Factors Influencing Supply Chain Responsiveness in Fast ...

Factors Influencing Supply Chain Responsiveness in Fast Moving Consumer Goods Industry: Case Study of PT. Unilever

Indonesia, Tbk

By:

Karina Dwi Andarin

014201300087

A skripsi presented to the Faculty of Economics President University

In partial fulfillment of the requirements for Bachelor Degree in Economics Major in Management

January 2017

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PANEL OF EXAMINERS

APPROVAL SHEET

The Panel of Examiners declares that the thesis entitled “Factors

Influencing Supply Chain Responsiveness in Fast Moving

Consumer Goods Industry: Case study of PT. Unilever Indonesia,

Tbk” that was submitted by Karina Dwi Andarin majoring in

Management from the Faculty of Economics was assessed and

approved to have passed the Oral Examinations on February 9, 2017.

Ir. Erny Hutabarat, M. BA

Chair – Panel of Examiners

Siska Purnama Manurung, S.Kom, M.M

Examiner 1

Maria Jacinta Arquisola, PhD, MHRM

Examiner 2

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SKRIPSI ADVISER

RECOMMENDATION LETTER

This skripsi entitled “Factors Influencing Supply Chain Responsiveness in

Fast Moving Consumer Goods Industry: Case Study of PT. Unilever

Indonesia, Tbk” prepared and submitted by Karina Dwi Andarin in partial

fulfillment of the requirements for the degree of bachelor in the Faculty of

Economics has been reviewed and found to have satisfied the requirements for a

thesis fit to be examined. I therefore recommend this thesis for Oral Defense.

Cikarang, January 27, 2017

Acknowledged by, Recommended by,

Dr. Dra. Genoveva, M.M. Maria Jacinta Arquisola, PhD, MHRM

Head of Management Study Program Thesis Adviser

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DECLARATION OF ORIGINALITY

I declare that this thesis, entitled “Factors Influencing Supply

Chain Responsiveness in Fast Moving Consumer Goods

Industry: Case Study of PT. Unilever Indonesia, Tbk.” is

to the best of my knowledge and belief, an original piece of work that

has not been submitted, either in whole or in part, to another

university to obtain a degree.

Cikarang, January 27, 2017

Karina Dwi Andarin

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ABSTRACT

Prediction shows that Indonesia will become a very potential market for global Fast Moving Consumer Goods (FMCG) industry to enter, however this will create a competition between local and multinational player. FMCG Industry is known with its large and various productions, which the product is perishable and obsolete. Many previous studies consider supply chain responsiveness is a key success of FMCG industry, therefore it lead the researcher to analysis factors influencing supply chain responsiveness on Fast Moving Consumer Goods Industry. PT. Unilever Indonesia, Tbk will be the perfect case of this study. Quantitative method is used in this research by distributing questionnaires to 60 respondents and additional 20 respondents for the pre-test using purposive sampling method. The result using multiple regression analysis found that only two independent variables, which are agile supply chain strategy and strategic supplier partnership has significant influence towards Unilever’s supply chain responsiveness. While the other two, which are lean supply chain strategy and postponement does not have any influence towards Unilever’s supply chain responsiveness. Keywords: Supply Chain Responsiveness, Lean Supply Chain Strategy, Agile Supply Chain Strategy, Strategic Supplier Partnership, Postponement, Fast Moving Consumer Goods Industry, PT. Unilever Indonesia, Tbk.

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ACKNOWLEDGEMENT

First of all, I am grateful to the Almighty Allah SWT, because only through

His grace and power I can finish this research wholeheartedly. Through this

opportunity, I also would like to send my gratitude to:

1. My beloved parents, brother and families, the one who motivate me, love

me and believe in me more than anyone else throughout all these years.

2. My best research advisor, Ms. Maria Jacinta Arquisola. Thank you for

your time, your guidance, and kindness to help me finish this research in

any condition. Thank you to my other lecturers, Mr. Anaconda, Mr.

Purwanto, Mr. Orlando and Ms. Siska for your sincere help during this

research.

3. SC Finance Line Manager of Unilever Indonesia, Bu Enny, my

supervisor, Pak Joko, HR Department, Pak Hidayat, thank you to Mba

Mia, and also my colleagues in Unilever who help me during the process,

Mba Anita, Mba Fitri, Mba Nita, Mba Tika, Mba Lia, Mas Denny, Mas

Hari, Mas Miko, Mas Angga and all respondents in Unilever MBAU and

savory factory in Cikarang, thank you for your time to fill the

questionnaire.

4. All of my beloved best friend who motivate me and help me to finish this

study, Azzahra Janet Thurai, Yenny Rahmawati, Putri Asri Azizah,

Andira Kintani Rachman, Byanka Firdaus, Kezia Shella Adelia, Merry

Susanti, Utami Nurima, Cynthia Elizabeth, Wirda Nasution, Kristian

Alfredo, Enrico Dimas Darmawan, Rido Bagas Julianto, Luqman Gandy,

Alief Ramdhani, Rita Amrina, Mega Rizky, Aci Widyaningtyas, Lois

Christina, Hanif Keanugraha, RP, Aseng Vidani, and my other friends

from Management and other major, Zahirul Ma’ala, and all of my friends

that I can not mention one by one.

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TABLE OF CONTENTS

PANEL OF EXAMINERS.........................................................................................................ii

SKRIPSI ADVISER...................................................................................................................iiiRECOMMENDATION LETTER.........................................................................................iii

DECLARATION OF ORIGINALITY..................................................................................iv

ABSTRACT....................................................................................................................................vACKNOWLEDGEMENT.........................................................................................................vi

TABLE OF CONTENTS.........................................................................................................viiLIST OF TABLES.......................................................................................................................ix

LIST OF FIGURES......................................................................................................................x

CHAPTER I - INTRODUCTION.........................................................................................101.1 Topic of the Study.......................................................................................................................11.2 Needs of the study.......................................................................................................................31.3 Problem Identification...............................................................................................................31.4 Research Questions....................................................................................................................51.5 Research Objectives...................................................................................................................51.6 Significance of Study.................................................................................................................61.7 Limitations.....................................................................................................................................61.6 Organization of the Skripsi......................................................................................................7

CHAPTER 2 - LITERATURE REVIEW..............................................................................82.1 Introduction...................................................................................................................................82.2 Supply Chain Responsiveness................................................................................................82.3 Lean Supply Chain Strategy..................................................................................................112.4 Relationship between Lean Supply Chain and Supply Chain Responsiveness....112.5 Agile Supply Chain Strategy.................................................................................................112.6 Relationship between Agile Supply Chain and Supply Chain Responsiveness...122.7 Strategic Supplier Partnership...............................................................................................122.8 Relationship between Strategic Supplier Partnership and Supply Chain Responsiveness......................................................................................................................................132.9 Postponement Strategy............................................................................................................132.10 Relationship between Postponement and Supply Chain Responsiveness...........132.11 Research Gaps..........................................................................................................................14

CHAPTER III - METHODOLOGY....................................................................................153.1 Introduction................................................................................................................................153.2 Theoretical Framework..........................................................................................................153.3 Hypotheses.................................................................................................................................163.4 Operational Definitions..........................................................................................................173.5 Research Design.......................................................................................................................183.6 Sampling.....................................................................................................................................183.7 Research Instrument................................................................................................................203.8 Descriptive Analysis................................................................................................................23

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3.8.1 Mean.........................................................................................................................................233.8.2. Standard Deviation.............................................................................................................24

3.9 Hypothesis Testing.......................................................................................................................243.9.1 Classic Assumption Test....................................................................................................243.9.2 Multiple Regression Analysis..........................................................................................25

CHAPTER IV - RESULT AND DISCUSSION..............................................................284.1 Descriptive Analysis....................................................................................................................28

4.1.1 Validity Test...........................................................................................................................284.1.2 Reliability Test......................................................................................................................294.1.3 Demographic Profile...........................................................................................................32

4.2 Inferential Analysis..................................................................................................................334.2.1 Descriptive Analysis...........................................................................................................334.2.2 Classical Assumption Test................................................................................................344.2.3 Hypothesis Testing..............................................................................................................38

4.3 Interpretation of Results..............................................................................................................43CHAPTER 5 - CONCLUSION AND RECOMMENDATION.................................47

5.1 Conclusion.......................................................................................................................................475.2 Recommendations.........................................................................................................................48

REFERENCES............................................................................................................................49

APPENDIX...................................................................................................................................58Appendix I..............................................................................................................................................58Appendix II – Raw Data.....................................................................................................................63

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LIST OF TABLES

Table 3.1 Operational Definition ...................................................................... 17

Table 3.2 Interpretation for Cronbach’s Alpha Value ...................................... 22

Table 4.1 Validity Test Result .......................................................................... 29

Table 4.2 Reliability Test: Supply Chain Responsiveness ............................... 30

Table 4.3 Reliability Test:Lean Supply Chain Strategy ................................... 30

Table 4.4 Reliability Test: Agile Supply Chain Strategy ................................. 31

Table 4.5 Reliability Test: Strategic Supplier Partnership ............................... 31

Table 4.6 Reliability Test: Postponement Strategy .......................................... 31

Table 4.8 Descriptive Statistic .......................................................................... 33

Table 4.9 Multicollinearity Test ....................................................................... 36

Table 4.10 Heteroscedascity Test: Park Test .................................................... 37

Table 4.11 Multiple Regression Analysis: Coefficient .................................... 38

Table 4.12 Anova Test ...................................................................................... 42

Table 4.13 Coefficient Corellation and Determinant ....................................... 42

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LIST OF FIGURES

Figure 2.1: Internalandexternalsupplychain ............................................. 9

Figure 3.1: Theoretical Framework .................................................................. 15

Figure 3.2: Likert Scale .................................................................................... 18

Figure 4.1: Gender Chart .................................................................................. 32

Figure 4.2: Age Chart ....................................................................................... 33

Figure 4.3: Normality Test: Histogram ............................................................ 34

Figure 4.4: Normality Test: P-Plot ................................................................... 35

Figure 4.5: Heteroscedastcity Test: Scatterplot Graph ..................................... 37

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CHAPTER I

INTRODUCTION

1.1 Topic of the Study

Nowadays, industries are competing for one another to serve the best to its

customers and look for factors that may attract customer to buy their product.

Thain and Braidley (2014) stated that the world’s largest Fast Moving Consumer

Goods (FMCG) companies have never been more successful or more interesting.

This is because FMCG drive the world’s advertising industries, like Google and

Facebook. FMCG have adapted and evolved faster than ever before to remain

connected to their purchaser in all over the world. As we can see, their products

are everywhere. FMCG industry itself is an industry where products are sold

quickly at relatively low cost with high volumes and large quantities.

FMCG industry is able to quickly attract customers to purchase their product,

especially because their products are useful for daily consumption. Despite the

fact that each of the products is not extremely costly, the total cost of the entire

product represents around 97% of a purchaser's monthly spending budget. This is

because consumers tend to purchase FMCG product very frequent and whenever

they need it, without wasting too much time deciding to buy the product, this is

also the reason why FMCG product are perishable and non-durable and that a

customer buys them as and when the needs is arise (Singh, 2014). FMCG

Industry have a wide range of consumer products such as personal care,

household care, food products and beverages (Singh and Chandhok, 2011).

FMCG industry is a fast, agile industry with a wide scope of products. FMCG

industry also portrayed as products which have short product life cycles and and

soon get to be distinctly out of date (Cheng and Choi, 2009). As highlighted by

Fisher, Obermeyer, Hammond and Raman (1994, Cited from Bala & Kumar

2011), the key differentiators of FMCG industry are their agility and rapid

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responsiveness. This is confirmed by Unilever (2014), which expressed that two

billion consumers worldwide use a Unilever product on any day, sold in more

than 190 countries, more than 50 years experience in Brazil, China, India and

Indonesia. Based on that, researcher conclude that responsiveness is one of

FMCG industry’s competitive advantage since they are able to deliver their high

amount of perishable product in the right time to many consumers from 190

countries and stay in the market for a very long-time.

Ellinger (2000, cited from Sukati, Hamid, Baharun, Alifiah, and Anuar, 2012)

expressed that competitive advantage gathers to those organization who are

responsive to the client needs and want. Lummus, Duclos, and Vokurka (2003)

also argued that in the future, supply chain compete with other supply chain. In

order to responds to that statement, business organization should understand

supply chain responsiveness towards customer needs, because creating

responsiveness of supply chain has been become a source of competitive

advantage of the firm (Lau and Hurley, 2001). Cousins (2006, cited from El-Tawy

and Gallear, 2012), contend that companies inside both the private and in addition

the general population divisions are perceiving the significance of supply chain

management and its part in making success inside their companies.

Moreover, Christopher (1992 cited from El-Tawy and Gallear, 2012) also

contends the similar statement saying that the competition later on won't be

between individual endeavors but between contending supply chains. While, Van

der Vorst (2004 cited from El-Tawy et al., 2012) proposes that the business

managers have perceived the significance of powerful planning, incorporating,

and managing of core procedures among all the supply chain members and

consider it as the key component influencing their company’s success. Li, Rao,

Ragu-Nathan, and Ragu-Nathen (2005) also said that companies additionally

consider supply chain management as a core method of success in the competitive

business surroundings and the vehicle that can empower them to give their

product or service offerings to the market place in a productive and effecient way.

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Li et al., (2005) contend that company must influence their supply chain if they

are to keep up a maintanable competitive position inside their market place.

Moreover, Seth and Panigrahi (2015) stated that shifting preferences, accessibility

of upgraded variation, product expansion, inventory issues because of various

package sizes and service level requirements in a powerfully fluctuating market

create supply chain responsiveness is a key consideration. Supply chain

responsiveness itself is the capacity of the supply chain to quickly indicate

changes and demands in the marketplace. Therefore supply chain responsiveness

is one of the key successes of FMCG companies to stay responsive and agile in

the global dynamic business environment.

1.2 Needs of the study

Aquilano, Chase and Davis (1995, cited from Somuyiwa and Adebayo, 2012)

stated that low cost, high quality and improved responsiveness are the three main

strategic imperatives to stay competitive in this century, therefore factors

influencing supply chain responsiveness is a need to be studied.

1.3 Problem Identification

Based on Kantar Worldpanel survey in Indonesia for Asian market, the growth of

Fast moving Consumer Goods (FMCG) industry is now up to 3% while in some

other Southeast Asia country such as Indonesia, Vietnam and Philippines show

higher growth compare to Asia as a whole. In Indonesia, the growth of FMCG in

the past year, in the third quarter of 2016 is 4.8% (www.mediaindonesia.com,

2016). This shows that Indonesia is a very potential market for FMCG to enter.

Fanny Murhayati, a new business development director of Kantar Worldpanel

Indonesia in 2014 stated that Indonesian consumers are the busiest if we compare

with other country’s consumers. It is being called busiest because within a year,

Indonesian customers shop for more than 400 times a year or about 31 times a

month (www.beritasatu.com, 2014). Moreover, Euromonitor, a global market

research company, predict that FMCG industry potential market in Indonesia

would have 80 million consumers or 40% of total ASEAN consumers in the next

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15 years (www.thejakartapost.com, 2014). This would lead Indonesia as a country

that will be dominated by all global industry.

However, if we compare with the previous year, 2013 until 2015, the growth of

FMCG in Asia is decreasing. In 2013, the growth of FMCG market is 10%

compared with 2012, while in 2014, the growth percentage is up to 15.2% and in

2015 is only 4.6% (www.swa.co.id, 2016). For now, Indonesian market

competition in FMCG industries is dominated by local products, which is a good

thing, Based on Kantar Worldpanel survey in Indonesia for Asian market, local

players in Asia contribute up to 74% and their growth is double times higher

compare with multinational players. This happen only in Indonesia and China,

which the local players give contribution of higher than 60% (www.swa.co.id,

2015). However, local players need to maintain their competitiveness to prepare

for future prediction, so that local players can keep on dominating and competing

with multinational players.

Therefore, the researcher sees the importance of strategies for FMCG industry, so

that both local players and multinational players of FMCG industry can compete

in Indonesian market place. Since it is stated that supply chain responsiveness can

be a smart tactics to keep up a reasonable aggressive position inside market place,

the researcher choose to investigate further about the factors that can influence

supply chain responsiveness in FMCG industry. However, In Indonesia, PT

Unilever Indonesia, Tbk is one of Indonesia’s biggest consumer goods producers.

PT. Unilever Indonesia, Tbk is one of multinational players that have been in

Indonesia for more than 80 years (Unilever, 2015). The organization's

arrangement of home and individual care and nourishments and refreshment

products contains different well-known brands, for example, Wall's, Lifebuoy,

Vaseline, Pepsodent, Rinso, Blue Band, Dove, Rexona, and Clear. The company,

which is for 85 percent possessed by its Dutch holding organization, is one of the

biggest companies as far as market capitalization in Indonesia Stock Exchange.

Additionally, Unilever also owns the greatest warehouse in Indonesia for FMCG

industries and the biggest Unilever distribution center on the planet in 2012,

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which is Unilever's Mega Distribution Center in Cibitung (West Java). With a

floor area of around 80,000 square meters, Unilever Indonesia's distribution

network includes more than one million outlets over the Indonesian archipelago,

the greater part of which is covered specifically by the company (www.indonesia-

investment.com, 2016).

Looking from the above information, the researcher conclude that PT. Unilever

Indonesia, Tbk is a standout multinational FMCG companies in Indonesia and is a

huge threat for local players to look out. Due to that, PT. Unilever Indonesia, Tbk

will be a perfect case for this study.

1.4 Research Questions

Specifically, this study aims to answer the questions below,

1. Is there is any significant influence of lean supply chain strategy towards

supply chain responsiveness in PT. Unilever Indonesia, Tbk?

2. Is there is any significant influence of agile supply chain strategy towards


3. Is there is any significant influence of strategic suppliers partnership

towards supply chain responsiveness in PT. Unilever Indonesia, Tbk?

4. Is there is any significant influence of postponement strategy towards


5. Is there is any simultaneous influence from lean supply chain strategy,

agile supply chain strategy, strategic supplier partnership and

postponement strategy towards supply chain responsiveness in PT.

Unilever Indonesia, Tbk?

1.5 Research Objectives

Based on the preceding question, the research objectives are:

1. To determine if there is any significant influence of lean supply chain

strategy on supply chain responsiveness in PT. Unilever Indonesia, Tbk.

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2. To determine if there is any significant influence of agile supply chain

strategy on supply chain responsiveness in PT. Unilever Indonesia, Tbk.

3. To determine if there is any significant influence of strategic supplier

partnership on supply chain responsiveness in PT. Unilever Indonesia,

Tbk.

4. To determine if there is any significant influence of postponement strategy

on supply chain responsiveness in PT. Unilever Indonesia, Tbk.

5. To determine if there is any simultaneous significant influence from lean

supply chain strategy, agile supply chain strategy, strategic supplier

partnership, and postponement strategy on supply chain responsiveness in

PT. Unilever Indonesia, Tbk.

1.6 Significance of Study

1. Researcher: to find out the researcher’s interest in finding out factors

influencing supply chain responsiveness in one of FMCG company in

Indonesia.

2. The Future Researcher: To help them expand their knowledge,

information, and suggestion so that it may help them acknowledge the

methods needed to investigate factors influencing supply chain

responsiveness.

3. The University: to increase the literature and studies in the field of supply

chain responsiveness and Fast Moving Consumer Goods Industry.

4. Fast Moving Consumer Goods Industry: to build awareness of the

important strategies that FMCG companies shall implement to increase

their supply chain responsiveness so that they are able to compete in the

market change.

1.7 Limitations

This research is only to study one company of FMCG industry in Indonesia,

which is PT. Unilever Indonesia, Tbk. This research is not to study the entire

workforce of PT. Unilever Indonesia, Tbk but only supply chain logistics

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department that located in Wisma Aldiron, MBAU, Jakarta Selatan with

approximately 60 employees.

1.8 Organization of the Skripsi

This skripsi consists of five chapters. Chapter I – Introduction, consists of a

background of the study, which explains the topic of the study, the need of the

study and the problem identification to identify the problem, which researcher

wants to conduct. In Chapter II- Literature Review, the concept of supply chain

responsiveness and the strategies that may improve supply chain responsiveness is

outlined. After outlining each of independent and dependent variable, the

relationship of each factor is discussed. In Chapter III – Methodology, the

method of performance measurement is described. After that, the important

factors indicators for supply chain responsiveness and result of the research is

being examined in Chapter IV – Results and Discussions. In Chapter V –

Conclussion and Recommendation, there is described the conclusion of the

research and recommendation for PT. Unilever Indonesia, Tbk and future studies.

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CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter will be explaining about the literature review and studies about the

dependent variable and the independent variable. In this chapter, the researcher

will also discuss about the relationship between each independent variable

towards the dependent variable.

2.1.1 Theories Guiding this Research

The researcher found that not many previous studies have researched about supply

chain responsiveness in a quantitative measure. Nevertheless, Qrunfleh and

Tarafdar (2013) and Azar (2015) have proved four determinants strategies have

influence on achieving and boosting supply chain responsiveness, which are lean

supply chain strategy, agile supply chain strategy, strategic suppliers partnership,

and postponement strategy. So, the researcher then concludes to investigate those

determinant strategies as the factors that may influencing supply chain

responsiveness in Fast Moving Consumer Goods (FMCG) Industry especially PT.

Unilever Indonesia, Tbk. These strategies and its relationship with supply chain

responsiveness will be discussed further in this chapter along with variable

theories guiding this research.

2.2 Supply Chain Responsiveness

Supply chain responsiveness is defined as the ability of speediness and how much

the supply chain can address changes in customer request (Holweg, 2005, cited

from Somuyiwa and Adebayo, 2012), while the definition of responsiveness itself

is accomplishment of adaptability and delivery execution (Al-Hawajreh and

Attiany, 2014). Based on Holweg (2005 cited from Li, Ye, and Sheu, 2014),

responsiveness can be defined as the ability to react intentionally and within a

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suitable time-scale to customer demand or changes in the marketplace, to cause or

keep up competitive advantage. Based on Yi, Ngai, and Moon (2011)

responsiveness is a consequence of a solitary organization's endeavors on inside

procedures, however, it is being recognize that more members are included in

physical and data streams of internal processes across supply systems and, thus,

responsiveness can also be enhanced additionally by including suppliers and

customer in integration efforts. Accordingly, literatures concur that internal and

external mix of supply chain can positively affect responsiveness. Internal supply

chain itself consist of the purchasing, production and distribution process inside

the company while external supply chain consist of the communication and

information from the suppliers side to the company and also the communication

and information from customers side to the company, as we can see on the Figure

2.1 below,

Figure 2.1 : Internal and External Supply Chain

Source: Bratić, (2011)

Based on Williams, Roh, Tokar, and Swink (2013) a supply chain organizations

internal integration skill gives reciprocal data handling capacities required to yield

expected responsiveness from more prominent inventory network visibility. Based

on Danese, Romano, and Formentini (2013), external integration can support to

improve responsiveness by distinguishing the ‘‘bullwhip effect’’, which happens

when data about the last clients' request turns out to be progressively contorted as

it moves in reverse toward the first-tier suppliers along the supply chain.

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Information contortion leads to inaccurate prediction demand and inefficient

resource distributions that result in longer delivery times, delay in deliveries and

turn absence of responsiveness. Reducing this contortion requires quick and

precise data sharing between business partners in the supply system and

arrangement of plans to final demand. External integration refers to the part when

producers build collaborative relationships and closeness, exchanges information

and jointly plans and organize supply chain activities with both suppliers and

customers.

Studies by Qrunfleh and Tarafdar (2013) indicate a positive relationship between

supply chain responsiveness and supply chain strategy. Based on Simchi-Levi

(2008, cited from Qrunfleh and Tarafdar, 2013) supply chain strategy is

characterized as an arrangement of methodologies used to coordinate suppliers,

manufacturing, warehouses, and stores with the goal that stock is produced and

distributed at the right quantities, to the right location, at the right time, in order to

limit system-wide costs while satisfying service level requirements. Fisher (1997,

cited from Roh, Hong, and Min, 2014) developed supply chain strategies into

three types, which are lean, agile, and leagile (combination of lean and agile).

While based on Huxel and Gelashvili (2014), the lean and agile strategies are

identified at the supply chain level also, adding to expanding its adequacy and

effectiveness. Therefore, the researcher concludes that lean and agile strategy as

other factors influencing supply chain responsiveness. It is also noted that

postponement helps the retailers to keep the inventory at an acceptable level and

fulfill the consumers demand responsively. This solution of postponement helps

the retailers to be more adaptable in their capacity to increase or decrease

production, to diminish the vulnerability and to meet the consumers’ request,

which mean more responsive (Qrunfleh et al., 2013). So, based on literature, the

researcher concludes four factors affecting supply chain responsiveness, which are

lean supply chain strategy and agile supply chain strategy, strategic supplier

partnership and postponement strategy.

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2.3 Lean Supply Chain Strategy

Leanness means developing a value stream to eliminate all waste, including time,

and to ensure a level schedule (Naylor and Berry (1999, cited from Huxel and

Gelashvili, 2014). Lean manufacturing itself concentrated on “zero inventory” and

“Just in time” approaches. Just-in-Time approach is when the company able to

produce the right part, right amount at the right time (Begam, Swamynathan, and

Sekkizhar, 2013). So, a lean supply chain is worried with cost decrease by

working the basic processes with at least waste and the essential target of a lean

supply chain can be acknowledged by utilizing the most fundamental types of

information correspondence on inventories, limits and delivery arrangements and

changes inside the structure of Just-In-Time (JIT) principles (Ambe, 2014).

2.4 Relationship between Lean Supply Chain and Supply Chain

Responsiveness

Lean give the ideal use of the expertise of the workforce by giving workers more

than one job, by assimilating direct and indirect work and by encouraging

unceasing enhancement activities. Consequently, lean production produces a

larger diversity of goods and services, at cheaper costs and greater quality, with

less of every input, compared to traditional mass production, which is known as

one of the main characteristics of FMCG industry. Thus, lean supply chain

strategy revolves around the minimal use of labor, space, investment and

development time. This will create a responsive supply chain network as a whole

(Rajagopal, Azar, Bahrin, Appasamy, and Sundram, 2016)

2.5 Agile Supply Chain Strategy

Agility in a supply chain is the capacity of supply chain as a whole and its

members to rapidly adjust the system and its operations to the dynamic and

turbulent necessities of customers. The fundamental concentration is on running

organizations in network structures with a satisfactory level of agility to react to

changes (responsiveness) and also proactively suspecting changes and looking for

new developing opportunities (Ambe, 2012). Thus, agile supply chain strategy

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aims to build up an adaptable and reconfigurable system with partners to share

abilities and market information keeping in mind the end goal to guarantee

survival and thriving in a fluctuating business sector environment by

accomplishing a quick reaction to market changes (Roh, Hong, and Min, 2014).

2.6 Relationship between Agile Supply Chain and Supply Chain

Responsiveness

Previous studies propose that agility is expected to upgrade the capacity of the

supply chain to react quicker to changes in customers request and in this manner

enhance the responsiveness of the supply chain. Lee (2004 cited from Orunfleh et

al., 2013) contends that agile supply chain strategy permits the supply chain to

react rapidly to short-term changes in customers request, and thusly empowers the

focal firm to deal with the uncertainty in the market. An agile strategy likewise

requires the supply chain to keep up a higher-capacity buffer in order to empower

it to react to the unpredictable market and hence brings about better supply chain

responsiveness. The agile approach has been embraced to supply chain

management, which proposed so that the companies may get an advantage of

winning technique by utilizing such approach in their supply chain. This is

intended to give an answer for the organizations and other member inside the

supply chains to react to the business market changes (El-Tawy et al., 2012).

2.7 Strategic Supplier Partnership

Strategic supplier partnership represents the long-term relationship between the

company and suppliers. A successful supplier partnership can be a crucial part of

a leading edge supply chain (Gharakhani, Mavi and Hamidi, 2012). Through

strategic supplier partnership, it empowers companies to work more effectively

with a few vital suppliers who are willing to share duty regarding the success of

the product. Suppliers taking an interest ahead of schedule in the product-design

process can propose more cost-effective design preferences, help select the best

components and technologies and help in design appraisal (Bratić, 2011).

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2.8 Relationship between Strategic Supplier Partnership and Supply

Chain Responsiveness

Strategic supplier partnerships including working intimately with suppliers to

design or redesign products and procedures, solve problems, and prepare backup

plans are important in achieving supply chain responsiveness (Thatte, Rao and

Ragu-Nathan, 2013). By permitting the information sharing, risk sharing,

acquiring mutual advantages and coordinating plans, it can allow the

improvement of the supply chain (Quesada, Gazo, and Sanchez, 2012).

2.9 Postponement Strategy

A postponement is one of the strategies used to lessen production cycle times and

time to market to react to the changes in downstream demand on a real-time basis

(Chaundhry and Hodge, 2012). Downstream process is the one that has the direct

contact with customers through the finished product. Postponement involves

delaying the product customization activity until more information is accessible

regarding the market demand (Chaundhry and Hodge, 2012). Postponement has

observed for so long in literature (Boone, Craighead, and Hanna 2007 cited from

Seth and Panigrahi, 2015). People can notice the overwhelming varieties of

postponement practices, applications, and associated underlying issues, both at

company and supply chain levels. To address variety and responsiveness

necessities, Zinn and Bowersox (1988 cited from Seth et al., 2015) discuss five

postponement strategies, which are labeling, packaging, assembly, and

manufacturing along with time.

2.10 Relationship between Postponement and Supply Chain Responsiveness

Practitioners have always noted postponement has the potential to create a

significant improvement in the supply chain. The development of supply chains

from conventional forecast driven the push to demand-driven pull systems have

paved a path for the postponement to play a significant role in a supply chain.

Furthermore, postponement is said to be a vital characteristic in the current and

14

competitive supply chains (Rajagopal, Azar, Bahrin, Appasamy, and Sundram,

2016).Seth et al., (2015), contended thatvarious factors which influence supply

chain responsiveness, as well as inventory considerations, proliferation, service

level requirements, have led many companies to consider adopting postponement

as a supply chain strategy for better responsiveness and service levels.

2.11 Research Gaps

Based on the previous studies, the researcher found the gaps of this research.

Qrunfleh et al., (2013) studied the role of strategic supplier partnership and

postponement respectively, on the relation between lean and agile supply chain

strategy and supply chain responsiveness and also study at the relation between

supply chain responsiveness and company performance. They also did the

research on the manufacturing firm in the USA, however, the gaps of this study is

only focuses on the lean and agile supply chain strategy, strategic supplier

partnership and postponement as the factors that affecting supply chain

responsiveness in one particular FMCG industry, which is Unilever at Indonesia.

Azar (2015) study on the determinant of supply chain responsiveness in supply

and distribution industry in Malaysia, however the study utilized survey data from

100 senior executives and managers in Malaysia, not one specific industry. Sukati

et al., (2012) also conducted a research about supply chain responsiveness with

the title of competitive advantage through supply chain responsiveness and supply

chain integration. The research is more about the impact of supply chain

responsiveness on competitive advantage in Malaysia Manufacturing industry, so

the gaps of this research is that this research only focus on four strategies

influencing supply chain responsiveness and only in one FMCG Industry which is

Unilever. Suhartati and Rosietta (2012) also conducted a research that discuss the

influence of supply chain management on firm performance moderated by

competitive strategy, it is quiet similar topic but the gaps is that they are focusing

on manufacture company quoted in IDX Indonesia.

15

CHAPTER III

METHODOLOGY

3.1 Introduction

This chapter will elaborate the methodology that is used for this research. This

investigation used quantitative method and questionnaire as the instrument tool

for the research. This section will consist of the theoretical framework,

hypothesis, and sampling that are used in this study.

3.2 Theoretical Framework

Figure 3.1: Theoretical Framework

Source: adapted from Azar (2015); Rajagopal et al., (2016)

Figure 3.1 illustrates the factors influencing supply chain responsiveness towards

PT. Unilever Indonesia, Tbk. This study uses four independent variables namely

lean supply chain strategy, agile supply chain strategy, strategic supplier

Supply Chain Responsiveness

(Y)

Lean Supply Chain Strategy

(X1)

Agile Supply Chain Strategy

(X2)

Strategic Supplier Partnership

(X3)

Postponement (X4)

16

partnership and postponement. Supply chain responsiveness acts as the dependent

variable that influenced from the above independent variables.

3.3 Hypotheses

Ha1: There is significant influence of lean supply chain strategy towards supply

chain responsiveness in PT. Unilever Indonesia, Tbk.

Ho1: There is no significant influence of lean supply chain strategy towards

supply chain responsiveness in PT. Unilever Indonesia, Tbk.

Ha2: There is significant influence of agile supply chain strategy towards supply


Ho2: There is no significant influence of agile supply chain strategy towards


Ha3: There is significant influence of strategic supplier partnership towards


Ho3: There is no significant influence of strategic supplier partnership towards


Ha4: There is significant influence of postponement strategy towards supply


Ho4: There is no significant influence of postponement strategy towards supply


Ha5: There is a simultaneous significant influence of lean and agile supply

chain strategy, strategic supplier partnership and postponement towards


Ho5: There is no simultaneous significant influence of lean and agile supply

chain strategy, strategic supplier partnership and postponement towards


17

3.4 Operational Definitions

Table 3.1 Operation definitions

Variables Definition Indicators

Supply Chain

Responsiveness

The ability of speediness and how

much the supply chain can

address changes in customer

request (Somuyiwa and Adebayo,

2012).

1. Various production

2. Capacity adjustment

3. Large production

Lean Supply

Chain Strategy

developing a value stream to

eliminate all waste, including

time, and to ensure a level

schedule (Huxel and Gelashvili,

2014)

1. Inventory management

2. Quality practice

3. Quality requirement

4. Inspection repetition

5. Reducing waste

Agile Supply

Chain Strategy

The capacity of supply chain as a

whole and its members to rapidly

adjust the system and its

operations to the dynamic and

turbulent necessities of customers

(Ambe, 2012).

1. Changes in delivery time

2. Changes in cost

3. Changes in design

4. Changes in product design

5. Product customization

6. Higher capacity buffer

Strategic

Supplier

Partnership

The long-term relationship

between the company and

suppliers (Somuyiwa, et al.,

2012).

1. Suppliers quality

2. Suppliers engagement

3. Suppliers contribution

Postponement

Process of delaying the product

customization activity until more

information is accessible

regarding the market demand

(Chaundhry et al., 2012).

1. Delay until customer order

receive

2. Delay until last position in

Supply Chain

3. Store in nearest distribution

point to customer.

18

Source: Somuyiwa et al., 2012; Huxel et al., 2014; Ambe et al., 2012; Chaundhry

et al., 2012

3.5 Research Design

The research design refers to the overall strategy that is chosen to integrate the

different components of the study in a coherent and logical way, thereby, ensuring

you will effectively address the research problem (De Vaus, 2006). The researcher

is using quantitative research, which means that the researcher is using scientific

tools and measurement. In order to answer statement of the problems,

questionnaire is created as the instrument of the research. A questionnaire is a

research instrument consisting of a series of questions and other guide in order to

gathering information from respondents (Susanto, 2013). The researcher is also

using Likert scale as a statistical tool for the survey. Likert scale is designed to

find out how strongly a subject agree or disagree with the statement on a scale

from one to five with the following anchors (Sekaran and Bougie, 2010):

Figure 3.2: Likert Scale

Source: Sekaran and Bougie, 2010

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

positive and negative indication will be scaled through Strongly Disagree,

Disagree, Neither Agree Nor Disagree, Agree, and Strongly Agree. An example

of the questionnaire is shown in Appendix I. The questionnaire includes supply

chain responsiveness variable, lean supply chain variable, agile supply chain

variable, strategic supplier partnership variable and postponement variable.

3.6 Sampling

According to Sekaran and Bougie, (2013), Sampling Design is a part of

methodology that is related with taking a portion part of population. Whenever a

sampling is done precisely, a true investigation can be performed to generalize the

19

entire population. There are two types of sampling that can be used: non-

probability and probability sampling.

1. non-probability sampling is used to select “representative” of the population.

2. Probability sampling, is used where the selection of units from the population is

made according to be known probabilities.

In this research, the researcher used purposive sampling, which is a non-

probability sample that is chosen based on characteristics of a population and the

purpose of the study (Crossman, 2016). Researcher see that this sampling method

is suitable since this topic specifically discuss about supply chain in Unilever

Indonesia, the sample size is only employees in regard with supply chain logistics

in Unilever Indonesia. Purposive sampling was considered suitable for

interpretative research where generalization was not the objective of the study

(Neuman, 2012). The purposive selection of data sources refer to the notion that it

is not important how much data were collected or from how many sources, but

whether the data that were gathered were adequately rich to bring improvement

and clarity to understanding an experience (Polkinghorne, 2006, p. 140), which

was the case in this study. However, in purposive sampling, there are four types of

purposive sampling which are maximum variation sampling, homogeneous

sampling, typical case sampling, extreme case sampling, critical case sampling,

total population sampling, and expert sampling (www.dissertation.laerd.com,

2012). However, this research is using total population sampling which is type of

purposive sampling technique where it choose to examine the entire population

(total population) that have particular set of characteristics

(www.dissertation.laerd.com, 2012). In this study, the entire population is supply

chain logistics division of PT. Unilever Indonesia, Tbk with approximately 60

employees.

3.6.1 Population

According to Sekaran and Bougie, (2013), Population relates to the entire group

of people that the researcher wishes to explore. It is the gathering of people to

20

which the researcher needs to make conclusion based on sampling statistics.

According to the theory, the researcher expected the total population of supply

chain logistics division at PT. Unilever Indonesia, Tbk that is 60 employees.

3.6.2 Sample

Sample is defined as a subset of a population. It includes a few individuals chose

from it. (Sekaran et al., 2013). Based on Roscoe (1975 cited from Hill, 2012, p.3),

the number of sample size of more than 30 and less than 500 is appropriate for

most research. Roscoe (1975) also stated that in multivariate research (multiple

regression analysis is included), the sample size supposed to be several times

(ideally 10 times or more) which is at least as large as the number of variables of

the study. Since this study is using 5 variables, the minimum of 5 x 10 = 50

respondents is acceptable. In this research, the researcher surveyed all supply

chain logistics division in PT. Unilever Indonesia, Tbk that are 60 respondents.

3.7. Research Instrument

In order to fulfill the validity of this research, the researcher uses only primary

data. According to (Sekaran et al., 2010) Primary data is explained to be data

acquired direct by the researcher on the variables chosen for the particular reason

for the review.

In order to gather the result of the primary data, the researcher used method of

quantitative by giving direct questionnaire to the specified respondents in PT.

Unilever Indonesia, Tbk that located in MBAU, Wisma Aldiron Jakarta Selatan.

3.7.1. Validity Test

Questionnaires have to be tested first, through the validity test. According to

Weiner (2007), validity of a measure is the degree to which any measurement

approach or instrument succeeds in describing or quantifying what it is designed

to measure. This test is usually used to measure the accuracy of a question on the

questionnaire. According to Cohen (2007), every questions of the questionnaire

can only be stated valid if the significance level is >α (0.05). In quantitative

21

research, it might improve the validity through careful sampling, appropriate

statistical treatments of the data. Every question of the survey can be stated valid

if the value of all fixed data is above the estimation of r table.

Equation 3.1 : Validity Formula

Source: Malhotra (2010)

Whereas:

Df = degree of freedom

n = number of respondents

3.7.2. Pearson Correlation Coefficient

Correlation between sets of data is a measure of how well the data is related. The

most common measure of correlation in statistics is the Pearson Correlation or

Pearson Product Moment Correlation. It shows the linear relationship between

two sets of data. The results will be between -1 and 1. Correlation is a technique

for investigating the relationship between two quantitative, continuous variables.

Pearson's correlation coefficient (r) is a measure of the strength of the association

between the two variables. The closer the value of r gets to zero, the greater the

variation the data points are around the line of best fit (Andale, 2013).

High correlation: 0.5 to 1.0 or -0.5 to 1.0

Medium correlation: 0.3 to 0.5 or -0.3 to 0.5

Low correlation: 0.1 to 0.3 or -0.1 to -0.3

Df = n-2

22

3.7.3. Reliability Test

Reliability test is purposed to show the general consistency of estimation and look

for the connection's reply between respondent (Malhotra, 2010). According to

Sekaran et al., (2010) the most popular test of inter-item consistency reliability is

Cronbach’s alpha coefficient. Hence, Cronbach alpha test is used in this study to

measure internal consistency of the instrument. Below is the formula and

interpretation for Cronbach’s Alpha value,

Equation 3.2: Cronbach’s alpha formula

Source: Hadinata (2016)

Where:

k = number of items

r = average correlation between any two items

α = reliability of the average or sum

Table 3.2: Interpretation for Cronbach’s Alpha Value

Source: Andale (2014)

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

23

3.8. Descriptive Analysis

Statistical Descriptive analysis is used to describe the data that has been collect

without intending to apply to general conclusions or generalized (Sugiyono 2011).

The analysis is concerned with the description of data such as calculate the

average value / mean and variance of raw data, using a table or graph describes

that raw data easier to read. Its function is to provide an overview of the data that

has been obtained. The results of Statistical Descriptive analysis grouped into two,

which are summary statistics that show characteristics of respondents and

summary that shows mean (Chandra, 2016). Based on thus information, Statistical

Description analysis will be giving out a description of the characteristics and

assumption of respondents in Unilever’s supply chain responsiveness.

3.8.1 Mean

The mean is similar to an arithmatic mean (the most common type of average);

which some data points contribute more than the other. The notion of mean plays

a role in descriptive statistic and also occurs in a more general form in several

others areas of mathematics (Chandra, 2016). Below is the formula of mean,

Equation 3.3: Mean formula

Source: Chandra et al., (2016)

Where:

= Mean Value

∑ = Summation

X = Score

N = Number of score

24

3.8.2. Standard Deviation

Standard deviation is the average or means of all the averages for multiple sets of

data (chemistry.about.com, 2013).

Equation 3.4 Standard Deviation Formula

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

= mean or average of the sample

3.9 Hypothesis Testing

3.9.1 Classic Assumption Test

3.9.1.1 Normality Test

According to Santoso (2010), the aim of doing a normality test it to find out

whether the distribution of a data follows or are near to the normal distribution,

which can be seen if the distribution data form a bell shaped. According to Hair,

Black, Babin, and Anderson (2010), “Good” data are data that has a pattern of

normal distribution, which can be seen by looking at the distribution data that is

not skew to the left or right. The simplest diagnostic test for normality is a visual

check of the histogram that compares the observed data values with a distribution

approximating the normal distribution. A more reliable approach is the normal

25

probability plot, which compares the cumulative distribution. The normal

distribution forms a straight diagonal line and plotted data values are compared

with the diagonal. If a distribution normal, the line representing the actual data

distribution closely follows the diagonal.

3.9.1.2. Multicollinearity Test

Multicollinearity test shows the variance inflation factor (VIF) of all the

independent variables, if it have the values greater than 0.1 but less than 10 it

means acceptable to be analyze and all tolerance values is acceptable if it is lower

than 1 (Lind, Marchal and Wathen, 2010).

3.9.1.3. Heteroscedascity Test

In a multiple regression equation, it is necessary to tested heteroscedascity test to

find out about the variance of the residuals of the observations is different or the

same with other observations. If there are some individuals, who have the same

variance, it is called homoscedascity. And if the residuals have the difference

variance, it is called heteroscedascity. Multiple regressions equation is good if

there is no heteroscedasticity (Chandra et al., 2016).

3.9.2 Multiple Regression Analysis

Regression analysis is a statistical procedure for estimating or predicting the

relationships among variables. The variable is predicted on the basis of other

variable is called dependent variable, which is the explained variable, and the

other is the independent variable, which is the predicting variable. Multiple

regressions itself is a technique which allows additional factors to enter the

analysis separately, so that the influence of each can be estimated and it is

appreciated for measuring the influence of various simultaneous influences upon a

single dependent variable (Pratibha and Archana, 2015). Multiple regressions can

be formulated as:

26

Equation 3.5 Multiple Linear Regression Formula

Y= b0 + b1X1 + b2X2 + b3X3

Whereas:

Y defined as dependent variable (Supply Chain Responsiveness)

X1 = coefficient of variable (Lean supply chain strategy)

X2 = coefficient of variable (agile supply chain strategy)

X3 = coefficient of variable (Informativeness)

b0 = constant

3.9.2.1. Significance F-test

F-test aims to test the overall multiple regression models. This test determines

whether there is a significant relationship between the dependent variable and

independent variables or not. If the F test shows that the level of significance

number is less than 0.05, it means that H0 is rejected and it conclude that there is

no relationship between variables, and if it higher than 0.05, it means that Ha is

accepted and it conclude that there is relationship between variables (Malhotra et

al., 2010).

3.9.2.2. T-test

This test aims to see whether independent variable partially influence dependent

variable or not. T-test usually using coefficient tables, which is used to determine

the effect of each independent variable towards dependent variable. If the

significant is higher than 0.05, then accept Ha and reject H0. Otherwise, if the

significant is less than 0.05, reject Ha and accept H0 (Malhotra et al., 2010).

27

3.9.2.3. The coefficient of Correlation (R) and the coefficient of determination

(R²)

The coefficient of Correlation (R) is to measures the relationship between

independent variable (X) and dependent variable (Y) (Susanto, 2013). The

coefficient of determination (R²) is to measures the proportion of the variation in

dependent variable (Y) that is explained by the set of independent variables (X).

The coefficient of correlation (R) and coefficient of determination (R²) is range

from zero to one. If the coefficient of correlation (R) is zero, it means that the

correlation between dependent variables (X) towards independent variable (Y) is

very low while, if the coefficient of correlation (R) is one, it means that the

variables X and Y have a very strong correlation (Tarigan, 2014). So do with the

coefficient of determination (R²), the closer the R² to one, the greater the

independent variables in providing information required to predict dependent

variable.

28

CHAPTER IV

RESULTS & DISCUSSIONS

4.1 Descriptive Analysis

This chapter will describe the result of the calculation conducted with SPSS ver.

21 starting from the pre-test which consist of validity and reliability test, and also

the multiple regression calculation and analysis that the researcher conducted for

this research.

4.1.1 Validity Test

This research tests the validity of the questionnaire by using Pearson’s Product

Moment Coefficient Correlation. In order to check the validity, we have to check

the r-value from the output of statistical analysis, while in order to be counted

valid, the r-value must be bigger than the standard value based on the r table. If

the r-value is lower than the standard value, the question will be counted invalid,

and it has to be changed or deleted from the questionnaire.

The researcher is using two tailed significant level, (α) = 5% and since the

researcher uses 20 respondents as the pre-test sample the n= 20 so, researcher

calculated the df with the below formula:

Source: Malhotra et al., (2010)

Based on the above formula, the result is 18, so the standard value based on r-

table is 0.444. This is mean that the result have to be bigger than 0.444 to be valid.

The result is summarized below,

Df = n-2

29

TABLE 4.1: Validity Test Result

Source: Data Processing Result of SPSS ver. 21 (2016)

4.1.2 Reliability Test

Reliability test is used to measure the internal consistency (reliability) of scale in

the likert questions of the survey/questionnaire that the researcher conducted.

Researcher used Cronbach’s Alpha to test the reliability of the data with the help

of statistical application SPSS version 21.

Variables Questions Pearson

Correlations (r) rtable Remarks

Supply Chain

Responsiveness

SCR1 0.763 0.444 Valid

SCR2 0.759 0.444 Valid

SCR3 0.818 0.444 Valid

Lean

Supply

Chain

LSC1 0.740 0.444 Valid

LSC2 0.492 0.444 Valid

LSC3 0.824 0.444 Valid

LSC4 0.844 0.444 Valid

LSC5 0.676 0.444 Valid

Agile

Supply

Chain

ASC1 0.492 0.444 Valid

ASC2 0.684 0.444 Valid

ASC3 0.605 0.444 Valid

ASC4 0.810 0.444 Valid

ASC5 0.754 0.444 Valid

ASC6 0.732 0.444 Valid

Strategic

Supplier

Partnership

SSP1 0.795 0.444 Valid

SSP2 0.864 0.444 Valid

SSP3 0.905 0.444 Valid

Postponement

POS1 0.887 0.444 Valid

POS2 0.946 0.444 Valid

POS3 0.847 0.444 Valid

30

So, below is the detail result of each variable’s of Cronbach’s Alpha:

1. Supply Chain Responsiveness

Table 4.2: Cronbach’s Alpha Supply Chain Responsiveness

Source: Data processing Result of SPSS ver. 21(2016)

Based on table 4.3, Cronbach’s Alpha supply chain responsiveness is in range 0.8

≤ α < 0.9, which means the internal consistency is on the range of good.

2. Lean Supply Chain Strategy

Table 4.3: Cronbach’s Alpha Lean Supply Chain Strategy

Source: Data processing Result of SPSS ver. 21(2016)

Based on table 4.4, Cronbach’s Alpha Lean Supply Chain Strategy is in the range

0.8 ≤ α < 0.9, which means the internal consistency is on the range of good.

3. Agile Supply Chain Strategy

Table 4.4: Cronbach’s Alpha Agile Supply Chain Strategy

Reliability Statistics Cronbach's

Alpha N of Items

.880 4


Alpha N of Items

.895 6

31

Source: Data processing Result of SPSS ver. 21 (2016)

Based on table 4.5, Cronbach’s Alpha agile supply chain strategy is in the range α

≥ 0.9, which means the internal consistency is on the range of excellent.

4. Strategic Supplier Partnership

Table 4.5: Cronbach’s Alpha Strategic Supplier Partnership


Based on table 4.6, Cronbach’s Alpha Strategic Supplier Partnership is in the

range α ≥ 0.9, which means the internal consistency is on the range of excellent.

5. Postponement Strategy

Table 4.6: Cronbach’s Alpha Postponement


Based on table 4.7 Cronbach’s Alpha postponement is in the range 0.8 ≤ α < 0.9,

which means the internal consistency is on the range of good.


Alpha N of Items

.950 4


Alpha N of Items

.938 4


Alpha N of Items

.888 7

32

4.1.3 Demographic Profile

Researcher took the data of the respondent in the section I of the distributed

questionnaire. The variety of respondents taken only based on their age and

gender difference because the respondents are all employees with mostly the same

educational level, occupation and income. Below are shown the result of the

respondent data that already gave their statements and influencing this research.

1. Gender

Firstly, the demographic profile will be categorized from their gender. There are

60 respondents with the gender of male and female, the result of the data collected

shown below:

Figure 4.1: Gender Chart

Source: construct by researcher (2016)

Based on above figure, it is shown that 63% of the population is male and 37% is

female. This is show that employees who work in supply chain field are mostly

men.

2. Age Group

The dominant age groups that involve in the supply chain are adult with the age of

31-40, which consist of 37% of the population, even though there is only a little

different percentage with the younger employees from 21-30 years old which is

37%

63%

Gender

Female

Male

33

33% of the population. The least age group is the age of below 20, only one

person, he is an intern, and also 2 person who are from the age above 50 years old.

This is shows that Unilever Indonesia supply chain employees are mostly in the

range age of 31-40 years old, which means that they are mostly experienced at

work.

Figure 4.2: Age Chart

Source: construct by researcher (2016)

4.2 Inferential Analysis

4.2.1 Descriptive Analysis

Descriptive analysis shows the mean and standard deviation of each dependent

variable as well as the independent variable. This test will point out the significant

variable that is mostly influencing independent variable.

Table 4.8: Descriptive Statistics

Descriptive Statistics N Minimum Maximum Mean Std. Deviation SCR 60 1.00 5.00 4.2000 .73184 LSCS 60 1.00 5.00 4.2167 .76117 ASCS 60 2.00 5.00 4.0000 .66384 SSP 60 2.00 5.00 3.9333 .68561 POS 60 2.00 5.00 3.5000 .83362 Valid N (listwise) 60

2%

33%

37%

25%

3% Age

>2021-3031-4041-50<50

34


Table 4.8 shows the data that obtained from the questionnaire with SCR (Supply

Chain Responsiveness) as the dependent variable and respectively LSCS (Lean

Supply Chain Strategy), ASCS (Agile Supply Chain Strategy) and POS

(Postponement) as the independent variable. With the biggest mean value of

4.2167, lean supply chain strategy (LSCS) is the most significant variable that

influences supply chain responsiveness. Followed by agile supply chain strategy

(ASCS) in 4.000 and the least is postponement with the mean value of 3.500.

4.2.2 Classical Assumption Test

This research used multiple regressions for analyzing the data. Therefore, the first

step is to do classic assumption test, which consist of three types of assumptions,

1. Normality Test

Normality Test is used to find out the independent variable (X) and the dependent

variable (Y) on the resulting regression equation, if it is normally distributed or

not. Normality Tests executed using the test chart Histogram and Normal P-Plot

Regression Standardized Residual to test the regression model residuals, which

are shown below.

Figure 4.3: Histogram

35


It can be seen the data is normally distributed, it will be shown in the histogram

graph that resemble into the bell shapes curve in the center, and it is either skewed

to the left or the right. Based on Figure 4.3 this data match with the standard shape

of normally distributed data. This means that this data can be used and qualified to

conduct this research.

While the other test, which is normal P-Plot of Regression, is shown in the figure

4.4 below. Based on the figure, we can see that there are several points which

some of them are spreading a little further away from the diagonal line, but the

direction of the spreading points are still following the diagonal line. This means

that the data in regression model still match with the normality standard and

eligible to use.

Figure 4.4: Normal P-P Plot of Regression Standardized Residual


36

2. Multicollinearity Test

Multicollinearity test shows the variance inflation factor (VIF) of all the

independent variables, if it have the values greater than 0.1 but less than 10 it

means acceptable to be analyze and all tolerance values is acceptable if it is lower

than 1 (Lind, Marchal & Wathen; 2010). Based on table 4.9, researcher can

conclude that the data is not facing multicollinearity and the researcher can

analyze using the Multiple Regression Model.

Table 4.9: Multicollinearity Test

Model Collinearity Statistics Tolerance VIF

1

(Constant)

TLSC .555 1.803 TASC .459 2.180 TSSP .459 2.178 TPOS .600 1.666

a. Dependent Variable: TSCR Source: Data Processing Result of SPSS ver. 21 (2016)

3. Heteroscedasticity Test

The scatterplot show that there is no interference from the same variants in one

observation to another observation. From this chart, it is shown that the plot is not

spread randomly, even though there are plot in negative area and plot in positive

area, the plots point out a form of organizing.

37

Figure 4.5: Scatterplot Graph


Therefore, researcher uses other way to test heteroscedasticity test. There are four

ways of testing heteroscedasticity, which are Glejser test, Park test, Spearman test

and scatterplot (Hidayat,2013). When using Park test, the researcher found that

the result is showing a positive result of no heteroscedacity problem is occurred.

Below is the result conducted using Park Test,

Table 4.10: Heteroscedasticity Test

Coefficientsa Model Unstandardized

Coefficients Standardized Coefficients

t Sig.

B Std. Error Beta

1

(Constant) -.476 .326 -1.463 .159

LnX2 .298 .253 .326 1.178 .253

LnX3 -.062 .192 -.082 -.321 .751

LnX4 -.101 .221 -.136 -.459 .651

LnX5 .020 .128 .039 .158 .876

a. Dependent Variable: SCR


38

When using Park Test, Ho is accepted if the result of t on the table is less than T

table of degree of freedom (df-2), therefore researcher see t table of 58 (60-2)

using two-tail (0.05) and the result is 2.002, as seen from above table, the t result

< 2.002, therefore there is no heteroscedasticity problem.

4.2.3 Hypothesis Testing

The researcher used multiple linear regression to specify the significant influence

of independent and dependent variable.

Table 4.11: Multiple Regression Analysis: Coefficient

Source: Data Processing Result of SPSS ver 21 (2016)

4.2.3.1 T-Test

T-test for the partial regression coefficients supposed to examine how far the

influence of one variable independent individually in explaining the dependent

variable. The T-Test can be finished manually by comparing the calculated T and

the T table (Chandra et al., 2016). In statistical calculation software, T-test is

according to the significant (sig) value of each one of independent variable. The

result of T-test of this research can be seen in table 4.11 above.

From Table 4.11, the regression model will be interpreted in the standardized

form of the equation as follow:

Coefficientsa Model Unstandardized

Coefficients Standardized Coefficients

t Sig. Collinearity Statistics

B Std. Error

Beta Tolerance VIF

1

(Constant) .935 .502 1.863 .068

TLSC .117 .130 .121 .900 .372 .555 1.803 TASC .335 .164 .304 2.049 .045 .459 2.180 TSSP .338 .158 .317 2.136 .037 .459 2.178 TPOS .030 .114 .034 .259 .796 .600 1.666

a. Dependent Variable: TSCR

39

Y = 0.935 + 0.335 X2 + 0.338 X3

Where,

Y = Supply Chain Responsiveness

X2 = Agile Supply Chain Strategy

X3 = Strategic Supplier Partnership

From the regression model above, it can describe as follows that the regression

coefficient of Agile Supply Chain Strategy (X2) is 0.335, it means that if the

values of Agile Supply Chain Strategy increase in one unit while the other

variables is constant, then Supply Chain Responsiveness (Y) will increase as

much 0.335 of unit and the same goes to Strategic Supplier Partnership (X3).

Based on the result it can also be conclude that there are only 2 independent

variables among 4 independent variables that have significant influence towards

supply chain responsiveness, therefore, the researcher only put the variables that

have significant influence in the equation based on Table 4.11. The model

regression above might be interpret as the following statement:

1. Agile supply chain strategy has significant influence towards supply chain

responsiveness, as shown that the significance value of agile supply chain

strategy is 0.045, which is less than α (0.05). Therefore, it can be concluded

that every 1% increased of agile supply chain strategy will able to increase

supply chain responsiveness by 0.045 percent.

2. Strategic supplier partnership has significant influence towards supply chain

responsiveness, as shown that the significance value of strategic supplier

partnership is 0.037, which is less than α (0.05). Therefore, it can be

concluded that every 1% increased of strategic supplier partnership will able

to increase supply chain responsiveness by 0.037 percent.

According to the data shown in table 4.11, the researcher also conclude the

analyses for each hypothesis of this research, which are:

40

1. Lean Supply Chain Strategy (X1)

H01: There is no significant influence of lean supply chain strategy on supply

chain responsiveness.

Ha1: There is significant influence of lean supply chain strategy on supply chain

responsiveness.

The significant value of lean supply chain strategy is 0.372, which is greater than

the α (0.05). It means that lean supply chain strategy does not have significant

influence on supply chain responsiveness. Therefore, the null hypothesis (H01) is

accepted and the alternative hypothesis (Ha1) is rejected.

2. Agile Supply Chain Strategy (X2)

H02: There is no significant influence of agile supply chain strategy on supply

chain responsiveness

Ha2: There is significant influence of agile supply chain strategy on supply chain

responsiveness

The significant value of agile supply chain strategy is 0.045, which is less than the

α (0.05). It means that agile supply chain strategy have significant influence on

supply chain responsiveness. Therefore, the null hypothesis (H02) is rejected and

the alternative hypothesis (Ha2) is accepted.

3. Strategic Supplier Partnership (X3)

H03: There is no significant influence of strategic supplier partnership on supply


Ha3: There is significant influence of strategic supplier partnership on supply


The significant value of strategic supplier partnership is 0.037, which is less

than the α (0.05). It means that strategic supplier partnership has significant

influence on supply chain responsiveness. Therefore, the null hypothesis

(H03) is rejected and the alternative hypothesis (Ha3) is accepted.

41

4. Postponement Strategy (X4)

H04: There is no significant influence of postponement strategy on supply chain

responsiveness.

Ha4: There is significant influence of postponement strategy on supply chain

responsiveness.

The significant value of postponement strategy is 0.796, which is greater than the

α (0.05). It means that postponement strategy does not have significant influence

on supply chain responsiveness. Therefore, the null hypothesis (H03) is accepted

and the alternative hypothesis (Ha3) is rejected.

4.2.3.2 F-Test

F-test or ANOVA table is used to determine the influence of all independent

variable toward dependent variables, it can show if there is influence

simultaneously from the independent variable on this model, which are lean

supply chain strategy (X1), agile supply chain strategy (X2), strategic supplier

partnership (X3) and postponement strategy (X4) towards the dependent variable

supply chain responsiveness (Y). This done by looking at the magnitude of the

probability value (p value) compared to 0.05 (Level of significance α = 5%). The

test criteria were used in accordance with the hypothesis 5 of this research:

HO5: There is no simultaneous significant influence of lean and agile supply chain

strategy, strategic supplier partnership and postponement strategy on supply


Ha5: There is simultaneous significant influence of lean and agile supply chain

strategy, strategic supplier partnership and postponement strategy on supply


42

Table 4.12: Anova Test


Based on Table 4.12, the F-value is 11.032 with a significance level of 0.000,

which is less than α (0.05). It means that lean supply chain strategy, agile supply

chain strategy, strategic supplier partnership and postponement simultaneously

influencing supply chain responsiveness. Therefore, the null hypothesis (H05) is

rejected and the alternative hypothesis (Ha5) is accepted.

4.2.3.3 Coefficient of Correlation (R) and Coefficient of Determination (R2)

The result of the coefficient of correlation (R) and the coefficient of determination

(R²) is shown in the table below:

Table 4.13: Multiple Regression Analysis

Coefficient Of Correlation (R) And Coefficient Of Determination (R2)


ANOVAa Model Sum of

Squares df Mean

Square F Sig.

1 Regression 14.067 4 3.517 11.032 .000b Residual 17.533 55 .319

Total 31.600 59

a. Dependent Variable: TSCR b. Predictors: (Constant), TPOS, TLSC, TSSP, TASC

Model Summaryb Model R R Square Adjusted R

Square Std. Error of the

Estimate Durbin-Watson

1 .667a .445 .405 .56461 1.893 a. Predictors: (Constant), TPOS, TLSC, TSSP, TASC b. Dependent Variable: TSCR

43

Table 4.13 show that the value of coefficient correlation is 0.667, it means that

correlation between four independent factors of lean and agile supply chain

strategy, strategic supplier partnership and postponement strategy towards supply

chain responsiveness is 66.7%, while the value of coefficient determination is

0.445, which means that supply chain responsiveness can be explained by the

independent variables of lean supply chain strategy, agile supply chain strategy,

strategic supplier partnership and postponement as much as 44.5%. While the rest

of 55.5% is the percentage of other factors that can explain supply chain

responsiveness but not examined in this research.

4.3 Interpretation of Results

The researcher conducted a research about the significance influence of lean

supply chain strategy, agile supply chain strategy, strategic supplier partnership

and postponement towards supply chain responsiveness in specifically Unilever

Indonesia. The data resulted from the research will be interpreted in this part.

4.3.1 The Influence of Lean Supply Chain Strategy towards Supply Chain

Responsiveness

Data analysis shows that lean supply chain strategy does not have significant

influence towards supply chain responsiveness in PT. Unilever Indonesia, Tbk.

This hypothesis is supported by the T-test data obtained from statistical analysis

using SPSS version 21. Lean supply chain strategy cause responsiveness by

managing their inventory to be so called “zero-inventory”, so they are focusing

more on quality requirement and practices and reducing waste. However, this

strategy shows no significant influence towards Unilever’s supply chain

responsiveness.

Based on the analysis, it is shown that respondents are not aware neither they

understand about lean supply chain strategy which resulted in no significant

influence towards supply chain responsiveness. This interpretation is also

supported by Begam et al., (2013), which stated that the main reasons for low

level of lean implementation lays on the concern of changing the mindset of

44

workers, lack of awareness and training about the lean concepts and cost and time

involved in lean management implementation.

Moreover, this result is also similar with the previous research by Qrunfleh et al.,

(2013), their research also shows no significant influence of lean supply chain

strategy on supply chain responsiveness at manufacturing firm in USA, they said

that it conclude that the company focuses only on waste elimination without

considering the deployment of suitable resources.

4.3.2 The Influence of Agile Supply Chain Strategy towards Supply Chain

Responsiveness

Data analysis shows that agile supply chain strategy has significant influence

towards supply chain responsiveness in PT. Unilever Indonesia, Tbk. This

hypothesis is supported by the T-test data resulted from statistical analysis using

SPSS version 21. Agile supply chain strategy cause responsiveness by quickly

responds to the changes in delivery time, cost, design, product customization, and

high capacity buffer.

This result shows that respondents of PT. Unilever Indonesia, Tbk agree that agile

supply chain responsiveness is critical and being used by Unilever to improve

their supply chain responsiveness. This result also shows that respondents are

aware that agile supply chain strategy is implemented in PT. Unilever Indonesia,

Tbk. It means that PT. Unilever Indonesia, Tbk is aware and reacts quickly with

all the changes require in the market changes. Compare from the previous

research, Azar et al., (2015) shows agile supply chain strategy has a significant

influence towards supply chain responsiveness. He also concludes that firms, who

follow agile characteristics, will always stay current with fulfilling customers’

needs, leading to perhaps maintained competitive advantage. It conclude that, the

greater extend of agile supply chain strategy will result in the greater supply chain

responsiveness.

4.3.3 The Influence of Strategic Supplier Partnership towards Supply Chain

Responsiveness

45

Data analysis shows that strategic supplier partnership has significant influence



SPSS version 21. Strategic supplier partnership cause responsiveness by focusing

on suppliers’ engagement and their qualities, and also encourages them to solve

problem together. If companies can maintain their relationship with the suppliers,

they can achieve significant benefit including responsiveness in supply chain.

Based on the result, respondents agree that the relationship between suppliers and

firm is implemented in PT. Unilever Indonesia, Tbk and cause supply chain

responsiveness. This result is also supported by previous research conducted by

Somuyiwa et al., (2012), who argued that strategic supplier partnership shows a

positive influence towards supply chain responsiveness. This result also follows

previous studies of Azar et al., (2015) who stated that strategic supplier

partnership has a significant influence on supply chain responsiveness.

4.3.4 The Influence of Postponement Strategy towards Supply Chain

Responsiveness

Finding shows that postponement strategy does not have significant influence



SPSS version 21. Postponement strategy cause supply chain responsiveness by

delaying the product until the last position point in the supply chain, which allow

flexibility and able to modify the product according to meet the changing

customer needs.

This result disconfirm previous studies based on Li, Ragu-Nathan, Ragu-Nathan

and Subba Rao, (2006 cited from Bratić et al., 2011), which argue that the

implementation of postponement may be suitable in the condition where products

are innovative, products with the monetary density, high specialization and wide

range, markets that categorized by long delivery time, low delivery frequency and

high demand uncertainty, and manufacturing or logistic systems with small

46

economies of scales and no need for special knowledge, even though all of the

condition match with Unilever, it is shown that postponement strategy doesn’t

have influence towards Unilever’s supply chain responsiveness. It might be

because of the respondents don’t fully understand and acknowledge about the

postponement strategy which resulted in no significant influence towards

Unilever’s supply chain responsiveness.

This result is also different with the previous research conducted by Azar et al.,

(2015) and Qrunfleh et al., (2013) who stated that postponement strategy have a

significant influence on supply chain responsiveness of their research.

4.3.5 The Influence of Lean and Agile Supply Chain Strategy, Strategic

Suppliers Partnership and Postponement Simultaneously on Supply Chain

Responsiveness

Data result shows that there is simultaneous significant influence of lean and agile

supply chain strategy, strategic suppliers partnership and postponement

simultaneously on supply chain responsiveness. This hypothesis is supported by

the data resulted from statistical analysis using SPSS version 21. From the

ANOVA test, it is shown that the F-value is 11.032 with a significance level of

0.000, which is less than α (0.05). So it proves that there is simultaneous

significant influence of all independent variables toward dependent variable.

47

CHAPTER 5

CONCLUSION AND RECOMMENDATION

On this last chapter of this research, the detail findings about the study will be

explained, and followed by the discussions about suggestions for future research.

5.1 Conclusion

Based on the result and discussion of the research in chapter IV, it can be

concluded that lean supply chain strategy accepted hypothesis HO1 and rejected

hypothesis Ha1. This result concludes that there is no significant influence of lean

supply chain strategy on supply chain responsiveness in PT. Unilever Indonesia,

Tbk. The second independent agile supply chain strategy rejected hypothesis HO2

and accepted Ha2. This result concludes that there is significant influence of agile

supply chain strategy on supply chain responsiveness in PT. Unilever Indonesia,

Tbk. The third independent variable strategic supplier partnership is also rejected

hypothesis H03 and accepted Ha3. This result concludes that there is significant

influence of strategic supplier partnership on supply chain responsiveness in PT.

Unilever Indonesia, Tbk. The fourth independent variable postponement accepted

hypothesis H04 and rejected Ha4. This result concludes that there is no significant

influence of postponement on supply chain responsiveness in PT. Unilever

Indonesia, Tbk. Based on F-test result, the researcher can also conclude that

variable dependent, supply chain responsiveness is simultaneous influenced by

lean and agile supply chain strategy, strategic supplier partnership and

postponement.

In summary, the result shows that only two variables, which are agile supply

chain strategy and strategic suppliers partnership that shows significant influence

towards supply chain responsiveness. With this research, one of the gaps in

understanding factors supply chain responsiveness in FMCG Industry of PT.

Unilever Indonesia, Tbk is now addressed.

48

5.2 Recommendations

Referred to the conclusions gained from this study, the recommendations

proposed as a complement to the results of the study as follows:

1. For PT. Unilever Indonesia, Tbk:

There are 4 variables of supply chain responsiveness which are lean supply chain

strategy, agile supply chain strategy, strategic supplier partnership and

postponement by which only 2 variables, agile supply chain strategy, and strategic

supplier partnership that have a significant influence on supply chain

responsiveness in PT. Unilever Indonesia, Tbk. It means that PT. Unilever

Indonesia, Tbk need to maintain their agile supply chain strategy and strategic

supplier partnership of their company in order to stay responsive, competitive and

have a greater responsiveness in their supply chain. However, the other two

insignificant strategies, which are lean supply chain strategy and postponement

strategy, needs to be studied further by PT. Unilever Indonesia, Tbk, and they

shall conduct more training and awareness about lean and postponement

strategies, so that employees become aware with the strategies and the company

can pick out if the strategy can match with their characteristics and policy, so that

it can improve more in their supply chain responsiveness.

2. For future researchers:

The future researchers may choose one of these four strategies to be researched

further, so that it can obtain more focus to generate better result. Future researcher

may also develop this study by finding out other factors, which might influence

supply chain responsiveness, such as information sharing, customer relationship,

top-level commitment, and organization factor. Recommendation from previous

studies (Rajagopal et al., 2016, Qrunfleh al., 2013) also suggest future research

choose industries that have short product life cycles, other productive

combinations of supply chain strategies may dominate, which lead researcher to

suggest fast fashion industry who have short product life cycles too. Future

researchers can also use other methods, for example through factor analysis, and

in-depth interviews, so that the information obtained can be varied.

49

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Supriyanto, B. (2016) Hasil survei, pertumbuhan FMCG Indonesia kalahkan Asia. Retrieved on January 23, 2017 from http://industri.bisnis.com/read/20161130/12/608198/hasil-survei-pertumbuhan-fmcg-indonesia-kalahkan-asia

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APPENDIX

Appendix I Questionnaire

Factors affecting Supply Chain Responsiveness in Fast Moving Consumer Goods Industry: A case study of Unilever Indonesia

Responden yang terhormat, Nama saya Karina Dwi Andarin, mahasiswi President University program studi Manajemen dengan konsentrasi Bisnis Internasional. Saat ini saya sedang mengumpulkan data untuk penelitian tugas akhir/ skripsi saya dengan tujuan untuk mengetahui faktor yang mempengaruhi ketanggapan rantai suplai di Unilever Indonesia. Oleh karena itu, saya ingin meminta bantuan Saudara/i yang bekerja di Unilever Indonesia bidang rantai suplai untuk meluangkan waktunya mengisi kuesioner ini. Terimakasih atas waktu dan dukungannya dalam menyelesaikan kueisioner ini. Dear Respondents, My name is Karina Dwi Andarin, student of President University majoring Management, concentration of International Business. I am currently involved in collecting data for my thesis research with the aims of determining factors affecting supply chain responsiveness in Unilever Indonesia. Therefore, I would like to ask a favor from you who currently work at Unilever Indonesia, especially who work in the field of supply chain, to spare time answering this questionnaire. Thank you for your time and support in completing this questionnaire. SECTION I (Please give mark (X) for your chosen answer) (mohon beri tanda (X) pada pilihan anda)

1. What is your gender? o Female (Perempuan) o Male (Pria)

2. How old are you?

o 20 - 30 o 31- 40 o 41 – 50 o > 50

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SECTION II

Cara Pengisian: Mohon beri lingkaran pada nomor yang menurut anda sesuai dan menyatakan rantai suplai di Unilever Indonesia, skala 1-5: (1) Sangat tidak setuju, (2) tidak setuju, (3) netral, (4) setuju, (5) sangat setuju. How to fill: Please circle the following question in regards to supply chain in Unilever Indonesia, scale 1-5 : (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, and (5) strongly agree.

Strongly disagree

Disagree Neutral Agree Strongly agree

1. Unilever dapat menghasilkan

produk dengan berbagai fitur pilihan, ukuran dan warna. (We able to produce products characterized by numerous features options, sizes and colors)

1 2 3 4 5

2. Unilever dapat menyesuaikan kapasitas dengan cepat dan fleksibel dalam menanggapi perubahan permintaan pasar. (we are able to rapidly adjust capacity so as to accelerate or decelerate production in response to changes in customer demand)

1

2

3

4

5

3. Unilever mampu dengan cepat memperkenalkan banyak variasi produk. (We are able to rapidly introduce large numbers of product variation)

1 2 3 4 5

4. Unilever mengelola inventaris dengan hanya mengirim apa yang dibutuhkan (We manages inventory by delivering what we need)

5. Unilever menerapkan pentingnya

1

1

2

2

3

3

4

4

5

5

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syarat kualitas. (We adopts quality practices as per our requirements)

6. Unilever mengelola segala hal sesuai dengan aspek kualitas. (We manages quality as per our requirements)

1 2 3 4 5

7. Kami sering kali memeriksa produk. (We inspect products frequently)

1 2 3 4 5

8. Unilever mengurangi segala jenis pembuangan/pemborosan (We reduce any kind of waste)

1 2 3 4 5

9. Kami dengan cepat merespon apabila terjadi perubahan syarat untuk waktu pengiriman (We respond quickly to our changing requirement of delivery time)

1 2 3 4 5

10. Kami dengan efektif merespon apabila terjadi perubahan syarat untuk biaya (We responds effectively to our changing requirements of cost)

1 2 3 4 5

11. Kami dengan efektif merespon apabila terjadi perubahan syarat dalam desain produk (We responds effectively to our changing requirements of design)

1 2 3 4 5

12. Kami dapat menangani perubahan dalam desain produk (We can handle changes in product design)

1 2 3 4 5

13. Kami menyesuaikan produk kami dengan menambahkan ciri model (We customizes our products by adding feature models as per our

1 2 3 4 5

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requirement)

14. Unilever menetapkan kapasitas produk yang lebih untuk jaga-jaga menanggapi pasar yang tidak stabil (We maintains a higher capacity buffer to response to volatile market)

1

2

3

4

5

15. Unilever memilih supplier berdasarkan kualitas (We select suppliers based on their quality)

1 2 3 4 5

16. Unilever menyelesaikan masalah bersama sama dengan supplier (We solve problems jointly with our suppliers)

1 2 3 4 5

17. Kami mengikutsertakan supplier utama kami dalam perencanaan dan kegiatan penetapan tujuan. (We include our key suppliers in our planning and goal-setting activities)

1

2

3

4

5

18. Kami menunda kegiatan perakitan produk akhir sampai pesanan konsumen benar-benar telah diterima. (We delay final product assembly activities until customer orders have actually been receive)

1 2 3 4 5

19. Kami menunda kegiatan perakitan produk akhir hingga ke posisi terakhir yang memungkinkan (atau yang terdekat dengan pelanggan) dalam rantai suplai. (We delay final product assembly activities until the last possible position (or nearest to customers) in the supply chain)

1 2 3 4 5

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20. Kami menyimpan bagian produk kami di titik distribusi yang terdekat dengan pelanggan. (We store our parts/products at distribution points closer to the customers)

1 2 3 4 5

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