Ijtra130523

International Journal of Technical Research and Applications e-ISSN: 2320-8163,

www.ijtra.com Volume 1, Issue 3 (july-aug 2013), PP. 68-75

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IMPROVEMENT OF SUPPLY CHAIN

MANAGEMENT BY MODEL ANALYSIS

R.K.VERMA1, K.M.MOEED

2, K.G.SINHA

3

1Research scholar, Integral University

2Associate Professor, Integral University

3Assistant Professor SR Institute of Management and Technology

Department of Mechanical Engineering

Abstract—This paper analyses the case of any production

system by making a model for the existing or a new industry we

can analyze the different aspects of manufacturing and then by

using various techniques we can minimize the flow from one end

to another end so that the lead time decreases and productivity

increases.

Index Terms—supply chain management, productivity, model

analysis. (key words)

I. INTRODUCTION

The 90’s have often been viewed as the decade of market

globalization, shorter product life cycles, and the disintegration

of many industries, facts that increased competition among

companies and that resulted in a race to improve supply

chains (Lee, 2001)[1]. Such a race established a new

competition logic where the potential to generate competitive

advantages is no longer limited to a single company or business

unit, but is extended to many other companies that take part in

the same supply chain (Pires, 1998)[2]. To be successful in the

supply chain competition logic, the integration and

optimization of the main processes across a chain should be

achieved under a Supply Chain Management (SCM)

perspective. Many companies have been discovering that

effective SCM is the next step they need to take to increase

profits and market share (Simchi- Levi et al., 2000)[3].

Taking these facts in consideration, this paper aims at

analyse a model for SCM analysis and also suggest a modified

model to decrease the time and increase the productivity in an

industry. Within a SCM perspective, the model systematizes

the analysis of relevant elements involved in supply chains,

which are here called SCM configurations. To apply it to the

industry, first, the study analyses the industry as an industrial

segment and, then, it selects the supply chains responsible for

the creation, production, and distribution to apply the model.

II. LITERATURE REVIEW

Simulation models are used in evaluating supply chain

configuration decisions because of their ability to represent the

problem realistically and also used to know the exact area of

problem. They can also be applied to select the most

appropriate configuration from a limited set of alternative

configurations. Existing models representing configuration,

related issues are grouped according to the simulation-

modelling approach used namely, process-oriented simulation,

object-oriented simulation, and agent-oriented simulation. We

discuss these below.

A. Process-Oriented Models

A simulation model is described by a sequence of processes

initiated by events occurring in the system. This approach is

attractive from the model integration point of view. Supply

chain process models can be transformed into simulation

models. Bowersox (1972)[4] presents an early study on

application of simulation for long-term distribution planning.

The model consists of standardized nodes representing

manufacturing plants with adjacent warehouses, distribution

centres, consolidated shipping points, and demand units. In the

case studies reported, simulation is used to evaluate several

preconfigured supply chain design alternatives. Decisions to be

made include capacity expansion and location of new facilities.

The author indicates that data availability and complex model

building are major obstacles for widespread use of simulation

in supply chain management. The business process orientation

is adopted by Van der Vorst et al. (2000)[5]. The supply chain

is defined as consisting of multiple business processes

governed by design variables, defined as configuration level

and operational level. Thus, a simulation model is used for

decision-making in both strategic and operational decisions.

The business process modelling formalism used is Petri-nets,

which are often considered over other process and network

modelling methods because they are based on sound theoretical

principles and enable some analytical evaluation. Strategic and

operational supply chain design decisions to be made are

identified following the principle that supply chain

performance can be improved by reducing the impact of



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various sources of uncertainty. Configuration-related decisions

are implementation of real-time inventory management

information systems and reallocation of some of the supply

chain management functions. The supply chain performance is

evaluated for numerous scenarios, where each scenario is

characterized by a set of design variables with specified values.

It is reported that the adoption of decisions made on the basis

of simulation modelling has resulted in major performance

improvements. Petri-nets also have been used for simulation of

the manufacturing supply chain by Dong and Chen

(2001)[6].Ganeshan et al. (2001)[7] simulate the performance

of a retailing supply chain. The simulation model takes the

supply chain network structure as an input parameter.

Inventory cycle time, return-on-investment (ROI), and service

levels are measured for several scenarios characterized by

forecasting accuracy, information exchange mechanism used,

and planning cycle length. Simulation results show that all

three factors have significant impact on supply chain

performance.

Process-oriented simulation of existing systems and

envisioned systems for a logistics services provider is explored

by Jain et al. (2001)[8]. The modelling objective is to evaluate

the possible benefits of replacing legacy IT systems and

business processes. Performance measures are service level,

inventory turns, and order-to-delivery lead time. Processes

represented in the model are order fulfilment, procurement, and

demand-and-supply planning. The authors emphasize the

importance of providing an adequate level of abstraction,

which should correspond to modelling objectives. Bagchi et al.

(1998) [9] describes a supply chain simulator developed at

IBM. This simulator defines seven typical supply chain

processes available for model composition: customer,

manufacturing, distribution, transportation, inventory planning,

forecasting, and supply planning processes. Performance

measures characterizing customer service, inventory, resources,

and returns are collected during simulation. Similarly, Ingalls

and Ingalls RG (1999) [10] present a simulation model used for

supply chain analysis at Compaq. The model is used to answer

managerial questions concerning customer service and

profitability of the entire supply chain, as well as that of

individual units. It includes eight standardized structures, such

as customer, company, inventory site, manufacturing site, geo

(i.e., sales component where revenue and costs are accounted

for), and country. Thus, the model emphasizes global aspects

of the supply chain. The model uses 59 input data tables and

provides 112 output data tables. Hung (2000) [11] describe a

tool, Supply Solver, which incorporates various specific

features for specifying supply chains. For instance, an interface

for inputting distances between supply chain units is provided.

Persson and Hager (2002) [12] apply simulation to select

among three alternative configurations of a manufacturing

supply chain. Simulation can be perceived similar to the

scenario-based approach, where a scenario is defined by a

particular supply chain configuration under evaluation.

B. Object-Oriented Models

The object-oriented approach allows the designing of

modular simulation models. In the case of supply chain

configuration, that implies compilation of the supply chain

network from a set of standardized objects. The object-oriented

approach also makes easier the transition between model

development and executable software.

Several existing works on object-oriented supply chain

simulation attempt to identify main classes characterizing

supply chains in general. Alfieri and R. R. Barton (1997) [13]

define key classes for representing demand points, factories,

stocking points, and routing. The authors show the use of

generalization and inheritance to describe specific management

policies. For instance, the general stock point class has two

child classes representing (R, Q) and (s, S) inventory

management policies, respectively. A simulation workbench

for analysing information-sharing policies at the operational

level is developed by Ng et al. (2002)[14]. It contains several

classes used to represent a supply chain that include decision-

making classes for forecasting and inventory planning, and

classes for structural supply chain elements. Van der Zee and

van der Vorst (2005) [15] use object-oriented simulation model

development to improve separation of concerns with particular

emphasis on better representation of control elements, which

tend to be dispersed anywhere in the simulation model. An

object-oriented supply chain simulation system named SISCO

has been developed by Chatfield et al. (2006) [16]. The system

allows users to specify supply chain structure and management

policies using a user friendly graphical interface. Users' inputs

are saved in an XML-based Supply Chain Modelling Language

(SCML) format. The XML document obtained is used to

generate an executable supply chain simulation model by

mapping its elements to specific classes in the supply chain

simulation library. The library contains the implementation of

classes representing order, supply chain arcs, and nodes, and

several manager and actor classes, which are implemented

using a general purpose programming language called Java.

Hung et al. (2006) [17] develop a supply chain simulation

model for production scheduling purposes. The supply chain

network is composed of generic nodes. Each node has three

components:

Inbound material management

Material conversion

Outbound material management

C. Agent-Based Models

Agent-based approaches attempt to capture collaborative

and implicit aspects of supply chain behaviour. Swaminathan

et al. (1998) [18] propose an agent-based architecture for

building and executing networked supply chain models.

Developed models allow describing issues related to network

structure evaluation according to lead time, transportation cost,

currency fluctuations, inventory control, information exchange,

supplier reliability, flexibility, and others. The architecture is



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based on employing a generic agent, which can be specialized

to perform various supply chain activities. Agents

communicate with each other by sending messages. The

processing of each message is governed by a set of rules (for

example, rules defining an inventory replenishment policy).

Types of agents and available control policies are structured

into the supply chain library. Reduction of model development

workload is identified as the key advantage of this architecture,

which is achieved by supporting modular model structure and

the reuse of existing components (agents and control policies).

A supply chain simulation framework proposed by Van der

Zee and Van der Vorst (2005) [15] also advocates use of the

agent-based approach.

Agents are used to represent infrastructural elements of the

supply chain as well as managers of these elements. External

agents represent suppliers and customers. Internal agents

operate according to allocated jobs and their local intelligence.

They transform available flow items (i.e., products,

information). The agent-based approach is implemented using

ARENA. The authors also list several key requirements for an

efficient supply chain simulation modelling tool. These include

an appropriate user interface, which facilitates trust building in

collaborative decision making, and ease of handling modelling

scenarios. Efficiency of agent-based modelling largely depends

upon developed agent capabilities, which are often limited to

most basic behaviour.

III. DEVELOPMENT OF SUPPLY CHAIN CONFIGURATION

SIMULATION MODELS

Development of simulation models is a complex process.

General simulation modelling methodologies have been

developed (Law and Kelton (2000) [19]. However, simulation

models tend to be rather case specific, thus requiring a major

development effort. Therefore, specific modelling templates,

methods, and tools for a particular problem domain are useful.

In the case of supply chain configuration, development of

simulation models can be facilitated by exploring several

specific characteristics including

A high level of abstraction

Representing two main elements, namely, supply

chain nodes and arcs connecting nodes

Interactions with other supply chain configuration

models

Commercially available simulation packages have attained

a high level of maturity. Therefore, using these packages for

development of supply chain configuration models and specific

utilities facilitating the development process is advisable,

instead of relying on custom tools. The benefits of using

Commercial-Of-The-Shelf (COTS) software in the framework

of optimization and simulation are also identified by Vamanan

et al. (2004) [20].

IV. APPROACH

The proposed simulation model building approach utilizes

two main concepts:

Separation between data and the model

A generic representation of supply chain units.

The main stages of the model building approach are shown

in Fig

Fig. 1 Integrated simulation model building.

A supply chain simulation model is developed using data

from the supply chain management information system, and is

initially specified using UML. If simulation is used to evaluate

supply chain configuration optimization results, then

optimization results are an important data source. The decision-

modelling system generates the simulation model by

transforming information models into a specific simulation

modeming language, which is generated on the basis of a

predefined template. The template does not contain any

simulation objects. It only contains procedures for executing

control of the generic functions and data declarations. The

procedures have a uniform design. Different procedures can be

developed to perform the same activity. Thus, different

management policies can be analysed. The generated

simulation model can also be manually edited by a user to

incorporate features not represented in the information models

or not supported by the model generation mechanism. The

decision-modelling system also transforms input data in a

format suitable for efficient execution of the simulation model.

This format is referred to as the modelling techniques’ specific

data model. The generated simulation model is executed by a

commercially available simulator.



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Fig 2 A generic representation of supply chain unit

Fig 3 A class diagram of generic supply chain unit.

V. MODEL GENERATION

The simulation model is generated on the basis of the

object diagram. The object diagram contains realizations of

classes shown in Fig.3. Realizations are created according to

optimization outcomes (for instance, optimization yields that

three out of five manufacturing units are to be opened at

selected locations; objects representing these three

manufacturing units are created) or for a given fixed supply

chain configuration to be evaluated.

Different mechanisms are used to represent various entities

from the supply chain object model. In the case of using

ARENA (Rockwell, 2001) [21] as a simulation modelling tool,

a supply chain unit object is represented as a standardized

sequence of simulation modelling blocks, customer zones are

represented using a differently structured sequence of

simulation modelling blocks, products and materials are

represented using simulation modelling entities, and resources

are represented using the resource module.

An ARENA sub model is generated for every supply chain

unit included in the configuration (i.e., for every Supply Chain

Unit object). The Flow- Transformation object is transformed

into a sequence of processes realizing manufacturing order for

processing, setting up resources, requesting materials from the

stock, and finally assembling the product. The object diagram

prescribes that flow transformation is needed and allows the

setting of variables in the ARENA model (for instance, the

setup Time attribute is used to generate a corresponding

variable in the ARENA simulation model). At the same time,

the object diagram does not specify the flow transformation

process. That is perceived as model method and modelling tool

specific data, which determine transformation of the object in

ARENA blocks. Products and materials are represented by

ARENA entities. Arrays are used to deal with multiple

products and resources. The model generator in the decision

support system is implemented using Visual Basic (VB). It

creates ARENA objects using the ActiveX technology

(actually, the same data model can be used to create a

simulation model in other simulation modelling environments

supporting the ActiveX technology).

A separate sub model is used to represent Customer Zone

objects. This sub-model is used to generate customer demand

and to serve as a final destination for finished products.

The main model generation transformations are

summarized as follows:

1. A sequence of simulation modelling blocks is

generated for each object of the Customer Zone type. This

sequence represents generation and queuing of demand orders

and receiving.

2. The ARENA resource table is populated by

generating an entry for each object of the Resource type.

3. An ARENA entity is generated for each object of the

Product type.

4. An ARENA sub model is generated for each Supply

Chain Unit object.

It consists of four sets of simulation modelling blocks

corresponding to objects that compose the Supply Chain Unit

object. The local control set is generated from the appropriate

object of the Local Control type. Other sets of blocks are

generated in a similar manner.

The modelling method specific data model is also

generated and populated during the model generation process.

The data model organizes data in a manner suitable for

execution of the simulation model. This ensures quick access

of necessary data items. The data model consists of multiple

spreadsheets containing information about structure and

operational characteristics of the system. At the beginning of

simulation, modelling data from the data model are loaded in

the simulation model. Before loading, intermediate data have

been created by converting the data model tables from the

Excel format into the text format because ARENA reads text

files much faster than Microsoft Excel files. Some of the data

tables are loaded into ARENA arrays for access by ARENA



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objects, while some others are loaded in VB arrays for access

by control functions.

VI. CASE STUDY: OMAX AUTOS LIMITED

This paper investigates the flow of manufacturing in an

industry and by using the model analysis the main aim is to

reduce the total time of manufacturing in the industry and

increase the total productivity

A. Present Model Of OMAX Autos Limited

Fig.4 Present Model of OMAX Autos Limited

Now from fig as found that there is problem of supply from

raw material to the finished

1) VARIOUS PROBLEM AREAS OF THE INDUSTRY

In this industry the setup of the machines are well and good

but there are some things can be improved so that easy supply

can be occur.

Figure.5 Improper Arrangement Of Raw Material After

First Cutting

Figure.6 Root Blockages Due To Raw Material

Figure.7 improper place for quality inspection

Figure.8 safety problems



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Fig.9 improper working on press machine

Figure.10 material handling problems

B. Proposed Model

Since OMAX AUTOS LIMITED have number of products

for TATA motors and we have studied the different operations

in existing layout and finally we came to the point that the

layout of machines are optimal hence our suggestions are not to

shift to any machine to anywhere which may create lot of time

in machining for other products but some important changes

can occur so many conclusions is to reduce the existing

machining time by modifying the material handling system so

it is better to modify the handling equipment’s

Fig .11 proposed model

C. Improvement Methods

In OMAX AUTOS LIMITED various improvements of the

existing models are:

1) Changes in existing model:

There are several changes in existing model we made

which is useful for improving the productivity. In these

changes we made some departments such as

Industrial Engineering Department: In this department

when volume of the products and types of the products

increases. Many problems created such as:

Machine problems

Tool problems

Workers problems

Material handling problems

To reduce these types of problems we add this department

a) Objectives Of This Department:

To fulfil all the machine requirements.

To make strategic planning.

To arrange the machinery in proper order.



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To reduce the time by using work study and

method study.

To make the good relation between workers and

management.

To select the proper machines by proper machine

selection by using existing technology.

b) Temporary Inventory Of Raw Material:

Documentation of the raw material should necessary for

any industry in this model we add a department for the proper

documentation. For this we use CIM for the proper documents.

c) Advantages Of This Department

In case you have a new job order design you can

just search the previous samples and modify them

to make a new one.

Reduce design procedure.

Can save time.

We can directly connect it to design department to

make designs and procedures.

d) Modification Of Handling Equipment

For Large Size Goods

For large size goods the supply is to be done by forklift for

the smooth supply of large goods we can make arrangement

such as supply can be easily done by pre determine allowance

limits of forklift machine.

Fig.12 Forklift for large Goods

For such type of forklift truck we make some

predetermined allowance limit and also made some stands so

that easy flow can occur.

For Medium Size Goods

We can supply the finished goods by small trollies so that

easily supply of raw material can be done. These trollies should

have small wheels so that can be movement can be done easily.

Fig.13 Trolley for Medium Size Goods

For Small Size Goods

We can supply the small goods by using trays such that

easily handling can be done it may be noted that handle on the

tray should be ergonomically correct due to avoid any

muscular pain of worker

Fig.14 Modified Equipment’s For Small Goods

VII. CONCLUSION

The investigation can be concluded in following words

If we use proper layout of the industry than

approximately 30-40 % problems of supply can be

reduced.

Material handling can also be the problem so if we

use proper material handling we can reduce time.

We can use ergonomics to eliminate the industrial

stress also.

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