Robust Supplier Selection
Systematic Approach using Data Envelopment Analysis: In the Post-Pandemic Era

Ganisha Nayanathara Premarathna Athaudage (2123704)
Lee Wing Ki (2121661)
University Canada West
MBAR 661 Consulting/Research Project (ONS-Winter 23-04)
Dr. Mazyar Zahedi-Seresht
June 11, 2023

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Table of Contents

ABSTRACT.................................................................................................................................... 3
INTRODUCTION .......................................................................................................................... 4
Introduction to Supply Chain Management ................................................................................ 4
About COVID-19.................................................................................................................... 5
How did COVID-19 affect on Supply Chain? ........................................................................ 7
Robust Supply Chain ............................................................................................................ 10
Importance of supplier selection in the post-COVID-19 world............................................ 13
Introducing Data Envelopment Analysis (DEA) and inputs and outputs to find a robust supplier
................................................................................................................................................... 14
Data Envelopment Analysis (DEA) ...................................................................................... 14
The need for a powerful benchmark tool for supplier selection ........................................... 22
Robust supplier ..................................................................................................................... 22
Integrating social and environmental factor for selecting supplier after COVID-19 ........... 23
Criteria of a robust supplier .................................................................................................. 23
Define Input and output variables on the DEA model .......................................................... 24
Inputs......................................................................................................................................... 25
Geographical distance ........................................................................................................... 25
Number of deliveries............................................................................................................. 26

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Number of employees ........................................................................................................... 27
Total health operating expenses ............................................................................................ 28
Outputs ...................................................................................................................................... 30
Sustainability Rank ............................................................................................................... 30
Investment in information technology .................................................................................. 31
Technology rank ................................................................................................................... 32
Research and Development (R&D) Personnel ..................................................................... 35
Number of ports that can reach ............................................................................................. 36
Percentage of machinery ....................................................................................................... 37
FINDINGS AND DISCUSSION .................................................................................................. 39
Step 1: Determining inputs/ outputs factors of suppliers .......................................................... 39
Inputs..................................................................................................................................... 40
Outputs .................................................................................................................................. 41
Step 2: Retrieving data for inputs/ outputs factors.................................................................... 44
Step 3: Efficiency score calculation .......................................................................................... 44
Data Normalization ............................................................................................................... 45
CONCLUSION ............................................................................................................................. 56
REFERENCES ............................................................................................................................. 58

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Robust Supplier Selection
Systematic Approach using Data Envelopment Analysis: In the Post-Pandemic Era
ABSTRACT
With the existence of COVID-19, the whole economy experienced an unprecedented
challenge. Organizations must be resilient to the ever-changing and unanticipated market to avoid
being out of the fierce competition. In an era of information explosion, managers require a
systematic, explicable, comparative, and traceable approach to evaluate and choose suppliers. In
recent years, procurement strategies have been revamped due to the disruption in the global supply
chain by the pandemic and war in Europe. A wrong supplier selection decision seriously damages
the company’s supply chain, operations, and reputation. Therefore, partnering with a sustainable
supplier is a prerequisite for business success. With the rising importance of sustainability,
choosing a competent supplier is one of the significant strategic management decisions. A
sustainable supplier impacts business operations and accelerates long-term growth, enhancing
efficiency and effectiveness. In the post-pandemic era, it is expected to have new approaches to
define inputs and outputs to rank suppliers and logistics firms. This study uses Data Envelopment
Analysis (DEA) to identify a sustainable supplier. Our approach involves selecting suitable inputs
and outputs, improving the accuracy and relevance of the study to find more robust suppliers.
The results of this research were implemented in the business intelligence system of Miraab
Company, which is known as a reliable industrial valve manufacturing company in Iran.
Key Words: Data Envelopment Analysis (DEA), Supply Chain, Robust Supplier, Efficiency,
Decision Making Unit (DMU)

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INTRODUCTION
Introduction to Supply Chain Management
As the definition by the Council of Supply Chain Management Professionals (CSCMP),
supply chain management (SCM) involves planning and managing all activities encompassing
procurement, conversion, and logistics-related services. It is a function of integrating the main
business into the other business operations within or among the stakeholder companies. Most
importantly, suppliers, intermediaries, and customers coordinate and collaborate on supply chain
activities related to sales, marketing, finance, production, procurement, logistics, etc. Apart from
that, previous literature also provided several definitions for SCM. Tang (2006) defined SCM as
“the management of material, information, and financial flows through a network of organizations
(i.e., suppliers, manufacturers, logistics providers, wholesalers/distributors, retailers) that aims to
produce and deliver products or services for the consumers.” Summing up the definitions given in
the previous literature, a supply chain can be defined as an integrated network of resources,
processes, and stakeholders who collaboratively manage the movement of materials, information,
and money from the raw material acquisition, transforming materials to work-in-processed and
finished goods to distributing finished products to the final customer to satisfy customer demand
(Athaudage et al., 2022 and Tang, 2006).
With globalization, business processes have become more complex and advanced. Supply
chain value and geographical range are immensely growing, and more stakeholders are joining
worldwide. As a result, supply chains are also getting more vulnerable than before (Xu et al., 2020;
Bier et al., 2020; Fagundes et al., 2020)). Not only that, but the market has also become more
competitive. Companies are always looking for improvement opportunities with increasing
competitiveness in the business context. International companies are on a continuous improvement

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to survive in the rapidly changing global market. As the supply chain (SC) plays a salient role in
the entire business function, companies pay more attention to continuous improvement.
With the increasing supply chain complexity, current supply chains encompass many
players such as suppliers, manufacturers, distributors, and retailers at different tiers in various
industries, which can sometimes be located across the world. Companies struggle to overcome
many barriers and sustain in this striving global market (Vishnu et al., 2019). They frequently
experience serious threats and many other risks. Some common instances are demand
uncertainties, internal uncertainties, and supply uncertainties. Demand uncertainties such as
product availability in the market, seasonality, trends, and affordability of consumers, refer to
random and external factors that create unexpected demand incline or decline. Supply uncertainties
are mainly capacity uncertainties, supply chain disruptions, and yield uncertainties, referring to
the uncertainties in ordering quantity resulting from product defects etc. Recently, many disastrous
events, such as terrorist attacks, pandemics, natural disasters, etc., have hugely interrupted supply
chains. As Urciuoli & Hintsa (2018) explained, changing business trends, globalization,
complexity, and specialization immensely drive risk and gradually decrease managerial power to
control operations (Urciuoli & Hintsa, 2018).
About COVID-19
A 55-year-old woman from Hubei province was the first COVID-19 case reported in
Wuhan, China, on 17th November 2019 (The Economic Times, 2020). The first media statement
on ‘viral pneumonia’ was given to the WHO country office of the People’s Republic of China on
the 31st of December 2019 by the Wuhan Municipal Health Commission (World Health
Organization, 2020). On 11th February, the International Committee on Taxonomy of Viruses
(ICTV) declared the name of the newly identified virus as the “Severe Acute Respiratory

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Syndrome Corona Virus 2 (SARS-CoV-2)” since the virus was genetically related to SARS in
2003, however it is distinct and separate from SARS. On the same day, WHO named the new
disease “COVID-19” (World Health Organization, 2020). The first case outside the People’s
Republic of China was recorded in Thailand on 13th January 2020. WHO characterized COVID19 as a pandemic on the 11th of March 2020, considering the shocking spread, severity, and
inaction (World Health Organization, 2020).
According to the reports of WHO, approximately 7.53 billion cases and 6.8 million deaths
have been recorded by the date of 30th January 2023 (World Health Organization, 2023). So far,
229 countries and territories worldwide have been affected by the disease. Among all regions,
Europe has recorded the maximum number of COVID-19 cases, which is about 2.71 billion with
2.18 million deaths. In contrast, the American region has the highest number of deaths, 2.90
million, with 1.99 billion cases (World Health Organization, 2023). Moreover, the US has
accounted for over 1 billion confirmed cases. China and India remain after the United States with
the confirmed cases of 98 million and 44 million, respectively. The top three countries which have
the highest burden of the COVID-19 outbreak have recorded approximately 32 percent of global
total confirmed cases (World Health Organization, 2023).
From the beginning of 2020, the pandemic engulfed the world. COVID-19 rapidly spread
across the globe and formed a public health emergency among countries. In one aspect, fear of the
disease pervades societies. Not only that, but strong economies of the world also downturned.
World trade, capital flow, tourism, commodity prices, and remittances were abandoned. The global
GDP rate was anticipated to slow down by nearly 2.3 percent due to COVID-19. Multilateral
organizations estimated the global GDP deteriorated by 3% in April 2020, worse than the 0.1%
shrinkage in 2009 (Alfaro & Jeong, 2020). Approximately 44 percent of 718 professionals and

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world leaders interviewed in the Global Risks Perception Survey (GRPS) think that the ‘erosion
of global supply chains’ is a risk for the economy, which would improve in 2020 compared to
2019 (World Economic Forum, 2020). The Institute for Supply Management (ISM) surveyed 600
supply chain professionals on the 10th March 2020. According to that, approximately 75% of
surveyed companies reported that their supply chains are disrupted, and 16% of companies have
experienced downward revenue targets (Industry Week, 2020).
Figure 1
Spread of COVID-19 - World map

Note. Global COVID-19 spread as of 30th January 2023. From WHO https://covid19.who.int/
How did COVID-19 affect on Supply Chain?
COVID-19 was unique among all disruptions as it has severely affected the global supply
chains (GSCs), creating dynamic and diversified issues throughout (Chowdhury et al., 2021; Xu
et al., 2020; Simchi-Levi, 2020). It was one of the most severe catastrophes in history (Raj et al.,
2022; Ivanov et al., 2017). It proved the complexity and interconnectivity of GSCs as the upstream
SCs are seriously affected due to the irregular and unpredictable behavior of downstream partners.
As a result, the impact of the bullwhip effect was very sharp and significant in the upstream supply

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chains, specifically small and medium-sized businesses. The COVID-19 pandemic disrupted all
phases of GSC, from raw material acquisition to delivering products to the final customer (Raj et
al., 2022; Xu et al., 2020; Ivanov et al., 2017). The activities within the supply chain were
interconnected. Therefore, disrupting one function causes a ripple effect (Chowdhury et al., 2021)
in several supply chains' supply, demand, and logistics side(Raj et al., 2022). According to a
Fortune report released on the 21st of February 2020, out of 1000 Fortune companies, 94% of
companies have experienced supply chain disruptions (Sherman, 2020). Furthermore, sudden
demand and supply volatility, shortage of labor, international trade barriers, and vehicle movement
restrictions were significant issues in all phases of the supply chain (Chowdhury et al., 2021) due
to COVID-19 preventive measures such as quarantine restrictions, travel barriers, and temporary
closures of some plants (Xu et al., 2020).
As a result of the pandemic, the gap between demand and supply increased (Raj et al.,
2022). Some sectors have shown sudden demand spikes, while some have declined. Generally,
products can be categorized into two types: functional products and innovative products.
Functional products such as face masks have turned into innovative products, showing severe
demand and supply fluctuations (Xu et al., 2020). For instance, the demand for face masks,
Personal Protective Equipment (PPE), medical equipment, and canned and dried foods has
significantly increased, creating a shortage in the market. Consumers experienced delays in online
and traditional delivery services. They tend to buy more than they require due to uncertain supply
chains, panic buying, and stockpiling behaviors (Chowdhury et al., 2021). At the same time,
demand for non-essential products has drastically declined due to the income loss and saving
money for an uncertain and ambiguous future (Chowdhury et al., 2021; Chiaramonti & Maniatis,
2020). Consequently, the price of essential commodities has increased, whereas non-essential

9
goods have decreased (Chowdhury et al., 2021). Many companies have adapted to Just-in-Time
(JIT) inventory management systems to manage their inventories, optimizing the cost. As a result,
those companies have struggled to maintain their inventory levels and realized that JIT is not a
proper strategy to combat a global disruption such as a pandemic (Raj et al., 2022).
Furthermore, lockdowns have led to many constraints in accessing resources. The limited
availability of labor, raw materials, and consumer goods suspended the functioning of some sectors
(Xu et al., 2020). Approximately 25% of the world’s population of around 2 billion workers belong
to work in emerging economies. Due to the lockdown restriction and COVID-19 preventative
measures, many workers failed to continue their jobs. Also, some of them lose their jobs because
of temporary closures. As a result of their wage reductions and loss of employment, many migrant
workers traveled back to their home countries. This reverse migration created enormous long-term
issues, especially in agriculture and apparel supply chains. For example, India's largest food
supplier, Azadpur Mandi has operated at only 25% of its standard capacity due to the labor
shortage (Raj et al., 2022).
Moreover, logistics and transportation were disrupted and experienced delays,
cancellations, and postponements due to travel restrictions and border closures. As World Trade
Organization predicted, international trade has been fined by 13% to 32% in 2020 due to the
pandemic (World Trade Organization, 2020). Commercial transportation was shut down, and air
cargo transportation was limited to medical supplies (Xu et al., 2020). Furthermore, some countries
imposed export and import restrictions. For instance, India, France, and the USA restricted
medicine exports, leading to delayed transactions. Maritime freight is crucial in international
logistics, representing 90% of global trade volume. The lack of truck drivers for container pickups, quarantine, and rigorous custom inspection caused delays in maritime cargo transportation

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(Xu et al., 2020). This has increased the lead time, exceeding the timelines of the overall supply
chain from raw material extraction to the final product (Hippold, 2020). According to
Enteprenuer.com, lead times have risen by an average of 20 days for the suppliers of Anviln China
(Raj et al., 2022).
As a result of the pandemic, many companies shifted to the blended distribution method of
online-offline from physical channels. Due to preventative measures, many physical distribution
channels had limited access or shut down (Dente & Hashimoto, 2020), causing multiple issues in
the supply chain. However, many companies have improved their online service capabilities to
survive this challenging time. Some retailers developed warehouses focusing exclusively on online
sales, while others struggled to improve logistics functions to address new markets (Mollenkopf
et al., 2021). Also, the relationship between supply chain partners has been significantly impacted
due to the restrictions. As a result, the organizations failed to integrate and collaborate in supply
decision-making, creating many adverse effects, including the impact of the bullwhip effect.
Robust Supply Chain
The coronavirus has created rapid changes in the business environment. Firms need to
proactively take action to mitigate the issue by integrating and revamping their capabilities. After
the pandemic, many organizations have improved supply chain resilience to strengthen operations
and manage supply chain disruptions. Supply chain resilience allows organizations to maintain
and enhance their market position (Birkie & Trucco, 2020). Previous research studies have proven
that resilience is vital in directing other organizations to the right path in a catastrophe (Ponomarov
& Holcomb, 2009). The collaborative research of Accenture and the World Economic Forum
revealed that 80% of global companies considered supply chain resilience after the pandemic

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(World Economic Forum, 2020). Many organizations have reconsidered their supply chain
strategy, design, and dependencies to avoid adverse impacts in future situations.
Chowdhury & Quaddus (2016) discussed three main dimensions of supply chain resilience:
preparedness, response, and recovery. The strategy considered preparedness for future disruption,
readiness to respond quickly, minimizing adverse impacts, and recovering into the original or a
better state (Chowdhury & Quaddus, 2016). As a result of the pandemic, organizations reconsider
resource allocation to address a disruption successfully. They have prioritized the importance of
tasks and allocated resources from non-prioritized activities to essential activities. Also, they have
realized to increase production capacities, addressing demand spikes in the short run. However,
researchers suggest utilizing temporary capabilities (Leite et al., 2021) by eliminating non-critical
tasks. Also, it is recommended to share resources among supply chain partners to minimize the
impact of disruption as the demand peaks at different points for different entities. Practically, the
overall process will shut down in case of raw materials shortage. Therefore, maintaining and
improving upstream supply chain resilience is crucial to continue the process. Supply chain
mapping is commonly used to identify the bottlenecks and their consequences, enhancing visibility
and formulating supplier-centric strategies. During COVID-19, most manufacturing companies
temporarily closed production due to single sourcing. Thus, it is recommended to maintain a
diversified supplier network and improve emergency sourcing procedures to address the disruption
(Chowdhury et al., 2021).
Some scholars have proposed to improve and redesign logistics facilities to enhance
responsiveness. Faster delivery methods, such as air transportation, are encouraged in the event of
disruption (Chowdhury et al., 2021). Nearshoring and back shoring are also recommended to
improve local capacities. Also, many entities adapted to offshoring strategies and equipped

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production facilities with the required logistic support to deal with a future catastrophe. Overdependency on international trading created multiple issues in the supply chain. Therefore, many
companies tried to balance local and international trade as a lesson of the pandemic (Chowdhury
et al., 2021). Selecting multiple suppliers near the primary production plant enhances the security
and reserves stock levels in the short term (Raj et al., 2022). Redesigning short supply chains with
a few partners also successfully maintains supply chain resilience. Furthermore, previous studies
recommend developing ICT in supply chains is beneficial in the long term to deal with disruption.
As a result of the pandemic, the popularity of adapting to technology was common. Consumers
prefer online purchasing and home delivery due to travel restrictions. Digital technologies help to
streamline, control, and monitor the process while mitigating the issues in a catastrophe (IbnMohammed et al., 2021; Remko, 2020). The entities adapt to Industry 4.0 as the new long-term
trend to deal with supply chain disruptions (Kumar et al., 2020). For instance, cloud and FOG
computing, 3D printing, artificial intelligence (AI), Internet of Things (IoT), blockchain, and big
data analytics are commonly used depending on the size of the business. According to Harvard
Business Review, Walmart applied artificial intelligence technology to strengthen supply chain
flexibility and resilience. By adopting the AI, Walmart automated the negotiation by chatbot with
procurement suppliers to streamline the process while the pilot of the solution was conducted in
January 2021 (Hoek et al., 2022).
Furthermore, some entities have developed automated systems to ensure smooth running
with limited staff due to social distancing (Ivanov & Das, 2020). Organizations value real-time
data in decision-making to gain a competitive advantage. Improving real-time transparency
through control towers utilizing big data has become a new business trend. These approaches
formulate business continuity plans and facilitate last-mile deliveries using autonomous vehicles

13
and drones (Raj et al., 2022). Digital transformation methodologies create real-time responsive
and flexible supply chains to deal with external disruptions. New supplier partnerships are
encouraged when revamping the supply chain, ensuring local proximity. Digital transformation
technologies such as 3D printing and artificial intelligence can use to enhance production
capabilities locally. Improving supply chain collaborations assists in mitigating adverse effects,
speeding up the recovery, and preparing for the events (Sharma et al., 2022; Chowdhury et al.,
2021). For instance, entities can implement knowledge management systems to share expertise
and information among supply chain partners to mitigate information ambiguity. Furthermore,
horizontal collaboration is encouraged at the national level, ensuring the continuous supply of
essential products (Chowdhury et al., 2021).
Importance of supplier selection in the post-COVID-19 world
The COVID-19 pandemic enormously challenges the whole economy in every industry,
affecting individuals and organizations (Mańkowski et al., 2022). It offers an opportunity for
business transformation; companies switch their business model and how they operate their
business (Naha & Nandy, 2022). Lockdowns, working from home, social distancing, panic buying,
and rising demand in e-commerce created unexpected disruptions in supply chains. The pandemic
exposes supply chain vulnerability with serious disturbance and delayed delivery (Sombultawee
et al., 2022). The smooth transportation of goods or services, raw materials, components, semifinished goods, or finished goods from a supplier to the customer on time and in the correct place
is not inevitable. Each participates in the supply chain inextricably and executes activities at each
stage to facilitate the cycle. In consequence, the performance of the supply chain is the crucial
factor in achieving business success and enhancing a company’s competitive edge (Čiković et al.,

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2022). Simultaneously, the rivalry between companies from all industries is fierce in the
digitalization era, and supplier selection is essential.
Choosing an ideal and reliable supplier is challenging; however, it enables a long-term
business partnership and enhances performance by minimizing potential risk, accelerating mutual
benefit, enhancing productivity, and optimizing profit. The selection of sustainable suppliers
embraces economic criteria, ethical business practices, and environmental and societal impact with
the raised awareness of corporative social responsibility (Čiković et al., 2022). Supplier evaluation
includes an ecological commitment to reduce adverse environmental effects by integrating green
supply chain management (Huang et al., 2022). The green concept is incorporated into the entire
supply chain procedure from procurement, production, packaging, storage, and distribution (Shin
& Cho, 2022). Successful supplier selection helps minimize operating costs, enhances customer
satisfaction, and creates positive brand value. As a result, a quality supplier is a crucial part of an
organization to achieve business success (Dutta et al., 2022)

Introducing Data Envelopment Analysis (DEA) and inputs and outputs to find a robust
supplier
Data Envelopment Analysis (DEA)
Data Envelopment Analysis (DEA) is widely used to measure productivity and efficiency
(Čiković et al., 2022). This methodology is a practical approach popularly adopted in various
industries, including healthcare, financial institutions, agriculture, armed services, sports, retail,
etc. (Čiković et al., 2022). It facilitates decision-makers in evaluating the efficiency of an
organization and conducting benchmarking (Mozaffari et al., 2022). DEA is a non-parametric
mathematical method for performance measurement. It is a data-oriented approach that directly

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compares the available data without any pre-assumed parameters (Čiković et al., 2022). One of the
biggest advantages of DEA is that it manages multiple input and output data. Also, DEA directly
compares the DMU against the rest of the DMUs and provides efficiency scores separately for
each DMU.
Farrell developed the traditional DEA method in 1957 (Hosseini-Nasab & Ettehadi, 2023).
Charnes, Cooper, and Rhodes introduced the first DEA model in their seminal paper titled
"Measuring the efficiency of decision-making units" in 1978 (Čiković et al., 2022; Charnes et al.,
1978). Initially, it was also called CCR since the model was developed by Charnes, Cooper, and
Rhodes (Mozaffari et al., 2022). Later, Banker, Charnes, and Cooper further developed the model,
and it was called BCC in 1984 (Singh et al., 202). Today, DEA has become one of the most crucial
analysis tools in decision-making. It helps to assess the efficiency and identify the production
(Čiković et al., 2022). DEA is a linear programming-based methodology (Shafiee et al., 2021)
used to calculate the production efficiency of suppliers by employing multiple input and output
variables (Hosseini-Nasab & Ettehadi, 2023). By this, vendors can be distinguished by their
efficiency levels. DEA is a mathematical method to calculate an economic unit's related
productivity or efficiency, and it allows measuring the efficiency of a set of “Decision Making
Units” (DMU).
A DMU refers to a homogeneous entity or productive unit offering similar products or
services. It can include different entities such as doctors, energy providers, hospitals, restaurants,
universities, banks, and countries. Besides, DMUs can generate multiple output variables based
on various input variables. Applying the mathematical programming technique of DEA helps
identify which DMU has the highest efficiency score and facilitates the selection of suppliers.

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After establishing a set of DMUs, the next step is to define the input and output variables.
The DEA model allows the incorporation of multiple input and output variables (Čiković et al.,
2022), and the number of input variables can differ from the number of output variables, such as
having two input variables with one output variable. The efficiency of DMU is affected by the
proportional changes in the input or output variables. In DEA, all DMUs utilize the same set of
input and output variables. To assess the efficiency of each DMU, a weighted ratio is assigned to
each input and output variable for each DMU. By utilizing these weight ratios, the efficiency rate
of each DMU can be calculated in the optimal condition with the maximum efficiency (Mozaffari
et al., 2022). The efficiency rate of a DMU can be expressed as a weighted sum of outputs/
weighted sum of inputs.
In the DEA model, the efficiency score of each DMU is determined with respect to an
efficiency frontier, which ranges from 0 to 1. The ratio of the weighted sum of outputs / weighted
sum of inputs for each DMU cannot exceed 1 (Demircioğlu & Özgüner, 2022). If the efficiency
score of a particular DMU is 1, it indicates that it operates at or very close to 100% efficiency. The
productivity levels are relatively inefficient for DUMs with an efficiency score of less than 1 (but
greater than 0). Furthermore, the DEA framework can identify the highest output level reached by
a particular input level (Shafiee et al., 2021). This model allows inefficient DMUs to adjust their
input and output levels to enhance efficiency and (Amirteimoori & Kordrostami, 2014) become
comparable to their peer group (Ruiz & Sirvent, 2022).
The DEA model is a widely accepted mathematical programming technique that is being
used in various industries. DEA model can be integrated with various multi-decision-making
(MCDM) techniques to enhance its capability (Dutta et al., 2022). These techniques include
Techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS), Artificial neural

17
network (ANN), Analytic Hierarchy Process (AHP), Analytical Network Process (ANP), fuzzy
DEA, etc. (Čiković et al., 2022). In addition, DEA can be combined with other methodologies
such as fuzzy Analytic Network Process (FANP), super efficiency DEA, and DEA-Assurance
Region (AR) model to attain more accurate results (Dutta et al., 2022).
Consider n number of DMUs with non-negative input vectors, 𝐱 𝐣 = (𝑥1𝑗 , 𝑥2𝑗 , …, 𝑥𝑚𝑗 ) and
output vectors, 𝐲𝐣 =(𝑦1𝑗 , 𝑦2𝑗 , … 𝑦𝑟𝑗 ), where 𝑥𝑖𝑗 denotes the value of the ith input and 𝑦𝑘𝑗 denotes
the value of the kth output of the DMUj, j = 1, 2, …, n in a typical DEA model. The efficiency
score of DMUq is calculated by dividing the weighted sum of outputs by the weighted sum of
inputs. They maximize the efficiency score of the evaluation unit (DMUq), assuming that the
efficiency scores of all other units are less than or equal to 1. The linear version of the model is
below.
Maximize
r

 q =  uk ykq
k =1

Subject to;
r

m

k =1

i =1

 uk ykj −  vi xij  0, j = 1, 2,..., n,
m

 v x = 1,
i =1

i iq

uk   , vi   .
𝑣𝑖 – Input weight of the ith input
uk – Output weight of the kth output
Non-Archimedean element 𝜀 > 0 ,

18
𝑣𝑖 and 𝑢𝑘 represent input and output weights for the i-th input and k-th output. On the other hand,
𝜀 > 0 is a non-Archimedean element, smaller than any positive real number.
The optimal objective function value 𝑞𝑞∗ = 1 shows the efficiency of the unit being
evaluated. The units that are being assessed less than 1 indicate the inefficiency. Reducing the
number of inputs helps to reach the efficient limit (Aswccfo et. al.).
For example, Assume that Ranch House, Inc is a fast-food restaurant chain operating in
five locations. They have collected their inputs and output data to develop the DEA model to find
the most efficient restaurant in the chain. Accordingly, they considered weekly hours of operation,
full-time staff, and weekly supply expenses as the input measures. Their output measures include
an average weekly contribution to profit, market share, and annual growth rate. The gathered input
and output data are illustrated below in Tables 1 and 2, respectively (Aswccfo et.al.).
Table 1
Input measures
Restaurant

Hours of Operation

Full-time Staff

Supplies ($)

Bardstown

96

16

850

Clarksville

110

22

1400

Jeffersonville

100

18

1200

New Albany

125

25

1500

St. Matthews

120

24

1600

Note. Input measures in five locations.

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Table 2
Output measures
Restaurant

Weekly Profit ($)

Market Share

Annual Growth Rate

Bardstown

3800

25

8.0

Clarksville

4600

32

8.5

Jeffersonville

4400

35

8.0

New Albany

6500

30

10.0

St. Matthews

6000

28

9.0

Note. Output measures in five locations
Developing the Mathematical Model for each DMU:
DEA model for Bradstown:
𝑀𝑎𝑥 𝜃 = 3800𝑢1 + 25𝑢2 + 8𝑢3
Subject to
3800𝑢1 + 25𝑢2 + 8𝑢3 − 96𝑣1 − 16𝑣2 − 850𝑣3 ≤ 0
4600𝑢1 + 32𝑢2 + 8.5𝑢3 − 110𝑣1 − 22𝑣2 − 1400𝑣3 ≤ 0
4400𝑢1 + 35𝑢2 + 8𝑢3 − 100𝑣1 − 18𝑣2 − 1200𝑣3 ≤ 0
6500𝑢1 + 30𝑢2 + 10𝑢3 − 125𝑣1 − 25𝑣2 − 1500𝑣3 ≤ 0
6000𝑢1 + 25𝑢2 + 9𝑢3 − 120𝑣1 − 24𝑣2 − 1600𝑣3 ≤ 0
96𝑣1 + 16𝑣2 + 850𝑣3 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 , 𝑣3 ≥ 𝜀
DEA model for Clarksville:
𝑀𝑎𝑥 𝜃 = 4600𝑢1 + 32𝑢2 + 8.5𝑢3
Subject to

20
3800𝑢1 + 25𝑢2 + 8𝑢3 − 96𝑣1 − 16𝑣2 − 850𝑣3 ≤ 0
4600𝑢1 + 32𝑢2 + 8.5𝑢3 − 110𝑣1 − 22𝑣2 − 1400𝑣3 ≤ 0
4400𝑢1 + 35𝑢2 + 8𝑢3 − 100𝑣1 − 18𝑣2 − 1200𝑣3 ≤ 0
6500𝑢1 + 30𝑢2 + 10𝑢3 − 125𝑣1 − 25𝑣2 − 1500𝑣3 ≤ 0
6000𝑢1 + 25𝑢2 + 9𝑢3 − 120𝑣1 − 24𝑣2 − 1600𝑣3 ≤ 0
110𝑣1 + 22𝑣2 + 1400𝑣3 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 , 𝑣3 ≥ 𝜀

DEA model for Jeffersonville:
𝑀𝑎𝑥 𝜃 = 4400𝑢1 + 35𝑢2 + 8𝑢3
Subject to
3800𝑢1 + 25𝑢2 + 8𝑢3 − 96𝑣1 − 16𝑣2 − 850𝑣3 ≤ 0
4600𝑢1 + 32𝑢2 + 8.5𝑢3 − 110𝑣1 − 22𝑣2 − 1400𝑣3 ≤ 0
4400𝑢1 + 35𝑢2 + 8𝑢3 − 100𝑣1 − 18𝑣2 − 1200𝑣3 ≤ 0
6500𝑢1 + 30𝑢2 + 10𝑢3 − 125𝑣1 − 25𝑣2 − 1500𝑣3 ≤ 0
6000𝑢1 + 25𝑢2 + 9𝑢3 − 120𝑣1 − 24𝑣2 − 1600𝑣3 ≤ 0
100𝑣1 + 18𝑣2 + 1200𝑣3 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 , 𝑣3 ≥ 𝜀
DEA model for New Alberny:
𝑀𝑎𝑥 𝜃 = 6500𝑢1 + 30𝑢2 + 10𝑢3
Subject to
3800𝑢1 + 25𝑢2 + 8𝑢3 − 96𝑣1 − 16𝑣2 − 850𝑣3 ≤ 0
4600𝑢1 + 32𝑢2 + 8.5𝑢3 − 110𝑣1 − 22𝑣2 − 1400𝑣3 ≤ 0

21
4400𝑢1 + 35𝑢2 + 8𝑢3 − 100𝑣1 − 18𝑣2 − 1200𝑣3 ≤ 0
6500𝑢1 + 30𝑢2 + 10𝑢3 − 125𝑣1 − 25𝑣2 − 1500𝑣3 ≤ 0
6000𝑢1 + 25𝑢2 + 9𝑢3 − 120𝑣1 − 24𝑣2 − 1600𝑣3 ≤ 0
125𝑣1 + 25𝑣2 + 1500𝑣3 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 , 𝑣3 ≥ 𝜀
DEA model for St. Matthews:
𝑀𝑎𝑥 𝜃 = 6000𝑢1 + 25𝑢2 + 9𝑢3
Subject to
3800𝑢1 + 25𝑢2 + 8𝑢3 − 96𝑣1 − 16𝑣2 − 850𝑣3 ≤ 0
4600𝑢1 + 32𝑢2 + 8.5𝑢3 − 110𝑣1 − 22𝑣2 − 1400𝑣3 ≤ 0
4400𝑢1 + 35𝑢2 + 8𝑢3 − 100𝑣1 − 18𝑣2 − 1200𝑣3 ≤ 0
6500𝑢1 + 30𝑢2 + 10𝑢3 − 125𝑣1 − 25𝑣2 − 1500𝑣3 ≤ 0
6000𝑢1 + 25𝑢2 + 9𝑢3 − 120𝑣1 − 24𝑣2 − 1600𝑣3 ≤ 0
120𝑣1 + 24𝑣2 + 1600𝑣3 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 , 𝑣3 ≥ 𝜀
Following table 3 shows the results taken by implementing and solving the mathematical model
using Excel:
Table 3
Optimal Efficiency Score
Restaurant

Optimal Efficiency Score

Bardstown

1

Clarksville

0.96

Jeffersonville

1

22
New Albany

1

St. Matthews

0.99

Note. Optimal efficiency score of five locations
Obtaining a score of 1 shows a lack of evidence in determining whether the composite unit
or the corresponding operating unit is relatively efficient. A score lower than one shows that the
composite unit is more efficient than the corresponding operating unit.
The need for a powerful benchmark tool for supplier selection
With the existence of COVID-19, the entire economy has experienced an unprecedented challenge.
Organizations need to be resilient to stay competitive and adapt to the ever-changing and
unanticipated market. Continuously evaluating their supply chain business partners, including raw
material vendors, manufacturers, distributors, logistic partners, retailers, etc., is indispensable. In
an era of abundant information, managers require a systematic, explicable, comparative, and
traceable approach to evaluate and choose suppliers. Making a wrong choice in supplier selection
seriously damages the company’s supply chain, operations, and reputation. Therefore, establishing
partnership with a reliable suppliers is a prerequisite for business success.
Robust supplier
The concept of Triple Bottom Line (TPL) was raised by Freer Speckley in 1981 by claiming
organizations need to maintain a balanced relationship of three areas, profit, people, and the planet.
This principle became corporate social responsibility and later developed as the basis of
sustainability (Wijonarko & Woro Astuti, 2022). In the 1990s, John Elkintong utilized the
sustainability concept to measure the sustainability of cooperation in three aspects, economic,
social, and environmental (Silva et al., 2022).

23
Sustainable suppliers should be conscious of society and the environment while operating
their business (Čiković et al., 2022). Instead of solely pursuing profit maximization, it is important
for suppliers to prioritize their daily operations and production outputs with the aim of mitigating
negative impacts on both human beings and the environment.
Integrating social and environmental factor for selecting supplier after COVID-19
With the rising importance of sustainability, choosing a competent supplier is one of the significant
strategic management decisions (Čiković et al., 2022). Solely focusing on price, quality, and
delivery time is insufficient in supplier selection (Ferreira & Silva, 2022). Over the years,
especially after experiencing the COVID-19 disaster, the traditional factors were found inadequate,
and additional factors need to be included (Ferreira & Silva, 2022). The existing methods are
reformulated, integrating social and environmental impact and developing a combination model
in supplier selection (Ghamari et al., 2022). Economic and environmental factors are critical during
the vendor selection process. (Ghamari et al., 2022). Sustainable suppliers enable a company to
maximize profit while minimizing hazardous effects on the planet and people (Čiković et al.,
2022).
Criteria of a robust supplier
A sustainable supplier requires engagement from top management for all green practices. It
impacts business operations and accelerates long-term growth, enhancing efficiency and
effectiveness (Čiković et al., 2022). Considering supplier location, resilience, flexibility,
reliability, and long-term relationship are critical priorities during the selection process, although
the significance of choosing suppliers differs by industry and product (Ghamari et al., 2022).
Moreover, the evaluation of routine operations, packaging quality, warranty, and refund policy are
significant factors in supplier selection (Javad et al., 2020). A sustainable supplier should obtain

24
professional knowledge of green practices. They perform regular environmental audits and acquire
certificates such as ISO 14000, proving less environmental footprint (Silva et al., 2022).
In recent years, procurement strategies have been revamped due to the disruption in the
global supply chain by the pandemic and war in Europe. On top of the three traditional concerns
of quality, delivery, and price, intangible factors such as supplier reputation, continuous
improvement, production responsiveness, technological development, and financial stability
become part of the selection criteria (Ferreira & Silva, 2022). To cultivate sustainable principles,
companies should consider environmental measures such as pollution control, carbon and dust
emission, and treatment of wastewater (Ghamari et al., 2022), green product design (Xie et al.,
2022), and R&D on environmental concerns utilizing latest technologies (Xie et al., 2022). Social
responsibility (Sureeyatanapas et al., 2018), work safety procedures

(Hasan et al., 2020),

information disclosure (Banaeian et al., 2018), and labor relations (Xie et al., 2022) are taken into
account in terms of social dimensions. Organizations are being cautious and devoting further
resources to locating sustainable partners. A successful selection of suppliers expedites the final
deliverables and increases customer satisfaction and the company’s reputation.
Define Input and output variables on the DEA model
The DEA model measures the relative efficiency of decision-making units based on a group of
inputs and outputs (Zhang & Li, 2017). To calculate the efficiency score, a set of input and output
variables are needed. Any resource consumed by a DMU is considered as an input. The output
represents the outcome or performance of transforming the input into either products or services
(Wong, 2021). The input and output index selection varies by industry, product, or service type.
The choice between input and output is based on the objective, whether input orientation or output
orientation. An input-oriented DEA model investigates the ability to generate a specific output

25
level with minimal input and resources (Alidrisi, 2021). On the other hand, an output-oriented
DEA model assesses how efficiently a DMU maximizes output with a particular input level..
There are no specific rules for identifying input and output variables in the DEA model.
Operation indicators such as total assets, capital, current liabilities, operating expense number of
staff, and overhead expenses can be considered as input variables. Similarly, operating income,
net profit, net sales, or revenue can be regarded as output variables (Wong, 2021) when applying
the DEA methodology to calculate the efficiency score for comparison.
Inputs
Geographical distance
Sourcing activities play a vital role across industries since it directly impact on business success..
Furthermore, strategic sourcing partners are essential for manufacturers, retailers or traders.
Globalization accelerates a more complex and dispersed supply chain. The global supply chain
network is more scattered and diffused with the growing number of warehouses, manufacturing
and assembly factories, and subsidiaries (Wiengarten & Ambrose, 2017). Geographical distance
is vital in selecting robust suppliers as it impacts the procurement process and may cause undesired
performance results (Wiengarten & Ambrose, 2017).
Distance between supplier and buyer directly impactsthe accuracy of delivery lead-time
and transportation time for goods or services from one stage to another. Unexpected shipment
delays affect inventory management and production schedule. With COVID-19, companies
experienced that any interruption of raw materials supply would damage the manufacturing
process and the entire production line. This harmed the company’s reputation and customer
satisfaction as the finished products failed to deliver as committed. In supplier selection,
organizations now prioritize reliability instead of the traditional cost consideration (Fonseca &

26
Azevedo, 2020). Therefore, transitioning from global to local sourcing enhances the supply
reliability and continuity of the production process (Juhász & Bányai, 2021).
Enterprises initiate efforts to lower risk and redundancy while enhancing resiliency and
agility in their supply chains, adapting to the post-pandemic environment. The risk incurred is
likely higher with a more significant geographic distance between suppliers and buyers (Fonseca
& Azevedo, 2020). It increases the possibility of supply chain disruption caused by political
instability, natural disasters, and unexpected events. Contingency plans, instant communication,
and remedy action are hard to execute due to the inflexibility of long shipment routes and time
zone differences. After the COVID-19 crisis, the finance minister from France urged French
companies to assess their supply chains and reduce reliance on China and other Asian countries
while preferring to switch to regional trade blocs (Fonseca & Azevedo, 2020). Therefore,
cooperations mitigated risk by nearshoring, localization, and regionalizing in facing global trade
uncertainty. The supply chain management trend focuses more on regionalization than
globalization (Wang & Sun, 2021).
Number of deliveries
On top of being agile and resilient, logistics flexibility is crucial in the rapidly changing business
economy. Logistic flexibility refers to a company’s ability to modify and tailor the procedure of
transport and storage of goods to meet the evolving requirement of each customer, which is
necessary for establishing a sustainable competitive advantage (Sandberg, 2020). In response to
the uncertainties of the global context, companies must enhance their flexibility (Hatmanto et al.,
2022), which is one of the assessment criteria of supply chain performance (D’Aleo & Sergi
Hatmanto, 2017) and warehouse operation performance (Dede & Çengel, 2020).

27
A logistic service provider with fewer deliveries can provide flexible service. Since the
operation of these companies is not fully optimized, they have more resources available to deal
with unexpected needs. They are well equipped to deal with an unforeseeable increase in demand
to avoid delay, error, and quality issues compared to the companies with a high volume of orders.
However, companies with substantial orders and at their maximum capacity spend much time and
resources monitoring the current process to avoid supply chain disruption. Therefore, it can hardly
provide reliable and timely service for a sudden surge in demand.
Companies with fewer deliveries are at a higher level of response flexibility. Adjustments
to the current process are easily made. They react faster to market shifts, and customer needs as
they have excess resources to address requirements and offer customized solutions to customers’
specific needs. The ad hoc activities arranged will not have a negative impact and cause a burden
on their existing business operation. The direct correlation between flexibility and performance
suggests that greater flexibility leads to higher firm performance (Hatmanto et al., 2022).
Number of employees
The COVID-19 outbreak has greatly affected numerous segments of the global community. The
pandemic has caused significant disruption in the supply chain. Many organizations faced
difficulties during the crisis. Some companies were forced to halt their operations, while others
laid off their staff (Ajripour, 2022). Being resilient and capable of reacting quickly to threatening
disruption are suitable strategies for maintaining operational stability and sustainability during
challenging times (Yang et al., 2022).
Suppliers or vendors with a smaller workforce allow operating with higher flexibility and
agility. Many small-to-medium-size companies took the initiative to transform their business
model after once under COVID-19 breakout by scaling up their e-commerce platforms. They

28
successfully leveraged the challenges and turned them into new business opportunities (Kien et
al., 2023). Besides, companies with lesser employees and fewer layers of hierarchy can also work
more efficiently and be easier to manage. They can have a fast response to the changing market
and unexpected circumstances. The decision-making process is simple and streamlined, which is
vital in times of crisis. In addition, these companies also offer more customized services and
products to meet specific customer needs in the rapidly changing environment.
Numerous enterprises suffered a severe drop in business operations and profit during the
COVID-19 pandemic. They face financial difficulties and either being forced to shut down or
maintain their business with minimal gains or on the margins (Akingbade, 2021). However,
enterprises with smaller workforces can operate with lesser expenses, such as salary, benefits,
training and development, office spaces, equipment and supplies, and relative administrative costs.
Having a supplier with less overhead costs makes them more reliable since they have a relatively
stable financial position. Besides, they will be more flexible in offering competitive prices and
payment terms, directly enhancing profitability.
After experiencing COVID-19, communication and information flow among trade partners
are crucial. It is to increase transparency and minimize uncertainties and risks. Companies with a
smaller workforce can build closer relationships with their trade partners, and their information
flow is simple and more straightforward. They can communicate and share information through
formal or informal meetings and anticipate potential risks. Trust and a close relationship are
essential for a manufacturer’s resilience (Yang et al., 2022).
Total health operating expenses
The outbreak of COVID-19 has adversely impacted many enterprises, resulting in financial losses
and supply chain disruption. Enterprises are becoming more cautious about managing costs;

29
focusing on monitoring and minimizing operating expenses to optimize financial performance.
Businesses are taking cost-cutting initiatives like reducing unnecessary utilities and marketing
expenses. Financial resources were recognized as an essential asset for sustaining a business, and
companies started to initiate new collaborations with suppliers (Bostan, 2021). Due to this,
companies are being more meticulous in their supplier selection process while aiming to reduce
financial burden and prioritizing trade partners that exhibit higher productivity.
Lower health operating expenses reflect the organizations investing less in employee health
costs. Companies can be more flexible in allocating funding with lower health operating expenses.
They can allocate saved expenditures on research and development, marketing and promotion,
technology enhancement, etc., enhancing their product and service quality. Ultimately, their trade
partners benefit from superior product quality and a higher level of services that fit the market
needs.
Spending a lesser health cost in a cooperative reflects that their employees are relatively
healthy, directly connected with a company’s performance and sustainability. Health risk factors
include excessive consumption of alcohol, obesity, high cholesterol, high blood pressure etc.,
taking around 25% of the employee’s total healthcare expenditures (Street & Lacey, 2019). A
healthy workforce team is regarded as one of the most desirable assets to an organization (Street
& Lacey, 2019), and there is a direct relation between employee health and productivity.
Employees with a high risk of a modifiable health situation, including nutrition problems
and tobacco and alcohol use, were 12.2% less productive than other colleagues (Street & Lacey,
2019). The more an organization spends due to employees’ poor health, such as compensation, the
lower morale, higher absenteeism and turnover (Street & Lacey, 2019). Major reasons for
workplace absence are typically mental health problems, such as anxiety, stress, and depression.

30
The problem affects approximately 20% of the working population (Suter et al., 2022; Steel et al.,
2014). It leads to an annual global productivity loss of USD1 trillion (Suter et al., 2022; Chisholm
et al., 2016). Besides mental health issues, an example from the study by the American Diabetes
Association in 2017 shows that diabetes impacts productivity since it causes an indirect cost of
USD 90 billion while damaging work efficiency (Norwitz et al., 2022).
Despite profitability, maintaining stakeholders’ well-being is vital to attain long-term
success. Organizations are required to provide a healthy and safe working environment
contributing to higher job satisfaction and engagement. A smaller proportion of spending on health
operating costs reflects that the company can promote the well-being of their employees with a
balance of financial performance and employees’ well-being, which is a factor for achieving
sustainability (Bosetti, 2022).
Outputs
Sustainability Rank
Companies should first evaluate themselves to identify their goals, business priorities, and
sustainable initiatives to manage supplier selection criteria effectively. They should communicate
their criteria to customers, ensuring that they have the potential to adhere to sustainability priorities
as requested by the company. Companies should assess their supplier's performance by
considering the environmental impact, social business dilemma, and ethical business practices.
Sustainable supplier selection can be challenging as it requires organizations to identify and assess
the suppliers' performance, aligning with sustainability. In order to overcome these challenges,
companies can use different frameworks such as Global Reporting Initiatives (GRI), Standards
defined by the Sustainability Accounting Standards Board (SASB), and International Organization
for Standards (ISO 14001) system. Practicing sustainable supplier selection brings out many

31
advantages to a company. Most importantly, it improves the brand image, reduces the risks of
unforeseen social and environmental impacts, and enhances business efficiency. Companies tend
to build long-term relationships with suppliers who adapt to sustainable practices, reducing supply
chain risks.
Investment in information technology
Information Technology (IT) has expanded business capacities in many aspects. As a result of
globalization, IT plays a salient role in maintaining the interactions and information flow among
upstream and downstream supply chain tiers (Tsai et al., 2021). Companies widely use digital
transformation technologies integrated with Industry 4.0 in data exchange and collection,
improving supply chains based on real-time technologies such as industrial Internet of Things
(IoT), additive manufacturing, big data, artificial intelligence, blockchain, and cloud computing.
Based on the requirements of Industry 4.0, many companies use improved and collaborative
technologies when dealing with suppliers (Ferreira & Silva, 2022).
However, companies increasingly invest in information and communication technology to
make their supply chain robust and resilient because of their learnings after the pandemic. Institute
of Supply Chain Management (ISCM) found that 80% to 95% of supply chains were adversely
impacted in March 2022 due to the COVID-19 pandemic. According to the survey done by
Bassware, 60% of procurement managers of over 700 respondents claimed that they experience a
lack of transparency in supply chains, causing a significant issue over supply chain resilience.
Remko (2020) studied the risks and challenges reported by SC executives and recommended some
strategies to reduce supply risks. Most importantly, he suggested initiating and accelerating
projects related to digital transformation technologies to address the problem. Due to the pandemic,
businesses increasingly moved toward e-commerce, online shopping, and self-checkouts,

32
promoting contactless purchasing practices. Also, many companies invested in logistics control
towers to enhance business transparency and smoothen the information flow across the supply
chains while reducing the Bullwhip effect. Therefore, the supplier's capacities and capabilities to
invest in technologies are among the most influential factors in the supplier selection process.
The supplier selection process plays a significant role in reducing purchasing risks.
Referring to the related literature, many authors have identified technology as one of the essential
criteria in terms of supplier selection. Ferreira & Silva (2022) highlighted some significant aspects
of supplier selection, including the delivery control system, improved communication, supplier
involvement in the design, less paperwork, and long-lasted relationship, emphasizing the
importance of investing in new technologies. Efficient communication channels significantly
enhance the relationship between two parties (Ferreira & Silva, 2022). Tsai et al. (2021) studied
the supplier selection in Taiwan's Thin-Film Transistor (TFT) Liquid-Crystal Displays (LCD)
industry using DEMATEL (Decision Making Trial and Evaluation Laboratory) and ANP (Analytic
Network Process) methods, evaluating the main factors in the supplier selection process.
Accordingly, he also emphasized adapting to technologies is a crucial factor in tech industries as
it strengthens the ability to minimize production time, develop the latest product designs and
processes, and integrate business operations (Tsai et al., 2021).
Technology rank
Measuring the suppliers' capability to initiate innovations and develop existing technologies is
crucial in supplier selection. Supplier innovation systematically leverages and accelerates
innovative capabilities, providing numerous benefits such as novel ideas, higher margins, and
reduced time-to-market, which leads to profit growth. McKinsey found that the externally sourced
innovation are 40% more commercialized than the internally generated ideas as the supplier has

33
already validates (Göβwein et al., 2019). Aston Martin, the UK based car manufacturer has worked
with US-based aerospace supplier, Flexsys to implement adjustable wings for high-end designs.
The supplier used flexible materials to develop the component, offering a better appearance and
performance than conventional designs. As we study, very few indexes measure the company's
innovation and potential. Most importantly, the index for the Capacity For Innovation (CFI)
measures the supplier's capability to improve innovation and technologies. The CFI index is
mainly based on the existing industry and the resources allocated to research and development
(R&D), which includes the variables in the workforce, budget allocation, and range of products
offered. The main two variables in assessing the supplier's CFI index are (1) the amount of the
R&D budget and (2) the R&D Staff ratio. The CFI index measures a supplier's potential based on
the abilities in the R&D, depending on the industry sectors (Wu et al., 2013).
In addition to that, companies also use the index for Proper Practice (PP) to determine the
supplier's attitude towards the client's Intellectual Property Rights and Resources (IP&R) and the
supplier's practices to protect the client. The PP index is influenced by the external legal and
cultural environment of the business and internally constrained by the influences of organizational
culture, management, and administration (Wu et al., 2013). Wu et al. (2013) introduced three main
variables to evaluate the PP index: Corporate Identity, IP rights index, and restraints index.
Corporate identity is more subjective and considers the supplier's overall image. The IP rights
index is an index that assesses suppliers' adherence to the terms and conditions of the agreement.
Based on the CPI and PP index, the suppliers can be categorized into four segments; dependable
suppliers, proficient suppliers, At-arm's-length suppliers, and risky suppliers, as explained in
Figure 2. Maintaining a proper PP index determines the supplier's trustworthiness and reliability,
adding extra value to the supplier selection process.

34
Furthermore, a few other indexes measure the supplier's innovative performance. For
example, Master card and Harvard Business Review Analytic Services (HBRAS) introduced the
"Business Innovators Index," considering five significant areas: process, human resource, funding,
customer experience, and technology. This index is scaled from 0 to 100, whereas 100 represents
the highest performance in an innovative strategy, frameworks, and behaviors (Mastercard, n.d.).
This framework categorizes businesses into three segments: leaders, followers, and laggards. The
study revealed that 17% of respondents were identified as leaders, while approximately half (41%)
were laggards, with a mean index of 37 (Baker, 2019). Also, the Product Vitality Index (PVI)
measures the company's innovation by evaluating the ability to introduce and deliver new products
or services. This metric was created in 1988 by 3M, considering the company's revenue generated
from the new product. The PVI is calculated by dividing the New Product Revenue by the Gross
revenue in a pre-defined time (Manor, 2021). This index varies subject to the factors such as the
nature of the product, the industry, and the product's current phase of the product lifecycle (Manor,
2021). Moreover, Sutardja Center for Entrepreneurship and Technology established a new index,
named "Berkeley Innovation Index," which measures the innovation capabilities in a more holistic
approach, considering four main aspects; (1) Strategy and Leadership, (2) Innovative culture from
the organization's perspective, (3) Operations across the organization, (4) Mindset of people and
(5) Tactical measures (Ikhlaq Sidhu et al., 2016).
Figure 2
Supplier Categorization based on the CPI and PP index

35

Note. Four supplier segments based on CPI and PP index
Research and Development (R&D) Personnel
R&D can be a vital consideration in supplier selection based on various factors. Companies prefer
that their suppliers have an R&D division, ensuring proper technical capabilities and welldeveloped business practices in safety and environment to maintain a robust and sustainable
business (Rajesh & Ravi, 2015). Suppliers with highly improved technologies can quickly adapt
to new business trends and recent technologies (Rajesh & Ravi, 2015; Schiele et al., 2011), which
rapidly change with Industry 4.0. It also assists suppliers to respond current market turbulence and
supply chain disruptions, mitigating supply chain risks. Furthermore, R&D activities drive the
product or service an extra mile by integrating technologies to maintain quality standards (Rajesh
& Ravi, 2015). Having the required quality standards and certifications proves the quality and
innovation capabilities of the company. Suppliers who obtain positive results in quality audits in
the long term are likelier to have a culture of continuous innovation and improvement (Schiele et
al., 2011).
Cousins et al. (2011) conducted a study to evaluate both skills and the resources available
for R&D activities, utilizing the mechanisms to gain the advantage of external connections

36
(Cousins et al., 2011). The complexity of supply chain networks dramatically increases, reflecting
the rise in R&D investment, complex products, shorter product lifecycles, challenges in managing
the changes in technology, and the requirement of knowledge and resources for innovation.
Therefore, companies cannot succeed alone; instead, they appreciate collaboration to withstand
the fast-paced changes and gain a competitive advantage in the global market. Many organizations
have redefined their boundaries and formed close and long-term supplier relationships, leveraging
their strengths and achieving mutual benefits (Cousins et al., 2011). Wang et al. (2009) assessed
supplier performance by considering the attributes such as R&D, cost, quality, service, and
response and developed a multiple attribute matrix (Wang et al., 2009). In their study, R&D and
improvements measure suppliers' involvement while the performance can be evaluated using
delivery, damages, and quality (Wang et al., 2009). Moreover, Wu et al. (2013) assessed a
supplier's suitability for outsourcing by measuring the supplier's potential for technological
innovation, including the investment in R&D and the ratio of employees in the R&D division to
the total number of employees (Wu et al., 2013).
Number of ports that can reach
In facing the serious disruptions caused by the COVID-19 outbreak, the ability to accessan
expanded number of ports by logistic companies becomes more important. Ports play an essential
role as they are the central part and provide linkage in the global supply chain network between
countries. Circulation of goods dropped to around 60% capacity due to the initial country
lockdown and closure of international borders with COVID-19, which created a vast supply chain
disruption globally (Grater & Chasomeris, 2022). During the pandemic, ports implemented health
and safety protocols such as health screening, social distancing, temperature checking, contact
tracing, and quarantine requirement. With the widespread coronavirus infections, ports faced

37
challenges and could not maintain operations due to labor shortages and port congestions (Kim et
al., 2022).
Regional hub ports are large-scale and significant ports that facilitating trade movement
and connections for different countries. They are located close to major shipping routes and usually
serve as the first or last station in a supply chain. A relatively large number of suppliers rely on
them to transport products. In addition, these hub ports may impose stricter health screenings or
quarantine measures, which leads to additional freight transportation delays. When a supplier has
limited access to a single hub port that is already experiencing a high level of congestion, the
supplier can only wait until the bottleneck is alleviated. Such a delay has severe implications for
the shipment schedule. The scenario is similar if applied to transshipment ports, which handle the
transportation of goods between vessels to the destination. Recently, companies have prioritized
agility and responsiveness in supply chain management, especially in unpredictable circumstances.
Suppliers who can access a greater number of ports are highly valued. Such support enables
broader geographic coverage and enhances timely delivery by allowing the selection of the most
efficient port and routes. As a result, addressing each customer's requirement under diverse
situations.
Percentage of machinery
In response to the emergency of the contagious virus COVID-19, countries implemented different
countermeasures, which led to a range of uncertainties in the supply chain in all industries.
Measures such as lockdowns, restrictions contact tracing, vaccination programs, social distancing,
quarantines, port & airport closures, etc., impacted on the labor supply market.
Manufacturing involves converting raw materials into finished goods, such as handling raw
materials, prototype testing, production, assembly, packaging, quality control, inspection, goods

38
dispatch, and delivery. In traditional manufacturing, especially in developing countries, daily
operations mainly relies on physical labor with minimal fmachines assistance for handling simple
and repetitive tasks. The manufacturing processes require a substantial amount of labor, while the
involvement of machinery is minimal. Consequently, many manufacturers have encountered a
severe labor shortage problem during the pandemic crisis, which has disrupted the normal
operations of factories. Manufacturing industries faced significant obstacles and complexities
during COVID-19 (Suguna Sinniah et al., 2022)
The trend in manufacturing industries is toward automation which uses machinery to
perform tasks that humans previously performed manually. Automation involves using advanced
software and technology, together with an increased percentage of machinery involved in
manufacturing. COVID-19 also expedites robotics adoption in services sectors; the service robots
are machines equipped with artificial intelligence and controlled by a computer system without
being impacted by social distancing or quarantine measures (Romero & Lado, 2021; Seyitoğlu &
Ivanov, 2020).
Compared to machinery, human performance is more prone to fluctuation. Labor
performance will be negatively affected when labor reaches a capacity over 70%. Workers may
experience stress, burnout, decreased concentration, and fatigued leading to increased absenteeism
or turnover (Cummings et al., 2022). Unlike humans, machines operate continuously and do not
experience fatigue or require motivation. In the post-pandemic world, companies seek robust,
reliable and consistent suppliers. Implementing automation into manufacturing processes mitigates
output variability and enhances efficiency (Cummings et al., 2022). Therefore, suppliers who
obtain a more significant proportion of machinery used in manufacturing are highly valued,
particularly in the post-pandemic world.

39

FINDINGS AND DISCUSSION
Step 1: Determining inputs/ outputs factors of suppliers
As discussed in Section 3, we found the most appropriate set of inputs and outputs based on the
literature review. The findings of inputs and outputs are summarized below in Table 4.
Table 4
List of new inputs and outputs
Inputs

Outputs

Geographical distance

Sustainability Rank

Number of deliveries

Investment in information technology

Number of employees

Technology rank

Total health operating expenses

Research and Development (R&D) Personnel
Number of ports that can reach
Percentage of machinery

Note. Inputs and outputs for supplier selection
However, due to a lack of existing data for the above input and output sets, the efficiency
score calculation in the upcoming section will be based on the currently accessible data.
Traditionally, price, delivery time, and quality were recognized as three major considering factors
during the supplier selection process (Caristi et al., 2022). This paper has included these three
traditional factors; price, quality and delivery grade, along with “number of employees” as an

40
additional input and “technology capability” as an additional output to demonstrate the calculation
of the efficiency score for the ten suppliers. Table 5 shows the list of inputs and outputs for
processing the efficiency score calculation.
Table 5
List of inputs and outputs that use for calculating efficiency score
Inputs

Outputs

Number of employees

Delivery grade

Price

Technological capability
Quality

Note. Two Inputs and three outputs for calculating efficiency score
Inputs
Number of employees: As discussed in the preceding section, organizations with smaller
workforces adhere less to formal procedures; this exhibits their higher level of agility and
flexibility during global crises (Kraus et al., 2020). According to Haneberg (2021), the response
to the crisis is quicker among smaller enterprises compared to larger enterprises. As a result, the
number of employees is one of the critical considerations during the supplier selection process. It
uses as one of the inputs for calculating the efficiency score.
Price: Enterprises have their supply chain strategy and have diverse factors in the supplier
selection process (Ferreira & Silva, 2022). Although the previous section mentioned that selecting
a supplier solely by product price is insufficient, price still plays a significant role during supplier
screening. Companies have assessed price as one of the critical supplier selection criteria over an

41
extended period (Ferreira & Silva, 2022). As one of the common purchasing strategies is to
minimize cost, having suppliers that offer a favorable price directly influences the cost of raw
materials, components, delivery cost, service charges, etc. Suppliers provide a relatively low price
impact directly on the enterprise’s financial performance, such as cost of goods sold, business
expenses, cash flow, profit margin, etc. Therefore, the price offered by the supplier is one of the
essential factors during the vendor selection process, which improves financial performance and
competitiveness (Sariaji & Juarna, 2022). During supplier assessment phase, companies must
consider various factors to ensure having the most appropriate supplier according to their
requirement. The ideal option is with the best and most accurate delivery timing at a better price
(Ferreira & Silva, 2022). However, as discussed, price alone cannot be the only determinating
factor. For example, while selecting a lower-priced supplier may have cost benefits for companies,
it also comes with a trade-off:a longer and less reliable delivery service, as well aslower product
quality. These factors, namely the delivery factor and quality factor, discussed in the upcoming
section. Therefore, organizations need to carefully evaluate and balance the input elements to
achieve an optimal outcome.
Outputs
Delivery grade: An organization’s performance is greatly influenced by choosing the right
supplier with excellent delivery performance. Research from Mirani et al. (2021) indicated that 86
percent of the companies prioritized timely delivery service by their suppliers. Timely delivery
directly impacts the efficiency of a company’s supply chain operation. On-time delivery is an
essential aspect of supply chain management; it is measured by the supplier’s ability to ship goods
or services within the committed timeframe, as agreed upon by both parties (Mantos et al., 2023).
An accurate delivery time enhances customer satisfaction and loyalty as customers are often

42
willing to pay a higher price for a product with better quality and faster delivery time (Wen &
Wang, 2022). Failing to fulfill the delivery schedule caused delays in shipment and resulted in
customer dissatisfaction (Pajić et al., 2022). Without reliable suppliers that are able to deliver
products on time, there can be a severe disruption not only in the procurement process but also in
the production and distribution of goods and services. Organizations experience many obstacles in
inventory planning. Overstocking results in extra holding costs and a higher risk of obsolescence.
On the other hand, understocking occurs, and companies do not have enough inventory to meet
the market demand. The above phenomenon was more evident during the COVID-19 pandemic,
where delays in shipment caused severe disruption to the entire supply chain. Inventory levels
were low, organizations faced challenges finding substitutes, and certain kinds of products and
components were not even available for an extended period.
Technological capability: As highlighted in the preceding section, investment in
information technology is another critical aspect of supplier selection. It reflects the organization’s
ability to adapt to technology and foster innovation. During the digital economy, advanced
technology facilities the exchange of knowledge, information sharing, and communication among
different parties within the supply chain network (Xie et al., 2022). The growing prevalence of
new technologies and digitalization drive notable changes in supply chain practices, enhancing
transparency, flexibility, communications, and productivity (Tavana et al., 2021). Supply chain
stakeholders can interact and collaborate more efficiently across geographic boundaries without
time constraints. Digitalization integrated the entire supply chain and enhanced the visibility to all
parties (Tavana et al., 2021). The utilization of advanced technology such as automation, artificial
intelligence (AI), machine learning, Internet of Things (IoT), blockchain technology (BCT) and
big data analytics (BDA), etc., provides significant support in logistic management, manufacturing

43
system, and supply chain management. These technologies facilitate better responsiveness,
efficiency, and accuracy and mitigate internal and external disruption (Xie et al., 2022).
Upon experiencing supply chain vulnerability due to the COVID-19 pandemic, firms
widely entered the digital transformation era by adopting new technologies and methodologies
into their business operations for future challenges (Wang et al., 2022). Nowadays, technology
capability is one of the top ten criteria during the supplier selection process (Kayani et al., 2023).
Therefore, technological capability is considered one of the output factors in the presented DEA
model.
Quality: Having the right supplier leads to business success, and customers are more
emphatic about product quality (Wen & Wang, 2022). Quality applies to both service and product.
According to Iqbal et al. (2020), Magdalena (2012), and Toloo & Nalchigar (2011), service quality
can be evaluated by the degree of adherence to the committed specification, and product quality
can be assessed on factors including durability, correctness, user-friendliness, and reliability
(Mantos et al., 2023). The first supplier selection research conducted by Dickson (1966) states that
quality has been recognized as the most crucial criterion by managers from 273 manufacturing
companies out of the 23 standards during the supplier selection process (Ulutaş et al., 2022). This
finding remains pertinent and applicable to this day. One of the studies by Mirani et al. (2021)
mentioned that quality was ranked as the most crucial supplier selection requirement. Besides,
having suppliers that are able to offer high-quality products and services mitigates risks (Liu et al.,
2023). With superior quality in products and services from suppliers, organizations can minimize
the chances of errors and associated costs for rectifying them. This enables organizations to
maintain a consistent production schedule, conduct accurate planning, and enhance inventory
management without encountering disruptions caused by inconsistent products or services.In the

44
vendor evaluation process, companies consider the trade-off between the input factor “price” and
the output factor “quality.” Suppliers whooffer lower costs may provide low-quality products,
and choosing a low-quality product can result in higher opportunity costs (Saputro et al., 2021).
Extra expenses are incurred for conducting quality control measures such as inspection, testing &
repairs. As defective items keep separately from other products, it incurs additional handling and
holding costs (Saputro et al., 2021). Moreover, lower-quality supplies may adversely impact the
quality of the final product and reduce customer satisfaction, company reputation, and brand
loyalty. Hence, organizations must evaluate their alternatives and weigh their options to attain
equilibrium between these input and output factors.
Step 2: Retrieving data for inputs/ outputs factors
The quantitative data for the above discussed input and output factors were collected from 10 key
suppliers of Miraab company. Miraab has four decades of experience in the valves industry and
has received international certificates, including product quality certificates from Lloyds Register
of England and DVGW of Germany, as well as IMS certificates including 2015:OHSAS
18001:2007, ISO 9001, and 2015:ISO 14001 from TÜVNORD.
Step 3: Efficiency score calculation
The retrieved data from Miraab company are shown below in Table 6. The dataset contains two
inputs and three outputs; however, they are measured in different scales.
Table 6
Inputs
Data of the
inputs and
outputs of
10 DMUs

No. of
employees
Grade
(Out of 10)

Outputs
Price
(Out of 10)

Delivery
grade
(Out of 20)

Technological
Capability
(Out of 100)

Quality
(Out of 100)

45

Supplier 1

1

9

13

70

10

Supplier 2

3

10

20

57

13

Supplier 3

5

8

17

40

60

Supplier 4

4

7

20

44

20

Supplier 5

6

3

4

40

90

Supplier 6

7

6

3

80

70

Supplier 7

2

4

18

57

90

Supplier 8

8

1

9

58

16

Supplier 9

10

2

6

88

22

Supplier 10

9

5

5

80

77

Note. Inputs and outputs grades form ten suppliers
Data Normalization
Data presented in Table 6 is comprised of various input and output variables, being measured in
different scales. The disparity in measurement scales among the data creates discrepancies that
lack comparability. Therefore, normalization is essential to standardize the values into a consistent
format for comparison and further analysis.
Normalization is a procedure of transforming variables or attributes from a dataset by
scaling them down (Kotsiantis et al., 2006). It refers to the process of scaling numerical data from
different features into a common scale. It is crucial to allow data to be comparable and combined
in a way that can be analyzed and presented (Muhammad & Peshawa, 2022).

Various

normalization techniques include min-max normalization, z-score, softmax, sigmoid, and decimal
scaling (Kumar et al., 2022). “Min-Max Normalization” is being employed in this research paper.
The inputs and outputs are weighted equally and carry an equivalent impact during the decisionmaking process by applying min-max data normalization. After completing min-max
normalization, the resulting values fall into a specific range of either [0,1] or [-1, 1]. By

46
implementing this process, it is to ensure that the normalized value of all inputs and outputs shown
in Table 7 falls within the range of 0 to 1 despite variations in the measurement units.
The mathematical formulation of Min-Max normalization is as below,
𝑋 − 𝑋𝑚𝑖𝑛

𝑋𝑛𝑜𝑟𝑚 = 𝑋 𝑖

𝑚𝑎𝑥 − 𝑋𝑚𝑖𝑛

𝑋𝑛𝑜𝑟𝑚

= Min-Max normalized value of X

𝑋𝑖

= 𝑖 𝑡ℎ value of X

𝑋𝑚𝑖𝑛

= Min. value of the dataset

𝑋𝑚𝑎𝑥

= Max. value of the dataset

The Min-Max normalization approach is one of the methods that is frequently used
(Srijiranon et al., 2021). It is one of the most common normalization techniques and ensures data
are given in the identical range based on the minimum and maximum values. The technique
establishes a new baseline for each data point by taking the difference between the minimum and
maximum values as a base. The main goal of this normalization technique is to bring all the
dataset’s features into the same scale, ensuring they weigh in a same way, providing a fair
comparison.
Min-max normalization finds the dataset’s minimum and maximum values. Next, the
difference between the actual and minimum values is divided by the difference between the
maximum and minimum values, generating a normalized value between 0 to 1 or -1 to 1. Table 7
demonstrates the values of the inputs and outputs of the 10 DMUs after normalization.
Table 7

47
Data of two inputs and three outputs of 10 DMUs after normalization
Input

Output

No. of
employees
Grade

Price

Delivery
grade

Technological
Capability

Quality

Supplier 1

0.00

0.89

0.59

0.63

0.00

Supplier 2

0.22

1.00

1.00

0.35

0.04

Supplier 3

0.44

0.78

0.82

0.00

0.63

Supplier 4

0.33

0.67

1.00

0.08

0.13

Supplier 5

0.56

0.22

0.06

0.00

1.00

Supplier 6

0.67

0.56

0.00

0.83

0.75

Supplier 7

0.11

0.33

0.88

0.35

1.00

Supplier 8

0.78

0.00

0.35

0.38

0.08

Supplier 9

1.00

0.11

0.18

1.00

0.15

Supplier 10
0.89
0.44
0.12
0.83
Note. Inputs and outputs grades form ten suppliers after normalization

0.84

Data Envelopment Analysis (DEA) measures the relative efficiency of each supplier compared to
other suppliers. Supplier performance is calculated using the weighted input and output ratios in
the supplier selection. The DEA model calculates weights that maximize the relative efficiency
score of a DMU, ensuring that the efficiency scores of all DMUs are less than or equal to one,
preventing the challenge of determining the weights of different DMUs. The objective is to find
the best suppliers from the ten available options. We have developed ten equations, considering
the problem in linear programming.
DEA model for Supplier 1:
Max 𝜃 = 0.59𝑢1 + 0.63𝑢2
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0

48
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.89𝑣2 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

DEA model for Supplier 2:
Max 𝜃 = 𝑢1 + 0.35𝑢2 + 0.04𝑢3
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0

49
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.22𝑣1 + 𝑣2 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

DEA model for Supplier 3:
Max 𝜃 = 0.82𝑢1 + 0.63𝑢3
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.44𝑣1 + 0.78𝑣2 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

DEA model for Supplier 4:
Max 𝜃 = 𝑢1 + 0.08𝑢2 + 0.13𝑢3

50
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.33𝑣1 + 0.67𝑣2 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

DEA model for Supplier 5:
Max 𝜃 = 0.06𝑢1 + 𝑢3
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0

51
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.56𝑣1 + 0.22𝑣2 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

DEA model for Supplier 6:
Max 𝜃 = 0.83𝑢2 + 0.75𝑢3
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.67𝑣1 + 0.56𝑣2 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

52
DEA model for Supplier 7:
Max 𝜃 = 0.88𝑢1 + 0.35𝑢2 + 𝑢3
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.11𝑣1 + 0.33𝑣2 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

DEA model for Supplier 8:
Max 𝜃 = 0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0

53
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.78𝑣1 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

DEA model for Supplier 9:
Max 𝜃 = 0.18𝑢1 + 𝑢2 + 0.15𝑢3
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
𝑣1 + 0.11𝑣2 = 1

54
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀

DEA model for Supplier 10:
Max 𝜃 = 0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3
Subject to;
0.59𝑢1 + 0.63𝑢2 − 0.89𝑣2 ≤ 0
𝑢1 + 0.35𝑢2 + 0.04𝑢3 − 0.22𝑣1 − 𝑣2 ≤ 0
0.82𝑢1 + 0.63𝑢3 − 0.44𝑣1 − 0.78𝑣2 ≤ 0
𝑢1 + 0.08𝑢2 + 0.13𝑢3 − 0.33𝑣1 − 0.67𝑣2 ≤ 0
0.06𝑢1 + 𝑢3 − 0.56𝑣1 − 0.22𝑣2 ≤ 0
0.83𝑢2 + 0.75𝑢3 − 0.67𝑣1 − 0.56𝑣2 ≤ 0
0.88𝑢1 + 0.35𝑢2 + 𝑢3 − 0.11𝑣1 − 0.33𝑣2 ≤ 0
0.35𝑢1 + 0.38𝑢2 + 0.08𝑢3 − 0.78𝑣1 ≤ 0
0.18𝑢1 + 𝑢2 + 0.15𝑢3 − 𝑣1 − 0.11𝑣2 ≤ 0
0.12𝑢1 + 0.83𝑢2 + 0.84𝑢3 − 0.89𝑣1 − 0.44𝑣2 ≤ 0
0.89𝑣1 + 0.44𝑣2 = 1
𝑢1 , 𝑢2 , 𝑢3 , 𝑣1 , 𝑣2 ≥ 𝜀
In order to find out the most efficient suppliers, we used the Excel solver function to determine the
efficiency scores of each supplier, as shown in Table 8. Figure 3 further illustrates the findings of
our study. Accordingly, two suppliers, supplier one and supplier seven, reported the highest
efficiency score 1. As depicted in Figure 3, the third efficient supplier of Supplier 2 shows an
efficiency score of 38%, which is considerably less than the first two.

55
Table 8
The efficiency score of suppliers
Efficiency Score

u1

u2

u3

v1

v2

Supplier1

1.00

0.00

1.60

0.00

1.73

1.13

Supplier2

0.38

0.00

2.82

0.00

3.04

1.99

Supplier3

0.00

0.00

1.06

0.00

1.14

0.75

Supplier4

0.10

0.00

1.21

0.00

1.30

0.85

Supplier5

0.00

0.00

0.00

0.00

1.00

0.00

Supplier6

0.00

0.00

0.97

0.00

1.05

0.69

Supplier7

1.00

0.00

0.00

0.00

1.80

0.00

Supplier8

0.00

0.00

0.00

0.00

1.50

0.00

Supplier9

0.08

0.00

0.00

0.00

1.29

0.00

Supplier10

0.00

0.00

0.00

0.00

1.13

0.00

Note. Efficiency score of ten suppliers
Figure 3
Efficiency scores of suppliers

Efficiency Score
Supplier 10

Supplier 8
Supplier 6
Supplier 5
Supplier 3
Supplier 9
Supplier 4
Supplier 2
Supplier 7
Supplier 1
0.00

0.20

0.40

0.60

Note. Bar chart of efficiency score of ten suppliers

0.80

1.00

56
CONCLUSION
Supplier selection is one of the most crucial components in a manufacturing and logistics
operation. It significantly influences the end-to-end supply chain functions, from sourcing raw
materials to producing final products and delivering them to the final consumer. After reviewing
related literature, we identified geographical distance, number of deliveries, number of employees,
and total health operating expenses as the few most impactful inputs in supplier selection.
Moreover, we found sustainability rank, information technology investment, up-to-date
technology rank, research and development (R&D) personnel, numbers of ports that can reach,
and percentage of machinery as significant outputs. Based on the context and data availability, we
selected two inputs; the number of employees and price, and three outputs; delivery grade,
technological capability, and quality, to develop the model.
This study used the Data Envelopment analysis model to determine the most suitable
supplier for Miraab, which is known as a reliable industrial valve manufacturing company in Iran,
based on the collected input and output data. Additionally, the decision-makers can rank the
suppliers and prioritize them based on the context. Ranking and prioritizing suppliers is crucial in
supply chain disruption, such as a pandemic, to mitigate the risk of production interruption.
Table 9
Comparing top-ranked suppliers

Inputs
Price

Delivery
Grade

Technological
Capability

Quality

0.89

0.59

0.63

0.00

Supplier 7
0.11
0.33
0.88
0.35
Note. Inputs and outputs grades of top two suppliers after normalization

1.00

Supplier 1

No. of
employees
0.00

Outputs

57
Considering suppliers 1 and 7, both suppliers have an efficiency score of 1. We extracted the
normalized input and output data from Table 9 to select the most appropriate supplier. In
comparing suppliers 1 and 7, the value of no. of employees for supplier 7 is significantly greater
than supplier 1. However, the price paid by Supplier 7 is considerably lower than Supplier 1.
Regarding the outputs, the quality of supplier 7 is also higher. In addition, the delivery grade is
higher for Supplier 7 compared to Supplier 1. Also, Supplier 7 performs better in deliveries, even
though its technological capability is less advanced than Supplier 1. As per the analysis, supplier
7 offers comparatively higher quality products at a lower price. Therefore, we recommend the
selected company, Miraab, a well-known valve manufacturing company, to keep Supplier 7 as the
leading supplier and Supplier 1 as the alternate supplier.
In this research, we consider a few input and output variables, however we found there are
many other variables that directly influence the supplier’s efficiency score. Therefore, we suggest
future research to integrate many input and output variables in the analysis as it increases the
accuracy of the results. Moreover, this research considers 10 DMUs with restrictions in gathering
data within the given time frame. In future research efforts, we suggest expanding the same
research by considering more DMUs, assisting companies inmaking clearer decisions on supplier
selection. Furthermore, we conduct general research without considering specific industry, and
apply it in an organization to validate the applicability of the model. However, we recommend
conducting the same research, focusing on particular industries. Future research can find out
specific input and output variables related to the industry, develop the model, and apply it in a few
companies in the related industry, proving the applicability of the model. This proposed research
methodology is expected to encourage companies to consider DEA in supplier selection process

58
as a benchmarking tool, increasing the reliability, continuity, and robustness of the overall supply
chain.

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