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
shashikashav37@gmail.com
Rajwinder Singh
Professor at Punjabi University,
School of Management Studies – Patiala – Punjab, India
rajwindergheer@gmail.com
Amir Shabani
PhD Scholar at Vrije Universiteit Amsterdam,
Faculty of Economics and Business Administration – Amsterdam, The Netherlands
a.shabani@vu.nl
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
1

Shashi, Rajwinder Singh, R., Shabani, A.: The identification of key success factors in sustainable cold chain management: Insights from the Indian food industry
ISSN: 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 Remarks
Food 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. deviation
Government regulatory requirements 1 6.04 1.312
Top management vision 2 5.83 1.826
Customer expectations 3 5.55 1.041
Market competition 4 5.47 1.405
Government ecological initiatives 5 4.79 1.578
NGOs 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. deviation
Accuracy 1 2.22 1.072
Quality 2 2.68 1.207
Product freshness 3 3.26 1.495
Cold warehouses and vehicles 4 3.59 1.363
Prices 5 4.02 1.850
Order fulfillment capacity 6 4.42 1.329
Quantity and cash discounts 7 5.10 2.152
Service rates 8 5.12 2.848
Geographical proximity 9 5.56 3.939
Variety 10 6.63 3.683
Delivery style 11 6.87 4.027
Certification 12 7.21 5.514
Credit based sales 13 8.22 5.430
Information sharing ability 14 8.43 5.793
Goodwill 15 9.43 5.461
Environmental issues 16 10.13 5.126
Long-term SC relationships 17 10.36 7.960
Social responsibility 18 11.77 7.633
Personal 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. deviation
Waste reduction 1 6.19 1.124
ISO 14001 Certification 2 6.12 1.314
Reduction of energy consumption 3 5.93 1.381
Lean management 4 5.83 1.279
Proper waste disposal 5 5.72 1.296

Recyclable packaging 6 5.43 1.574
Safety and agile health 7 5.30 1.514
Lower levels of greenhouse emissions 8 5.29 1.207
Resource management efficiency 9 5.12 1.337
Adoption of latest technology 10 4.98 1.672
Reverse logistics 11 4.64 1.450
Product life cycle management 12 4.31 1.625
Solar energy utilization 13 3.98 1.067
Use of passive CC 14 3.85 1.463
Green 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. deviation
Lower price 1 6.18 1.323
Purity 2 6.17 1.204
Quality 3 6.08 1.135
Organic food 4 5.93 1.436
Fresh food 5 5.86 1.310
Taste 6 5.83 1.438
Prompt delivery 7 5.72 1.183
Less supplier lead time 8 5.69 1.203
Environmentally friendly packaging 9 5.60 1.317
Availability 10 5.59 1.562
Proper Labeling 11 5.56 1.336
Less manufacturer lead time 12 5.24 1.478
Grading 14 4.94 2.583
Sorting 15 4.76 1.610
Low carbon emissions 16 4.41 1.939
Variety 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. deviation
Risk management 1 5.32 1.287
Cold logistics integration 2 5.25 1.463
Technical integration 3 4.99 1.316
Learning 4 4.81 1.614
Stakeholder management 5 4.65 1.692
Strategic orientation 6 4.47 1.535
Supply chain continuity 7 4.40 1.684
Innovation 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. deviation
Significant 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 1.009
Strong relationships with the community 4 5.56 1.211
Use of clean production technology 5 5.47 1.224
Reduction in operational costs 6 5.46 1.467
Physical layout designed to optimize
materials and energy
7 5.32 1.365
Recycling 9 5.14 1.643
Purchase 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 1.572
Use of renewable energy sources 13 4.26 1.565
Use 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. deviation
Regular meetings 1 6.23 1.177
Knowledge sharing 2 6.15 1.249
Knowledge acquisition and evaluation 3 5.93 1.392
Licensing 4 5.90 1.285
Partner-based synergies 5 5.87 1.363
Transparency 6 5.76 1.668
Partner development programs 7 5.64 1.403
Common IT System 8 5.41 1.574
Partner training 9 5.28 1.780
Joint 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. deviation
Reduction in waste rate 1 6.04 1.132
Reduction in customer complaint rate 2 5.98 1.211
Reduction in carbon emission rate 3 5.97 1.186

Customer satisfaction rate 4 5.94 1.185
Reduction in overall energy use 5 5.86 1.716
Reduced transportation costs 6 5.79 1.481
Shipping accuracy rate 7 5.76 1.379
Reduction in lead time 8 5.72 1.652
Improved product quality rate 9 5.71 1.307
Increased profits 10 5.69 1.373
Reduction in cooling costs 11 5.64 1.439
Reduced inventory costs 12 5.62 1.377
Staff retention 13 5.62 1.247
Order returns 14 5.56 1.624
Reduced processing costs 15 5.54 1.407
Recycling rate 16 5.40 1.438
Reduced warehousing costs 17 5.35 1.512
Reduced 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. deviation
Inadequate CC infrastructure 1 6.32 1.134
High investment costs 2 6.30 1.136
Lack of CC expertise 3 6.18 1.092
High energy costs 4 6.14 1.120
Complexity of designing ways to reduce re-
source/energy consumption
5 6.01 1.078
High costs of hazardous waste disposal 6 5.98 1.236
Lack of CC integration 7 5.97 1.203
Costs of environment friendly packaging 8 5.96 1.404
Lack of specific environmental goals 9 5.95 2.270

Unavailability of CC performance measurements 10 5.89 1.117
Uneven installation of CC centers 11 5.86 1.280
Lack of technology 12 5.81 1.538
Complexity of designing ways to recycle used
products
13 5.78 1.248
Inefficient processes 14 5.73 2.437
Lack of training courses 15 5.62 1.381
Lack of awareness about adopting reverse logistics 16 5.42 1.363
Lack 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. deviation
Goodwill 1 6.18 1.200
Customer satisfaction 2 6.17 1.199
Less lead time 3 6.08 1.386
Large amount of added value 4 5.93 1.357
Reductions in waste 5 5.88 1.148
Improved quality 6 5.87 1.240
Improved working conditions 7 5.80 1.235
Reductions in stocks 8 5.73 1.356
Flexibility 9 5.63 2.049
Reductions in energy costs 10 5.54 1.121
Strong CC relationships 11 5.54 1.883
Development 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).Akeyperformancemeasuresforevalu-
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

Submitted 06.03.2016. Approved 15.09.2016.
Evaluated by double blind review process.
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
lokendra2910@gmail.com
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.
Volume 9 • Number 2 • July - December 2016 http:///dx.doi/10.12660/joscmv9n2p17-27
17

Devangan, L. K.: An Integrated Production, Inventory, Warehouse Location and Distribution Model
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.
2. LITERATURE REVIEW
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/distributionplanninginsupplychains:Aninvitedreview.
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

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

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

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (18)

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

Similar a JOSCM - Journal of Operations and Supply Chain Management - n. 02 | Jul/Dec 2016 (20)

Más de FGV | Fundação Getulio Vargas

Más de FGV | Fundação Getulio Vargas (20)

Último

Último (20)

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