ANALYSIS OF TRANSPORTATION, DISTRIBUTION CENTER AREA …

ANALYSIS OF TRANSPORTATION, DISTRIBUTION

CENTER AREA AND INVENTORY THAT

INFLUENCE DELIVERY EFFICIENCY OF

LOGISTIC MANAGEMENT

(A Case Study of PT. Nestle Indonesia Distribution

Center, Cikarang)

By

Faisal Faturrahman

ID no. 014201100158

A skripsi presented to the

Faculty of Business President University

in partial fulfillment of the requirements for

Bachelor Degree in Economics Major of Management

2015

i

SKRIPSI ADVISER

RECOMMENDATION LETTER

This skripsi entitled “ANALYSIS OF TRANSPORTATION,

DISTRIBUTION CENTER AREA, AND INVENTORY THAT

INFLUENCE DELIVERY EFFICIENCY OF LOGISTIC

MANAGEMENT (A Study Case in PT. Nestle Indonesia

Distribution Center, Cikarang” prepared and submitted by Faisal

Faturrahman in partial fulfillment of the requirements for the degree of

Bachelor in the Faculty of Business has been reviewed and found to

have satisfied the requirements for a skripsi fit to be examined. I

therefore recommend this skripsi for Oral Defense.

Cikarang, Indonesia, February 10th , 2015

Acknowledged by, Recommended by,

Vinsensius Jajat K., MM, MBA Filda Rahmiati. MBA

Head of Management Study Program Thesis Adviser

ii

DECLARATION OF

ORIGINALITY

I declare that this skripsi, entitled “ANALYSIS OF

TRANSPORTATION, DISTRIBUTION CENTER AREA AND

INVENTORY THAT INFLUENCE DELIVERY EFFICIENCY OF

LOGISTIC MANAGEMENT (A Study Case in PT. Nestle Indonesia

Distribution Center, Cikarang” is, to the best of my knowledge and

beliefs, an original piece of work that has not been submitted, either in a

whole or in a part, to another university to obtain a degree.

Cikarang, Indonesia, February 10th , 2015

FAISAL FATURRAHMAN

iii

PANEL OF EXAMINERS

APPROVAL SHEET

The Panel of Examiners declares that the skripsi entitled “ANALYSIS

OF TRANSPORTATION, DISTRIBUTION CENTER AREA AND

INVENTORY THAT INFLUENCE DELIVERY EFFICIENCY OF

LOGISTIC MANAGEMENT (A Study Case in PT. Nestle Indonesia

Distribution Center, Cikarang)” that was submitted by Faisal

Faturrahman majoring in Management from the Faculty of Economics

was assessed and approved to have passed the Oral Examinations on

(Date of Defense).

LISWANDI.Spd.MM

Chair – Panel of Examiners

Ir. Erny Hutabarat, MBA

Examiner 1

Filda Rahmiati. MBA

Examiner 2

iv

ABSTRACT

This study explored the analysis of transportation, distribution center area and

inventory that influence delivery efficiency of logistic management. PT. Nestle

Indonesia has several issues such as dispersed customer, damage products, distance

and road condition created problems of delivery efficiency of logistic management in

PT Nestle Indonesia. PT Nestle Indonesia joint venture with PT GAC logistics Ocean

try to deliver products as good as efficient but the elaboration above would be a bit of

an obstacle in the process of fulfilling the customer's needs. The result of this

research shows the most dominant variable is distribution center area and in the

analysis that all variable (Transportation, Distribution Center Area, and Inventory) in

this research have significant influence towards delivery efficiency. In this research,

the data collected are primary data, by spreading questioner to the 71 respondents.

Inside the questioner, tool for measure the degree of agreement from respondents is

Likert Scale. Test that include in quantitative analysis are reliability and validity test,

classical assumptions test, and linear multiple regression to conclude the hypothesis

testing through F-test, T-test, and coefficient determination (R2).

Key words: Transportation, Distribution Center Area, Inventory, Logistic

management, Delivery Efficiency

.

v

ACKNOWLEDGEMENT

“Life is never flat so make it awesome”

This time with the tears of happiness, the moment that i wait for a long time is finally

come true. This is the happiest moment after struggling with my undergraduate thesis

and finishing my study at President University. I still remember three years ago,

Mom and Dad accompany me to President University and moving in to dormitory,

time goes by and now I’m finished my undergraduate skripsi and graduated from

President University.

Through this opportunity, I would like to show my gratitude to

1. Thank you just the word that can I say to Allah SWT for the blessing so that I

can complete this last assignment in university.

2. My Parents, Mom and Dad. I don’t know how to describe my gratitude to

both of you in words, but I am so glad and proud to have parents like you

mom and dad. You are the best parents in the world. Thanks for everything,

thanks for your unlimited support and care.

3. My Brother, Thank you for your love and support during this thesis period.

4. My Thesis Adviser, Mr. Orlando Santos MBA, Thank you so much for your

guidance, attention, patience, care, time, and kindness. You are the friendliest

lecturer ever. I’m so proud to have you as my thesis adviser, thanks for being

my thesis adviser.

5. My second Thesis Adviser, Mrs Filda Rahmiati Thank you so much for your

guidance, attention for being my thesis adviser

6. Nestle Indonesia Finance and control has already accept me became a part of

you in 4 month for the knowledge and experiences

vi

7. Bromance of F&C Nestle Head office that already make my life when

internship period is different.

8. Darsane Haji Karta as the whole life friends as the president university

student for the craziness and support until I can complete this thesis and

working together to achieve every dreams.

9. Nestle Indonesia distribution center Cikarang as specialy Mr Susilo as the

head of Cikarang distribution center, that kindness receive me to distribute

the questioner and the data I get , it is very useful for this thesis

10. All respondents in Cikarang distribution center that very help me and

kindness to fulfill the questioner

11. Eva Yuliana Sa’diah that always remain me to complete this thesis, thank

you for your kindness and support.

12. Geovani Septio, Ardisa, Kartika that help me to made SPSS so that very

helpful and make this thesis complete.

13. The last but not least DiverVenture X that already support me to make this

thesis.

vii

List of Table

SKRIPSI ADVISER RECOMMENDATION LETTER .............................................. i

DECLARATION OF ORIGINALITY ....................................................................... ii

PANEL OF EXAMINERS APPROVAL SHEET ...................................................... iii

ABSTRACT ................................................................................................................ iv

ACKNOWLEDGEMENT ............................................................................................ v

List of Table ............................................................................................................... vii

List of Figure .............................................................................................................. xii

CHAPTER 1 INTRODUCTION .................................................................................. 1

1.1. Background of Study ...................................................................................... 1

1.2 Problems Identification ....................................................................................... 6

1.3 Statement of Problem .......................................................................................... 7

1.4 Research Objective .............................................................................................. 8

1.5 Scope and Limitation .......................................................................................... 8

1.6 Definition of Term .............................................................................................. 9

1.7 Significance of the study ................................................................................... 10

Chapter 2 Literature Review ....................................................................................... 11

2.1 Theoretical Review ........................................................................................... 11

2.1.1 Logistic Management ............................................................................. 11

2.1.2 Logistic .................................................................................................. 12

2.1.3 Delivery .................................................................................................... 14

2.1.4 Delivery Efficiency ................................................................................ 16

2.1.5 Transportation. ............................................................................................ 17

2.1.6 Transportation and Delivery Efficiency .................................................. 20

2.1.7 Distribution Center Area .......................................................................... 21

2.1.9 Distribution Center Area to Delivery Efficiency ...................................... 23

2.1.10 Inventory ................................................................................................. 24

2.1.11 Inventory to Delivery Efficiency ............................................................ 25

viii

2.2 Previous Research ............................................................................................. 26

2.3 Theoretical Framework ..................................................................................... 28

2.4 Operational Definition ....................................................................................... 29

2.5 Hypothesis ......................................................................................................... 31

Chapter III RESEARCH METHODOLOGY ............................................................. 32

3.1 Research Design ........................................................................................... 32

3.2 Research Framework .................................................................................... 33

3.3 Research Instrument ..................................................................................... 35

3.3.1 Primary Data ......................................................................................... 36

3.3.2 Secondary Data ..................................................................................... 37

3.4 Sampling Design .......................................................................................... 37

3.4.1 Population ............................................................................................. 38

3.4.2 Sample ................................................................................................... 38

3.5 Statistical Treatment ..................................................................................... 39

3.5.1 Likert Scale ........................................................................................... 40

3.5.2 Weighted Mean ..................................................................................... 41

3.5.3. Standard Deviation .................................................................................... 42

3.6 Data Analysis ............................................................................................... 42

3.7 Reliability and Validity ..................................................................................... 43

3.7.1. Reliability Test........................................................................................... 43

3.7.2. Validity Testing ......................................................................................... 44

3.8.1 Classical Assumption Test ............................................................................. 46

3.8.1.1 Normality Test ......................................................................................... 46

3.8.1.2 Multicollinearity test ................................................................................ 46

3.8.1.3 Heteroscedasticity Test ........................................................................ 47

3.8.2 Linear Multiple Regression ...................................................................... 47

3.8.3 Paired Sample T- test ( Partial Test) ......................................................... 49

3.8.4 F- Test (Simultaneous Test)...................................................................... 50

3.8.5 R2 Test (Coefficient of Determination) .......................................................... 51

ix

CHAPTER IV ANALYSIS AND INTERPRETATION ............................................ 52

4.1Corporate Profile ................................................................................................ 52

4.1.1. Vision and Mission .................................................................................... 52

4.1.2. Corporate Values ....................................................................................... 54

4.1.3 Core Organization Activities ...................................................................... 55

4.1.4. Products ..................................................................................................... 57

4.2 Data Result Analysis ......................................................................................... 58

4.2.1 Reliability Test ...................................................................................... 59

4.2.2 Validity Test .......................................................................................... 60

4.2.3 Respondent Profiles .............................................................................. 70

4.2.4 Respondent Responses .......................................................................... 74

4.2.5 Descriptive Statistics ............................................................................. 95

4.2.6 Classic Assumption Test ....................................................................... 96

4.2.7 Multiple Regression Analysis ............................................................... 99

4.2.8 Hypotesting Testing .................................................................................. 100

4.3 Interpretation of Results ............................................................................. 105

Chapter V .................................................................................................................. 110

CONCLUSIONS AND RECOMMENDATIONS ................................................... 110

5.1 Conclusions ................................................................................................ 110

5.2 Recommendations ...................................................................................... 111

5.2.1. For Nestle Indonesia ................................................................................ 111

5.2.2 For Future Research .................................................................................. 112

REFERENCES ...................................................................................................... 114

APPENDIX A ....................................................................................................... 117

APPENDIX B ....................................................................................................... 121

APPENDIX C ....................................................................................................... 130

x

LIST OF TABLES

Pages

Table 1.1: Damage products value ·························································· 6

Table 1.2: Variant short lead by in –Accuracy stock Cikarang DC ···················· 7

Table 2.1: Previous Research ······························································· 28

Table 2.2: Operational Definition………………………………………………...…29

Table 4.1: Cronbach’s Alpha of Transportation ·········································· 54

Table 4.2: Cronbach’s Alpha of Distribution Center Area ····························· 55

Table 4.3: Cronbach’s Alpha of Inventory ················································ 55

Table 4.4: Cronbach’s Alpha of Distribution center performance ····················· 55

Table 4.5: Pearson Correlation of Transportation ········································ 58

Table 4.6: Pearson Correlation of Distribution center area ····························· 59

Table 4.7: Pearson Correlation of Inventory ·············································· 60

Table 4.8: Pearson Correlation of Distribution Center performance ·················· 63

Table 4.9: Respondent Profiles: Current age ············································· 65

Table 4.10: Respondent Profiles: Last Education ········································ 67

Table 4.11: Respondent Profiles: Working in this situation ··························· 69

Table 4.12: Respondent Profiles: Working in this Company ··························· 71

Table 4.13: Variable 1 Description ························································· 77







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Table 4.22: Variable 10 Description ······················································· 84











Table 4.34: Descriptive Statistics ··························································· 92

Table 4.35 Multicollinearity Test: Tolerance and VIF Value ························· 93

Table 4.36 Multicollinearity Test: Coefficient Correlations…………………….…95

Table 4.37 Multiple Regression Analysis: Coefficients ································ 99

Table 4.38 Multiple Regression Analysis: F-Test (ANOVA)…………………….100

Table 4.39 Multiple Regression Analysis: Coefficient Determination (R²) ……...102

xii

List of Figure

Figure 3.1: Research Framework ................................................................................ 35

Figure 3.2 Data Collection Method ............................................................................ 36

Figure 3.3: Likert Scale ............................................................................................... 40

Figure 3.4: Likert Scale Questionnaire ....................................................................... 40

Figure 4.1. Nestle Corporate Value ............................................................................ 54

Figure 4.1 Respondent Profiles: Current Age ............................................................. 71

Figure 4.2 Respondent Profiles: last Education .......................................................... 72

Figure 4.3 Respondent Profiles: Working in this position .......................................... 73

Figure 4.4 Respondent Profiles: Working in this company ........................................ 74

Figure 4.5 Normality Test: Histogram ........................................................................ 96

Figure 4.6 Normality Test: P-P Plot Graph ................................................................ 97

Figure 4.7 Heteroscedascity Test: Scatter Plot Graph ................................................ 98

1

CHAPTER 1

INTRODUCTION

1.1. Background of Study

Logistics has been playing a fundamental role in global development for almost

5,000 years. Since the construction of the pyramids in ancient Egypt, logistics

has made remarkable strides. Time and again, brilliant logistics solutions have

formed the basis for the transition to a new historical and economic era.

Examples of this fundamental progress include the invention of the sea-cargo

container and the creation of novel service systems during the 20th century. Both

are integral parts of globalization today.

Information networking made the next paradigm shift in logistics management.

Around that time, the improvement of transportation technologies and

deregulation of transportation had also occurred. So, many logistics researcher

sized transportation change, but I think that the increased importance of

information technology had a much greater effect on logistics management

changes. Using the infrastructure of telecommunication, logistics changed

dramatically. The traditional functions of maker, distributor, and retailer had to

be reconsidered to exploit information network power. This means 5 traditional

trading ways had to be changed under the information -networking era.

Global competition began to arise and spread in the 1970s and accelerated in the

1990s. Globalization is still moving forward today. Efficient logistics creates a

crucial competitive edge for companies that are expanding in global markets.

Successful logistics efforts in international supply chains can fuel the

development of global markets.

2

Logistics is an increasingly prevalent term in business. It’s about getting the

product to the customer in the most efficient, timely and cost effective manner.

Transport and logistics managers play a key role in fulfilling manufacturers’

promises to their customers and in meeting those customers’ expectations. They

are responsible for managing the execution, direction, and coordination of all

transportation matters within the organization. This includes managing budgets,

organizing schedules & routes, ensuring that vehicles are safe and meet legal

requirements, and making sure that drivers are aware of their duties.

Logistics links all the processes involved, from obtaining the raw materials

through to delivering the finished goods to the customer. The management of

this supply chain is now recognized as one of the most important factors in

making companies efficient and competitive in today’s global economy. Linked

logistics distribution consists of a set of facilities, which each consisting of a

production plant with a warehouse that is connected, and a set of customers.

Each plant with capacity has known and limited. And every customer is placed

or connected to facilities with a particular plan for customer demand typically

form a seasonal pattern. Because each warehouse associated with a particular

plant, it is assumed that the cost of transportation between the factory and

warehouse, including in production costs, and no transportation among

warehouses. Decisions made must consider placement of customers to facilities

and the location and size of the inventory. Both of these should be set in a policy

which puts the customer at the facility by taking into account the location and

amount of inventory must be optimized as a function placement of customers.

Basically, consumers expect to get the product which has the benefit at an

acceptable price level. In Order To realize the consumer desires, each company

seeks optimally to use all the assets and capabilities held to provide value to the

consumer expectations. Implementation this effort would lead to consequences

that costs vary companies including competitors. To be able to offer products

3

that interesting with competitive price level, every company should strive press

or reduce the entire cost without reducing the quality of the product and

standards that have been defined. Appropriate mix of location of factories

and distribution centers to serve the market of customers, and use the location,

vehicle routing analysis, dynamic programming and, of course, traditional

logistics optimization to maximize the efficiency of the distribution. From the

description and definition above, we can see that logistics as an activity or

business process will always be there. And even existence has been there since

the transformation activity of goods and distribution to the final consumer

begins. Stalls and mini-market, at the same time he became very bottom end

(downstream) product distribution for distributing many products at once. The

company also has to distribute its product with a long distance and road

conditions do not support of distribution be a problem itself.

Figure 1.1 PT Nestle Indonesia delivery maps

Source: Nestle Distribution Center Cikarang performance 2014

Cikarang distribution center, as the supplier of 40% of the total PT. Nestlé

Indonesian productss are distributed to all parts of Indonesia and with an area

4

large enough distribution center of 60,000 sqm site, Existing capacity of 60,000

pallet positions, and Over 100 trucks to 120 destination dispatched per day

definitely need a logistic distribution as a management tool is appropriate and

efficient in the distribution products. The massive increasing customer demand

about the products PT. Nestle Indonesia from year to year, the demage products

occure when distribution of products to the customer like this figure 1.2 explain.

Figure 1.2 Damage product by Place


This table explain from period January – September 2014 damage inbound by

place od delivery, this is the destination of delivery and cause the damage. First

rank from Kejayan Factory located in west java 63%, import products 24 %,

Gempol Distrbution Center 10% and others delivery take 3%. It might because

the long distance distribution and amount of products from Kejayan factory

became the first rank of damage products by the location.

5

Figure 1.3 Damage Inbound by Products


Figure 1.3 Explain what product is contribute the high damage in period January

– September 2014 which is dairy contribute 51%, Liquid 19%, Nutrition 14%,

NBC 12% and others 4%. It has proved by table 1.1 the demand of diary is high

so that the products that deliver also high so the damage is high.

Table 1.1 Damage product value and percentage


6

From table 1.1 the total amount of damage products is quite high in the 9 month

it reach 833.249.253 IDR it have to decrease the damage so that the delivery

will more efficient.

Table 1.2 VARIANT SHORT Lead by In-Accuracy Stock Cikarang DC


The Stock accuracy it have to be the attention for the staff because in majority

increasing every month and one of obstacle that have to solve as soon as

possible. From the data already mention it cause problems and difficulties to

deliver the product in efficient.

1.2 Problems Identification

The logistics role and system in the company in general still has limitations in

the application, as the largest food production company in the world, Nestle

Indonesia focus to improve nutrition (nutrition), health (health), and well-being

(wellness) from our customers. The employees are dedicated and motivated to

produce quality products and build brands that meet consumer needs. But with

so many existing customers, limitation and problems that already mention before

of course there is problems appear to supply the products to the customers. From

some cases experienced one delay outlet product into an outlet and the waiting

7

time can be one until three-day of, it will effect on the supply of goods to outlets

in meeting the needs of customers because of its empty stock (inventory). As for

the issues that will be discussed in this study is the delay in distribution centers

in supplying products to the customers, stock accuracy and transportation

problems are frequently encountered. Because the factors of these problems is

something that is dynamic so as to minimize the problem of the delivery of

logistics flexibility required for effective and efficient handling in the face of

any changes that occur in field. From this the researcher would like to research

about “Analysis that influence delivery efficiency of logistic management”

(A Case Study of PT. Nestle Indonesia Distribution Center, Cikarang).

1.3 Statement of Problem

This research aims to answer the following questions:

1. Is there any partial significant influence of transportation towards delivery

efficiency of logistic management?

2. Is there any partial significant influence of distribution center area towards

delivery efficiency logistic management?

3. Is there any partial significant influence inventory towards delivery efficiency

logistic management?

4. Is there any simultaneons significant influence of transportation, distribution

center area, and inventory toward delivery efficiency logistic management?

8

1.4 Research Objective

1. To find out any partial significant influence of transportation towards

delivery efficiency logistic management.

2. To find out any partial significant influence of distribution center area

towards delivery efficiency logistic management.

3. To find out any partial significant influence inventory towards delivery

efficiency logistic management.

4. To find out any simultaneons significant influence of transportation,

distribution center area, and inventory towards delivery efficiency logistic

management.

1.5 Scope and Limitation

This research attempts to understand the efficiency of delivery and learn in the

scope of Cikarang distribution center Pt. Nestle Indonesia. The researcher

chooses the respondents are the staff of Cikarang distribution of Nestle Indonesia

through giving direct questioners. From this research will give information about

analysis that can influance of the effectiveness delivery in the Pt. Nestle

Indonesia in other hand from that research what sector that most effective so

from the company can improve from that sector. The population was 87 staff

Cikarang distribution center Nestle Indonesia. Samples are 71 staff (the

respondents data will be explain in Chapter 3) and data collection techniques that

used were questionnaire. This study focus on the effectiveness of delivery which

consist of three independent variables. That are transportation, distribution center

area, and inventory in Cikarang distribution center Pt. Nestle Indonesia.

9

1.6 Definition of Term

The definitions of that are relevant to the study are listed below:

A) Delivery :

The process of transporting goods from a source location to a predefined

destination.

B) Distribution center:

A warehouse or other specialized building, often with refrigeration or air

conditioning, which is stocked with products (goods) to be redistributed to

retailers, to wholesalers, or directly to consumers. A distribution center is a

principal part, the order processing element, of the entire order fulfillment

process. Distribution centers are usually thought of as being demand driven.

C) Efficiency:

Generally describes the extent to which time, effort or cost is well used for

the intended task or purpose. It is often used with the specific purpose of

relaying the capability of a specific application of effort to produce a specific

outcome effectively with a minimum amount or quantity of waste, expense,

or unnecessary effort.

D) Inventory:

Inventory is the raw materials, work-in-process goods and completely

finished goods that are considered to be the portion of a business's assets that

are ready or will be ready for sale

E) Logistics:

Management of the flow of goods between the point of origin and the point

of consumption in order to meet some requirements, of customers or

corporations. The resources managed in logistics can include physical items,

such as food, materials, animals, equipment and liquids, as well as abstract

items, such as time, information, particles, and energy. The logistics of

10

physical items usually involves the integration of information flow, material

handling, production, packaging, inventory, transportation, warehousing, and

often security.

1.7 Significance of the study

The purpose of this study was to describe factors impact of delivery efficiency of

logistic management as study case in Cikarang distribution center PT. Nestle

Indonesia.

This study addresses the information about management capabilities are hoped to

be significant to the following:

A) For the Company

This study is expected to provide the benefit to the company that in this case

is Pt Nestle Indonesia to know the influence of delivery efficiency of logistic

management so from that analysis which one is most efficient so that

company can improve company development.

B) For the Students

This research is also expected to findings analysis influence the efficiency

product deliver so that the student can understand more and especially for

those who have interest in the distribution logistic. This research also can be

used as the references for the other research.

C) For the Future Researchers

The researcher hopes that this research could be integrated for the future

researcher also research could serve in the perspective of supply chain

management especially in the distribution channel also the product of this

study will give researcher’s knowledge about analysis that influence the

efficiency and hope can be useful someday.

11

Chapter 2

Literature Review

2.1 Theoretical Review

2.1.1 Logistic Management

According to Council of Supply Chain Management Professionals (CSCMP),

logistics management is the part of supply chain management that plans,

implements, and controls the efficient, effective forward and reverse flow and

storage of goods, services, and related information between the point of origin

and the point of consumption in order to meet customers’ requirements. Logistics

management activities typically include inbound and outbound transportation

management, fleet management, warehousing, materials handling, order

fulfillment, logistics network design, inventory management, supply/demand

planning and management of third party logistics services providers. To varying

degrees, the logistics function also includes sourcing and procurement,

production planning and scheduling, packaging and assembly, and customer

service. It is involved in all levels of planning and execution – strategic,

operational, and tactical. Logistics management is an integrating function which

coordinates and optimizes all logistics activities with other functions in supply

chain and logistics management, including marketing, sales, manufacturing,

finance, and information technology.

The indicators of logistics management in SCM by Martin Christopher quoted

Eko Indrajit (2002: 42) are:

1. Location

2. Transportation

3. Inventory and Forecasting

12

4. Marketing and Channels restructuring

5. Source and Supplier Management

6. Information and Electronic Media

7. Care and After-sales

8. Logistics Turnover and Latest Issue

9. Outsourcing and Alliance Strategy

2.1.2 Logistic

“Logistics is the process of strategically managing the acquisition, movement and

storage of materials, parts and finished inventory from suppliers though the firm

and on to customers.” It requires right product in the right place at the right time.

(Christopher 1994, 1)

Logistics is part of supply chains. It connects the relationship between producing

and consumption. It is essential for planning and operating a distribution system

successfully. The objectives are supplying the right products to the right places at

the right times for the least costs. Logistics appears with the development of the

economy and the appearance of goods and products, therefore logistics a

traditional and old economic activity. (Attwood 1992, 2)

Logistics process is a key to execution and achieving results. The objectives in

general are accomplishing things and creating value. Detailed speaking, logistics

process coordinates all activities involved in acquiring, converting and

distributing goods from raw materials source to target group to satisfy customers’

need. And deliver the required levels of customer service in an efficient, cost

effective manner.However, pleasing customers is not the only goal for logistics

process, but also must operate productively to bring profits for the company.

(Byrne & Markham 1991, 31)

Logistics according to the Council of Supply Chain Management Professionals

(CLM, 2000) is part of supply chain management in planning, implementing, and

13

controlling the flow and storage goods, information, and services effectively and

efficiently from point of origin to the point purposes in accordance with

consumer demand. To stream of goods from point origin to point of destination

will require some activity known with 'key activity in logistics' include: 1)

customer service, 2) demand forecasting / planning, 3) inventory management, 4)

logistics communications, 5) material handling, 6) traffic and transportation, and

7) warehousing and storage (Lambert et al., 1998).

In Blueprint for Development of the National Logistics System (Presidential

Decree No. 26 of 2012), logistics is defined as part of the supply chain which

handles the flow of goods, information, and money through the process

procurement, storage (warehousing), transportation, distribution, and service

delivery . The preparation of the logistics system is intended to increase security,

efficiency, and effective movement of goods, information, and money started

from the point of origin to the point of destination according to the type, quality,

quantity, time and consumers demand. Another definition of logistic that more

structural according to Bowersox (1978), that is “The process of strategically

managing the movement and storage of materials, part and finish inventory,

enterprise facilities, and customer”

And Logistic will always connect with delivery, manufacture, distribution and

customer, so logistic have to deliver products and service that customer need to

more efficient, and the mission that logistic is (Ballou,1992)” The mission of

logistics to get the right goods, or service to the right place, at the right time and

in the desired condition, while making the greatest contribution to the firm”. In

general logistics activities consist of two (2) activities that are activities

movement (move) and storage activities (store), so if both This activity is planned

and controlled strictly, then the system issues logistics as a whole will be able to

be resolved properly. two activities The main decomposed into several activities

that are processing orders, transportation, inventory, handling of goods, facilities

14

and system structure information and communication. Seventh activities are

considered the logistics activity mix where all the activity is not unavoidable

presence in a supply chain system.

In this distribution system are many factors that affect success or failure, while

the factor are (1) whether facilities and adequate transport infrastructure, in order

to deliver the goods to the destination in a timely manner (transportation) (2) does

believe that the number of items delivered is certainly appropriate DO (Delivery

Order) issued by the Department Sales (inventory), (3) Is the distribution centers

(Warehouse) and facilities supporters were ready, so that goods to customers not

constrained (structure facility), (4) whether the goods handling systems are

adequate, so there is no damage and loss in the distribution (material handling),

(5) whether the information and communication systems owned / used is in

conformity with the requirements (communication and information

2.1.3 Delivery

Delivery is the process of transporting goods from a source location to a

predefined destination. There are different delivery types. Cargo (physical goods)

are primarily delivered via roads and railroads on land, shipping lanes on the sea

and airline networks in the air. Certain specialized goods may be delivered via

other networks, such as pipelines for liquid goods, power grids for electrical

power and computer networks such as the Internet or broadcast networks for

electronic information.

The general process of delivering goods is known as distribution. The study of

effective processes for delivery and disposition of goods and personnel is called

logistics. Firms that specialize in delivering commercial goods from point of

production or storage to point of sale are generally known as distributors, while

those that specialize in the delivery of goods to the consumer are known as

15

delivery services. Postal, courier, and relocation services also deliver goods for

commercial and private interests.

Definition of the delivery according to Tjiptono Fandy (1997: 185) is as follows:

"Delivery is a marketing activity that seeks expedite and facilitate the distribution

of goods and services from producers to consumers, so their use in accordance

with the required (type, quantity, price, place, and time required)."

According to Basu Swastha and Irawan (2003: 179) explains that the

implementation process delivery will involve three aspects, so that the

distribution process goes well, are: 1.The company's transportation system

2. Storage Systems 3. Selection of distribution channels in the transportation

system, among others, the satisfaction of the tool transport (aircraft, trains, trucks,

ships, pipelines), the determination of the delivery schedule, which must

determine the location of the warehouse, the type of equipment used to handle

material and other equipment. While the selection of distribution channels

involving decisions about the use of distributors. Most consumer goods are

delivered from a point of production (factory or farm) through one or more points

of storage (warehouses) to a point of sale (retail store), where the consumer buys

the good and is responsible for its transportation to point of consumption. There

are many variations on this model for specific types of goods and modes of sale.

Products sold via catalogue or the Internet may be delivered directly from the

manufacturer or warehouse to the consumer's home, or to an automated delivery

booth. Small manufacturers may deliver their products directly to retail stores

without warehousing. Some manufacturers maintain factory outlets which serve

as both warehouse and retail store, selling products directly to consumers at

wholesale prices (although many retail stores falsely advertise as factory outlets).

Building, construction, landscaping and like materials are generally delivered to

the consumer by a contractor as part of another service. Some highly perishable

16

or hazardous goods such as radioisotopes used in medical imaging, are delivered

directly from manufacturer to consumer

2.1.4 Delivery Efficiency

Deliver efficiency covers only part of manufacturers, distributors and customers,

either directly or indirectly delivery to meet customer demand. Delivery

efficiency only covers transporters, warehouses, retailers, and even customers

themselves. Delivery efficiency is dynamic and involves the flow constant

information, products, and finance among the different levels. In fact, the main

purpose of the various delivery is fulfilling customer needs and in the process,

generating profits for the company. Size distribution logistics performance,

include:

1. The quality (level of customer satisfaction, customer loyalty, accuracy

shipping).

2. Time (total replenishment time, business cycle time).

3. Cost (total delivered cost, the efficiency of value-added).

4. Flexibility (number and specifications) in the development of SCM Logistics

Distribution.

Development of SCM Logistics Distribution can also defined network of

organizations regarding the relationship to the upstream and downstream, in a

different process and generate value in the form of goods / services in the hands

of final customers (the ultimate customer / end user).The competitive advantage

of logistics distribution is how ability to manage the flow of goods or products in

a supply chain, in other words logistic distribution network model is an activity

important to be done on the supply chain management.

Implementation supply chain strategy more effective if supply chain have a

network with the appropriate configuration (Punjawan.IN, 2005) because

network configuration can determine whether a delivery will be responsive or

17

efficient. Basically the logistics distribution network is the result of some

Strategic action. The action are location strategic distribution center, warehouse

facilities, labor reliability, smoothness transportation and availability of

products.

In line with the philosophy that requires the integration of delivery efficiency

between systems, performance measurement in delivery efficiency designed by

process. The process is a collection of activities that cross time and place, has a

beginning, end and a clear input and output. To connect markets, distribution

networks, manufacturing processes and procurement activities so that consumers

are served at high level but at a lower cost the other words to achieve

competitive advantage it is necessary to reduce costs and improve service.

2.1.5 Transportation.

Transportation is the service activities so Waters (2003, 310) said that. “One of

the most important impacts of transportation is customer service and the most

important transportation service characteristics are dependability, time-in-transit,

market coverage, flexibility, loss and damage performance and the ability of

carrier. There are a lot of methods for transporting goods from one place to

another one or other areas, such as rail, air, water, pipelines, motor”.

According to Waters (2003, 309) Transport is responsible for the physical

movement of materials between points in the supply chain. It moves a company’s

products to markets at a certain long distances due to geography factors. Another

main function is for warehousing in a short time. There are some major business

decisions affected by transportation, like product decisions, market area

decisions, purchasing decisions, location decisions and pricing decisions.

Transportation also used to move between the different products in the supply

chain. Just like other triggers, transport also has a great impact to the Company's

ability to respond and efficiency. In general, the company has three (3) alternative

18

transportation capabilities. First, private fleet equipment purchased or rented.

Second, special contracts can be arranged by specialist transport to get shipping

services contract. Third, a company can obtain the services of a licensed transport

company (legally authorized) who offer transport from one place to another for a

fee.

The type of transport chosen by the company may also affect the supply and

distribution center locations in the supply chain. According Nasution (2008 )

there are elements of transport include : (a ) No cargo transported , ( b ) available

vehicles as a means of conveyance , ( c ) there are streets / paths that can be taken

, ( d ) there is a terminal of origin and destination terminal , and ( e ) human

resources and or management that drives the transport activity

2.1.5.1 Transportation problems

Transportation problem is one thing that is very important to be solved and is

always happen in the delivery of products, the causes are changed from time to

time because a lot of things surrounding it, the unpredicted weather,the bad road

condition, the vehicle does not meet standards on a daily basis. And according to

(Wiley & Sons 1985, 124) An unreasonable transportation has many aspects. For

example, a roundabout and repeating route, wrong choices of consignment

methods and long distance transporting may happen during the process due to

artificial operational mistakes, weather conditions, lack of technology or other

risks and accidents. If facilities are under poor conditions and ratios of

professional transportation stands too low.

In the period of transporting goods, traffic accidents will lead to damages and

loss. Besides, bad management, control, theft or destroying goods on purpose by

workers can affect the effectiveness of transporting activities. There are also

many risks including weather conditions, defect of goods, mistakes of wrong

direction guide and information etc. In a customer's perspective, transportation

19

may sometimes lead to a delay and delivering goods later. That will cause some

fees, except more transportation fees, like charges of delay, rent, distribution fees

etc. (Bowersox & Closs & Cooper 2010, 244).

2.1.5.2 Route Determination

In the transportation problem has been mentioned that the route to be taken

should pay attention to the distance to the warehouse and the warehouse storage

capacity. The distance which is the main component in this case should be noted

that the shortest route is not the main thing that must be considered but consider

also the capacity of the warehouse because people are always wrong perception

by giving priority to such factors that little impact for the delivery of goods.

Selection the right route it should pay attention to many things such as distance,

travel time, fuel used or security travel route. All these things must be taken to

ensure that the risk of errors can be minimized so can not cause too much harm.

Understanding the characteristics of the route can be used to avoid the high cost

and causes the products storage too long in warehouse. According to (Bowersox

& Closs & Cooper 2010, 355) Types of transporting routes should be based on

place of departure and destination. The shortest, the most convenient and the

most economic benefit route should be considered and designed through

geometry, such as simple annular, compound annular, pattern of crossing lines

etc. Especially, the start place can be the same one as the final destination. Only

in this way, can the goods be delivered and carried in double ways. The targets

of choosing routes are high effectiveness, low costs, best distribution service

level, the shortest mileage and the smallest volume of the circular flow.

20

2.1.6 Transportation and Delivery Efficiency

According to Haizer and Render (2005) relationship between transportation to

delivery efficiency is how supply can avoid the risk of traffic jump and

infrastructure on the road. The need for the distribution of goods to be inhibited

so increase the delivery time. The condition of road that use not only for delivery

the products it also increasing of crowd of road and the increasing total

individual transportation like motorcycle and car unbalance with the increasing

of road infrastructure Activity distribution of goods also can not be done only on

night These conditions do if the distribution between cities or island that require

long travel time. Whereas for distribution in the city is not possible to rely on the

condition of the night. Whereas in the process of transportation / distribution of

goods so on required in accordance with the mission efficiency of time in

logistics. So that jams can encourage high transportation costs. The movement

of goods in Indonesia dominated by road transport by trucks that tend to bring

overload has caused problems of road damage. This road damage course must be

corrected with the maintenance program, so that the distribution of goods can

distribute.

Activity distribution of goods also can not be done only on night these

conditions do if the distribution between cities or island that require long travel

time. Whereas for distribution in the city is not possible to rely on the condition

of the night. Whereas in the process of transportation / distribution of goods so

on required in accordance with the mission time efficiency of logistics. So that

jams can encourage high transportation costs. The movement of goods in

Indonesia dominated by road transport by trucks that tend to bring overload has

caused problems of road damage. This road damage course must be corrected

with the maintenance program, so that the distribution of goods remain to walk.

Function, transport is a complex endeavor and expensive. In a simple timer, the

sender often think in terms of the type of carrier, such as rail cars, ships, or

21

aircraft. Transportation is not just shipping. It is closely linked to the location

and operation of some number of warehouses. It determines the possibility

routing and therefore a critical decision. Thus, an interest increase in

warehousing contracts, that warehousing functions. Thus requiring a strong

transport.

2.1.7 Distribution Center Area

Being in the right location is a key ingredient in a business's success. If a

company selects the wrong location, it may have adequate access to customers,

workers, transportation, materials, and so on. Consequently, location often plays

a significant role in a company's profit and overall success. A location strategy is

a plan for obtaining the optimal location for a company by identifying company

needs and objectives, and searching for locations with offerings that are

compatible with these needs and objectives. Generally, this means the firm will

attempt to maximize opportunity while minimizing costs and risks.

A company's location strategy should conform with, and be part of, its overall

corporate strategy. Hence, if a company strives to become a global leader in

telecommunications equipment, for example, it must consider establishing plants

and warehouses in regions that are consistent with its strategy and that are

optimally located to serve its global customers.

According to Weber's theory (1909) the selection of industrial site is based on

cost minimization. Weber states that the location of each industry depends on

the total cost of transportation and labor where the summation both should be

the minimum and a place where the total cost of transport and energy to work is

synonymous with maximum profit level. According to Weber, there are three

factors that affect the location of industry that are the cost of transportation,

labor, and the strength of agglomeration or deagglomeration. The last stages in

the logistics and supply chain systems is how to determine a strategic location

22

for a distribution center storage products to be supplied to the customer. Because

of the central functions distribution as a provider of goods or distributors will

always provide the needs of customer.

According to Talley-Seijn, Margaret (2004) Formulating a location strategy

typically involves the following factors:

1. Facilities. Facilities planning involves determining what kind of space a

company will need given its short-term and long-term goals.

2. Feasibility. Feasibility analysis is an assessment of the different operating

costs and other factors associated with different locations.

3. Logistics. Logistics evaluation is the appraisal of the transportation options

and costs for the prospective manufacturing and warehousing facilities.

4. Labor. Labor analysis determines whether prospective locations can meet a

company's labor needs given its short-term and long-term goals.

5. Community and site. Community and site evaluation involves examining

whether a company and a prospective community and site will be compatible in

the long-term.

6. Trade zones. Companies may want to consider the benefits offered by free-

trade zones, which are closed facilities monitored by customs service where

goods can be brought without the usual customs requirements. The United States

has about 170 free-trade zones and other countries have them as well.

7. Political risk. Companies considering expanding into other countries must

take political risk into consideration when developing a location strategy. Since

some countries have unstable political environments, companies must be

prepared for upheaval and turmoil if they plan long-term operations in such

countries.

8. Governmental regulation. Companies also may face government barriers and

heavy restrictions and regulation if they intend to expand into other countries.

23

Therefore, companies must examine governmental—as well as cultural—

obstacles in other countries when developing location strategies.

9. Environmental regulation. Companies should consider the various

environmental regulations that might affect their operations in different

locations. Environmental regulation also may have an impact on the relationship

between a company and the community around a prospective location.

10. Incentives. Incentive negotiation is the process by which a company and a

community negotiate property and any benefits the company will receive, such

as tax breaks. Incentives may place a significant role in a company's selection of

a site.

Depending on the type of business, companies also may have to examine other

aspects of prospective locations and communities. Based on these

considerations, companies are able to choose a site that will best serve their

needs and help them achieve their goals.

2.1.9 Distribution Center Area to Delivery Efficiency

The goals to be achieved from any distribution logistics supply chain

management is to maximize the value generated (Chopra, 2001). Integrated

logistics distribution will increase the overall value generated by the supply

chain and logistics that support his system. So in its development strategic

location of distribution centers has an influence on the performance of the

delivery efficiency.

PT. GAC Samudra Logistics joint venture with PT. Nestle Indonesia in terms of

delivery of goods to all over Indonesia. PT. GAC Samudra Logistics has a

Distribution centers are located in major cities one of them is in Cikarang, West

Java, which supplies goods to the area of Java and South Kalimantan and

Sumatra south and supplying goods to 40% of the total products that exist and

function for storage of the product that will be distributed to a subscriber nestle

24

as many outlets supplied, then the distance a strategic location in the distribution

center will be able to accomplish with consideration of all possible risks that

occur

To implement the delivery efficiency needed one distribution system of delivery

of products so company have to pay attention to the location of distribution

Center that can reach all areas of marketing, so that optimizing the supply of

products fulfilled. Location decisions in the design a logistics system is centered

on the warehouse, where the warehouse established if it can provide the service

or benefit costs in a certain markets (Bowersox, 1978; 16).

The most fundamental problem in the analysis of the strategic location of the

distribution center is how to decide the strategic central of location for demand

and market area.

2.1.10 Inventory

Inventory is the raw materials, work-in-process goods and completely finished

goods that are considered to be the portion of a business's assets that are ready or

will be ready for sale. Inventory represents one of the most important assets that

most businesses possess, because the turnover of inventory represents one of the

primary sources of revenue generation and subsequent earnings for the

company's shareholders/owners.

According to Management Study guide (2012) Inventory is a necessary evil in

any organization engaged in production, sale or trading of products. Inventory is

held in various forms including Raw Materials, Semi finished Goods, Finished

Goods and Spares.

Every unit of inventory has an economic value and is considered an asset of the

organization irrespective of where the inventory is located or in which form it is

available. Even scrap has residual economic value attached to it.

25

Inventory Controllers are engaged in managing Inventory. Inventory

management involves several critical areas. Primary focus of inventory

controllers is to maintain optimum inventory levels and determine

order/replenishment schedules and quantities. They try to balance inventory all

the time and maintain optimum levels to avoid excess inventory or lower

inventory, which can cause damage to the business.

The necessity of well-executed inventory is knowing certainly the cost of goods

sold. Besides, for ensure the smooth flow of traffic of goods. We need to hold

records of all receipts of goods from suppliers, goods ordered by customer, sold

goods, goods that are returned by the customer and the adjustment of the goods,

so the record will be known , which are over-stock items and goods which must

be ordered back to the supplier for supply is running low, in case ordering goods

to the supplier, then it should also be noted reservations to get all information

about inventory, if all the transaction has not recorded properly Mentioned will

find the Difficulties later for the example the Difficulties for knowing the

amount stocks that exist and such trouble to find out how many existing

inventories and are already in marketed as well as the amount of goods that have

been ordered by customer (Quantity Committed) and the amount of goods

ordered to the supplier (Quantity Sold) and other important information. Reduce

inventory items. Inventory is an asset company that range between 30% - 40%,

while the cost of storage of goods ranging from 20% - 40% of the value goods

stored.

2.1.11 Inventory to Delivery Efficiency

The relationship between inventory and logistics distribution is like 2 sides

currency which can not be separated. In addition, consumer demand from nestle

in an increasingly demanding and more variety, making the company need to

find ways to improve the effectiveness and efficiency of availability products in

the distribution center. In the journal Effect of Competitive Strategy Against

26

Relationships Supply Chain Management Performance by Titi Suhartati and

Hilda Rosietta, One of today's competitive advantage is accuracy in the

management establish any relation to the performance of existing logistics

distribution in the supply and determine supply chain management is an

important value to be able to compete in the market because the inventory we

can hold the role of the existence of products on the market in order to meet the

needs of consumers. Increasing the level of service provided through the capture

order efficiently, the existence of products, timely delivery, transparency

information and improve response. Porter (1985) states that the company must

have a clear competitive strategy with the aim to compete effectively and gain a

sustainable competitive advantage. Strategy compete is the most competitive

positioning expected by companies occurred in the industry (Porter, 1985).

Competitive strategy aimed to build profits and survive the opposite position

forces that determine industry competition. Differences any competitive strategy

used by the company in the arena of competition in the industry can creating a

competitive advantage.

2.2 Previous Research

Table 2.1 Previous Research

Authors Research title Research methodology Result

Ronnie

Roter,(1985)

Storage and International

Distribution Solutions",

Industrial Management

& Data Systems

This article takes a

logistic service provider's

perspective and is based

on a multiple case study

of six companies. The

analysis is based on cross‐

case analysis, and

empirical, as well as

theoretical. Examines the

interdependence between

Information system,

Recognizing the problems

besetting companies with

storage difficulties the David

Martin Group (headquarters

at Colnbrook) has started to

provide storage space for the

high‐tech sector of its client

list, including names like

Wang, Honeywell and

Digital. Every document

passing through the Group's

system is processed by

27

location and marketing computer to eradicate tariff

and record discrepancies.

The service helps the client

in two ways by supplying

specialized warehousing

under controlled conditions

and by having products

packaged and ready to move

when the client wants

Michael A.

Haughton, Al

an J. Stenger,

(1997)

Semi‐variable delivery

routes and the efficiency

of outbound logistic

Using extensive

experimental data,

develops a regression

model that efficiently and

accurately estimates this

productivity increase, and

illustrates how

spreadsheets can be used

as a decision support

medium for using the

model in pedagogical and

applied settings.

Examines the


Transportation, after

sales, inventory, and

information system

Maintaining efficiency in

despatching goods from a

depot to geographically

dispersed customers may

require management at the

depot to adjust its delivery

routes daily if these

customers’ demands

fluctuate from day to day.

One type of adjustment is to

give drivers daily “skip lists”

instructing them not to visit

customers who have

indicated that they do not

need delivery on the day in

question. This adjustment,

which is appropriately

referred to as semi‐variable

routes, increases the depot’s

outbound

logistic productivity by

eliminating some

unnecessary travelling

Vaidyanathan

Jayaraman,

(1998)

"Transportation, facility

location and inventory

issues in distribution

network design: An

investigation"

Examines the


facility location,

transportation and

inventory issues in a

distribution network

Management of inventories,

determination of

transportation policy, and

location of plants and

distribution centers are

normally carried out by

28

design problem. different groups of people in

an organization. These

activities interact, however,

when the transportation is

used to replace inventory, an

increase in the number of

warehouses increases total

system inventory or location

of warehouses would dictate

the type of transportation

mode choice or carrier that

needs to be used.

2.3 Theoretical Framework

Framework describe the connection between independent variables towards

dependent which aims to facilitate the research process. The theoretical framework

can be seen on this figure 2.1

Figure 2.1: Theoretical Framework

Source: "Transportation, facility location and inventory issues in distribution network

design: An investigation (1998)"(Developed by Researcher)

Delivery Efficiency

(Y)

Transportation

(X1)

Distribution center area

(X2)

Inventory

(X3)

29

2.4 Operational Definition

Table 2.2 Operational Definition

Independent

Variable

Definition Benefit to Delivery

Efficiency

Indicator

Transportation Responsible for the

physical movement

of materials

between points in

the supply chain.

As tools of

equipment used to

increase the delivery

efficiency in terms of

dependability, time-

in-transit, quality,

model transportation,

flexibility, loss and

damage performance

and the ability of

carrier.

1.Number of vehicles has been

met the requirement products to

distributors

2. Quality of vehicles in

accordance with the standard

requirements that company

assigned

3. Vehicle capacity sufficient to

maximize delivery of products

4. Schedule and timing arrival

and departure transportation

already achieve target

5. Condition of road already

support the lead time for supply

products.

6. Selection of transport was

able to save costs and time for

Supply products

Distribution

center area

The location of

each industry

depends on the total

cost of

As tools of

equipment Used to

increase the delivery

efficiency in terms of

1.The distribution center is

located in a strategic place (So

easily to distribute the

products).

30

transportation and

labor where the

summation both

should be the

minimum and a

place where the

total cost of

transport and

energy to work is

synonymous with

maximum profit

level

cost of delivery, time

consuming and

access to customers,

workers,

transportation,

materials,

2.Infrastructure at distribution

center has sufficient to supply

the products

3.Placement of distribution

centers can reduce

transportation costs

4.Distribution center has many

access to in and out

Inventory The raw materials,

work-in-process

goods and

completely finished

goods that are

considered to be the

portion of a

business's assets

that are ready or

will be ready for

sale

Used to increase the

delivery efficiency in

terms of system used,

and allocation of

product.

1.Size of distribution center is

sufficient for the products

loading and unloading

2.Setting products use FIFO

system

3.Allocation of each products

storage is clear

4.Stock accuracy during the

stock taking is high

5.The current system used is

easy to track the products to be

distributed

31

2.5 Hypothesis

1 .Partial significant influence of transportation towards delivery efficiency

Ho1: There is no partial significant influence transportation of logistic

management toward delivery efficiency

Ha1: There is a partial significant influence transportation of logistic

management toward delivery efficiency.

2. Partial significant influence of distribution center area towards delivery efficiency

Ho2: There is no partial significant influence distribution center area of

logistic management toward delivery efficiency

Ha2: There is a partial significant influence distribution center area of logistic


3. Partial significant influence of inventory towards delivery efficiency

Ho3: There is no partial significant influence inventory of logistic


Ha3: There is a partial significant influence inventory of logistic management

toward delivery efficiency

4. Simultaneons significant influence of transportation, distribution center area, and

inventory of logistic management toward delivery efficiency.

Ho4: There is no simultaneons significant influence of transportation,

distribution center area, and inventory of logistic management toward

delivery efficiency

Ha4: There is a simultaneons significant influence of transportation,

distribution center area, and inventory of logistic management toward

delivery efficiency

32

Chapter III

RESEARCH METHODOLOGY

3.1 Research Design

There are two methods in doing scientific research those are qualitative and

quantitative research. The differences between qualitative and quantitative research

are the type of data, research process, instrument in collecting data and the purpose of

research.

Qualitative method usually gathered by observations, interviews or focus

groups and the data also is gathered from written documents and through case

studies, it less emphasis on counting numbers of people who think or behave

in certain ways and more emphasis on explaining why people think and

behave in certain ways.

Quantitative method involves smaller numbers of respondents, Utilizes open-

ended questionnaires or protocols, Best used to answer how and why

questions. (Civicpartnership.org ,2013)

Quantitative observations are made using scientific tools and measurements. The

results can be measured or counted,` and any other person trying to quantitatively

assess the same situation should end up with the same results. In Quantitative method

pieces of information that can be counted mathematically, it usually gathered by

surveys from large numbers of respondents selected randomly and it is analyzed

using statistical methods Best used to answer what, when and who questions

(Civicpartnership.org,2013). The researcher use quantitative method in conducting

research..

Multiple Regressions analysis is an extension of simple linear regression. It is used

when we want to predict the value of a variable based on the value of two or more

33

other variables. The variable to be predicted is called the dependent variable (or

sometimes, the outcome, target or criterion variable). The variables that used to

predict the value of the dependent variable are called the independent variables (or

sometimes, the predictor, explanatory or regressor variables) (statistics.laerd.com,

2013).

Therefore, this study uses the quantitative method with Factor Analysis and Multiple

regressions Analysis to answer the research questions.

3.2 Research Framework

The main topic of this research is analysis that influance of delivery efficiency of

logistic management in Pt. Nestle Indonesia distribution center, Cikarang. As

described in Chapter 2, Delivery efficiency is valuable asset that a company has, and

it creates value for customers and company. Therefore, the transportation, the

strategic distribution center area and inventory to make the efficient.

This research specifically investigates analysis of delivery efficiency of logistic

management in Cikarang distribution center Pt. Nestle Indonesia. Before conducting

this research, the researcher had to collect data of performance Cikarang distribution

center of Pt. Nestle Indonesia in 2014.

After collecting the data, the researcher directly proceeded to the problem

identification. From the data obtained, Cikarang distribution center is the busies

nestle distribution center in Indonesia because 40% of total product is deliver from

Cikarang distribution center and have some problem about the lead time, product

damage and not accuracy inventory. This encouraged the researcher’s curiosity to

find out why it could happen. After reading some supporting passages from journals

and articles, it could be identified that the decision of transportation, the strategic

distribution center area and good inventory forcasting will be effect to the delivery

34

efficiency. Furthermore, the problem statement was constructed as the basic view of

the topic. To support the problem statement, theories and opinions are explored. All

those findings are expressed in Chapter 2 of Literature Review. This then brings

benefits to the construction of the questionnaires.

Questionnaires were checked for validity and reliability test. Pearson correlation

matrix used to measure the validity and Cronbach Alpha used to measure the

reliability of the questionnaire. Pearson correlation matrix will indicate the direction,

strength, and significance of the bivariate relationships among all the variables that

were measured at an interval or ratio level (Sekaran and Bougie, 2010, pp.321).

Cronbach Alpha is a reliability coefficient that shows how well the items in a set are

positively correlated to one another (Sekaran and Bougie, 2010, pp.324). in here the

respondents were divided in two, 20 for pre test and if found valid and reliable , the

remaining 51 respondents will be use in the real test.

Before being spread, the questionnaires went through the stage of “Tryout.” 20

different people were selected and gathered by the researcher to examine whether the

statements in the questionnaires were clear enough to understand. This stage is also

intended to revise some statements, so that every respondent will have the same

perception towards them. After some reviews and proof readings, the questionnaires

were finally spread to 51 respondents whose characteristics have been provided in

one of the explanations below.

In this research, SPSS was utilized to analyze the data. Finally, the points of

conclusion and recommendation are drafted. All steps conducted by the researcher

from problem identification to the result accomplishment are reflected in the

following figure of research framework.

35

Source: Developed by Researcher

3.3 Research Instrument

Research Instrument is the tool that used to answer the research questions that stated

in the previous chapter. The Researcher intention is to gather the information from as

much various sources. Data can be obtained from primary or secondary data, Primary

data refers to information obtained first-hand by the researcher on the variables of

interest for specific purpose of the study and secondary data refer to information

gathered from sources that already exist (Sekaran, Bougie, 2010). In order to fulfill

Figure 3.1: Research Framework

36

the validity of this research, the researcher use both primary and secondary data as

shown in the figure 3.2 below:

Figure

Source: Developed by Researcher

3.3.1 Primary Data

Primary data is the specific information collected by the person who is doing the

research. It can be obtained through clinical trials, case studies, true experiments and

randomized controlled studies. This information can be analyzed by other experts

who may decide to test the validity of the data by repeating the same experiments

(Ehow.com, 2013).

RESEARCH

DATA

COLLECTION

PRIMARY DATA SECONDARY DATA

SURVEY BOOKS AND

JOURNALS

LITERATURE

STUDY

ARTICLES ON

INTERNET

DATA SELECTION

Figure 3.2 Data Collection Method

37

Primary data in this research of “Analysis influance of delivery efficiency in logistic

management in Pt. Nestle Indonesia distribution center, Cikarang” is obtained

directly from the questionnaires that used for survey. Questionnaires are a technique

of data collection done by giving series of written statements that are consists of

research variables. These questionnaires will be spread to the numbers of samples.

3.3.2 Secondary Data

Secondary data is information gathered for purposes other than the completion of a

research project and Secondary data is also used to gain initial insight into the

research problem (steppingstones.ca, 2013). Secondary data is the data that have

been already collected by and readily available from other sources. Such data are

cheaper and more quickly obtainable than the primary data and also may be available

when primary data cannot be obtained at all (managementstudyguide.com, 2013).

Secondary data on this research is the literature studies. A literature studies is a

technique of data collection based on information gathered from books and journals

related to the research discussion. Data collected by learning and selecting from

previous literature studies, books, journals and related websites.

3.4 Sampling Design

Sampling Design is part of statistical methodology that related in taking a portion of

the population. If a sampling is done correctly, statistical analysis can be used to

generalize a whole population. There are two major types of sampling design:

probability and nonprobability sampling. In probability sampling, the elements in the

population have some known non-zero chance or probability of being selected as

sample subjects. In non-probability sampling, the elements do not have a known or

predetermined chance of being selected as subjects (Sekaran, Bougie, 2010).

38

3.4.1 Population

Population is all elements, individuals, or units that meet the selection criteria for a

group to be studied (businessdictionary.com, 2013). The Population refers to the

entire group of people, events, or things of interest that the researcher wishes to

investigate (Sekaran, Bougie, 2010, pp. 262). In this study, research population is

focused on staff of PT. Nestle Indonesia distribution center,Cikarang

This research is aimed to analyze within the company that influence delivery

efficiency of logistic management in PT. Nestle Indonesia Distribution Center,

Cikarang, therefore, the population include all of the party that involved in

distribution center, staff of GAC Samudra Logistic and supply chain management

who undertaken the delivery product.

In this research, the population are the people in Nestle staff in Cikarang

Distribution center and Supply chain division . Whereas they are Nestle Cikarang

distributor center (12 people), staff of GAC Samudra Logistic (67 people) and

supply chain management who undertaken the delivery product. (8 people).

Therefore the total population is 87 people.

3.4.2 Sample

Sample is a group of subjects for a study in such a way that individuals represent

the larger group from which they were selected. This representative portion of a

population is called a sample (Ary, 1987).

There are specified criteria where in respondents must belong, which is the one

who involved in purchasing operation. For the purpose of conducting the sampling

strategy, the researcher used judgmental sampling. Judgemental sampling is the

sampling design of this study; since it is the most appropriate design to be used.

39

The researcher used judgmental sampling in choosing the sample. The researcher

will ask those who meet the criteria to participate in the research study. From the

mentioned population of 87 people, the sample size will be taken according to the

following formula:

n = N

1 + Ne2

n = 87

1 + (87)(0.05)2

n = 87 ; n = 71

1 + 0.1625

Where:

n = sample size

N = population

e2 = level of confidence 95%

Therefore, the total respondents in this research is 71 persons and will be

separated into two parts; 20 respondents for pre-test and 51 respondents for real

test. The researcher will spread the questionnaires based on researcher’s personal

judgement towards the respondent who have knowledge and involved in the

operation that are going to be analyzed in this study.

3.5 Statistical Treatment

There are 3 (three) statistical tools used in this study, which are Likert Scale,

Weighted Mean, and Standard Deviation.

40

3.5.1 Likert Scale

The Likert scale is designed to examine how strongly subjects agree or disagree with

statements on a five-point scale with the following anchors (Sekaran, Bougie, 2010):

(Source: Sekaran, Bougie, 2010)

The Questionnaire uses Likert Scale and all statements that express either a favorable

and unfavorable attitude will be scaled through Strongly Disagree, Disagree, Neither

Agree Nor Disagree, Agree, and Strongly Agree.

The figure of the questionnaire is shown below:

(Source: Develop by Researcher)

Note:

1. For Strongly Disagree

2. For Disagree

No. Statements 1 2 3 4 5

1

2

3

4

5

Figure 3.4: Likert Scale Questionnaire

Figure 3.3: Likert Scale

41

3. For Neutral

4. For Agree

5. For Strongly Agree

Each of the five responses would have a numerical value which would be used to

measure the attitude under investigation.

Likert Scales have the advantage that they do not expect a simple yes / no answer

from the respondent, but rather allow for degrees of opinion, and even no opinion at

all. Therefore quantitative data is obtained, which means that the data can be

analyzed with relative ease.

The Validity and Reliability testing must be done before the questionnaire spreads to

the respondents. Pre testing is conducted to check if the statements are proper as

research instrument.

3.5.2 Weighted Mean

Arithmetic mean computed by considering relative importance of each items is called

weighted mean. To give due importance to each item under consideration, numberis

called weight to each item in proportion to its relative importance. Weighted Mean is

computed by using following formula (Emathzone.com, 2013):

Which means:

42

Where:

= Weighted Mean of the factors related

W = Corresponding Weight

X = A set of number designated / rate of importance

3.5.3. Standard Deviation

The Standard Deviation is a measure of how spreads out numbers are. Standard

Deviation is used when data is drawn from a larger set chemistry.about.com (2013).

The sample standard deviation is used when a sample of data is analyzed. In this

equation:

s = sample standard deviation

N = number of scores in a sample

N-1 = degrees of freedom or Bessel's correction

x = value of a sample

x bar = mean or average of the sample

3.6 Data Analysis

In analyzing the data obtained, the researcher uses two major programs that are

statistic-related. The first program that the researcher uses is Microsoft Excel. The

employment of this program is intended to tabulate the data obtained from

questionnaires distribution. It simplifies the researcher to analyze the data.

43

The second program is Statistical Package for Social Science (SPSS). SPSS is

commonly utilized by researchers to quantitatively examine the data obtained from

questionnaires distribution. It has been recognized to be helpful to investigate

statistical data. SPSS in this research was used to analyze reliability, validity,

weighted mean, factor analysis, classic assumption and multiple linear regression

analysis.

3.7 Reliability and Validity

3.7.1. Reliability Test

The first requirement of a good instrument was reliability. The Reliability test of a

measure indicates the extent to which it is without bias (error free) and hence ensures

consistent measurement across the time and across the various items in the

instrument. In other words, the reliability of a measure is an indication of the stability

and consistency with which the instruments measures the concept and helps to assess

the goodness of measure (Sekaran, Bougie, 2010). Accurate questionnaire may

deflect the right question which is means when the question is asked for several

times, the interpretation would be the same from one respondent to another.

Measurement of Reliability (Internal-Consistency) in this research will use the

Cronbach’s Alpha Coefficient; the equation is(Janzengroup.net, 2013):

Where,

k = number of items

r = average correlation between any two items

α = reliability of the average or sum

44

3.7.2. Validity Testing

The purpose of validity testing is to eliminate the proper question that will answer the

research objectives. The Pearson product-moment correlation coefficient (or Pearson

correlation coefficient for short) is a measure of the strength of a linear association

between two variables and is denoted by r. Basically, a Pearson product-moment

correlation attempts to draw a line of best fit through the data of two variables, and

the Pearson correlation coefficient, r, indicates how far away all these data points are

to this line of best fit (how well the data points fit this new model/line of best fit)

(Statistic.laerd.com ,2013). The valid data is a representative statement of variables

that are ready to spread to the respondents.

In Pearson Correlations, results are between -1 and 1. A result of -1 means that there

is a perfect negative correlation between the two values at all, while a result of 1

means that there is a perfect positive correlation between the two variables. A result

of 0, on the other hand, means that there is no linear relationship between the two

variables. Most research will very rarely get a correlation of 0, -1 or 1. Result would

Cronbach's alpha Internal consistency

α ≥ 0.9 Excellent

0.8 ≤ α < 0.9 Good

0.7 ≤ α < 0.8 Acceptable

0.6 ≤ α < 0.7 Questionable

0.5 ≤ α < 0.6 Poor

α < 0.5 Unacceptable

45

be somewhere in between. The closer the value of r gets to zero, the greater the

variation the data points are around the line of best fit.

The Quantitative interpretation of the degree of linear relationship existing is shown

in the following range of values.

±1.00 perfect Positive (negative) correlation

±0.91 - ± 0.99 very high positive (negative) correlation

±0.71 - ± 0.90 high Positive (negative) correlation

±0.51 - ± 0.70 moderately positive (negative) correlation

±0.31 - ± 0.50 low positive (negative) correlation

±0.01 - ± 0.30 negligible positive (negative) correlation

0.0 no correlation

Correlation r formula:

For any two variables, X and Y, the correlation coefficient between them is given by

the formula:

Where

n = number pair of scores

∑𝑥𝑦= sum of the products of pair scores

46

∑𝑥 = sum of x scores

∑𝑦 = sum of y scores

∑𝑥 ² = sum of squared x scores

∑𝑦 ² = sum of squared y scores

The first requirement of a good instrument was validity. Thus, the researcher chooses

Pearson Product Moment Correlation by using the software SPSS 16.0 to fulfill the

requirement of the instrument’s validity.

3.8.1 Classical Assumption Test

Below are several test of classic assumption test, namely normality test

multicollinearity test, heteroscedasticity test, and autocorrelation test.

3.8.1.1 Normality Test

Normality test is to see whether the residual values are normally distributed or not.

A good regression model has a normal distribution or at least semi-normal

(Ghozali, 2001). Normality test can be done with the test histograms, normal test

P Plot, Chi Square test, skewness and Kurtosis or Kolmogorov Smirnov. If

residuals are not normal but closer to the critical value (eg Kolmogorov Smirnov

significance of 0.049) it can be tested by other methods which may provide

justification to normal. But if far from the normal value, then it can be done

several steps: data transformation, perform data trimming outliers or add

observation data. The transformation can be made into a form of natural

logarithm, square root, inverse, or other forms depending on the normal curve

shape, whether leaning to the left, right, collects in the middle or spread to the

right and left side

3.8.1.2 Multicollinearity test

47

Multicollinearity test is the test to determine whether there is any correlation

among the independent variables in the regression model (Ghozali, 2001). If there

is a high correlation between independent variables, then the relationship between

the independent variable on the dependent variable to be disturbed. Statistical tool

that is often used to test multicollinearity problems are with the variance inflation

factor (VIF), Pearson correlation between the free variables, or by looking at the

eigenvalues and condition index (CI).

3.8.1.3 Heteroscedasticity Test

Heteroscedasticity test is a text to determine whether or not the regression model

has difference in variance from one event to another (Ghozali, 2001). This test to

see whether there is inequality of variance of the residuals of the observations to

other observations. Regression models that meet the requirements are where there

is equality of variance of the residual one observation to another observation fixed

or called homoskedastisitas.

Detection of heteroscedasticity can be done using scatter plots with plotted the

ZPRED value (predicted value) with SRESID (residual value), where Y axis is the

predicted value and X axis is a residual value (Ghozali, 2001). A good model is

obtained if there is no particular pattern on the graph, such as collects in the

middle, narrowed and then widened or otherwise widened and then narrowed. The

statistical test can be used are Glejser test, test test Park or White.

3.8.2 Linear Multiple Regression

In this research, the researcher uses multiple regression technique to exa the

influence of several independent variables (variable X) on the dependent variable

(variable Y). Multiple regression analysis is the analysis to asses the strength of a

relationship between on dependent and two or more independent variables (Mark

Saunder, Philip Lewis, et all, 2011). This analysis involves combining several

48

predictor variables in a single regression equation. Multiple regressions are used

to assess the effect of multiple predictor variables on the dependent measures. It is

to establish between the single continuous Y-variable and several X variables

(Jackson, 2011, p. 165). Formally we can say that if the significance value is

greater than 0.05, it means that the independent variable being measured does not

have significant influence toward the dependent variable (Santoso, 2009). The

interpretation of unstandardized regression coefficient is that it represents the

amount of change in the dependent variable associated with one-unit change in

that independent variable, with all other independent variables held constant (Rae

R. Newton, 2012).

The equation of regression model is as follows;

𝑦=𝑎+𝑏1𝑥1+𝑏2𝑥2+𝑏3𝑥3+𝑏4𝑥4+

Figure 3.5 Multiple Regression Equation

𝑦 : Value of the Dependent Variable, which is Delivery efficiency

𝑎 : Constant or intercept

b1: Regression Coefficient between transportation toward Delivery Efficiency

b2: Regression coefficient between Distribution Center Area toward Delivery

Efficiency

b3: Regression coefficient between Inventory toward Delivery Efficiency

x1 : Dimension score of Transportation

x2 : Dimension score of Distribution Center area

x3 : Dimension score of Inventory

49

The researcher will be using SPSS 21.0 software for Windows to help analyze the

multiple regressions analysis and the result will be used to accept or reject the

hypothesis as to observe whether there is any dependency between dependent

variable which is Delivery efficiency (Y) and independent variables which are

Transportation (X1), Distribution Center Area(X2), and Inventory (X3).

3.8.3 Paired Sample T- test ( Partial Test)

Basically, Creswell (1994) stated that the paired sample t-test is the most

commonly used method to evaluate the differences in means between two

samples. Theoretically, the t-test can be used even if the sample sizes are very

small, as long as the variables are normally distributed within each group and the

variation of scores in the two groups is not reliably different (Creswell, 1994). T

test used to evaluate the influence of independent variable toward dependent

variable partially.The equation of t-test for manual calculation is stated as follows:

t = bj – βj

Sbj

Where:

t = statistic test for t-distribution

bj = sample slope

βj = slope of the population

Sbj = standard error of the slope

If Tcount < Ttable at α = 5% significance level, so H0 accepted and Ha rejected. It

means that independent variable has no significant influence towards dependent

variable. If Tcount > Ttable at α = 5% significance level, so H0 rejected and Ha

50

accepted. It means that independent variable has significant influence towards

dependent variable.

The requirements of this test is, Hypothesis is accepted if significance value is

greater than 0.05 on α = 5%, and if the number in t-column is greater than the

value in t-table (Arifin, 2008).

3.8.4 F- Test (Simultaneous Test)

F test used to evaluate the influence of all independent variable towards dependent

variables simultaneously. This method used to measure if there are a significant

influence independent (transportation, Distribution Center Area, Inventory) toward

dependent simultaneously.

The equation of F-test for manual calculation is stated as follows:

F = [ R2 / k ]

[ ( 1 – R2 ) / ( n – k – 1 ) ]

Where:

F = statistics test for F distribution

R2 = coefficient of determination

k = number of independent variables in the regression model

n = number of samples

If Fcount < Ftable at α = 5% significance level, so H0 accepted, means that

independent variables has no significant influence towards dependent variable

simultaneously. If Fcount > Ftable at α = 5% significance level, so Ha accepted,

51

means that independent variables has significant influence towards dependent

variable simultaneously.

Formally we can say that if the significance value is greater than 0.05 we have to

reject the null hypothesis. The general ways to evaluate influence of independent

variables towards dependent variable simultaneously is by analyzing the F column in

ANOVA table (Arifin, 2008)

3.8.5 R2 Test (Coefficient of Determination)

The coefficient of determination (R2) was essentially measures how far the model’s

ability to explain the variation in the dependent variable. The coefficient of

determination is between 0 and 1. The closer to 1 the value is, it indicates that the

independent variables provide almost all the information needed to predict the

dependent variables (Sirkin, 2006).

52

CHAPTER IV

ANALYSIS AND INTERPRETATION

4.1Corporate Profile

Nestlé Indonesia is the subsidiary of Nestlé SA, leading company in the field of

nutrition, health and wellness, which headquartered in Vevey, Switzerland. Nestlé

SA was founded more than 140 years ago by Henri Nestlé, a pharmacist who

managed to mix baby cereal or pap to help a mother save her baby that was very sick

and not able to receive breast milk.

Nestlé has been operating in Indonesia since 1971, and currently employs more than

2,600 employees to produce a variety of products for Nestlé in four factories: Plant

Kejayan, Pasuruan, East Java to process dairy products such as DANCOW, BEAR

BRAND, and Nestlé DANCOW IDEAL; Panjang factory in Lampung to process

NESCAFÉ instant coffee and Cikupa factory in Banten to produce confectionery

products and POLO'S FOX. The fourth new factory opened in 2013 is located in

Karawang to produce DANCOW, MILO, and Nestlé CERELAC baby porridge.

Nestlé's motto is "Good Food, Good Life" describes the company's ongoing

commitment to combine science and technology to provide products that meet basic

human needs for food and drinks nutritious, and safe to eat with delicious taste.

4.1.1. Vision and Mission

The Mission of PT Nestlé Indonesia as one of the biggest food companies in

Indonesia is to actualize healthier society in Indonesia. To achieve that mission, the

Visions of PT Nestlé Indonesia are:

53

1. Obtain the trust of the consumers, and become the leading and respected food

and nutrient company in Indonesia.

2. Ensuring profitability and sustainability of long-term growth with efficient

capital for the company, through a service that is able to improve the quality

of life of consumers.

3. Become a market leader or become in the first position. Besides for the vision

and mission, PT Nestlé Indonesia also assigns their company motto, which is

"Passion for Our Consumers". Through this motto, PT Nestlé Indonesia will

always strive to give the best to its customers.

Based on the visions, PT. Nestlé Indonesia adopted several policies Quality and

Environmental Safety and Health Policy, which are:

1. Product and service never neglect safety factors.

2. Always follow the regulations.

3. Zero waste and zero defect.

4. Committed to increase the quality standard.

5. Employees and business partners are very important.

6. Implementing sustainable business practices.

7. Comply with all environmental regulations and K3.

8. Nullify occupational accidents and complaints.

9. Continuous improvements in the fields of environment and PT Nestlé

Indonesia has always apply values that have been the foundation for the

company and all employees, the values are known as the "PRIDE", which

stands for Passion, Respect, Integrity, Determination, and Excellence.

Mission of PT. Nestlé Indonesia is to actualize healthier Indonesian society through

their products which qualified, nutritious and delicious taste. Besides that, PT. Nestlé

Indonesia also focuses on giving the information and knowledge for all customers,

such as noted in each of the product packaging. In conducting its business, PT. Nestlé

Indonesia strives to always carry the responsibility to the community and create

54

benefits. The purpose of PT. Nestlé is a strong desire to provide healthy products to

the general public around the world so that people around the world can be assured of

their health with the present of Nestlé products. In addition Nestlé has a goal like the

other companies which is Nestlé want to compete with other companies with its

competition and to dominate the world market. Now the purpose of the Nestlé that is

to dominate the world market in a healthy manner is almost realized by using a good

market strategy and also with hard work Nestlé is getting stronger and growing

rapidly.

4.1.2. Corporate Values

Nestle has established six corporate values which serve as guidelines for all

employees in their effort to run the Company. The six corporate values are:

Figure 4.1. Nestle Corporate Value

Source: Nestle.com

55

4.1.3 Core Organization Activities

Nestlé is committed to the following Business Principles in all countries, taking into

account local legislation, cultural and religious practices:

1. Nutrition, Health and Wellness

Our core aim is to enhance the quality of consumers’ lives every day, everywhere by

offering tastier and healthier food and beverage choices and encouraging a healthy

life style. We express this via our corporate proposition 'Good Food, Good Life'.

2. Quality Assurance and product safety

Everywhere in the world, the Nestlé name represents a promise to the consumer that

the product is safe and of high standard.

3. Consumer Communication

We are committed to responsible, reliable consumer communication that empowers

consumers to exercise their right to informed choice and promotes healthier diets. We

respect consumer privacy.

4. Human rights in our business activities

We fully support the United Nations Global Compact’s (UNGC) guiding principles

on human rights and labor and aim to provide an example of good human rights’ and

labor practices throughout our business activities.

5. Leadership and personal responsibility

Our success is based on our people. We treat each other with respect and dignity and

expect everyone to promote a sense of personal responsibility. We recruit competent

and motivated people who respect our values, provide equal opportunities for their

development and advancement protect their privacy and do not tolerate any form of

harassment or discrimination.

56

6. Safety and health at work

We are committed to preventing accidents, injuries and illness related to work, and to

protect employees, contractors and others involved along the value chain.

7. Supplier and customer relations

We require our suppliers, agents, subcontractors and their employees to demonstrate

honesty, integrity and fairness, and to adhere to our non-negotiable standards. In the

same way, we are committed towards our own customers.

8. Agriculture and rural development

We contribute to improvements in agricultural production, the social and economic

status of farmers, rural communities and in production systems to make them more

environmentally sustainable.

9. Environmental sustainability

We commit ourselves to environmentally sustainable business practices. At all stages

of the product life cycle we strive to use natural resources efficiently, favor the use of

sustainably-managed renewable resources, and target zero waste.

10. Water

We are committed to the sustainable use of water and continuous improvement in

water management. We recognize that the world faces a growing water challenge and

that responsible management of the world’s resources by all water users is an

absolute necessity.

57

4.1.4. Products

In 2010 Corporate Equity Monitor stated that Nestlé has improved rapidly in

innovation, renovation, proximity to consumers, enjoyment, as well as corporate

social responsibility. In product development, Nestlé apply the Nestlé Nutritional

Profiling System to ensure that the products provide good nutritional value for

consumers. The brands that Nestlé produces are divided into several business units.

58

4.2 Data Result Analysis

The data result analysis reports on the results of the analysis that influence delivery


Cikarang. The researcher distributed questionnaire to the staff in Cikarang

distribution Center. The Questionnaire consists of three parts. Part I consist of

general description of respondent profile, Part II consist of instruction of

questionnaire filling, Part III contains the statement for respondents of delivery


Cikarang.

The respondents had provided information that assisted in meeting the objectives for

the study. In the questionnaire, the first part was used to obtain the basic information

of respondents regarding to their profile, part two were the instruction for

respondents to fill the questionnaire, part three is the statement of respondent of the

analysis that influence delivery efficiency of logistic management in PT. Nestle

Indonesia Distribution Center, Cikarang. Questions were arranged as such: question

1-5 focused on perceived transportation; question 1-4 focused on brand distribution

center area; question 1-5 focused on inventory; question 1-5 focused on performance

Cikarang distribution center. The questionnaire used in this research has been tested

for reliability and validity as followed.

59

4.2.1 Reliability Test

Reliability test was conducted by employing SPSS and arranged data from Microsoft

Excel to tabulate Cronbach’s Alpha of the research instruments. The results are as

followed.

Table 4.2: Cronbach’s Alpha of Transportation

Table (4.2) shows reliability coefficient of Cronbach’s Alpha of .784 on

Transportation which means that this parameter had a good reliability rate (over 0.7).

Table 4.3: Cronbach’s Alpha of Distribution Center Area

Reliability Statistics

Cronbach's

Alpha

N of Items

.816 5

Table (4.3) shows reliability coefficient of Cronbach’s Alpha of .816 on Distribution

Center Area which means that this parameter had a good reliability rate (over 0.8)


Cronbach's

Alpha

N of Items

.784 7

60

Table 4.4: Cronbach’s Alpha of Inventory


Cronbach's

Alpha

N of Items

.722 6

Table (4.4) shows reliability coefficient of Cronbach’s Alpha of .722 on Inventory

which means that this parameter had a good reliability rate (over 0.7).

Table (4.5) shows reliability coefficient of Cronbach’s Alpha of .743 Distribution

Center Performance on which means that this parameter had a good reliability rate

(over 0.7).

4.2.2 Validity Test

Validity test was conducted by employing SPSS to tabulate pearson correlation

matrix of the questionnaires. Data was first arranged in Microsoft Excel and then

analyzed in SPSS. The results are as followed.


Cronbach's

Alpha

N of Items

.743 6

Table 4.5: Cronbach’s Alpha of Distribution Center Performance

61

Table 4.6: Pearson Correlation of Transportation

Correlations

Transportation1 Transportation

Total

Transportation1

Pearson Correlation 1 .804**

Sig. (2-tailed) .000

N 20 20

Transportation Total

Pearson Correlation .804** 1


N 20 20

**. Correlation is significant at the 0.01 level (2-tailed).

Correlations

Transportation

Total

Transportation2




N 20 20

Transportation2



N 20 20


Correlations

Transportation

Total

Transportation3

TransportationTotal



N 20 20

Transportation3



N 20 20


62

Correlations

Transportation

Total

Transportation4




N 20 20

Transportation4



N 20 20


Correlations

Transportation

Total

Transpotation5


Pearson Correlation 1 .495


N 20 20

Transpotation5

Pearson Correlation .395 1


N 20 20

Correlations

Transportation

Total

Transportation6

TransportationTotal



N 20 20

Transportation6



N 20 20


63

Table 4.6 Pearson Correlation of Transportation shows Transportation1-

Transportation6 in place of Question 1-6 with Transportation Total as its total.

Transportation1 was rated .804 in Pearson Correlation Analysis which means that

there is high positive correlation with perceived quality. Transportation2 was rated

.897 which means that there is high positive correlation. Transportation3 also shows

high positive correlation with Pearson correlation of .725. Transportation4 was rated

.829 which means that there is high positive correlation. Transportation5 was rated

.495 which means that there is low positive correlation but still valid.

Transportation6 have similar with the Transportation1, transportation2,

Transportation3, and Transportation4 the Pearson correlation of .671 indicates that

there is high positive correlation with perceived quality. All in all, table 4.6 indicated

that question 1-6 is correlated with Transportation, base on the r table with 0.468

value.

Table 4.7: Pearson Correlation of Distribution Center Area

Correlations

DCA1 DCA Total

DCA1



N 20 20

DCATotal



N 20 20


Correlations

DCATotal DCA2

DCATotal



N 20 20

DCA2 Pearson Correlation .782** 1


64

N 20 20


Correlations

DCATotal DCA3

DCATotal



N 20 20

DCA3



N 20 20


Correlations

DCATotal DCA4

DCATotal



N 20 20

DCA4



N 20 20


Table 4.7 Pearson Correlation of Distribution Center Area shows DCA1-DCA4 in

place of Question 1-4 with DCATotal as its total. DCA1 was rated .793 in Pearson

Correlation Analysis which means that there is moderately positive correlation with

brand awareness. DCA2 was rated .782 which means that there is high positive

correlation. DCA3 also shows high positive correlation with Pearson correlation of

.894. Similar with the DCA1, the DCA4 indicates another moderately positive

correlation at .732. All in all, table 4.7 indicated that question 1-4 is correlated with

Distribution Center Area, base on the r table with 0.468 value.

65

Table 4.8 Pearson Correlation of Inventory

Correlations

Inventory1 InventoryTotal

Inventory1

Pearson Correlation 1 .469*


N 20 20

InventoryTotal

Pearson Correlation .469* 1


N 20 20

*. Correlation is significant at the 0.05 level (2-tailed).

Correlations

InventoryTotal Inventory2

InventoryTotal



N 20 20

Inventory2



N 20 20


Correlations


InventoryTotal



N 20 20

Inventory3



N 20 20


66

Correlations


InventoryTotal



N 20 20

Inventory4



N 20 20


Correlations


InventoryTotal



N 20 20

Inventory5



N 20 20


Table 4.8 Pearson Correlation of Inventory shows Inventory1-Inventory4 in place of

Question 1-5 with InventoryTotal as its total. Inventory1 was rated .469 in Pearson

Correlation Analysis which means that there is a positive correlation with Inventory.

Inventory2 was rated .526 which means that there is a positive correlation with the

inventory. Inventory3 also shows high positive correlation with Pearson correlation

of .717. Inventory4 was rated .774 which means that there is a positive correlation

with the inventory. Similar with the Inventory1-Inventory4, the Inventory5 indicates

negative correlation at .499. in table 4.8 indicated that question 1-5 are correlated

with delivery efficiency, based on the r table with 0.468 value.

67

Table 4.9 Pearson Correlation of Distribution Center Performance

Correlations

DCP1 DCPTotal

DCP1



N 20 20

DCPTotal



N 20 20


Correlations

DCPTotal DCP2

DCPTotal



N 20 20

DCP2



N 20 20


Correlations

DCPTotal DCP3

DCPTotal



N 20 20

DCP3



N 20 20


Correlations

68

DCPTotal DCP4

DCPTotal



N 20 20

DCP4



N 20 20


Correlations

DCPTotal DCP5

DCPTotal



N 20 20

DCP5



N 20 20


Table 4.9 Pearson Correlation of distribution center performance shows DCP1-DCP5

in place of Question 1-5 with DCPTotal as its total. DCP1 was rated .570 in Pearson

Correlation Analysis which means that there is a positive correlation with distribution

center performance. DCP2 was rated .743 which means that there is high positive

correlation with the distribution center performance. DCP3 also shows high positive

correlation with Pearson correlation of .704. DCP4 also shows a positive correlation

with Pearson correlation of .647. Similar with the DCP1-DCP4, the DCP5 indicates

another a positive correlation at .516. All in all, table 4.9 indicated that question 1-5

is correlated with distribution center performance.

69

Table 4. 10: validity Test

Variable Corrected item

Total Correlation

R Table Validity

Transportation1 .804 .468 Valid






DCA1 .793 .468 Valid




Inventory1 .469 .468 Valid





DCP1 .570 .468 Valid


70




4.2.3 Respondent Profiles

The Respondent profiles data gathered to gain insight about the characteristics of

respondents in this study through the questionnaires. Whereas they are Nestle

Cikarang distributor center (12 people), staff of GAC Samudra Logistic (67 people)

and supply chain management who undertaken the delivery product. (8 people).

Therefore the total population is 87 people. After included in the sample formula the

data that collect only 71 people. Data obtained were recorded as follows:

4.2.3.1 Current Age

Table 4.10 Respondent Profiles: Current Age

Your Current Age Respondents Percentage (%)

Between 20 years to 30

years.

28 39%

Above 30 years to 40

years

34 48%

In the 40 years up to 50

years

9 13%

Above 50 years 0 0%

Total 71 100%

71

Figure 4.1 Respondent Profiles: Current Age

As it is shown in table 4.10 and figure 4.1 about respondent profiles of current age in

this research, 28 people (39%) were between 20 years to 30 years respondents and 34

people (48%) were Above 30 years to 40 years respondents,9 people (13%) were in

the 40 years up to 50 years and above 50 years 0 % Therefore, the majority

respondents were above 30 years to 40 years.

4.2.3.2 Last Education

Table 4.11 Respondent Profiles: Last education

Last Education Respondents Percentage (%)

High School 23 32%

Diploma 23 32%

S1 24 34%

S2 1 2%

S3 0 0%

Total 71 100%

39%

48%

13%0%

Between 20 years to 30 years. Above 30 years to 40 years

In the 40 years up to 50 years Above 50 years

72

Figure 4.2 Respondent Profiles: last Education

As it is shown in table 4.11 and figure 4.2 about respondent profiles of last education

in this research, 23 people (32%) were high school respondents , 23 people (32%)

were diploma respondents,24 people (34%) were S1,1 people (2%) and above S3 0%

Therefore, the majority respondents were S1.

4.2.3.3. Time have been working in this position.

Table 4.12 Respondent Profiles: Working in this situation

Working in this position Respondents l Percentage (%)

1 year to 2 years 35 49%

Up to 2 years to 8 years 25 35%

Above 8years to 12 years 11 16%

Above 12 years to15 years 0 0%

Above 15 years 0 0%

Total 71 100%

32%

32%

34%

2%0%

High School Diploma S1 S2 S3

73

Figure 4.3 Respondent Profiles: Working in this position

As it is shown in table 4.12 and figure 4.3 about respondent profiles of working in

this position in this research, 35 people (49%) were 1 years to 2 years respondents,

25 people (35%) were up to 2 years to 8 years respondents,11 people (16%) were

Above 12 years to15 years, 0 people (0%) ware Above 15 years Therefore, the

majority respondents were 1 years to 2 years.

4.2.3.4. Working in this Company

Table 4.13 Respondent Profiles: Working in this Company

Working in this Company Respondents Percentage(%)

Lest than 5 years 35 49.2

Above 5 years to 10 years 29 40.8

Above 10 years to 15 years 5 7.2

Above 15 years to 20 years 0 0

Above 20 years 2 2.8

Total 71 100

49%

35%

16%0%0%

1 years to 2 years up to 2 years to 8 years Above 8 years to 12 years

Above 12 years to 15 years Above 15 years

74

Figure 4.4 Respondent Profiles: Working in this company

As it is shown in table 4.13 and figure 4.4 about respondent profiles of working in

this company in this research, 35 people (49%) were less than5 years respondents, 23

people (41%) were up to 5 years to 10 years respondents, 5 people (7%) were above

10 years to15 years, 0 people (0%) ware Above 15 years to 20 years and 2 people

(3%) ware above 20 years. Therefore, the majority respondents were less than 5

years.

4.2.4 Respondent Responses

Based on the questionnaires, the respondent’s assessment refers to facilitate the

assessment of the respondents, answers will be based on the Likert scales as

followed:

Strongly Disagree 1

Disagree 2

Neither Agree or Disagree 3

Agree 4

Strongly Agree 5

49%

41%

7%0%3%

Lest than 5 years Above 5 years to 10 years Above 10 years to 15 years

Above 15 years to 20 years Above 20 years

75

4.2.4.1 Respondent Responses Assessing Transportation (X1)

The distribution frequency of Variable 1 (Number of vehicle has been met the

requirement product to distributor) is presented in Table 4.14 above. From the total

of 71 respondents, 3 respondents (4,2%) strongly agree with the statement; 45

respondents (63,3%) agree with the statement; 9 respondents (12,6%) are neutral; 13

respondents (18,3%) disagree with the statement; and the rest 1 respondent (1,4%)

strongly disagree with the statement. From the percentage, it can be summarized that

most respondents (63,3 agree and 4,2 % strongly agree) have tendency to agree with

the statement that Number of vehicle has been met the requirement product to

distributor.

Table 4.14: Variable 1 Description

Variable 1: Number of vehicle has been met the requirement

product to distributor

Scale Respondents Percent Cumulative Percent

SD 1 1.4 1.4

D 13 18.3 19.7

N 9 12.6 32.3

A 45 63.3 95.6

SA 3 4.2 100

Total 71 100

76

The distribution frequency of Variable 2 (Quality of vehicle in accordance with

standard requirement that company assigned) is presented in Table 4.15 above. From

the total of 71 respondents, 8 respondents (11,2%) strongly agree with the statement;

33 respondents (46,5%) agree with the statement; 16 respondents (22,5%) are

neutral; 13 respondents (18,3%) disagree with the statement; and the rest 1

respondent (1,4%) strongly disagree with the statement. From the percentage, it can

be summarized that most respondents (46,5 agree and 11,2 % strongly agree) have

tendency to agree with the statement that Quality of vehicle in accordance with

standard requirement that company assigned.


Variable 2: Quality of vehicle in accordance with standard

requirement that company assigned


SD 1 1.4 1.4

D 13 18.3 19.7

N 16 22.5 42.3

A 33 46.5 88.8

SA 8 11.2 100

Total 71 100

77

The distribution frequency of Variable 3 (Vehicles capacity has sufficient to

maximize supply of the products) is presented in Table 4.16 above. From the total of

71 respondents, 12 respondents (16,9%) strongly agree with the statement; 35




most respondents (49,2 agree and 16,9 % strongly agree) have tendency to agree with

the statement that. Vehicles capacity has sufficient to maximize supply of the

products.


Variable 3: Vehicles capacity has sufficient to maximize supply of the

products


SD 1 1.4 1.4

D 6 8.4 9.8

N 17 23.9 33.7

A 35 49.5 83.1

SA 12 16.9 100

Total 71 100

78

The distribution frequency of Variable 4 (Schedule of arrival and departure timing of

the transportation has already achieved the target) is presented in Table 4.17 above.

From the total of 71 respondents, 6 respondents (8,4%) strongly agree with the

statement; 33 respondents (43,7%) agree with the statement; 20 respondents (28,1%)

are neutral; 13 respondents (18,3%) disagree with the statement; and the rest 1

respondent (1,4%) strongly disagree with the statement. From the percentage, it can

be summarized that most respondents (43,7agree and 8,4 % strongly agree) have

tendency to agree with the statement that Schedule of arrival and departure timing of

the transportation has already achieved the target.


Variable 4: Schedule of arrival and departure timing of the

transportation has already achieved the target


SD 1 1.4 1.4

D 6 8.4 9.8

N 17 23.9 33.7

A 35 49.5 83.1

SA 12 16.9 100

Total 71 100

79

The distribution frequency of Variable 5 (Condition of road has already considered

when calculate the lead time) is presented in Table 4.18 above. From the total of 71

respondents, 9 respondents (12,7%) strongly agree with the statement; 36


respondents (7,04%) disagree with the statement; and the rest 0 respondent strongly

disagree with the statement. From the percentage, it can be summarized that most

respondents (50,7% agree and 12,7 % strongly agree) have tendency to agree with

the statement that Condition of road has already considered when calculate the lead

time.

Variable 5 : Condition of road has already considered when calculate

the lead time


SD 0 0 0

D 5 7.4 7.4

N 21 29.6 37

A 36 50.7 87.7

SA 9 12.3 100

Total 71 100


80

The distribution frequency of Variable 6 (Selection modal transportation was able to

save cost and time to supply products) is presented in Table 4.19 above. From the

total of 71 respondents, 7 respondents (9,8%) strongly agree with the statement; 44

respondents (62%) agree with the statement; 11 respondents (15,4%) are neutral; 9



respondents (62 % agree and 9,8/ % strongly agree) have tendency to agree with the

statement that selection modal transportation was able to save cost and time to supply

products.


Variable 6 : Selection modal transportation was able to save cost and

time to supply products


SD 0 0 0

D 9 12.7 12.7

N 11 15.4 28.1

A 44 62 90.2

SA 7 9.8 100

Total 71 100

81

4.2.4.2 Distribution Center Area (X2)

The distribution frequency of Variable 7 (The distribution center is located in a

strategic place (easy to distribute the products)) is presented in Table 4.20 above.

From the total of 71 respondents, 14 respondents (19,7%) strongly agree with the

statement; 40 respondents (56,3%) agree with the statement; 12 respondents (16,9

%) are neutral; 4 respondents (5,5%) disagree with the statement; and the rest 1

respondent strongly disagree with the statement. From the percentage, it can be

summarized that most respondents (56,3 % agree and 19,7 % strongly agree) have

tendency to agree with the statement that The distribution center is located in a

strategic place (easy to distribute the products).


Variable 7 : The distribution center is located in a strategic place

(easy to distribute the products)


SD 1 1.4 1.4

D 4 5.5 6.9

N 12 16.9 23.8

A 40 56.4 80.2

SA 14 19.8 100

Total 71 100

82

The distribution frequency of Variable 8 (Infrastructure at distribution center is

sufficient to supply the products) is presented in Table 4.21 above. From the total of





respondents (59,1 % agree and 22,5% strongly agree) have tendency to agree with

the statement that Infrastructure at distribution center is sufficient to supply the

products.


Variable 8 : Infrastructure at distribution center is sufficient to supply

the products


SD 0 0 0

D 7 9.8 9.8

N 6 8.6 18.4

A 42 59.1 77.5

SA 16 22.5 100

Total 71 100

83

The distribution frequency of Variable 9 (Placement of distribution center can

reduce the transportation costs) is presented in Table 4.22 above. From the total of





respondents (50,7 % agree and 21,1% strongly agree) have tendency to agree with

the statement that Placement of distribution center can reduce the transportation

costs.


Variable 9 : Placement of distribution center can reduce the



SD 2 2.8 2.8

D 2 2.8 5.6

N 16 22.5 28.1

A 36 50.7 78.8

SA 15 21.2 100

Total 71 100

84

The distribution frequency of Variable 10 (Distribution Center has many access to in

and out) is presented in Table 4.23 above. From the total of 71 respondents, 17

respondents (24,1%) strongly agree with the statement; 43 respondents (60,5%)

agree with the statement; 6 respondents (8,4%) are neutral; 2 respondents (2,8%)

disagree with the statement; and the rest 3 respondent strongly disagree with the

statement. From the percentage, it can be summarized that most respondents (60,5 %

agree and 24,1% strongly agree) have tendency to agree with the statement that

distribution Center has many access to in and out.


Variable 10: Distribution Center has many access to in and out


SD 3 4.2 4.2

D 2 2.8 7

N 6 8.4 15.4

A 43 60.5 75.9

SA 17 24.1 100

Total 71 100

85

2.5.1.1 Inventory (X3)

The distribution frequency of Variable 11 (Size of distribution center is sufficient for

the products loading and unloading) is presented in Table 4.24 above. From the total

of 71 respondents, 8 respondents (11,4%) strongly agree with the statement; 37


respondents (9,8%) disagree with the statement; and the rest 6 respondent (8,4)


most respondents (52,1 % agree and 11,4% strongly agree) have tendency to agree

with the statement that size of distribution center is sufficient for the products loading

and unloading.


Variable 11: Size of distribution center is sufficient for the products

loading and unloading


SD 6 8.4 8.4

D 7 9.8 18.2

N 13 18.3 36.5

A 37 52.1 88.6

SA 8 11.4 100

Total 71 100

86

The distribution frequency of Variable 12 (Setting product use FIFO system) is

presented in Table 4.25 above. From the total of 71 respondents, 11 respondents

(15,4%) strongly agree with the statement; 12 respondents (16,9%) agree with the

statement; 11 respondents (15,5%) are neutral; 30 respondents (42,2%) disagree with

the statement; and the rest 7 respondent (9,8%) strongly disagree with the statement.

From the percentage, it can be summarized that most respondents (42,2 % disagree

and 9,8 strongly disagree) have tendency to disagree with the statement that setting

product use FIFO system.

Variable 12: Setting product use FIFO system


SD 7 9.8 9.8

D 30 42.2 52

N 11 15.4 67.6

A 12 16.9 84.5

SA 11 15.5 100

Total 71 100


87

The distribution frequency of Variable 13 (Allocation of each products storage is

clear) is presented in Table 4.26 above. From the total of 71 respondents, 10



disagree with the statement; and the rest 1 respondent (1,4%) strongly disagree with

the statement. From the percentage, it can be summarized that most respondents

(47,8 % agree and 14,2 strongly agree) have tendency to agree with the statement that

allocation of each products storage is clear.


Variable 13: Allocation of each products storage is clear


SD 1 1.4 1.4

D 9 12.7 14.1

N 17 23.9 38

A 34 47.8 85.8

SA 10 14.2 100

Total 71 100

88

The distribution frequency of Variable 14 (Stock accuracy during the stock taking is

high) is presented in Table 4.27 above. From the total of 71 respondents, 2

respondents (2,8%) strongly agree with the statement; 32 respondents (45,2%) agree

with the statement; 16 respondents (22,5%) are neutral; 6 respondents (8,4%)

disagree with the statement; and the rest 15respondent (21,1%) strongly disagree

with the statement. From the percentage, it can be summarized that most respondents

(45,2 % agree and 2,8 strongly disagree) have tendency to agree with the statement

that stock accuracy during the stock taking is high.


Variable 14:Stock accuracy during the stock taking is high


SD 15 21.1 21.1

D 6 8.4 29.5

N 16 22.5 52

A 32 45.2 97.2

SA 2 2.8 100

Total 71 100

89

The distribution frequency of Variable 15 (The current system used is easy to track

the products to distributed) is presented in Table 4.28 above. From the total of 71

respondents, 8 respondents (11,26%) strongly agree with the statement; 36




respondents (50,3 % agree and 11,3 strongly disagree) have tendency to agree with

the statement that the current system used is easy to track the products to distributed.


Variable 15:The current system used is easy to track the products to

distributed


SD 1 1.4 1.4

D 8 11.3 12.7

N 19 26.7 39.4

A 36 50.3 89.7

SA 8 11.3 100

Total 71 100

90

4.2.4.4. Distribution Center Performance (X4)

The distribution frequency of Variable 16 (Damaged products showed a declining

trend) is presented in Table 4.29 above. From the total of 71 respondents, 8



disagree with the statement; and the rest 10 respondent (14,8%) strongly disagree

with the statement. From the percentage, it can be summarized that most respondents

(36,6 % agree and 11,3 strongly disagree) have tendency to agree with the statement

that damaged products showed a declining trend.


Variable 16: Damaged products showed a declining trend


SD 10 14.8 14.8

D 9 12.7 27.5

N 18 25.3 52.8

A 26 36.6 89.7

SA 8 11.3 100

Total 71 100

91

The distribution frequency of Variable 17 (Product availability has already met the

customer demand to fulfill order) is presented in Table 4.30 above. From the total of





most respondents (50,2 % agree and 4,2 strongly disagree) have tendency to agree

with the statement that product availability has already met the customer demand to

fulfill order.


Variable 17: Product availability has already met the customer

demand to fulfill order


SD 0 0 0

D 7 9.8 9.8

N 25 35.2 45

A 36 50.2 95.2

SA 3 4.2 100

Total 71 100

92

The distribution frequency of Variable 18 (Lead time accuracy of product delivery

shows increasing trend) is presented in Table 4.31 above. From the total of 71

respondents, 3 respondents (4,3%) strongly agree with the statement; 29 respondents

(40,8%) agree with the statement; 27 respondents (38%) are neutral; 5 respondents

(7,1%) disagree with the statement; and the rest 7 respondent (9,8%) strongly


respondents (40,8 % agree and 4,3 strongly disagree) have tendency to agree with the

statement that lead time accuracy of product delivery shows increasing trend.


Variable 18: Lead time accuracy of product delivery shows increasing

trend


SD 7 9.8 9.8

D 5 7.1 16.9

N 27 38 54.9

A 29 40.8 95.7

SA 3 4.3 100

Total 71 100

93

The distribution frequency of Variable 19 (Delivery plan as per schedule) is

presented in Table 4.32 above. From the total of 71 respondents, 15 respondents

(21,1%) strongly agree with the statement; 40 respondents (56,3%) agree with the

statement; 11 respondents (15,5%) are neutral; 5 respondents (7,1%) disagree with

the statement; and the rest 0 respondent strongly disagree with the statement. From

the percentage, it can be summarized that most respondents (56,3 % agree and 21,3

strongly disagree) have tendency to agree with the statement that delivery plan as per

schedule.


Variable 19: Delivery plan as per schedule


SD 0 0 0

D 5 7 7

N 11 15.5 22.5

A 40 56.3 78.8

SA 15 21.2 100

Total 71 100

94

The distribution frequency of Variable 20 (The current delivery schedule plan has

already in corporated the peak seasons to fulfill the customer demand) is presented

in Table 4.37 above. From the total of 71 respondents, 7 respondents (9,8%) strongly

agree with the statement; 47 respondents (66,3%) agree with the statement; 11

respondents (15,5%) are neutral; 6 respondents (8,4%) disagree with the statement;

and the rest 0 respondent strongly disagree with the statement. From the percentage,

it can be summarized that most respondents (66,3 % agree and 9,8% strongly

disagree) have tendency to agree with the statement that the current delivery schedule

plan has already in corporated the peak seasons to fulfill the customer demand.


Variable 20: The current delivery schedule plan has already in

corporated the peak seasons to fulfill the customer demand


SD 0 0 0

D 6 8,4 8,4

N 11 15,5 23,9

A 47 66,3 80,2

SA 7 9,8 100

Total 71 100

95

4.2.5 Descriptive Statistics

Descriptive statistics show the mean and standard deviation on transportation,

distribution center area, inventory, and delivery efficiency according to respondent

responses. Weighted mean is the most widespreadway to find out which variable is

the most (and least) dominant from all variables based on the mean value. Standard

Deviation is a measure of how spreads out numbers are.The result is shown below in

Table 4.34.

Table 4.34 Descriptive Statistics

Mean Std Devision N

Transportation Total 4.28 3.46 71

DCA Total 2.96 2.53 71

Inventory Total 3.44 2.47 71

DCP Total 3.5 2.66 71

total 3.5 2.78

From Table 4.34, it can be noted that the most dominant impact of delivery

efficiency in this case study in Cikarang distributor center is transportation with the

mean value of 4.28, followed by inventory with the mean value of DCP Total.

Company always concern to improve the capacity and efficiency to delivery product

with improving the transportation.

The least dominant factor is the distribution center area with the mean value of 2.96.

Looking at the variables, it makes sense that why it is appears least dominant,

according to the Head of Cikarang distributor center of Nestle Indonesia ,the location

96

of distribution center has already planning before its build so just low impact from

that three variable there.

4.2.6 Classic Assumption Test

In order to use multiple regression models, classic assumption test need to implement

such as normality testing, heteroscedascity testing and multicollinearity.

4.2.6.1 Normality Test

Normality Test used to test the independent variable (X) and the dependent

variable(Y) on the resulting regression question, whether normally distributed or not

distributed normally. Normality Tests performed using the test chart. Histogram and

P-P plots to test the regression model residuals are shown in Figure 4.5 and 4.6

following.

Figure 4.5 Normality Test: Histogram

97

Figure 4.6 Normality Test: P-P Plot Graph

Graphs of normal probability p-p plot in figure 4.6 suggests that the data spread in

around the diagonal line and follow the direction of the diagonal line, then the

regression model meet the assumption of normality

4.2.6.2Heteroscedascity Testing

In a multiple regression equation, it is needed to be tested for the same or not the

variance of the residuals of the observations without herobservations.

If the residuals have the same variance, it is called homoscedascity. And if the

residuals have the difference variance, it is called heteroscedascity

(dawaisimfoni.wordpress.com, 2013).Multiple regressions equation is good if there is

noheteroscedasticity. Heteroscedasticity test generates chart patterns point spread

(scatterplot) as shown in Figure 4.7 below.

98

Figure 4.7 Heteroscedascity Test: Scatter Plot Graph

Heteroscedasticity test results on Figure 4.7 indicate that the points are not form a

certain pattern or no clear pattern and the points spread above and below the number

0 (zero) on the Y axis, then there is no heteroscedasticity.

4.2.6.3Multicollinearity test

Multicollinearity is the existence of such a high degree of correlation between

supposedly independent variable being used to estimate a dependent variable that

contribution of each independent variable to variation in the dependent variables in

the multiple regression.

Multicollinearity test has purpose to test whether the regression model found a

correlation between the independent variables. A good regression models should

have no correlation between independent variable. Since multicollinearity increases

the standard errors of the coefficients. increased standard errors in turn means that

coefficients for some independent variables may be found not to be significantly

99

different from 0, whereas without multicollinearrity and lower standard errors,

those same coefficients might have been found to be significant and researcher may

not have come to null findings in the first place. Multicollinarity is indicated for a

particural variable if the tolerance value is 0.01 or less and if the VIF greater than

10 as indicative of multicollinerity (Mayers, 2006).

Table 4.35 Multicollinearity Test: Tolerance and VIF Value

From the Table 4.35 Multicollinearity Test, there is no variable that have VIF value

more than 10 and no tolerance less than 0.1 indicating that there is nomulticollinerity

4.2.7 Multiple Regression Analysis

Multiple linear regression analysis was used in this research to determine whether

there is the influence of independent variables on the dependent variable.

Statistical calculations in a multiple linear regression analysis were used in SPSS.

Summary of results of data processing by using The SPSS program was as follows.

100

Table 4.36 Multiple Regression Analysis: Coefficients

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) -.717 3.431 -.209 .835

TransportationTotal .242 .109 .268 2.226 .031

DCATotal .404 .199 .239 2.033 .048

InventoryTotal .379 .130 .371 2.918 .005

a. Dependent Variable: DCPTotal

From the result in Table 4.36, if written in the standardized form of the equation, the

regression is as follows:

Y = -.717 + 0.242 X1 + 0.404 X2 + 0.379 X3

Where,

Y = Delivery Efficiency

X1 = Transportation

X2 = Distribution Center Area

X3 = Inventory

4.2.8 Hypotesting Testing

4.2.8.1 T-Test

T-test for the partial regression coefficient is intended to determine how far the

influence of one variable independent (transportation, distribution center area, and

inventory) individually in explaining the dependent variable (delivery efficiency).

101

Transportation

Ho1 There is no positive and significant relationship between transportation and


Ha1There is a positive and significant relationship between transportation and


The test using SPSS for the variable X1 (Transportation) obtained the t value = 2.226

with significance level of 0.031. By using the 0.05 limit, the significance value is

smaller than the level of 5%, the t value is > t table (1.972) with df = 199 which

means that It means that the variable already met the requirement of significant

influence variable.

Distribution Center Area

Ho2 There is no positive and significant relationship between distribution center area

and delivery efficiency.

Ha2 There is a positive and significant relationship between distribution center area

and delivery efficiency.

The test using SPSS for the variable X2 (Distribution Center area) obtained the t

value = 2.033 with significance level of 0.048. By using the 0.05 limit, the

significance value is smaller than the level of 5%, the t value is > t table (1.972) with

df = 199 which means that It means that the variable already met the requirement of

significant influence variable.

Inventory

Ho3 There is no positive and significant relationship between inventory and delivery

efficiency.

Ha3 There is a positive and significant relationship between inventory and delivery

efficiency.

102

The test using SPSS for the variable X3 (inventory) obtained the t value = 2.918 with

significance level of 0.005. By using the 0.05 limit, the significance value is smaller

than the level of 5%, the t value is > t table (1.972) with df = 199 which means that It

means that the variable already met the requirement of significant influence variable.

From the results of multiple linear regression and t-test in table 4.41 shows that all

regression coefficient is positive and significant. From the regression model above, it

can be further described as follows:

1. Variable transportation (X1) has a significant influence to Delivery

efficiency (Y) with a regression value 0.268 and t value= 2.226 with a

significance level of 0.031.

2. Variable Distribution Center Area (X2) has a significant influence to

delivery efficiency (Y) with a regression value 0.239 and t value=2.033

with a significance level of 0.048.

3. Variable inventory (X3) has a significant influence to delivery efficiency

(Y) with a regression value 0.371and t value=- 2.918 with a significance

level of 0.005.

4.2.8.2 F-Test

F-Test is also used to determine the influence of transportation, distribution center

area, inventory together on delivery efficiency. If F value > F table, Ho rejected and

Ha accepted. Oppositely, if F value < F table, then Ho accepted and Ha rejected. The

result of F-Test (ANOVA) is shown in the following Table 4.37.

103

Table 4.37 Multiple Regression Analysis: F-Test (ANOVA)

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 156.024 3 52.008 12.433 .000b

Residual 196.603 47 4.183

Total 352.627 50

a. Dependent Variable: DCPTotal

b. Predictors: (Constant), InventoryTotal, DCATotal, TransportationTotal

Hypothesis:

Ho4: There is no a simultaneous significant influence of Transportation,

distribution center area, inventory have positive and significant relationship with

delivery efficiency

Ha4: There is a simultaneous significant influence of Transportation, distribution

center area, inventory have positive and significant relationship with delivery

efficiency

The result of this F-test shows the F value = 12.433 with a significance level of

0.000. The F table value is found on the F table with df1 = 4 and df2 = 295, thus the

F table value is 2.3719. F value > F table (53.846 > 2.3719) and significance level of

0.000 means that there is a simultaneous of Transportation, distribution center area,

inventory have positive and significant relationship with delivery efficiency.

104

4.2.8.3 Coefficient Determination (R²)

The coefficient of determination (R²) was essentially measures how much the ability

of the model to explain the variations dependent variable. The coefficient of

determination is between zero and one. The coefficient of determination represented

in Table 4.38 below

Table 4.38 Multiple Regression Analysis: Coefficient Determination (R²):

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Durbin-Watson

1 .665a .442 .407 2.04525 1.192

a. Predictors: (Constant), InventoryTotal, DCATotal, TransportationTotal

b. Dependent Variable: DCPTotal

Results calculated using SPSS can be seen that the R square value of 0.442 is

obtained. The 44,2% means that delivery efficiency can be explained by the

variable Transportation, distribution center area, and inventory, while the rest is

55,8% of delivery efficiency is influenced by other variables which are not

examined in this research (Marketing and Channels restructuring, Source and

Supplier Management, Information and Electronic Media, Care and After-sales,

Logistics Turnover and Latest Issue, and Outsourcing and Alliance Strategy).

105

4.3 Interpretation of Results

The Reliability Test shows the value of Cronbach Alpha from every variable. Each

variable’s cronbach alpha values that greater than 0.468 means that the questionnaire

which is the indicators of these variables is reliable. This can be seen from the results

of the testing that has been done as follows: Transportation (X1) Cronbach Alpha

value of 0.784, Distribution Center Area (X2) Cronbach Alpha value of 0.816,

Inventory (X3) Cronbach Alpha value of 0.722, and Delivery Efficiency (Y) value of

0.743.

The Validity Test shows the r values from each variable are greater than 0.468 which

indicate moderate and high positive relationship with significance level less than

0.0468. The result of validity test can be seen as follows:

1. Transportation (X1)

Transportation r values determined with the indicators of: Transportation1 with

value of 0.804; Transportation2 with value of 0.897; Transportation3 with value

of 0.725; Transportation4 with value of 0.829.with value of 0.495:

Transportation6 with value of 0.671. All indicators show a positive relationship.

2. Distribution Center Area (X2)

Brand Awareness r values determined with the indicators of: DCA1 with value of

0.793; DCA2 with value of 0.782; DCA3 with value of 0.894; DCA4 with value

of 0.732. BAW1 and All indicators show high positive relationship.

3. Inventory (X3)

Brand Associations r values determined with the indicators of: Inventory1 with

value of 0.469; Inventory2 with value of 0.529; Inventory3 with value of 0.717;

BAS4 with value of 0.774; Inventory 5 with value 0.449. All indicators show

positive relationship.

106

4. Delivery efficiency (Y)

Purchase Decision r values determined with the indicators of: DCP1 with value

of 0.570; DCP2 with value of 0.743; DCP3 with value of 0.703; DCP4 with value

of 0.647; DCP5 with value of 0.516. All indicators show positive relationship.

From the result above, it can be concluded that each variables is valid.

The three independent variables have been tested individually through t-test. All

variables have significant relationship on delivery efficiency. The most dominant

variable that influences the delivery efficiency is Inventory with t value of 2.918

(>1.972) and significance value 0.005 (<0.05) as shown in Table 4.36.The next

variable that have significant role on delivery efficiency is transportation with its t

value of 2.226 and significance value 0.031. And Distribution Center area t-value is

2.033 (<1.972) with significance level of 0.048 (<0.05). So, from all variable have

significant relationship with delivery efficiency. According to the respondent

responses in table 4.34. The mean score for Transportation and inventory are 4.28

and 3.44 showing that transportation and inventory is the main concern of Cikarang

distribution center to increasing the efficiency of delivery efficiency of logistic

management. Compering from the previous research Vaidyanathan Jayaraman, (1998)

"Transportation, facility location and inventory issues in distribution network design: An

investigation" in the missisippi USA that all variable that is transportation, facilities location

and inventory have high mean and significant to distribution network.

The Impact of Transportation on Delivery Efficiency

Hypothesis 1 testing results shows that variable Transportation has a significant

effect on delivery efficiency through the result of the regression analysis with

significance level of 0.031 below 0.05.This means that company has already choose

the right transportation to delivery products efficiency. In this research, according to

staff of Nestle Cikarang distributor responses, they assess schedule of arrival and

departure timing of transportation has already achieved the target. This variable

107

transportation then become one factor that high affect to delivery efficiency as it is

shown in the table 4.34, the mean score of transportation is 4.28 and delivery

efficiency only 3.5. The assessment of question 1 to get that number of vehicles for

distribution is efficient. This is demonstrated by the number of vehicles that would

minimize the burden of efficient distribution made by the company. The assessment

of question 2 shows that the quality of the vehicle for distributing already meet the

standards. This is demonstrated by the quality of vehicles for distribution will result

in a faster distribution performance. Top rating question 3 regarding the capacity of

the vehicle to be revealed in good conditions. This is demonstrated by a good vehicle

capacity will shorten the time of distribution of the goods from the warehouse to the

outlet. The assessment of question 4 concerning schedule of arrival and departure

timing of the transportation has already achieve the target. This is demonstrated by

the target that company has already set, already achieved. Top 5 questions about the

assessment of condition of road has already considered when calculate lead time.

This is demonstrated by the condition of road already calculate to predict the lead

time of distribution. The assessment of question 6 route distribution of goods to the

outlet is in good condition. This is demonstrated by setting a good and proper routes

will speed up the process and time distribution of goods to the outlet from the

warehouse distributor.

The Impact of Distribution Center area on Delivery Efficiency

Hypothesis 2 testing results shows that variable distribution center area has significant

effect on delivery efficiency. It is proven from the results of the regression analysis

showed significant level of 0.048 which is over the maximum limit error tolerance,

0.05 or 5%. Thus H3 is supported by data.

The location of distribution center, infrastructure, placement of distribution center

can reduce cost, and access of distribution center easy to in and out and it is proven

by 2.96 of distribution center area mean score of respondent responses.

108

Although the distribution center area got the low mean score of delivery efficiency,

don’t underestimate because location of distribution centers that will facilitate the

strategic goals and ways of delivering the products to the consumer, infrastructure

that support would also be useful to increase the efficiency of distribution of goods as

well as the strategic location also can reduce cost of transportation.

The Impact of Inventory on Delivery efficiency

Hypothesis 3 testing results shows that variable inventory has a significant effect on

delivery efficiency through the result of the regression analysis with significance

level of 0.005 below 0.05. This means that inventory that cikarang distribution do has

good strategy. In this study, the staff in cikarang distribution center are refused that

in cikarang distributor use the FIFO system but use FEFO (First Expaired First Out)

because as the food company nestle must underline products that have the little time

to consume. Researcher think it is right choice cause after the product has expaired it

can be dangerous and not have profitability to company.

From question 1-5 of inventory, it can be found that most have the right strategy to

inventory that keep in the distribution center like the stock accuracy during the stock

taking is high cause in the peak season the customer demand is high so company

have to consider it to fulfill the demand and the allocation of product storage is clear

so make easy to staff that this area is the place of koko crunch for example, the

delivery efficiency can increase if the inventory is clear.

Then from the results of the F test showed that the effect together of all the

independent variables (Transportation, Distribution Center area, and Inventory) on

delivery efficiency of logistic management showed significant results. It is evident

from the magnitude F value of 12.533 with a significance level of 0.000 (less than

0.05).

109

Meanwhile, from the calculation of the coefficient of determination (R²), it can

conclude that the independent variables in this study were able to explain 44,2% of

the delivery efficiency of logistic management while the remaining 55,8% is

explained by other variables not included in this study.

110

Chapter V

CONCLUSIONS AND RECOMMENDATIONS

In this final chapter of the research, the researcher draws the conclusion and

recommendation developed from the wholly integrated quantitative analysis,

specifically the multiple regression analysis, about the analysis of impact delivery

efficiency of logistic management in PT. Nestle Indonesia a case study of distribution

center,Cikarang. The analysis is conducted to discover the specifically impact of

transportation, distribution center area, and inventory on delivery efficiency.

5.1 Conclusions

Based on the research about the impact transportation, distribution center area, and

inventory on delivery efficiency of logistic management in PT. Nestle Indonesia a

case study of distribution center, Cikarang, the conclusions are obtained as follows:

From the results of multiple linear regression and t-test in table 4.36 shows that two

of the regression coefficient is significant and two of the regression coefficient is not

significant. The regression model that can be further described as follows:

1. The results of respondents responses in table 4.34 shows that the variable

transportation (X1) has means score amounted to 4.29 which is high

scores. , it show the big influence on delivery efficiency where it mean

scores only 3.5. High influence of transportation affect to delivery

efficiency. Transportation multiple linear regression coefficient result was

0.268 where the Variable Transportation (X1) has positive and significant

impact on Delivery efficiency (Y) with t-value of 2.226 and significance

level of 0.031.

2. The results of the respondents responses in table 4.34 shows that the

variable Distribution Center area (X2) has means score amounted to 2.94

111

which is below scores. Distribution center area mean score is not enough

and not have influence on purchase decision where it mean scores 3.5.

Low distribution center area affect to the low delivery efficiency.

Distribution center area multiple linear regression coefficient result was

0.239 where the Variable Distribution Center area (X2) has positive and

significant impact on Delivery efficiency (Y) with t-value of 2.033 and

significance level of 0.048.


variable Inventory (X3) has means score amounted to 3.44 which is

second high scores after transportation. The result of inventory mean

score is above the distribution center performance, it show the influence

on delivery efficiency where it mean scores 3.5. Inventory multiple linear

regression coefficient result was 0.371 where the Variable inventory (X3)

has positive and significant impact on Delivery efficiency (Y) with t-

value of 2.918 and significance level of 0.005.


Distribution center performance (Y) has means score amounted to 3.5

which is result of distribution center performance mean score is same as

total, it show the influence on delivery efficiency where it mean scores

3.5.

5.2 Recommendations

Based on the conclusions obtained in this study, the recommendations proposed as a

complement to the results of the study as follows:

5.2.1. For Nestle Indonesia

1. In the peak season where demand from consumers is high such as lebaran,

Christmas and new year. The company had to prepare in the stock accuracy

product so that consumers do not experience shortages of stock during the

112

peak season. Because the efficiency of delivery product has achieve when the

demand is high and company can fulfill.

2. The good products will come in customer hand without any damage so the

demantion of products damage have to press in the delivery of products

because it can reduce the cost and increasing the customers trust.

3. Clear for products storage will also ease and speed up in the distribution

process then the allocation goods more efficient, and would be very helpful so

staff will not get confused where this product is storage.

4. with the increasing number of transport models in the business of distribution

of goods will be more able to suppress cost of transportation and time taken

in delivery products, there is a choice of distribution via rail can be one

alternative in the distribution of goods because of the density of the road with

the high number of vehicle that is not in balance in the growth of the road

then the company must always rotate the brain in order to squeeze time and

cost.

5. The long distances in the delivery of the products is also one of the obstacles

with only the 4 distribution centers throughout Indonesia but already efficient

in delivering the goods to the consumer, then the company should maintain

the existing logistics distribution conditions. Good conditions of distribution

logistics will increase consumer confidence against Nestle Indonesia

5.2.2 For Future Research

1. For future research, it is needed doing a further research in other factors

besides Transportation, Distribution Center area, and Inventory affecting the

Delivery efficiency of logistic management. This is because the three

independent variables in this study were able to explain 44,2% of the delivery

efficiency of logistic management 55,8% is explained by other variables not

included in this study such as Marketing and Channels restructuring, Source

and Supplier Management Information Electronic Media, Care and After-

113

sales, Logistics Turnover and Latest Issue, Outsourcing and Alliance

Strategy.

2. Future studies are advised to examine the other company with take another

example of the influence of also different Transportation, Distribution Center

area, and Inventory affecting the Delivery efficiency of logistic management,

so the variables that influence delivery efficiency also different. It can be used

as a comparison and complements in this research.

3. For future studies it is advisable to look for another different populations and

the wider population this study. The sample used should also be much more

than the sample in this study, thus further research can further provide a more

specific on the effect Transportation, Distribution Center area, and Inventory

affecting the Delivery efficiency of logistic management.

114

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117

APPENDIX A

QUESTIONNAIRE

RESEARCH QUESTIONNAIRE

ANALYSIS THAT INFLUENCE DELIVERY EFFICIENCY OF

LOGISTIC MANAGEMENT

(A Case Study of PT. Nestle Indonesia Distribution Center,

Cikarang)

Dear Respondents,

I would like to ask your valuable time to participate on this research questioner .This

questioner is part of my thesis related delivery efficiency which taken part on Cikarang

Distribution Center.

Your perception will give contribution to the efficiency of logistic management in the

future. So we hope to fill this questioner honestly, objectively, and it is very significant

for this study. All data from this questioner will be sticky use for my education purpose

only and will be treat as confidential.

.

Jakarta, December 2104

Sincerely,

Faisal Faturrahman

118

Personal Data:

1. Your current age:

(1) Between 20 years to 30 years.

(2) Above 30 years to 40 years.

(3) In the 40 years up to 50 years.

(4) Above 50 years

2. Last Education:

(1) High School

(2) Diploma

(3) S1

(4) S2

(5) S3

3. How long have you been working in the field of Distribution:

(1) 1 year to 2 years

(2) Up to 2 years to 8 years

(3) Above 8 years to 12 years

(4) Above 12 years to 15 years

(5) Above 15 years

4. How long you've been working in this company:

(1) Less than 5 years.




119

Transportation SD D N A SA

1 Number of vehicles has been met the

requirement products to distributors

2 Quality of vehicles in accordance with the

standard requirements that company assigned

3 Vehicle capacity sufficient to maximize

delivery of products

4 Schedule and timing arrival and departure

transportation already achieve target

5 Condition of road already support the lead time

for supply products.

6 Selection of transport was able to save costs

and time for Supply products

Distribution Center area SD D N A SA

7 The distribution center is located in a strategic

place (So easily to distribute the products).

8 Infrastructure at distribution center has

sufficient to supply the products

9 Placement of distribution centers can reduce


10 Distribution center has many access to in and

out

120

Inventory SD D N A SA

11 Size of distribution center is sufficient for the

products loading and unloading

12 Setting products use FIFO system

13 Allocation of each products storage is clear

14 Stock accuracy during the stock taking is high

15 The current system used is easy to track the

products to be distributed

Dimensions of Performance

Distribution Logistics

SD D N A SA

16 Damaged product showed a declining trend

17 Product availability has already met the

customer demand to fulfill order

18 Lead time accuracy of products delivery

shows increasing trend

19 Delivery plan as per schedule

20 The current delivery schedule plan has already

in corporated the peak seasons to fulfill the

customer order

121

APPENDIX B

RAW DATA MTERIAL

Transportation and Distribution center area

Tran

sport

ation

1

Tran

sport

ation

2

Tran

sport

ation

3

Tran

sport

ation

4

Tra

nspo

tatio

n5

Tran

sport

ation

6

Trans

portat

ionTo

tal

D

C

A

1

D

C

A

2

D

C

A

3

D

C

A

4

D

C

AT

ota

l

3 4 4 4 3 4 22 4 4 5 5 18

4 5 5 4 3 4 25 5 4 3 4 16

2 3 4 4 2 4 19 3 4 4 4 15

2 2 2 2 4 3 15 4 4 4 4 16

2 1 3 2 3 2 13 3 2 2 1 8

4 5 4 4 4 4 25 4 4 4 4 16

4 3 4 2 3 4 20 4 4 4 4 16

4 5 4 4 4 4 25 5 4 5 4 18

4 5 4 4 4 4 25 4 4 4 4 16

4 4 4 4 4 4 24 4 4 4 4 16

2 4 4 3 3 3 19 4 4 3 4 15

122

2 2 4 2 2 2 14 2 4 2 4 12

4 4 4 3 4 3 22 3 3 4 3 13

4 4 5 4 4 4 25 4 4 4 4 16

4 4 4 3 3 4 22 4 4 4 3 15

4 2 4 3 4 2 19 4 2 3 4 13

4 2 4 3 4 2 19 4 2 3 4 13

1 3 2 2 3 4 15 1 2 1 3 7

4 5 5 4 4 4 26 4 4 4 4 16

4 4 4 5 2 4 23 3 4 3 4 14

4 4 4 5 5 4 26 3 4 3 4 14

4 4 4 4 5 5 26 3 3 4 4 14

4 5 2 4 4 2 21 4 4 4 4 16

4 2 4 4 2 4 20 2 4 5 5 16

4 2 4 3 5 4 22 3 4 5 5 17

3 2 4 4 4 5 22 4 5 5 4 18

4 2 5 2 5 3 21 4 5 4 5 18

4 4 3 4 4 4 23 5 5 5 5 20

123

5 2 3 4 3 4 21 5 5 4 4 18

4 4 5 4 4 4 25 5 3 3 4 15

3 4 3 4 4 2 20 4 5 2 3 14

4 4 2 3 4 2 19 4 5 4 3 16

2 2 4 4 4 3 19 5 5 3 4 17

5 4 4 4 3 4 24 3 3 3 2 11

5 4 4 4 4 5 26 3 3 2 5 13

4 4 4 5 4 4 25 5 4 4 4 17

4 3 5 4 4 5 25 4 5 4 4 17

4 5 4 3 4 5 25 4 5 2 5 16

2 3 3 4 5 4 21 5 3 4 4 16

3 2 4 5 3 4 21 3 4 5 5 17

3 2 4 5 3 4 21 3 2 5 5 15

3 2 4 3 3 3 18 4 2 3 4 13

2 1 3 2 3 2 13 3 2 2 1 8

4 2 4 3 4 2 19 4 2 3 4 13

4 5 5 4 4 4 26 4 4 4 4 16

124

4 4 4 4 5 5 26 3 3 4 4 14

4 4 4 5 5 4 26 3 4 3 4 14

4 4 2 4 3 4 21 3 4 3 4 14

4 5 5 4 4 4 26 4 4 4 4 16

1 3 2 2 3 4 15 1 2 1 3 7

4 4 5 2 1 4 20 3 1 4 4 12

4 2 4 3 4 2 19 4 2 3 4 13

4 5 4 2 5 4 24 4 3 3 4 14

4 4 4 3 3 4 22 4 3 4 3 14

4 4 5 3 4 3 23 4 4 3 4 15

4 4 4 4 3 3 22 3 4 3 3 13

2 2 4 4 2 2 16 2 4 2 4 12

2 4 4 4 3 3 20 4 4 3 4 15

4 4 4 2 4 4 22 4 4 4 4 16

4 5 4 3 4 4 24 4 4 4 4 16

4 5 4 3 4 4 24 4 5 4 5 18

4 3 4 2 3 4 20 4 4 4 4 16

125

2 2 2 2 4 3 15 4 4 4 4 16

2 4 4 4 2 4 20 3 4 4 4 15

2 4 5 1 3 4 19 5 4 3 4 16

3 4 4 1 3 4 19 4 4 5 5 18

4 5 4 3 4 4 24 4 4 4 4 16

4 4 3 3 4 4 22 2 3 4 4 13

4 4 4 1 2 3 18 4 5 3 4 16

4 5 4 3 4 5 25 3 4 5 5 17

3 4 5 4 4 4 24 4 5 4 4 17

Inventory and Distribution Center Performance

Inve

ntory

1

Inve

ntory

2

Inve

ntory

3

Inve

ntory

4

Inve

ntory

5

Invento

ryTotal

D

C

P1

D

C

P2

D

C

P3

D

C

P4

D

C

P5

3 5 3 4 3 18 3 4 3 3 3

5 4 3 4 3 19 3 4 4 4 4

4 2 3 1 4 14 4 4 3 4 4

126

4 1 5 2 5 17 1 4 3 4 4

2 1 2 1 2 8 2 2 1 2 2

4 4 4 4 4 20 4 4 4 4 4

4 1 4 4 4 17 3 3 3 3 4

4 5 5 4 4 22 4 4 4 5 4

4 1 5 4 4 18 4 3 3 3 4

4 2 4 4 4 18 3 3 3 3 3

4 4 4 3 4 19 3 4 4 3 4

4 2 2 1 4 13 4 2 1 4 4

3 2 3 2 4 14 3 3 3 4 4

4 4 4 4 4 20 4 4 4 4 4

4 4 4 4 4 20 4 4 3 4 4

4 4 4 1 3 16 1 3 3 4 4

4 4 4 1 3 16 1 3 3 4 4

4 5 3 1 2 15 5 4 1 5 2

3 3 3 3 4 16 4 4 3 4 4

2 4 4 4 4 18 4 3 4 5 3

127

2 4 4 4 4 18 4 3 2 5 3

4 4 3 4 4 19 3 4 4 4 4

3 3 3 3 4 16 3 4 3 3 4

2 2 4 4 4 16 4 4 4 4 4

5 3 3 3 4 18 2 4 4 4 4

3 5 4 3 5 20 4 4 3 5 5

5 5 4 4 5 23 4 5 4 4 4

3 2 4 4 5 18 4 4 5 4 4

4 4 3 4 3 18 4 4 4 4 4

2 2 4 4 4 16 5 5 3 4 4

4 4 5 4 5 22 5 4 4 4 4

5 3 4 4 4 20 4 3 4 5 4

4 4 5 3 2 18 4 4 5 2 5

4 3 3 3 4 17 2 4 4 4 4

3 3 5 5 2 18 5 3 4 5 3

3 3 2 3 4 15 5 2 5 4 4

4 3 4 3 4 18 4 4 3 5 5

128

3 4 2 3 4 16 3 4 2 4 4

5 5 4 4 3 21 2 3 4 5 5

5 2 2 4 4 17 5 3 2 4 4

5 4 4 3 3 19 3 3 2 3 4

3 2 4 4 4 17 3 3 2 3 4

2 1 2 1 2 8 2 2 1 2 2

4 4 4 1 3 16 1 3 3 4 4

3 3 4 3 3 16 4 3 3 4 4

4 4 3 4 4 19 3 4 4 4 4

3 4 4 4 4 19 4 3 4 5 3

3 4 4 2 4 17 4 3 4 5 3

3 3 3 3 4 16 3 4 3 5 2

4 5 1 1 2 13 5 4 1 5 2

4 2 4 5 3 18 4 3 4 5 3

4 4 4 1 3 16 1 3 1 2 2

3 4 4 3 5 19 2 4 3 2 3

3 4 3 3 3 16 2 2 4 4 3

129

4 4 4 1 2 15 3 4 4 4 4

2 3 2 4 3 14 3 3 4 4 4

4 2 2 1 4 13 4 2 1 4 4

4 4 4 2 4 18 3 3 4 4 5

4 4 4 1 3 16 1 4 4 3 3

4 4 2 3 3 16 1 3 3 4 4

4 4 5 2 4 19 1 5 4 5 4

4 1 4 4 4 17 1 3 3 4 5

4 1 5 2 5 17 1 4 3 4 4

4 2 3 1 5 15 2 4 3 4 4

5 4 3 4 4 20 2 4 4 4 4

3 5 3 4 3 18 4 3 3 3 4

4 4 4 4 3 19 3 4 4 4 4

4 5 4 4 3 20 4 4 3 3 4

3 4 4 1 2 14 3 2 3 4 5

4 5 5 4 3 21 4 4 3 4 4

4 5 5 4 4 22 5 4 4 4 4

130

APPENDIX C

N α = 0.05 α = 0.01

4 0.950 0.999

5 0.878 0.959

6 0.811 0.917

7 0.754 0.875

8 0.707 0.834

9 0.666 0.798

10 0.632 0.765

11 0.602 0.735

12 0.576 0.708

13 0.553 0.684

14 0.532 0.661

15 0.514 0.641

16 0.497 0.623

17 0.482 0.606

18 0.468 0.590

19 0.456 0.575

20 0.444 0.561

25 0.396 0.505

30 0.361 0.463

35 0.335 0.430

40 0.312 0.402

131

45 0.294 0.378

50 0.279 0.361

60 0.254 0.330

70 0.236 0.305

80 0.220 0.286

90 0.207 0.269

100 0.196 0.256

ANALYSIS OF TRANSPORTATION, DISTRIBUTION CENTER AREA …

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