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Annals of Operations Research
https://doi.org/10.1007/s10479-021-04454-w
ORIGINAL RESEARCH
Evaluating sustainability initiatives in warehouse
for measuring sustainability performance: an emerging
economy perspective
Sadia Samar Ali1·Rajbir Kaur2·Shahbaz Khan3
Accepted: 16 November 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Abstract
Adding green elements in logistics functions have biggest impact in shaping the supply chains
towards sustainability. Business strategies must promote environmentally conscious thinking
through continuous integration of green and evaluation of resultant business and environment
sustainability performance. The approach is illustrated and validated through the development
and analysis of sustainability initiatives implemented in warehouses of frozen food supply
chains in Saudi Arabia. Modelled on a case study basis, this three-phase study builds on
theoretical concepts of contingency theory and triple bottom line approach. It incorporates
identification and ranking of essential sustainable practices of warehousing using literature
analysis, participation of practitioners in fuzzy Delphi and Best Worst Method. Further,
study establishes its uniqueness by applying combined compromise solution to rank the
resultant sustainability performance improvement in warehouses. The results draw attention
to green operations for energy and resource conservations, promotes the role of sustainable
work culture, sustainable strategies, and policies for their role in encouraging sustainability
performance outcomes.
Keywords Frozen food warehouse ·Sustainable-green practices ·Sustainability
performance ·Fuzzy Delphi ·BWM ·CoCoSo ·Saudi Arabian context
BSadia Samar Ali
ssaali@kau.edu.sa
Rajbir Kaur
rajbir00@gmail.com
Shahbaz Khan
shahbaz.me12@gmail.com
1Department of Industrial Engineering, Faculty of Engineering, King Abdul-Aziz University,
Jeddah, Saudi Arabia
2Govt Girls College, Panchkula, Haryana 134001, India
3GLA University, Mathura 281406, India
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Annals of Operations Research
1 Introduction
Saudi Arabia is marching towards sustainable growth as predicted through its futuristic vision
20301that aims to combat all related challenges through innovative and progressive decisions.
Numerous projects have been undertaken in Kingdom of Saudi Arabia (KSA) to convert it
into environmentally responsible country through smart and sustainable developments. The
key decision makers are striving to identify the challenges that could pose major threats to
the sustainability efforts and once such identified challenge is the food waste. The global
frozen food market is surging with an estimated 244.3 billion USD growth in 2020 which
would reach 312 billion USD by 2025 and European frozen food market is expected to
reach 95.7 billion by 2025.2Growing trends of convenience frozen food has pressurized the
cold supply chains of emerging economies which are already dealing with problems of poor
infrastructure, funding constraints and regulations.
Competitive emerging economies like India and China also face the similar problems
of fragmented food industry, product, or food loss due to lack of or old infrastructure and
environment deterioration. Efforts are underway in these emerging economies to upgrade
their infrastructural facilities to overcome these problems for sustainable development. A
comparative analysis of leading emerging economies China and India with Saudi Arabia is
presented below in Table 1.
KSA classified by United Nations3in 2017 as emerging and high-income country suffers
from the problem of food waste and spoilage specially at the retail end of the food chain
(Baig et al., 2019). This in turn increase unnecessary imports and food wastage cost lead-
ing to economic downturns (Meneghetti & Monti, 2014). Food waste is considered a large
greenhouse gas emitter impacting environmental sustainability hence it must be addressed.
Another point of discussion is the growing market of frozen packaged foods and ready meals,
beverages which is estimated to reach 59 billion USD by 2021.4Resultantly, it is influenc-
ing the supermarkets and distributions networks which struggle to maintain the quality of
food due to extremity of weather conditions. While some organizations are optimizing their
supply chains to minimize the food waste by networking with different regional suppli-
ers and distribution networks. Others are remodeling their warehouses for minimizing food
and energy waste for economic impacts while keeping environmental issues at the fore-
front. They are reconfiguring their supply chains to become more responsive and interactive
through innovations, integrations of newer concepts and technologies in all their major func-
tions (Choudhary et al., 2020;Duetal.,2017). Corporate sustainability strategies insist on
business process and supply chain model based on environmental sustainability dimensions,
so organizations are converting their supply chains to green supply chains (Brandenburg
et al., 2015; Mangla et al., 2018). Green supply chains are expected to manage environmen-
tal degradation problems by turning all main activities into green procurement, operations
or manufacturing, cleaner production, reverse logistics, and distribution (Choi et al., 2017;
Malesios et al., 2018; Bai et al., 2018). Warehouse operations are central to supply chain
logistics strategies as they have strong impact on productivity operation cost and supply
chain performance (Centobelli et al., 2017;Guetal.,2010). Warehouse operations constitute
1https://vision2030.gov.sa/en.
2https://www.marketsandmarkets.com/Market-Reports/global-frozen-and-convenience-food-market-
advanced-technologies-and-global-market-130.html.
3https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/WESP2019_BOOK-ANNEX-en.
pdf.
4https://www.grandviewresearch.com/industry-analysis/frozen-food.
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Table 1 Emerging economics and food supply chains
Emerging
Economies
Growth of cold
chain logistics
market
Growth of
frozen food
market
Food waste Global
sustainability
index
Warehousing
and storage
market
China From 40.46 bn
to reach 74.95
bn USD in
2026a
39% of overall
food
consumption
of 20 million
metric
tonnesb
1.8 bn
tonnes per
yearc
136dExpected to
reach USD
646.77. bn
by 2022e
India 17.53 bn USD in
2019 to 47.2
bn USD by
2022f
From INR
85.27 bn to
INR 192.96
bn by 2024 g
1.6 bn
tonnes per
yearh
63iExpected to
reach USD
384.034 bn
by 2024j
Saudi Arabia Projected to
reach USD
2,572 million
by 2024 k
Expected to
reach USD
531 million
by 2021 l
13.3. bn
tonnes per
annumm
161nExpected to
reach USD
117 bn in
2020o
ahttps://www.prnewswire.com/news-releases/china-cold-chain-logistics-industry-report-2020-2026-
301051738.html
bhttps://www.businesswire.com/news/home/20170504005554/en/Cold-Chain-Market-China-Grow-
CAGR-17
chttps://www.ft.com/content/9790db76-5d16-4569-aa88-d29dd8b53007
dhttps://earth.org/global_sustain/china-global-sustainability-index/
ehttps://www.globenewswire.com/news-release/2020/02/20/1987593/0/en/Warehousing-And-Storage-
Global-Market-Report-2020.html
fhttps://bdbipl.com/blogs/indian-cold-chain-industry-outlook/
ghttps://www.researchandmarkets.com/reports/5017306/frozen-foods-market-in-india-2020
hhttps://swachhindia.ndtv.com/independence-day-how-to-reduce-food-wastage-23834/
ihttps://earth.org/global_sustain/india-ranked-63rd-in-the-global-sustainability-index/
jhttps://www.businesswire.com/news/home/20200521005412/en/Warehousing-Market-India-2020-
Expected-Worth-INR
khttps://www.researchandmarkets.com/reports/4591591/saudi-arabia-cold-storage-market-2018-2024
lhttps://www.insightssuccess.in/outlook-frozen-food-market-gulf-region/
mhttps://www.insightssuccess.in/outlook-frozen-food-market-gulf-region/
nhttps://earth.org/global_sustain/saudi-arabia-ranked-161st-in-the-global-sustainability-index/
ohttps://www.marketwatch.com/press-release/refrigerated-warehousing-and-storage-market-global-insights-
trends-and-huge-business-opportunities-2020-to-2023-2020-07-09?tesla=y
24% of the logistics cost (Richards, 2017), hence focus on warehouse design for managing
total cost and carbon footprint can deliver favorable results (Accorsi et al., 2017). Similar
ideas are shared by Ries et al. (2016) who argued that environmental sustainability is possible
in context of warehouse for reducing operational cost and carbon footprint (Du et al., 2017).
Warehouses of cold supply chain can benefit by turning green as they not only eliminate
food waste but economize on energy and resource efficiencies too. Following the changing
trends, these supply chains must redesign their warehouse strategies to accommodate the
requisite changes for sustainable performances (Meneghetti & Monti, 2014). How to bring
these changes is another strategical decision as which green practice offer what kind of result
is yet to be established (Yakavenka et al., 2019). Waste minimization, energy and resource
savings comes with its own set of challenges hence it is vital to identify essential greening
strategies or practices which will allow to overcome these challenges (Rezaee et al., 2017).
Furthermore, any strategic decision undertaken must justify its result, hence evaluation of
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performance is essential to understand and justify the integration of sustainability efforts
(Tseng et al., 2018a,2018b; Kopphiahraj et al., 2021). Clearly, it is important to identify and
analyze sustainable practices responsible for sustainable performance so moving forwards,
we state the following objectives of the study:
•To identify the sustainable-green practices applicable in warehouse functions of supply
chains,
•To examine these practices by estimating their importance weights in sustainable perfor-
mance improvement,
•To establish a relationship framework of these practices and performances based on
research findings.
The study proceeds further to fulfill the above stated objectives by approach as explained:
first review of the literature is undertaken to identify the most suitable green-sustainable prac-
tices for warehouse performance improvement. Further key performance indicators reflecting
expected performance improvement are shortlisted from the review. Theoretical founda-
tions of sustainability parameters are explored by evaluating landscapes of triple bottom
approach and contingency theory. Next experts from warehousing and supply chain industry
are approached for validation of these practices and key performance improvement indica-
tors. Further, Best Worst Method (BWM) is applied to rank the identified sustainable-green
practices for their importance. Then combined compromise solution (CoCoSo) is used to rank
the performance based on sustainable-green practices. The details of methodology and case
companies are discussed in Sect. 3,and4respectively. The results and detailed discussion
followed by conclusions and future implications are provided in consecutive sections.
2 Theoretical foundations for building sustainability initiatives
in warehouse for sustainable performance
This section sheds significant light on existing literature on established sustainability theories,
sustainable-green practices adopted for performance evaluations, metrics, and measurements.
2.1 Article selection
Relevant scientific articles were selected for review by using keywords such as warehouse
sustainability, performance evaluation, food supply chain, middle east region. Scholarly
databases like Science Direct, Scopus, Wiley, Google Scholar, Emerald, Springer, and Taylor
and Francis were used for selection of articles. The articles written in English language and
peer-reviewed are considered for further contemplation.
2.2 Exploration of sustainability concepts: sustainability as a derivative
of contingency
Sustainable development as proposed by Brundtland Report5in 1987 signifies the impor-
tance of protection of present resources for sustenance of future generations (Ali et al.,
2020a). Triple bottom theory (TBL) proposed by Elkington (1997) stresses the importance
of resource savings from three critical dimensions: social, economic, and environmental.
5https://sustainabledevelopment.un.org.
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Industrial paradigms are however more tilted towards commercial and business parameter
rather than environmental (Govindan et al., 2019). In terms of business and manufacturing,
sustainability entails a focus on serving the demands of an organization’s direct and indirect
stakeholders, which could include an individual, a corporation, a community, a city, or a gov-
ernment. (Nechi et al., 2020), while accommodating future stakeholders’ interest. Keeping
economic interest as a priority, sustainability also considers continuation of business opera-
tions which may lead to environmental degradation and societal deprivation (George et al.,
2016; Jabbour et al., 2019). Hence it is imperative that organizations must measure their
progress towards sustainable performance (Luthra et al., 2020) rather than keeping a narrow
focus on operational and economic performance to improve their sustainability indices (Ver-
rier et al., 2016).The theoretical underpinnings of contingency theory (Lawrence & Lorsch,
1967) state that organization behavior and performance is dependent on the organizational and
contextual factors which are further impacted by environment in which it operates (Jabbour
& de Sousa Jabbour, 2016). Organizational and contextual factors/drivers or alternatively
called external and internal factors either motivate or pressurize them to overcome barriers
and adopt green/sustainable practices which can help in achieving sustainability performance
(Garza-Reyes, 2015). As contingency theory explains that any managerial action must jus-
tify its context or objectives for meaningful implications hence proportionate fit quotient
between organizational structure and contextual or contingency factors is essential (Don-
aldson, 2006). This means that any sustainable practice implemented in organization must
comply or fit with its expected sustainability performance outcomes. Contextual factors as
mentioned by Faber et al. (2017)iscomplexity a derivative of organization’s inner envi-
ronment and uncertainty a derivative of organizations’ external environment. It is essential
to manage the contingency factors of socio, economic and environmental uncertainty, and
organization’s structural, functional, and operational complexities for optimum sustainable
performance outcomes (Govindan & Sivakumar, 2016) and warehouse management should
follow the same principle (Richards, 2017). Baruffaldi et al. (2019) develop a decision sup-
port system for the warehouse management system customization by considering the cost of
the information sharing, data viability, and the uncertainty involved in the quantification of
ROI. Similar to this, Salhieh and Alswaer (2021) proposed a maturity model for improving
the performance of warehouse and this model could use for benchmarking the warehouse
performance improvement. Islam et al. (2021) proposed a model to the predict the key per-
formances indicators of overall warehouse performance with a low forecasting error. The
proposed model PSOGM (1,1) is an extension of the GM (1, 1) model by incorporating the
PSO algorithm to minimize the grey model’sdevelopment coefficients PSO algorithm. Nantee
and Sureeyatanapas (2021), proposed a sustainability assessment framework for sustainable
warehousing in the industry 4.0 environment using the combined approach of item-objective
congruence index and Q-sort method.
The capability of organizations to stay ahead is contingent upon their ability to channelize
contingency environmental factors, implement sustainability measures (Sousa & Voss, 2008)
and a continuous analysis of business sustainability performances (Fichtinger et al., 2015;
Rentizelas et al., 2018) for combating climate change. External contingency elements are
outside the control of management, so the organisation must strategize to deal with them (Li
et al., 2020). Worldwide energy consumption of warehouse functions has multiplied espe-
cially in case of cold supply chains which consume 15% of total energy (Ries et al., 2016).
The refrigeration requirements of cold supply chains for cooling and maintaining food quality
exerts pressures on energy demand which in turns influences sustainability of these supply
chains (Yakavenka et al., 2019). Food based cold supply warehouse operational strategies
must consider inventory models that can moderate storage temperature, time, and inventory
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filling level to understand their impact on energy consumption (Meneghetti & Monti, 2014).
Sustainable cold food supply chains require balance of temperature and storage time for
reducing food waste along with balancing cost (Zanono and Zaanella, 2012). Strong focus
on inventory policies for minimizing cost, environmental issues, energy efficient transport
(Oberhofer & Dieplinger, 2013), design for reduction in CO2emissions (Du et al., 2017)
should be encouraged. Environmental sustainability in warehouse can be achieved by work-
ing on its design, facilities, operations, and equipment (Accorsi et al., 2017;Guetal.,2010)
processes recycling and waste management, human resource management (Faber et al., 2013).
Sustainability practices implemented in warehouse for sustainability performance must con-
sider these factors like advent of new technologies, competitive pressures, market pressures,
new government policies or regulation, supply chain disruptions, cost of raw materials, lack
of resources (Ries et al., 2016). Additionally, these sustainable-green practices should match
the intensity of the contingency of individual factor (Allevi et al., 2018). New environmental
policies on climate change, waste management and carbon reduction has brought attention
towards logistics activities (Ali et al., 2020b;Duetal.,2017) which are considered a main
source of environmental pollution in supply chains along with warehousing activities (De
Koster et al., 2017). Globally green practices in supply chains have seen positive outcomes as
discussed by many authors explain results in individual context. As warehousing functions
constitute major part of logistics cost (Richards, 2017), inclusion of practices of green waste
management and recycling in warehousing could offer operational cost reduction (Laari et al.,
2017) and performance improvement at regional regions (Ali and Kaur, 2021). Additionally,
preservation of natural resources through decreased material inventory, decreased energy use,
and reduction in emission of hazardous materials (Maletiˇcetal.,2016) enables sustainable
organizational growth.
2.3 Sustainability measures/practices adopted in warehouse function
for sustainable performance
(i) Energy efficient operations Use of advanced technologies allow green warehousing oper-
ation that can considerably reduce resource utility and improve economic performance.
Innovative technologies can be used for optimizing future demand (Ali et al., 2021), opti-
mizing warehouse activities for reduction in operation cost and time influencing CO2
emission rate (Meneghetti & Monti, 2014). Management of packaging waste through cat-
egorizing of inbound material, regrouping, reclassifying outbound material, efficient cross
docking for avoiding inventory pileups save warehouse resources (Accorsi et al., 2017).
Innovative technologies can also be applied for route and order picking, for dynamic
stock keeping units (SKU) capacity utilization, improved forklift operations, for optimized
work capacity and personnel loads and energy saving through reduced travelling distance
and time (De Koster et al., 2017).
(ii) Green transportation Inhouse transportation and outbound logistics have major role in fuel
usage and emission control. Focus on quality of fuel used, alternative fuel for long distance
deliveries, even electric vehicles are now used to reduce the emissions rate (Oberhofer &
Dieplinger, 2013).
(iii) Green building Warehouse building and infrastructure should integrate resource saving
processes like air cooled equipment and chemical treatments and building material in its
foundational strategies. However, the existing building can also be modified to some extent
for water conservations and energy saving. Rainwater harvesting, green roofs, eco-friendly
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Annals of Operations Research
sanitation equipment (Gu et al., 2010) can minimize dependence of outsourced water. Ware-
house operations require temperature control which to some extent is possible with insulation
in walls but product’s nature, facilities location, region of operations, weather conditions are
also important for understanding this issue (Tappia et al., 2015). Noise control is another
important aspect which is possible with green building.
(iv) Employees’ training of energy efficient practices and operations Regular training of
employees motivates employees to consider sustainability aspects in more comprehensive
manner and change their thinking about the way they execute work. Such training not only
change their work behavior but improves their lifestyles and thought process too (Rentizelas
et al., 2018).
(v) Establishing sustainable work culture Promoting a culture of ethical, clean, and sustain-
able work culture allows organizations to remodel their work methods, preserve ethical values
and support the initiatives adopted by the organizations. Keeping the premises clean, regular
habits of less use of energy/electric appliances, switching off the machines and appliances
when not in use to conserve energy. Sustainable practices followed by managers can encour-
age employees to follow their footsteps (Muduli et al., 2020) and improve social aspects in
sustainable supply chain (Jabbour et al., 2020).
(vi) Green/eco-friendly strategies and policies It is for formulation of corporate sustainable
strategies, lean green policies, and low carbon policies (Chen et al., 2016;Duetal.,2017;Li
et al., 2020). Sustainability reporting (Laari et al., 2017) prepares organizations to integrate
sustainability practices in their warehouse functioning on a continuous basis. These strategies
and policies are outcomes of exogenous or external contingency factors like government
pressures.
(vii) Monitoring control measures Formulation of policies and strategies is beneficial for
sustainability but their assessment for the impacts also need to be monitored through a
proper reporting system of sustainability performance evaluation (He et al., 2017). Formal
and informal control measures should be adopted to assess and evaluate the effectiveness of
environmental strategies and policies (George et al., 2016).
(viii) Green waste management Elimination of waste from business processes by focusing on
main dimensions of waste like, inventory overload, energy and fuel waste through inhouse
transportation, wrongful processing motions, waiting signifying waste of time, energy or
opportunity, and waste of human capital and potential (Verrier et al., 2016). Clean and green
technology has become an important aspect for elimination or reduction of waste and per-
formance improvement (Yadav et al., 2018). Cloud computing is frequently used for storing,
managing, and accessing data anywhere in workspaces. This allows easy information retrieval
for easier displays thus minimizing dependence on papers. Paperless operations eventually
facilitate reduction of CO2emissions, elimination of solid waste and minimization of water
consumption (Tappia et al., 2015).
Discussions of sustainable-green practices and performance measures are provided in
Table s 2and 3. The triple bottom line along with the contingency framework is a theoretical
concept of this research study. Figure 1explains how the internal and external contextual
contingency elements of green practices support performance indicators of triple bottom line
in terms of economic, environmental, and social framework of pillars of sustainability.
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Table 2 Sustainable-green practices of warehouse sustainability performance
SN Sustainable practices Description References
1 Development of green
building and
infrastructure
Use of eco efficient construction
material and adding green or
vegetative cover provides
protection from extremities of
weather, facilitate temperature
control, and promote energy
saving which results in decrease
in cost of energy consumption.
Allowing source of natural lighting
through use of energy efficient
thermal control glass will reduce
electricity consumption
Gu et al. (2010)
2 Adoption of energy
efficient operations
Warehouse operations are energy
intensive, so inclusion of
technologies allow faster and
efficient operations allowing
effective use of material thus
reducing inventory pileups which
will allow decrease in inventory
levels, solid waste and will
improve capacity utilization
Meneghetti and Monti
(2014)
3 Adoption of green
transportation
Implementation of green initiatives
or practices in transportation
supports cost and pollution control
and drive supply chain towards
decrease in harmful emissions
Centobelli et al. (2017),
Ahi and Searcy (2013)
and Mirzaei et al. (2021)
4 Implementation of green
strategies, policies, and
regulations
Strategies and policies formulated
based on international and climate
friendly guidelines provides
support and motivation to
sustainability efforts in a
warehouse. Environmental
certifications, development of
clean technologies, partnership
with green organizations, green R
& D and pollution control
initiatives will improve
organization image, carbon
performance
Chen et al. (2016)
5 Implementation of green
waste management
practices
Life cycle analysis, use of
eco-friendly green or organic
material for packing, paperless
operations allow decrease in solid
waste. Tie ups with
environmentally conscious
partners for sustainable scrap
management measures will allow
increase in scrap and used
material. Association with local
retailers for sale of soon to be
expired material increase in
investment recovery on excess
inventories and allow effective
capacity utilization
Luthra et al. (2020)
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Table 2 (continued)
SN Sustainable practices Description References
6 Establishing sustainable
work culture
Building sustainability work culture
promotes employee motivation
and creates ethical habits of
resource conservations among
them
Muduli et al. (2020)and
Jabbour et al. (2020)
7 Implementation of
environmental
monitoring and control
measures
Provides clarity on the real situation
and allow organization to take
requisite and timely improvement
steps and make changes in their
process actions and movements
which results in decrease in
environmental accidents
George et al. (2016)and
Al et al. (2020a,2020b)
8 Employees’ training of
energy efficient
practices and operations
Regular training about sustainable
practices and methods will
encourage employees to learn
more and improve their efficiencies
which in turn results in improved
output, both work and workers
Rentizelas et al. (2018)
Table3 Sustainable warehouse performance
Code Sustainable performance Referred by
PO1 Decrease inventory levels Mangla et al. (2018)
PO2 Reduce electricity consumption through natural lighting De Koster et al. (2017)
PO3 Improved capacity utilization Richards (2017)
PO4 Reduction of solid waste Yadav et al. (2018)
PO5 Increased environmental work and worker output Accorsi et al. (2017)
PO6 Image of organization and country of operations on
global front based on their carbon performance
Zanono and Zavanella (2012)
PO7 Increase in investment recovery (IR) (sale) of excess
inventories/materials
Faber et al. (2017)
PO8 Increase in sale of scrap and used materials Laari et al. (2017)
PO9 Increased market share Ali et al. (2019)
PO10 Decrease of frequency of environmental accidents He et al. (2017)
PO11 Reduce hazardous/harmful/toxic/hazardous material Tappia et al. (2015) and Ali et al.
(2020a,2020b))
PO12 Decrease of cost for energy consumption Ries et al. (2016)
PO13 Employee increased motivation Baker and Canessa (2009)
3 Research gap identification
Researchers have contributed immensely towards sustainable supply chain literature from
various perspective (Mangla et al., 2018; Bai et al., 2018; Choudhary et al., 2020) but literature
on warehouse sustainability concepts requires more attention (De Koster et al., 2017). The
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Fig. 1 Graphical representation of theoretical framework
sustainability adoption, practices, and performances from developing countries perspective
need more contributions.
Research literature on triple bottom line in context of warehouse is explored by many
authors (Bank & Murphy, 2013; Faber et al., 2017). All works named herein shared theo-
retical discussions but analytical implications of individual aspects of social, economic, and
environmental along with expected outcomes need further explorations. Contingency theory
is explored in context of sustainable supply chains from external and internal contingent
factors (Sousa & Voss, 2008; Faber et al., 2017; Volberda et al., 2012). But the impact of
these factors on adoption of green practices and resultant outcomes need exploration using
multi-criteria decision-making (MCDM) approaches.
Literature on sustainability practices and performance in warehouse covered from the-
oretical concepts of relationship between practices and performance (Tappia et al., 2015;
Meneghetti & Monti, 2014;Riesetal.,2016; Faber et al., 2017). But MCDM technique
like Best Worst Method (BWM) along with Combined Compromise Solution (CoCoSo) for
establishing relationship between sustainable practices and performance in warehouse has
not been explored in existing studies.
Above mentioned research gaps are filled by extending the study further by drawing on
case companies of Saudi Arabian frozen food warehouse cluster. Building on the sustainable
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practices and performance indicators identified through literature review and validated by
experts, the study constructs a framework. This framework is analyzed by using BWM and
CoCoSo.
4 Methodology
Objectives of the study are achieved using a three phased methodology framework as shown
in Fig. 2. First phase included identification of sustainable-green practices and performance
measures of warehouse for achieving the sustainable performances using the integrated
approach of literature survey and fuzzy Delphi method. Second phase determines the pairwise
comparison-based ranking of sustainable practices of warehouses for achieving the sustain-
able performances using BWM. Finally, the hybrid approach is used in third phase evaluates
the sustainable performances of warehouse in by considering the adopting the sustainable
practices using CoCoSo.
Fig. 2 Proposed research framework for this study
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4.1 Fuzzy Delphi
Fuzzy Delphi method is the extension of the Delphi method that is developed by Ishikawa
et al. (1993). The traditional Delphi method requires several numbers of survey rounds to
develop the consensus among the experts’ opinion which resulted into higher execution time
and cost (Ishikawa et al., 1993). In addition, the feedback of the experts cannot be adequately
expressed in quantitative terms, due to the varying meanings of their responses. Fuzzy set
theory is combined with the traditional Delphi methodology to overcome these shortcomings
and is known as the fuzzy Delphi method. This method has benefit over the Delphi method as
it decreases the number of survey rounds and consequently time (Tseng et al., 2018a,2018b).
The detailed steps of the fuzzy Delphi method are provided in the Appendix A1.
4.2 BWM method
Best worst method (BWM) is a recent multi-criteria decision making (MCDM) technique
that is developed by Rezaei in 2015 (Rezaei, 2015). Due to the lesser number of pairwise
comparison among the factors (in this study sustainable-green practices) and less mathemat-
ical complexity, this method gains wide acceptability among the academia and some recent
studies related to BWM is provided in Table 4.
In addition, BWM deals effectively with the inconsistency obtained from pairwise com-
parisons. In this method, only reference comparison is performed, which refers that all
sustainable practices are only compared with respect to the best and worst sustainable prac-
tices. The preference of the best sustainable practices over the other sustainable practices is
determined and the preference of other sustainable practices is obtained over the worst sus-
tainable practices while reference comparison is done using usually 9-point scale on “1–9”.
The stepwise procedure of the BWM method is provided in the Appendix A2 as proposed
by Rezaei (2015).
4.3 Combined Compromise Solution (CoCoSo)
Recently, Yazdani et al. (2019) developed the CoCoSo method that is one of the effective
MCDM technique. This method is based on the combination of the simple additive weight-
ing and exponentially weighted product model. This method deals with the ranking of the
alternatives (in this study sustainable performance measures) which are evaluated against the
certain criteria (in this study sustainable-green practices). The CoCoSo is a relatively newer
approach and has now gained momentum in supply chain and related research areas. Some
of the recent studies related to CoCoSo application are provided in Table 5.
The details of the steps of CoCoSo are given in Appendix A3 as proposed by Yazdani
et al. (2019).
5 Case study
5.1 Case companies’ information
All the companies selected for the research purpose are in the industrial region of Al-Khomra
in south Jeddah. This region is an established industrial cluster situated near Jeddah Islamic
Port and comprises of warehouses and distribution centers for logistics, FMCG, and light
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Table 4 Recent studies of the application of BWM method
Authors Applied method Focus area Sample size Objectives
Alietal.(2021) BWM Technology 8 Decision making
framework for the
Drone integration
in the emerging
economics
Moktadir et al.
(2020a)
BWM +
DEMATEL
Circular economy 15 Identification and
assessment of
critical success
factors (CSFs) of
CE practices
implementation
Moktadir et al.
(2020b)
BWM Sustainability 07 Identification and
investigation of
key performance
indicators (KPIs)
of operational
excellence towards
sustainability
Choudhary et al.
(2020)
BWM +
DEMATEL
Sustainable supply
chain management
09 Evaluate the
effectiveness
environmentally
sustainable supply
chain management
dimensions
Gupta et al. (2020) BWM Supply chain
management
08 Investigation of the
barriers and their
overcoming
strategies of the
supply chain
sustainability
innovation in the
context of an
emerging economy
Govindan et al.
(2020)
BWM +
DEMATEL
Sharing economy 06 Identification and
investigation of
barriers related to
industrial sharing
economy
Chen and Ming
(2020)
BWM Method development 05 Selection of smart
product service
module
Khan et al. (2021) Fuzzy BWM Risk assessment 08 Risk assessment in
halal supply chain
industry. The organization were selected based on their area of operations, number of oper-
ational years, employee base and green practices implementation stages. All the selected
organizations were from frozen food supply chain and were operational for more than 10 years
and had minimum employee strength of 100. In the first round 67 companies were selected
for conducting the research out of which only 19 expressed willingness to participate. The
information about the organizations was not easily available on their websites, hence multiple
telephonic calls were made to obtain information. Telephonic conversations were made on
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Table 5 Evolution and application of CoCoSo method
Authors Applied method Focus area Sample
size
Objectives
Yazdani et al. (2019) CoCoSo Supplier selection 07 Extend the
CoCoSo with
grey numbers, to
measure the
supplier’s
performance in a
construction
company
Uluta¸s et al. (2020) fuzzy SWARA +
CoCoSo
Logistics 08 Location selection
for logistics
center
Ecer and Pamucar
(2020)
fuzzy BWM +
CoCoSo’B
Supply chain
management
03 Identify and
evaluate the
Sustainable
Supply chain
management
practices for
sustainable
supplier selection
Yazdani et al. (2020)DEA+R-FUCOM +
R-CoCoSo
Logistics 04 Development a
decision model to
select the
establishment of
logistics centres
the autonomous
communities of
Spain
Khan and Heleem
(2021)
CoCoSo Circular practices 05 Proposed a
framework to
rank the circular
practices
Liu et al. (2021) Pythagorean fuzzy
CoCoSo
Medical waste
management
04 Proposed a
framework for
medical waste
treatment
technology
selection
Wen et al. (2019) SWARA and CoCoSo Cold chain
logistics
04 Selection of drug
cold chain
logistics
pre-decided times for clarification and validation of responses. Nine experts expressed their
inability to participate further due to time constraints and finally we received 10 responses
which were considered sufficient for continuation of the research.
All the consenting experts were sent an open-ended questionnaire pertaining to sustain-
able practices applicable in the warehouses of food cold chain organizations. A questionnaire
pertaining to these practices was sent to experts through e-mails. These practices were final-
ized using fuzzy Delphi method based on the responses received by ten experts. The same
specialists were also involved in the evaluation of sustainable practices and their associated
performance using the BWM and CoCoSo methods. Data is obtained in this manner with the
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support of ten experts using three questionnaires (fuzzy Delphi, BWM, and CoCoSo). The
data is collected in two phases, one for the finalising the sustainable practice and second for
the evaluation of the sustainable performance.
5.2 Background of experts
All the experts provided extensive support with their responses and telephonic conversations
to provide us with extra information. Experts were mainly from engineering and business
management backgrounds who had minimum 10 years of work experience. Seven experts had
proper industrial specially warehouse related hands-on experience whereas three experts were
corporate consultants having academic associations too. Seven experts had held managerial
positions in warehouses and were familiar with challenges of frozen food supply chain and
sustainability concepts. The details of experts are shared in Table 11 (Appendix A).
The number of experts was limited to ten as the sample size in expert-based studies,
especially for the MCDM technique, might range from four to thirty (Rezaei et al., 2015).
The small number of experts (two to three) makes it difficult to generalize the results, but
the big number of experts (twenty to thirty) makes it difficult to reach to an agreement or
consensuses which may lead to a high level of inconsistency (Pun & Hui, 2001). As a result,
this study utilized the optimal number of ten experts are considered for the contributions
who were competent to draw conclusions. Some prominent studies are already mentioned in
Table s 4and 5used the expert size of three to fifteen.
5.3 Finalization of sustainable practices and sustainable performance measures
In order to finalise the sustainable practices and performance measures a combined approach
of literature review and fuzzy Delphi is applied (Mosayebi et al., 2020). Initially, twelve
sustainable-green practices and seventeen performance measures of the warehouse were
identified through the literature review. After that, a questionnaire was prepared that were
sent to the experts for collection of the responses. Ten valid responses are received, upon
which fuzzy Delphi analysis was applied to finalise the sustainable-green practices and perfor-
mance measures shown in Table 12 (Appendix B). Further processing related to questionnaire
preparation is done to collect the expert’s response for finalized sustainable-green practices
and performance measures.
5.4 Prioritization of sustainable practice
In this phase, we have prioritised the sustainable-green practices responses using the BWM.
In order to apply the BWM, the best and the worst sustainable-green practice is identified
using the expert’s input with the help of questionnaire. The best and worst sustainable practice
recognised by the ten experts are shown in Table 13.
After that the preference of the best sustainable-green practices over the other sustainable-
green practices are specified through the input of ten experts using nine-point scale (1–9).
Table 14 shows the response of one of the experts. Similar to this, the preference of the other
sustainable-green practices over the worst sustainable-green practices are also specified the
ten experts using nine-point scale (1–9). The response of one expert is shown in Table 15.
Subsequently, the optimal weights of the sustainable practices are calculated, by solving
the optimization model 2 [Eq. (A4), Appendix A2] for each of the ten experts and same are
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Table 6 Weight and rank of sustainable practices
Sustainable practices Weights Rank
Development of green building and infrastructure (SP1) 0.038294 8
Adoption of energy efficient operations (SP2) 0.291308 1
Adoption of green transportation (SP3) 0.16615 3
Implementation of green strategies, policies, and regulations (SP4) 0.220431 2
Implementation of green waste management practices (SP5) 0.099421 4
Establishing sustainable work culture (SP6) 0.076104 5
Implementation of environmental monitoring and control measures (SP7) 0.058696 6
Employees’ training of energy efficient practices and operations (SP8) 0.049596 7
Average Consistency Ratio (CR) =0.07180
showninTable16. Further, average for each sustainable practice are calculated to get a single
weight vector, as shown in Table 6. From the Table 6, it is evident that the average consistency
ratio (CR) is close to zero (Rezaei, 2015,2016), hence the comparisons are highly consistent
and reliable. Based on the weight of each sustainable practices, the rank is computed and
shown in Table 6.
5.5 Prioritization of performance measures
In the third phase, the sustainable performance measures are priorities using the CoCoSo
method. In order to apply the CoCoSo method, a linguistic decision matrix is provided to
each expert and asked them to evaluate each sustainable performance as per their importance
using the sustainable-green practices as an evaluation criterion. In this manner, ten linguistic
matrix decision matrices are obtained which are converted into decision-making matrix by
replacing the linguistic terms with the crisp values as per Table 10 (Appendix B). This matrix
is transformed into initial decision-making matrix using the altermatic average of the ten
decision-making matrices and shown in Table 17 (Appendix B).
Further, the initial decision-making matrix is normalised using Eqs. (A6)and(A7).
This normalised decision matrix is shown in Table 18, after that, a weighted comparabil-
ity sequence and their summation (Sj) is calculated for each sustainable performance using
Eq. (A8). The weighted comparability sequence and SjisshowninTable18. Similarly, the
power weighted comparability sequence, and their summation (Pi) is computed for each of
the sustainable performance using Eq. (A9). The power weighted comparability sequence,
and the calculated value of summation (Pi) is shown in Table 19.
In the last step of CoCoSo method, three aggregation methods are employed to compute the
relative weights (kia,kib,kic) of each sustainable performance using the Eqs. (A10)–(A12).
These relative weights are applied to determine the final weights by using Eq. (A13)and
shown in Table 20. Based on final weights, each sustainable practice is prioritised, and final
ranks are shown in Table 7above.
5.6 Robustness of the model
MCDM strategies exist to assist decision makers in understanding their problem as well as the
different aspects that can influence it and then arriving at a “Good” enough solution (Vincke,
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Table 7 Relative weights, final weigh and raking of performance measures
Performance KaRanking KbRanking KcRanking K Final ranking
PO1 0.08208 6 3.91872 10 0.90680 6 2.29904 8
PO2 0.05877 12 2.92558 12 0.64927 12 1.69270 12
PO3 0.05528 13 4.21877 8 0.61072 13 2.15049 11
PO4 0.08329 5 4.41186 6 0.92015 5 2.50175 6
PO5 0.06843 10 4.88858 5 0.75600 10 2.53673 5
PO6 0.07703 9 4.03178 9 0.85104 9 2.29504 9
PO7 0.08090 7 3.83580 11 0.89382 7 2.25567 10
PO8 0.05910 11 2.15680 13 0.65288 11 1.39284 13
PO9 0.07885 8 4.37682 7 0.87110 8 2.44548 7
PO10 0.08827 4 5.29759 3 0.97525 4 2.89011 3
PO11 0.08918 2 5.52376 2 0.98527 2 2.98528 2
PO12 0.09051 1 5.78995 1 1.00000 1 3.09973 1
PO13 0.08831 3 5.25719 4 0.97566 3 2.87505 4
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Table 8 Sustainable performance rank in different test
POs Original Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Test 9
PO18 898987777
PO2 12 12 12 12 12 12 12 12 12 10
PO3 11 11 11 11 7 6 4 4 3 2
PO46 556678899
PO55 1085532211
PO6 9 7 7 9 11 11 11 11 11 13
PO710 910101010 9 9 8 8
PO8 13 13 13 13 13 13 13 13 13 11
PO9 7 6 6 7 8 9 10 10 10 12
PO103 233456666
PO112 112345555
PO121 321111123
PO134 444223344
1999). Generally, variations in selected set of variables or even in endogenous variables also
serve as robustness indications (Bouoiyour, 2014). In our study we have considered sustain-
ability performance in warehousing as an endogenous variable, which could be considered
as an indication of the robustness. The result obtained from the MCDM analyses could be
influenced by the data imprecision, vagueness, and subjective judgment of experts. Studies
shows that a small variation in criteria weights may vary the ranking (Khan & Haleem, 2021).
Hence, it is essential to evaluate the robustness of the computed ranking. In order to test the
robustness of result, a sensitivity analysis is performed (Asjad & Khan, 2017).
Sensitivity analysis is performed by varying the weight of sustainable practices that have
highest weight. The highest weight of sustainable practices is ‘adoption of green operation
(SP2)’ and hence the weight of SP2 was varied from 0.1 to 0.9 with the increments of 0.1
to generate nine tests (test1 to test 9). Due to the change in SP2 weight, the corresponding
change is also seen in other sustainable practices weights and same is shown in Tables 21
and 22. These weight changes in different tests forced to change the rank of the sustainability
performance in corresponding test. The obtained ranking of the sustainable performance
using CoCoSo method in nine different tests is shown in Table 8.
From Fig. 3, it is perceived that most of the sustainable performances remains the same
or slightly changed in all tests. Therefore, we can conclude that the proposed method is
sufficiently reliable, robust, and stable to obtain the result.
6 Results and discussions
6.1 Results of BWM
The priority ranking allotted to sustainable-green practices is found as green operations >
implementation of green policies and strategies > employee training about green practices
> adoption of green transportation > green work environment and culture > adoption of
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0
5
10
15
Original
Test 1
Test 2
Test 3
Test 4
Test 5
Test 6
Test 7
Test 8
Test 9
PO1
PO2
PO3
PO4
PO5
PO6
PO7
PO8
PO9
PO10
Fig. 3 The illustrative ranking of sustainable performances in nine tests
ecofriendly waste management practices > implementation of environmental monitoring and
control > development of green building and infrastructure.
The results explain the value of each practice in obtaining the sustainability perfor-
mance in warehouse operations. Clearly green operations have taken precedence over others
because of the benefits they offer. Technologies have proven to be main driver of sustainable
operations. Technical integrations provide necessary thrust to organizations for improving
operational efficiencies of their systems. Predictive analytics for forecasting, assessment of
future demand, RFID scanners for labelling, cloud computing for data storage and retrieval
facilitate smooth working in warehouse. Green policies and strategies adopted by organi-
zations strengthen its intent to enforce sustainability in every aspect of their operations and
functions and encourage their corporate social responsibilities actions. Additionally, orga-
nizations tend to improve their image and carbon performance through their sustainability
actions. Employee training about environmental sensibilities and awareness is crucial as
they are the main actors of entire operations and their commitment, training empowers the
functionality of the system. Continuous training programs provide impetus to employees to
outperform which resultantly provides competitive advantage to organization. Green trans-
portation minimizes the logistics emission ratio which could provide substantial respite to
environmental control measures implemented. Sustainable work culture and environment
motivate the employees and employer to collaborate towards achievement of sustainable
goals but in the current study, the working regime being strictly adhering to the rule of
the state hence it received average ranking. Sustainable waste management could provide
economic benefits to organizations but in current study, it received average score as food
waste is a serious issue in this country. Climatic conditions, and perishability of frozen food
along with demand for high electricity and cooling systems makes it vital for organizations
to channelize their efforts to minimize waste. Association with local food and eateries help
in reduction of some type of food item but not to great extent. Informal and formal control
systems were prevalent in all organizations and only three organizations did not have formal
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and professionally drafted quality control measures implemented in their organizations. This
practice being extremely crucial for food quality and safety as it is present universally in
all food related warehouses hence it was not ranked that high. The least ranking is assigned
to green building and infrastructure as mostly organizations are already established and do
not have funds to rebuild or relocate. Minor cosmetic changes, renovations work can offer
remedial respite, but infrastructure investments are difficult to manage and hence the ranking.
6.2 Result of CoCoSo
The result provides reality of the sustainability performance of warehouse engaged in imple-
menting sustainable-green practices. The key performance indicator decrease in cost of
energy consumption is rated first because of the use and integration of sustainable eco-friendly
operations. Since the warehouses targeted had implemented emission control policies (with-
out which they cannot operate) so their performance of decrease in toxic emissions was
rated at two. Formulation of employee welfare policies, supervision and control measure
allowed them to reduce their annual number of environmental accidents. Cooperative work
environment supports employee motivation which was rated as four. Green operations and
technological integrations provided efficiencies in workand worker output and allowed reduc-
tion of solid waste which was rated at six. Improvement in market share received average
score of seven followed by decrease in inventory levels at eight. The performance of organi-
zation in terms of its global image, carbon footprints was rated at nine followed by increase in
investment recovery of excess inventory at ten. The bottom three performance were improved
capacity utilization at eleven, reduced electricity consumption at twelve and increase in sale
of scrap and used material at thirteen.
6.3 Discussions
The objectives of this study are to investigate the type of sustainable-green practices that
get integrated in warehouse operations under the influence of external and internal contin-
gent factors and to further probe how these integrations would improve the sustainability
performance of warehouse operations. The results have identified crucial sustainable-green
warehousing practices of green operations, green policies and employee training of green
integrations or practices as the main and most favored or adopted practices that are inte-
grated in most of the warehousing operations in the targeted organizations. Further these
green practices are preferred by the chosen experts as they contribute effectively towards the
three identified sustainability indicators-economic, social, and environmental (Jabbour et al.,
2020; Ali & Kaur, 2021). Technology integrations in supply chain for channelizing its efforts
towards sustainability has been proven to be immensely beneficial as discussed by Luthra
et al. (2020), Yadav et al. (2018). Like wise use of technologies in cold supply chains for
improving operational efficiencies are promoted by Sharma et al. (2017), Ackerman et al.
(2017), Meneghetti and Monti (2014), Mangla et al. (2018). The results of this study collab-
orate with the previous studies in establishing the significance of technologies in warehouse
operations for sustainability improvements. The performance indicators of decrease in cost
for energy consumption, reduction in harmful material, and environmental accidents adds
to the sustainability index from the social, economic, and environmental perspectives (Li
et al., 2020). The integration of green elements in organization’s policies and strategies could
have positive impact on the sustainability performance of supply chains from economic and
environmental perspectives (Laari et al., 2017). Food supply chains are high risk categories
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due to deteriorative nature of products that might result in energy and product loss and higher
logistics cost (Ali et al., 2020b). Hence the business strategies that aim for energy efficiency
or product innovation (Gerstlberger et al., 2014) or low carbon logistics (He et al., 2017)must
be encouraged. Low carbon cap and trade policies (Chen et al., 2016) must be promoted for
sustainable warehouse performance. The study supports the claims of previous authors for
green policies and strategies as indicated by reduced environmental accidents and improved
labelling of carbon footprint on products. This is especially important for food products
as the consumers are increasing becoming more conscious of their consumption behavior
and responsibility towards the environment. Corporate sustainability strategies strengthen
the organizations resolve to explore the possibilities and increase the amplitude of efforts
affiliated with sustainable development (Du et al., 2017; Rantala et al., 2018). Backed by
such strategies, organizations inculcate employee training programs and create sustainable
work culture (Rentizelas et al., 2018) for preparing employees towards sustainability move-
ment which is validated by our study also. Employee training enhances their motivates and
encourage them to comprehend the general warehousing and food specific problems areas
and acquaint them with solution-oriented measures to prepare them for their effective con-
tribution towards sustainability. Our results validate the claim that proper training programs
results in improved efficiencies of workers which in turn improve the quality of work outputs
(Faber et al., 2013). Along with training, waste management practices could also be fol-
lowed to measure food waste and fixing reduction objectives based on strategies and policies
formulated (Zanoni & Zavanella, 2012). Increasing awareness of food waste among employ-
ees, proper management of food storing in warehouses, tie ups with local stakeholders for
distribution of soon to be expired food items, has enabled the understudy organizations to
curb the food waste to some extent. Precursory measures to avoid solid waste also includes
proper forecasting, improved packaging, and food donations. It is expected that integration
of green strategies promotes lean green movements that means lower inventory levels, effec-
tive movements and operations, operational improvement, and hence better operational and
business performance (Verrier et al., 2016; Yakavenka et al., 2019). Contradictory to the
expectations, our results do not support the lower inventory and better market share claims.
This is as explained by experts, the problem areas that needed more focus and attention of
the organizations. Sustainable practices adopted because of contingency factors like pres-
sures from international bodies like World Commission on Environment and Development
(WCED) and United Nations (UN) or Intergovernmental Panel on Climate Change (IPCC)
etc. project country’s image at the global front (Ali et al., 2019,2020a,2020b). Saudi Arabia
warehouses have not been able to achieve its competitive and global image and the same is
reflected in our study. Warehouse performance in terms of sustainability also means better
capacity utilization, reduced electricity consumption and increase in sale of scrap and used
material. However, the organizations understudy struggled with their warehouse capacities
and electricity consumption as temperature (peak summers) during the time of study posed
a big problem. Green buildings and infrastructure contribute immensely towards the mini-
mizing electricity consumption which is major source of emissions (Gu et al., 2010). The
infrastructural problem faced by these organizations posed problems in capacity utilizations
as the perishable nature (Ali et al., 2013) of the food products stored did not provide moving
flexibility.
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7 Implications of the study
7.1 Managerial implication of the study
The finding of the study can help managers working in cold/frozen food supply chain and
related industries in understanding necessity of sustainability initiatives in their organiza-
tions. It also guides them to understand each initiative in respect to expected sustainable
performance to prioritize it according to their requirement and capacity or capability. The
case companies offer unique insights into the working of warehouses in frozen food cate-
gories. The challenges and possibilities related to operations and sustainability of food and
waste issues are handled and discussed through exploration of possibilities. Green operations,
stringent policies, and corporate strategies allow organizations to reduce their energy cost
which offers economic respite to organizations. Employee welfare and motivation looked
after through a sustainable work culture and employee training which offers social sus-
tainability to organizations. Waste reduction, emission control, through control measures
and monitoring, scrap handling could allow environmental protection. Decrease in harmful
emissions, and hazardous waste allows organizations to improve their carbon performance
and hence boost their image at global front.
8 Research implications of the study
This study contributes significantly to business strategy and sustainability literature. It justi-
fies the theoretical concepts of contingency elements as driver of sustainability integrations
and supporter of ‘triple bottom line (TBL) and comprehensively covers ‘three pillars of
sustainability-economic, social, and environmental’ aspects.
It explores various contributions related to sustainability in supply chain and specifically
to sustainability issues in warehouse management (Bai et al., 2018) and further strengthen
and extend previous studies that examine these issues. Further it covers the sustainability
in warehouse from emerging economy perspective which is stated to be lacking (De Koster
et al., 2017).
The study explores the sustainability practices adopted in warehouses context from TBL
perspective (Bank & Murphy, 2013). It further strengthens the literature with analytical
implications of individual aspects of social, economic, and environmentalalong with expected
sustainability outcomes through ranking of each practice by industry experts.
Contingency theory perspective is limited and majorly defined in research literature from
theoretical perspective of role of the contingent factors (Sousa & Voss, 2008; Volberda et al.,
2012). This study extends the role of contingency factors into the adoption and further evaluate
the results of such actions by analytically examining this relation using MCDM technique.
This study further extends the literature with its focus on moderating role of sustainable-
green practices on sustainable warehouse performance by applying MCDM approaches
proposed by earlier researchers. The study has applied the use of BWM with CoCoSo which
has not been applied before. Hence this is a unique contribution of our research study.
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Annals of Operations Research
9 Conclusion, limitations, and future research directions
Business supply chains are highly influenced by new industrial paradigms, complex prod-
ucts, diverse demands, and intense competitive landscape. Busines logistics strategies insist
on business models and processes that offer real time visibility, relevant information about
dynamic market requirements. Resultantly, supply chains tend to become more responsive
and interactive though innovations, integrations of newer concepts and technologies in all
their major functions. Simultaneously, industrial sectors are directed towards sustainabil-
ity to revamp their business strategies and are assessing their sustainability performance by
paying special attention to warehouse operations. Warehouse operations either manual or
machine based or automated, are energy intensive and resource dependent, hence decrease in
energy consumption, water usage, material usage or handling, recycling, sustainable pack-
aging permeate resource sustainability. Resounding with ‘Sustainable Goals’ promoted by
United Nations organizations are innovating through accommodation of three dimension
of sustainability-economic, social, and environmental in their business models. The present
study proposes a sustainability framework of green warehouse practices for measuring sus-
tainable performance using BWM and CoCoSo. The results clearly confirm the positive
role of integration of sustainable measures for positive outcomes and describes green opera-
tions for energy and resource conservations, the results also promote the role of sustainable
work culture, sustainable strategies and policies for their role in encouraging sustainability
performance outcomes. The results are validated though expert opinion gathered from the
organizations dealing in frozen food supply chain in the Kingdom of Saudi Arabia.
There are some limitations in the current study which could be addressed in the
future studies. The study was carried out in the one geographical region of Al-Khomra
in south Jeddah and it could be extended to different geographical region or even dif-
ferent countries. Further, this study considered the warehouse of frozen food supply
chains, it could also be experimented with warehouses of several other sectors like
tire/rubber/chemical/Petroleum/Construction or any other manufacturing enterprises. Last,
the study could be altered by combining it with other techniques like fuzzy VIKOR, fuzzy
TOPSIS or even this combination could be used to explore other aspects of sustainability in
warehouses.
Acknowledgements Wewant to express our appreciation to our guest editors and reviewers who have provided
us with positive feedback that has helped us enhance the quality of our manuscript.
Funding This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University,
Jeddah, under Grant No. G: 216-144-1442. The authors, therefore, acknowledge with thanks DSR for technical
and financial support.
Appendix A
Appendix A1: Detail steps of fuzzy Delphi
Step 1. Identify the factors/criterion
In this step the reasonable factors/criterion (in this study sustainable practices &performance
measures) associated to the identified problem has been identified through literature sur-
vey/questionnaire/interview.
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Table 9 Linguistic scale and their
associated TFNs Scale Level of significance Triangular fuzzy number
1 Very low (0.1,0.1,0.3)
2 Low (0.1,0.3,0.5)
3 Medium (0.3,0.5,0.7)
4 High (0.5,0.7,0.9)
5 Very high (0.7,0.9,0.9)
Step 2. Collecting the opinions of expert group
The expert’s opinion is collected for the significance of the sustainable practices through the
questionnaire survey. This study uses the questionnaire to collect the expert’s opinion using
five-point Likert scale and same is shown Table 9.
Step 3. Setting up of the triangular fuzzy numbers
As per the Table 1, the experts’ inputs are transformed into the TFNs. The minimum and
maximum values of experts’ inputs are calculated using TFNs. This study applied the geo-
metric mean (MA) to indicate consensus of the expert group. The computational procedure
is provided as follows:
Suppose the evaluation value of the significance of the jth element given by ith expert
among the n experts is; ˜wij =(lij,mij,uij),i=1, 2,….n and j =1,2,….m. Then fuzzy
weighting ˜wjof jth element is:
˜wj=lj,mj,uj
lj=minilij
mj=n
n
i
mij
uj=maxiuij(A1)
where wij signifies that ith expert’s evaluation for sustainable practices j,ljcharacterise the
lowest appraisal values of sustainable practices j,mjindicate the geometric mean of all the
expert assessment values for element j,andujindicates the highest expert assessment values
for criterion j.
Step 4. Defuzzification of the TFNs
TFNs is converted into crisp number (Si) of each sustainable practice using the center of
gravity method as per Eq. (A2)
Sj =lj+mj+uj
3(A2)
Step 5. Finalisation of the sustainable practices
The last step of the fuzzy Delphi is the finalisation of the sustainable practices. To obtain
the significant sustainable practices, the weights attained for each sustainable practice is
compared with a threshold value (λ). The logic behind the sustainable practice selection
processisasfollows:
If Si≥λ,then the enabler iis selected.
If Si<λ,then the enabler iis rejected.
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Appendix A2: Detail steps of BWM
Step 1. Identification of sustainable practices
In this step, the significant sustainable practices (“n” number of sustainable practices: SP1,
SP2,SP
3,….SPn) are identified through the literature review and fuzzy Delphi.
Step 2. Determine the best and sustainable practices
In this step, the experts identify the best and the worst sustainable practices among the
finalised sustainable practices. The best and worst sustainable practices is denoted as cB,and
cWrespectively.
Step 3. Perform the reference comparisons with sustainable practices
The preference of the best sustainable practices is determined over all the other sustainable
practices using 9-point scale (1–9) through expert input and represented by the ABvector as:
AB=(aB1,aB2 ,..., aBn)
where ABthe Best-to-Others (BO) vectors, aBj denotes the preference of the best sustainable
practices B over sustainable practices j and aBB =1.
Step 4. Perform the reference comparisons with worst sustainable practices
The preference of the other sustainable practices is determined over the worst sustainable
practices using 9-point scale (1–9) through expert input and represented by AWvector as:
AW=(a1W,a2W ,..., anW)T
where ABthe Others-to-Worst (OW) vector, ajw refers the preference of the sustainable
practices j over the worst sustainable practices W and aww =1.
Step 5. Determine the optimal weights
The optimal weight for each sustainable practice is the one where, for each pair wB/wjand
wj/wW, it should have wB/wj=aBj and wj/wW=ajW . To satisfy these conditions for all
j, maximum absolute differences minimized of the set {|wB−aBjwj|, |wj−ajW wW|}. This
problem can be represented as following model:
min max{
wB−aBjwj
,
wj−ajWwW
}.
Subject to:
j
wj=1
wj≥0;∀ j(A3)
Model (1) can be transformed into following linear problem.
min ξL
s.t.
wB
wj
−aBj
≤ξLfor all j
wj
wW
−ajW
≤ξLfor all j
j
wj=1
wj≥0forallj (A4)
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Annals of Operations Research
Table 10 Linguistic Scale and
associated crisp value Linguistic Scale Crisp value
Ver y L o w ( V L ) 1
Low (L) 2
Medium (M) 3
High (H) 4
Very High (VH) 5
The optimal weights of each sustainable practices (w∗
1,w
∗
2,w
∗
3.....w
∗
n)and optimal value
of ξLobtained by solving the linear problem (A4). Further, the consistency ratio of the
comparisons is checked. Consistency of the comparison depends on the value of ξL,avalue
closer to 0 indicates higher consistency and this value is > 0.1 desired for consistency (Rezaei,
2016).
Appendix A3: Detail steps of CoCoSo
Step 1. Initial decision-making matrix is formulated using the linguistic terms with respect
to the evaluation criteria. The structure of the Initial decision-making matrix is as follows:
Xij =⎡
⎢
⎢
⎢
⎣
x11 x12 ··· x1n
x21 x22 ··· x2n
.
.
..
.
.....
.
.
xm1xm2··· xmn
⎤
⎥
⎥
⎥
⎦
;i=1,2, ....n,j=1,2, ....m(A5)
The matrix [X]m×nshows the initial decision-making matrix which include the m- num-
ber of alternative and n-evaluation criteria. The element of the matrix “xij”represent the
performance of ith alternative with respect to jth criterion. In this study the alternative are
the “performances” and criterion are the “sustainable practices”. In this context, “xij”shows
the accomplishment of the ith performance by adopting the jth sustainable practices (Table
10).
Step 2. The normalisation of the initial decision-making matrix is performed using the Eqs.
(A6)and(A7) (please refer Zeleny, 1973):
For benefit criteria
rij =
xij −min
ixij
max
ixij −min
ixij
;(A6)
For non-benefit/cost criteria
rij =
max
ixij −xij
max
ixij −min
ixij
;(A7)
Step 3. The weighted comparability sequence (Si) of the each alternative and power weight
of comparability sequences (Pi) of each alternative is calculated using the Eqs. (A8)and
(A9) respectively.
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Annals of Operations Research
Table 11 Details of the experts participated in the study
SN Experts Education Experience (in
years)
Major roles and responsibilities
1 Manager B. Tech, MBA 10 +Management of warehouse
function
2 Manager B. Tech MBA 10 +Monitoring warehouse functions,
inventory, and waste
management
3 Manager (legal affairs) L.L.B., MBA 12 +Supply chain planning and
strategy formulations for
efficiencies improvement
4 Consultant/Visiting
Academician
M. Tech
Pursuing PhD
12 +Consultancy related to
warehouse management and
delivering specialized course
5 Senior Manager B. E. MBA 15 +Senior supply chain manager,
identifications of problem areas
and management of warehouse
operations
6 Assistant Manager B. Tech MBA 10 Warehouse maintenance and
management
7 Consultant/Academician MBA PhD 15 +Consultancy related to
environmental integration
related to warehouse
management and associated
with university for the
specialized course
8 Senior Manager B. Tech MBA 15 +Planning of inventory
management and warehouse
functions
9 Manager B. E. MBA 12 +Vendor selection for technology
and equipment for warehouse
functions
10 Consultant/Academician MBA pursuing
PhD
10 +Academics and consultancy
related to green warehouses
practices
Si=
n
j=1
(w jrij)(A8)
Pi=
n
j=1rijwj(A9)
Step 4. Relative weights of each alternatives is calculated using the three aggregation
approaches, which are provided as Eqs. (A10)–(A12):
kia =Si+Pi
m
i=1(Pi+Si);(A10)
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Annals of Operations Research
Equation (A10) shows the arithmetic mean of sums of scores, weighted sum measure (Si)
and weight power measure (Pi)
kib =Si
min
iSi
+Pi
min
iPi
(A11)
Equation (A11) delivers a sum of relative scores of weighted comparability sequence (Si)
and power weighted comparability sequence (Pi) compared to the best.
kic =λ(Si)+(1−λ)(Pi)
(λ max
iSi+(1−λ)max
iPi)(A12)
Equation (A12) signifies the balanced compromise of weighted comparability sequence
(Si) and power weighted comparability sequence (Pi) score. The value of the parameter λis
usually 0.5 or it might be chosen by experts as per the requirements.
Step 5. The weight of the alternatives is relied on the value of ki,and it is computed using
Eq. (A13).
ki=(kiakibkic)1
3+1
3(kia +kib +kic)(A13)
The final ranking of the alternatives is provided as per the descending order of kivalues
i.e. the alternative having the greater value of kiis more significant (Table 11).
Appendix B
See Tables 12,13,14,15,16,17,18,19,20,21 and 22.
Table 12 Identification of the sustainable practices and sustainable performances
SN Sustainable practices Min Geometric Mean Max De-fuzzy Accept/Reject
1 Adoption of energy
efficient operations
0.5 0.777275 0.9 0.725758 Accept
2 Adoptions of
international standards
0.3 0.587425 0.9 0.595808 Reject
3 Adoption of energy
efficient operations
0.5 0.777275 0.9 0.725758 Accept
4 Adoption of green
transportation
0.5 0.793725 0.9 0.731242 Accept
5 Adoption of
biodegradable
packaging
0.1 0.283833 0.7 0.361278 Reject
6 Implementation of
green strategies,
policies, and
regulations
0.5 0.793725 0.9 0.731242 Accept
7 Implementation of
green waste
management practices
0.5 0.777275 0.9 0.725758 Accept
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Annals of Operations Research
Table 12 (continued)
SN Sustainable practices Min Geometric Mean Max De-fuzzy Accept/Reject
8 Integrate green
accounting system
0.1 0.243064 0.9 0.414355 Reject
9 Establish carbon-based
credit system
0.1 0.180737 0.7 0.326912 Reject
10 Establishing sustainable
work culture
0.5 0.761166 0.9 0.720389 Accept
11 Implementation of
environmental
monitoring and
control measures
0.5 0.761166 0.9 0.720389 Accept
12 Employees’ training of
energy efficient
practices and
operations
0.5 0.761166 0.9 0.720389 Accept
SN Sustainable performances Min Geometric
Mean
Max De-fuzzy Accept/Reject
1 Decrease inventory levels 0.5 0.761166 0.9 0.720389 Accept
2 Increase green information system 0.1 0.215667 0.7 0.338556 Reject
3 Reduce electricity consumption
through natural lighting
0.5 0.777275 0.9 0.725758 Accept
4 Improved capacity utilization 0.5 0.761166 0.9 0.720389 Accept
5 Increase carbon-based accounting 0.1 0.172098 0.7 0.324033 Reject
6 Reduction of solid waste 0.5 0.777275 0.9 0.725758 Accept
7 Increase green human performance 0.1 0.236343 0.7 0.345448 Reject
8 Improved efficiencies in work and
worker output
0.5 0.761166 0.9 0.720389 Accept
9 Image of organization and country
of operations on global front
based on their carbon
performance
0.5 0.761166 0.9 0.720389 Accept
10 Increase in investment recovery
(IR) (sale) of excess
inventories/materials
0.5 0.793725 0.9 0.731242 Accept
11 Increase end of life product return 0.5 0.793725 0.9 0.731242 Reject
12 Increase in sale of scrap and used
materials
0.5 0.793725 0.9 0.731242 Accept
13 Increased market share 0.3 0.74012 0.9 0.646707 Accept
14 Decrease of frequency of
environmental accidents
0.5 0.777275 0.9 0.725758 Accept
15 Reduce haz-
ardous/harmful/toxic/hazardous
material
0.5 0.777275 0.9 0.725758 Accept
16 Decrease of cost for energy
consumption
0.5 0.745391 0.9 0.71513 Accept
17 Employee increased motivation 0.5 0.793725 0.9 0.731242 Accept
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Annals of Operations Research
Table 13 Best and worst
sustainable practices Sustainable
practices
Determined as best Determined as worst
SP1 E1, E2, E6, E7, E8,
E10
SP2 E1, E3, E4, E6, E8, E9
SP3 E3
SP4 E2, E5, E7
SP5
SP6
SP7 E5, E9
SP8 E3, E4,
Table 14 Best sustainable practice preference over the other sustainable practice for Expert 1
Best to Others SP1 SP2 SP3 SP4 SP5 SP6 SP7 SP8
SP2 91325667
Table 15 Preference of all
sustainable practice over the
worst sustainable practice for
Expert 1
Others to the worst SP1
SP1 1
SP2 8
SP3 6
SP4 7
SP5 4
SP6 4
SP7 3
SP8 2
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Annals of Operations Research
Table 16 Weights of the sustainable practices obtained from each expert
SP Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 Expert 7 Expert 8 Expert 9 Expert 10 Weight
SP1 0.03435 0.02950 0.05415 0.05017 0.04055 0.03146 0.03230 0.02874 0.05173 0.02999 0.03829
SP2 0.34347 0.20015 0.34657 0.33445 0.26240 0.34085 0.20594 0.33046 0.34187 0.20692 0.29131
SP3 0.13739 0.13343 0.10830 0.20067 0.16221 0.13284 0.13729 0.10057 0.20692 0.34187 0.16615
SP4 0.20608 0.33288 0.21661 0.13378 0.31966 0.19927 0.31900 0.20115 0.13795 0.13795 0.22043
SP5 0.08243 0.10008 0.08664 0.10033 0.08111 0.09963 0.10297 0.13410 0.10346 0.10346 0.09942
SP6 0.06869 0.08006 0.08664 0.08027 0.06489 0.07971 0.08237 0.08046 0.06897 0.06897 0.07610
SP7 0.06869 0.06672 0.07220 0.06689 0.02863 0.06642 0.06865 0.06705 0.02999 0.05173 0.05870
SP8 0.05888 0.05719 0.02888 0.03344 0.04055 0.04982 0.05148 0.05747 0.05912 0.05912 0.04960
CR 0.06869 0.06742 0.08664 0.06689 0.06202 0.05768 0.09287 0.07184 0.07197 0.07197 0.07180
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Annals of Operations Research
Table 17 Initial decision Matrix
Performance measures SP1 SP2 SP3 SP4 SP5 SP6 SP7 SP8
PO1 3.3 3.4 3.2 2.4 3.5 2.8 2.4 2.5
PO2 3.9 3.3 3.5 2.3 2.4 2.1 1.7 2.3
PO3 1.9 4.6 3.8 2.3 2 2.2 2.5 1.7
PO4 3.6 3.2 3.3 2.4 4.1 3.4 3.3 3.9
PO5 3.3 4.7 3.8 2.3 2 2.8 2.4 1.7
PO6 2.1 3.1 3.4 2.8 3.2 3.6 2.7 2.5
PO7 3.2 3.3 3.2 2.2 2.7 3.9 3.7 2.3
PO8 2.3 3.3 2.5 1.8 2.7 3.2 3.1 2
PO9 3.4 3.1 3.2 2.5 4.3 3.6 3.2 4.6
PO10 3.4 3.5 4 2.5 3.6 3.6 4 2.9
PO11 3.2 3.5 3.7 3.2 3.5 3.3 3.3 2.7
PO12 2.8 4.3 3.4 2.8 3.7 3.4 3.2 2
PO13 2.2 4.1 3.5 2.6 3.6 3.1 3.2 2.2
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Table 18 Normalized decision matrix
Performance Measures SP1 SP2 SP3 SP4 SP5 SP6 SP7 SP8
PO1 0.7 0.1875 0.466667 0.428571 0.652174 0.388889 0.304348 0.275862
PO2 1 0.125 0.666667 0.357143 0.173913 0 0 0.206897
PO3 0 0.9375 0.866667 0.357143 0 0.055556 0.347826 0
PO4 0.85 0.0625 0.533333 0.428571 0.913043 0.722222 0.695652 0.758621
PO5 0.7 1 0.866667 0.357143 0 0.388889 0.304348 0
PO6 0.1 0 0.6 0.714286 0.521739 0.833333 0.434783 0.275862
PO7 0.65 0.125 0.466667 0.285714 0.304348 1 0.869565 0.206897
PO8 0.2 0.125 0 0 0.304348 0.611111 0.608696 0.103448
PO9 0.75 0 0.466667 0.5 1 0.833333 0.652174 1
PO10 0.75 0.25 1 0.5 0.695652 0.833333 1 0.413793
PO11 0.65 0.25 0.8 1 0.652174 0.666667 0.695652 0.344828
PO12 0.45 0.75 0.6 0.714286 0.73913 0.722222 0.652174 0.103448
PO13 0.15 0.625 0.666667 0.571429 0.695652 0.555556 0.652174 0.172414
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Table 19 Weighted comparability sequence matrix
Performance Measures SP1 SP2 SP3 SP4 SP5 SP6 SP7 SP8 Sj
PO1 0.02681 0.05462 0.07754 0.09447 0.06484 0.02960 0.01786 0.01368 0.37941
PO2 0.03829 0.03641 0.11077 0.07873 0.01729 0.00000 0.00000 0.01026 0.29175
PO3 0.00000 0.27310 0.14400 0.07873 0.00000 0.00423 0.02042 0.00000 0.52047
PO4 0.03255 0.01821 0.08861 0.09447 0.09078 0.05496 0.04083 0.03762 0.45804
PO5 0.02681 0.29131 0.14400 0.07873 0.00000 0.02960 0.01786 0.00000 0.58830
PO6 0.00383 0.00000 0.09969 0.15745 0.05187 0.06342 0.02552 0.01368 0.41546
PO7 0.02489 0.03641 0.07754 0.06298 0.03026 0.07610 0.05104 0.01026 0.36949
PO8 0.00766 0.03641 0.00000 0.00000 0.03026 0.04651 0.03573 0.00513 0.16170
PO9 0.02872 0.00000 0.07754 0.11022 0.09942 0.06342 0.03828 0.04960 0.46719
PO10 0.02872 0.07283 0.16615 0.11022 0.06916 0.06342 0.05870 0.02052 0.58971
PO11 0.02489 0.07283 0.13292 0.22043 0.06484 0.05074 0.04083 0.01710 0.62458
PO12 0.01723 0.21848 0.09969 0.15745 0.07349 0.05496 0.03828 0.00513 0.66471
PO13 0.00574 0.18207 0.11077 0.12596 0.06916 0.04228 0.03828 0.00855 0.58281
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Table 20 Exponentially comparability sequence matrix
Performance
Measures
SP1 SP2 SP3 SP4 SP5 SP6 SP7 SP8 Pj
PO1 0.9864 0.6141 0.8811 0.8296 0.9584 0.9306 0.9326 0.9381 7.0709
PO2 1.0000 0.5457 0.9349 0.7970 0.8404 0.0000 0.0000 0.9248 5.0427
PO3 0.0000 0.9814 0.9765 0.7970 0.0000 0.8025 0.9399 0.0000 4.4973
PO4 0.9938 0.4459 0.9008 0.8296 0.9910 0.9755 0.9789 0.9864 7.1020
PO5 0.9864 1.0000 0.9765 0.7970 0.0000 0.9306 0.9326 0.0000 5.6231
PO6 0.9156 0.0000 0.9186 0.9285 0.9374 0.9862 0.9523 0.9381 6.5767
PO7 0.9836 0.5457 0.8811 0.7587 0.8885 1.0000 0.9918 0.9248 6.9742
PO8 0.9402 0.5457 0.0000 0.0000 0.8885 0.9632 0.9713 0.8936 5.2024
PO9 0.9890 0.0000 0.8811 0.8583 1.0000 0.9862 0.9752 1.0000 6.6899
PO10 0.9890 0.6678 1.0000 0.8583 0.9646 0.9862 1.0000 0.9572 7.4231
PO11 0.9836 0.6678 0.9636 1.0000 0.9584 0.9696 0.9789 0.9486 7.4705
PO12 0.9699 0.9196 0.9186 0.9285 0.9704 0.9755 0.9752 0.8936 7.5514
PO13 0.9299 0.8720 0.9349 0.8839 0.9646 0.9563 0.9752 0.9165 7.4333
Table 21 Sustainable practices weight for sensitivity analysis
SPs Original Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Test 9
SP1 0.038 0.049 0.043 0.038 0.032 0.027 0.022 0.016 0.011 0.005
SP2 0.291 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800 0.900
SP3 0.166 0.211 0.188 0.164 0.141 0.117 0.094 0.070 0.047 0.023
SP4 0.220 0.280 0.249 0.218 0.187 0.156 0.124 0.093 0.062 0.031
SP5 0.099 0.126 0.112 0.098 0.084 0.070 0.056 0.042 0.028 0.014
SP6 0.076 0.097 0.086 0.075 0.064 0.054 0.043 0.032 0.021 0.011
SP7 0.059 0.075 0.066 0.058 0.050 0.041 0.033 0.025 0.017 0.008
SP8 0.050 0.063 0.056 0.049 0.042 0.035 0.028 0.021 0.014 0.007
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Table 22 K values of the sustainable performance for sensitivity analysis
POs Original Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Test 9
PO1 2.299 2.391 2.347 2.294 2.241 2.187 2.130 2.071 2.119 2.726
PO2 1.693 1.788 1.740 1.688 1.637 1.587 1.535 1.482 1.506 1.912
PO3 2.150 1.801 1.983 2.167 2.360 2.564 2.779 3.007 3.592 6.448
PO4 2.502 2.736 2.617 2.491 2.362 2.229 2.090 1.944 1.868 2.064
PO5 2.537 2.183 2.367 2.553 2.748 2.953 3.169 3.399 4.013 7.067
PO6 2.295 2.482 2.392 2.285 2.169 2.042 1.903 1.753 1.644 1.636
PO7 2.256 2.388 2.323 2.249 2.175 2.099 2.019 1.937 1.939 2.348
PO8 1.393 1.431 1.412 1.391 1.374 1.359 1.346 1.335 1.388 1.795
PO9 2.445 2.663 2.558 2.434 2.300 2.155 1.997 1.826 1.701 1.690
PO10 2.890 3.053 2.974 2.882 2.786 2.685 2.579 2.468 2.504 3.290
PO11 2.985 3.167 3.078 2.976 2.869 2.757 2.638 2.514 2.540 3.323
PO12 3.100 2.950 3.031 3.106 3.184 3.265 3.350 3.441 3.848 6.170
PO13 2.875 2.765 2.826 2.880 2.935 2.993 3.053 3.119 3.452 5.399
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