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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
Table 4.14: Variable 2 Description ························································· 78
Table 4.15: Variable 3 Description ························································· 79
Table 4.16: Variable 4 Description ························································· 79
Table 4.17: Variable 5 Description ························································· 80
Table 4.18: Variable 6 Description ························································· 81
Table 4.19: Variable 7 Description ························································· 81
xi
Table 4.20: Variable 8 Description ························································· 82
Table 4.21: Variable 9 Description ························································· 83
Table 4.22: Variable 10 Description ······················································· 84
Table 4.23: Variable 11 Description ······················································· 84
Table 4.24: Variable 12 Description ······················································· 85
Table 4.25: Variable 13 Description ······················································· 85
Table 4.26: Variable 14 Description ······················································· 86
Table 4.27: Variable 15 Description ······················································· 87
Table 4.28: Variable 16 Description ······················································· 88
Table 4.29: Variable 17 Description ······················································· 89
Table 4.30: Variable 18 Description ······················································· 90
Table 4.31: Variable 19 Description ······················································· 90
Table 4.32: Variable 20 Description ······················································· 91
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
Source: Nestle Distribution Center Cikarang performance 2014
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
Source: Nestle Distribution Center Cikarang performance 2014
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
Source: Nestle Distribution Center Cikarang performance 2014
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
Source: Nestle Distribution Center Cikarang performance 2014
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
interdependence between
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
interdependence between
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
management toward delivery efficiency
3. Partial significant influence of inventory towards delivery efficiency
Ho3: There is no partial significant influence inventory of logistic
management toward delivery efficiency
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
efficiency of logistic management in PT. Nestle Indonesia Distribution Center,
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
efficiency of logistic management in PT. Nestle Indonesia Distribution Center,
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)
Reliability Statistics
Cronbach's
Alpha
N of Items
.784 7
60
Table 4.4: Cronbach’s Alpha of Inventory
Reliability Statistics
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.
Reliability Statistics
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
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
Transportation
Total
Transportation2
Transportation Total
Pearson Correlation 1 .897**
Sig. (2-tailed) .000
N 20 20
Transportation2
Pearson Correlation .897** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
Transportation
Total
Transportation3
TransportationTotal
Pearson Correlation 1 .725**
Sig. (2-tailed) .000
N 20 20
Transportation3
Pearson Correlation .725** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
62
Correlations
Transportation
Total
Transportation4
Transportation Total
Pearson Correlation 1 .829**
Sig. (2-tailed) .000
N 20 20
Transportation4
Pearson Correlation .829** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
Transportation
Total
Transpotation5
Transportation Total
Pearson Correlation 1 .495
Sig. (2-tailed) .085
N 20 20
Transpotation5
Pearson Correlation .395 1
Sig. (2-tailed) .085
N 20 20
Correlations
Transportation
Total
Transportation6
TransportationTotal
Pearson Correlation 1 .671**
Sig. (2-tailed) .001
N 20 20
Transportation6
Pearson Correlation .671** 1
Sig. (2-tailed) .001
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
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
Pearson Correlation 1 .793**
Sig. (2-tailed) .000
N 20 20
DCATotal
Pearson Correlation .793** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
DCATotal DCA2
DCATotal
Pearson Correlation 1 .782**
Sig. (2-tailed) .000
N 20 20
DCA2 Pearson Correlation .782** 1
Sig. (2-tailed) .000
64
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
DCATotal DCA3
DCATotal
Pearson Correlation 1 .894**
Sig. (2-tailed) .000
N 20 20
DCA3
Pearson Correlation .894** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
DCATotal DCA4
DCATotal
Pearson Correlation 1 .732**
Sig. (2-tailed) .000
N 20 20
DCA4
Pearson Correlation .732** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
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*
Sig. (2-tailed) .037
N 20 20
InventoryTotal
Pearson Correlation .469* 1
Sig. (2-tailed) .037
N 20 20
*. Correlation is significant at the 0.05 level (2-tailed).
Correlations
InventoryTotal Inventory2
InventoryTotal
Pearson Correlation 1 .526*
Sig. (2-tailed) .017
N 20 20
Inventory2
Pearson Correlation .526* 1
Sig. (2-tailed) .017
N 20 20
*. Correlation is significant at the 0.05 level (2-tailed).
Correlations
InventoryTotal Inventory3
InventoryTotal
Pearson Correlation 1 .717**
Sig. (2-tailed) .000
N 20 20
Inventory3
Pearson Correlation .717** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
66
Correlations
InventoryTotal Inventory4
InventoryTotal
Pearson Correlation 1 .774**
Sig. (2-tailed) .000
N 20 20
Inventory4
Pearson Correlation .774** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
InventoryTotal Inventory5
InventoryTotal
Pearson Correlation 1 .499*
Sig. (2-tailed) .042
N 20 20
Inventory5
Pearson Correlation .499* 1
Sig. (2-tailed) .042
N 20 20
*. Correlation is significant at the 0.05 level (2-tailed).
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
Pearson Correlation 1 .570**
Sig. (2-tailed) .009
N 20 20
DCPTotal
Pearson Correlation .570** 1
Sig. (2-tailed) .009
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
DCPTotal DCP2
DCPTotal
Pearson Correlation 1 .743**
Sig. (2-tailed) .000
N 20 20
DCP2
Pearson Correlation .743** 1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
DCPTotal DCP3
DCPTotal
Pearson Correlation 1 .703**
Sig. (2-tailed) .001
N 20 20
DCP3
Pearson Correlation .703** 1
Sig. (2-tailed) .001
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
68
DCPTotal DCP4
DCPTotal
Pearson Correlation 1 .647**
Sig. (2-tailed) .002
N 20 20
DCP4
Pearson Correlation .647** 1
Sig. (2-tailed) .002
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
DCPTotal DCP5
DCPTotal
Pearson Correlation 1 .516*
Sig. (2-tailed) .020
N 20 20
DCP5
Pearson Correlation .516* 1
Sig. (2-tailed) .020
N 20 20
*. Correlation is significant at the 0.05 level (2-tailed).
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
Transportation2 .897 .468 Valid
Transportation3 .725 .468 Valid
Transportation4 .829 .468 Valid
Transportation5 .495 .468 Valid
Transportation6 .671 .468 Valid
DCA1 .793 .468 Valid
DCA2 .782 .468 Valid
DCA3 .894 .468 Valid
DCA4 .732 .468 Valid
Inventory1 .469 .468 Valid
Inventory2 .526 .468 Valid
Inventory3 .717 .468 Valid
Inventory4 .774 .468 Valid
Inventory5 .499 .468 Valid
DCP1 .570 .468 Valid
DCP2 .743 .468 Valid
70
DCP3 .703 .468 Valid
DCP4 .647 .468 Valid
DCP5 .516 .468 Valid
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.
Table 4.15: Variable 2 Description
Variable 2: Quality of vehicle in accordance with standard
requirement that company assigned
Scale Respondents Percent Cumulative Percent
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
respondents (49,2%) agree with the statement; 17 respondents (23,9%) are neutral; 6
respondents (8,4%) 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 (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.
Table 4.16: Variable 3 Description
Variable 3: Vehicles capacity has sufficient to maximize supply of the
products
Scale Respondents Percent Cumulative Percent
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.
Table 4.17: Variable 4 Description
Variable 4: Schedule of arrival and departure timing of the
transportation has already achieved the target
Scale Respondents Percent Cumulative Percent
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 (50,7%) agree with the statement; 21 respondents (29,6%) are neutral; 5
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
Scale Respondents Percent Cumulative Percent
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
Table 4.18: Variable 5 Description
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 (12,7%) disagree with the statement; and the rest 0 respondent strongly
disagree with the statement. From the percentage, it can be summarized that most
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.
Table 4.19: Variable 6 Description
Variable 6 : Selection modal transportation was able to save cost and
time to supply products
Scale Respondents Percent Cumulative Percent
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).
Table 4.20: Variable 7 Description
Variable 7 : The distribution center is located in a strategic place
(easy to distribute the products)
Scale Respondents Percent Cumulative Percent
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
71 respondents, 16 respondents (22,5%) strongly agree with the statement; 42
respondents (59,1%) agree with the statement; 6 respondents (8,6%) are neutral; 7
respondents (9,8%) disagree with the statement; and the rest 0 respondent strongly
disagree with the statement. From the percentage, it can be summarized that most
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.
Table 4.21: Variable 8 Description
Variable 8 : Infrastructure at distribution center is sufficient to supply
the products
Scale Respondents Percent Cumulative Percent
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
71 respondents, 15 respondents (21,1%) strongly agree with the statement; 36
respondents (50,7%) agree with the statement; 16 respondents (22,5%) are neutral; 2
respondents (2,8%) disagree with the statement; and the rest 2 respondent strongly
disagree with the statement. From the percentage, it can be summarized that most
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.
Table 4.22: Variable 9 Description
Variable 9 : Placement of distribution center can reduce the
transportation costs
Scale Respondents Percent Cumulative Percent
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.
Table 4.23: Variable 10 Description
Variable 10: Distribution Center has many access to in and out
Scale Respondents Percent Cumulative Percent
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 (52,1%) agree with the statement; 13 respondents (18,3%) are neutral; 7
respondents (9,8%) disagree with the statement; and the rest 6 respondent (8,4)
strongly disagree with the statement. From the percentage, it can be summarized that
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.
Table 4.24: Variable 11 Description
Variable 11: Size of distribution center is sufficient for the products
loading and unloading
Scale Respondents Percent Cumulative Percent
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
Scale Respondents Percent Cumulative Percent
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
Table 4.25: Variable 12 Description
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
respondents (14,2%) strongly agree with the statement; 34 respondents (47,8%)
agree with the statement; 17 respondents (23,9%) are neutral; 9 respondents (12,7%)
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.
Table 4.26: Variable 13 Description
Variable 13: Allocation of each products storage is clear
Scale Respondents Percent Cumulative Percent
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.
Table 4.27: Variable 14 Description
Variable 14:Stock accuracy during the stock taking is high
Scale Respondents Percent Cumulative Percent
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 with the statement; 19 respondents (26,7%) are neutral; 8
respondents (11,3%) disagree with the statement; and the rest 0 respondent strongly
disagree with the statement. From the percentage, it can be summarized that most
respondents (50,3 % 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.
Table 4.28: Variable 15 Description
Variable 15:The current system used is easy to track the products to
distributed
Scale Respondents Percent Cumulative Percent
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
respondents (11,26%) strongly agree with the statement; 26 respondents (36,6%)
agree with the statement; 18 respondents (25,3%) are neutral; 9 respondents (12,7%)
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.
Table 4.29: Variable 16 Description
Variable 16: Damaged products showed a declining trend
Scale Respondents Percent Cumulative Percent
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
71 respondents, 3 respondents (4,2%) strongly agree with the statement; 36
respondents (50,2%) agree with the statement; 25 respondents (35,2%) are neutral; 9
respondents (12,7%) 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 (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.
Table 4.30: Variable 17 Description
Variable 17: Product availability has already met the customer
demand to fulfill order
Scale Respondents Percent Cumulative Percent
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
disagree with the statement. From the percentage, it can be summarized that most
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.
Table 4.31: Variable 18 Description
Variable 18: Lead time accuracy of product delivery shows increasing
trend
Scale Respondents Percent Cumulative Percent
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
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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.
Table 4.32: Variable 19 Description
Variable 19: Delivery plan as per schedule
Scale Respondents Percent Cumulative Percent
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
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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.
Table 4.33: Variable 20 Description
Variable 20: The current delivery schedule plan has already in
corporated the peak seasons to fulfill the customer demand
Scale Respondents Percent Cumulative Percent
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
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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
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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
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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.
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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
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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.
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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).
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Transportation
Ho1 There is no positive and significant relationship between transportation and
delivery efficiency.
Ha1There is a positive and significant relationship between transportation and
delivery efficiency.
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.
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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.
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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.
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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).
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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.
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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
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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.
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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).
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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.
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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
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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.
3. The results of the respondents responses in table 4.34 shows that the
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.
4. The results of the respondents responses in table 4.34 shows that the
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
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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-
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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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Indonesia.
Tersine, Richard J., 1994, Principle of Inventory and Materials Management, Prentice
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Whidya Utami, Christina. 2006. Manajemen Ritel. Salemba Empat, Jakarta
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.
(2) Above 5 years to 10 years.
(3) Above 10 years to 15 years.
(4) Above 15 years to 20 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
transportation costs
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