoming goods in the inventory, and the changes associated with creation of a second transportation line in the area.

The values with respect to time and travel distance from lines 25 and 27 to loading areas LF29 L and LF31 R would be decreased respectfully. This arises from the creating the connection between line 25 and the loading area pointed out above. Also if the change is not implemented in this manner the change in the transportation control would decrease the transportation time and the travel distance.

All the values would be increased in a way of extending the travel distance due to the implementation of the second transportation line that would serve the loading area. The increase derives from the fact that the AGVs should switch lines, which insufficiently extends the travel distance. Since this suggestion is conceptual and it is not calculated in details, it would be wrong to estimate the degree of the changes in the travel times and the distances associated with the traveling.

The travel distance and the time necessary for delivering pallets from the new pick up conveyor area would be a new variable that would be presented in the process activity map. Since the new conveyor pick up position is following the same direction of the material flow and it is situated close to the existing conveyor pick up place the new values in the process map would be slightly increased from the existing ones.

According to the new suggestions given in the future state model of the inventory system figure 5-4 was developed. The figure presents the new nodes being created as well as the ones that have been removed from the system. The transportations activities that would take place and the possible scenarios according to which the flow of material would be are presented.

Figure 5-4. A material flow map between stations, with respect to transportation activities carried out by the AGV system in the future state model

Analysis

62

6 Analysis

This chapter is aiming to analyzing the finding from the current system and discusses the implementation of the new suggestions being given.

The results in the previous chapter of this thesis present the current conditions in Kinnarps inventory. The mapping of the activities in the area of this research gave the opportunity to estimate the negative factors influencing the material flow in the system. By doing so and establishing the connections between the empirical findings from the different variables being considered it was possible to establish a good picture of the processes.

Based on the findings in the current state of the inventory and the usage of all the data generated from all the statistical analysis made it was reasonable to give appropriate suggestions for changes in the process. These suggestions were already described in the result section. The ideas and the base according to which these suggestions were reflected on were coming both from the present and the past state conditions of the system. The past state in accordance to the present state system gave the attainability to establish correlations between certain variables in the system. That leaded to realistic and reliable judgments in accordance to the factors subjected to changes in the suggested future model of the system in the inventory.

The analysis corresponding to the different parts of the system and their connections would be carried out in the following sections in comparison to the different states of the system. Their evaluation would be carried out in accordance to the estimated theories in the stream fields being considered.

6.1 Transportations and distribution of goods

The material flow in Kinnarps’ inventory was investigated and a connection between the activities was established. After the investigations being carried out a deep understanding of the material flow and the material handling activities was found. According to (Bowersox, 1999; Harrison, 2002) this understanding let to focused evaluation and improvement in the performance of the system with respect to the manufacturing output.

Based on the transportation activities carried out by the AGV system there were several noticeable characteristics of the material flow. According to the activities carried out by the AGVs in the current state system the AGVs delivering goods to the inventory are making unnecessary transportations which can be considered as a transportation waste according to Goldsby (2005).

Analysis

63

Though, in the new model suggestion in the transportation system the incoming goods would be entering the inventory by a conveyor system. This will result in achieving the elimination of unnecessary movements of the AGVs, which would reflect in eliminating transportation waste. According to Öjmertz and Johansson (1997) the material handling activity effort would be decreased and unnecessary time needed for executing this task would be minimized. Another important consideration within this change is the fact that by creating a system boundary with the implementation of a conveyor system the AGVs serving the inventory wouldn’t be disturbed by the AGVs entering the area.

According to Öjmertz (1998a) the degree of disorder is a measurement of a value-adding activity. From the present state map it was estimated that the disorder presented by entering of the AGVs in the area which would be eliminated in the new model. Another important consideration is the number of AGVs being involved in the system, as being described by Mantel and Landeweerd (1995) and Ujvari and Hilmola (2006) the proper number of AGVs is important in estimating less blocking as shown on figure 2-3. By changing the way the goods are incoming in the inventory a proper number of AGVs can result in less interruptions and less disorder in the system. Since there is no synchronization between the incoming goods in the inventory that creates disturbances in the material flow reflecting in bottlenecks referred to (Harrison, 2002).

The goods that are currently stored in inventory 7, but they are not supposed to be loaded in the same area are creating a backflow, which prevent the stable and continuous flow, which is considered by Harrison (2002) and Sekine (1992), the data is registered in appendix 2. From another perspective referred to Hines and Rich (1997) that is regarded as an unnecessary transportation, and from another perspective creates unnecessary inventory within the area. From the demand amplification mapping of all the incoming and outgoing goods found in appendix 2, it was estimated that the number of incoming goods with respect to all outgoing is higher. That can be considered as a waste, since it creates unnecessary inventory and occupies space (Goldsby, 2005).

In the new model suggestion the transportation of the pallets, which are not supposed to be loaded in inventory 7, but currently are stored is going to be carried out by the AGV system outside of the area. In this way the necessary amount of AGVs is going to be kept at equal level, and will not be disturbed. Though, this transportation is still regarded as waste due to unnecessary transportation and double handling Monden (1993).

The pallets that are arriving from the conveyor system are also including the ones that are currently stored in the other company’s inventory. According to Goldsby (2005) and Womack (1996), that is regarded as an unnecessary movement and results in time consumptions.

Analysis

64

6.2 Pallets and rack system

One issue that was recognized during the investigation was the fact that pallets are not fully utilized. Hence, more transportation is required in order to fulfill the demands for loading a distribution container, according to Goldsby (2005) unnecessary transportation is a big cost associated in logistics and is considered as one of the non-adding value activities that should be eliminated.

The type D pallets within the loading area are occupying approximately 50 % of the places and are functioning as an inventory instead of a loading area. This is considered to be a major issue since this inventory occupies space and serves as unnecessary inventory according to Goldsby (2005). That takes away some of the capacity in the loading area for using loading pallets places. The secondary data form appendix 4 presents that there are in average 5 orders for 18 hours per day from these places. The existence of this inventory disturbs the entire material flow, since their localization is not part of the overall sequential material flow. That restricts and interrupts the proper material flow in the system, breaking the flow in certain divisions (Öjmertz and Johansson, 1997).

Another important aspect that was detected by the observations made is that long queues are created among the AGVs on the transportation line when carrying out deliveries to the rack system. This is due to the fact that when the AGVs are dealing with orders to the rack system near this central line, the other AGVs stop due to safety reasons, and wait until the orders are carried out. Since this line is the only line the AGVs are using for delivering goods a blocking scenario is initiated. According to Mantel and Landeweerd (1995), Arifin and Egbelu (2000) when the system is disturbed due to blocking then the ability for the AGVs to deal with more orders declines. The same transportation conditions occur with respect to the central cardinal line which is used for looping among the rack system and delivering pallets to the loading area.

According to the new model and mentioned in the result D pallets have been arranged only in the rack system, occupying the first two rows on the side of the loading area and the first three rows on the other side of the parallel side figure 5-2. Table 4-4 presents the new available places compared to table 4-1. The main reasoning behind this solution was initiated through the investigation of the data from the company found in appendix 3, which confirms that 15 % of the total demand orders for delivery belong to D pallets and 85 % the rest. This new arrangement of the D pallets decreases the queuing problem on the transportation line which is a problem in the current status since the possibility for AGVs to receive an order from D pallets is approximately 6 times less. That probabilistically will result in less probability for the AGVs to wait on each other, with the same proportion as the arrival of the orders.

The amount of AGVs interacting with each other in a restricted area is one the three important factors which affect the time variables in an AGV system described by Mantel and Landeweerd (1995). This was confirmed through observation and the secondary data from appendix 9, figure 4-4, a disorder and high waiting time took place in the middle of the rack system due to the stocking policy of the AGVs for picking and delivery goods. As described in the result table 4-4, the pallets A, B, C are spread equally among the rack system which will result in spreading the AGVs equally which means less interacting, hence less AGVs waiting on each other.

Analysis

65

The pallets places available in the new system as show in table 4-4 have 34 flexible places for types A, B, C which will be stored above type pallet D. Due to this flexibility it is possible to decrease the amount of goods being stored in the other company’s inventory which was the case before. If the demand for certain type of pallets exists the system would be able to fulfill it. The non-adding activity such as unnecessary movements will be reduced and less deliver time to the loading area. The sum of the fixed new places for pallets A, B, C will be 172 for each type, less than the current system 4.1, but there would be 103 places with floating type of pallets. For example, if there is a high demand for type A the total number of places would be 275. At the same time the time necessary for delivering pallets from the rack system to the loading area would decrease due to less interaction of the AGVs, which will result in faster turn-over of the pallets stored in the rack system. That according to Goldsby (2005) is a crucial aspect of the material flow system in terms of increasing the speed by reducing the time consumption for the activities.

6.3 Transportation layout

According to the present state of the system the transportation layout was identified to have certain disadvantages, this judgment arises from the secondary data and observations in the company. One disadvantage was identified to be that there is not established connection between the line 27 and loading places LF29 L and LF31 R. According to the interviews carried out it was found out that this connection is possible to be established. That will result in less transportation distance and time consuming for making a delivery, which according to Goldsby (2005) is considered as an important aspect in transportation logistics. As well as the material handling activity will provide the process with higher value adding aspect (Liker, 2004).

This will also result is the ability of the AGVs to choose the shortest distance to achieve the destination point (Mantel and Landeweerd, 1995). As well as that will result in the fact that the AGVs will not use other transportation lines in carrying out a delivery that are used by others. In this manner the AGVs would be spread in the distribution area.

The other suggestion being given is the implementation of a second transportation line parallel to the central cardinal line as shown on the future state map on figure 5-2. The effect of this line will be analyzed from two different perspectives as follows.

The first one is the fact that with the implementation of a second line the AGVs which are looping among the rack system will be eased by using the current central line. That will ease their activity in a way that probabilistically there will not be AGVs occupying the line, which are carrying out deliveries to the loading area. That will result in lower waiting time and blocking free routine which according to Mantel and Landeweerd (1995) is important for avoiding congestions.

Analysis

66

The second reasoning is the fact that since the material handling activity is regarded as non-value adding but necessary, and since they exist the focus should be on improving them. In the same manner the AGVs delivering pallets to the loading area are providing goods necessary for loading. These goods are important as for the people to the loading area and for the customers, and the speed in delivering these goods is important from logistical time perspective Goldsby (2005). Öjmertz (1998b) emphasizes on the importance of fulfilling the requirements among the supply chain in more effective manner. The AGVs delivering goods will not be involved in waiting time due to other AGVs occupying the same line and at the same time, which will result in higher possibility for the loading personnel to receive the pallets for loading on time.

According to statements given by Arifin and Egbelu (2000) the implementation of a second central transportation line would reduce the blocking conditions, which results in decreasing the time variables in the AGV system and gives the possibility for the AGVs to deal with more orders.

6.3.1 Transportation control and routing

The present state transportation control is part of the whole transportation control system of the company. That implies the AGVs being involved in all the transportation activities in the company. In other words, in the conditions when there is a demand from the inventory 7 and the AGVs are not present in the area that would imply in high transportation times in reaching the order point. Since the availability of the AGVs is not constant in the inventory area the times associated with the AGV’s activity is high. These types of transportations are considered as unnecessary and highly time consuming according to Goldsby (2005). The routing in the present state of the system implies the possibility of congestions, since there is a high activity in the exit of the inventory. It was pointed out in the result part of this thesis that the incoming and outgoing AGVs from the inventory are creating disorder and congestions, based on the descriptions made by Öjmertz (1996b) and Mantel and Landeweerd (1995). That results in blocking of the AGVs and high waiting times, which Ujvari and Hilmola (2006) discussed as candidates for changes in the system modeling and transportation control. According to Mantel and Landeweerd (1995) a separate control system within the inventory area results in consideration of the AGV’s activity, where the vehicles are routed in the system. The future state suggestion is aiming to having a separate control system from the Kinnarps overall one, where all the transportation orders are involving all the AGVs in the company. In this manner there is a higher possibility for implementing effective transportation techniques considered by Mantel and Landeweerd (1995). That will result in decreasing the time variables of the AGVs.

Analysis

67

6.3.2 Number of AGVs

In the current state model the number of AGVs is not fixed, but varies, as being explained in the result part of this thesis. This variation results in many factors associated with the properness of the material flow system. Since the AGVs number increases the waiting time increases as well. That waiting time is considered as a waste by Goldsby (1995), and according to Ujvari and Hilmola (2006) the unrealistic increase of AGVs in the system leads to overcrowding. Through the observations carried out and the statistical data obtained it was noticed that in some areas and times during the day when the AGVs are more in the system the waiting time exists.

Ujvari and Hilmola (2006) emphasize on the fact that if the number of AGVs is more then the system requires the loaded travel time increases as there is a transportation delay. That refers clearly to the present conditions in the system as the information is referred both to the secondary data and the observations carried out.

In the new model suggestion the system would have fixed amount serving the orders in the inventory area. Ujvari and Hilmola (2006) emphasize of the importance that the driving factor of an AGV’s transportation system is the proper number of vehicles serving it. In accordance to the new model and Ujvari and Hilmola (2006) the proper number of AGVs required in the system minimizes the total travel of loaded vehicles as well as the empty travel time.

According to the models proposed by Mantel and Landeweerd (1995) the estimation of the proper number of the AGVs serving the inventory was established. The times used were real and they were involving both the AGVs within the inventory 7 and the ones coming from outside of the area. As being discussed above the AGVs entering the inventory are associated with higher transportation times. In the calculations one of the variables used is regarding the empty travel time. On figure 2-4 the connections between the variables regarded as AGVs time variables are presented. After the calculations were made it was estimated that the required number of AGVs in the present system is 22 AGVs.

The other variables considered in the calculations are respectfully loaded vehicle travel time and assignment waiting time. These variables are influenced by the empty travel time. Due to the fact that the AGVs are periodically entering and leaving the inventory, the orders within the inventory are taken, both from the AGVs within the inventory and from the ones outside of it. It can be analyzed but at the same time not possible to measure, what part of these AGVs took orders from outside of the inventory 7.

Analysis

68

The result achieved by the usage of the model developed by Koo, Jang and Suh, (2005) gave another perspective of the estimated number of vehicles. From the result obtained it was clearly seen that the measured empty travel time from the secondary data corresponding to 3.25 minutes , see appendix 12, is more then the one based on the model used where the calculated value is 1.11 minutes. The reason for that is associated with the fact that the AGVs are fixed within the area of the inventory according to the assumptions made in the model by Koo, Jang and Suh (2005). The number calculated corresponds to 11 AGVs during the busiest time of the system, and the random vehicle selection rule. The number arising from the nearest selection rule is 10. Both of these cases are considered since it is important to perceive the importance with which the different transportation control rules affect the result.

The other scenario that was used when the pallets are equally distributed among the rack system provides us with the fact that the vehicle travel time would increase from 4.04 to 4.041 minutes, which is insufficient in its value. The reason for that increase is the fact that there are equal numbers of pallets from every line in inventory 7. Though, some of these lines have higher travelling times then others, which results in an increase of the overall calculated travel distance. The biggest benefit is that due to spreading out of the AGVs among the rack system and having the proper number to serve the orders the blocking will be reduced. Even thought, there is a slight increase in the travel time due to higher travel distance, the reduction of the interdependence among the AGVs in certain areas will result in less waiting time.

Further on the reduction of the waiting time will reduce the time variables used in these calculations, which are taken from appendix 13. The interdependence of the AGVs will be less and this will affect the travel time used in these calculations (Ujvari and Hilmola, 2006). This will lead to lower travel time, which will affect the material flow system in a positive way as stated by Goldsby (2005).

Another important remark is the fact that due to the unstable environment with respect to the arrival rate of orders the model used by Koo, Jang and Suh, (2005) is unable to estimate the part waiting time properly. As being explained another approximation was used, but it is not ideally proper, since the time period that it covers is one hour and it sums up the orders during that period of time.

According to the future model demands though the number of orders will increase, which will result in higher fleet size managing with them. As being described in the result section there are different alternatives according to which the choice of the proper fleet size can be determined. According to Ujvari and Hilmola (2006), when the system is having less orders and the number of the AGVs exceeds the proper number at that moment the idle conditions will increase.

The effectiveness is important consideration considered by Öjmertz and Johansson (1997) in satisfying the customer demands. Harrison (2002) comments on the importance of being competitive when the environment is uncertain by having the ability to respond to the customer demand. In the case of selecting the size of the fleet, it can be commented that it is important to design the system to respond to all the demands, even thought there would be times when the AGVs will be idle.

Analysis

69

The most important consideration is the fact that the fixed amount of AGVs will result in decreasing the empty travel time variable. That results in decreasing the assignment waiting time and vehicle travel time according to Koo, Jang and Suh (2005). That contributes to increasing the speed of the material flow and decreasing the time consumption for the activities, which according to Goldsby (2005) and Öjmertz and Johansson (1997) is of valuable importance for the material flow.

6.4 Loading area

In the current state map the number of places for loading goods was considered to be insufficient. The reason for that statement, as being described in the result section of this thesis, is the fact that it creates waiting. The reason for that is that the AGV system most of the time is not able to respond quickly to the new demand of pallets to arrive. In other words the time necessary for loading a pallet is less then the time needed for delivering a pallet. That statement was generated from the secondary data and the observations carried out. According to Goldsby (2005) and Hines and Rich (1997), that creates waiting and results in non-value adding time consumption. The other waiting time considered as a waste is the time when the workers are waiting for the right pallet to be delivered

According to the future suggestion the available places for loading pallets would be increased. In this manner the probability for waiting would be reduced. The workers would have more available places from where they can load the distribution containers. At the same time the new pallets that needed to be delivered would have higher time range to reach the destination point, since the workers would have more places to load the containers from. The other aspect is the fact that with the presence of more pallets the chance for waiting for the right pallets to be loaded following the sequence of loading would be reduced, which will result in less time needed to load a container as well. That will, according to Harrison (2002), result in synchronization of the material flow and higher coordination and undisturbed flow. That change will result in improving the time difference between the parts of the supply chain, since Öjmertz (1998b), emphasizes on the importance of connecting the parts of the supply chain in time periods. That will increase the effectiveness in which the workers loading the containers will benefit by that change (Öjmertz and Johansson, 1997). At the same time that part of the material flow will increase the possibility for fulfilling certain objectives in improving the flow (Harrison, 2002).

The design of the AGV system in retrieving the empty pallets from the loading functions in a manner that when the AGVs are entering the loading area they pick up the order and then they are already occupied with a pallet by delivering it to the empty pallets area.

In the new model suggested these places should be positioned as shown on figure 5-2. In this manner the AGVs can deliver pallets to the places along the line and by leaving the line they can pick up that order. That will result in fulfilling two orders with a single transportation. That would result in eliminating unnecessary transportation (Goldsby, 2005). According to Öjmertz (1998b), the material handling adds more value by orienting and sequencing the orders, which results in improving the effectiveness of the system. Coyle et al. (1996) and Womack (1996) refers to the importance of coordinating and sequencing the material handling activities resulting in better outcome of the material flow.

Analysis

70

The comparison made between the past and the present system gave another important interrelation between the orders in the inventory. Sheridan and Wickens (2000) emphasized on the importance of identifying the connection between the variables, regardless of the fact that the process are separate. The empty pallets transportations affect the possibility for the AGV system to deliver goods to the loading area. The empty pallets transportation by the AGV’s system is considered as a non-value adding activity, but necessary. And their negative effect on the value-adding chain can be reduced by changes in the operations. From another perspective the empty pallets transportation can be regarded as a wasted motion disturbing the AGV’s material handling system.

It can be stated, from the comparison made in chapter 4.2 and the result obtained on figure 4-6, that the empty pallet orders are taking away the possibility to withdraw more orders from the rack system to the loading area. Three of the order variables in the inventory were taken into consideration respectfully to the orders from the rack system, the orders from the distribution conveyors and the empty pallets orders.

The percentage of the orders of empty pallets in the present state system is 15.5 % in comparison to 20.3 % in the past one. It can be stated that the reduction in the orders for empty pallets contributed to increase of the orders from the rack system to the loading area respectfully from 52.3 % to 56.7 % in the present state. The improvement with respect to the delivery ability was estimated to be 7.8 % to the past system.

An analysis was conducted in the same manner as the one generating figure 4-6, though the empty pallets transportations served by the AGVs are entirely removed. Figure 6-1 presents the results obtained in percentage of involvement of the variables.

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

%


conveyor 27.37

empty pallets 20.37


conveyor 32.90

empty palltes 0.00

1

Figure 6-1. Comparison of variables from the present and the past state, when the empty pallets are not served by the AGV system

Analysis

71

From the result obtained it can be seen that by eliminating the orders of the empty pallets by the AGV system there is an increase of the possibility of the AGV system to deliver pallets to the loading area. There is an increase in this variable from 52, 3 % to 67.1 %. At the same time the AGVs are more able to fulfill orders from the conveyor system to the loading area since there is an increase from 27.4 % to 32.9 %, which gives higher possibility for the entire turn over of goods in the inventory. According to Monden (1993) these activities as the empty pallets transportations regarded as non-value adding but necessary can be removed by changing the way of their operations. That can be associated with creating a retrieving system that would save the AGVs capacity, that as mentioned in the result section can be achieved by a conveyor system. The output of the system in overall improvement in comparison to the past one is presented in figure 6-2.

0.00

5.00

10.00

15.00

20.00

25.00

%

improvements

improvements 22.12 16.79 0.00

1 2 3

Figure 6-2. Improvements range of the variables, in term of higher output order, when the empty pallets are removed by the AGV system in comparison to 2007

The extent of the improvements can be measured in comparing the present system and the future system. The comparison is based on analyzing the improvements being achieved from the two different time periods being considered. Figure 6-3 presents the improvements from the present and the future system.

Figure 6-3. Improvements range of variables, in terms of higher output order, when the empty pallets are removed by the AGV system (2008)

0

5

10

15

20

%

Improvements

Improvements 14.3 15.27 0

1 2 3

Analysis

72

If the system is reconfigured to that extend there would be more available loading places at the loading area. Since the empty pallets place and the storage system are not going to be used, the places can be used for loading places. The benefits associated with that were explained above in this section.

The extent to which the changes are made will have a direct impact of the possibility to load more distribution container. That arises from the possibility to reduce the necessary but non-value adding material handling activities in the inventory (Bowersox, 1999; Coyle, 1996).

6.5 Empty pallets area

As being presented the empty pallets from the empty pallets conveyor system are processed by the AGVs system and moved in upstream direction to the workshop areas. These non-adding value movements are not only occupying essential time of the AGVs but also prevents them to deal with value-adding orders in the inventory instead of empty pallets, which according to Fung (1998) and Cunningham (1996) has a direct impact on customer satisfaction.

Another issue that has to be considered is the fact that according to the new model the AGVs will not create a bottleneck on their way out from inventory 7. That is the case in the current system, which results in high waiting times and overcrowding of the exit area, considered as an undesirable circumstance in an AGV’s transportation system (Ujvari and Hilmola, 2006).

The improvement within the empty pallets area became an important factor to solve since the AGVs are already overloaded with many different operations in the inventory area. On figure 4-1 can be seen the conceptual solution associated with this area, the empty pallets conveyor directs the pallets outside inventory 7 where the empty pallets are processed by AGVs not serving inventory 7. Through the secondary data it was acquired, from appendix 23, that there are 54 orders being managed by the AGV system from these conveyors. This number of orders corresponds approximately to the possibility to deliver the same amount of pallets to the loading area. That approximately corresponds to the needed pallets for loading one distribution container. To sum up, this solution will allow the AGVs to work with more efficient activity which contribute more value both to the customer and Kinnarps Production AB. That would gave the possibility for the AGVs to deal with more value adding activities in the inventory area, which according to Shingo (1981) and Monden (1994) will result in having more resources for handling the value-adding tasks.

6.6 Stocking policy

In the present model of the system the stocking policy starts from line 21 in the rack system. That creates bottle neck among the rack system since all the AGVs are aiming to this area. Respectfully from another perspective the waiting time that the AGVs are spending waiting on each other is high. According to Ujvari and Hilmola (2006), by overcrowding certain areas by the AGVs influence the system in a negative way. This results in increase of the travel time variance according to Mantel and Landeweerd (1995). Through the observations and the secondary data it was seen that due to the stocking policy the AGVs are highly involved in traffic conditions.

Analysis

73

The change in the stocking policy in the future model will ease the system in many ways. First the AGVs will be spread among the rack system, which will result in less opportunity for congestions and decrease in the travel time. Secondly the chance for the AGVs looping among the rack system would decrease the possibility for blocking the other AGVs involved in transportation activities using the main transportation lines. That would result in fewer chances for congestions and less transportation delay, which will increase the speed of deliveries.

The reason why the present system was designed to function in this manner is due to the fact that line 21 is situated at equal distances to all the gates in the loading area. From the process mapping found in appendix 8 it was found out that the average time necessary for delivering from this line to the loading area is not the minimum. Table 6-1 presents the arrangement of the times from the different lines in the rack system to the loading area in a descending order.

max Lines-loading area 0:03:51 25 0:03:28 27 0:03:02 28 0:02:53 21 0:02:45 23 0:02:44 24 0:02:37 20 0:02:33 22 0:02:20 30 0:02:12 26

min

Table 6-1. Average time necessary for delivering goods from the different lines in the rack system to the loading area

From the measurements and the process mapping it was estimated that from line 21 to the loading area the time consumption is not least. Instead it was found that line 26 corresponds to that issue.

It has to be taken into consideration the time consumption from the conveyor system to the rack system respectfully to the lines in it. The following table 6-2 summarizes the average time necessary for delivery from the conveyor system to the different lines in the rack system. The data is arranged in a descending order with respect to the different lines.

Analysis

74

max Conveyors-lines 0:03:12 28 0:03:05 24 0:02:58 30 0:02:53 20 0:02:44 23 0:02:37 26 0:02:14 27 0:02:00 22 0:01:44 21 0:01:37 25

min

Table 6-2. Average time necessary for delivering goods from the distribution conveyors to the different lines in the rack system

In order to level the times in an average manner the two tables are combined and the average time was taken respectfully to the lines. That takes into consideration both of the material handling activities and gives more realistic picture. As Hines and Rich (1997) defined the rearrangement in the material flow system results in higher efficiency and better flow pattern. The generated table 6-3 presents the most suitable positioning of the pallets among the rack system. The average times of the two transportation scenarios are arranged in an ascending order respectfully to the lines in the rack system as playing the core variable in the analysis.

line Rack system-loading area Conveyors-rack system Average time 22 0:02:33 0:02:00 0:02:16 21 0:02:53 0:01:44 0:02:18 26 0:02:12 0:02:37 0:02:25 30 0:02:20 0:02:58 0:02:39 25 0:03:51 0:01:37 0:02:44 23 0:02:45 0:02:44 0:02:45 20 0:02:37 0:02:53 0:02:45 27 0:03:17 0:02:14 0:02:46 24 0:02:44 0:03:05 0:02:54 28 0:03:02 0:03:12 0:03:07

Table 6-3. Average time necessary for a material handling activity respectfully both to the rack system and the loading area.

Table 6-3 demonstrates that if the pallets are stored equally among the rack system that will not affect the time consumption so much, since line 22, 21, 26, 25 and 30 present similar short average time measurement. At the same time these lines are spread almost averagely among the rack system. By connecting the interdependence analysis and the following waiting time after that and the result from the empty travel time when determining the number of AGVs. The new stocking policy will influence the material flow system with higher efficiency and effectiveness (Öjmertz and Johansson, 1997) as contributing to stable and continuous flow considered by Harrison (2002), Sekine (1992).

Analysis

75

From the analysis regarding the pallets and rack system it was discussed that the effect of leveling of all the different types of pallets equally among the rack system would reduce the interdependence of the AGVs. Based on the new arrangement of the pallets in the rack system the stocking policy would be able to reduce the probability for the AGVs to occupy certain areas more. That will result in less congestions and less overcrowding, which decreases the waiting time and the transportation times (Mantel and Landeweerd (1995); Ujvari and Hilmola (2006)).

6.6.1 Stoppages and Errors

The past state of the system was useful to identify the importance of the stoppages of the AGV system in the inventory. That gave the opportunity to relate the connection between the variables and their interdependence. According to Sheridan and Wickens (2000) there is a possibility for estimating connections between the variables and their interdependency.

When the effect of the bumper stop errors is analyzed it is important to consider the number of orders within the respected areas being investigated. Presumably the number of orders results in higher activity with certain areas and that defines the occurrence of more bumper stops. Though, from the figure 4-9 it can be seen that there is no dependability between the number of orders and the AGVs registered with bumper stop error. Even though the orders for empty pallets in the sample from 2007 are more then the ones in the sample from 2008 the errors registered are less. As well as it can be seen that the orders from the conveyors in the sample from 2008 are more but the errors during that period are less. It can be stated that the bumper stop errors occur and they are not dependant on the number of orders and high activity.

The more important variable when considering the occurrence of the errors is the error due to high waiting time. According to figure 4-8 where the errors are interrelated in accordance to the number of orders it can be seen that the errors are dependant on the number of orders. In other words, if the number of orders is higher in some areas the AGVs’ activity is high as well, this results in more frequent occurrence of that error. It is seen that since the AGVs activity is higher in the sample from 2008 in the rack system in comparison to the sample from 2007 the number of registered errors is higher. Thus, the same logic follows among the other areas as well.

It can be stated that the AGVs are subject to high involvement of that error if they are concentrated in certain areas. According to Harrison (2002) and Sekine (1992) there is necessity for improving the material flow system in accordance to fewer stoppages. According to Öjmertz (1998b) by investigating the material flow and estimating the existing losses it can be seen how these losses influence each other. Though it can be stated that the error due to high waiting time defines high losses for the material flow, and the reason for that loss is the high involvement of the AGVs in certain areas.

Analysis

76

According to the new model the rack system is leveled with all the different kinds of pallets, the stocking policy is equally spread among the rack system and there is a second transportation line. Thus, the AGVs are not going to be concentrated in certain areas. That will result in less AGVs being involved in stoppages and errors, which will grade the loss as low in accordance to the overall material flow (Öjmertz, 1998b), and reduce the waste in the system, Goldsby (2005). The overall effect will be that there will be less transportation delay and waiting time in the system, which will increase the speed and will improve the stable and continuous flow (Harrison, 2002; Sekine, 1992).

Another important consideration is the fact that the bumper stop errors are occurring in areas, where the material handling activity doesn’t interact with the workers. These errors are result of the material handling equipment. The interviews carried out in the company rose up the question if the material handling equipment with respect to the AGV system is old and due to that reason these errors occur. Bowersox (1999) and Arifin and Egbelu (2000) emphasize on the importance of the material handling equipment and its result on the overall logistics cost.

6.7 Process mapping activity, transportation delay and waiting time

By mapping the current system in terms of transportations and activities it was possible to gain a holistic picture of the processes and the time variables associated with that. The generated map in appendix 8 aimed to identifying the flow in terms of ideal transportation times when there are no negative factors affecting the material handling activity and prevent the undisturbed flow. The evaluation of the material flow efficiency was carried out by the ability to compare the ideal values from the generated map with the real values that differ from that system. That differentiation was important to be state that there are factors that prevent the appropriate and smooth flow.

The terms ideal flow can be regarded as the flow when there are no obstacles preventing the estimated travel time. The ideal transportation system in terms of time was used as a reference system in estimating the degree of disturbances as Öjmertz (1998) emphasizes on. These disturbances results in a transportation delay as being seen in the case of Kinnarps.

There were number of factors being identified as the reason for the interruptions in the material flow that resulted in the inability of value-adding character to the material flow. As a result it was seen the prevention of the uniform continuous flow as stated by Womack (1996). The factors influencing the transportation delay were summarized as follows:

• Excessive number of AGVs • Stocking policy • Pallet arrangement • Transportation layout • Stoppages and errors

Analysis

77

All these areas above were analyzed and their effect on the transportation times was considered. All these factors are interrelated and influence the system performance in a negative way, by decreasing the speed for deliveries. According to Mantel and Landeweerd (1995) and Ujvari and Hilmola (2006) when an AGV system is designed there are number of factors influencing the system by taking into consideration the number of AGVs as presented on figure 2-3.

The fixed and estimated proper number of AGVs in the transportation system will decrease the possibility for overcrowding and higher travel time as been pointed out by Ujvari and Hilmola (2006) as an important consideration.

The stocking policy will naturally spread the AGVs fulfilling orders equally among the rack system, which will result in higher travel distance, thus higher number of AGVs. Though, this issue was previously analyzed and it resembles that the result of the change in terms of travel distance and number of AGVs is sufficiently vague. The desirable outcome would reduce the interdependence of the AGVs in the system and reduce the waiting time conditions, when the AGVs are waiting on each other.

The new pallets arrangement will reduce the waiting time conditions on the central lines used for delivering as being discussed above in this chapter. Since all the different types of pallets are presented in the rack system there would not be certain bias of areas in demand of certain types of pallets, which will lead to less interaction of the AGVs. The pallets distribution is mainly associated with the stocking policy in the system.

The changes in the transportation layout as being discussed will reduce the possibility for interaction of the AGVs by having blocking free route. That results in fewer conditions for waiting time and interaction which will result in less transportation delay.

As discussed in the chapter regarding the stoppages and delays the bumper stoppages are unpredictable and occur not following any reasoning. It can be stated that the effect of the errors on the overall material flow will decrease by implementing the changes according to the new system model design. The minimized interaction of the AGVs in certain area will result in less possibility for involvement of the AGVs in stoppages and errors, which will result in less transportation delay.

By applying the same model for estimating the proper number of AGVs proposed by Koo, Jang and Suh (2005) it was estimated that if the ideal times generated in appendix 8 are used in the calculations the AGV’s time variables will decrease. This is an ideal case and it is not likely to appear in real time conditions, though it presents the effect of the extent of the negative factors preventing the transportation times. The result obtained based on the stochastic model and iteratively repeating the calculations determines that the travel loaded time will be 2.4 minutes in ideal conditions in the system.

Analysis

78

By considering the factors being mentioned with respect to their measurements and impact on the material flow system, referring to the ideal reference travel time base, it is possible to evaluate the efficiency of the changes in the entire material flow, as analyzed by Öjmertz (1998). According to Harrison (2002), Womack (1996), Goldsby (2005) by eliminating the negative factor influencing the material flow a stable continuous flow will be achieved, decreased lead time and value-adding material handling activities.

The waiting time is part of the time variables in an AGV system, which is the sum of the assignment waiting time of the orders and the empty travel time. According to Koo, Jang and Suh (2005) by considering figure 2-4 it can be seen how the waiting time is affected by the empty travel time and the assignment waiting time. The assignment waiting time is affected by the number of AGVs in the system, and it is affected by the arrival rate of the orders in the system. Special emphasize will be done on the empty travel time.

The empty travel time within the present transportation system in the inventory was estimated to be average of 3.3 minutes in appendix 24, when the orders from the rack system, the empty pallets orders and distribution conveyors are taken into consideration. The factors that affect the most this AGV time variable are:

• Transportation loaded travel time • Fixed number of AGVs • Control system

The transportation time as being discussed earlier is influenced by the negative factors affecting the transportation conditions in the system. Since the empty travel time is associated with the unloaded travel time when the vehicles are heading to pick up the orders, the travel time delay is a big consideration. By considering all the areas influencing the empty travel time in the future state model the empty travel time would be reduced.

According to the model proposed by Koo, Jang and Suh (2005) the estimated empty travel time with the current stocking policy is 1.13 minutes. That value is in accordance with random vehicle selection and 0.53 minutes for a nearest vehicle selection. The times used in the calculations are based on appendix 13, where real time system delay is used. The difference in the estimated value is coming from the fact that the model is based on system conditions where the AGVs are fixed in the inventory area. That predominates that the travel distances are shorten, since there is a constant availability of the AGVs within the system. According to the new stocking policy the travel distance will increase, but the difference in the estimated values is insufficient, which makes the system less sensitive.

Analysis

79

According to Ujvari and Hilmola (2006) the proper number of the AGVs is important to be estimated, since if this number exceeds the estimated values the empty travel time would decrease, but the transportation time would increase. This is due to the fact that the system becomes overcrowded and as a result the AGV time variables increase. According to the future changes made discussed above in the analysis section the AGVs would serve only the inventory area and will not leave the area serving other orders as it is in the current system. That would lead to shorter travel distances and constant availability of the AGVs within the area, which is the driving factor for an AGV system (Ujvari and Hilmola, 2006). That results in decreasing wasted motions and shorter lead times, which is important consideration of the material flow system in terms of transportation activities (Womack, 1996; Goldsby, 2005).

The control system is important consideration that affects the empty travel time. In the result section it was estimated that there is a difference between the empty travel time when the random vehicle selection rule is used and nearest vehicle selection. Under random selection the empty travel time is 1.11 minutes and in the case of nearest selection rule is 0.53 minutes. In the new suggestion the inventory will have a separate control system where different control abilities can influence the effectiveness of the system.

All the travel times used in the model are based on a determined delay in the system. If iteratively the procedure is repeated in the stochastic model by Koo, Jang and Suh (2005) and the ideal times are used, then the empty travel time would be 0.5 minutes, under the random vehicle selection rule. That case is unrealistic in real time environment, but though shows the importance of decreasing the negative factors in the system.

The assignment waiting time is dependant on the availability of the AGVs to take orders fast. Within the future state model of the system the AGVs will be fixed only in the inventory. As being analyzed the time variables are reduced with respect to travel time and empty travel time the AGVs will result in less lead time. In a combination of the constant presence of AGVs in the area and the possibility for the AGVs to fulfill transportation orders faster the assignment waiting time will decrease. That results in increase of the efficiency in the material flow system, since the consumption of time resources necessary to carry out an order decrease (Öjmertz and Johansson, 1997).

As being explained earlier figure 4-4 corresponds to the waiting time in the system respectfully to the lines in the rack system. According to the new model the goods would be spread equally among the rack system, and by taking into consideration the removal of all the negative factors in the system, the waiting time would decrease. The high peaks found in figure 4-4 would not be presented, and due to less interaction of the AGVs in the system and the decrease of the empty travel time the average value of the waiting time will drop down. That would contribute to undisturbed and smooth material flow, with higher transportation speed, which is important consideration in the logistics perspective (Harrison, 2002; Goldsby, 2005). According to the new state model the bottleneck presented in figure 4-4 with respect to the lines around 21 would be eliminated, which derives from the better synchronization and coordination of the activities, regarded by Harrison (2002).

Analysis

80

Another important aspect is the analysis of the availability of the AGVs in the system. As can be judged based on figure 4-5 the AGV concentration is between lines 23 and 22, even though the orders are higher than the other lines, still, the average waiting time is less in comparison with the other lines. That is due to the fact that the AGVs are picking the nearest order and that results in higher waiting time in the range of lines 24 to 30. Lines 27 and 25 are having higher waiting time as well, due to the fact that the AGVs are entering the rack system from this way, and create blocking case scenario. In consideration with the analysis carried out above, the concentration of the AGVs results in increase of the transportation time, due to congestions (Mantel and Landeweerd, 1995). At the same time the time will be reduced, which will result in less time the goods are spending in a transition by shrinking the lead times (Goldsby, 2005).

By following the same analytical consideration the times being considered in this analysis are real and present the real time perspective environment. By implementing all the suggestions being given, the times will tend to reach the ideal times being estimated. That would reduce the overall average of the time variables resulting in increased speed, which is of crucial importance for the logistics system (Zhang, 2002; Goldsby, 2005).

The process mapping activity analysis will be carried out in accordance to comparison of the present state system and the future one. The material flow maps being developed in the result part section of this thesis will be analyzed. The issues that are considered are associated with dependences and linkages between the steps as well as the activities carried out. The possibility to move certain nodes in a different sequential order, with respect to the entire material flow, would be taken into consideration according to Öjmertz and Johansson (1997). Also the importance of the nodes within the system and their importance with respect to the value-adding character they have with respect to Monden (1993) and (Öjmertz, 1998b) was considered. The following figure 6-4 present the material flow map from the current and the future state system.

Figure 6-4. Comparison of the present and the future material flow map

ConveyorsRack system,Pallets type D

Loading areaEmpty palletsArea

Material flow activities performed by the AGVs in

the inventory

Conveyors

Material flow activities performed by AGVs not in

the invenory

System Boundary

Emptypallets

Future state mapCurrent state map

Conveyors Rack system

Loading area Inventory Pallet type

D

Empty pallets

Empty palletsArea

Material flow

Analysis

81

According to the present state of the system the possible transportation activities with respect to the different nodes in the system can be listed as follows:

• Conveyors o Inventory Pallet type D o Rack system

• Rack system o Outside inventory 7 o Loading area

• Outside the area o Rack system

• Inventory Pallet type D o Loading area o Outside inventory 7

• Loading area o Empty pallets Area

• Empty pallets Area o Outside inventory 7

The total requirement of activities is 9.

According to the new model by following the same procedure the activities are listed as:

• Conveyors o Rack system, Pallets type D

• Rack system o Loading area o Conveyors

• Loading area o Empty pallets area

The total requirement of activities is 4

With respect to the new system the AGVs activities within the inventory area will be reduced from 9 possible transportation scenarios to 4. The sequential order within the new system is in line with the entire material flow in the system, which contributes to better synchronization of the activities. As well as the material flow is expressed with less divisions, which restrict and interrupt the proper flow (Öjmertz and Johansson, 1997). Some of the activities being removed are considered to be unnecessary and time consuming for the AGVs and the entire material flow. According to Öjmertz (1996) the activities of the material handling should be analyzed as separate function and not been seen always as waste. The analyze of the current system has been done with the respect to the level of these activities providing value to the products which according to Öjmertz (1996) is the proper method when evaluating a material flow system, since material handling can not be eliminated totally.

Analysis

82

The clear picture was gained by investigating the material flow in terms of the importance of the nodes and their connection with respect to their value-adding nature (Öjmertz and Johansson, 1997). As being analyzed above the different areas were graded as value-adding, non-value adding and non-value adding, but necessary to be carried out. Based on that grading, the future process flow and design of the new system was based on Monden (1993). The main aim when creating the future state model was to simply unload all the unnecessary activities being carried out of the AGVs and make them more available to deal with more value-adding.

The new system boundaries predominates the possibility to establish a separate control system, which will optimize the control of the transportation orders and prevent the disturbances being registered in the present system.

If the extent of the changes reaches the level when the non-value adding activity but necessary, with respect to the empty pallets transportation by the AGVs is removed, then the system would be associated with only value-adding ones. As being discussed that will increase the system capability and create only value-adding flow of materials stressed by Womack (1996).

Conclusions

83

7 Conclusions

This section of the thesis is aiming to pointing out the importance of the findings in the research being carried out. It points out the contribution of the suggestions being given. Also reflections on the work have being given, suggestions for future research and critics associated with the work done.

The findings in the analysis present the effect of changing the way goods are incoming in the inventory. The creation of a new distribution conveyor would reduce the transportation and the time consuming waste for the AGVs serving the inventory. At the same time it would reduce the disorder created by the AGVs entering the inventory area resulting in high interdependence creating waiting time.

The changes associated with the pallets and the rack system will increase the loading places by moving the D pallets type among the rack system. At the same time the mixture of all the pallet types equally among the rack system and their positioning will reduce the waiting time and the interdependence of the AGVs in certain areas. The new pallets places will increase the possibility to store more pallets in a more flexible manner.

From the conducted analysis it was estimated that according to the new stocking policy the increased average travel time is not substantial. The benefits associated with the new stocking policy are reflecting in less possibility for congestions. That results in decreasing the time variables of the AGV system, which contributes to a more stable and continuous flow.

All the changes in the transportation layout will result in decreasing the possibility for blocking conditions. That results in fulfilling the demand for delivering pallets to the loading area in a more efficient manner.

From the analysis it was estimated that proper fixed amount of AGVs serving the inventory area is the driving factor for decreasing the empty travel variable and reducing the congestions. This improvement will lead to decreasing the assignment waiting time and vehicle travel time.

Another important consideration in the analysis states that even though there are stoppages due to different errors the overall effect on the material flow will be minimized. This derives from the fact that according to the changes being made there would be less AGVs being involved in stoppages, which will reduce the disturbances in the material flow and the time variables.

The changes associated with the loading area will affect the workers and the material flow itself. By increasing the loading places the waiting time for the pallets will decrease and in this manner the workers loading rate will increase. This will increase the workers effectiveness in terms of the improved flow, which will result in the possibility for loading more daily containers. At the same time the change in the sequence with respect to the empty pallets transportation will add more value to the material handling activity, by decreasing the transportation activities.

Conclusions

84

Based on the analysis done it was estimated that if the empty pallets activities are not processed by the AGV system, the extent of improvements within the inventory will increase. That will enlarge the possibility for the AGV system to fulfill more orders from the rack system and the distribution conveyors.

The analysis conducted states that the elimination of the activities by carrying out the empty pallets transportation from the empty pallets area by the AGVs in the inventory will give the possibility for the same AGVs to fulfill more adding-value operations in inventory 7.

It can be concluded that by the changes being made all the negative factors affecting the material flow are going to be eliminated or reduced. The sum of the outcome within these changes will result in decreasing all the time variables which leads to overall improvement within the system. That will contribute to an increase of the speed of the entire material flow in inventory 7 which will have a direct impact on the possibility to load more containers.

7.1 Discussion

The area of this thesis was associated with investigating the material handling activities performed by an AGV system. Due to the complex nature of these systems and the inexperience of the authors of this paper, hence the pre study was time consuming. That involved deep understanding of the AGV behavior through observations and literature reviews.

The methods being used in encountering the research were broad. By using these methods the data generation was hard and time consuming. It was hard to estimate the connection between the different variables and the treatment of the high amount of data used. Though, the authors believe that the methods used were necessary in order to truly understand the problem areas and to be able to provide relevant and accurate solutions. Which otherwise would not be achieved by not following the approached methodology.

7.2 Future research

As being stated in the delimitation part of this thesis, the authors believe that pallet utilization and the planning will have a great impact on improving the material flow in Kinnarps, therefore these factors should be taken into consideration for future research.

Some of the suggestions being given in this thesis are conceptual. It is a matter of precise calculations to evaluate the design and the scale necessary for implementing them.

7.3 Criticism of the thesis

There were a number of tools being used for investigating the transportation system and the appropriate amount of AGVs required in the system. Though, important aspect that has to be considered is the fact that a simulation tool wasn’t used. By the usage of a simulation it would be possible to measure and visualize the positive effect of the changes being made.

Conclusions

85

The achievements in this work would be higher if the pallet utilization aspect was taken into consideration. If that aspect was considered then the number of transportations, in the area of that research, would be decreased. That would result in higher output of the system in terms of more distribution containers being loaded with the same amount of orders presented.

The planning was an important factor that had to be considered in this paper. With better production planning there is possibility for establishing takt timing with respect to the arrival rate of pallets in the inventory. That would result in facilitating the planning in the inventory with respect to loading the containers, which can establish a smooth rate of orders without peaks and nadirs. In this manner the system capabilities would be able to carry out more deliveries, which would result in more loaded containers. This will give the possibility for more efficient scheduling, which will decrease the nervousness of the system.

The statistics being used in this thesis was taken from different sources in order to provide the work with proved and realistic data. The sample during year 2007 is covering one month period in comparison to 2008 where it is two months, even though the comparison is based in the same amount of days. This is due to the fact that the secondary data from the company in year 2007 was restricted in a one month period. In this manner the outcome of this comparison would have been more adequate and trustworthy.

Due to the fact that it was impossible to receive precise transportation times with respect to different coordinates an average time needed to be generated. That average time had to be used in the calculations being made, which led to imprecise estimation of certain variables. Though, since the entire material flow was investigated, the researchers consider that these averaging did not highly influence the final outcome of the findings.

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90

9 Appendix List

Appendix 1. Statistics for number of pallets, incoming-outgoing

Appendix 2. Difference between the outgoing goods from inventory and to the loading area in 7

Appendix 3. Average usage of the different types of pallets in percentage

Appendix 4. Statistics for pallet orders from different places in the inventory

Appendix 5. Average number of orders per hour during a day for 27 days

Appendix 6. Bumper stops statistics for 2007 and 2008

Appendix 7. Error due to waiting time statistics for 2007 and 2008

Appendix 8. Process activity map with respect to times and distances

Appendix 9. Sum and percentage of the waiting time in the rack system

Appendix 10. Statistics with respect to transportation times compared with the ideal times estimated

Appendix 11. Statistics for empty pallets, conveyor orders and outgoing goods, relation between the variables in percentage

Appendix 12. Estimation of required number of AGVs by using the model proposed by Mantel and Landeweerd (1995)

Appendix 13. Process map including ideal time and the average transportation delay within lines according to the secondary data

Appendix 14. Travel times between the different locations in the inventory

Appendix 15. Requested rates for delivery between different locations in the inventory

Appendix 16. Time requirements for delivering to different locations in the inventory

Appendix 17.Delivery requirements to different locations Appendix 18. Percentage of activities for certain periods during the days with respect to number of orders Appendix 19. Requested rates for delivery between different locations in the inventory, orders are evenly spread among the rack system Appendix 20. Delivery requirements to different locations, orders are equally spread among the rack system Appendix 21. Average number of orders per hour during a day for 27 days, the future state demand Appendix 22. Percentage of activities for certain periods during the days with respect to number of orders, according to the future demand in the system Appendix 23. Number of orders from the empty pallets conveyor system Appendix 24. Average empty travel time Appendix 25. Questionnaire

91

Appendix 1. Statistics for number of pallets, incoming-outgoing

Dates Outgoing All incoming Conveyors Incoming AGVs

80205 602 464 224 240 80206 633 596 324 272 80207 574 669 308 361 80211 591 632 333 299 80212 605 733 325 408 80213 616 645 210 435 80214 565 639 262 377 80218 690 728 330 398 80219 566 521 236 285 80220 596 608 251 357 80221 554 608 272 336 80225 596 589 323 266 80226 535 493 197 296 80227 530 631 349 282 80228 463 512 279 233 80303 542 546 287 259 80304 507 395 183 212 80305 653 788 398 390 80306 551 567 232 335 80310 617 547 404 143 80311 563 735 345 390 80312 595 570 243 327 80313 488 611 252 359 80318 538 628 216 412 80319 479 638 253 385 80320 504 677 306 371 80325 590 687 324 363

Sum 16457 7666 8791 Percentage 0.46582 0.534179984 Average 568.2593 609.5185185 283.9 325

92

Appendix 2. Difference between the outgoing goods from inventory and to the loading area in 7 and the difference between the all outgoing goods to incoming

Dates All outgoing

Incoming Outgoing

Difference between all outgoing and outgoing

Difference incoming-all outgoing

Tuesday 80205 715 638 676 39 -77 Wednesday 80206 634 642 558 76 8 Thursday 80207 651 771 624 27 120 Monday 80211 619 729 619 0 110 Tuesday 80212 629 782 603 26 153 Wednesday 80213 758 699 756 2 -59 Thursday 80214 658 690 658 0 32 Monday 80218 763 835 614 149 72 Tuesday 80219 707 668 687 20 -39 Wednesday 80220 748 743 663 85 -5 Thursday 80221 657 700 639 18 43 Monday 80225 739 704 737 2 -35 Tuesday 80226 691 684 663 28 -7 Wednesday 80227 592 656 592 0 64 Thursday 80228 595 589 595 0 -6 Monday 80303 553 612 512 41 59 Tuesday 80304 554 454 553 1 -100 Wednesday 80305 711 878 711 0 167 Thursday 80306 639 679 638 1 40 Monday 80310 703 692 693 10 -11 Tuesday 80311 677 862 671 6 185 Wednesday 80312 687 595 628 59 -92 Thursday 80313 628 737 619 9 109 Tuesday 80318 582 676 536 46 94 Wednesday 80319 582 672 545 37 90 Thursday 80320 586 750 584 2 164 Tuesday 80325 795 729 709 86 -66 average 661.22222 638 632.7037 28.51852 -77

93

Appendix 3. Average usage of the different types of pallets in percentage

Pallet types dates A,B,C D

80205 492 82 80206 529 100 80207 489 83 80211 498 93 80212 518 89 80213 552 66 80214 489 83 80218 583 100 80219 495 69 80220 513 73 80221 463 87 80225 490 102 80226 463 72 80227 443 86 80228 401 60 80303 449 96 80304 423 80 80305 544 100 80306 462 91 80310 529 91 80311 470 94 80312 519 76 80313 412 76 80318 464 72 80319 419 58 80320 415 89 80325 492 98

percentage 85.1721 14.8279

94

Appendix 4. Statistics for pallet orders from different places in the inventory

95

Appendix 5. Average number of orders per hour during a day for 27 days

daily hours average orders per hour

1 9.11 2 20.93 3 11.93 4 20.48 5 10.48 6 11.30 7 40.52 8 25.81 9 26.70

10 27.59 11 35.81 12 21.63 13 37.41 14 25.26 15 37.22 16 37.37 17 27.70 18 25.44 19 28.78 20 41.30 21 39.22 22 24.04 23 31.15 24 15.52

sum 632.70

96

Appendix 6. Bumper stops statistics for 2007 and 2008

bumpers 2008

Rack system

Loading area Conveyor sum all

days 19 20 30 5 55 21 27 66 15 108 25 35 65 11 111 26 67 48 5 120 27 17 38 3 58 28 22 43 3 68 3 41 31 20 92 5 30 21 20 71 6 51 57 4 112

10 24 41 11 76 11 26 44 9 79 12 20 24 3 47 13 18 26 1 45 17 22 65 7 94 18 29 33 2 64 20 15 49 5 69

sum 464 681 124 1269 0.36564224 0.53664303 0.097715 percentage 36.5642238 53.6643026 9.771474

bumpers 2007

Rack system

Loading area Conveyor sum all

dates 8/6/2007 42 37 19 98 8/7/2007 43 31 16 90 8/8/2007 18 29 5 52 8/9/2007 24 19 7 50

8/13/2007 25 48 20 93 8/14/2007 47 72 14 133 8/15/2007 26 84 11 121 8/16/2007 34 27 5 66 8/20/2007 59 67 24 150 8/21/2007 46 69 26 141 8/22/2007 11 31 7 49 8/23/2007 27 40 6 73 8/27/2007 31 27 19 77 8/28/2007 31 30 7 68 8/29/2007 28 53 10 81 8/30/2007 17 36 5 58

sum 509 700 201 1400 0.36357143 0.5 0.143571 percentage 36.3571429 50 14.35714

97

Appendix 7. Error due to waiting time statistics for 2007 and 2008

error due to waiting time 2008

dates rack system loading area conveyor sum all

2/18/2008 32 11 2 45 2/19/2008 13 20 0 33 2/20/2008 77 42 9 128 2/21/2008 39 22 6 67 2/25/2008 68 46 19 133 2/26/2008 51 14 9 74 2/27/2008 39 18 8 65 2/28/2008 48 25 1 74

3/3/2008 28 28 15 71 3/4/2008 14 7 2 23 3/5/2008 82 55 18 155 3/6/2008 77 20 14 111

3/10/2008 69 25 16 110 3/11/2008 47 55 5 107 3/12/2008 43 20 9 72 3/13/2008 32 8 8 48 3/17/2008 38 24 7 69 3/18/2008 27 14 4 45 3/19/2008 80 37 19 136 3/20/2008 73 47 10 130

sum 977 538 181 1696 0.576061321 0.31721698 0.1067217 percentage 57.60613208 31.7216981 10.6721698

error due to waiting time 2007

dates Rack system Loading area Conveyor sum all

8/6/2007 36 18 4 58 8/7/2007 24 14 10 48 8/8/2007 11 27 0 38 8/9/2007 7 14 0 21

8/13/2007 7 27 8 42 8/14/2007 32 32 1 65 8/15/2007 26 48 10 84 8/16/2007 28 24 5 57 8/20/2007 44 33 7 84 8/21/2007 72 46 2 120 8/22/2007 24 23 0 47 8/23/2007 24 23 0 47 8/27/2007 43 25 3 71 8/28/2007 19 23 4 46 8/29/2007 29 26 5 60 8/30/2007 23 18 12 53

sum 449 421 71 941 0.477151966 0.44739639 0.07545165 percentage 47.7151966 44.7396387 7.54516472

98

Appendix 8. Process activity map with respect to times and distances

Pick-up, Rack system

Drop point

Distance (meters)

Ideal Time (min:sec)

Average Distance (meters)

Average Ideal Time (min:sec)

27 LF29 L 154,532 00:03:43 125,911375 00:03:17 R 82,536 00:02:23 LF31 L 130,338 00:03:18 R 125,17 00:02:57 LF33 L 141,389 00:03:40 R 102,148 00:02:55 LF35 L 157,717 00:03:49 R 113,461 00:03:32





22 LF29 L 82,477 00:02:19 89,8325 00:02:33 R 78,752 00:02:25 LF31 L 58,293 00:01:58 R 54,297 00:02:08 LF33 L 142,169 00:03:35 R 30,103 00:01:06

99

LF35 L 158,326 00:03:50 R 114,243 00:03:00





Conveyors Drop point

C 27 75,253 00:02:14 91,3844 00:02:31 C 25 39,474 00:01:37 C 23 92,356 00:02:44 C 21 56,479 00:01:44 C 20 109,456 00:02:53 C 22 73,624 00:02:00 C 24 126,556 00:03:05 C 26 90,679 00:02:37 C 28 144,345 00:03:12 C 30 105,622 00:02:58 Empty Pallets LF35 109,638 00:02:41 85,628 00:02:20 LF33 85,851 00:02:19 LF31 61,395 00:01:59

100

Appendix 9. Sum and percentage of the waiting time in the rack system

101

Appendix 10. Statistics with respect to transportation times compared with the ideal times estimated

80211

Average Ideal times

Measured Average Times 80213

Average Ideal times


Average Ideal times

Measured Average Times

27 0:03:17 0:03:47 27 0:03:17 0:04:05 27 0:03:17 0:03:58 25 0:03:51 0:05:39 25 0:03:51 0:04:38 25 0:03:51 0:05:22 23 0:02:45 0:03:21 23 0:02:45 0:03:39 23 0:02:45 0:03:31 21 0:02:53 0:04:00 21 0:02:53 0:04:20 21 0:02:53 0:03:13 20 0:02:37 0:04:19 20 0:02:37 0:04:03 20 0:02:37 0:03:20 22 0:02:33 0:03:11 22 0:02:33 0:03:05 22 0:02:33 0:03:07 24 0:02:44 0:03:43 24 0:02:44 0:04:03 24 0:02:44 0:03:44 26 0:02:12 0:03:50 26 0:02:12 0:03:09 26 0:02:12 0:02:57 28 0:03:02 0:03:53 28 0:03:02 0:04:22 28 0:03:02 0:03:27 30 0:02:20 0:02:47 30 0:02:20 0:03:45 30 0:02:20 0:02:48

LF31 0:01:59 0:02:32 LF31 0:01:59 0:02:20 LF31 0:01:59 0:02:33 LF33 0:02:19 0:03:49 LF33 0:02:19 0:03:38 LF33 0:02:19 0:02:45 LF35 0:02:41 0:05:17 LF35 0:02:41 0:03:54 LF35 0:02:41 0:04:01 Conveyor 0:02:31 0:03:51 Conveyor 0:02:31 0:03:46 Conveyor 0:02:31 0:03:26

80225

Average Ideal times


Average Ideal times


Average Ideal times


27 0:03:17 0:04:52 27 0:03:17 0:04:27 27 0:03:17 0:03:43 25 0:03:51 0:08:34 25 0:03:51 0:04:47 25 0:03:51 0:04:57 23 0:02:45 0:04:08 23 0:02:45 0:03:54 23 0:02:45 0:03:25 21 0:02:53 0:04:50 21 0:02:53 0:03:35 21 0:02:53 0:03:33 20 0:02:37 0:03:46 20 0:02:37 0:03:52 20 0:02:37 0:03:24 22 0:02:33 0:03:25 22 0:02:33 0:03:48 22 0:02:33 0:03:13 24 0:02:44 0:04:12 24 0:02:44 0:03:41 24 0:02:44 0:03:21 26 0:02:12 0:02:55 26 0:02:12 0:02:49 26 0:02:12 0:02:45 28 0:03:02 0:05:27 28 0:03:02 0:04:42 28 0:03:02 0:03:44 30 0:02:20 0:02:22 30 0:02:20 0:03:04 30 0:02:20 0:02:49


102

80304

Average Ideal times


Average Ideal times


Average Ideal times


27 0:03:17 0:04:35 27 0:03:17 0:04:16 27 0:03:17 0:04:37 25 0:03:51 0:04:06 25 0:03:51 0:05:09 25 0:03:51 0:06:02 23 0:02:45 0:03:24 23 0:02:45 0:03:59 23 0:02:45 0:03:46 21 0:02:53 0:03:34 21 0:02:53 0:04:15 21 0:02:53 0:04:26 20 0:02:37 0:03:40 20 0:02:37 0:04:07 20 0:02:37 0:04:19 22 0:02:33 0:03:01 22 0:02:33 0:03:27 22 0:02:33 0:03:20 24 0:02:44 0:03:40 24 0:02:44 0:03:43 24 0:02:44 0:03:42 26 0:02:12 0:03:20 26 0:02:12 0:03:00 26 0:02:12 0:03:41 28 0:03:02 0:03:34 28 0:03:02 0:03:55 28 0:03:02 0:04:19 30 0:02:20 0:02:50 30 0:02:20 0:04:00 30 0:02:20 0:03:05


80320

Average Ideal times


27 0:03:17 0:05:42 25 0:03:51 0:05:26 23 0:02:45 0:04:11 21 0:02:53 0:04:09 20 0:02:37 0:04:05 22 0:02:33 0:03:29 24 0:02:44 0:04:01 26 0:02:12 0:03:05 28 0:03:02 0:05:50 30 0:02:20 0:02:59

LF31 0:01:59 0:03:22 LF33 0:02:19 0:03:34 LF35 0:02:41 0:04:42 Conveyor 0:02:31 0:04:12

Location

Ideal Average time

Average of the Measured Average times

Delayed average time

27 00:03:17 00:04:24 00:01:07 25 00:03:51 00:05:28 00:01:37 23 00:02:45 00:03:44 00:00:59 21 00:02:53 00:03:59 00:01:06 20 00:02:37 00:03:54 00:01:16 22 00:02:33 00:03:19 00:00:46 24 00:02:44 00:03:47 00:01:03 26 00:02:12 00:03:09 00:00:57 28 00:03:02 00:04:19 00:01:17 30 00:02:20 00:03:03 00:00:43

LF31 00:01:59 00:02:42 00:00:43 LF33 00:02:19 00:03:26 00:01:07 LF35 00:02:41 00:04:27 00:01:46 Conveyor 00:02:31 00:03:49 00:01:19

103

Appendix 11. Statistics for empty pallets, conveyor orders and outgoing goods, relation between the variables in percentage

2007 24hours 2008 18hours

15days Out Conveyors Empty pallets 15days Out Conveyors

Empty pallets

70806 482 268 237 80211 591 333 173 70807 258 250 90 80213 647 210 133 70808 383 142 177 80214 598 262 201 70809 344 142 105 80218 541 330 142 70813 615 195 267 80219 566 236 156 70814 528 233 238 80220 596 251 166 70815 517 248 220 80221 536 272 160 70816 461 152 196 80225 625 323 184 70820 545 470 202 80227 558 349 155 70821 651 329 216 80228 484 279 126 70822 496 218 193 80304 506 183 150 70827 446 265 128 80305 653 398 176 70828 443 274 169 80306 653 232 130 70829 484 331 190 80310 607 287 189 70830 499 229 159 80320 510 306 131

sum 7152 3746 2787 sum 8671 4251 2372 0.52 0.27 0.20 0.57 0.28 0.16 percentage 52.26 27.37 20.37 percentage 56.70 27.80 15.51

Proportion between the percentages 0.078 0.015 -0.313 Improvements 7.82 1.52 -31.31

104

Appendix 12. Estimation of required number of AGVs by using the model proposed by Mantel and Landeweerd (1995)

date 80305

Order point\time

Total empty travel time (hours:min:sec)

Total loaded time (hours:min:sec)

Total waiting time (hours:min:sec) Orders

Empty pallets 10:00:45 11:20:28 33:34:16 176.00 line 27 1:16:12 2:18:07 4:39:48 32.00 line 25 2:32:53 2:59:17 6:37:29 35.00 line 23 3:56:05 4:45:28 10:17:33 71.00 line 21 6:35:06 7:56:15 19:22:10 109.00 line 20 6:42:36 6:59:36 18:00:14 105.00 line 22 5:23:25 4:57:30 10:13:37 83.00 line 24 2:33:00 4:02:34 10:01:59 65.00 line 26 2:20:58 2:38:41 5:55:16 53.00 line 28 1:56:19 2:10:02 6:12:34 34.00 line 30 1:28:38 1:34:25 3:55:18 24.00 loading area D type pallets 1:55:17 2:31:57 5:05:13 33 conveyors 43:39:08 54:54:00 61:24:59 788.00 Sum 90:20:22 109:08:20 195:20:26 1608.00 Total sum 394:49:08 AGV availability (hours) 18:00:00 Average times 0:03:25 0:03:55 Number of AGV required 22

105

Appendix 13. Process map including ideal time and the average transportation delay within lines according to the secondary data

Pick-up, Rack system

Drop point

Ideal times (min:sec)

Average transportation delay (min:sec)

Ideal+Delay (min:sec)

Minutes and seconds

27 LF29 L 0:03:43 0:01:07 0:04:50 4.50 R 0:02:23 0:01:07 0:03:30 3.3 LF31 L 0:03:18 0:01:07 0:04:25 4.25 R 0:02:57 0:01:07 0:04:04 4.04 LF33 L 0:03:40 0:01:07 0:04:47 4.47 R 0:02:55 0:01:07 0:04:02 4.02 LF35 L 0:03:49 0:01:07 0:04:56 4.56 R 0:03:32 0:01:07 0:04:39 4.39





22 LF29 L 0:02:19 0:00:46 0:03:05 3.05 R 0:02:25 0:00:46 0:03:11 3.11 LF31 L 0:01:58 0:00:46 0:02:44 2.44 R 0:02:08 0:00:46 0:02:54 2.54 LF33 L 0:03:35 0:00:46 0:04:21 4.21

106

R 0:01:06 0:00:46 0:01:52 1.52 LF35 L 0:03:50 0:00:46 0:04:36 4.36 R 0:03:00 0:00:46 0:03:46 3.46





Conveyors Drop point

C 27 0:02:14 00:01:19 0:03:33 3.33 C 25 0:01:37 00:01:19 0:02:56 2.56 C 23 0:02:44 00:01:19 0:04:03 4.03 C 21 0:01:44 00:01:19 0:03:02 3.02 C 20 0:02:53 00:01:19 0:04:12 4.12 C 22 0:02:00 00:01:19 0:03:19 3.19 C 24 0:03:05 00:01:19 0:04:24 4.24 C 26 0:02:37 00:01:19 0:03:56 3.56 C 28 0:03:12 00:01:19 0:04:31 4.31 C 30 0:02:58 00:01:19 0:04:17 4.17 Empty Pallets LF35 0:02:41 00:01:46 0:04:27 4.27 LF33 0:02:19 00:01:07 0:03:26 3.26 LF31 0:01:59 00:00:43 0:02:42 2.42

107

Appendix 14. Travel times between the different locations in the inventory

108

Appendix 15. Requested rates for delivery between different locations in the inventory

109

Appendix 16. Time requirements for delivering to different locations in the inventory

110

Appendix 17.Delivery requirements to different locations

111

Appendix 18. Percentage of activities for certain periods during the days with respect to number of orders

Hour Orders Percentage

7 57 0.087289433 8 37 0.056661562 9 28 0.04287902

10 37 0.056661562 11 46 0.070444104 12 30 0.045941807 13 58 0.088820827 14 37 0.056661562 15 31 0.047473201 16 35 0.053598775 17 26 0.039816233 18 22 0.033690658 19 42 0.06431853 20 47 0.071975498 21 57 0.087289433 22 16 0.024502297 23 35 0.053598775 24 12 0.018376723

sum 653

112

Appendix 19. Requested rates for delivery between different locations in the inventory, orders are evenly spread among the rack system

113

Appendix 20. Delivery requirements to different locations, orders are equally spread among the rack system

114

Appendix 21. Average number of orders per hour during a day for 27 days, the future state demand Daily hours Average orders per hour

1 14.85413375 2 34.11620151 3 19.44321573 4 33.39160962 5 17.08829208 6 18.41671055 7 66.05862735 8 42.0867123 9 43.53589609

10 44.98507987 11 58.39002984 12 35.263472 13 60.98648411 14 41.18097244 15 60.68457083 16 60.92610146 17 45.16622784 18 41.48288573 19 46.91732491 20 67.32666316 21 63.94523433 22 39.18834474 23 50.78181499 24 25.30033351

sum 1031.516939

115

Appendix 22. Percentage of activities for certain periods during the days with respect to number of orders, according to the future demand in the system Hour Order Percentage Minimum Maximum

1 14.85 0.014 14.85 2 34.12 0.033 3 19.44 0.019 4 33.39 0.032 5 17.09 0.017 6 18.42 0.018 7 66.06 0.064 8 42.09 0.041 9 43.54 0.042

10 44.99 0.044 11 58.39 0.057 12 35.26 0.034 13 60.99 0.059 14 41.18 0.040 15 60.68 0.059 16 60.93 0.059 17 45.17 0.044 18 41.48 0.040 19 46.92 0.045 20 67.33 0.065 67.33 21 63.95 0.062 22 39.19 0.038 23 50.78 0.049 24 25.30 0.025

sum 1031.52

116

Appendix 23. Number of orders from the empty pallets conveyor system Date\Lane Empty pallets conveyor

80205 42 80206 46 80207 54 80211 49 80212 50 80213 57 80214 64 80218 45 80219 56 80220 59 80221 61 80225 58 80226 54 80227 62 80228 57 80303 52 80304 48 80305 57 80306 57 80310 51 80311 45 80312 60 80313 57 80318 57 80319 54 80320 57 80325 57

Average 54.2962963

117

Appendix 24. Average empty travel time dates Empty travel time (min:sec)

80211 0:02:53 80213 0:04:05 80214 0:03:12 80225 0:03:50 80304 0:03:42 80305 0:03:55 80227 0:03:05 80228 0:03:18 80320 0:03:47 80306 0:03:10

Average 0:03:30

118

Appendix 25. Questionnaire Comfort questionnaire In order to for us to know how you experience your work environment we are grateful to submit this survey. The questionnaire is responded entirely. The loading area environment: 1. Do you find your work stressful? 2. Are there sufficient aids for heavy lifting? 3. What impact do the AGVs have in your loading ability? 4. How is your loading rate affected by the incoming material flow? 5. How do you consider the stocking policy of the orders in the containers? The control room 1. Which areas in the company do stoppages of the AGVs occur mostly? 2. How is the path of the AGVs preceded when entering inventory 7 for

delivering/picking up orders? 3. How is your work affected by these stoppages? 4. How is the current stocking policy in the rack system ranged and why? 5. How is the amount of the AGVs within inventory 7 affecting each other and their

loading ability? The planning room 1. How is the planning procedure undertaken and how is it related to the amount of

orders? 2. What factors are affecting the amount of out delivery of the containers? 3. What is the daily average for out delivery of the containers? 4. Why is the amount of out delivery during weekends so less than during the week? 5. What is the main reason that goods that should be in inventory 7 are instead stored

in the other company’s inventory?

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