Decision Analytics Journal 8 (2023) 100293

Contents lists available at ScienceDirect

Decision Analytics Journal

journal homepage: www.elsevier.com/locate/dajour

A grey decision-making trial and evaluation laboratory model for digital

warehouse management in supply chain networks
Syed Imran Zaman a,b , Sherbaz Khan c , Syed Ahsan Ali Zaman a,d,e , Sharfuddin Ahmed Khan f ,∗
a School of Economics and Management, Southwest Jiaotong University, Chengdu, China
b
Department of Business Administration, Jinnah University of Women, Karachi, Pakistan
c
Logistics and Supply chain management, College of Business Management, Institute of Business Management, Karachi, Pakistan
d
School of Economics and Business, Kaunas University of Technology, Kaunas, Lithuania
e
Department of Business Management, University of Religions and Denominations, Qum, Iran
f
Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, 3737 Wascana Pkwy, Regina, SK, Canada, S4S 0A2

ARTICLE INFO ABSTRACT

Keywords: Integrating digitalization and warehouse management systems (WMS) is a crucial aspect of enhancing supply
Digitalization chain performance for strategic competitiveness. Multiple technologies promote digital development and
Warehouse management system supply chain management (SCM) transformation. They include artificial intelligence and robotics, cloud
Supply chain management
computing, 3D printing, advanced analytics, blockchain, augmented reality, radio frequency identification
Performance assessment
(RFID), the internet of things (IoT), and cloud technology. This research aims to identify and evaluate the
Grey-DEMATEL
factors of digitalization, WMS, and supply chain performance by combining a comprehensive literature review
analysis with the grey decision-making trial and evaluation laboratory (DEMATEL) method. An extensive
literature review is conducted to identify the primary determinants of supply chain performance. Subsequently,
the expert panel from the textile industry is consulted to obtain expert opinions on these factors’ relative
importance. The findings of this study demonstrated that by considering the interdependencies on supply
chain performance and the uncertainties related to expert judgments, the suggested comprehensive model is
highly capable of addressing the digitalization WMS problem

1. Introduction uninterrupted operations, even in the event of adverse circumstances

[6]. Materials shortages, capacity limits, labour shortages, quality is-
Supply chains (SCs) have become more susceptible to disruptions sues, transportation delays, demand shifts, and supply ruptures are
as a result of their increased size and complexity as a direct result of typical SC failure mechanisms [7]. Due to customer demand, the com-
globalization and outsourcing [1]. Supply Chains must adopt innova- plicated and constantly shifting business environment, and the need
tive techniques to adapt quickly and economically to rapidly shifting for businesses to be flexible and adaptive [8]. The advent of digital
dynamics in the market that are becoming more chaotic in volume technologies, including sensors, RFID chips, cyber–physical systems,
and diversity [2]. Due to large customization, inventory reduction,
and the internet of things (IoT), has brought about significant changes
and global rivalry, controlling cost and quality has made supply chain
in the manufacturing and services sectors of the supply chain [9].
information flow problematic [3]. Various types of disruptions, such
The term ‘‘digitalization’’ refers to the ongoing trend of digital
as power outages, system crashes, network failures, or unforeseen
technology becoming pervasive in all aspects of human existence [10].
events, have the potential to initiate a chain reaction of problems
The concept of digitalization can be perceived as a response to the
that culminate in critical service continuity SC failure situations. The
aforementioned scenarios may entail a diverse array of difficulties, constantly evolving demands of both organizations and the broader
including but not limited to loss of data, interruptions in applica- community, rather than a novel notion. The intricate interdependence
tion functionality, breakdowns in communication, and compromised of interruptions, digitalization, and service continuity necessitates that
security [4]. These issues pose a threat to the seamless operation businesses acknowledge and strategize for these factors. The advent
and dependability of critical services and systems [5]. Anticipating of digitalization has led to a transformation of the risk landscape,
and proactively addressing potential disruptions is a critical aspect for rendering traditional risk management methods potentially insufficient
organizations to mitigate the risk of supply chain failure and ensure [11]. Consequently, risk management strategies have progressed to

∗ Corresponding author.
E-mail addresses: s.imranzaman@gmail.com (S.I. Zaman), analyzeus@gmail.com (S. Khan), syed.zaman@ktu.edu (S.A.A. Zaman),
Sharfuddin.Khan@uregina.ca (S.A. Khan).

https://doi.org/10.1016/j.dajour.2023.100293
Received 15 June 2023; Received in revised form 13 July 2023; Accepted 23 July 2023
Available online 27 July 2023
2772-6622/© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
S.I. Zaman, S. Khan, S.A.A. Zaman et al. Decision Analytics Journal 8 (2023) 100293

encompass the proactive forecasting and readiness for such potential country. This study takes into consideration the inconsistent results and
disruptions. As the landscape of the digital realm undergoes constant overlooked aspects in the current body of literature through he below
evolution, it is imperative for companies to remain adaptable and research question.
poised to confront potential disruptions. In order to ensure the con- RQ: How do digitalization and WMS affect the efficiency of the textile
tinuous provision of products and services in the contemporary era, industry’s supply chain in a developing nation?
businesses must adopt a strategic approach that takes into consideration This paper is organized as Section 2 will present a comprehensive
the dynamic nature of the digital realm and the associated risks [12]. literature review of the digitalization and Warehouse Management
In the current era of digitalization, businesses may not solely achieve Systems (WMS) in the context of supply chain performance. Sections 3
success unless they adhere to certain practices [13]. Companies can and 4 will describe the methodology employed, including variable
trace raw materials, components, semi-finished items, and final goods measurement and questionnaire analysis. In Section 5, the discus-
in real time using sophisticated technology. Information flow makes the sion, implications, conclusions, and limitations of the study will be
supply chain more trustworthy and robust [14]. Small and medium- presented.
sized enterprises need digitalization even more and need more cautious
2. Literature review
management than large firms and multinationals [15].
Warehouse management systems (WMS) are commonly utilized by
2.1. Digitalization
various types of companies, including manufacturers, retailers, whole-
salers, and logistics providers. The primary objective of WMS is to
Nowadays, one hears the term ‘‘digitalization’’, which refers to the
enhance the efficiency of warehouse operations by optimizing speed, process of an economy becoming digital [10]. One may think of it as
accuracy, and output. This objective is achieved through the provision a change in how business operations are conducted that makes use of
of real-time monitoring of stock quantities, locations, and movements, information technology [25]. Seah et al. [26] assert that one of the key
thereby enabling business owners to effectively manage their inventory themes that is currently transforming society and business on both a
[16]. The implementation of WMS can potentially enhance inven- short-term and long-term basis is digitalization. It must be stated that
tory management, minimize errors, and improve order accuracy and a supportive digital infrastructure underlies the digitalization process.
timeliness for enterprises [17]. The optimization of warehouse ar- This infrastructure is made up of essential digital inputs, such as
chitecture and storage tactics results in the maximization of space digital skills, regulatory frameworks, and innovation mechanisms, as
utilization and minimization of unnecessary motion within the system. well as digital accelerators such socio-cultural factors [27]. Addition-
This system facilitates the scheduling and monitoring of workforce ally, the use of sensors, RFID chips, cyber–physical systems, and the
operations, thereby enhancing productivity and optimizing resource al- Internet of Things are revolutionizing supply chain production and
location [18]. WMSs handle a high-volume warehouse operation in real services [9]. RFID has been utilized in a variety of fields, but it can
time. These devices improve product handling and storage [19]. Some be employed particularly in supply-chain management and logistics
companies still utilize antiquated warehouse management practices, systems for identification, tracing, and tracking since it enables accu-
which raises the risk of high operating expenses and lost investments rate system monitoring by employing real-time data [28]. Innovative
and commodities [20]. WMS maximizes storage space, speeds up order- business models, new production techniques, and the development of
ing and delivery, improves manufacturer services, simplifies data access knowledge-based goods and services are prevalent as a result of this
[19]. A good warehouse management system cuts costs and boosts digital transformation, which is defined by the fusing of cutting-edge
customer satisfaction [21]. WMS improves supply chain performance, technology and the integration of physical and digital systems [29].
for example, wireless barcodes with management information systems Numerous research endeavours have been undertaken to expound
(MIS) may reduce costs, enhance flexibility, minimize inventory, and upon the concept of digitalization. The phenomenon of digitalization
cut delivery times, improving customer satisfaction [21]. encompasses the utilization of digital technology in both industrial
The burgeoning potential and remarkable capabilities of digitaliza- and societal contexts, and the consequent alterations in individuals’
tion in enhancing supply chain operations have been underscored by interconnectivity with both tangible objects and one another [30].
recent research [22]. The aforementioned characteristics, particularly According to Reis et al. [31], digitalization refers to the utilization of
within the industrial domain, present a promising outlook. However, digital technology and data to improve business operations, generate
the advantages of a digitalized supply chain have yet to be comprehen- revenue, modify or replace corporate procedures, and create a digital
business environment with digital information at its core. This was
sively investigated. Rad et al. [23] have demonstrated the favourable
demonstrated in a study conducted by the authors. A recent investi-
aspects of contemporary digital technologies in the context of supply
gation portrays digitalization as a phenomenon characterized by an
chains. However, they have also expressed their reservations regarding
increase in the generation, examination, and utilization of data for the
the inadequacy of comprehensive assessments of these advantages. As
purpose of creating value digitalization is presented as the application
a result of the increasing level of competition in the business realm,
of digital technology and data to generate income, enhance opera-
the implementation of digital transformation has transitioned from a
tions, replace or modify corporate procedures, and establish a setting
desirable option to an essential requirement [6]. Notwithstanding the
for digital business, with digital information at its centre [31]. The
significance of enhancing their supply chains, numerous enterprises
integration of information and communication technologies (ICTs) is
encounter difficulties in determining the appropriate technology to imperative for digitalization, and it is essential that these technologies
adopt [15]. This underscores the heightened necessity for conducting are incorporated into diverse facets of a company’s operations and
comprehensive research in this domain. Recently, Aljoghaiman and products [32]. Drawing upon previous conceptualizations and knowl-
Bhatti [24] conducted a study that sheds light on the transformative edge within the field, certain scholars characterize digitalization as
influence of e-business technology on supply chain performance, with the utilization of digital technology within industrial ecosystems to
a particular focus on the textile sector in emerging economies. The facilitate the development of novel business models, generate fresh
present investigation contributes to our comprehension of the matter sources of revenue, and foster opportunities for value generation [33].
under consideration, yet it also underscores the significance of carrying The definition presented by Parida et al. [34] underscores the notion
out further inquiry. The review of past literature indicates that the that the utilization of diverse digital technologies constitutes merely
digital transformation of the supply chain industry has garnered some a single facet of the broader concept of digitalization. Devereux and
attention but remains an area that has not been extensively researched, Vella [35] explicate the phenomenon of digitalization as entailing the
particularly with respect to empirical validation. This research aims widespread adoption of a versatile technology. According to Haoua
to investigate the impact of digitalization and warehouse management and Moutahaddib [36], the proliferation of smart devices and mobile
systems on the performance of the textile supply chain in an emerging applications has contributed to the acceleration of digitization.

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2.2. Warehouse management system (WMS) resilient methods also affect supply chain effectiveness and compet-
itiveness. Cost, quality, variety, and service level may increase with
Due to the dynamic nature of commodities intake and outflow, a better integration [53].
warehouse management system (WMS) is needed to effectively man- The conceptual framework depicts the inter-relationship among
age a warehouse’s operations [37]. Baruffaldi et al. [38] assert that the three keywords, which as reviewed as follows. As highlighted
the Warehouse Management System (WMS) manages a warehouse’s in the literature cited above, several researches have been compiled
physical and informational flows, including arriving and departing on digitalization, warehouse management system, and supply chain
activities. Invoicing, reporting, activity coordination, and cycle count- performance. Digitalization has been defined as a change in business
ing, are part of modern warehouse management systems. According to processes, using information technology [10]. As for WMS, it is a man-
Madurapperuma et al. [39], this method improves ERP system inter- agement information system that controls the physical and information
action. Information on a company’s assets, commodities, and activities flows within the warehouse, for inbound and outbound operations [38].
is collected, stored, and distributed via WMS. The system captures and Whereas, supply chain performance has been defined as the supply
documents transactions and sends important data to other ERP modules chain’s activities in meeting end-customer requirements [50]. In the
[40]. Warehouse activities are documented by a database-driven ware- next section, the methodology of the paper shall be discussed in detail.
house management system to monitor put-away and ensure inventory
correctness [41]. An excellent warehouse management system may 3. Methodology
improve operational efficiency and provide valuable business insights,
saving manufacturing and distribution companies money [42]. DEMATEL is a systematic method used to build and analyze the
The advent of WMSs at supply chain nodes simplify information structure of complicated causal relations between a set of factors with
infrastructures for procurement, manufacture, storage, and delivery matrices or digraphs. It is widely used to identify the key factors
[38]. WMS may now combine with complex technologies like RFID in various systems. This study uses grey DEMATEL and a literature
and speech recognition and be utilized alone or as part of an ERP soft- review to find factors of digitalization, warehouse management and
ware [41]. Another research found that WMS helps companies manage supply chain performance. Conducting a comprehensive review of the
inventory and provide reports quickly and accurately [43]. Woźni- literature is necessary to ascertain the most crucial factors that con-
akowski et al. [19] explain that the WMS program detects third-party tribute to enhancing the overall performance of the supply chain.
vendors, determines internal origin, and allows warehouse delivery and Initially, twenty-three factors were selected by the authors from a
pickup. They assert it controls product quality and quantity and chooses comprehensive literature review pertaining to digitalization, warehouse
the optimal storage location. A warehouse management system is also management and supply chain performance.
used to improve logistical operations [41]. WMS systems let businesses Professionals with expertise in the field of textiles are employed to
manage, supervise, and regulate products and material transportation carry out this form of examination. This study utilizes the expertise and
and storage [19]. According to Assis and Sagawa [44], identify the insights of prominent figures in the industry to discern the key deter-
system’s advantages as administration, process order definition, storage minants that affect the effectiveness of supply chain operations. These
location selection, flow dependability, and operation monitoring. experts emphasize the importance of conducting a comprehensive anal-
ysis of these components and their interrelationships and narrowed
2.3. Supply chain performance down the factors to sixteen in total (Table A.1 in Appendix). Subse-
quently, it is imperative to ascertain the causal connections between
Supply chain management performance must be evaluated for effi- the aforementioned variables, as this will facilitate comprehension of
ciency and profitability [45]. Performance in supply chain management their impact on the efficacy of the supply chain.
is organizing operations to fulfil end-user needs [46]. It may also The literature evaluates causality using TISM, ISM, DEMATEL, and
be characterized as information about processes and products’ results ANP [54]. Ocampo [55] use ISM and TISM can identify structural
that can be compared to goals, trends, historical performance, and linkages among factors, but they do not indicate strength [56]. DEMA-
other processes and goods [47]. This word refers to the company’s TEL can assess relationship quality [57–59]. Traditional DEMATEL has
supply chain functions’ operational results. This encompasses all ac- fuzzy expert input. The grey-DEMATEL approach can remove these re-
tions, information, and activities related to moving and converting strictions [60]. Thus, we determined digitalization, warehouse manage-
items from raw resources to consumers [48]. Effectiveness optimizes ment and supply chain performance causality using the grey-DEMATEL
the supply chain by increasing customer satisfaction, whereas efficiency [61,62]. The experts studied the factors and filled the questionnaires.
maximizes production while minimizing costs and waste [45]. Supply Their interrelationships, the formation of causal relationships was ac-
chain performance and connectivity management depend on infor- tualized after the implementation of the grey-DEMATEL method. Khan
mation communication [8]. Many companies understand performance et al. [63] use a linguistic scale to determine how one factor affects
evaluation and how to use it for a successful supply chain [49]. others. Experts’ direct-relation matrix completes grey-DEMATEL.
Supply Chain Performance (SCP) has been studied extensively. The procedure for the grey-DEMATEL is as follows:
SCP study is growing rapidly on this area [47]. Kurdi et al. [46] Step 1: Define the grey semantic scale.
describe SCP as a set of operations that put customers first and make A five-level grey semantic scale was used in this study. The scale
products available via speedy delivery. Another research found that items were as follows:
it measures how effectively supply chain activities improve company
• No influence = [0, 0]
competitiveness [45]. Mubarik et al. [50] define SCP as the measures
• Very low influence = [0, 0.25]
performed by the extended supply chain to rapidly and efficiently
• Low influence = [0.25, 0.5]
satisfy end-customer needs, such as availability of goods and on-time
• High influence = [0.5, 0.75]
delivery. Another study defined SCP aspects as delivery speed, qual-
• Very high influence = [0.75, 1]
ity, affordability, adaptability, and visibility [48]. Supplier-oriented
performance, customer-oriented performance, cost-containment perfor- Step 2: Develop the grey direct-relation matrix X.
mance, time-based performance, and reliability performance have also Three experts with a deep understanding of a developing coun-
been found [51]. Another research considers performance metrics for try’s textile industry were invited to participate in this study. The
flexibility, efficiency, responsiveness, and quality. It may also be used questionnaires, which contained the definitions of the factors of dig-
to measure performance increases by availability, variety, innovation, italization, WMS, and supply chain performance with relevant filling
timeliness, cost, and pricing [8]. Altay et al. [52] claim that agile and rules, were distributed to the respondents to collect the data needed

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S.I. Zaman, S. Khan, S.A.A. Zaman et al. Decision Analytics Journal 8 (2023) 100293

for our study. Table 1 shows the completed direct-relation matrices. 4. Results and analysis
The direct-relation matrices were transformed into grey direct-relation
matrices based on the grey semantic scale [64]. According to the value of net effect (𝑅𝑖 − 𝐶𝑗 ), the factors could be
Step 3: Compute the overall crisp direct-relationship matrix Z. divided into the following three categories:
By referencing previous studies, the crisp matrix was obtained The ‘‘cause’’ group: sales and operations planning (F9), RFID (F1),
using the modified-CFCS method. Subsequently, the overall crisp direct sensors (F3), RF handheld devices/vehicle mounted units (VMU) (F6),
relationship matrix 𝑍 was created using Eq. (1): cloud computing (F4), and augmented reality (AR) (F2). The 𝑅i − 𝐶j
( )
1 1 value of these factors was greater than zero, which means that these
𝑧𝑖𝑗 = 𝑧 + 𝑧2𝑖𝑗 + ⋯ + 𝑧𝑘𝑖𝑗 (1)
𝑘 𝑖𝑗 affected other factors more significantly than they were affected.
Step 4: Compute the normalized direct-relation matrix 𝑁. The ‘‘effect’’ group: cycle count accuracy (F8), quality (F12), reli-
The normalized direct-relation matrix 𝑁 was calculated using Equa- ability performance (F16), blockchain technology (F5), material plan-
tions. (2) and (3): ning (F10), customer-oriented performance (F15), barcode label (F7),
delivery speed (F11), cost (F13), and flexibility (F14). The 𝑅i −𝐶j value
𝑁 = 𝑠𝑍 (2)
of these factors was less than zero, which means that they were more
1 affected by other factors.
𝑠= ∑𝑛 (3)
max1≤𝑖≤𝑛 𝑗=1 𝑧𝑖𝑗 In addition, a critical line was drawn on the mean value of promi-
Step 5: Compute the total relation matrix 𝑇 . nence (𝑅i +𝐶j ), and the factors were further divided into two groups ac-
The total relation matrix 𝑇 was developed using Eq. (4), where 𝐼 cording to prominence value. Factors with a prominence value greater
represent the identity matrix: than the mean value were considered central factors, which means
that they significantly affected others or were significantly affected by
∑
∞
𝑇 = 𝑁 + 𝑁2 + 𝑁3 + ⋯ = 𝑁 𝑖 = 𝑁(𝐼 − 𝑁)−1 (4) others.
𝑖=1 Therefore, the factors in the ‘‘cause’’ group with a prominence value
Step 6: Compute the prominence and net effect of the factors. greater than the mean value were considered critical. They significantly
The prominence and net effect of the factors were computed using affected others more than they were affected, and they represented the
Eqs. (5)–(8), as Table 4 shows. core factors in the network. Specifically, sales and operations planning
∑
𝑛 (F9), RFID (F1), sensors (F3), RF handheld devices/vehicle mounted
𝑅𝑖 = 𝑡𝑖𝑗 , ∀𝑗 (5) units (VMU) (F6), cloud computing (F4), and augmented reality (AR)
𝑗=1 (F2) were found to be the key factors. Additionally, the normalized and
∑𝑛
total relation matrix are mentioned (See Tables 2 and 3.)
𝐶𝑗 = 𝑡𝑖𝑗 , ∀𝑗 (6) The presented tabular data depicts a normalized direct relationship
𝑖=1
{ } matrix that is commonly utilized in the decision-making trial and eval-
𝑃𝑖 = 𝑅𝑖 + 𝐶𝑗 ∣ 𝑖 = 𝑗 (7) uation laboratory (DEMATEL) technique. The DEMATEL methodology
{ }
𝐸 𝑖 = 𝑅 𝑖 − 𝐶𝑗 ∣ 𝑖 = 𝑗 (8) is a significant analytical approach that assesses the structural inter-
dependencies among various criteria, facilitating the comprehension of
Step 7: Plot the prominence–causal diagram. intricate systems.
The prominence (𝑅𝑖 + 𝐶𝑗 ) is represented on the horizontal axis, The table denotes the extent of direct impact of the factor ‘‘Fi’’ on
and the net effect (𝑅𝑖 − 𝐶𝑗 ) is represented on the vertical axis in the
‘‘Fj’’ through each cell (i, j). The range of values spans from 0 to 0.0745,
causal-effect graph.
with the former denoting a lack of influence and the latter representing
the most significant direct impact. It is noteworthy that the diagonal
3.1. Case study
line consisting of 0s, extending from the upper left corner to the lower
right corner, denotes the absence of impact of factors on themselves, as
A significant exporting and job-producing industry is textile. Any
factors do not exert influence on their own outcomes. (See Figs. 1 and
progress made in this area will raise peoples’ standards of living and
2.)
help fight poverty [65]. Pakistan is a developing country with a high
The aforementioned figure displays the various factors within a
labour intensity that mostly depends on the textile industry for jobs
given system that were assessed through the utilization of the decision-
and export revenue. More than 60% of export revenues and 8.5%
making trial and evaluation laboratory (DEMATEL) methodology. The
of GDP are contributed by just this industry [66]. Another research
supports this notion by stating that presently, nearly one-fourth of rank of each factor is established based on its prominence value, which
industrial added value, 40% of industrial labour, 40% of bank loans, serves as a metric of its impact within the system. A lower rank denotes
and 60% of exports are attributed to the textile industry [67]. There greater prominence, implying that the factor in question has a signifi-
are 560 companies listed on the Pakistan stock exchange (PSE), with 35 cant impact on other variables. The variable with the highest impact is
different industries represented. The textile industry, which comprises ‘reliability performance’, which holds the first position in the ranking.
all 153 of these businesses and is Pakistan’s second-largest industry Conversely, the variable with the lowest impact is ‘blockchain technol-
after agriculture, is dominated by that country [65]. Since the adoption ogy’, which is positioned at the 16th rank. The process of ranking aids in
of global supply chains (SCs), the SC in this industry has been exposed comprehending the hierarchical interplay of factors, thereby facilitating
to significant risks that have frequently come to pass. Examples of these the establishment of priorities and informed decision-making.
risks include late deliveries, lengthy lead times between returns and The presented figure showcases the evaluation and prioritization
resending to customers, stock-outs or overstocks, etc. McMaster et al. of various factors utilizing the DEMATEL (decision making trial and
[68]. Sometimes the labour markets are exploited, and firms suffer evaluation laboratory) methodology. The provided Sankey diagram
from heavy wages or strikes, which increase the cost of production shows how various factors are connected to and contribute to the
for the firm. Political instability has a negative impact on economic ‘‘Cause’’ and ‘‘Effect’’ categories. Causal factors, exemplified by RFID
development by lowering the rates of productivity growth. Inflation which holds the third rank, exert a substantial impact on other factors.
is the result of political unrest [69]. The economy is facing a massive Conversely, effect factors, such as reliability performance which holds
industry drain in the past few years as around 41% of the industry has the first rank, are predominantly influenced by other factors. This
moved abroad, which become the issue of concern for the state to take comprehension facilitates the identification of pivotal leverage points
effective step to stop further industrial drain [70]. for strategic decision-making and interventions.

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Table 1
Direct relation matrix.
DRM F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15 F16
F1 [0, 0] [0.75, 1] [0.75, 1] [0.75, 1] [0.75, 1] [0.5, 0.75] [0.75, 1] [0.75, 1] [0.25, 0.5] [0.75, 1] [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.5, 0.75] [0.75, 1] [0.75, 1]
F2 [0.75, 1] [0, 0] [0.75, 1] [0.75, 1] [0.5, 0.75] [0.5, 0.75] [0.75, 1] [0.75, 1] [0.25, 0.5] [0.75, 1] [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.5, 0.75] [0.75, 1] [0.75, 1]
F3 [0.75, 1] [0.75, 1] [0, 0] [0.75, 1] [0.5, 0.75] [0.5, 0.75] [0.75, 1] [0.75, 1] [0.25, 0.5] [0.75, 1] [0.75, 1] [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.75, 1] [0.75, 1]
F4 [0.75, 1] [0.75, 1] [0.75, 1] [0, 0] [0.75, 1] [0.5, 0.75] [0.75, 1] [0.75, 1] [0.5, 0.75] [0.75, 1] [0.75, 1] [0.25, 0.5] [0.25, 0.5] [0.75, 1] [0.5, 0.75] [0.75, 1]
F5 [0, 0.25] [0, 0.25] [0, 0.25] [0, 0.25] [0, 0] [0, 0.25] [0.5, 0.75] [0.5, 0.75] [0.25, 0.5] [0, 0.25] [0, 0] [0, 0] [0.75, 1] [0.5, 0.75] [0, 0.25] [0.75, 1]
F6 [0.75, 1] [0, 0.25] [0.75, 1] [0.75, 1] [0.25, 0.5] [0, 0] [0.5, 0.75] [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.75, 1] [0, 0] [0.5, 0.75] [0.5, 0.75] [0.75, 1] [0.75, 1]
5

F7 [0.75, 1] [0.25, 0.5] [0.75, 1] [0.75, 1] [0, 0.25] [0.5, 0.75] [0, 0] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.25, 0.5] [0.25, 0.5] [0.5, 0.75] [0.5, 0.75] [0.75, 1] [0.75, 1]
F8 [0.75, 1] [0, 0] [0, 0] [0, 0] [0, 0] [0.25, 0.5] [0.5, 0.75] [0, 0] [0.75, 1] [0.75, 1] [0, 0.25] [0, 0.25] [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.75, 1]
F9 [0.75, 1] [0.25, 0.5] [0.75, 1] [0.75, 1] [0, 0.25] [0, 0.25] [0.5, 0.75] [0.5, 0.75] [0, 0] [0.75, 1] [0, 0.25] [0, 0.25] [0.5, 0.75] [0.25, 0.5] [0.75, 1] [0.5, 0.75]
F10 [0.75, 1] [0, 0] [0.5, 0.75] [0.5, 0.75] [0, 0] [0, 0] [0.75, 1] [0.75, 1] [0.5, 0.75] [0, 0] [0, 0.25] [0.5, 0.75] [0.75, 1] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75]
F11 [0, 0] [0, 0] [0, 0] [0, 0] [0, 0] [0.75, 1] [0.75, 1] [0.25, 0.5] [0.25, 0.5] [0.5, 0.75] [0, 0] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75]
F12 [0, 0] [0, 0] [0, 0.25] [0, 0] [0, 0.25] [0, 0.25] [0, 0] [0, 0] [0.75, 1] [0, 0] [0, 0.25] [0, 0] [0.75, 1] [0.5, 0.75] [0.75, 1] [0.5, 0.75]
F13 [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0, 0.25] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.75, 1] [0.75, 1] [0, 0] [0.25, 0.5] [0.5, 0.75] [0.5, 0.75]
F14 [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.5, 0.75] [0.25, 0.5] [0.5, 0.75] [0.25, 0.5] [0.25, 0.5] [0.25, 0.5] [0.5, 0.75] [0.75, 1] [0, 0] [0.75, 1] [0.75, 1]
F15 [0.75, 1] [0.75, 1] [0.75, 1] [0.5, 0.75] [0.5, 0.75] [0.25, 0.5] [0.5, 0.75] [0.25, 0.5] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.75, 1] [0.75, 1] [0.25, 0.5] [0, 0] [0.75, 1]
F16 [0.5, 0.75] [0.5, 0.75] [0.75, 1] [0.5, 0.75] [0.75, 1] [0.75, 1] [0.75, 1] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.5, 0.75] [0.75, 1] [0.5, 0.75] [0.25, 0.5] [0.75, 1] [0, 0]

Decision Analytics Journal 8 (2023) 100293

S.I. Zaman, S. Khan, S.A.A. Zaman et al. Decision Analytics Journal 8 (2023) 100293

Table 2
Normalized direct relation matrix.
X F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15 F16
F1 0.0000 0.0745 0.0745 0.0745 0.0039 0.0745 0.0745 0.0745 0.0745 0.0745 0.0000 0.0000 0.0510 0.0274 0.0745 0.0510
F2 0.0745 0.0000 0.0745 0.0745 0.0039 0.0039 0.0274 0.0000 0.0274 0.0000 0.0000 0.0000 0.0510 0.0274 0.0745 0.0510
F3 0.0745 0.0745 0.0000 0.0745 0.0039 0.0745 0.0745 0.0000 0.0745 0.0510 0.0000 0.0039 0.0510 0.0274 0.0745 0.0745
F4 0.0745 0.0745 0.0745 0.0000 0.0039 0.0745 0.0745 0.0000 0.0745 0.0510 0.0000 0.0000 0.0510 0.0274 0.0510 0.0510
F5 0.0745 0.0510 0.0510 0.0745 0.0000 0.0274 0.0039 0.0000 0.0039 0.0000 0.0000 0.0039 0.0039 0.0274 0.0510 0.0745
F6 0.0510 0.0510 0.0510 0.0510 0.0039 0.0000 0.0510 0.0274 0.0039 0.0000 0.0745 0.0039 0.0510 0.0510 0.0274 0.0745
F7 0.0745 0.0745 0.0745 0.0745 0.0510 0.0510 0.0000 0.0510 0.0510 0.0745 0.0745 0.0000 0.0510 0.0274 0.0510 0.0745
F8 0.0745 0.0745 0.0745 0.0745 0.0510 0.0274 0.0510 0.0000 0.0510 0.0745 0.0274 0.0000 0.0510 0.0510 0.0274 0.0510
F9 0.0274 0.0274 0.0274 0.0510 0.0274 0.0274 0.0510 0.0745 0.0000 0.0510 0.0274 0.0745 0.0510 0.0274 0.0510 0.0510
F10 0.0745 0.0745 0.0745 0.0745 0.0039 0.0274 0.0510 0.0745 0.0745 0.0000 0.0510 0.0000 0.0510 0.0274 0.0510 0.0510
F11 0.0274 0.0274 0.0745 0.0745 0.0000 0.0745 0.0274 0.0039 0.0039 0.0039 0.0000 0.0039 0.0745 0.0274 0.0510 0.0510
F12 0.0274 0.0274 0.0274 0.0274 0.0000 0.0000 0.0274 0.0039 0.0039 0.0510 0.0510 0.0000 0.0745 0.0510 0.0745 0.0745
F13 0.0274 0.0274 0.0274 0.0274 0.0745 0.0510 0.0510 0.0274 0.0510 0.0745 0.0510 0.0745 0.0000 0.0745 0.0745 0.0510
F14 0.0510 0.0510 0.0274 0.0745 0.0510 0.0510 0.0510 0.0274 0.0274 0.0510 0.0510 0.0510 0.0274 0.0000 0.0274 0.0274
F15 0.0745 0.0745 0.0745 0.0510 0.0039 0.0745 0.0745 0.0274 0.0745 0.0510 0.0510 0.0745 0.0510 0.0745 0.0000 0.0745
F16 0.0745 0.0745 0.0745 0.0745 0.0745 0.0745 0.0745 0.0745 0.0510 0.0510 0.0510 0.0510 0.0510 0.0745 0.0745 0.0000

Table 3
Total relationship matrix.
TRM F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15 F16
F1 0.006 0.235 0.236 0.243 0.072 0.208 0.222 0.165 0.206 0.197 0.094 0.066 0.185 0.142 0.223 0.207
F2 0.177 0.001 0.176 0.181 0.047 0.1 0.128 0.06 0.117 0.086 0.057 0.046 0.136 0.102 0.172 0.151
F3 0.222 0.221 0.001 0.229 0.066 0.199 0.211 0.091 0.194 0.165 0.088 0.067 0.174 0.133 0.213 0.216
F4 0.211 0.209 0.21 0.004 0.061 0.189 0.2 0.084 0.185 0.156 0.081 0.057 0.165 0.124 0.181 0.184
F5 0.167 0.145 0.145 0.17 0.003 0.11 0.094 0.053 0.083 0.072 0.049 0.041 0.082 0.093 0.139 0.161
F6 0.165 0.164 0.167 0.173 0.054 0.001 0.156 0.09 0.097 0.09 0.137 0.05 0.146 0.131 0.138 0.182
F7 0.242 0.239 0.243 0.251 0.116 0.194 0.001 0.144 0.186 0.197 0.163 0.066 0.189 0.144 0.209 0.234
F8 0.224 0.222 0.223 0.232 0.11 0.156 0.187 0.008 0.173 0.186 0.108 0.058 0.173 0.152 0.171 0.194
F9 0.157 0.155 0.157 0.184 0.081 0.134 0.166 0.144 0.001 0.151 0.102 0.124 0.158 0.12 0.171 0.175
F10 0.225 0.224 0.226 0.234 0.067 0.159 0.191 0.158 0.197 0.001 0.132 0.061 0.178 0.133 0.194 0.196
F11 0.135 0.134 0.178 0.182 0.045 0.167 0.128 0.062 0.092 0.087 0.006 0.049 0.161 0.105 0.151 0.154
F12 0.135 0.134 0.136 0.141 0.048 0.097 0.127 0.067 0.093 0.134 0.114 0.004 0.163 0.129 0.175 0.175
F13 0.175 0.172 0.174 0.184 0.131 0.172 0.18 0.109 0.162 0.182 0.136 0.133 0.001 0.176 0.209 0.194
F14 0.174 0.172 0.153 0.203 0.097 0.154 0.161 0.095 0.125 0.142 0.119 0.095 0.131 0.008 0.145 0.149
F15 0.246 0.245 0.247 0.236 0.077 0.22 0.233 0.128 0.211 0.185 0.152 0.143 0.199 0.195 0.001 0.243
F16 0.265 0.262 0.265 0.276 0.148 0.233 0.245 0.176 0.201 0.195 0.157 0.123 0.209 0.205 0.252 0.001

Table 4
Causal-prominence values.
Code Factors 𝑅𝑖 + 𝐶𝑗 𝑅𝑖 − 𝐶𝑗 Rank Prominence
F8 Cycle count accuracy 4.367 −0.943 13 Effect
F12 Quality 3.145 −0.689 15 Effect
F16 Reliability performance 6.405 −0.397 1 Effect
F5 Blockchain technology 2.896 −0.384 16 Effect
F10 Material planning 5.04 −0.35 8 Effect
F15 Customer-oriented performance 6.041 −0.217 2 Effect
F7 Barcode label 5.756 −0.188 4 Effect
F11 Delivery 3.643 −0.141 14 Effect
F13 Cost 5.184 −0.04 7 Effect
F14 Flexibility 4.371 −0.031 12 Effect
F9 Sales and Operations planning (SOP) 4.707 0.143 10 Cause
F1 RFID 5.955 0.219 3 Cause
F3 Sensors 5.729 0.447 5 Cause
F6 RF handheld devices/Vehicle mounted units (VMU) 4.64 0.552 11 Cause
F4 Cloud computing 5.712 0.822 6 Cause
F2 Augmented reality (AR) 4.883 1.197 9 Cause

5. Discussion count accuracy (F8), delivery (F11), quality (F12), and block chain
technology (F5).
The grey-DEMATEL results have divided the factors into cause-and-
5.1. Top-most cause factors
effects. As per Table 4 and Fig. 3, the importance order of the cause
factors is: RFID (F1), sensors (F3), cloud computing (F4), Augmented
5.1.1. RFID (F1)
reality (AR) (F2), sales and operations planning (S&OP) (F9), and RF Businesses are keen in RFID technology’s benefits whether used
handheld devices (F6). independently or in a supply chain. RFID has increased sales by 2%
Subsequently, the importance order of the effect factors is reliabil- for US clothing business gap by reducing stock-outs [71]. J Crew, a
ity performance (F16), customer-oriented performance (F15), barcode popular US apparel company, uses RFID to speed up inventory keeping
label (F7), cost (F13), material planning (F10), flexibility (F14), cycle from five to eight times. RFID has additional uses. Experts reveal that

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S.I. Zaman, S. Khan, S.A.A. Zaman et al. Decision Analytics Journal 8 (2023) 100293

Fig. 1. Ranking of factors.

Fig. 2. Cause and effect factors.

it can be used for care labelling by adding washing instructions on it. 5.1.3. Cloud computing (F4)
It can also help improve stock visibility, which has twofold benefits. Cloud computing could save companies cost on servers, internal
It simplifies stock inspections that might normally take days. Second, software, and storage systems. Textile companies may create, manufac-
inventory and product monitoring reduce loss and theft. RFID helps ture, and invest in other technologies. These are easy to rent and use
warehouses monitor goods and prevent theft [72]. [75]. This would save a lot of company time and save IT investment
costs. Cloud computing improves supply chains by aiding production
5.1.2. Sensors (F3) planning, raw material management, pricing, order processing, and
Sensors are used in textiles nowadays. Smart sensors automate delivery. Cloud computing may also facilitate communication between
inventory counts and predictive maintenance, improving efficiency and foreign supply chain partners. Enhanced communication may also alert
accuracy. It also boosts machine efficiency, reduces material waste, textile manufacturers to value chain concerns like material receipt
and lowers costs, according to research [73]. It also prevents machine delays (decreased on-time delivery) so they can respond quickly.
mishaps. Pakistani firms also use this in machineries like air-jet looms.
A sensor stops activities when a thread breaks so a worker may fix it. 5.1.4. Augmented reality (F2)
In finishing facilities, metal-free zones may identify remaining metal Augmented reality (AR) streamlines the supply chain by enabling
fragments in clothing, and sensors can monitor warehouse humidity. interactive 3D visualization, item tracking, inventory accounting, and
Sensors can also automate processes, manage inventory in real time automation. AR headsets let managers swiftly identify, assign, and
to forecast demand, and save maintenance costs and downtime via move about the warehouse to get cost-saving data [76]. AR would au-
improved monitoring [74]. tomate and improve order picking and sourcing, according to insights.

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Fig. 3. Inter-relationship diagram.

AR systems will offer a warehouse, logistics, and distribution centre performance. The client obtains the final product with the desired
overview, saving time and improving warehouse efficiency. Order se- design and fabric at the desired cost and quality [83].
lection is another costly warehouse activity. AR headsets attached to
mobile devices let employees choose at the proper areas quickly. Visual 5.2.3. Barcode label (F7)
recognition AR systems will detect and validate the merchandise [77]. Barcode labels assist monitor product location and identity and
are cheaper than other automated identifying systems. It may im-
5.2. Top-most effect factors prove textile supply chain traceability and transparency by storing
sustainability information from a product’s whole value chain. Product
5.2.1. Reliability performance (F16) information must be lightweight, on-demand, and dependable [84].
The results have ranked reliability as one of the top-most effect Barcode-scanning mobile phones are one illustration of this rising need.
factors. It has been identified that this dimension reflects the output Barcode labels also save man-hours, inventory management problems,
of supply chain operations and its capability to meet customer require- paperwork loss, and organizational mobility. This low-cost method may
ments [78]. The role of reliability performance in the supply chain of be adopted on a big scale, benefiting textile enterprises that deal in
a textile sector is important. A textile firm in an emerging country enormous product volumes. As they progress towards Warehouse Man-
experienced high reliability due to the country’s logistic capabilities agement Systems, Pakistani textile manufacturers are focusing more on
being well established which helps facilitate the flow of goods, infor- barcode labels in the warehouse to manage inventory [85].
mation, and raw materials [78]. This leads to the insight that if other
developing countries also improve the logistics’ infrastructure, then 5.2.4. Cost (F13)
reliability performance of firms can be improved. Another insight has A textile supply chain’s distribution, inventory keeping, and over-
been found that in textile firms the proximity to suppliers is one of the head expenses (such energy and labour) are high. With consumer brand
factors which helps improve reliability performance [79]. awareness rising, corporations must cut expenses and pass the savings
on to customers [86]. This also shows good supply chain performance.
5.2.2. Customer-oriented performance (F15) Many textile companies compete in Pakistan’s market. It also attracts
The second important effect factor is customer-oriented perfor- more clients since developing countries consumers have less buying
mance. Experts reveal that successful companies use this dimension of power. Therefore, lowering these prices allows customers to get greater
supply chain performance at the corporate level. It includes measures value from a firm’s goods. In addition, the cost of living is growing
like cost, environment, quality, and delivery [80]. These measures are due to the economic crisis, thus more customers are deferring textile
associated with the external system as they express the link of an purchases because this is an expense that can be avoided as long as
organization with its customers and suppliers. In today’s competitive essential physiological demands are met [87].
environment, it has been found essential for textile firms to understand
the increasingly dynamic needs of customers [81]. These evolving 5.3. Practical implications and managerial implications
demands may include sustainability, reusing resources (partly due to
increasing inflation), employing sea-weed-based textiles, and so on. This paper has found some digitalization technologies, namely
More findings show that clients may want to pre-order certain designs RFID, sensors, cloud computing, and augmented reality are prominent
online with their preferred fabric before they are made [82]. This cause factors that affect supply chain performance factors like reliabil-
gives them custom-made designs, which may boost customer-oriented ity, customer-oriented performance, and cost [88]. The study also fails

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to relate WMS to supply chain performance variables. This contradicts overstocking, which improves the operation’s efficiency and reduces
prior research that linked these technologies to quality, flexibility, and storage costs. The findings help the textile industry to identify key fac-
delivery speed (main impact variables) of an organization [89]. This tors and get the best advice to improve their supply chain performance
study also examined how these technologies may be used in Pakistan. and competitive advantage. The literature highlights the transformative
Firms use RFID and sensor-based technologies, while the rest are rare impact that such technology-related systems can have on a firm’s
[90]. Cloud computing and AR may still be beneficial. According to supply chain performance. However, the results have highlighted that
this research, the government should first raise knowledge of these although digitalization technologies have a significant relationship with
technologies and their ROI so enterprises might consider them [33]. the dependent variable, but WMS is not an important cause factor.
Building IT infrastructure in the nation may also help textile manu- Nevertheless, these are future avenues which still need to be explored
facturers use new technology and improve their operations. Further by academicians, government, and managers alike in order to maintain
research should examine if bigger textile enterprises are more likely pace with the current competitive landscape.
to profit from such technologies than smaller firms since they demand
substantial expenditures. This requires quantitative and qualitative 5.5. Limitations and future direction
study and may have implications for textile sector.
Digitalization technologies appear to improve textile firms’ supply The limitations of this study are several. This paper focused on
chains the most [91]. This research identified emerging technologies the analysis of only textile industry for supply chain performance.
that may impact textile supply chain performance. Organizations may Other industries may also be explored in the context of a developing
face security issues with this technology. However, their efficient usage country in order to attain broader insights. Hence, future researchers
may save costs (connected to distribution, inventory, storage, waste, may conduct this study for other industries as well. This may also help
theft, etc.), boost customer satisfaction with high order fulfilment rates, conduct a comparative analysis among different industries to explore
and so on. WMS did not affect supply chain performance, either. Such individual industry differences. Moreover, the research question was
organizations may lack advanced WMS [92]. Managers may need to explored using cross-sectional data. In this regard, a longitudinal study
examine a system to reap its advantages. Technology-related systems may provide further in-depth understanding of the study objective.
are the future, thus managers should invest in them [93]. Cloud com- Moreover, due to limited time and resources, the authors have used
puting will become more important. However, smart warehousing, only the free literature article available online. Access to latest paid ar-
cloud computing, AR, and others are understood in many nations [94]. ticles may have resulted in a richer understanding of the topic. Finally,
In this regard, firms which take lead in their adoption may reap the the respondents of the study belonged to two textile firms. In the future,
benefits first and become industry leaders. This is because achieving more respondents may be sought to generalize the results further.
higher supply chain performance is one of the factors essential for not
only success, but also survival.
Declaration of competing interest

5.4. Conclusion
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared to
Supply chain has become complex as the product life cycle has
influence the work reported in this paper.
shortened owing to frequently changing customer demand. Although,
this evolution like the supply chain has enabled firms to perform
and compete efficiently in their business environment. In the modern Data availability
business environment, competition has shifted from single organization
to supply chains and effective supply chain management has become All data used is available in manuscript
increasingly significant in securing competitive advantage and improv-
ing organizational performance. With a WMS, supply chain managers Appendix
can track inventory levels in real-time, allowing for better inventory
control and management. This helps reduce the risk of stock outs and See Table A.1.

Table A.1
Key factors influencing digitalization, warehouse management system, and supply chain performance.
Sr. No. Factors Description
Digitalization
1 RFID (F1) RFID stands for the radio frequency identification, a generic term for technologies and systems that
use radio waves to transmit and automatically identify people or objects.
2 Augmented reality (AR) (F2) An interactive digital experience of a real-world environment where objects are augmented by
computer generated perceptual data which may include visual, auditory, haptic, somatosensory and
olfactory perceptions.
3 Sensors (F3) Detect physical phenomenon (i.e., pressure, force, acceleration, temperature, etc.) can convert data
into an output typically in the form of an electronic signal.
4 Cloud computing (F4) A massively scalable computing paradigm that offers software, infrastructure and platforms as a
service, providing real time data sharing capability throughout the supply chain.
5 Blockchain technology (F5) Incorruptible digital ledgers of transactions that are programmed to record value of any type of
transaction, allowing for a secure and transparent form of sharing transactional data.
Warehouse management system
6 RF handheld devices/vehicle mounted units (VMU) (F6) These devices help performing the day-to-day warehouse activities much faster and with very low
user error. As, these are integrated with the WMS, it helps in validating the data that the user is
attempted to scan from the product.
7 Barcode label (F7) Barcode label is a visual that consists of a composition of bars that have a set of numbers underneath
it. It is a representation of a data that can be translated by a certain type of machine.
8 Cycle count accuracy (F8) Cycle count accuracy represents how closely the system stock matches to the physical stock. Higher
accuracy means the WMS system stock location and the physical stock location are closely matched.
(continued on next page)

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Table A.1 (continued).

9 Sales and operations planning (SOP) (F9) The ability to read the reports on production output and inventory to effectively plan the sales and
operations.
10 Material planning (F10) The ability to plan long-term purchasing contracts, just in time purchasing, effectively do the stock
transfers, manage offsite material storage, and manage subcontracting.
Supply chain performance
11 Delivery speed (F11) Delivery speed comprises producing and delivering products/services much faster than others. The
dimensions of delivery speed include response customer-time, on-time delivery, lead-time, and
fill-rates.
12 Quality (F12) Quality considers getting orders from customers and making sure that the agreed standards are met at
all times. Customer satisfaction is enhanced when their preferred standards are met in the supply
chain operations; failure to achieve this will dip performance.
13 Cost (F13) The cost implication around distribution, manufacturing, inventory, warehousing, incentive cost and
subsidies, intangible cost and overhead cost. When these costs are managed efficiently, firms in the
supply chain operation turn to be more competitive and have an overall enhanced SCP.
14 Flexibility (F14) Firms with high flexibility are largely innovative and serve customers appropriately. It also allows for
the production of goods and services that move along with changing times.
15 Customer-oriented performance (F15) The performance of producers in servicing customers in the context of quality, flexibility, delivery in
the downstream supply chain
16 Reliability performance (F16) The order fulfilment rates, inventory turnover rate, safety stocks, obsolete inventories and the number
of product guarantee claims

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