JOSCM - Journal of Operations and Supply Chain Management - n. 02 | Jul/Dec 2016

Submitted 09.01.2016. Approved 29.06.2016. Evaluated by double blind review process.

THE IDENTIFICATION OF KEY SUCCESS FACTORS IN SUSTAINABLE COLD CHAIN

MANAGEMENT: INSIGHTS FROM THE INDIAN FOOD INDUSTRY

Shashi PhD Scholar at Punjabi University,

School of Management Studies – Patiala – Punjab, India [email protected]

Rajwinder Singh Professor at Punjabi University,

School of Management Studies – Patiala – Punjab, India [email protected]

Amir Shabani PhD Scholar at Vrije Universiteit Amsterdam,

Faculty of Economics and Business Administration – Amsterdam, The Netherlands [email protected]

ABSTRACT: Supply chain sustainability has emerged as an indispensable research agenda for gov-ernments, industriesand non-governmental organizations. Due to the country’s status as a developing nation, cold supply chain management in India is still in its infancy.Today, due to health consciousness and a greater focus on sustainability, customers are demandingfresh, toxic free, highly eco-friendly food products. However, sustainable cold chains have not yet received sufficientattention throughout the world. Therefore, this paper seeks to address cold chain sustainability issues. After an extensive review of the literature and after discussions with cold chain practitioners, we have formulated ten sustainable cold chain constructs. We have then taken this proposed framework and validated it with an empirical study of the Indian food industry. This study includes several alarming findings. Specifi-cally in India: i) environmental issues and social responsibility are not as important as other supplier selection criteria; ii)social responsibility ranks 18th among 19 food supplier selection criteria; iii) low carbon emissionsareviewed as a less important value added trait in comparison with other traits (this means that in India buyers focus more on individual and immediate benefits rather than longer last-ing advantages); iv) life cycle analyses, renewable energy sources and passive cold chains are the least often implemented cold chain practices; v) the government usually encouragescompanies to adopt and implement sustainability in their operations, but in actual practice, training programs that provide guidance in terms of sustainability are less rigorous in comparison to the actual requirements; but on the bright side; vi) business sustainability builds trust between companies and all of their stakeholders and thus contributes to strong chain relationships.

KEYWORDS: Food industry,cold supply chain, sustainability, production, supply chain practices.

Volume 9 • Number 2 • July - December 2016 http:///dx.doi/10.12660/joscmv9n2p01-16

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Shashi, Rajwinder Singh, R., Shabani, A.: The identification of key success factors in sustainable cold chain management: Insights from the Indian food industryISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 01 – 162

1. INTRODUCTION

Over the past few years, the practical implementation and study of sustainable supply chain management (SSCM) has been growing rapidly to include ecologi-cal, social and financial benefits (Ageron, Gunasek-aran, & Spalanzani, 2012; Zailani, Jeyaraman, Ven-gadasan, & Premkumar, 2012; Bourlakis, Maglaras, Aktas, & Gallear, 2014). Today, sustainability in sup-ply chains (SC) has become an unavoidable subject (Porter & Kramer, 2006) and plays a critical role in ef-ficient SC execution. It enables companies to achieve a high level of efficiency through optimal resource planning (Rao & Hotl, 2005; Beske, Land, & Seuring, 2014). Globally, however, research on sustainabil-ity in cold chain (CC) management has not received enough attention. Indeed, sustainable cold chain management (SCCM) is a strategic tool for achieving social, ecological and economic goals in managing SC activities that deal with perishable products like medicine, blood, dairy, meat, food, vegetable, mush-room, flower and fruit products, etc., which must be processed, kept, stored and distributed under special time and environmental conditions.

One of the important branches of CC deals with the food supply. The food industry is subject to regular changes in customer demand patterns (Aramyan, Kooten, Vorst, &Oude Lansink, 2007; Beske et al., 2014; Bourlakis et al., 2014). However, food CC can be divided into “fresh agricultural products” (e.g. vegetables and fruits) and “processed food prod-ucts” (e.g. convenience food and soft drinks). Gen-erally, SCCM demands practices like environmental friendly packaging, the use of passive CC (using ice and water to maintain the temperature of perishable products), temperature-controlled production, cold logistics systems, the use of recyclable packaging, and the systematic handling of returned orders and proper waste disposal, etc. As a consequence, CC re-quires huge amounts of power to maintain the tem-perature of perishable foodstuffs during warehous-ing, transportation and the retail end, which leads to CC producing one percent of all world carbon emis-sions (Bozorgi, Zabinski, Pazour, &Nazzal, 2015). In addition to this, in many developed and developing nations, firms do not accurately dispose of large quantities of these wastes (Nandy et al., 2015).

Food production in India was 264.80 million tons in 2013-14, and this figure declined 3% to 257.07 mil-lion tons in 2014-15. Here it is interesting to note that 30-40% of farm products are spoiled due to a lack of cold storage facilities in India. Moreover,India

is currently facing high inflation in terms of food prices (Devi, 2014). Thus, declining production, in-creasing waste, environmental issues, new health problems and a growing population indicate that unfavorable conditions will continue in the near fu-ture. Thus, focusing on SCCM will help to cope with the problems we have discussed above. The main fo-cus of this article will be on studying the following research questions:

• What are the reasons behind the adoption of sus-tainable CC practices?

• What are the food supplier selection criteria?

• What are current sustainability environmental issues?

• How does SCCM add value for firms and cus-tomers?

• What are the categories of sustainable CC?

• What are effective CC practices and the dynamic capabilities needed to attain sustainability?

• What are the most effective indicators for mea-suring sustainable CC performance?

• What are the major hurdles to,and possible pay-backs from sustainable CC?

To the best of the authors’ knowledge, this is the first study to focus on CC sustainability in order to as-sist companies in identifying the key success factors, so that all the economic, environmental and social goals can be satisfied simultaneously. This paper has several distinctive features:

• For the first time all factors that are likely to in-fluence the performance of SCCM have been identified.

• The most important implemented industrial sus-tainable practices and their benefits for enterpris-es as well as for society are discussed.

• A number of promising performance indicators for evaluating SCCM have been identified through co-operation with Indian food industry firms.

• This paper will provide firms as well as their stakeholders with a clear understanding of what is important to them and what they need to do. Thus, it will surely improve their competitive-ness in meeting sustainability expectations.


The ensuing sections discuss the state of the art lit-erature in the relevant fields, proposed approach, and results after implementing it in the context of analyz-ing SCCM. There are 7 main sections. Section 2 is a review of the literature related to SSCM. Section 3 presents our conceptual model of SCCM. We discuss our research methodology and empirical analysis in Sections 4 and 5. Finally, Sections 6 and 7 consist of a discussion of the results and our concluding remarks.

2. LITERATURE REVIEW

In this section, we review the SC sustainability lit-erature on in order to identify existing gaps. SC sus-tainability has remained at the top of the research agenda over the past few decades in industry as well as academia. The negative impact of industri-al growth and high resource consumption during the 1970s and 1980s has led to an increased general awareness of SSCM (Barber, 2007). Shashi and Singh (2015) address cold logistics management as an im-portant exercise in food SC, focusing on as it focuses on strategic, transparent integrated cooperation and the attaining of company ecological, social and fi-nancial goals through inter-organizational trade processes.Moreover, Gimenez, Sierra, and Rodon (2012) address CC sustainability as a triple bottom line for stakeholder satisfaction.

Sustainability in food CC deals with how organiza-tions may be depleting their resources (Bourlakis et al., 2014). Like other products SC, food chain pro-cessing also generates industrial effluents and other wastes (NCCD, 2012). These wastes are also one of the most pervasive concerns in terms of sustainabil-ity. CC by itself may account for 1% of world car-bon emissions (Bozorgi et al., 2015). Thus, there is a strong need to decrease this carbon emission rateby using macroscopic CC methods (Guo & Shao, 2012). Basically, transportation and distribution cost show the level of competency of a company’s CC logistics operations. It thus indicates the sustainable capacity of companies to reduce their fuel consumption, costs and wastes. Meanwhile, exact route planning can re-duce lead times, food spoilage, fuel costs and carbon emissions (Carter & Dresner, 2001; Bogataj, Bogataj, & Vodopivec, 2005). This implies that CC logistics management can not only help attain environmental sustainability, it can minimize costs.

Generally, factors such asthe cross-modal links, in-frastructure networks, the amount and nature of in-vestments, rules, coordination and company visions

affect CC sustainability (Subin, 2011). Indeed, appro-priate stockroom location, temperature monitoring and the adequate disposal of hazardous materials add sustainability to business processes. Ma and Wang (2010) discuss the importance of freezing at produc-tion, storage and distribution points. In the same vein, Clark (2007) emphasizes that SSCM requires the implementation of a product-oriented approach and a shift towards more valuable product manufactur-ing that can meet buyer expectations. In addition, all upstream and downstream partners should apply specific sustainability practices during their stages (Hanson, Melnyk, & Calantone, 2004).

Today, companies demand more from their vendors to help them attain a competitive position. Better buyer-supplier relationships can foster flexibility, customer responsiveness, green purchasing, qual-ity control, added value, reverse logistics and re-cycling (Vachon & Mao, 2008). Shreay, Chouinard, and McCluskey (2016) address the fact that efforts to improvesustainable practices on the part of sup-pliers can minimize total costs and maximize con-sumer satisfaction.This highlights the importance of strategic supplier development programsin gain-ing competence in sustainability. Hence, appropri-ate supplier selection and evaluation can enhance organizational social responsibility in terms of the environment and society (Vachon & Mao, 2006; An-dersen & Skjoett-Larsen, 2009).

Moreover, national and local governments, the World Health Organization and other NGOs have been work-ing to save the environment and protect consumers from food scandals (Gruber & Panasiak, 2011). In do-ing so, many governments have also started providing grants to firms to sustain their sustainability programs. This can help firms and their suppliers mitigate the risk of environmental and political uncertainty (Liu, Ke, Wei, Gu, & Chen, 2010). Besides,using recyclable pack-aging material (Shreay et al., 2016), reducing waste and pollution, and using carbon free energy can improve a company’s image (Beske et al., 2014).It is obvious that there is a vast amount of literature available that deals with SSCM. Nevertheless, most of these studies fail to highlight CC sustainability issues. Hence, this study attempts to fill this gap.

3. A CONCEPTUAL MODEL FOR SUSTAINABLE COLD SUPPLY CHAIN MANAGEMENT

Based on the literature discussed above, we have developed a conceptual model for SCCM for this


study based on Ageron et al. (2012). This article uses tenSCCM constructs, namely: (1) reasons behind the adoption of sustainability, (2) food supplier selec-tion criteria, (3) environmental awareness, (4) add-ing value through sustainable CC, (5) sustainable CC categories, (6) sustainable CC practices, (7) sus-tainable CC dynamic activities, (8) sustainable CC performance indicators, (9) sustainable CC hurdles, and (10) sustainable CC paybacks.

3.1. Reasons/motivations behind the adoption of sustain-ability

In business, each and every task has a specific objec-tive. These days, the accelerating rise in world tem-perature, the depletion of available resources, and large quantities of soil, water and air pollution due to increased industrialization and large amounts of food waste are some key concerns that must be controlled within a specific period of time (Doonan, Lanoie, & Laplante, 2005; Papargyropoulou, Colenbrander, Sudmant, Gouldson, & Tin, 2015). Global competi-tion is another factor that has made sustainability more important in securing competitive benefits (Ka-diti, 2013). Moreover, customer expectations, govern-ment initiatives, and pressure from related national/international food safety bodies and health organiza-tions, as well as financial institutions and NGOs have obliged companies and their chain partners to adopt sustainability in their business operations. As a con-sequence, firm managers are taking sustainability se-riously in terms of their business visions.

3.2. Food supplier selection

Suppliers are known as the engine of business. Or-ganizations expecttheir suppliers to adopt sustain-able SC practicesto maximize the firm’s integrity. The incorporation of technology on the part of pri-mary suppliers has a significant impact on organi-zational profitability, and supplier performance also has a profound influence on SC performanceoverall (Ageron et al., 2012; Rezitis & Kalantzi, 2016). Ac-cording to Fritz and Schiefer (2008) and Chapbell, Mhlanga, and Lesschaeve (2016), consumers de-mand fresh, safe and value-added food for consump-tion at reasonable prices, as well as its availability through prompt delivery at locations near them. In this regard, selecting an appropriate food supplier is a major decisionthat involves the consideration of criteria such as product freshness, its commitment to fulfilling orders, cost, quality, prompt delivery, environmental friendly operations, service rates and

supplier certifications, etc. Our review of the litera-ture has helped us in this identification of food sup-plier selection criteria (Losito, Visciano, Genualdo, & Cardone, 2011; Palak, Ekşioğlu, & Geunes, 2014; Grimm, Hofstetter, and Sarkis, 2014).

3.3. Environmental awareness

Environmental awareness issues revolve around all spheres of life. It is essential that all business part-ners should be more familiar with these issues in order to develop a healthy ecological, economic and social environment. Some of the popular sustain-ability issues are organic production, reductions in resource utilization and waste, and the proper dis-posal of waste, green sourcing, lean processing, re-cyclable packaging and logistics, etc. (Guo, Liang, & Xu, 2008; Gunasekaran & Spalanzani, 2012).

3.4. Value adding factors for Sustainable CC

Today, adding value at each stage of CC is a para-mount business objective and is associated with con-sumer buying behavior. Therefore, the development of sustainable food value chains could help firms and their partners increase their profits (Martinez et al., 2006). Moreover, value chainsrequire close collaboration between various stakeholders, name-ly: farmers, agribusinesses, governments and civil society who all add value to agricultural products through sorting, grading, processing, green packag-ing, refining purity and taste, etc. (Shashi & Singh, 2015) In this way, the value added through sustain-ability can be used as a diagnostic tool to manage operations, investments and buying decisions that affect CC performance.

3.5.Sustainable CC categories

CC categories are frequently implied by the man-agement of equipment and employees. Making de-cisions relating to partners, learning programs and transportation systems, etc. are critical sustainable CC categories inthe food business. Thus, thecom-panyhas to map out the most promising sustainable CC categories to deal with risks and opportunities. These categories can be classified as: partner devel-opment, partner selection, joint development, tech-nical integration, cold logistics integration (Guo & Shao, 2012), organizational learning, stakeholder management, and innovation and life cycle assess-ment (Bai, Sarkis, Wei, & Koh, 2012). However,the selection of sustainability categories dependsvery


much on a firm’s size and the availability of its re-sources. It can foster strategic planning pertain-ing to energy, products, transportation and mate-rial management,and can also develop a culture of learning and development throughout the chain.

3.6. Sustainable CC practices

Sustainable CC practices are important elements in the food industry. As we discussedabove, SCCM permits organizations to implement practices like green sourcing, green packaging, reprocessing and proper waste dumping (James & James, 2010). It significantly affects sustainable CC performance. Environmental sustainability is not possible without adopting SCCM practices. Moreover, the integra-tion of sustainable practices between upstream and downstream partners can increase the effectiveness of operational performance and resource utilization (Carter & Rogers, 2008).

3.7. Sustainable CC dynamic activities

A firm’s dynamic activities help develop, expand or/and adjust its resources to obtain greater cost-effec-tiveness than its competitors. These organizational activities can comein the form of knowledge assess-ment, knowledge acquisition, ability development, partner development, product development, cold lo-gistics integration and CC relationship management. Reflective control over the whole CC process per-mitsnew resource configurations and makes it pos-sible to adapt to sudden changes. Furthermore, these activities help organizations improve chain traceabil-ity and monitoring to satisfy customer expectations.

3.8. Sustainable CC performance indicators

Performance measurements play a vital role in eval-uating firm efficiencies and inefficiencies to make the necessary changes in existing structures (Aramy-an et al., 2007). Therefore the right selection of per-formance indicators is of great importance to SCCM. These performance indicators should include reduc-tions in processing costs, inventory costs, waste rates, energy consumption rates, order return rates and an increase in the use of passive CC, etc. (Guo & Shao, 2012). Agricultural products are produced on a seasonal basis; therefore, food safety and control over the food supply during all chain stages is very important for effective performance management (Martinez, Poole, Skinner, Illes, & Lehota, 2006; Fritz & Schiefer, 2008). We have selected these SCCM per-

formance indicators after considering all stages of the food supply, starting with production and end-ing with retail stores.

3.9. Sustainable CC hurdles

The hurdles that block the implementation of CC sustainability are different compared to general SC. It is essential to identify these hurdles in order to mitigate their impact on a firm’s overall perfor-mance. Some of the major hurdles that have restrict-ed CC sustainability are inadequate CC infrastruc-ture, uneven installation of CC centers, high energy costs, a lack of CC integration, inefficient processes, a lack of effective environmental measures, a lack of government support and a lack of CC expertise (Subin, 2011; Bozorgi et al., 2015). In this section we will underline the major hurdles to the implementa-tion of CC sustainability in a firm’s operations.

3.10. Sustainable CC paybacks

There are a number of paybacks to implementing CC sustainability that occur in different forms. These can be in terms of reducing risks, costs, inventory levels, lead times, waste and adding more value, offering greater flexibility, customer satisfaction, improved quality, brand value, improved working conditions and strong inter-organizational relationships, etc. (Barber, 2007; Pagell, Krause, & Klassen, 2008; Luth-ra, Kumar, Kumar, & Haleem, 2011). Therefore, it is important for organizations to carefully evaluate CC sustainability paybacks. This will enable organiza-tions to maintain strong positions in relation to their competitors and mitigate the risks associated with political uncertainty.

4. RESEARCH METHODOLOGY

At this juncture, after formulating the conceptual model for SCCM, we will shed some light on our research methodology for this study.

To address this study, we have formulated a semi-structured questionnaire to answer our research questions through primary data. Here we were in-terested in covering all aspects of SCCM. The whole questionnaire was divided into two parts: organiza-tional characteristics and firm sustainability.Organi-zational characteristics were associatedwiththe firm profilewhile thethefirm’s sustainability was divided into 10 proposed conceptual framework constructs.


Regarding our questionnaire, we ignored the 5 point Likert scale due to its inability to deal with question sensitiveness (Finstad, 2010). As an alternative, we used two 7-point Likert scales and one rank scale to record feedbacks. The first scale covered the 5 sustainable CC constructs (strongly disagree (1) to strongly agree (7)). The aim of this is to underline the firm’s considerations pertaining to the reasons for adopting CC sustainability, environmental aware-ness, performance measurement indicators, hurdles and paybacks. The second scale covered one con-struct with 19 variables of supplier selection criteria-on the basis of a ranking ((1) most important to (19) least important). The intention behind the measure-ment of this construct was to identify the top priori-ties of companies in terms of upstream integration. The third scale covered 4 constructs, namely: add-ing value through sustainability, CC categories, CC practices and dynamic activities (very low extent (1) to a very high extent (7)). The focus behind this is to identify how companies are working to achieve their sustainability goals.

The content validity of the proposed questionnaire was examined by sending it to 14 food CC experts. At this point, the aim was to ascertain that the content of our investigation was measuring what we pro-posed to measure. Experts were then asked to give

their suggestions, and using them we refined our questionnaire. Afterwards, improved questionnaire was sent to a pilot study to identify any remaining shortcomings. This pilot survey helped us eliminate a few unimportant variables from the questionnaire.

Finally, the full-fledged scale survey was conducted in Indian food industry CC from November 2014 to March 2015. A total of 674 questionnaires were sent through the mail to perishable food product CC practitioners. The list of respondents included CEOs, purchase managers, production managers, quality assurance managers, marketing & sales managers, SC managers, retail managers and others. In total, we received 487 filled out questionnaires in return. We only digitalized 463 out of the 487 re-turned questionnaires in SPSS because of (missing values and zero standard deviations) with 24 ques-tionnaires. Descriptive statistics (means, standard deviations and rankings) were used to answer the research questions.

The survey findings indicate that the businesses with the greatest representation (32.81%) were from the food processing area. In addition, SC managers accounted for the largest portion of survey respon-dents, equivalent to 21.02%. Table 1lists the business area and job profile for each of the respondents.

Table 1: Digitalized survey profile

Business Nature Remarks Respondent Profile RemarksFood processing firms 63 (32.81%) CEO 14 (3.02%)Cold logistics service providers 46 (23.95%) Purchase manager 78 (16.84%)Distribution firms 49 (25.52%) Production manager 65 (14.03%)Retail firms 34 (17.70%) Quality assurance manager 58 (12.52%)Total 192 (100%) Marketing & sales manager 63 (13.60%)

Supply chain manager 104 (21.09%)Retail manager 52 (11.23%)Others 29 (6.26%)Total 463 (100%)

5. EMPIRICAL ANALYSIS

Sustained practices in CC mostly come from out-side India. The country’s CC segment is highly fragmented and not developed properly to attract a large number of domestic specialists. It is clear that the rate of energy usage by CC technologies directly affects both the feasibility and finances of sustain-

ability. Unfortunately, due to the use of obsolete equipment and machinery, CC consumes a high rate of energy in India (KPMG, 2009). Thus, authorities in the agriculture, power, education and food seg-ments must work together to encourage the use of advanced CC technology, modern logistics systems, and the development of CC infrastructure networks


and expertise. In addition, the government must keep on encouraging more private players to invest in Indian CC in order to bring significant compe-tence in sustainability to the food sector.

The results obtained in terms of the reasons and mo-tivations behind applying sustainable practices in Indian CCs are displayed in Table 2. Our findings show government rules and regulations are the main reasons that companies have adopted sustainability in both their own operations and in their supplier’s operations. This indicates that government regu-latory requirements are playing a leading role in protecting the environment and society. Moreover, sustainability is a strategic concern, and without top-management support it is difficult to achieve. Our findings indicate that the vision of top manage-ment frequently incorporates financial, societal and ecological responsibilities in their organizational ac-

tions and strategic plans.

Sustainability refers to social, economic and eco-logical concerns which advocate a better care of cus-tomer expectations. Green packaging, lower prices, higher quality, lower carbon emissions and prompt delivery, etc. are the key drivers of sustainability. In today’s marketplace, firms that ignore sustainability will be ignored by customers when they make their purchases. Moreover, our analysis emphasizes that both customer expectations and market competitive-ness have significantly encouraged sustainable CC practices. Government ecological initiatives have also had a significant impact on the understanding of sustainability issues. In our list of reasons behind the adoption of sustainability, the role of NGOs re-ceived the lowest ranking, while in many studies, pressure on firms to adopt sustainability is said to be significant.

Table 2: Reasons for the adoption of sustainability

Reasons for the adoption of sustainability Rank Mean scores Std. deviationGovernment regulatory requirements 1 6.04 1.312Top management vision 2 5.83 1.826Customer expectations 3 5.55 1.041Market competition 4 5.47 1.405Government ecological initiatives 5 4.79 1.578NGOs 6 4.32 1.733

In terms of food supplier selection, accuracy (2.24), quality (2.68), product freshness (3.26), cold ware-houses and vehicles (3.59) and price (4.02) are the most promising variables considered. Likewise the supplier order fulfillment capacity (4.42), quantity and cash discounts (4.84) and service rates (5.10) also significantly affect supplier selection. In addi-tion, these firms also give preference to those suppli-ers who are nearest in terms of geographical location (5.56). This is surely to cut inbound costs, leadtimes and reduce food spoilage during transportation.

We also can observe here that credit-based sales (8.22) attract firms to buying material in bulk quan-

tity from suppliers. One astonishing result of our findings reveals that despite the remarkable global attention paid to the subject of sustainability (i.e. the simultaneous concentration on social, environ-mental and economic goals), in India, environmen-tal issues (10.13) and social responsibility (11.77) are not as important as other economic supplier selection criteria. In spite of this, environmental is-sues are fortunately more important for firms at the time of supplier selection compared to long-term SC relationships (10.36) and personal relationships (12.49). In Table 3, we display the food supplier se-lection criteria.


Table 3: Food supplier selection criteria

Criteria Rank Mean scores Std. deviationAccuracy 1 2.22 1.072Quality 2 2.68 1.207Product freshness 3 3.26 1.495Cold warehouses and vehicles 4 3.59 1.363Prices 5 4.02 1.850Order fulfillment capacity 6 4.42 1.329Quantity and cash discounts 7 5.10 2.152Service rates 8 5.12 2.848Geographical proximity 9 5.56 3.939Variety 10 6.63 3.683Delivery style 11 6.87 4.027Certification 12 7.21 5.514Credit based sales 13 8.22 5.430Information sharing ability 14 8.43 5.793Goodwill 15 9.43 5.461Environmental issues 16 10.13 5.126Long-term SC relationships 17 10.36 7.960Social responsibility 18 11.77 7.633Personal relationships 19 12.49 6.485

Aspects related to the environmental awareness of sustainability are reported in Table 4. This is very im-portant because it shows how environmental aware-ness helps business by lowering overhead costs, offsetting power usage, reducing the cost of waste removal, as well as boosting, easing and reducing the costs of paperless processes, etc. A company may have the most ambitious environmental policy, but unless it makes all of its stockholders environmentally aware so that they understand the philosophy behind their policy, the goals that the company is aiming for will not be achieved. Our findings indicate that the

use of green transportation channels, solar energy and passive CC has not yet received serious attention as expected. This may be happening because less CC expertise is available and the complex designing so-lar energy projects in India. Indeed, stricter ecological policies and regulation, reverse logistics and product lifecycle management have started to receive atten-tion. It is also interesting that Indian firms consider getting ISO 14001 certification and reducing both waste and energy consumption to be promising solu-tions for achieving environmental awareness for sus-tainable CC.

Table 4: Major aspects of environmental awareness in sustainability

Aspects Rank Mean scores Std. deviationWaste reduction 1 6.19 1.124ISO 14001 Certification 2 6.12 1.314Reduction of energy consumption 3 5.93 1.381Lean management 4 5.83 1.279Proper waste disposal 5 5.72 1.296


Recyclable packaging 6 5.43 1.574Safety and agile health 7 5.30 1.514Lower levels of greenhouse emissions 8 5.29 1.207Resource management efficiency 9 5.12 1.337Adoption of latest technology 10 4.98 1.672Reverse logistics 11 4.64 1.450Product life cycle management 12 4.31 1.625Solar energy utilization 13 3.98 1.067Use of passive CC 14 3.85 1.463Green transportation channels 15 3.63 1.853

Moreover, prompt delivery, taste, freshness and proper food labeling are important concerns in terms of VA food traits due to their short shelf life. An unpleasant taste for processed farm products has a detrimental effect on their consumption. A large quantity of perishable products gets spoiled during shipping; hence, packaging and expiration dates can help suppliers handle these products within an ap-propriate timeframe. Furthermore, this helps make buyers aware of this product attribute in terms of consumption and controlling food hazards. In ad-dition, having farm products available during the entire year will provide significant added value for

customers. Table 5 lists the results for the value add-ed by sustainable CC construct.

Our survey results show that low carbon emis-sions are viewed as providing less added value than other traits. This emphasizes that in India, buyers focus more on their individual and immediate ben-efits such as money savings, quality, taste, labeling, availability and less lead time rather than long last-ing benefits such as a healthy environment. We can see that effectively adding value is good for a firm’s business and that of its partners. Not surprisingly, lower prices are the most important VA factor.

Table 5: Value adding factors for Sustainable CC

Value Adding Factors for Sustainable CC Rank Mean scores Std. deviationLower price 1 6.18 1.323Purity 2 6.17 1.204Quality 3 6.08 1.135Organic food 4 5.93 1.436Fresh food 5 5.86 1.310Taste 6 5.83 1.438Prompt delivery 7 5.72 1.183Less supplier lead time 8 5.69 1.203Environmentally friendly packaging 9 5.60 1.317Availability 10 5.59 1.562Proper Labeling 11 5.56 1.336Less manufacturer lead time 12 5.24 1.478Reverse logistics 13 5.13 1.372Grading 14 4.94 2.583Sorting 15 4.76 1.610Low carbon emissions 16 4.41 1.939Variety 17 4.35 1.717


Now we turn to the part of this study that deals with sustainable CC categories. Our results emphasize that risk management and CC integration are almost equally important sustainability categories. Firms have adopted technical integration to gain mutual benefits through combining available technologies. Meanwhile, organizations are giving greater pref-erence to learning from the internal as well as the

external business environment to maintain their business competitiveness. Stakeholder management is essential for tackling business uncertainty. Here the innovation category (4.00) is neglected to some extent. Strategic orientationin trade related areas is important in the effective management of the entire SC. Table 6 lists sustainable CC categories in order of their importance.

Table 6: Sustainable CC categories

Sustainable CC categories Rank Mean scores Std. deviationRisk management 1 5.32 1.287Cold logistics integration 2 5.25 1.463Technical integration 3 4.99 1.316Learning 4 4.81 1.614Stakeholder management 5 4.65 1.692Strategic orientation 6 4.47 1.535Supply chain continuity 7 4.40 1.684Innovation 8 4.00 1.892

Developing sustainable CC practices is not only critical to business growth, but is also beneficial to future generations. Globalization, climatic change and changes in consumption patterns and in-creased middle class purchasing power have simul-taneously raised the need for improved sustainable CC practices. In this regard, improving ecological

standards is viewed as the most important sustain-able CC practice. Reducing energy consumption and waste are also receiving attention from firms. Similarly, reducing hazardous/toxic materials in food products is also viewed as important. We have listed sustainable CC practices in the order of their importance in Table 7:

Table 7: Sustainable CC practices

Practice Rank Mean scores Std. deviationSignificant improvement in fulfillment of ecological standards 1 5.93 1.196

Significant reduction in hazardous/toxic materials 2 5.79 1.298

Achieving waste reduction goals 3 5.78 1.009Strong relationships with the community 4 5.56 1.211Use of clean production technology 5 5.47 1.224Reduction in operational costs 6 5.46 1.467Physical layout designed to optimize materials and energy 7 5.32 1.365

Reverse logistics 8 5.26 1.972Recycling 9 5.14 1.643Purchase of packaging that is of lighter weight 10 5.10 2.115

Purchase of recyclable packaging material 11 5.06 1.631


Use of life cycle analysis 12 4.63 1.572Use of renewable energy sources 13 4.26 1.565Use of passive CC 14 4.01 1.390

Firms are using regular meetings and large amounts of knowledge sharing as the most implemented dy-namic activities. Similarly, knowledge acquisition and evaluation, licensing and partner based synergies are other important major activities that firms have adopted in their organization to promote sustainabil-ity. At this point, the joint development of products is viewed as the least important dynamic activity. Thus we can conclude that there is a focus on the part of of businesses on their own core competencies.

Our analysis reveals that the rest of the sustain-able dynamic activity variables are implemented to a great extent by the firm to develop and maintain sustainability. We can also see that the quality of shared knowledge is more crucial than the transpar-ency of the actions taken. Here in Table 8, then, we list the sustainable CC dynamic activities that assist firms in implementation.

Table 8: Sustainable CC dynamic activities

Dynamic activity Rank Mean scores Std. deviationRegular meetings 1 6.23 1.177Knowledge sharing 2 6.15 1.249Knowledge acquisition and evaluation 3 5.93 1.392Licensing 4 5.90 1.285Partner-based synergies 5 5.87 1.363Transparency 6 5.76 1.668Partner development programs 7 5.64 1.403Common IT System 8 5.41 1.574Partner training 9 5.28 1.780Joint development of products 10 4.82 1.813

When sustainability is implemented, it needs to be measured in order to make changes in existing pat-terns to accomplish predefined sustainability objec-tives. Hence, CC performance measurement is the most important step towards successful and effec-tive SCCM.

As we can see from Table 9, reducing the rate of waste is the most appreciated sustainable CC perfor-mance indicator in terms of helping firms quantify

their cash and material savings. Reduced levels of carbon emissions and customer complaints and im-proved customer satisfaction rates are also consid-ered in evaluating CC operations. Since CC requires a large amount of energy sources, the evaluation of reduced energy consumption is also a key indicator to ensuring CC sustainability. Overall, firms con-sider sustainable CC performance indicators to be important,since the score of the least important indi-cator (i.e. reduced maintenance costs) is 5.32.

Table 9: Sustainable CC performance indicators

Sustainable CC performance indicators Rank Mean scores Std. deviationReduction in waste rate 1 6.04 1.132Reduction in customer complaint rate 2 5.98 1.211Reduction in carbon emission rate 3 5.97 1.186


Customer satisfaction rate 4 5.94 1.185Reduction in overall energy use 5 5.86 1.716Reduced transportation costs 6 5.79 1.481Shipping accuracy rate 7 5.76 1.379Reduction in lead time 8 5.72 1.652Improved product quality rate 9 5.71 1.307Increased profits 10 5.69 1.373Reduction in cooling costs 11 5.64 1.439Reduced inventory costs 12 5.62 1.377Staff retention 13 5.62 1.247Order returns 14 5.56 1.624Reduced processing costs 15 5.54 1.407Recycling rate 16 5.40 1.438Reduced warehousing costs 17 5.35 1.512Reduced maintenance costs 18 5.32 1.420

A list of hurdles that have stifled sustainability is shown in Table 10. These obstacles to implementing sustainable cold chains in India have been hot topics in discussions about why India has yet to become the “Food Basket of the World.” In India, inadequate CC infrastructure, high investment costs, a lack of CC expertise, high energy costs and the complex-ity of designing ways to reduce the consumption of resources and energy are considered to be the big-gest obstacles which have impeded the adoption of CC sustainability. One interesting finding here is that a lack of government support received a value of 4.38, while a lack of training courses is ranked 15th.This means that the government usually has

backed firms in the adoption and implementation of sustainability in their operations, but that training courses to guide this process have not been made a requirement.

Moreover, available CC has been installed uneven-ly which shows up in the unavailability of multi-commodity based CC capacity. A report published by the Emerson Group emphasizes that most of the available CC technologies in the country are outdat-ed. Hence, the existing structure requires more CC coordination, ideal arrangements, consistent pro-cesses, specific environmental goals and the latest CC technology.

Table 10: Sustainable CC hurdles

Sustainable CC hurdles Rank Mean scores Std. deviationInadequate CC infrastructure 1 6.32 1.134High investment costs 2 6.30 1.136Lack of CC expertise 3 6.18 1.092High energy costs 4 6.14 1.120Complexity of designing ways to reduce re-source/energy consumption 5 6.01 1.078

High costs of hazardous waste disposal 6 5.98 1.236Lack of CC integration 7 5.97 1.203Costs of environment friendly packaging 8 5.96 1.404Lack of specific environmental goals 9 5.95 2.270


Unavailability of CC performance measurements 10 5.89 1.117Uneven installation of CC centers 11 5.86 1.280Lack of technology 12 5.81 1.538Complexity of designing ways to recycle used products 13 5.78 1.248

Inefficient processes 14 5.73 2.437Lack of training courses 15 5.62 1.381Lack of awareness about adopting reverse logistics 16 5.42 1.363Lack of government support 17 4.38 1.136

According to the figures reported in Table 11, the ma-jor paybacks of sustainable CC are goodwill, higher customer satisfaction and less lead time. It also leads to significantly more added value, better quality and reduction in waste. As companies have implemented

their sustainability plans, working and living condi-tions have definitely improved. Thus, we can observe that business sustainability builds trust between the government, suppliers, firms and all of the stakehold-ers involved in building strong CC relationships.

Table 11: Paybacks of sustainable CC

Paybacks of sustainable CC Rank Mean scores Std. deviationGoodwill 1 6.18 1.200Customer satisfaction 2 6.17 1.199Less lead time 3 6.08 1.386Large amount of added value 4 5.93 1.357Reductions in waste 5 5.88 1.148Improved quality 6 5.87 1.240Improved working conditions 7 5.80 1.235Reductions in stocks 8 5.73 1.356Flexibility 9 5.63 2.049Reductions in energy costs 10 5.54 1.121Strong CC relationships 11 5.54 1.883Development of trust 12 5.53 1.943

6. DISCUSSION

Ten sustainable CC constructs have been used to develop the theoretical model framework for this study. In this study, we have specifically conducted an analysis of the Indian food industry in terms of CC sustainability practices. To test and validate this model framework we have developed a semi-struc-tured questionnaire.

Perhaps government regulatory requirements create fear among firms in terms of fulfilling the sustainabil-ity prerequisites. The evasion of these prerequisites creates regulatory problems for profit-oriented or-

ganizations that can lead to the cancelling of licenses and cash fines. Nonetheless, there is a strong need to evaluate the regulatory compliance rate in small scale industries. The findings of this study indicate that CC infrastructure is a prime area for improvement. Therefore the government should promote private in-vestment in the CC sector, which would be beneficial for sustainable development. Similarly, government efforts in the domain of emission control technology, awareness and expertise could significantly contrib-ute to attaining sustainability.

In addition to this, companies need to set their year-ly sustainability goals, and to accomplish these goals


companies they need to be fully integrated with their upstream and downstream partners and also be concerned with their own performance. More emphasis should be placed on waste reduction be-cause it also affects waste disposal costs. Likewise, employee retention and training may enable firms to wield better control over emissions during the production process. Usually authorities do not put much emphasison evaluating the workflow of these business units. Managers frequently are not very dedicated towards their entrepreneurial and social responsibility responsibilities, which leads to apa-thy on the part of middle and lower level workers. Thusapathy of this kind can foretell greater carbon emissions, raw material waste, energy waste and internal conflicts in the future. We deem this to be another interesting finding as no previous work has confirmed the impact of management commitment upon the performance of their subordinates.

Indeed, firms are giving more preference to deliver-ing pure, quality products to consumers to make them healthier, but not to reduce carbon emissions. Though product purity and quality have only the positive ef-fects on the firm’s consumers, company negligence towards lowering the rate of carbon emissions has a toxic effect on the health of the entire world. In terms of this serious issue, high customer expectations and social pressures are two important aspects of green consciousness. Normally, a lack of awareness on the part of customers and society tends to diminish vol-untary contributions from NGOs, governments and corporate houses. Hence, regular pressure from so-ciety and customers is needed to maintain company progress in terms of sustainability.

The partners of any organization are commonly known as the backbone of a business. If the suppli-ers supply low quality raw materials, then it will directly affect the quality of the finished product and the quality of these finished products will nega-tively affect the company’s brand name in the mar-ket. Similarly, the quality of other supplier services also affecta company’s brand name. Thus, CC sup-pliers and sub-suppliers need to pay more attention to improving the accuracy of product orders, qual-ity, freshness, cold warehouse standards, vehicle sustainability and product pricing. Before selecting suppliers, organizations should be more aware of previous sustainability efforts in order to increase their enterprise’s efficiency in protecting the en-vironment. The use of the latest technology and trained manpower can be a game changer in terms

of waste reduction. The reduction of waste maximiz-es the rate of product processing, energy conserva-tion and savings in terms of other necessary inputs. Furthermore, these inputs can be used in the next production batch, which will satisfy sustainability expectations (lowering pollution rates, carbon emis-sions, production lead times and fuel usage, etc.)

Partner based synergies and information transpar-ency clarify business objectives and facilitate sus-tainable practices among a firm’s partners. Mutual synergies help firms tackle internal and external business hurdles. Since dissatisfied customers are quick to switch to other brands in today’s market-place, reducing customer complaints and optimal problem solving should be considered prime busi-ness imperatives. Our discussions with those in-volved in CC have revealed that CC lead timesare important and noticeable, because as CC lead times increase, the chances of food spoiling also increase along with fuel consumption and monetary losses. Indian companies do not have specific environmen-tal goals and frequently firms resist implementing sustainable practices. Thus, the absence of specific environment management goals, neglect and an unwillingness to tackle this issue are major hurdles that have hindered firms from reaping the benefits of CC sustainability.

Previous studies of sustainability have measured safety and agile health issues. We have included this in our investigation, and our findings indicate deci-sively that this is important in terms of sustainabil-ity. This fact should encourage companies to place a higher priority on the safety and health of their em-ployees, society and other living things. Retaining re-liable, experienced and knowledgeable staff reduces customer dissatisfaction and helps builda healthy working environment. In addition to this, a success-ful partner development program enriches firm com-petenceand helps solvefinancial difficulties.

7. CONCLUDING REMARKS

In this study, we have developed and analyzed ten SCCM constructs within the context of CC. For each of these constructs, we have in turn identified the major reasons for adopting sustainability, supplier selection criteria, environmental awareness, sus-tainability practices, value adding factors, as well as sustainable CC performance measures, hurdles and possible paybacks. We have discussed the most important implemented industrial sustainable


practices and their benefits for enterprises. This article also highlights the gap between required CC capacity and existing CC capacity in India. This study covers almost every aspect of CC sus-tainability. This study’s results argue that sustain-ability could have a profound impact on CC per-formance. However, consumers are also not very aware of the benefits of low carbon emission levels. Thus, this is hurting the efforts of various levels of government and other environment management authorities. Due to high initial costs, developing re-newable energy source infrastructure and passive CC systems are less preferable choices for the food industry. Moreover, as the survey points out, there is a strong need for close integration to compen-sate for the absence of resources. The government and NGOs will have to work in a unified manner to promote training programs to achieve their sus-tainability objects. These programs could also in-crease the efficiency of operational staff, cut waste and make the economy less dependent on carbon.

7.1 Study limitations and future avenues for research

One limitation of this study is that CEOs represent-ed only 3.02% of the responses in our survey. Thus, a greater involvement on the part of the industrial elite would be more helpful and would make future survey findings more interesting. Another possible avenue for future research would be applying the proposed model framework to the pharmaceutical industry to measure its approaches to sustainability. Structural equation modeling (SEM) could be used to test the relationships between the sustainability constructs we have developed.

REFERENCES

Ageron, B., Gunasekaran, A., & Spalanzani, A. (2012). Sustainable supply management: An empirical study. International Jour-nal of Production Economics, 140(1), 168-182. doi:10.1016/j.ijpe.2011.04.007

Andersen, M., & Skjoett-Larsen, T. (2009). Corporate so-cial responsibility in global supply chains. Supply Chain Management: An International Journal, 14(2), 75-86. doi:10.1108/13598540910941948

Aramyan, L. H., Kooten, O., Vorst, J. G., & Oude Lansink, A. G. (2007). Performance measurement in agri-food supply chains: A case study. Supply Chain Management: Interna-tional Journal, 12(4), 304-315. doi:10.1108/13598540710759826

Bai, C., Sarkis, J., Wei, X., & Koh, L. (2012). Evaluating ecologi-cal sustainable performance measures for supply chain man-agement. Supply Chain Management: International Journal, 17(1), 78-92. doi:10.1108/13598541211212221

Barber, J. (2007). Mapping the movement to achieve sustain-able production and consumption in North America. Jour-nal of Cleaner Production, 15(6), 499-512. doi:10.1016/j.jclepro.2006.05.010

Beske, P., Land, A., & Seuring, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the food industry: Acritical analysis of the literature. International Journal of Production Economics, 152, 131-143. doi:10.1016/j.ijpe.2013.12.026

Bogataj, M., Bogataj, L., & Vodopivec, R. (2005). Stability of per-ishable goods in cold logistic chains. International Journal of Production Economics ,93-94(8), 345-356. doi:10.1016/j.ijpe.2004.06.032

Bourlakis, M., Maglaras, G., Aktas, E., & Gallear, D. (2014). Firm size and sustainable performance in food supply: Insights from Greek SMEs. International Journal of Production Eco-nomics, 152, 112-130.doi:10.1016/j.ijpe.2013.12.029

Bozorgi, A., Zabinski, J., Pazour, J., & Nazzal, D. (2015). Cold supply chains and carbon emissions: Recent works and rec-ommendations. Working Paper.

Carter, C. R., & Dresner, M. (2001). Purchasing’s role in envi-ronmental management: Cross-functional development of grounded theory. Journal of Supply Chain Management, 37(2), 12-27. doi:10.1111/j.1745-493x.2001.tb00102.x

Carter, C. R., & Rogers, D. S.(2008). A framework of sustainable supply chain management: Moving toward new theory. In-ternational Journal of Physical Distribution & Logistics Man-agement, 38(5), 360-387. doi:10.1108/09600030810882816

Chapbell, B. L., Mhlanga, S., & Lesschaeve, I. (2016). Market dy-namics associated with Canadian ethnic vegetable produc-tion. Agribusiness, 32(1), 64-78. doi:10.1002/agr.21426

Clark, G. (2007). Evolution of the global sustainable consump-tion and production policy and the United Nations En-vironment Programme’s (UNEP) supporting activities. Journal of Cleaner Production, 15(6), 492-498. doi:10.1016/j.jclepro.2006.05.017

Devi, C. U. (2014). Trade performance of Indian processed foods in the international market. Procedia - Social and Behavioral Sciences, 133, 84-92. doi:10.1016/j.sbspro.2014.04.172

Doonan, J., Lanoie, P., & Laplante, B. (2005). Determinants of environment performance in the Canadian pulp and pa-per industries: An assessment from inside the industry. Ecological Economics, 55(1), 73-84. doi:10.1016/j.ecole-con.2004.10.017

Finstad, K. (2010). Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Studies, 5(3), 104-110.

Fritz, M., & Schiefer, G. (2008). Food chain management for sus-tainable food system development: A European research agenda. Agribusiness, 24(4), 440-452.doi:10.1002/agr.20172

Gimenez, C., Sierra, V., & Rodon, J. (2012). Sustainable op-erations: Their impact on the triple bottom line. Interna-tional Journal of Production Economics, 140(1), 149-159. doi:10.1016/j.ijpe.2012.01.035


Grimm, J. H., Hofstetter, J. S., & Sarkis, J. (2014). Critical factors for sub-supplier management: Asustainable food supply chains perspective. International Journal of Production Eco-nomics, 152(1), 159-173.

Gruber, J., & Panasiak, D. (2011). Regulation and non-regulatory guidance in Australia and New Zealand with implications for food factory design. In J. Holah, & H. L. M. Lelieveld (Eds.), Hygienic Design of Food Factories (pp. 115–142). Pad-stow, UK: Woodhead Publishing.

Gunasekaran, A., & Spalanzani, A. (2012). Sustainability of man-ufacturing and services: Investigations for research and ap-plications. International Journal of Production Economics, 140(1), 35-47. doi:10.1016/j.ijpe.2011.05.011

Guo, H., & Shao, M. (2012). Process reengineering of cold chain logistics of agricultural products based on low-carbon econ-omy. Asian Agricultural Research, 4(2), 59-62.

Guo, Q., Liang, L., & Xu, C. (2008). A joint inventory model for an open-loop reverse supply chain. International Jour-nal of Production Economics, 116(1), 28-42. doi:10.1016/j.ijpe.2008.07.009

Hanson, J. D., Melnyk, S. A., & Calantone, R. J. (2004). Core values and environmental management: A strong inference approach. Greener Management International, 46(Summer), 29-40.

James, S. J., & James, C. (2010). The food cold-chain and cli-mate change. Food Research International, 43(7), 1944-1956. doi:10.1016/j.foodres.2010.02.001

Kaditi, E. A. (2013). Market dynamics in food supply chains: The impact of globalization and consolidation on firms’ market power. Agribusiness, 29(4), 410-425. doi:10.1002/agr.21301

KPMG. (2009). Food processing and agribusiness. Retrieved from https://www.kpmg.de/Topics/16338.htm

Liu, H., Ke, W., Wei, K. K., Gu, J., & Chen, H. (2010). The role of institutional pressures and organizational culture in the firm’s intention to adopt internet-enabled supply chain man-agement systems. Journal of Operations Management, 28(5), 372-384. doi:10.1016/j.jom.2009.11.010

Losito, P., Visciano, P., Genualdo, M., & Cardone, G. (2011). Food supplier qualification by an Italian large-scale-distrib-utor: Auditing system and non-conformances. Food Control, 22(12), 2047-2051. doi:10.1016/j.foodcont.2011.05.027

Luthra, S., Kumar, V., Kumar, S., & Haleem, A. (2011). Barriers to implement green supply chain management in automobile industry using interpretive structural modeling technique: An Indian perspective. Journal of Industrial Engineering and Man-agement, 4(2), 231-257. doi:10.3926/jiem..v4n2.p231-257

Ma, Z., & Wang, S. (2010). A systematic optimization and opera-tion of central chilling systems for energy efficiency and sus-tainability. The Sixth International Conference on Improv-ing Energy Efficiency in Commercial Buildings, 13-14 April Frankfurt, Germany.

Martinez, M. G., Poole, N., Skinner, C., Illes, C., & Lehota, J. (2006). Food safety performance in European Union accession coun-tries: Benchmarking the fresh produce import sector in Hun-gary. Agribusiness, 22(1) 69-89. doi:10.1002/agr.20073

Nandy, B., Sharma, G., Garg, S., Kumari, S., George, T., Sunanda, Y., & Sinha, B. (2015). Recovery of consumer waste in India – A mass flow analysis for paper, plastic and glass and the contribution of households and the informal sector. Resources, Conservation and Recycling, 101, 167-181. doi:10.1016/j.resconrec.2015.05.012

National Center for Cold-chain Development. (2012). Connectiv-ity and post harvest marketing. Retrieved from http://www.nccd.gov.in/PDF/CSCL-Report.pdf

Pagell, M, Krause, D., & Klassen, R. (2008). Sustainable sup-ply chain management: Theory and practice. Journal of Supply Chain Management, 44(1), 85. doi:10.1111/j.1745-493X.2008.00048.x

Palak, G., Ekşioğlu, S. D., & Geunes, J. (2014). Analyzing the im-pacts of carbon regulatory mechanisms on supplier and mode selection decisions: An application to a biofuel supply chain. International Journal of Production Economics, 154, 198-216.

Papargyropoulou, E., Colenbrander, S., Sudmant, A. H., Gouldson, A., & Tin, L. C. (2015). The economic case for low carbon waste management in rapidly growing cities in the developing world: The case of Palembang, Indonesia. Journal of Environment Management, 163(1), 11-19. doi:10.1016/j.jenvman.2015.08.001

Porter, M. E., & Kramer, M. R.(2006). Strategy and society: The link between competitive advantage and corporate social responsibility. Harvard Business Review, 84(12). Retrieved from https://hbr.org/

Rao, P., & Holt, D. (2005). Do green supply chains lead to com-petitiveness and economic performance? International Jour-nal of Operations and Production Management, 25(9), 898-916. doi:10.1108/01443570510613956

Rezitis, A. N., & Kalantzi, M. A. (2016). Investigating technical effi-ciency and its determinants by data envelopment analysis: An application in the Greek food and beverages manufacturing industry. Agribusiness, 32(2), 254-271. doi:10.1002/agr.21432

Shashi, S., & Singh, R. (2015). A key performance measures for evalu-ating cold supply chain performance in farm industry. Manage-ment Science Letters, 5(8), 721-738. doi:10.5267/j.msl.2015.6.005

Shashi. S., & Singh, R.(2015). Modeling cold supply chain envi-ronment of organized farm product retailing in India. Uncer-tain Supply Chain Management, 3(3), 197-212. doi:10.5267/j.uscm.2015.4.004

Shreay, S., Chouinard, H. H., & McCluskey, J. J. (2016). Prod-uct differentiation by package size. Agribusiness, 32(1), 3-15. doi:10.1002/agr.21425

Subin, R. (2011). Country: India’s cold chain industry, Indo-American Chamber of Commerce. Retrieved from https://www.iaccindia.com/userfiles/files/Indials%20Cold%20 Chain%20Industry.pdf

Vachon, S., & Mao, Z. (2006). Extending green practices across the supply chain: The impact of upstream and downstream in-tegration. International Journal of Operations & Production Management, 26(7), 795-821. doi:10.1108/01443570610672248

Zailani, S., Jeyaraman, K., Vengadasan, G., & Premkumar, R. (2012). Sustainable supply chain management (SSCM) in Malaysia: A survey. International Journal of Production Eco-nomics, 14(1), 330-340.doi:10.1016/j.ijpe.2012.02.008


AN INTEGRATED PRODUCTION, INVENTORY, WAREHOUSE LOCATION

AND DISTRIBUTION MODEL

Lokendra Kumar Devangan Masters in Industrial and Management Engineering from

the Indian Institute of Technology – Kanpur – Uttar Pradesh, India [email protected]

ABSTRACT: This paper proposes an integrated production and distribution planning optimization model for multiple manufacturing locations, producing multiple products with deterministic demand at multiple locations. There are multiple modes of transport from plants to demand locations and warehouses. This study presents a model which allows decision makers to optimize plant production, transport and warehouse location simultaneously to fulfill the demands at customer locations within a multi-plant, multi-product, and multi-route supply chain system when the locations of the plants are already fixed. The proposed model is solved for sample problems and tested using real data from a ce-ment manufacturing company in India. An analysis of the results suggests that this model can be used for various strategic and tactical production and planning decisions.

KEYWORDS: Supply chain management, integrated models, logistics, transshipment, warehouse location.


17

Devangan, L. K.: An Integrated Production, Inventory, Warehouse Location and Distribution ModelISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 17 – 2718

1. INTRODUCTION

Extensive research has been performed to opti-mize production planning, inventory, warehouse location and vehicle routing, which have each been addressed as independent problems by several re-searchers (Fawcett & Magnan, 2002). Ganeshan and Harrison (1995) classify supply chain functions into four categories - location, production, inventory and transportation. The independent modeling of sup-ply chain functions has led to suboptimal solutions. There have generally been tradeoffs between com-putational efforts and optimality. Traditionally, in-dependent optimization modeling of different stag-es of the supply chain has been pursued mainly due to limited computational resources.

With recent advances in terms of computational re-sources, studies featuring an integrated approach to modeling supply chain functions have been pro-posed (Erengüç, Simpson, and Vakharia 1999, and Kaur, Kanda, and Deshmukh, 2006). As manufac-turing and economic conditions have become more dynamic, there has been a greater need to study sup-ply chain functions using an integrated approach in terms of global supply chain operations. This ap-proach is based on integrating the decision making related to various functions - location, production, inventory and distribution allocations - into a single optimization problem. The fundamental objective of this integrated approach is to optimize the variables for various functions simultaneously instead of se-quentially as they have been optimized in the past. There are several advantages to integrated model-ing such as reduced storage costs, less time spent on product customization and greater visibility in terms of demand.

This paper is organized as follows. The following section presents a brief review of the literature in the area of integrated supply chain optimization. Next section presents the proposed integrated sup-ply chain optimization model in detail. After that, the results for problems of varying sizes including a problem with real data are discussed. The final section presents my conclusions and future av-enues for research.


From the operational perspective, the issues of pro-duction scheduling, inventory policy and distri-bution routes have been modeled and optimized separately (Fawcett & Magnan, 2002). Reviews of

integrated models for production, inventory and distribution problems can be found in Erengüç et al. (1999). Chandra and Fisher (1994) compare the com-putational aspects of solving production and distri-bution problems separately as opposed to using a combined model. Dror and Ball (1987) and Chandra (1993) address the coordination of the inventory and distribution functions. Reviews of previous studies of multi-period international facility supply chain location problems have been provided by Canel and Khumawala (1997). They formulate a mixed-integer programming (MIP) model and solve it us-ing a branch and bound design algorithm to decide where to place manufacturing facilities and how to determine production and shipping levels. Ca-nel and Khumawala (2001) also develop a heuristic procedure for solving this MIP model considering a similar problem. Lei, Liu, Ruszczynski, and Park (2006) use a two-phase method to simultaneously solve the production, inventory, and routing prob-lems. Fumero and Vercellis (1991) use Lagrangian relaxation (LR) to solve an MIP model for the inte-grated multi-period optimization of production and logistics operations. They compare the results pro-duced by modeling separately and in an integrated fashion. To minimize costs in the integrated model, they use Lagrangian relaxation (LR) to permit the separation of the production and logistics functions. In this decoupled approach, the production and lo-gistics problems are solved independently in two different optimization models. Pirkul and Jayara-man (1996) develop a cost minimization problem to model a multi-product, 3-echelon, plant capacity and warehouse location problem. Lagrangian relax-ation (LR) is used to find the lower bound and then a heuristic method is used to solve the problem. Dasci and Verter (2001) consider approximated costs and demand to integrate the production and distribu-tion functions. Closed form solutions are obtained to minimize the fixed costs of facility location, op-erations, and transport costs.

Arntzen, Brown, Harrison, and Trafton (1995) use a branch and bound algorithm to solve a global supply chain model at Digital Equipment Corporation for multiple products, facilities, echelons, time periods, and transport modes. They then solve it using branch and bound enumeration. Kumanan, Venkatesan, and Kumar (2007) develop two search techniques to minimize total production and distribution costs in a supply chain network. Camm et al. (1997) inte-grate distribution-location problems and a product sourcing problem as two supply chain problems at


Proctor and Gamble. They use a geographical infor-mation system along with integer programming and network optimization models to solve the two sub-problems. Dhaenens-Flipo and Finke (2001) model a network flow problem to minimize the cost of a supply chain within a multi-facility, multi-product and multi-period environment. Sabri and Beamon (2000) model stochastic demand, production and supply lead-times in a multi-objective, multi-prod-uct, and multi-echelon model to address strategic and operational planning. Lodree, Jang, and Klein (2004) propose the integration of customer waiting time with the production and distribution functions in the supply chain to determine the production rate and the sequence of vehicle shipments.

Garcia, Sánchez, Guerrero, Calle, and Smith (2004) and Silva et al. (2005) model an integrated optimiza-tion problem for perishable products. Garcia et al. (2004) consider a ready-mix concrete production and distribution problem, in which the selection of orders to be processed by a ready-mix concrete manufac-turing plant has to be made, and orders have to have a fixed due date and need to be delivered directly to the customer site. Silva et al. (2005) also study a pro-duction and distribution problem with ready-mix concrete. Patel, Wei, Dessouky, Hao, and Pasakdee (2009) propose a model to minimize the total cost of distribution, storage, inventory and operations, and the determining of production levels appropriate to customer demand. They solve it using two heuristic methods and are able to provide a solution close to an optimal result which offers significant savings in runtime. Jolayemi (2010) develops two versions of a fully optimized model and a partially optimized model for production-distribution and transporta-tion planning in three stage supply chain scenarios. Rong, Akkerman, and Grunow (2011) propose a mixed integer programming model for the integra-tion of food quality for production and distribution in a food supply chain. They introduce the dynam-ics of food degradation by considering factors like storage temperature and transportation equipment in the proposed model. Larbi, Bekrar, Trentesaux, and Beldjilali (2012) formulate an integrated multi-objective supply chain model for an Algerian com-pany in modular form to minimize the cost and time for quality control. Bashiri, Badri, and Talebi (2012) present a mathematical model addressing strategic and tactical planning in a multi-stage, multi-prod-uct production-distribution supply chain network and solve the optimization problem for illustrative numerical problems. Cóccola, Zamarripa, Méndez,

and Espuña (2013) and Tang, Goetschalckx, and Mc-Ginnis (2013) propose an integrated production and distribution supply chain problem as a cost minimi-zation problem applied to the chemical and aircraft manufacturing industries. Yu, Normasari, and Lu-ong (2015) develop a cost minimization problem for small and medium size companies to decide how many plants and distribution centers to open and where to open them for a multi-stage supply chain network. Maleki and Cruz-Machado (2013) present a review of the integrated supply chain model and identify theoretical gaps in the integration model.

3. THE INTEGRATED PRODUCTION AND DIS-TRIBUTION PLANNING MODEL

3.1 Problem statement

In this study, we have developed an integrated production and distribution planning (IPDP) op-timization model for a multi-product, multi-plant, multi-location and multi-echelon supply chain en-vironment with multiple transport options includ-ing railways and roads. The manufacturing plants have the capacity to produce any product combina-tion within the company’s portfolio. The production capacity at the plant is shared among all the prod-ucts which means that plants do not possess sepa-rate production lines for each type of product. In the literature, two types of costs are defined for any production plant. First we have fixed costs which include administrative costs and construction costs, etc. Second, we have variable input costs which de-pend on the quantity of the product manufactured. The production costs are made up of labor costs, the costs of procuring raw materials, packaging costs and costs related to the processing of the raw materi-als to produce the finished product. In this study, we assume that the unit production costs and unit pack-aging costs have been computed in such a way that they also account for the associated fixed costs of production and the packaging of the products at the plant. These costs vary from plant to plant due to lo-cal geopolitical and economic reasons. The distances between the manufacturing plants to the customer locations, between the plants to the warehouse, and between the warehouse and the customer locations are assumed to be known. Hence, the unit transport cost between two points is known which varies by the mode of transport. In this study, we assume that railways and roads are two of the types of transport available. Since the transportation capacity by any mode of transport from a plant to a warehouse or a


customer location is bounded, we need to consider the type of transport in the optimization model. The warehouse inbound and outbound handling costs are dependent on the mode of transport which also makes this problem interesting. The implied unit sales price and taxes vary at each location.

The integrated profit maximization model proposed in this paper has been formulated as a mixed inte-ger programming model which determines how to allocate production and distribution to fulfill de-mand at the customer location. The model also de-termines where to place subcontracted warehouses, and allows the user to decide the status of existing warehouses as to whether they should continue or end their operations. This model integrates the op-timization of production, distribution, transport and warehouse location. The integrated production and distribution planning model is formulated as a mixed integer programming model to optimize pro-duction and distribution allocation within produc-tion constraints, obeying transport capacity and the given demand at various customer locations.

3.2 Assumptions

The assumptions for this integrated optimization model are as follows:

i. Integrated production and distribution optimi-zation has been developed for a planning hori-zon period, which may be a month for example

ii. The variable, production and packaging costs have been computed in such a way that they also account for the associated fixed costs of production and the packaging of the products at the plant.

iii. Each plant does not the capacity to produce every type of product.

iv. The total production capacity of a plant is shared among all the types of products and each prod-uct has its own limited capacity at each plant.

v. There is no inventory stored at the plants.

vi. Each plant can handle all types of packaging.

vii. The selling price for a product varies from customer to customer depending on what has been negotiated.

viii. Tax implications vary by customer location.

ix. A less than truckload (LTL) shipment is al-lowed without any penalty.

x. Customer demands can be fulfilled by sup-plying products directly from plants or ware-houses.

3.3 The formulation of the model

The parameters and decision variables used to for-mulate the model are described below:

Decision Variables


Parameters

The profit maximizing objective function and constraints are expressed below:

Maximize

Subject to


3.4 Model Interpretation

Equation (1) expresses the goal of the profit maximi-zation problem which is a function of the revenue from the customers, production costs, packaging costs, transport costs, handling costs at warehous-es, excise duty at retailers, and setup costs or rental costs at warehouses. Equation (2) ensures that the quantity of supply of any product type for any plant, warehouse and customer demand location combi-nation does not exceed the production capacity of this product type at that plant. Equation (3) ensures that the quantity of supply of all type of products for any plant, warehouse and customer demand lo-cation combination does not exceed the production capacity of the plant. Equation (4) ensures that the quantity of supply of a product with a given type of packaging for any plant, warehouse and customer demand location combination does not exceed the packaging type capacity at that plant. Equation (5) ensures that quantity of supply of any product for any plant, warehouse and customer demand loca-tion combination by a given mode of transport does not exceed the transport capacity allocated for that mode of transport. Equation (6) ensures that the quantity of supply of a product type with a given type of packaging for any plant, warehouse and customer demand location combination does not

exceed the maximum demand for this product with this given packaging type at that customer demand location. Equation (7) ensures that the total of supply of a product type with a given type of packaging for any plant, warehouse and customer demand loca-tion combination fulfills the minimum supply of the product for the given packaging type as promised for this customer demand location. This ensures the attainment of a minimum service level agreement as signed with the customer. Equation (8) ensures that no inventory is stored at warehouses on a continual basis and warehouses are used as a transshipment point. Equation (9) ensures that decision variable δj = 1 if warehouse wj is used as a transshipment point.

4. IMPLEMENTATION AND ANALYSIS

4.1 Numerical Examples and Illustrations

In this subsection, I will discuss the two numeri-cal examples used to illustrate the proposed mod-el. The examples are based on supply chains with three stages consisting of production units, ware-houses and customer demand locations. The model is solved both for two and three product types. A summary of the results obtained for these different problem sizes are shown in Table 4.1 below.

Table 1: Problem cases

Case # Plant

# Ware-house

# Trans Mode

# Item type

# Pack-aging Type

# Location # Constraint # Decision Variable

# Itera-tion

# Ware-house Located

1 2 2 2 2 2 10 104 354 56 0

2 3 5 2 3 2 50 659 4,985 436 4

3 9 318 2 2 4 4,783 16,811 141,068 10312 150


From the table it is clear that the integrated production and distribution planning model is able to solve prob-lems of various sizes. Cases 1 and 2 are examples of op-timization problems, whereas Case 3 is a real problem involving a cement manufacturing company.

Case 1 optimizes the production and distribution plan for 2 plants with a 10 location supply chain with 2 types of products, 2 types of packaging, and 2 types of transport modes. It takes 56 iterations to produce the optmimum solution. It does not pre-scribe any warehouse as this is a simple supply chain. Case 2 is an illustrative exmple for a rela-tively more complex supply chain. The optimum solution prescribes subcontracting 4 warehouses to fullfill demand. Case 3 involves 9 plants and 4,783 customer demand locations with two products and two modes of transport, and takes 10,312 iterations to produce its optimum solution and ends up rec-ommending the subcontracting of 150 warehouses. So the proposed model recommends warehouses as transhipment points for complex supply chains whereas it does not for a simple supply chain.

Case 3 will be discussed in detail in the next subsec-tion. All of the three cases were solved using SAS OR software (SAS Institute Inc. (2011) and the time taken for all of them was less than 2 minutes. OPT-MODEL, an algebraic modeling language, was used by the SAS/OR software to solve the problems. The OPTMODEL procedure allows efficient program-ming of large optimization problems. It uses the branch and cut algorithm to solve the proposed MIP optimization model.

4.2 Case study

The proposed model was implemented for a cement manufacturing organization in India which has 9 manufacturing plants spread across the country serving customers all over the country. Production capacity data for each of the plants was collected from the plant operation managers. The transporta-tion cost, transportation capacity and demand data were collected from the supply chain planning man-agers. They also shared the data for minimum sup-ply agreements and the selling price at different cus-tomer locations. This cement manufacturer produces two types of cement called OPC and PPC which are sold in the market with four types of packaging. In an emerging market like India, there are two types of demand for cement: bulk and retail. The cement manufacturing industry serves the demands of dif-

ferent geographical locations and has contracts with dealers to serve end customers. Dealers are the cus-tomers of this manufacturing company. They also serve the bulk demands of the construction indus-try. Manufacturing plants are set up near sources of raw materials. The rate of demand is not constant over time in each region as infrastructure construc-tion activities move to different locations over time. The proposed model which integrates production, warehouse location and distribution planning is ideal for scenarios where the rate of demand is not constant in a region. The proposed model allows the manufacturer to set up or subcontract warehouses to act as transshipment points and serve the demands of customers who are located very far away from the manufacturing plants. The model has also integrat-ed the minimum demand fulfillment agreement and does not differentiate between bulk demand and re-tail demand because the model has been formulated as a profit maximization model.

How best to configure cement production, distribu-tion and warehouse planning has been solved using real data collected from this manufacturing organiza-tion. There are 4,783 demand locations and 9 cement plants to serve demand. 318 is the number of ware-houses available to be used as transshipment points. Railways and roads are two of the transport modes. In reality every plant is connected to every ware-house and demand location, but transport cost data is not available for some of the routes. In the baseline scenario, the total demand is 781,321 tons and the ob-jective value is 2,861,932,772 INR. 0.02% of demand is not fulfilled due to lack of transportation route data. The total number of iterations required to solve the problem was 10,312 using a four thread 32GB RAM machine. The optimized solution recommends 150 warehouses. The overall utilization rate is 67%, and there are two plants which are highly underutilized (<=30%), and four plants with high utilization (>80%).

The results obtained using real data from this manu-facturing company are in sync with the geographical locations of the plant, warehouse and customer lo-cations. The nearer customer locations are supplied directly from the plant, whereas customer locations that are farther away get supplied by the warehous-es. The warehouses are supplied by various plants. The discussion of these results with plant managers also suggests a reduction in the cost components. The results also suggest that the optimal number of warehouses required is substantially smaller than the number of warehouses operating currently.


4.3 Sensitivity analysis

In a given process, a utilization rate of more than 80% strains the entire process. Table 2 shows the ob-jective value for different levels of demand in pro-portion to baseline demand, and Table 3 shows plant

Table 2 Objective value for different levels of demand

Iteration %Profit %Demand Overall Plant Utilization # Warehouses

.80*Baseline 79.99% 99.980% 53% 147

.90*Baseline 89.93% 99.980% 60% 148

Baseline Demand 100.00% 99.980% 67% 150

1.10*Baseline 109.85% 99.979% 73% 153

1.20*Baseline 119.55% 99.980% 80% 154

1.30*Baseline 128.84% 99.980% 87% 153

utilization at different levels of demand. Approxi-mately 100% of demand is fulfilled in all of the levels considered. Though the overall utilization rate rang-es from 53% to 87%, plant P4 is always 100% utilized for economic reasons, whereas plants P7 and P9 are the least utilized in every case.

Table 3 Utilization for different levels of demand

Scenario/Plants P1 P2 P3 P4 P5 P6 P7 P8 P9

.80*Baseline 61% 83% 64% 100% 45% 42% 17% 62% 24%

.90*Baseline 69% 100% 83% 100% 52% 48% 19% 70% 27%

Baseline Demand 83% 100% 100% 100% 60% 53% 21% 80% 30%

1.10*Baseline 100% 100% 100% 100% 89% 59% 24% 92% 33%

1.20*Baseline 100% 100% 100% 100% 100% 77% 34% 100% 43%

1.30*Baseline 100% 100% 100% 100% 100% 99% 47% 100% 48%

These observations have been used to create differ-ent scenarios for production capacities which are applicable to the manufacturing industry. Solu-tions to these scenarios are helpful in production planning and affect profitability in different situa-tions. The objective value and utilization of each of the plants in these scenarios are presented in Tables 4 and 5. The production capacity scenarios are dis-cussed below:

a. Scenario: All 9 of the plants are operating at 90% of production capacity. This is analogous to the situation that occurs when plants undergo peri-odic maintenance activities.

b. Scenario: Plant P7 is closed. The utilization of plant P7 is only 21% for the baseline demand case. Management may want to close this plant.

c. Scenario: Plants P7 & P9 are closed. Similar to P7, plant P9 is also highly underutilized.

Management may decide to close both of these plants.

d. Scenario: Plants P7 & P9 are closed, and plants P2, P3 & P4 are operating at 90% of production capacity. The remaining plants are operating at 100% capacity. This is analogous to the situa-tion that occurs when P7 and P9 are closed and 100% of the utilized plants are available with 90% capacity occurring due to maintenance ac-tivities.

e. Scenario: Plant P4 is closed. Plant P4 is the most economical plant, and is 100% utilized even when demand is at 80%. This plant will have to be shut down for maintenance due to a critical breakdown situation.

f. Scenario: Plant P4 is closed and plants P1, P2, P3, P5 & P8 are operating at 90% due to mainte-nance activities.


Table 4 Utilization for different production capacity

Plant Code Baseline Utilization a) Scenario b) Scenario c) Scenario d) Scenario e) Scenario f) Scenario

P1 83% 90% 88% 91% 96% 100% 90%

P2 100% 90% 100% 100% 90% 100% 90%

P3 100% 90% 100% 100% 90% 100% 90%

P4 100% 90% 100% 100% 90%

P5 60% 84% 60% 75% 100% 100% 90%

P6 53% 54% 61% 69% 72% 77% 90%

P7 21% 23% 34% 45%

P8 80% 84% 80% 81% 85% 100% 90%

P9 30% 30% 40% 35% 37%

Objective Value

100% 99% 98% 98% 100% 96% 95%

Table 5 Objective value for different production capacities

Scenarios Objective Value as % of Baseline Objective # Warehouses

a) Scenario 98.79% 145

b) Scenario 97.78% 144

c) Scenario 97.63% 143

d) Scenario 99.87% 152

e) Scenario 95.79% 150

f) Scenario 95.07% 150

5. THE OPTMODEL PROCEDURE IN SAS /OR

The data manipulation ability of SAS software makes it easy to handle problems of any size. In the implementation of Case 3 in which real data is used, plant managers know that all plants are not connect-ed by usable transportation routes to the destination points (customer locations or warehouses) hence all possible combinations are not present in the data used for transportation costs. Also it is known from the expected demand data that all customer loca-tions will not have demand for all types of products. In such problems the number of constraint and deci-sion variables cannot be correctly enumerated using permutations and combinations. Considering all the combinations in the programming, some of which are infeasible, also reduces its efficiency as it does not add any value to the objective function. These kinds of challenges in real problems can be very eas-ily programmed using SAS OPTMODE to solve in-tegrated optimization problems. The OPTMODEL procedure is discussed briefly in the next subsection.

6. CONCLUSIONS AND FUTURE WORK

The model proposed in this study determines the optimal integrated production, warehouse location and distribution planning as part of a profit maximi-zation problem. Other studies in the literature have formulated the integrated optimization problem as a cost minimization problem. The analysis of solu-tions for a large real supply chain problem involv-ing a cement manufacturing company shows that complex supply chains can be modeled and have good performance. This study presents a model that enables decision makers to simultaneously optimize product and customer allocations and warehouse locations within a multi-plant, multi-product and multi-route supply chain system. The various sce-narios discussed in terms of sensitivity analysis are useful in understanding what leverages profitability and redundant production capacity. These scenarios are highly relevant in a manufacturing industry. In the electronics, apparel and food processing indus-tries, where a large variety of products is produced


and transported to many market locations, the pro-posed integrated production and distribution plan-ning model is highly relevant. The model is also useful for planning annual maintenance work or temporary plant shutdowns. In the presented ap-proach demand is deterministic. This can be fore-casted periodically using historical sales data, and then it can be used as input into proposed models of supply chain optimization. Future research deal-ing with stochastic demand and the lead time in the model for perishable items would be useful. The proposed model can also be extended to consider the penalties that result from half truck loads and routing decisions in the integrated model.

REFERENCES

Arntzen, B. C., Brown, G. G., Harrison, T. P., & Trafton, L. L. (1995). Global supply chain management at Digital Equip-ment Corporation. Interfaces, 25(1), 69-93.

Bashiri, M., Badri H., & Talebi J.( 2012). A new approach to tac-tical and strategic planning in production-distribution net-works. Applied Mathematical Modelling, 36(4), 1703-1717. doi:10.1016/j.apm.2011.09.018

Canel, C., & Khumawala, B. M. (1997). Multi-period interna-tional facilities location: An algorithm and application. In-ternational Journal of Production Research, 35(7), 1891-1910. doi:10.1080/002075497194976

Canel, C., & Khumawala, B. M. (2001). International facilities location: A heuristic procedure for the dynamic incapaci-tated problem. International Journal of Production Research, 39(17), 3975-4000.

Camm, J. D., Chorman, T. E., Dill, F. A., Evans, J. R., Sweeney, D. J., & Wegryn, G. W. (1997). Blending OR/MS, judgment, and GIS: Restructuring P&G’s supply chain. Interfaces, 27(1), 128-142. doi:10.1287/inte.27.1.128

Chandra, P. A. (1993). A dynamic distribution model with warehouse and customer replenishment requirements. Journal of the Operational Research Society, 44(7), 681-692. doi:10.2307/2584042

Chandra, P., & Fisher, M. L. (1994). Coordination of production and distribution planning. European Journal of Operational Research, 72(3), 503-517. doi:10.1016/0377-2217(94)90419-7

ACKNOWLEDGEMENTS

The author would like to acknowledge the con-tributions of the anonymous manufacturing company in the form of real data and manage-rial input.

Cóccola, M. E., Zamarripa, M., Méndez, C. A., & Espuña A. (2013). Toward integrated production and distribution management in multi-echelon supply chains. Computers and Chemical Engineering, 57, 78–94. doi:10.1016/j.comp-chemeng.2013.01.004

Dasci, A., & Verter, V., (2001). A continuous model for pro-duction-distribution system design. European Journal of Operational Research, 129(2), 287-298. doi:10.1016/S0377-2217(00)00226-5

Dhaenens-Flipo, C., & Finke, G., (2001). An integrated model for an industrial-distribution problem. IIE Transactions, 33(9), 705-715. doi:10.1023/A:1010937614432

Dror, M., & Ball M. (1987). Inventory routing: Reduction from an annual to a short period. Naval Research Logistics, 34, 891-905. doi:10.1002/1520-6750(198712)34:6<891::aid-nav3220340613>3.0.co;2-j

Erengüç, S. S., Simpson, N. C., & Vakharia, A. J. (1999). Integrated pro-duction/distribution planning in supply chains: An invited review. European Journal of Operational Research, 115(2), 219-236.

Fawcett, S. E., & Magnan, G. M. (2002). The rhetoric and reality of supply chain integration. International Journal of Physi-cal Distribution and Logistics Management, 32(5), 339-361. doi:10.1108/09600030210436222

Fumero, F., & Vercellis, C. (1991). Synchronized development of production, inventory, and distribution schedules. Transpor-tation Science, 33(3), 330-340. doi:10.1287/trsc.33.3.330

Ganeshan, R., & Harrison, T. P. (1995). An introduction to supply chain management. Retrieved from http://lcm.csa.iisc.ernet.in/scm/supply_chain_intro. html.

Garcia, J. M., Sánchez, L. S., Guerrero, F., Calle, M., & Smith. K. (2004). Production and vehicle scheduling for ready-mix operations. Computers & Industrial Engineering, 46(4), 803-826. doi:10.1016/j.cie.2004.05.011

Jolayemi, J. K. (2010). Optimum production-distribution and transportation planning in three-stage supply chains. Inter-national Journal of Business and Management, 5(12), 29-40. doi:10.5539/ijbm.v5n12p29

Kaur, A., Kanda, A., & Deshmukh, S. G. (2006). A graph theo-retic approach for supply chain coordination. International Journal of Logistics Systems and Management, 2(4), 321-341. doi:10.1504/IJLSM.2006.010379

Kumanan, S., Venkatesan, S. P., & Kumar, J. P., (2007). Optimisa-tion of supply chain logistics network using random search techniques. International Journal of Logistics Systems and Management, 3(2), 252-266. doi:10.1504/IJLSM.2007.011824

Larbi, E. A. S., Bekrar, A., Trentesaux, D., & Beldjilali, B. (2012). Multi-stage optimization in supply chain: an industrial case study. Paper presented at 9th International Conference of Modelling, Optimization and Simulation, Bordeaux France.

Lei, L., Liu, S, Ruszczynski, A, & Park, S., (2006). On the integrat-ed production, inventory, and distribution routing problem. IIE Transactions, 38(11), 955-970.

Lodree, E., Jang, W., & Klein, C. M. (2004). Minimizing re-sponse time in a two-stage supply chain system with


variable lead time and stochastic demand. Internation-al Journal of Production Research, 42(11), 2263-2278. doi:10.1080/0020754042000197676

Maleki, M., & Cruz-Machado, V. (2013). A review on supply chain integration: Vertical and functional perspective and integration models. Economics and Management, 18(2), 340-350. doi:10.5755/j01.em.18.2.2968

Patel, M. H., Wei, W., Dessouky, Y., Hao, Z., & Pasakdee, R. (2009). Modeling and solving an integrated supply chain sys-tem. International Journal of Industrial Engineering, 16(1), 13-22.

Rong, A., Akkerman, R., & Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. International Journal of Production Econom-ics, 131(1), 421-429. doi:10.1016/j.ijpe.2009.11.026

SAS Institute Inc. (2011) SAS/OR® 9.3 User’s Guide: Mathemati-cal Programming. Cary, NC.

Silva, C. A., Faria, J. M., Abrantes, P., Sous,a J. M. C., Surico, M., & Naso, D. (2005). Concrete delivery using a combination of GA and ACO. Proceedings of the 44th IEEE conference on decision and control, and the European control conference, 7633-7638.

Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in sup-ply chain design. Omega, 28(5), 581-598. doi:10.1016/S0305-0483(99)00080-8

Tang, Z., Goetschalckx, M., & McGinnis, L. (2013). Modeling-based design of strategic supply chain networks for aircraft manufacturing. Procedia Computer Science, 16, 611-620. doi:10.1016/j.procs.2013.01.064

Yu, V. F., Normasari, N. M. E, & Luong, H. T. (2015). Integrated location-production-distribution planning in a multiprod-ucts supply chain network design model. Mathematical Problems in Engineering, 2015, 1-13. doi:10.1155/2015/473172


A SIMULATION OF CONTRACT FARMING USING AGENT BASED MODELING

Yuanita Handayati Professor at School of Business and Management,

Bandung Institute of Technology - Bandung, Jawa Barat, Indonesia [email protected]

Togar M. Simatupang Professor at School of Business and Management,


Tomy Perdana Professor at Department of Agribusiness,

Faculty of Agriculture, Padjadjaran University - Sumedang, Indonesia [email protected]

Manahan Siallagan Professor at School of Business and Management,


ABSTRACT: This study aims to simulate the effects of contract farming and farmer commitment to contract farming on supply chain performance by using agent based modeling as a methodology. Sup-ply chain performance is represented by profits and service levels. The simulation results indicate that farmers should pay attention to customer requirements and plan their agricultural activities in order to fulfill these requirements. Contract farming helps farmers deal with demand and price uncertainties. We also find that farmer commitment is crucial to fulfilling contract requirements. This study contrib-utes to this field from a conceptual as well as a practical point of view. From the conceptual point of view, our simulation results show that different levels of farmer commitment have an impact on farmer performance when implementing contract farming. From a practical point of view, the uncertainty faced by farmers and the market can be managed by implementing cultivation and harvesting schedul-ing, information sharing, and collective learning as ways of committing to contract farming.

KEYWORDS: Contract farming, agent based modeling and simulation, commitment, cultivation and harvest scheduling, supply chain performance.


28

Handayati, Y., Simatupang, T. M., Perdana, T., Siallagan, M.: A Simulation of Contract Farming Using Agent Based ModelingISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 28 – 4829

1. INTRODUCTION

These days consumers of agricultural products have become increasingly aware of and concerned about the availability, quality, and safety of the food they consume (Engelseth, Takeno, & Alm, 2009; Hastuti, 2007). This change in consumer be-havior represents a challenge to firms, exporters, intermediaries and retailers of traditional and mod-ern distribution channels in terms of meeting con-sumer demands (Adebanjo, 2009). This challenge is related to the ability of agricultural producers to supply the quality required by their customers. In developing countries, this challenge is more com-plex due to the characteristics of farmers as agricul-tural producers. They are usually small-scale farm-ers who have limited knowledge and limited access to technology and market information. They farm using traditional methods, and their agricultural routines are bases on their own needs and wants, rather than consumer needs.

Agricultural supply chains in developing countries, especially in Indonesia, are typically long supply chains, which mean that they consist of more than two actors. Moreover, farmers depend on interme-diaries who provide them with access to the mar-ket. The products that these farmers sell fluctuate in terms of quality, quantity and price (Sutopo, His-jam, & Yuniaristanto, 2012). These farmers do not have much bargaining power in negotiating with these traders, because they have limited resources. Therefore, they tend to accept any price that the in-termediaries offer (Widyarini, Simatupang, & En-gelseth, 2016). There is no written contract in these partnerships, and farmers and intermediaries can pull out of their agreements at any time, so it is hard to maintain long, sustained partnerships. This leads to uncertainties in terms of demand and prices for farmers and uncertainty in terms of agricultural supply for intermediaries.

Contract farming is proposed not only as an alterna-tive for dealing with demand and price uncertain-ties, but also as global optimization and a win-win solution for agricultural producers, intermediar-ies, firms, exporters, and customers (Pandit, Lal, & Rana, 2015; Young & Hobbs, 2002). Contract farming establishes a fixed price that the farmers will receive as agricultural producers. Moreover, the contract farmer also sets the quantity, quality, and the time of delivery of these agricultural products, which are supplied by farmers to intermediaries, firms, or ex-porters based on their customer requirements (Pan-

dit et al., 2015; Young & Hobbs, 2002). Consumers can thus find the good and safe agricultural prod-ucts that they want (Pandit et al., 2015).

Several studies mention that the implementation of contract farming requires commitment from all of the involved parties (Imbruce, 2008; Minot, 2007; Pultrone & Silva, 2012). Commitment is a willing-ness to give your time and energy to something that you believe in. In this study, commitment is used in the context of the farmers’ willingness to give their time and energy in fulfilling what has been written in the farming contract. The terms of farming con-tracts are usually violated by farmers who provide agricultural products of low quality that does not meet the farming contract’s requirements, or by sell-ing their products to other parties who offer a higher price (Guo, Jolly, & Zhu, 2007). Regarding the com-mitment of farmers to contract farming, Guo et al. (2007) state that the education level of farmers has a positive relationship with their participation in, and their commitment to, contract farming. However, previous studies have not sufficiently elaborated the impact of this coordination process and farmer com-mitment to contract farming on supply chain per-formance. Therefore, this study seeks to analyze the implementation of contract farming. We have adapt-ed a case study within the Pangalengan cluster in West Java, Indonesia. A preliminary study has been carried out to describe the journey of the Pangalen-gan cluster to gaining access to a structured market, focusing on the problems that the farmers faced and the coordination mechanism that they have imple-mented. The current study extends the coordination process and studies the impact of this coordination mechanism and the commitment to contract farm-ing on farmer supply chain performance.

This study is organized in the following manner. First we have offered some background to this re-search area. Second, we will present a review of the literature related to contract farming and agri-culture in Indonesia. Third, we will discuss agent based modeling and our research framework as part of this study’s methodology. We then present and discuss the simulation results. Finally, we will offer our conclusions.


In this section, the concept of contract farming, and the specific characteristics of agriculture and the case study are explained.


2.1 Contract Farming

Definitions of contract farming have been offered by several researchers:

“a binding arrangement between a firm or contractor and an individual producer or contractee in the form of a ‘forward agreement’ with well-defined obligations and remuneration for tasks done, often with specifications on product properties such as volume, quality, and timing of delivery” (Catelo & Costales, 2008).

“Agricultural production carried out according to a prior agreement in which the farmer commits to producing a given product in a given manner and the buyer commits to purchasing it” (Minot, 2007).

“A contractual arrangement between farmers and other firms, whether oral or written, specifying one or more conditions of production, and one or more conditions of marketing, for an agricultural product, which is non-transferable” (Rehber, 2007).

From these definitions, contract farming can be defined as agreements between farmers as produc-ers and firms as buyers to stipulate the specifica-tions of an agricultural product (volume, quality, and delivery time) at an agreed to price. Moreover, according to Rehber (2007), contract farming oc-curs when farmers and firms make a contractual arrangement involving production and marketing conditions for an agricultural product and the con-tract is non-transferable. The production conditions may include product types and characteristics, farming schedules, and the use of specific farm-ing techniques and resources. As for the market-ing conditions, they usually include a fixed price for the products and payment time. These condi-tions help farmers in obtaining more stable prod-uct prices and knowledge about current farming techniques, making the agricultural product mar-ket more structured. Contract farming also entails a commitment to fulfilling contract requirements by both the farmers and the firms.

People started applying the concept of contract farming around 100 years ago. It was first used by the Japanese in Taiwan during the nineteenth centu-ry (Little & Watts, 1994). It was also used in Europe, specifically within the seed industry, before the Sec-ond World War (Rehber, 2007). Since then, contract farming has become widespread, and it accounted for up to 39% of the total value of US agricultural production in 2001 (Young & Hobbs, 2002).

The use of contract farming in other areas of the world has also increased quite significantly, espe-cially in Southeast Asia. In Indonesia, contract farm-ing is promoted by the government through the Fed-eral Land Development Agency (FELDA) (Rehber, 2007). In Malaysia, contract farming mostly involves state-promoted out grower arrangements (Morri-son, Murray, & Ngidang, 2006). In Vietnam, 90% of cotton and fresh milk, and over 40% of rice and tea also come from contract farming (UNCTAD, 2009).

Based on the detailed information mentioned in the contract, farming contracts can be categorized into three types, namely market-specification contracts, resource-providing contracts, and production-man-agement contracts (Prowse, 2012). First, market-specification contracts give a guarantee to farmers in terms of price, delivery time, and marketing outlets, as long as the customer’s quality requirements are met. In this type of contract, farmers retain full con-trol over the production process. Second, resource-providing contracts state that agricultural input will be provided to the farmers in order to assure the quality of the agricultural product. This contract is usually used for crops that require specific inputs or quality standards, where farmers find it difficult to supply the agricultural inputs. Third, production-management contracts discuss the role of firms, in-termediaries, exporters, and/or retailers in the pro-duction process. In this type of contract, farmers do not have full control over their production process.

2.2 Agricultural Product Characteristics

The specific characteristics of agricultural prod-ucts that differ from other products are seasonal-ity, perishability, safety, and traceability through-out an end-to-end supply network (Taylor & Fearne, 2006; Van der Vorst, Beulens, & Van Beek, 2000). Furthermore, intermediary trading orga-nizations in the food industry have been facing the challenges of coordinating retail promotions with lead time requirements and a generally low degree of supply chain flexibility and limited sup-ply requirements (Adebanjo, 2009). Moreover, Bijman, Omta, Trienkens, Wijnands, and Wub-ben (2006) have suggested that an increased inter-organizational collaboration in terms of the food supply may be due to: the rise of food safety as a prominent societal issue; the raw materials in food distribution that often closely resemble the fin-ished product; and the fact that foods are to vary-ing degrees always perishable goods. Fresh foods,


e.g. freshly-packed seafood, are perishable products. This may then limit the timeframe for their transfor-mation during the supply stage.

Fresh food distribution involves specific challenges, i.e. “… (i) fresh products are not standard and are subject to quality deterioration, (ii) there is a lack of clear product descriptions and coding standardiza-tion, (iii) information requirements differ with each customer, making standardization complex, and (iv) farmers utilize a relatively low degree of automa-tion” (Van der Vorst et al., 2000).

Furthermore, food consumption is a vital aspect of human well-being. Thus, food supply chains seek to balance food safety, which is an ethically-laden societal aim, with the economic needs of supply-ing “quality” (Engelseth et al., 2009). “Safety” in the food supply denotes food product features mea-surable through the supply chain in terms of hu-man well-being, which are dependent on technical details, while “quality” involves product attributes measured in relation to customer satisfaction. Ac-cording to Becker (2000), food product quality in-volves product-oriented quality, process-oriented quality, and consumer-oriented quality.

2.3 Agriculture in Indonesia: A Case Study

In contrast to farmers in developed countries who have greater access to markets, and more knowl-edge, skill, and information about current farming technology, farmers in Indonesia are predominantly small scale and are characterized by not having ac-cess to the market, mostly selling their agriculture products to intermediaries, and a lack of knowledge and information about current farming technology. In practice, most small Indonesian farmers sell their agricultural products to intermediaries. Moreover, the relationships between farmers and intermedi-aries are not only based on transactions and com-merce. They are also based on family, religious, and ethnic links. In these partnerships, there is an infor-mation gap in terms of demand and prices. Farmers do not have the bargaining power to push demand and set the selling prices that intermediaries offer (Widyarini et al., 2016).

In addition, these farmers have to deal with high uncertainty along with high risks, since they do not know the extent of market demand and prices may

fluctuate based on the number of products on the market. The more agricultural products available on the market, the lower the price will be. With these conditions, it is difficult to improve the welfare of these small farmers. Moreover, the use of traditional farming methods can be observed from farmers’ cul-tivating and harvesting decisions. Small-scale farm-ers usually look at their neighbors in deciding which products to plant, and do not have plans in terms of when to plant and how much to plant. Moreover, these farmers do not have plans related to when they should harvest and how much they should harvest within a given period. Usually harvesting occurs during a short period of time, and selective harvesting is not implemented.

In our current study, we use a case study adminis-tered within the Indonesian horticulture industry. In fact, this industry is dominated by small-scale farmers who are usually scattered all over the coun-try. In addition, there is a relatively low level of competency in terms of farming and management practices. Another characteristic of this industry is the role of the intermediate actors, who are known as “tengkulak” (middlemen). They play an impor-tant role in the distribution of horticultural products in Indonesia. These characteristics have made food production and efficient coordinated food distribu-tion in Indonesia challenging.

We have selected the Pangalengan region in West Java for this case study due to its importance in producing and distributing horticultural products, produced not only for the domestic market, but also for international markets as well (see Figure 1). The Pangalengan cluster is one of the most vital fruit and vegetable centers in West Java, Indonesia. In the past, farmers had limited information related to market demand and prices. Farmers did not have any ac-cess to a structured market, and only dealt with in-termediaries or brokers to sell their products. Direct sales to intermediaries or brokers did not require good post-harvest handling for fruit and vegetable products. By developing a partnership model within a cluster, these farmers have been able to deal with several constraints. Price fluctuation is a major chal-lenge to these fruit and vegetable farmers. In addi-tion, these farmers have limited access to appropri-ate technologies, counseling services, and therefore perceive themselves individually as weak players in this regional marketplace.


Figure 1. The Pangalengan Region as a Case Study

In 2009, Padjajaran University established the Val-ue Chain Center (VCC) which has played the role of connecting farmers with the market in order to raise the competitiveness of West Javan agribusi-ness (Perdana & Kusnandar, 2012). In the same year, the VCC also began a partnership with farm-ers in Pangalengan. The partnership went well, so in September 2013 the cluster became an orga-nized entity created by the VCC to maintain price stability from the farmers to the market. Besides this, using the concept of a “cluster” has meant that farmers can make direct sales to a structured market and build partnerships with private com-panies and governmental agencies. Furthermore, the role of the VCC in creating the Pangalengan cluster has given other companies, willing to join and build partnerships with the cluster, access to a structured market.

In the beginning of this partnership between farm-ers and structured markets, there is a contract that sets out the structured market requirements in-cluding the quantity of agricultural products, their quality and price, and when they need to be de-livered. Structured markets know the needs and wants of their consumers. Consumer information needs are shared with the farmers. These consumer needs are fulfilled by farmers by applying one or more coordination mechanisms, such as collective learning, joint decision making, and information sharing with other farmers or with the VCC and other institutions as well as with the structured

market. These coordination mechanisms are used to achieve goals, namely selecting the best agricul-tural inputs, designing cultivation, planning har-vesting, and developing and implementing stan-dard operational procedures, etc.

Practically, providing a structured market with agricultural products that can fulfill their require-ments is not easy. Farmers choose specific varieties in accordance with cultivation techniques that may produce expected yields. These farmers used to im-plement traditional cultivation techniques or learn on their own. If the market desires particular agri-cultural products, it is important to provide farm-ers with training in terms of the necessary cultiva-tion techniques. The VCC can also help train these farmers. To maintain continual high productivity, cultivation and harvest scheduling needs to be well planned. Cultivation and harvest scheduling are im-portant to making sure that the market has a sustain-able supply of agricultural products from farmers. If cultivation and harvest scheduling are not imple-mented by these farmers, there will be periods when farmers cannot supply the market with enough of their agriculture products because there is no farmer cultivating these agricultural products at the time. There will also be periods in which the supply will be excessive because most of the farmers’ agricul-tural production occurs at the same time. In terms of implementation, farmers still lack the commitment to follow these schedules, which have already been proposed by the cluster and the VCC.


3. THE AGENT BASED MODEL

3.1 Model Objective and Overview

In this study, we use agent based modeling to simu-late three different scenarios related to the implemen-tation and impact of contract farming and farmer commitment to meeting the contract requirements. Each scenario simulates agent interactions that have an impact on supply chain performance. As men-tioned by Tangpong, Hung, and Li (2014), the interac-tions among supply chain agents gives an idea of the emerging properties of a supply chain system. In this study, the impact of each scenario in terms of supply chain performance is represented by farmer profits and service levels. This study uses tomatoes as the simulated commodity. The differences between the three scenarios can be seen in Appendix 1.

This study simulates the coordination process and farmer behavior in cultivating tomatoes, harvesting them until the tomato plants die off, sending the har-vested tomatoes to the farmer association (KATATA) for contract quality products and to intermediar-ies (the traditional market) for non-contract quality products, and the farmers receive payments from both the structured and traditional markets through KATATA. The values for the variables in this simula-tion have been derived from interviewing the man-ager of KATATA (the coordinator of the Pangalengan cluster), the head farmer, and the farmer advisor, conducting on-site observations, and searching for secondary data especially in terms of tomato prices in local markets. The interviews were conducted from January through April 2016. The interviews ranged from 30 to 120 minutes in length.

For all of the scenarios, the farmers’ harvested prod-ucts can be categorized into two types, namely con-tract quality products that meet the farming contract requirements, and non-contract quality products that do not meet the farming contract requirements. The contract quality products are sold at the price fixed in the farming contract, while the non-contract quality products are sold at a free market price. In the first scenario, there is no farming contract in the partnerships between the farmers and the free mar-ket or intermediaries. Information about demand and prices is not available before farmers start their cultivation activities. Therefore in this scenario, farmers make their decisions in terms of when to plant and how much to plant based on their experi-ence, not on customer orders. Based on the interview with the farmers, the prices that farmers get when

they sell their products on the free market fluctu-ate in every period. Therefore in our simulation, the agricultural product price is random and follows a normal distribution, with the minimum price being IDR 200 per kg and the maximum price being IDR 10,000 per kg. However, the free market can absorb all the harvested products produced by the farmers.

In the second scenario, a farming contract is used in the partnerships between the farmers and the structured market. The farming contract sets out the demand requirements that need to be met by farm-ers in each period, and the price that farmers will receive when they sell their agricultural products to the structured market. The structured market has an initial demand of 3 metric tons of tomatoes. In the second scenario, the price that the farmers will receive is fixed. The structured market’s price for to-matoes is IDR 7,750 per kg. In the second scenario, farmers are still not committed to contract farming in their efforts to meet the requirements agreed to by both the farmers and the structured market. De-cisions in terms of when to plant are still made by each farmer and are not made jointly. In the third scenario, the farmers are committed to contract farming and implement joint decision making, in-formation sharing, and collective learning to meet contract requirements.

Based on the simulation flow in Appendix 2, the simulation begins with farmer decisions related to when to plant and how much to plant. In scenarios 1 and 2, the number of farmers that plant the ag-ricultural product in every period is not the same. This happens because in scenarios 1 and 2 the farm-ers do not use cultivation and harvest scheduling. From our interview with the farmers, we know that 1 tomato plant with good agricultural practices can yield a maximum of 2 kg of tomatoes per week dur-ing the harvesting period. Each harvesting period lasts six consecutive weeks. The quantity of products harvested each week during the harvesting period is not the same. In weeks 1 and 6, farmers can only har-vest 40% of the expected yield (0.168 metric tons per farmer). In weeks 2 and 5, farmers can only harvest 50% of the expected yield (0.21 metric tons per farm-er). During weeks 3 and 4, with the farmers are able to harvest 90% of the expected yield (0.378 metric tons per farmer). In scenario 3, there are 10 farmers assigned to plant this agricultural product in each period in order to meet demand requirements and achieve a stable supply. Each farmer should there-fore cultivate 210 plants. While the tomatoes are still


harvestable, the farmers repeat the process of send-ing tomatoes to KATATA and receiving money from KATATA. KATATA also repeats the process of send-ing tomatoes to structured and traditional markets, and sends the money from both markets to the farm-ers. The farmer service levels and profits are calcu-lated for each period.

When farmers use cultivation and harvest schedul-ing, the second group of farmers starts their culti-vating activities 28 days after the previous group has begun their cultivation activities. For example, if group 1 has a 0-day delay, group 2 will have a 28-day delay, group 3 will have a 56-day delay, and so on. After planting their agricultural products, the farmers wait for 90 days until their tomatoes are ripe and ready to be harvested. If the scheduling option is not used, each farmer can individually choose when to plant their agricultural products. To validate the model, the simulation results have been confirmed with the manager of KATATA.

This study uses Relogo, a simple version of Repast, a Java Groovy-based programming language for agent based modeling and simulations (http://www. repast.sourceforge.net). It is an open-source simu-lation framework for agent based modeling, based on the Java programming language. Basically, the Java version of Repast is supposed to be used by us-ers familiar with Java who need to create advanced complex simulations. For new users with limited knowledge of Java, it is suggested that they use Re-logo, a simpler version of Repast, which is based on Groovy, a programming framework for Java. While in our simulation the customization of some agent actions is needed, this tool makes it easier to create advanced complex simulations.

We use an agent based simulation to prove the hy-pothesis of this study. Our hypothesis is that supply chains featuring contract farming with farmer com-mitment outperform those featuring contract farm-ing without farmer commitment and are better than the free market mechanism based on average profits and service levels. With a commitment to contract farming, farmers can coordinate their agricultural activities to fulfill contract requirements.

3.2 Agents

Based on our case study, there are four main agents in the Pangalengan cluster, namely the free market; the structured market; KATATA as the coordinator of the Pangalengan cluster; and the Pangalengan

farmers. Pangalengan farmers are divided into three categories based on their agricultural practices. Farmers within the first group have poor agricul-tural practices. Fifteen farmers from the Pangalen-gan cluster can be included in this category. Farmers within the second group have fair agricultural prac-tices. Fifteen farmers can be included in this catego-ry. Farmers within the third group have good agri-cultural practices. Twenty farmers can be included in this category.

These different types of farmers produce different quantities of contract quality products. Only 10% of the tomatoes produced by farmers with poor ag-ricultural practices are of sufficient quality to meet contract standards, and it will cost them IDR 3,000 per plant, given that one plant is equal to 2 kilograms of tomatoes. Farmers with fair agricultural practices will have 30% contract quality tomatoes and it will cost them IDR 4,000 per plant, while farmers with good agricultural practices will produce 80% con-tract quality tomatoes, but it will cost them IDR 6,000 per plant. It is assumed that all of the contract quality tomatoes will be sold to the structured mar-ket, and all of the non-contract quality tomatoes that cannot fulfill the contract requirements will be sold on the free market.

3.3 Agent Behavior

In the third scenario, farmers can change their be-havior based on their collective behavior. In this simulation, farmers can change their agricultural practices based on their observation of their neigh-bors’ behavior. As mentioned by Bianchi and Squaz-zoni (2015), our decision making is usually influ-enced by observing the behavior of others. Farmers will improve their agricultural practices if they ob-serve their neighbors implementing better agricul-tural practices and getting better profits. There is also a chance that farmers will degrade their agri-cultural practices if their neighbors get better profits though implementing worse agricultural practices. They might also decide to quit contract farming if the neighbors implement worse agricultural prac-tices and have greater profits with the profit coming mostly from the traditional market.

Another behavior simulated in this study is related to social punishment. Social punishment is another form of indirect reciprocity (Bianchi & Squazzoni, 2015). In this study, social punishment is represent-ed by the decision of farmers to exclude a farmer


who cannot fulfill contract requirements because the farmer will not follow Standard Operating Procedures (SOP). This decision would be made because other farmers believe that the behavior of this farmer could hurt their partnership with the structured market.

4. SIMULATION RESULTS

The simulation results compare the impact of con-tract farming and farmer commitment to fulfilling contract requirements on supply chain performance, namely profits and service levels. Table 1 illustrates the comparison of profits and service levels achieved by farmers in each scenario along with the standard deviation. The average profit and service level are obtained by running the simulation 700 times, and each running consists of 500 periods. The number of repetitions was calculated based on Kelton, Sad-owski, and Sadowski (2002). Moreover, to test the

differences in terms of profit in Scenarios 1, 2, and 3, we used a one-way analysis of variance (ANOVA). In addition, we used the Tukey-Kramer procedure to identify which Scenario group is different. In or-der to test the differences among service levels in Scenarios 2 and 3, we used F-Tests for two-sample variances and t-Tests for two-sample variances as-suming equal variances. The results of these tests can be seen in Tables 2 and 3.

Table 1 shows that without contract farming (Sce-nario 1) farmers earn the lowest profit compared with implementing contract farming. Moreover, if contract farming is used by farmers but farmers do not commit to fulfilling contract requirements (Sce-nario 2), the farmers earned profit is lower than if the farmers commit to fulfilling contract requirements (Scenario 3). During each week of the harvesting period, farmers in Scenario 2 earned profits of IDR 11,776,746.97 on average. In Scenario 3, the farmers earned profits of IDR 15,369,211.58 per week.

Table 1. Comparison of Profits in Scenarios 1, 2, and 3

Scenario 1 Scenario 2 Scenario 3Profits IDR 5,368,258.30 IDR 11,776,746.97 IDR 15,369,211.58Standard deviation IDR 5,074,632.90 IDR 6,029,624.23 IDR 2,855,707.02

Table 2. Result of Anova: Single Factor Test

SUMMARY

Groups Count Sum Average Variance

Scenario 1 700 3,757,791,227.50 5,68,274.61 40,929,666,151.05

Scenario 2 700 8,243,742,278.32 11,776,774.68 51,319,696,247.12

Scenario 3 700 10,758,448,105.33 15,369,211.58 43,732,459,023.36

ANOVA

Source of Variation SS df MS F p-value F-crit

Between Groups 3.59318E+16 2 1.79659E+16 396,359.05 0 4.615298

Within Groups 9.50513E+13 2097 45,327,273,807.18

Total 3.60268E+16 2099

The hypotheses used in the ANOVA are: H0: means for Scenario 1 = Scenario 2 = Scenario 3H1: means for the scenarios are not all the same

The result of the ANOVA test with a significance of 1% in Table 2 shows that the means for Scenar-ios 1, 2, and 3 are not all the same (F-test = 396,359, p-value = 0).


Table 3. Results of the Tukey-Kramer Procedure

Scenario Comparison Mean Differences Critical RangeScenario 1 – Scenario 2 6,408,500.07 3.31Scenario 1 – Scenario 3 10,000,936.97Scenario 2 – Scenario 3 3,592,436.90

The Tukey-Kramer procedure with a critical range of 3.31 shows that all of the Scenarios are different (Table 3). It can be concluded that Scenario 3 is better than Scenario 2 in terms of profits, and that Scenario 2 is better than Scenario 1.

Table 4. Comparison of Service Levels for Scenarios 1, 2, and 3

Scenario 1 Scenario 2 Scenario 3Service Level - 42.16% 93.86%Standard Deviation - 15.93% 14.25%

The simulation also shows the different service levels for each Scenario, which are displayed in Table 4. There is no service level performance in Scenario 1, because there is no information related to customer demand. The farmer service level performance in Scenario 2 is 2 times lower than in Scenario 3. The standard devia-tion of service level in Scenario 2 is also lower than Scenario 3. This means that using Scenario 2, there is only 42% of structured market demand that is being met by farmers. In Scenario 3 on the other hand, the service level shows that farmers managed to meet structured market demand. In order to check the service levels of Scenarios 2 and 3, we used a t-test pooled variance to test the difference.

Table 5. F-Test for Two-Sample Variances

Scenario 2 Scenario 3Mean 42.15919963 93.8578608Variance 0.093151249 2.761344456Observations 700 700Df 699 699F 0.03373402P(F<f) one-tail 0F-critical one-tail 0.882926931

Table 6. t-Test: Two-Sample Variances Assuming Equal Variances

Scenario 2 Scenario 3Mean 42.15919963 93.8578608Variance 0.093151249 2.761344456Observations 700 700Pooled Variance 1.427247852Hypothesized Mean Difference 0Df 1398T Stat -809.5871746P (T<=t) one-tail 0T Critical one-tail 1.645944318P (T<=t) two-tail 0T Critical two-tail 1.961662333


Tables 5 and 6 show that Scenario 3 is significantly

different from Scenario 2 (t test = -809, p value = 0). It

can be concluded that the service level in Scenario 3

is better than in Scenario 2.

Figure 2. Service Level Performance in Scenario 3 Illustrates Farmer Behavior

The simulation result in Figure 2 illustrates that in Scenario 3, the farmer service level can be improved over time. The improvements in service level oc-curred because farmers interact with other farm-ers and the market. In this interaction, information sharing, collective learning, and joint decision mak-ing are implemented. This implementation is related to farmer behavior in terms of SOP to increase their service levels. Social punishment is also illustrated in the simulation by assessing farmer service levels in meeting contract requirements. If over several periods, farmers cannot increase their service levels

and cannot improve their SOP, the farmer cluster will ask them to withdraw from the partnership.

5. DISCUSSION

In this section, we explain the simulation results based on practice, observation, and our interviews. Furthermore, farmer commitment to implementing contract farming is also discussed.

5.1 Simulation Discussion for Farming without Con-tracts (Scenario 1)

Figure 3. Farmer Product Supply in Scenario 1


The farmer partnership in farming without con-tracts is an illustration of farmer partnerships with intermediaries as in the traditional market. In these partnerships, the quantity of demand and prices are not known in advance. In this kind of situation, farmers face the risk that not all of their agricultural products will be purchased by intermediaries, or that their agricultural products will be sold at low prices, which will mean their losing a lot of money.

As can be seen in Figure 3, the agricultural supply in Scenario 1 fluctuates. This fluctuation occurs be-cause there is no coordination among the farmers. Moreover, farmers still do not realize that there is a need for coordination, because there is no informa-tion related to the customer requirements that they need to fulfill. These farmers also do not use collab-orative planning in terms of cultivation and harvest scheduling. Therefore, there will be periods when farmers plant the same commodity and harvest at the same time, which will result in an excessive sup-ply of their agricultural products. There will also be periods when their agricultural products will be very scarce, because there will be few farmers plant-ing these commodities.

The simulation results for this situation show that without contract farming to inform them of custom-er demand, farmers cannot plan their cultivation de-cisions. Farmer decisions related to what, when, and how much to plant are only based on their past ex-perience or the experiences of other farmers. With-out cultivation planning, farmers cannot provide a sustainable supply of product for their customers because farmers do not have sustained, continuous demand from their customers. Moreover, in this situation, farmers only implement their traditional methods of farming without considering ways to apply new technology or knowledge to improve their productivity. This situation occurs because farmers have no incentive to change their current farming activities. Moreover, there is no partner-ship mechanism to encourage farmers to adjust or improve their farming activities or cultivation and harvest decisions.

These simulation results were confirmed by the KATATA manager and the farmer advisor in a vali-dation process. The KATATA manager and farmer advisor agreed that when contract farming is not implemented, the situation that is illustrated in the simulation result occurs. Farmer cultivation deci-sions are based on their neighbors’ decisions, and harvesting takes place without implementing se-lective harvesting. Sometimes early harvesting oc-curs because the farmers need money for their daily needs. In practice, sometimes an extreme situation occurs, when farmers do not harvest their agricul-tural products because the market price is very low. Thus, the costs of harvesting would make them suf-fer even greater losses.

5.2 Simulation Discussion for Contract Farming without Commitment (Scenario 2)

Contract farming illustrates the farmer partner-ship with the structured market. In contract farm-ing, there is a demand quantity for the agricultural product and its price is clearly stated (Arshinder & Deshmukh, 2008). The demand quantity is fixed rel-atively for every period so that farmers can carry out cultivation planning to fulfill the demand for each period. The key to contract farming is sustained and continuous demand and zero fluctuation in terms of demand and price. In the beginning of the con-tract farming implementation process, it is difficult for farmers to shift from their traditional methods of farming to more well-planned farming. The most basic way to achieve a sustained and continuous supply of agricultural products is to implementing cultivation and harvest scheduling. The implemen-tation of cultivation and harvest scheduling involves joint decision making, information sharing, and col-lective learning between farmers and the market. This is in accordance with the concept of Collab-orative Planning, Forecasting, and Replenishment which aims to reduce supply chain costs and pro-mote greater integration, visibility and cooperation among supply chain members (HolmstrÖm, Främ-ling, Kaipia, & Saranenet, 2002).


Figure 4. Farmer Product Supply in Scenario 2

In the beginning of the contract farming implemen-tation process, farmers still do not know how to car-ry out cultivation and harvest scheduling. The fluc-tuation in the supply of contract quality products occurs because farmers do not implement cultiva-tion and harvest scheduling, as can be seen in Figure 4. Moreover, with contract farming, farmers’ losses can be avoided because contract farming offers a stable price. Thus, farmers can make more accurate calculations of their revenue. This differs from mar-ket prices which can be very low and can result in farmers losing a lot of money.

Like the previous simulation results, the simulation results of the second scenario have been validated by confirming them with the KATATA manager and the farmer advisor. They confirmed that the simulation results are in accordance with the KATATA situation, where contract farming for a structured market has just been implemented. Having a partnership with the structured market is a new experience for KATA-TA, especially in terms of the structured market’s complex requirements. Farming contracts spell out detailed demand requirements. Agricultural product characteristics, namely product weight, color, and shape are also spelled out in the farming contract. The quantities of agricultural products that are to be supplied every period are also stated. The price that farmers get from implementing contract farming is stable and higher than the average market price. Farmers realize that with contract farming their prof-its will increase. However, by comparing the complex

requirements of contract farming with the require-ments of the traditional market, farmers realize that they need to improve their farming activities. With the assistance of the farmer advisor, these farmers are slowly changing their cultivation decisions.

5.3 Simulation Discussion for Contract Farming with Commitment (Scenario 3)

In contrast to the second scenario, the third scenar-io shows that farmers have already completed the adaptation process and have adjusted their farm-ing activities by developing cultivation and harvest scheduling to meet contract requirements. Farm-ers work together to arrange their cultivation and harvest scheduling. Farmers get together to decide when each one should plant, what to plant, and how much to plant, so that they can offer a continuous and sustained supply of agricultural products.

KATATA confirmed that the simulation results were in accordance with what has happened in KATATA when cultivation and harvesting scheduling have been implemented. The stable demand of contract farming allows farmers to develop cultivation plan-ning. Farmers can also plan in terms of how much to plant, when to plant, when to harvest, and how much to harvest.

Using cultivation and harvest scheduling, the farm-ers that join KATATA should coordinate their cultiva-tion decisions. There is a commitment from farmers to meet contract requirements, because both farmers


and the structured market benefit from this partner-ship mechanism. Farmers benefit from higher profits compared with their partnerships with intermediar-ies. The structured market benefits from higher mar-gins because they obtain their product supply direct-ly from farmers, not from intermediaries. Moreover, the structured market can maintain higher quality because the distribution chain is shorter.

Figure 5. The Level of Commitment and Supply Chain Performance

The simulation results for each scenario in the im-plementation of contract farming illustrate the dif-ferent levels of farmer commitment, which in turn illustrates farmer commitment and its implications in terms of supply chain performance. Without con-tract farming, farmers do not have information relat-ed to the quantity of agricultural products that they will be able to supply during each period, so farm-ers do not have a commitment to meeting market demands. Furthermore, communication between farmers and their consumers is very limited. There is no bonding or incentive to encourage farmers to evaluate their farming activities to improve harvest-ing quantity and quality. The demand requirements in this partnership are very loose. Moreover, with contract farming, the commitment between farmers and the structured market is greater than with the traditional market in which contract farming is not implemented. Contract farming encourages farm-ers to have a supply target that they need to meet to achieve higher revenue. In the second scenario, farmer commitment has begun, but it is still in its early stages, because farmers are still adapting to the new partnership mechanism and the requirements can be negotiated. This situation has also been confirmed by Stringer and Sang (2009) and Burer, Jones, and Lowe (2008). Furthermore, in this second scenario, farmer commitment to contract farming

requirements is still questionable. The simulation results from the third scenario show that the com-mitment between farmers and the structured market is well developed. Moreover, in the third scenario, the farmer commitment to meeting contract require-ments is greater than in the second scenario. With this increased commitment, supply chain perfor-mance also increases. This situation has been illus-trated by Kuwornu, Kuiper, and Pennings (2009), and Zylbersztajn and Miele (2005).

This study has practical implications. Supply chain performance can be improved by establishing bet-ter communication among farmers. This communi-cation aims to motivate farmers to make a serious commitment to meeting contract requirements, and to monitor farmers in terms of the implementation of cultivation and harvest scheduling and SOP for Good Agricultural Practices. Thus, farmers who do not follow SOP, and farmers that only halfhearted-ly follow SOP, are encouraged to fully adopt SOP. This simulation also proves that farmers who follow SOP will have higher productivity and better supply chain performance compared to farmers who do not follow SOP or follow them halfheartedly. In general, contract farming can only have a positive impact on supply chain performance, especially in terms of farmer profits and service levels, if farmers have a commitment to meeting contract requirements by implementing cultivation and harvest scheduling and good agricultural practices.

This study has several limitations that can be im-proved upon by further research. This study only uses one case study to illustrate the implementation of contract farming and its effect on supply chain performance. The only commodity that was simu-lated in this study is the tomato, even though in real-ity, farming contracts can be required for more than one commodity, and one farmer can cultivate more than one commodity. The agent based simulation in this study excludes external factors such as the en-vironment and climate which can affect agricultural production. The farming contracts illustrated in this study are fixed and given. This study does not simu-late the interactions between actors in the agricul-tural supply chain that go into reaching agreements on the terms of these contracts.

6. CONCLUSIONS

The simulation results of the three scenarios illus-trate that a stable supply of agricultural products


can be achieved when contact farming is implement-ed and affects farmer cultivation decisions. Contract farming makes farmers eager to improve their farm-ing activities by developing cultivation and harvest scheduling. A comparison of farmer profits in these three scenarios shows that farmer profits are lowest when there are farmer partnerships with intermedi-aries to meet traditional market requirements. The highest profits are achieved by implementing con-tract farming. Instead of a market price that is always fluctuating, prices in contract farming are stable and higher than average market prices. Moreover, culti-vation and harvest scheduling make farmers create a stable supply of products and stable profits.

Williamson (1981) has stated that contract farming is used to manage uncertainty. Williamson also added that the implementation of contract farming needs to consider agent characteristics in terms of bound-ed rationality and opportunistic behavior. Bounded rationality is related to the agent limitations in for-mulating and solving complex problems and in pro-cessing information. Opportunistic behavior charac-terizes agents trying to maximize their own profits regardless of how this affects other agents. In this study, agent characteristics are reflected in farmer commitment to meeting contract requirements. The contribution of this study is related to the coordina-tion process of fulfilling farming contracts and the impact of contract farming and farmer commitment on supply chain performance.

The commitment between farmers and their cus-tomers is greater when contract farming is imple-mented. Farmers should commit to meeting contract requirements. Greater farmer commitment can be seen by the implementation of cultivation and har-vest scheduling. Contract farming also encourages farmers to improve their farming activities because farmers have a commitment to improve their pro-ductivity and quality, which is never even discussed when farmers supply intermediaries through tradi-tional markets.

This study makes practical as well as conceptual contributions to the field. From the conceptual point of view, it differs from previous studies that have examined contract arrangements by assess-ing the interactions between actors (Chambers & King, 2002; Kuwornu et al., 2009). This study has been modeled and proves that implementing con-tract farming affects the coordination of farmer cul-tivation decisions, and shows that different levels of farmer commitment affect farmer performance

when contract farming is implemented. This study also shows that by using agent based modeling, the actor or agent decision making process based on their preferences can be illustrated. This study also has practical implications. Contract farming is ben-eficial for both farmers and the market because it efficiently manages uncertainty in terms of demand and prices for farmers and uncertainty in terms of sustainable product supply for the market. More-over, this simulation shows that contract farming will provide farmers with larger profits. However, contract farming without farmer commitment will ruin the partnership between farmers and the mar-ket, because the farmers will not be able to fulfill the contract requirements. This simulation also illus-trates that farmer commitment to contract farming will increase profits and service levels.

The practical implication of this is that the simula-tion’s coordination process shows that farmer com-mitment to contract farming can be achieved by collective learning, information sharing, and joint decision making. Collective learning is related to the implementation of SOP which can be used by all farmers. Information sharing is related to the behavior of comparing profits and service levels as a result of implementing SOP. Joint decision mak-ing is related to cultivation and harvest scheduling that is agreed to and implemented by all farmers, by informing them that in order to fulfill contract requirements and achieve good service levels and profits, farmers need to have closer interactions with other farmers and their customers. Farmers should be supervised or need an advisor to provide greater knowledge and improved technology so that farm-ers can improve their farming activities and cultiva-tion decisions to enter a more valuable market.

To deal with the limitations of this study, future research should focus on the coordination process or coordination mechanism at each stage of these agricultural activities. In the initial stage of agricul-tural activities, farming contracts are agreed to. The interactions between actors in the agricultural sup-ply chain should be explained and included in this simulation to explain the process of agreeing to the terms of farming contracts. The coordination mecha-nism is not the sole mechanism for contract farm-ing, but other mechanisms are usually used in the coordination process. The coordination mechanism should be included also in the simulation to capture the interactions between actors in the agricultural supply chain. The interaction and learning process


of each agent in the coordination process at each stage should also be better simulated. Furthermore, external factors such as climate and environmental conditions should be included in the simulation to describe their role in affecting agricultural produc-tivity and their influence on the coordination pro-cess. In further research, the simulation should in-clude more than one farmer planted commodity to indicate farmer capabilities and limitations in terms of meeting contract requirements.

REFERENCES

Adebanjo, D. (2009). Understanding demand management chal-lenges in intermediary food trading: A case study. Supply Chain Management: An International Journal, 14(3), 224-233. doi:10.1108/13598540910954566

Arshinder, K.A., & Deshmukh, S. G. (2008). Supply chain coordi-nation: Perspective, empirical studies and research direction. International Journal Production Economics, 115(2), 316-335. doi:10.1016/j.ijpe.2008.05.011

Becker, T. (2000). Consumer perception of fresh meat quality: A framework for analysis. British Food Journal, 102(3), 158-176. doi:10.1108/00070700010371707

Bianchi, F., & Squazzoni, F. (2015). Agent-based model in sociolo-gy. WIREs Computational Statistics, 7(4), 284-306. doi:10.1002/wics.1356

Bijman, J., Omta, S.W.F., Trienkens, J.H., Wijnands, J.H.M., & Wubben, E.F.M. (2006). International agri-food chains and net-works. Management and Organization. Wagengingen, NL: Wa-geningen Academic Publishers. doi:10.3920/978-90-8686-573-4

Burer, S., Jones, P.C., & Lowe, T.J. (2008). Coordinating the sup-ply chain in the agricultural seed industry. European Jour-nal of Operational Research, 185(1), 354-377. doi:10.1016/j.ejor.2006.12.015

CCatelo, M.O., & Costales, A. (2008). Contract farming and other market institutions as mechanism for integrating smallhold-er livestock producers in the growth and development of the livestock sector in developing countries. Pro-Poor Livestock Policy Initiative, Working Paper, 45. Retrieved from http://www.fao.org/3/a-bp187e.pdf

Chambers, W., & King, R.P. (2002). Changing agricultural mar-kets: Industrialization and vertical integration in the dry ed-ible bean industry. Review of Agricultural Economic, 24(2), 495-511. doi:10.2307/1349774

Engelseth, P., Takeno, T., & Alm, K. (2009). Food safety, qual-ity, and ethics in supply chains – a case study focusing on informing in international fish distribution. In A. Lindgreen, M. Hingley, & J. Vanhamme, (Eds.), The Crisis of Food Brands: Sustaining Safe, Innovative and Competitive Food Supply. Alder-shot UK: Gower.

Guo, H., Jolly, R. W., & Zhu, J. (2007). Contract farming in Chi-na: Perspectives of farm households and agribusiness firms. Comparative Economy Studies, 49(2), 285-312.

Hastuti, E.Y. (2007). The influence of agribusiness system applied to vegetables farmer’s income improvement in Boyolali regency. Semarang, Indonesia: Diponegoro University.

HolmstrÖm, J., Främling, K., Kaipia, R., & Saranen, J. (2002). Collaborative planning forecasting and replenishment: New solutions needed for mass collaboration. Supply Chain Management: An International Journal, 7(3), 136-145. doi:10.1108/13598540210436595

Imbruce, V. (2008). The production relations of contract farming in Honduras. Geo Journal, 73(1), 67-82. doi:10.1007/s10708-008-9179-z

Kelton, W.D., Sadowski, R.P., & Sadowski, D.A. (2002). Simula-tion with arena. McGraw Hill.

Kuwornu, J.K.M., Kuiper, W.E., & Pennings, J.M.E. (2009). Agency problem and hedging in agri-food chains: Model and application. Journal of Marketing Channels, 16(3), 265-289. doi:10.1080/10466690902934557

Little, P.D., & Watts, M.J. (1994). Living under contract: Contract farming and agrarian transformation in sub-Saharan Africa. Mad-ison: University of Wisconsin Press.

Minot, N. (2007). Contract farming in developing countries: Pat-terns, impact, and policy implications. Case Study 6-3 of the Program, Food Policy for Developing Countries: The Role of Gov-ernment in the Global Food System, Cornel University.

Morrison, P.S., Murray, W.E, & Ngidang, D. (2006). Promoting indigenous entrepreneurship through small scale contract farming: The poultry sector in Sarawak, Malaysia. Retrieved from FAO website: http://www.fao.org/ag/ags/contract-farming/agsli-brary/detail-fr/fr/item/3797/icode/7/?no_cache=1.

Pandit, A., Lal, B., & Rana, R.K. (2015). An assessment of potato contract farming in West Bengal State, India. Potato Research, 58(1), 1-14. doi:10.1007/s11540-014-9259-z

Perdana, T., & Kusnandar. (2012). The triple helix model for fruits and vegetables supply chain management develop-ment involving small farmers in order to fulfill the global market demand: A case study in “value chain center (vcc) universitas padjadjaran”. Procedia Social and Behavioral, 52, 80-89. doi:10.1016/j.sbspro.2012.09.444

Prowse, M. (2012). Contract farming in developing countries – a re-view. A Savoir, Institute of Development Policy and Manage-ment, University of Antwerp.

Pultrone, C., & Silva, C.A. (2012). Guiding principles for responsible contract farming operations. Rome, Italy: Rural Infrastructure and Agro-industries Division, FAO. Retrieved from http://www.fao.org/docrep/016/i2858e/i2858e.pdf

Rehber, E. (2007). Contract farming: Theory and practice. Hyder-abad, India: ICFAI University Press.

Stringer, R., & Sang, S. (2009). Producers, processors, and pro-curement decisions: The case of vegetable supply chains in china. World Development, 37(11), 1773-17801. doi:10.1016/j.worlddev.2008.08.027

Sutopo, W., Hisjam, M., & Yuniaristanto. (2012). An agri-food supply chain model to empower farmers as supplier for modern retailer using corporate social responsibility activi-


ties on deteriorated product. Proceedings of the International Multi Conference of Engineers and Computer Scientists, 2, 1-5. doi:10.1007/978-94-007-5651-9_14

Tangpong, C., Hung, K.T., & Li, J. (2014). Agent-system co-devel-opment in supply chain research: Propositions and demon-strative findings. Journal of Operations Management, 32(4), 154-174. doi:10.1016/j.jom.2014.03.002

Taylor, D., & Fearne, A. (2006). Towards a framework for im-provement in the management of agri-food SCs. Supply Chain Management: An International Journal, 11(5), 379-384. doi:10.1108/13598540610682381

United Nations Conference on Trade and Development. (2009). World Investment Report 2009 on Transnational Corporations, Agricultural Production and Development. Geneva: UNCTAD/DIAE/2009. Retrieved from: http://unctad.org/en/docs/wir2009_en.pdf

Van Der Vorst, J.G.A.J., Beulens, A.J.M., & Van Beek, P. (2000). Modelling and simulating multi-echelon food systems.

European Journal of Operational Research, 122(2), 354-366. doi:10.1016/S0377-2217(99)00238-6

Widyarini, M., Simatupang, T.M., & Engelseth, P. (2016). Social interaction and price transmission in multi-tier food supply chains. Journal of Operations and Supply Chain Management, 9(1), 110-128. doi:10.12660/joscmv9n1p110-128

Williamson, O.E. (1981). The Economics of organization: The transaction cost approach. American Journal of Sociology, 87(3), 548-577. doi:10.1086/227496

Young, L.M., & Hobbs, J.E. (2002). Vertical linkages in agri-food supply chains: Changing roles for producers, commodity groups, and government policy. Review of Agricultural Eco-nomics, 24(2), 428-441. doi:10.1111/1467-9353.00107

Zylbersztajn, D., & Miele, M. (2005). Stability of contracts in the Brazilian wine industry. Revista de Economia e Sociologia Ru-ral, 43(2), 353-371. doi:10.1590/S0103-20032005000200008


Appendix

Appendix 1. Scenario Differences in the Simulation Model

AspectsScenario

Scenario 1 (Transactional) Scenario 2 (Contractual) Scenario 3 (Committed)

Farmer access to market

•Traditional market through intermediaries for all agricultural products produced by farmers

•Structured market for contract quality products based on contract requirements

•Traditional market for non-contract quality products

•Structured market for contract quality products based on contract requirements

•Traditional market for non-contract quality products

Available information before cultivation

- •Demand requirements•Prices

•Demand requirements•Prices•SOP and Good Agricultural

Practices

Agents

•Pangalengan cluster farmers in terms of Good Agricultural Practices:- Traditional method of farming

(15 farmers)- Partially implement Good

Agricultural Practices (15 farmers)

- Good Agricultural Practices (20 farmers)

•Traditional market through intermediaries











Coordination mechanism •Contract farming

•Contract farming•Joint decision making between

farmers to planning cultivation and harvesting scheduling in advance

•Information sharing and collective learning between farmers and structured market to implement Good Agricultural Practices and follow SOP

Agent behavior

•Farmers do not have planning related to when to plant and how much to plant. Therefore it is random.

•Farmers do not have planning related to when to plant and how much to plant. Therefore it is random.

•Which farmers should plant, when to plant, how much to plant, when to harvest, how much to harvest are based on cultivation and harvesting scheduling.

•Information sharing and collective learning is implemented so that farmers have the ability to learn and change their behavior in implementing SOP and GAP. With this coordination mechanism, farmers can improve their agricultural practices.


AspectsScenario

Scenario 1 (Transactional) Scenario 2 (Contractual) Scenario 3 (Committed)

Assumptions

•The initial stage of the simulation in each scenario has the same number of farmers with the same configuration of levels of agricultural practices.

•Demand and prices of the traditional market are unknown

•Farmers only sell their agricultural products to the free market. The free market price fluctuates, with a minimum price of 200 IDR and a maximum price of 10,000 IDR. The price is set at random with normal distribution.

•Farmer production fluctuates greatly

•Demand and prices of structured market are stable and known before cultivation

•Farmers sell their contract quality products to the structured market at a stable price, 7,750 IDR.

•Farmers sell their non-contract quality products to the free market with fluctuating prices.

•Farmer production fluctuates greatly

•Farmers sell their contract quality products to the structured market at a stable price, 7,750 IDR.

•Farmers sell their non-contract quality products to the free market with fluctuating prices.

•Farmer production is relatively stable compared with the other two scenarios

•Farmers share information with other farmers, and benefit from collective learning related to agricultural practices. Therefore, farmers can change their level of agricultural practices and follow their neighbors who have higher levels of agricultural practices.

•Farmers do not share information with other farmers, so farmers cannot change their level of agricultural practices

•Farmers do not share information with other farmers, so farmers cannot change their level of agricultural practices

•Each farmer plants 210 tomato plants, with each plant yielding 2 kilograms of tomatoes. The total amount of harvested product will also depend on the harvest count. On the first and sixth harvest, they will yield 40% of total expected products, which will amount to around 0.168 metric tons. On the second and fifth harvest, they will yield 50% of total expected products, which will amount to around 0.210 metric tons. On the third and fourth harvest, they will yield 90% of total expected products, which will amount to around 0.378 metric tons. However, the percentage of contract quality products depends on the type of farmers. Farmers in the first group have 40% contract quality products because they do not implement SOP and Good Agricultural Practices. Farmers in the second group can achieve yields of 60% contract quality products. Meanwhile the farmers of the third group can achieve yields of 80% because of the implementation of SOP and Good Agricultural Practices


Appendix 2. Simulation Flow in Scenario 1

Poor agricultural practices

15 farmers(yield 10% on-grade

products)

Good agricultural practices


products)

Each farmer plant 210 tomato plants. On the first & sixth harvest, they will yield 0.168 tonsOn the second & fifth harvest, they will yield 0.21 tonsOn the third & fourth harvest, they will yield 0.378 tons

Define 3 types of farmers in producing tomatoes

1st scenario

Fair agricultural practices


products)

Each farmer decides when to plant

Waiting > 90 days after planting

Farmers harvest tomatoes until

tomatoes age > 130 days

Sell all harvested product to traditional

market

Farmers start planting without cultivation

scheduling




15 farmers(yield 10% on-grade products)



products)

Each farmer plant 210 tomato plants. On the first & sixth harvest, they will yield 0.168 tonsOn the second & fifth harvest, they will yield 0.21 tonsOn the third & fourth harvest, they will yield 0.378 tons


2nd scenario


15 farmers(yield 30% on-grade products)

sell the off-grade tomato to

traditional market weekly

Waiting > 90 days after

planting

Farmers harvest tomato weekly

until tomato age > 130 days

sell on-grade tomato to

structured market weekly

Contract farming negotiation

Contract farming of demand

requirement and price

Each farmer decides when to

plant without joint decision making

On-grade

Yes

No





products)

3rd scenario


20 farmers(yield 80% on-

grade products)

Each farmer plant 210 tomato plants. On the first & sixth harvest, they will yield 0.168 tons

On the second & fifth harvest, they will yield 0.21 tonsOn the third & fourth harvest, they will yield 0.378 tons



15 farmers(yield 30% on-

grade products)

sell off-grade tomato to traditional

market weekly

Waiting > 90 days after

planting

Farmers harvest tomato until tomato age > 130

days

sell on-grade tomato to structured

market weekly

Contract farming negotiation

Contract farming of demand requirement and

price

Each farmer assigned when to plant based on

cultivation and harvesting scheduling

On-grade

By joint decision making farmers cluster design

cultivation and harvesting scheduling

Yes

No Farmers with lower agricultural practices

compare their own profit from structured market and traditional market weekly

Structured market

profit is higher than traditional

market profit

Farmers keep their

own agricultural practices Farmers look for information about others’ profit

from KATATA and neighbors to make the decision of keeping, improving, or degrading

their agricultural practices, or quiting KATATA

Yes No

Decision to stay in KATATA is higher than

decision to quit KATATA

Farmers quit KATATA

Decision to change agricultural practices is

higher than decision to keep agricultural practices

Farmers change

agricultural practices

No

Yes

Replacement farmers join

KATATA

Decision to keep agricultural practices increases

Yes

No

Submitted 10.10.2016. Approved 19.12.2016. Evaluated by double blind review process. Scientific Editor: José Barros Neto

SIX SIGMA BENCHMARKING OF PROCESS CAPABILITY ANALYSIS AND MAPPING

OF PROCESS PARAMETERS

Jagadeesh Rajashekharaiah Professor at SDM Institute for Management Development - Mysore, India

[email protected]

ABSTRACT: Process capability analysis (PCA) is a vital step in ascertaining the quality of the output from a production process. Particularly in batch and mass production of components with specified quality characteristics, PCA helps to decide about accepting the process and later to continue with it. In this paper, the application of PCA using process capability indices is demonstrated using data from the field and benchmarked against Six Sigma as a motivation to improve to meet the global standards. Further, how the two important process parameters namely mean and the standard deviation can be monitored is illustrated with the help of what if analysis feature of Excel. Finally, the paper enables to determine the improvement efforts using simulation to act as a quick reference for decision makers. The global benchmarking in the form of Six Sigma capability of the process is expected to give valuable insight towards process improvement.

KEYWORDS: Benchmarking, process capability, Six Sigma, mapping, control chart.


60

Rajashekharaiah, J.: Six Sigma Benchmarking of Process Capability Analysis and Mapping of Process ParametersISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 2 p 60 – 7161

1. INTRODUCTION

Quality control and improvement is the most impor-tant part of organizations engaged in the manufac-turing of products or delivery of service. Monitoring the quality using the standard references, or metrics, is vital to any organization that cares about custom-ers and ultimately helps the organization to capture the market.

The advent of new technologies, increased demand for high quality products, and quality based competi-tion mandate close scrutiny and careful selection of processes. The overall cost depends on the judicious selection of the process and thus process capability analysis (PCA) is considered as a vital step in ensur-ing the quality of products. Increasing competition, availability of low-cost suppliers, global supply chains and information technology driven manufac-turing, all have caused new paradigms in process de-cisions. Manufacturers are moving towards more of outsourcing and trying to cut down the cost of pro-duction. In addition, these decisions are more influ-enced by global standards, and benchmarking with best practices. Hence it is imperative to ascertain the quality of output from a production process before that process is identified for batch or mass produc-tion. Further it is necessary to find out how the pro-cess can further be improved to meet the global stan-dards so as to remain competitive in the market.

2. ORGANIZATION OF THE PAPER

In this paper, first a brief overview of PCA is pro-vided and the numerical measures in the form of Process Capability Indices (PCI) are described. The mathematical formulae to calculate these PCI’s along with their interpretation are also given. A descrip-tion of relevant concepts of benchmarking and Six Sigma are also provided for completeness as well as continuity. Later, using field data PCA is performed and benchmarked against Six Sigma standards. The two key process parameters namely process mean and the variance are optimized to accomplish the required Six Sigma standards, using Excel’s “what if” analysis. Next, using simulated data how much of improvement efforts are needed to reach global standards is demonstrated. Finally mapping of pro-cess parameters with respect to the required level of Six Sigma (SS) standards is carried out and how the two process parameters are simultaneously tracked is illustrated. This enables continuous monitoring and improvement and helps to set the goals clearly.

All the calculations, scenario building, optimiza-tion, and simulation, including the development of charts, have been done using Excel 2013 version, which has powerful features to support the current research work.

3. BRIEF LITERATURE REVIEW

Process capability analysis (PCA) forms the initial step in establishing acceptability of a production pro-cess to produce output as per the specified tolerance. The literature on process capability studies, besides being rich and diversified, has a long historical back-ground. Different aspects of process capability have been covered with varying details to satisfy the needs of practitioners and researchers. It is not the intention here to provide an exhaustive coverage of PCA, but a brief overview of the major aspects relevant to this paper, is presented in the following sections.

Process capability refers to the ability of a process to produce the output namely a product or a service, according to the specifications as suggested or pre-scribed by the designer or the customer. Because of the variations that occur in a process due to assign-able as well as the chance causes, a process will not be always performing as per the expectations and hence the output quality can deviate from the pre-set standards. Process capability studies help to ver-ify whether the processes adopted by the manufac-turer or the service provider are capable of meeting the specifications. In addition, process capability as-sessment studies have several objectives as follows, (Summers, 2005):

1. To ascertain the extent to which the process will be able to meet the specifications.

2. To determine whether the process will be able to meet the future demand placed on it in terms of the specifications.

3. To help the industries to meet the customers’ de-mands.

4. To enable improved decision making regard-ing product or process specifications, selection of production methods, selection of equipment, and thus improve the overall quality.

Besides the above, it is reported that process capabil-ity studies help in vendor certification, performance monitoring and comparison and also for setting tar-gets for continuous improvement.


Process capability indices are simple numerical measures to express the potential and performance capabilities of a process under different conditions. These indices essentially link the key parameters like process mean and variance to design specifications of a quality characteristic. Thus they act as a bridge between the fixed or static tolerance values and the dynamic process values as seen over a period of time. A lucid paper by Kane (1986) explains the fun-damentals of process capability indices along with numerical illustrations. The importance of sampling for the estimation of process capability indices has been vividly presented by Barnett (1990). A detailed explanation about the process capability indices is available in Porter and Oakland (1991). It is impor-tant to observe that the process capability measures are all basically sample based and both the sample size chosen and the method of sampling need to be carefully considered. In addition, it is essential that the process be stable and in the state of statistical control before taken up for capability assessment.

Assessment of process capability is commonly done using process capability indices and Table 1

shows the different types of indices used in prac-tice. The corresponding formulas are also shown in the Table 1. A Cp of 1.00 indicates that the pro-cess is judged capable. It is generally necessary to estimate the process standard deviation so as to estimate Cp of the process. Due to sampling varia-tion and machine setting limitations, Cp = 1.00 is not used as a minimally acceptable value, and a minimum acceptable value of Cp is 1.33 which ensures acceptable quantity within the specifica-tions, as a shift in the process mean from the tar-get value and a change in the process variance oc-cur over a period of time. Since the Cp and Cpk indices do not take into account the departure from the target/nominal value, Chan, Cheng, and Spiring (1988) have introduced another measure of process capability, called the Cpm index. This index takes into account the proximity to the tar-get value as well as the process variation when assessing process performance. The Cpm index is also referred to as “Taguchi capability index’, as illustrated by Boyles (1991) and Balamurali and Kalyanasundaram (2002).

Table 1: Process Capability Indices

Name Index Formula

Process Potential Index Cp 6USL LSLCp

σ−

=

Scaled Distance Factor K ( )2

mK USL LSL

µ−=

−

Process Performance Index Cpk Cpk = Cp (1 – K)

Cpu 3USLCpu µ

σ−

=

Cpl

3

( )2

LSLCpl

mK USL LSL

µσ

µ

−=

−=

−


Taguchi Capability Index Cpm 2 2 26 ( )1

USL LSL CpCpm or CpmT Tσ µ µ

σ

−= =

+ − − +

Third Generation Process Capability Index

Cpmk

Minpmk

(USL - , - LSL) = C 3µ µσ

pkpmk

2

C = C - T1 + ( )µσ

Symbols and notations used:

USL = Upper specification Limit

LSL = Lower Specification Limit

σ = Standard Deviation

μ = Process Mean

T = Target value

m = Midpoint of USL and LSL

Process capability indices that are commonly used are all based on the assumption of normally dis-tributed data, however, the case of non-normality is also discussed in the literature, for example, Clements (1989), Somerville and Montgomery (1996), Rao and Xia, (1999), and Hou and Wang (2012), to name a few. But typically across the in-dustries the assumption of normality is followed and rarely the non-normality is taken into account because of complexity of calculations, and long procedures. Further, it is interesting to note that the process capability indices are categorized as first, second and third generation indices depend-ing upon what process conditions are being ex-plored and indicate also their relative sensitivity in recognizing process changes. The process capa-bility index Cp is considered as the first generation index and Cpk and Cpm are regarded as the “sec-ond generation” indices. Pearn, Kotz, and Johnson (1992) while discussing the distributional and in-ferential properties of process capability indices, also propose a ‘third generation’ process capabil-ity index and two new multivariate indices. These are claimed to be possessing better properties than the earlier developed indices. However, in most of

the industries it is still the first and second genera-tion indices that are commonly used for process assessment and monitoring. The interpretations to be made based on the values of the indices are as shown in Table 2.

Table 2: Process Capability Index Cpk and Inter-pretation

Value of Cpk Interpretation< 1 Not at all capable= 1 Not capable= 1.33 Minimum requirement= 1.67 Promising= 2 Total confidence with process

The procedure used in PCA, as well the recommend-ed values need to be ascertained against statistical basis and analysis. Montgomery (1986), comments that some of the industry practices do not satisfy the statistical tests. Thus serious doubts arise about the validity of such measures calculated using those in-dustries prescribed procedures. The recommended values according to Montgomery (1986) are given in the Table 3.

Table 3: Recommended Minimum Values of Cp

Two-sided specifications

One sided specification

1.33 1.251.50 1.451.50 1.451.67 1.60


1.1 Six Sigma and benchmarking of the process capability

Six Sigma (SS) needs no introduction, as it is now re-garded as an intensive approach to improve the pro-cess quality and be able to meet the global standards. The concept of Six Sigma is basically to produce error free output. Sigma, s, is a letter in the Greek alpha-bet used by statisticians to measure the variability in any process and the Greek letter σ, represents stan-dard deviation. Today a company’s performance is measured by the sigma level of their business pro-cesses, (Breyfogle, 1999). Traditionally companies followed three or four sigma performance levels as the norm, despite the fact that these processes cre-ated between 6,200 and 67,000 problems per million opportunities. However, the Six Sigma standard of 3.4 defects per million opportunities is a response to the increasing expectations of customers, who want their products to be free from defects.

SS is defined in many ways as researchers, prac-titioners, and corporate people have given differ-ent perspectives about Six Sigma. Consequently, many definitions have been put forth to indicate what SS is all about. Some of the definitions of SS are as follows:

◊ According to Pyzdek (2003), SS is the applica-tion of the scientific methods to the design and operation of management systems and business processes which enable employees to deliver the greatest value to customers and owners.

◊ Persico (1992) states Six Sigma as a direct ex-tension of total quality management which, in turn, is based on the principles and teachings of W. Edwards Deming, the legendary quality guru.

◊ Six Sigma is a disciplined, quantitative approach for improvement-based on defined metrics-in manufacturing, service, or financial processes, (Hahn, Hill, Hoerl, & Zinkgraf, 1999).

In many industry and business environments, the Six Sigma culture is deployed through a systematic and uniform approach and set of techniques for con-tinuous quality improvement, (Harry, 1998). A Six Sigma program leads to better decision making by developing a system that prompts everyone in the organization to collect, analyze, and display data in a consistent way (Maleyeff & Kaminsky, 2002), and hence appreciated in the industries.

As a good amount of literature is available about the technique and applications of SS, a detailed descrip-tion of SS technique is not attempted in this paper, but useful references are quoted to provide the nec-essary initial reading. Some of the useful resources suggested are Hahn, Doganaksoy, and Hoerl (2000), Hammer and Goding (2001), and Pande and Holpp (2002). Many authors have discussed about the goodness of SS by thoroughly reviewing the litera-ture, and hence the following reviews should be ad-equate for understanding the growth and expansion of the literature pertaining to Six Sigma:

1. Six Sigma Literature: A Review and Agenda for Future Research, (Brady& Allen, 2006),

2. Six Sigma: Literature review and key future re-search areas, (Nonthaleerak & Hendry, 2006)

3. Six Sigma: A literature review, (Oke, 2007)

4. The origin, history and definition of Six Sigma: a literature review, (Prabhushankar, Devadasan, Shalij, & Thirunavukkarasu, 2008)

5. Six sigma: A literature review analysis, (Cagnazzo & Taticchi, 2009)

6. Six Sigma: A literature review, (Tjahjono et al., 2010)

All these reviews cover the concepts, theory and the applications of the SS technique in many diverse ar-eas which has prompted many companies to adopt the technique.

Table 4: Conversion between DPMO and process sigma level

Shift (sigma)1.5 1 0.5 0

SigmaArea between both Z values

DPMO (1.5 sigma shift))

DPMO (1 sigma shift))

DPMO (0.5 sigma shift))

DPMO (No sigma shift))

1 0.68268949 691462 500000.0 308537.5 158655.31.5 0.86638560 500000 308537.5 158655.3 66807.2


2 0.95449974 308538 158655.3 66807.2 22750.12.5 0.98758067 158655 66807.2 22750.1 6209.73 0.99730020 66807 22750.1 6209.7 1349.93.5 0.99953474 22750 6209.7 1349.9 232.64 0.99993666 6210 1349.9 232.6 31.74.5 0.99999320 1350 232.6 31.7 3.45 0.99999943 233 31.7 3.4 0.35.5 0.99999996 32 3.4 0.3 0.06 1.00000000 3.4 0.3 0.0 0.0

An important metric in the SS technique is the De-fects per million opportunities, (DPMO), which refers to the number of unacceptable fraction ex-pressed as a ratio of one million opportunities. A typical conversion between DPMO and the process sigma level is shown in Table 4, (Six Sigma Daily, 2016). Because quite often the acceptable quantity expressed as percent of output is expressed, the cor-responding Sigma levels are shown in Table 5.

Table 5: Percent output acceptable from the process and the corresponding Sigma level

Percent acceptable DPMO Sigma level30.9 6,90,000.0 162.9 3,08,000.0 293.3 66,800.0 399.4 6,210.0 499.98 320.0 599.9997 3.4 6

When the process sigma level is plotted against the percent acceptable quantity, the relationship takes the form as shown in Figure 1. From this figure it is evident that as the quantity acceptable approaches 100%, the process sigma level reaches six and fur-ther increase is not necessary. Though process sigma level leads to 3.4 defects per million, it is considered a zero defects. It is observed that almost like a habit companies continue to accept three or four sigma performance levels as the norm, despite the fact that these processes can have between 6,200 and 67,000 problems per million opportunities, (Pyzdek, 2003). Considering this comment, in this paper the optimi-zation attempt is towards reaching four sigma first and later moving towards Six Sigma. But essentially the global standards mandate that the processes be benchmarked against Six Sigma only as Six Sigma is considered the ultimate stamp of acceptance in a highly competitive environment.

Figure 1. Process sigma level and acceptable output

1.2 Why benchmarking is required to ascertain quality?

Continuous quality improvement of products and/or service offered by a company is essential for sur-vival in the market and meeting the demands of the customers. Hence the organizations are continuous-ly searching for new techniques and tools to enable them to improve quality. Benchmarking is one such quality improvement technique that helps qual-ity improvement by comparing the performance or any other measurable attribute with those who are doing it better. In essence benchmarking involves comparison with the superior performer, identify the gaps, and take proper action to overcome those gaps, thereby improving the quality. This process is not a one-time application but has to be used as an on-going process. Since new benchmarks are regu-larly created, it is necessary that the spirit of bench-marking is maintained. Benchmarking has been historically used as a technique for comparison of anything, a product, service, performance, output, or any measurable characteristic, with the superior performer or the “best in class” so as to find the gaps that prompt for improvement. After the publication of the success story of Xerox Corporation of USA, which adopted the technique to defend against the


stiff competition from the Japanese manufacturers in the copier market, (Camp, 1989, the application of benchmarking has increases manifold. Though benchmarking exercises have been in existence for a long time, it is in the recent times customary to probe whether the subject under consideration has been benchmarked against the best in class, (Elmuti & Kathawala, 2013). The term “benchmark” was included in the guidelines of the prestigious US Quality Award, Malcolm Baldrige National Quality Award, in 1985, and benchmarking became a quali-fying criterion to participate in the award process.

The literature related to benchmarking for quality improvement that covers the concepts, models, and applications, is abundant and has thus attracted the attention of several researches who have provided a comprehensive picture of the growth and spread of research based on benchmarking studies across the globe. For a complete list of literature on the process of benchmarking literature, some of the prominent review papers on benchmarking can be consulted, (Zairi & Youssef, 1995), (Kozak & Nield, 2001), (Scott, 2011) and (Dattakumar & Jagadeesh, 2003).

These papers also illustrate the various applications of benchmarking besides indicating the popularity of the topic of benchmarking and its applications.

The present paper which is focused on improving the process performance through benchmarking the process capability, decided to develop a generic benchmark which can be statistically established and capable of expressing using the main process parameters, namely process mean and process stan-dard deviation. These two parameters are also the building block of the process capability assessment. In this context, the globally accepted ultimate per-formance level namely Six Sigma was selected as the “benchmark” to be used to ascertain the quality of the process. Any process that exhibits the Six Sigma standard of performance would obviously be con-sidered as the “best performer” and this paper has used Six Sigma to essentially mean the ultimate level of comparison for a given process to be considered as on par with the global standard.

1.3 The problem on hand

The problem considered here pertains to a discrete manufacturing process adopted in a company which used to supply components to major auto manufac-turers in India. Though many different components were produced by the company, for the purpose of

illustrating the methodology, only one component, namely a threaded fastener is considered here. The component has one critical dimension namely the core diameter which had a specification of 3.4 ± 0.05 millimeter, and thus considered as “critical to qual-ity”, (CTQ). The data pertaining to a batch of 125 components is shown in Table 6, which shows the core diameter values in millimeter pertaining to 125 values under 25 subgroups with each subgroup hav-ing five components.

2. PRELIMINARY ANALYSIS

Before the process output data was used to analyze the process capability, the preliminary analysis in-cluded (1) Plotting the histogram, (2) Testing the data for normality, and (3) plotting the control charts.

The histogram of the data collected is shown in Fig-ure 2. Using the Kolmogorov-Smirnov test, the value of P is found to be 0.016, which barely indicates nor-mality. Further, the normal probability plot was also drawn and the data was found to be only approxi-mately normally distributed. This is well accepted as perfect normality is not expected in an industrial process, as is the case on hand.

Figure 2. Histogram of core diameter values

The typical control charts, namely x-bar chart and R- chart were plotted to ascertain the stability of the process. These two charts are shown in Figures 3 and 4 respectively. From these charts it is evident that the process is under statistical control and also stable. Further, the charts do not exhibit any questionable patterns and hence further analysis was carried out.

2.1 Process capability analysis

Considering the data available, the typical process capability assessment was made and the typical pro-


cess capability indices have been calculated. These are shown in Table 6. It is observed that the process spread exceeds the specification spread and thus out of specification values are expected. Further, process mean is not centered and process variance also needs

to be reduced. However, the process capability indi-ces clearly reveal that the process is not capable of meeting the requirements, and currently producing defects. This obviously demands improvement of the process by proper process control.

Figure 3. Control chart for averages

Figure 4. Control for ranges

Table 6: Process capability analysis

Process parameters Core DiameterLower specification limit, LSL 3.35Upper specification limit, USL 3.45Target value, T 3.4Process Mean, μ 3.4248

Process standard deviation, σ 0.0225

Process capability measures / indices Process capability, 6 σ 0.1350

Process potential index, Cp 0.7407Process performance index, Cpk 0.3733Taguchi capability index, Cpm 0.4977Scaled distance factor, K 0.496

In the next step, when the process is assessed for Six Sigma capability, it is observed that the pro-cess is currently performing at the sigma level of 2.62635812, as shown in Table 7, which is too in-adequate. Considering a sigma value of at least 4.00, it was decided to find out the values of pro-cess mean and standard deviation to reach the de-


sired result. Using a process sigma level of 4.00 as the threshold value, Excel’s what if analysis is performed and the possible values and combina-tions of process mean and standard deviation are established. These values are shown in Table 8. Be-cause the intention is to have a process sigma of at least 4.00, only those desirable combinations of process mean and standard deviation are selected and shown in Table 9.

Table 8: Combinations of process mean and standard deviation for different sigma level

Process St. deviation

Process mean3.38 3.39 3.40 3.41 3.42 3.43

0.0150 #NUM! #NUM! #NUM! 4.309557537 3.525651912 2.841610.0175 #NUM! #NUM! 4.66050891 3.834468896 3.229182321 2.6493830.0200 #NUM! #NUM! 4.0856508 3.525651912 3.010505032 2.5055940.0225 #NUM! 4.309558 3.76409138 3.294455072 2.841609503 2.3939230.0250 4.5344 3.965158 3.52565191 3.112290281 2.706980784 2.3046690.0275 4.1432 3.719935 3.33614272 2.964360975 2.597064745 2.231688

Table 9. Desirable combinations of process mean and standard deviation for sigma level greater than 4.00 and corresponding DPMO values

Process sigma Greater than 4 Process mean Std. Dev. DPMO (rounded)

4.53436 3.38000 0.02500 12054.14315 3.38000 0.02750 41074.30956 3.39000 0.02250 24814.66051 3.40000 0.01750 7884.08565 3.40000 0.02000 48604.30956 3.41000 0.01500 2480

Table 7: Si Sigma capability analysis

Performance AnalysisMean + 3 Sigma 3.4923Mean - 3 Sigma 3.3573Area to the left of LSL 0Area to the right of USL 0.130006983Total area 0.130006983DPMO (total rejects X million) 130006.983Process Sigma Level 2.62635812

From the Table 9, it is understood that if by strict monitoring and proper centering, process mean can be controlled within a distance of 0.01 mm from the target value of 3.4 mm, then the process standard deviation could be ranging from 0.015 to 0.0225 mm, to yield a process sigma of more than 4.00. Hence, the process manager can decide as to which quality parameter can be easily “fixed”, mean or the vari-ance. By controlling the process standard deviation within a range of 0.015 to 0.0225 mm, and ensuring the process mean is within the range of 3.39 to 3.41 mm, the process manager should be able to reach a process sigma value greater than 4.00. This kind of a trade-off enables better process control and leads to

substantially lowering the rejects as observed under the DPMO values column in Table 9, from a previ-ous DPMO of 130006 when the process sigma level was less than 3.00. The lowest value of DPMO is 788 occurring for process mean of 3.40 mm and standard deviation of 0.01750 mm, with a corresponding sig-ma level of 4.66051.

2.2 Reinforcing the analysis with simulation

Having find out the desirable combinations of pro-cess mean and the standard deviation, it is now possible to go backward to find out the desirable values of the individual variable which is the core diameter so that the process sigma level is at least


4.00. This can be easily done by generating a set of normally distributed values using the optimum values of process mean and standard deviation as given in the Table 9. For example, for a combina-tion of process mean of 3.38 and standard deviation 0.02500, a desired set of values of the core diameter can be generated using Excel itself, and then ob-serve how the individual values should be existing. As these values are under hypothetical conditions, it is important to note that these are only the ex-pected values of the core diameter and hence not to be taken as the actual output from the process. Further, the actual output is influenced by several variables and that pattern is not captured by the

simple simulation model described here. For a bet-ter visualization of the changes in process mean and standard deviation affecting the process sigma level, the values from Table 9 are mapped with the Six Sigma scale, as shown in Figure 5, with process sigma values along the vertical axis, and process mean along the horizontal axis. In this Figure, the different values of process mean are plotted against various values of process standard deviation lead-ing to process sigma values ranging from 2 to 5. As the desired target value of process sigma level is 4 and above, only those combinations of mean and standard deviation need to be selected. This is shown as the shaded area of the figure, at the top of the chart which can be called as the feasible region.

Figure 5. Mapping of process sigma level (vertical axis) for process mean (horizontal axis) and process standard deviation along the curves

Using Figure 5, it is now possible to identify the op-erating levels in the process which enable the desired sigma level performance. For a given sigma level of performance, the process mean and the standard de-viation can be identified and used as process param-eters. For example, if the process standard deviation is 0.0275 mm, the process mean when set at 3.38 mm would yield 4 sigma level of performance and then will decrease with the increase in the mean value. This clearly indicates that if both the process mean and the standard deviation increase, the sigma level of the process decreases. Thus it is now left to the process manager to decide as what level of sigma is to be targeted and accordingly decide the process parameters as found from Figure 5, and then aim to

maintain the same in the process. Thus Figure 5 can be suggested as decision making aid to enable the selection of process parameters for a desired sigma level of performance by the process.

3. CONCLUSION

Statistical process control in the earlier times typi-cally involved ensuring that the process is under sta-tistical control and the process is stable. The control charts served the purpose of assessment and the ad-ditionally done process capability analysis complet-ed the assessment. These two assessments helped the process managers to control the processes to en-


sure better output and thus enabled smoother pro-duction. With the advent of process improvement, and Six Sigma becoming a major development, it became necessary for the process managers to con-tinuously improve and also assure minimum global standards. This requires a thorough understanding of the Six Sigma metrics, which are globally recog-nized and used as common measures of process quality. Both the Six Sigma level of the process, and the DPMO have to be continuously monitored.

Today it is well known that “error free” output is expected by the customers and hence the process managers have to redefine the process performance as per the new standards set by the customers. In this context the process managers obviously look for benchmarks against which they can compare their processes and thus understand the gaps to proceed towards improvement. Six Sigma is one such bench-mark that is easy to understand and convince the cus-tomers when selecting the benchmarking initiatives. As Six Sigma is commonly accepted benchmark to define the quality, it is quite logical and prudent to choose Six Sigma for the purpose of comparison. In this paper such an attempt has been made to illus-trate how global benchmarking of the process needs to be done using Six Sigma metrics and further, how using “what if” analysis feature of Excel, it is possi-ble to get a clear picture of the desirable values of the process parameters. The inherent assumptions like normality of the output, and unchanged process be-havior, are here also made, and the usual limitation of making the assessments based on sample based values, also exist. However, the paper serves an im-portant purpose of helping the process managers to benchmark against the Six Sigma metrics, and thus aim towards global standards. The charts and tables illustrated in this paper provide a convenient meth-od of selecting the process parameters for a desired level of performance of the process. This tradeoff provides a wide opportunity of setting the process parameters depending on the resources. The idea is to illustrate how process improvements can happen by controlling the process parameters and get in to a predictive model to enable improved results. The overall objective is developing and demonstrating a decision making model through the established techniques.

REFERENCES

Balamurali, S., & Kalyanasundaram, M. (2002). Con-struction of a generalized robust Taguchi capabil-

ity index. Journal of Applied Statistics, 29(7), 967-971. doi:10.1080/0266476022000006676

Barnett, N. S. (1990). Process control and product quality: The Cp and Cpk revisited. International Journal of Quality and Reliabil-ity Management, 7(5), 34-43. doi:10.1108/02656719010135040

Boyles, R. A. (1991). The Taguchi capability index. Journal of Quality Technology, 23(1), 17-26.

Brady, J. E., & T. T. Allen (2006). Six Sigma: A literature review and suggestions for future research. Quality and Reliability Engineering International, 22(3), 335-367. doi:10.1002/qre.769

Breyfogle, F. (1999). Implementing Six Sigma (1st ed.). New York, USA: John Wiley.

Cagnazzo, L., & Taticchi, P. (2009). Six sigma: A literature review analysis. Proceedings of the 8th WSEAS International Conference on E-Activities and information security and privacy, 29-34.

Camp, R. C. (1989). Benchmarking: The search for industry best prac-tices that lead to superior performance. Milwaukee: ASQC Press.

Chan, L. K., Cheng, S.W., & Spiring, F. A. (1988). A new mea-sure of process capability: Cpm. Journal of Quality Technology, 20(3), 162-175.

Clements, J. A. (1989). Process capability calculations for non-normal distributions. Quality Progress, 22(9), 95-100.

Dattakumar, R., & Jagadeesh, R. (2003). A review of literature on benchmarking. Benchmarking: An International Journal, 10(3), 176-209. doi:10.1108/14635770310477744

Elmuti, D., & Kathawala, Y. (2013). An overview of benchmark-ing process: A tool for continuous improvement and com-petitive advantage. Benchmarking for Quality Management & Technology, 4(4), 229-243. doi:10.1108/14635779710195087

Hahn, G. J., Doganaksoy, N., & Hoerl, R. W. (2000). The evolu-tion of Six Sigma. Quality Engineering, 12(3), 317-326.

Hahn, G., Hill, W., Hoerl, R., & Zinkgraf, S. (1999). The impact of Six Sigma improvement: A Glimpse into the future of statistics. The American Statistician, 53(3), 208-215. doi:10.2307/2686099

Hammer, M., & Goding, J. (2001). Putting Six Sigma in perspec-tive. Quality, 40(10), 58-62.

Harry, M. J. (1998). Six Sigma: A breakthrough strategy for prof-itability. Quality Progress, 31(5), 60-64.

Hou, Y., & Wang, B. (2012). Analysis of non-normal process capability based on Rosenblatt transforma-tion system. AMR, 482-484, 2238-2242.

Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41-50.

Kozak, M., & Nield, K. (2001). An overview of benchmarking literature: Its strengths and weaknesses. Journal of Quality As-surance in Hospitality & Tourism, 2(3-4), 7-23.

Maleyeff, J., & Kaminsky, F. C. (2002). Six Sigma and introduc-tory statistics education. Education + Training, 44(2), 82-89.


Montgomery, D. C. (1986). Introduction to statistical quality control. New York, USA: John Wiley & Sons.

Nonthaleerak, P., & Hendry, L. (2006). Six Sigma: Literature re-view and key future research areas. International Journal of Six Sigma and Competitive Advantage, 2(2), 105-161.

Oke, S. (2007). Six Sigma: A literature review. South African Jour-nal of Industrial Engineering, 18(2), 109-129.

Pande, P., & Holpp, L. (2002). What is Six Sigma? New York, USA: McGraw-Hill.

Pearn, W. L., Kotz, S., & Johnson, N. L. (1992). Distributional and inferential properties of process capability indices. Journal of Quality Technology, 24(4), 216-231.

Persico, J. (Ed.) (1992). The TQM transformation: A model for organizational change. Quality Resources. New York, USA: White Plains.

Porter, L. J., & Oakland, J. B. (1991). Process capability indices: An overview of theory and practices. Quality and Reliability Engineering International, 7, 437-448.

Prabhushankar, G., Devadasan, S., Shalij, P., & Thirunavukkara-su, V. (2008). The origin, history and definition of Six Sigma: A literature review. International Journal Of Six Sigma And Competitive Advantage, 4(2), 133-150.

Pyzdek, T. (2003). The Six Sigma handbook. New York, USA: Mc-Graw-Hill.

Rao, S., & Xia, W. (1999). Measuring quality by process capability indices and the effects of non-normality. Journal of Statistics and Management Systems, 2(2-3), 127-142.

Scott, R. (2011). Benchmarking: A literature review. Academic Excellence Centre for Learning and Development, Edith Cowan University. Retrieved from http://intranet.ecu.edu.au/ data/assets/pdf_file/0010/ 357193/Benchmarking-Litera-ture-Review.pdf

Six Sigma Daily. (2016). DPMO to sigma level relationship. [on-line] Retrieved from http://www.sixsigmadaily.com/dpmo-to-sigma-level-relationship/

Somerville, S., & Montgomery, D. (1996). Process capability indi-ces and non-normal distributions. Quality Engineering, 9(2), 305-316.

Summers, D. (2005). Quality management (1st ed.). Upper Saddle River, USA: Pearson Prentice Hall.

Tjahjono, B., Ball, P., Vitanov, V., Scorzafave, C., Nogueira, J., & Calleja, J. ...Yadav, A. (2010). Six Sigma: A literature review. International Journal of Lean Six Sigma, 1(3), 216-233.

Zairi, M., & Youssef, M. (1995). A review of key publications on benchmarking part I. Benchmarking for Quality Management & Technology, 2(1), 65-72.

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