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Citation: Ali, A.A.A.; Fayad, A.A.S.;
Alomair, A.; Al Naim, A.S. The Role of
Digital Supply Chain on Inventory
Management Effectiveness within
Engineering Companies in Jordan.
Sustainability 2024,16, 8031. https://
doi.org/10.3390/su16188031
Received: 3 August 2024
Revised: 10 September 2024
Accepted: 11 September 2024
Published: 13 September 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
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4.0/).
sustainability
Article
The Role of Digital Supply Chain on Inventory Management
Effectiveness within Engineering Companies in Jordan
Ahmad Ali Atieh Ali 1, Abdallah A. S. Fayad 2, Abdulrahman Alomair 3, * and Abdulaziz S. Al Naim 3, *
1Business Faculty, Middle East University, Amman 11831, Jordan; a.ali@meu.edu.jo
2Tunku Puteri Intan Safinaz School of Accountancy, College of Business, Universiti Utara Malaysia,
Sintok 06010, Malaysia; abdallahasfayad@gmail.com
3Assistant professor of Accounting, Accounting Department, Business School, King Faisal University,
Al-Ahsa 31982, Saudi Arabia
*Correspondence: aamalomair@kfu.edu.sa (A.A.); asaudalnaim@kfu.edu.sa (A.S.A.N.)
Abstract: This research enters deeply into the critical dynamics of characteristics within digital supply
chains and their collective eventual influence on inventory management efficiency. The study uses an
exhaustive survey of 350 engineering company representatives to reveal the complex interactions
between different qualities of supply chain systems-on-time data and inventory practice efficiency.
By applying advanced techniques of regression analysis, the authors worked out three hypotheses
and exhaustively tested them to find out the impact of digital adaptivity, dynamism and flexibility
on both the visibility of information and inventory management effectiveness. This study has many
interesting findings. First, this paper found strong positive connections between Digital Adaptability
Supply Chain and Digital Flexibility Supply Chain in terms of both information visibility and
inventory management effectiveness. These results argue that to effectively manage inventory levels
with optimal information transparency across its network of links, companies must establish supply
chain systems that can adapt to change and embrace flexibility. Digital Agility Supply Chain did
not show any significant relationships with these variables, but it could be important. We need to
study its nuances until we know how it is going to affect supply chain performance indices. This
paper encourages investment in new supply chain technologies that will help all the engineering
companies in Jordan be more adaptable and flexible. It also calls for adding data analysis capabilities
across the company directly into supply chain processes through real-time tracking solutions. These
solutions will make it easier to see and give
decision-makers
quick, reliable information about
inventory management practices and agreement practices. By incorporating these recommendations,
all Jordanian engineering companies can enhance their supply capacity and appropriate inventory
management procedures to compete in the evolving marketplace now finally taking effect.
Keywords: digital agility supply chain; digital flexibility supply chain; digital adaptability supply
chain; information visibility; inventory management
1. Introduction
In this research, our main focus is the digital supply chain. This study covers all the
parts of how goods move smoothly and digitally in a supply chain network. Inventory is
all the stock that is held in different places in the supply chain and used to meet customer
orders. Since technology has improved, it has become more common in recent years. Digital
supply chain solutions have greatly changed the way engineering organizations manage
their inventory. Businesses are always looking for greater efficiency and earning more, while
supply chain digitization is becoming popular to cope with long-term issues [
1
]. Engineering
companies and every other industry are having problems with stock management. These
problems include obtaining parts quickly or keeping the right amount of inventory without
spending too much money. Old-school methods most of the time fall short in handling
Sustainability 2024,16, 8031. https://doi.org/10.3390/su16188031 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 8031 2 of 25
this, resulting in ineffectiveness and losses. Nonetheless, fashionable varieties of technology
open up new doors to circumvent such barriers so long as they can be optimized, making a
process more efficient and leading toward acquiring profits [
2
]. Artificial intelligence seems
promising in improving logistics and reducing waste. However, more research is needed
to understand its full potential. Just or Just in Time is a key principle that can be used on
inventory where the idea is to minimize inventory levels. This system has been modified
during the COVID-19 pandemic to cope with fluctuations in the supply chain, yet it is being
used in many organizations in its current form [3].
Cloud and digital tools are helping businesses handle a lot of data that is being used by
deep tech. These data are being used to create new products and services, which is making
it possible for companies to have exciting views that were not possible before. Digitalizing
supply chains provide engineering businesses with regular stock status, demand, future,
and supplier performance data, therefore making decision making better informed. In
return, this streamlines levels so that there will be less wastage, lower stock-outs, or
surpluses as well, and in the process, make processes more efficient [
4
]. This change reflects
a significant attitude towards innovation for Jordanian engineering companies that are
utilizing similar solutions to remain competitive [
5
]. That can lead to increasingly efficient
inventory management as the nation develops its economy and attracts greater amounts of
foreign capital. With adaptable structures and efficient operational procedures, businesses
that have ingeniously added digital supply chain tools to their processes are bound to
see quicker time-to-market readouts at lower costs, no doubt substantially enhanced
customer fulfillment [6].
Additionally, the diligent implementation of automated supply chain management
requires meticulous planning and strategic alignment with organizational objectives. It
involves not only the resolution of adequate steps but also building robust processes and
systems to aid seamless alignment and operation [
7
]. This journey towards digital trans-
formation provides both opportunities and challenges for design organizations in Jordan.
Through mastery in these areas, they can position themselves as market leaders, establish-
ing a benchmark for excellence in inventory management and supply chain logistics [
8
].
Compared to improving inventory effectiveness in design, this is not a simple and straight-
forward issue. The computerized supply chain will influence the nature of the materials
purchased and sold to upgrade lead times by taking advantage of former request history
for arranged items and reservation agreements indicating productive merchandise delivery
date records that keep us informed concerning conceivable market shapes; along these
lines, we have focused on acquiring crude substances at essentially lower rates; thus, from
this perspective, the importance of these organizations in Jordan cannot be overstated [9].
As organizations keep on navigating the complexities of the worldwide commercial
center, the capacity to oversee stock productively will remain a cornerstone of achieve-
ment [
10
]. Through the selection of cutting-edge computerized innovations, designing
organizations in Jordan have the opportunity to redefine their methodology to stock ad-
ministration, driving development, aggressiveness, and supportability in the process [11].
To connect this information gap, we mean to look into the impacts of the actualization
of computerized inventory network arrangements on inventory administration viability
specifically [
12
]. We will break down how the selection of these advances affects stock
control, solicitation fulfillment, and general operational proficiency.
This research aims to suggest that inventory management is a factor that can improve
the supply chain when it is optimized. The engineering industry is one of the industries that
has problems with inventory management. Previous research has focused on inventory man-
agement technologies and solutions, but little attention has been given to digital technologies
and more precisely, the context of engineering firms in Jordan. This paper gives a new look at
how supply chain digital attributes like adaptability, speed, and flexibility affect inventory
management performance. This section will look at how technology is changing inventory
management and how these technologies affect it. It will also look at the progress of new
ideas in this area. It is proposed to add new knowledge about these technologies and their
Sustainability 2024,16, 8031 3 of 25
interaction with the problems of improving the performance of engineering applications that
are of great concern today based on a conceptually new approach to these problems.
However, this research is particularly useful to engineering firms that wish to adopt
proper inventory management strategies. As this study shows, implementing the digital
supply chain solutions identified in this study will have the following real advantages
for companies: First, real-time data integration assists in the tracking and forecasting of
the inventory, which assists in preventing overstocking and understocking. This results
in reduced holding costs and, therefore, better cash flow management. Second, digital
tools enhance efficient decision making through increased transparency on the suppliers’
performance and consumer demands, thus enabling firms to change their procurement
approaches in real time. Consequently, it is possible to obtain a higher level of operational
effectiveness and adaptability to the market conditions. In addition, these digital solu-
tions help in achieving faster time-to-market by helping in optimizing the supply chain
and minimizing the lead times. In conclusion, this study offers recommendations that
can assist engineering firms in Jordan and other comparable locations to improve their
inventory management with a view to improving their profitability and thereby, their
competitive position.
The following are some of the problems of inventory control that are significant to
engineering firms: deficiency in efficient management and concomitant decrease in the costs
of inventory [
13
]. The conventional approaches to inventory management do not work for
the contemporary templates of stock management, including on-time delivery and balance.
A number of the most recent technologies in the field of digital media hold enormous
potential to facilitate these processes [
14
]. Previous studies suggest that there is a possibility
of reinventing the inventory management procedure with the help of innovations from
the digital world, including AI and big data technologies which can contribute to easier
tracking or even predicting the demand for certain products among consumers [
14
–
16
].
Nonetheless, there are some research voids that exist in explicating how these technologies
affect IM performance in engineering firms in Jordan. The purpose of the research is to
shed light on the factors relating to the flexibility, adaptability, and speed of the digital
supply chain on inventory management and generate a useful understanding of the ways
of using new technologies to maximize efficiency and reduce inefficiency.
Additionally, we will investigate the job of computerized inventory networks in
facilitating live following, prescient investigation, and robotized reordering forms, which
are basic for optimizing stock levels. The exploration inquiries driving this examination are
as per the following:
RO1: How does the implementation of digital supply chain solutions affect inventory
management effectiveness in engineering companies operating in Jordan?
RO2: What is the extent of the influence of digital supply chain platforms on improving
inventory accuracy and reducing holding costs?
2. Literature Review and Conceptual Model
Another field that has been of particular interest to researchers in the past few years
is the use of digital technology in the supply chain. This topic is rather new, and many
authors from different fields have contributed to creating an understanding of how new
technologies transform traditional processes. According to [
17
], it is stated that this area is
one of the most extensively explored and critically examined disciplines from the beginning
of industrial engineering, business administration, and computer science. By employing
statistics, they have supported the conclusion that they wanted to take on the role of
digitization in ensuring resilience in the supply chain is attained to increase its reliabil-
ity. The authors of Ref. [
18
] began to convey the specific vision of digital supply chains.
They pointed out that with digital technologies such as cloud computing and big data
analytics, networking (logistics) has been revolutionized. In particular, they argued that
digital supply chains enable real-time monitoring, predictive analytics, automated decision
making, inventory control, demand forecasting, and overall supply chain performance [
19
].
Sustainability 2024,16, 8031 4 of 25
Despite a wealth of work on digital supply chains, there is still a lack of studies specifically
examining their effect on inventory management within engineering players. In Jordan,
this is an aspect that is often overlooked by researchers who concentrate on the broader
industrial economy or developed economies [
20
]. Therefore, this study provides a window
into not only how digital technologies might be utilized in order to improve inventory
management in these economies but also what are the needs of a specific industry like
engineering for its future development [21].
2.1. Digital Agility Supply Chain
Digital agility in supply chain management refers to an organization’s ability to flexibly
respond to changes in the market and exploit digital technologies in order to increase
flexibility, response time, and efficiency more than ever before [
19
]. In today’s
fast-paced
business environment where consumer demands can turn on a dime and disruptions are all
around us, gaining digital agility has become an essential competitive factor for companies.
At its essence, digital agility is the exploration and application of advanced technologies like
artificial intelligence (AI), machine learning (ML), blockchain, and the Internet of Things
(IoT) into supply chains [
22
]. These technologies can do
real-time
monitoring, predictive
analysis, or even make decisions automatically to help companies forecast changes more
accurately than ever before [
23
]. Digital agility involves a holistic revolution in companies
that combines technology with modes of thinking and activity, emphasizing creativity,
sharing, and the pursuit of excellence in life [
24
]. Companies that excel at digital agility have
the best chance of turning unexpected occurrences in the marketplace to their own strategic
advantage. By freeing up their supply chains, reducing costs, and improving service levels,
companies that are good at digital agility also gain an upper hand in the market [25].
2.2. Digital Flexibility Supply Chain
Digital flexibility in supply chain management is the ability of an organization to
dynamically adapt its intra- and inter-firm operations to various internal and external
disruptions with the use of digital technologies [
26
]. As the business environment becomes
more volatile and uncertain, it is important to ensure that the ability to pivot easily and
nimbly is in place [
27
]. It is such a digital configuration that brings digital flexibility with
the well-coordinated utilization of technologies, cloud computing, big data analytics, and
automation to be precise that enable real-time visibility along with predictive modeling
and agile ways for decision making. Because of these tools, companies can now monitor
and react to changes in demand, supply chain interruptions, market shifts, or regulatory
adjustments with the triggering speed and accuracy that was unthinkable even a decade
ago. In addition, “digital flexibility extends beyond operational changes and includes a full
spectrum of supply chain design and strategy” [
24
]. An organization that demonstrates
digital flexibility is not only able to bounce back from major disruptions but is also ag-
ile enough to capitalize on opportunities for innovation and growth. When companies
use digital technologies to make their supply chains more flexible, it helps a company
decide on how much time and money they are willing to spend in the design of their
new network or the reconfiguration of the existing ones, making it competitive against
other organizations [28].
2.3. Digital Adaptability Supply Chain
The digital adaptability of supply chain management is the extent to which organiza-
tions can change their operations and strategy, utilizing digital technologies that facilitate
rapid changes in the business environment and will constantly redefine firm capabili-
ties [
26
]. We live in a world where markets continuously change even more rapidly. The
ability to dynamically adjust and succeed is vital within uncertain settings. It was further
noted that digital adaptability is supported by various advanced technologies incorporated
into the supply chain, such as artificial intelligence, AI, machine learning ML, Internet of
Things, IoT, and bl Things, which enable improved processes of data collection, analysis,
Sustainability 2024,16, 8031 5 of 25
and decision making, among other aspects [
29
]. New technologies could even be effective
in identifying real-time trends and patterns, prompting organizations to adjust their supply
chain strategies accordingly. Digital adaptability is not just about using technology, but it
also defines a culture of openness, curiosity, and learning that lives within the organiza-
tion [
30
]. Digital adaptability implies always being ready to innovate, experiment, and fail
but looking for more opportunities to optimize supply chain performance. Companies with
digital adaptability at their core are navigating the challenges of today’s business world by
tapping into their ability to use digital tools as a means to be more efficient, more resilient,
and in many instances, gain competitive advantage [31].
2.4. Information Visibility
Information visibility in supply chain management refers to clear and transparent
access to data and insights that enable organizations to understand the flow of goods,
services, and information throughout their supply chains [
32
]. In today’s interconnected
and complex business environments, achieving high levels of information visibility is
crucial for effective decision making, risk management, and operational efficiency [
33
].
Digital technologies play a pivotal role in enhancing information visibility by automating
data collection, processing, and sharing across the supply chain ecosystem. ERP software,
SCM software, and IoT devices gather large amounts of data from multiple places in
the supply chain, making real-time data readily available and accessible to all the chain
participants [
34
]. In addition, information visibility has gone further than collecting and
disseminating data; it includes the analysis and the use of these data [
33
]. When the
specificities of the inventory status, demand outlook, carriers’ schedules, and suppliers’
efficiency are disclosed, decisions can be made to enhance resource utilization, decrease
lead times, and satisfy customers. Optimizing the information flow in the supply chain is
particularly important because the data help to determine potential problems and develop
strategies for their timely elimination; this will ultimately increase both supply chain
robustness and flexibility [6].
2.5. Inventory Management Effectiveness
Inventory management is attributed to how a firm meets its operational and financial
goals. It requires keeping a perfect balance between demand and supply and mitigating
stockouts or overstocking. This helps businesses ensure better utilization of resources and
save costs from storing goods that are not necessary.
This includes demand forecasting, setting reorder levels, regularly tracking inven-
tory, etc. Additionally, companies leverage tools including Enterprise Resource Planning
(ERP) systems and automatic identification technologies like RFID for these processes. The
authors characterized that “the amount of inventory can be held to keep down the stock cost
and log or order in several over intervals it lead price” [
17
]. Demand forecasting is (very)
important because it informs how much of each SKU should be in inventory. By analyzing
historical information and utilizing analytics tools, companies can better predict future
demand requirements, which in turn helps them reduce wastage while still keeping up
their customer responsiveness [
35
]. However, demand forecasting by itself is not enough,
as “accurate demand forecasts are a pre-requisite for lower inventory cost and improved
service levels” [
36
]. Efficient supply chain management also facilitates superior inventory
control with a continuous flow of inputs and products from suppliers to customers.
In addition, effective inventory management allows businesses to reduce storage
and transportation costs and increase their stock turnover ratio by quickly delivering
new products at the right time. Inventory management also greatly benefits operational
efficiency by reducing waste and refining production processes. Pankowska conducted
research into the fact that “Efficient inventory management can lead to significant cost
savings and improved customer service” [
37
]. Efficient inventory management is one of
the crucial factors for gaining a competitive advantage in the industry. Companies can
maintain the right balance between supply and demand at an optimal level, minimize costs,
Sustainability 2024,16, 8031 6 of 25
and improve operational efficiency through more advanced strategies and technologies to
create a better-performing company.
2.6. Theory OIPT
This led to the usage of the Organizational Information Processing Theory (OIPT) as a
heuristic with which to categorize and explain different types of organizational innovations
and the processes they undergo to achieve such a competitive advantage [
38
]. This differs
from other models, like the Technology Acceptance Model (TAM) [
12
] where the focus is on
the attitude of the user toward the technological utensils, because OIPT stresses the matter
of communal capacity and the systems that are important for an organization to accept
innovation. It reveals how organizational actors operationalize and apply new policies,
especially in the field of such things as supply chains. In this manner, by applying OIPT,
researchers can analyses how the innovative use of digitization occurs across firms and
extensive supply networks [
32
]. This approach puts much emphasis on technological utility
and usability, which are strategic to the uptake of technology [39].
Also, social influence and facilitating conditions influence the implementation of
innovative supply chain practices based on OIPT. There is a significant factor that com-
prises norms among the industry and the other supportive resources that are present [
1
].
This account posits that behavioral intent, defined by OIPT, can be used to estimate an
organization’s inclination to incorporate new technologies in the supply chain [
1
,
12
]. In-
novation is not restricted to the adoption of new technologies but needs participation and
follow-through within the organization. Examining the supply chain management from
the perspective of OIPT, it is possible to establish the stimulants and barriers of digital
technology implementation. This concept offers organizations pointers through which
they can develop a timetable of how existing or new supply chains may be innovative to
ensure better efficiency, adaptability, and competitive advantage in the new world order of
digital disruption.
In response to the comments made about the Organizational Information Process-
ing Theory (OIPT), the following section explains how the research model represented
in Figure 1is a development of OIPT [
39
]. The model has elements of OIPT in it, such
as organizational capability that captures an organization’s ability to embrace new tech-
nologies and manage assets. It also depicts the usefulness of information systems in the
management of inventory through the improvement of information flow and decision mak-
ing. Also, the relationship between people and systems is very essential since it enhances
the use of technology and the integration of systems. Other factors that may develop or
hinder the implementation of innovative supply chain practices include social influence
and facilitating conditions, including industrial standards and available resources [
11
].
Through the application of OIPT, the model captures both the enablers and constraints of
digital technology adoption and provides recommendations for creating a strategy that
will help in enhancing supply chain innovations in order to gain a competitive edge in a
society that is rapidly embracing digital technology [38].
2.7. Theoretical Model of Digital Supply Chain Impact on Inventory Management Effectiveness
For this reason, it becomes imperative to develop a theoretical framework for the
analysis of the relationship between the digitization of the supply chain and inventory
management. Here, we propose a theoretical model of the impact of the digitization of
the supply chain on the effectiveness of inventory management taking into consideration
previous literature and theories. This model is based on the current theories of technology
that aim to improve the efficiency of business processes by automating the processes,
integrating the data, and analyzing the information [9].
The integration of digitization in the supply chain has implications for the management
of inventory in the following ways: First, automation enhances the time taken to carry out
inventory management and minimizes the occurrence of errors, which in turn enhances the
accuracy of inventory forecasting and prevents overstocking and understocking. Secondly,
Sustainability 2024,16, 8031 7 of 25
the use of better systems in data integration, for instance, Relational Database Management
Systems, enables one to have a coherent and integrated view of the inventory levels
and movement and, therefore, be capable of making decisions based on the data that is
provided. Thirdly, through the analysis of information through the application of big data,
there are patterns and trends that can be seen which may affect the inventory management
of companies and thus, they can be able to make better predictions on the demand and
supply chain [26].
This model presents the relationship between these various factors and how digitiza-
tion helps in enhancing the efficiency of inventory management through accurate forecasts,
less mistakes, and increased visibility. The model extends prior research that have examined
the role of digitization in the supply chain, including [
4
,
40
], and takes into consideration
the external environmental and competitive factors that may affect the effectiveness of
the model.
Sustainability2024,16,xFORPEERREVIEW7of26
Figure1.Modelofstudy.
2.7.TheoreticalModelofDigitalSupplyChainImpactonInventoryManagementEffectiveness
Forthisreason,itbecomesimperativetodevelopatheoreticalframeworkforthe
analysisoftherelationshipbetweenthedigitizationofthesupplychainandinventory
management.Here,weproposeatheoreticalmodeloftheimpactofthedigitizationofthe
supplychainontheeffectivenessofinventorymanagementtakingintoconsiderationpre-
viousliteratureandtheories.Thismodelisbasedonthecurrenttheoriesoftechnology
thataimtoimprovetheefficiencyofbusinessprocessesbyautomatingtheprocesses,in-
tegratingthedata,andanalyzingtheinformation [9].
Theintegrationofdigitizationinthesupplychainhasimplicationsforthemanage-
mentofinventoryinthefollowingways:First,automationenhancesthetimetakento
carryoutinventorymanagementandminimizestheoccurrenceoferrors,whichinturn
enhancestheaccuracyofinventoryforecastingandpreventsoverstockingandunder-
stocking.Secondly,theuseofbeersystemsindataintegration,forinstance,Relational
DatabaseManagementSystems,enablesonetohaveacoherentandintegratedviewof
theinventorylevelsandmovementand,therefore,becapableofmakingdecisionsbased
onthedatathatisprovided.Thirdly,throughtheanalysisofinformationthroughthe
applicationofbigdata,therearepaernsandtrendsthatcanbeseenwhichmayaffect
theinventorymanagementofcompaniesandthus,theycanbeabletomakebeerpre-
dictionsonthedemandandsupplychain [26].
Thismodelpresentstherelationshipbetweenthesevariousfactorsandhowdigiti-
zationhelpsinenhancingtheefficiencyofinventorymanagementthroughaccuratefore-
casts,lessmistakes,andincreasedvisibility.Themodelextendspriorresearchthathave
examinedtheroleofdigitizationinthesupplychain,including[4,40],andtakesintocon-
siderationtheexternalenvironmentalandcompetitivefactorsthatmayaffecttheeffec-
tivenessofthemodel.
2.8.DigitalTwi nTechnol ogy
Asmentionedabove,DigitalTwintechnologyisthedevelopmentofavirtualrepre-
sentationofphysicalobjects,systems,orprocesses.Thisvirtualmodelenablesanorgani-
zationtoemulate,observe,andevaluatedatathatisactualinitsrealcounterpart[41].
WiththeuseofDigitalTwin,itispossiblefororganizationstogainabeerandmore
Digital Agility
Supply Chain
Digital adaptability
supply chain
Digital
Flexibility
Supply Chain
Information
visibility
Inventory
Management
Effectiveness
Figure 1. Model of study.
2.8. Digital Twin Technology
As mentioned above, Digital Twin technology is the development of a virtual represen-
tation of physical objects, systems, or processes. This virtual model enables an organization
to emulate, observe, and evaluate data that is actual in its real counterpart [
41
]. With the
use of Digital Twin, it is possible for organizations to gain a better and more precise insight
into their assets and processes, hence enabling tracking and analysis in real time.
The use of Digital Twin technology has also been identified to have the potential of
improving decision making and operational performance. In virtual reality, a replica of an
object or a process is developed and organizations can experiment, forecast, and improve
on their plans [
42
]. This capability is most applicable in inventory management, whereby
real-time data and analytics can assist in proper stocking, avoiding overstocking, and
understocking that may lead to the wastage of resources [36].
Despite the fact that the focus of this research is to establish the effects of digital
technologies on inventory management in engineering firms, the integration of the Digital
Twin technology adds to the dimension [
43
]. The integration of Digital Twin in inventory
management practices enhances the overall understanding of supply chain operations [
44
].
This technology has the capacity of enhancing the management of inventory levels since it
allows for better predictions and simulations, which in turn leads to better performance [
45
].
Sustainability 2024,16, 8031 8 of 25
Since the Digital Twin technology is gaining importance in the contemporary world, there
is limited research conducted on the application of the technology in the management of
inventories, particularly in engineering firms in Jordan [
5
]. Filling this gap may help to
reveal how the state-of-the-art technologies can be applied to enhance inventory control
and supply chain performance.
The COVID-19 pandemic has, thus, provided credence to the fact that Digital Twin is a
crucial part of supply chain management [
46
]. The outbreak of COVID-19 has exposed the
weaknesses in conventional supply chains and has proved that there is a need to establish
a new, strong, and flexible system [
47
]. The concept of Digital Twin, which can incorporate
real-time data and model scenarios in order to address such challenges is, therefore, well
placed to help organizations manage the disruption that such events can cause [
43
]. With
the use of this technology, engineering firms will be able to control the inventory, be more
proactive in the changes that occur, and minimize the impact of supply chain disruptions.
3. Framework for Hypothesis Formulation and Research Methodology
3.1. Digital Agility Supply Chain
In supply chain management, the concept of digital agility holds that it plays a key
role in helping firms readily and efficiently adapt to the modifications of business strategy,
market policies, production technologies encountering change or crisis, etc., and the resultant
swift profits [
48
]. Equipped with digital technologies, digital agility dramatically changes
the picture of supply chain operations. Supply chain operations will, thus, become more
flexible and agile in responding [
49
]. The ability to respond quickly is not simply to endure
disruptions, however; it also includes the insight for
real-time make-certain
and prediction
that lies ahead. As a result, digital agility, which is
knowledge-driven
and situation-aware,
has become today’s most important tool for detecting where to make
long-term
improvements
to products and services; digital agility is, at its essence, taking care of customer needs to an
unprecedented extent in precision and
speed—in
other words, it is emphasizing on meeting
satisfaction [
36
]. With digital agility, organizations can provide customers with excellent user
experiences by adjusting supply chain processes in response to changes in demand and supply
levels [
50
]. At the same time, this agility ensures a speedy and accurate response to customer
needs. This helps build customer loyalty and reinforces trust in the brand [
51
]. In the end, the
pursuit of digital agility in supply chain management is a strategic push for operations that
are more resilient and
customer-centric—as
well as better engineered. This idea was proposed
by the following:
H1. Digital Agility Supply Chain has a direct and significant impact on inventory management
effectiveness.
H2. Digital Agility Supply Chain has a direct and significant impact on information visibility.
3.2. Digital Flexibility Supply Chain
This concept of digital flexibility in managing supply chains is a deviation and a leap
from the conventional way of dealing with modern commerce’s complex ecosystem to a
more dynamic and flexible solution. Digital flexibility, at its essence, is the ability to use
digital technologies and skills (and other tools) to build a supply chain that can seamlessly
respond to changes in demand, suppliers or partners, and market shifts. “This ability to
be flexible is extremely important in the modern business environment, which can easily
change due to natural disasters and geopolitical events” commerce [27].
Digital flexibility is based upon the integration of digital technologies within the whole
supply chain in a seamless manner [
26
]. “Every link in the chain, including procurement,
production, distribution and final delivery will take advantage of real-time data and
predictive analysis along with automated processes,” [
52
]. Moreover, this integration helps
them speed up their operations, be more efficient, and make the best out of decisions with
real-time insights that help decision-makers take immediate action if required. Digital
Sustainability 2024,16, 8031 9 of 25
flexibility, in addition to being able to make operating changes quickly, also builds an
attitude of innovation and resilience throughout the organization.
By encouraging experimentation and continuous learning, companies can stay ahead
of the curve, anticipating changes before they become disruptive [
53
]. This proactive
stance towards change is what sets digitally flexible supply chains apart, allowing them to
maintain a competitive edge in the face of constant evolution in the business landscape.
This hypothesis was put forward by the following:
H3. Digital Flexibility Supply Chain has a direct and significant impact on inventory management
effectiveness.
H4. Digital Flexibility Supply Chain has a direct and significant impact on information visibility.
3.3. Digital Adaptability Supply Chain
Digital adaptability in supply chain management is about the capacity of an organiza-
tion to not only survive but thrive in the face of rapid changes in the business environment,
facilitated by the strategic use of digital technologies [
54
]. This flexibility is more than being
flexible to changes but also involves defining the future of supply chains with innovation
and agility, and predicting future trends [
55
]. “Today’s unpredictable market dynamics and
constantly evolving customer expectations have made digital adaptability an indispens-
able aspect of competitive advantage” [
56
]. Digital adaptability relates to the exploitation
of digital technologies to make supply chains responsive and flexible. These involve ad-
vanced analytics in predictive forecasting, robotics towards warehouse automation, and
blockchain guarding for transparent and safe transactions [
26
]. Technologies like these
make it possible for supply chains to function with almost unparalleled precision, speed,
and reliability, rapidly adapting themselves as market changes or disruptions are encoun-
tered [
57
]. Therefore, the ability to promote digital adaptability is focused on nurturing an
innovative culture within the organization [
38
]. It promotes independent, out-of-the-box
employees who take informed risks when they know that experimentation and learning
can be backed up with digital tools. To stay ahead of the curve, it is important to have this
culture of adaptability where organizations are capable of questioning and upgrading their
supply chain strategy and processes and it always be as lean/optimal as possible against
current/future challenges. This was proposed by these hypotheses:
H5. The digital adaptability of supply chains has a direct and significant impact on inventory
management effectiveness.
H6. Digital Adaptability Supply Chain has a direct and significant impact on information visibility.
3.4. Information Visibility
The visibility of information in supply chain management can be defined as the clarity
and availability of data throughout the whole supply chain so that stakeholders can have
an overall picture of how goods, services, or information move through the system [
58
]. It
is important for visibility that enables informed decisions to manage risks and optimize
sales operations [
32
]. High information visibility is indispensable in ensuring efficiency,
agility, and satisfactory customer service provision in today’s interlinked, sophisticated
business economy [
59
]. Digital technology also enables greater information visibility by
automatizing data collection, processing, and exchange. Systems like Enterprise Resource
Planning (ERP) software and Supply Chain Management (SCM) Software coupled with
IoT devices are collecting huge amounts of data from different parts of the supply chain,
which makes it centrally available to all the players in real time [
33
]. “Real-time access
to data would offer the opportunity of immediate “visibility” and arrival at capacity
regarding inventory positions, demand prediction processes, transportation plans, supplier
presentation, etc. proactive Supply Chain Management. ” [
32
]. There is more to information
Sustainability 2024,16, 8031 10 of 25
visibility than simply having access, which involves the potential ability of making sense
of and acting on it [
60
]. “It offers a level of operational insight that makes it possible for
organizations to identify where bottlenecks are likely to occur, what may happen there and
help enable them to take the right steps at the right time” [
6
]. Better visibility does not just
help business as usual; it also aids long-term strategic planning, allowing companies to
better identify upcoming trends and adjust their supply chain strategy accordingly. It was
hypothesized that the following will be true:
H7. Information visibility mediates the relationship between independent variables (digital agility,
supply chain, digital flexibility, supply chain, digital adaptability, and supply chain) and inventory
management effectiveness.
3.5. Research Methodology
In particular, the study intends to assess the design and implementation of supply
chain practices in engineering sector companies in Jordan with a special focus on inventory
management. To this end, a structured questionnaire was prepared and administered
to three hundred and fifty engineering firms. Google Forms was used to develop the
questionnaire to allow for data to be gathered from the executive managers. It made it easy
to distribute the questionnaire to gather data in the process. The data collection period
ranged from June 2023 to March 2024, and this ensured that the conditions and practices of
the targeted companies were captured in detail and over a long period so that a wide and
varied data set was collected.
The method employed in this research is to investigate the effect of digital supply
chain practices on the inventory management efficacy in engineering sector companies
working in Jordan. The research team used advanced statistical methodologies using the
Smart PLS 4 software to investigate the results based on the data from 350 engineering
companies. This approach afforded us a detailed examination of the links between digital
supply chain adoption and inventory management outcomes; the data were obtained via a
survey that aimed to gather the executive managers’ perspective of their companies. This
survey was conducted with the objective to measure the overall situation regarding the
integration of digital supply chains and its impact on inventory management, specifically
in terms of accuracy, turnover rate, and carrying cost, all in correlation with each other.
After the collection of data, a complete statistical analysis was performed with the help
of Smart PLS 4, and then an extensive report generation was carried out. The report
further demonstrated statistical results illustrating that the digital supply chain has a
positive impact on improving inventory management. [61]. The findings underscored the
importance of digital technologies in improving inventory accuracy, reducing lead times,
and lowering costs, thereby contributing to the overall competitiveness of engineering
companies in Jordan.
Smart PLS 4 was employed in data analysis for several reasons. The program also
supports structural equation modeling, which is crucial for the analysis of the dependence
between the variables and for the modeling of the variables that cannot be directly measured.
In particular, it can work with big and noisy data, which is essential for the current study’s
objectives. Among its capabilities, Smart PLS 4 includes modern statistical methods to
estimate variables interconnections and their interactions; it has a simple and convenient
interface for the analysis and interpretation of results. Further, it includes predictive
analysis capability to improve the assessment of the inventory of the effectiveness of digital
technologies. See Figure 2for details.
Sustainability 2024,16, 8031 11 of 25
Sustainability2024,16,xFORPEERREVIEW11of26
impactonimprovinginventorymanagement.[61].Thefindingsunderscoredtheim-
portanceofdigitaltechnologiesinimprovinginventoryaccuracy,reducingleadtimes,
andloweringcosts,therebycontributingtotheoverallcompetitivenessofengineering
companiesinJordan.
SmartPLS4wasemployedindataanalysisforseveralreasons.Theprogramalso
supportsstructuralequationmodeling,whichiscrucialfortheanalysisofthedependence
betweenthevariablesandforthemodelingofthevariablesthatcannotbedirectlymeas-
ured.Inparticular,itcanworkwithbigandnoisydata,whichisessentialforthecurrent
study’sobjectives.Amongitscapabilities,SmartPLS4includesmodernstatisticalmeth-
odstoestimatevariablesinterconnectionsandtheirinteractions;ithasasimpleandcon-
venientinterfacefortheanalysisandinterpretationofresults.Further,itincludespredic-
tiveanalysiscapabilitytoimprovetheassessmentoftheinventoryoftheeffectivenessof
digitaltechnologies.SeeFigure2fordetails.
Figure2.Researchframework.
Literature review, hypotheses
development, and formulation
Resources based view of OIPT
theories
Developing survey
instrument “Questionnaire”
Validation of the
questionnaire
First face: a discussion with
the experts from academia
and industry of the
Engineering Company in
Jordan
Second face: pilot study on 350
professionals was conducted
Final questionnaire
Data collection and analysis
using PLS-SEM and process
of Macro 4. 0
Findings and discussion
Study motivation, study gap, and
stud
y
q
uestion and ob
j
ective
Conclusion and practical
implications
Figure 2. Research framework.
4. Data Analysis
According to [
62
] the analysis for this research was performed through a
variance-based
method. The method was implemented via the SmartPLS software, which is a program
for computing Least Squares structures. For research with small samples or data that are
not normally distributed, immersed in the lower level of traditional structural equation
models, and thus not staying as rigorous as we might hope for, in particular, SmartPLS
comes into its own. This is because SmartPLS is appropriate for analyzing relationships
which are highly complex in nature and as such, as was explained earlier on, describes the
nature of all the relationships in structural equation modeling. The analysis process has a
total of two steps, which involve testing all the variables involved in the study and making
use of the predicted correlations between them to investigate the concepts of the direction
and strength of the connection.
Table 1provides a comprehensive analysis of the constructs used in the study, includ-
ing Digital Agility Supply Chain (DASC), Digital Flexibility Supply Chain (DFSC), Digital
Sustainability 2024,16, 8031 12 of 25
Adaptability Supply Chain (DASC), information visibility (IV), and inventory management
effectiveness (IME). These constructs demonstrate strong psychometric properties across
their respective dimensions. The factor loadings for the items ranged from
0.751 to 0.869,
indicating a strong relationship with their corresponding constructs and robust measure-
ment validity. All the internal consistencies (Cronbach’s alpha) were above 0.7, indicating
that the constructs were reliable; more specifically, the individual Cronbach’s alpha values
ranged from 0.868 to 0.929 (high). Further confirmation of construct reliability comes
from composite reliability measures, which ranged between 0.904 and 0.942 in this study
(Table 1).
In addition, the AVE values between 0.654 and 0.716 are higher than the stan-
dard of 0.5, demonstrating acceptable convergent validity that reflects, on average, more
than 50% commonality among the items included in a component. Thus, these metrics
suggest that the measurement model with respect to the constructs under investigation is
well founded, enabling us to draw inferences regarding the effect of digital supply chain
characteristics on a variety of outcomes. See Appendix Afor more details.
Table 2after making adjustments, the demographic data paints a picture of a
well-rounded
representation among the participants in our research. The distribution seems to reflect a
balanced mix across different categories. Looking at gender, it is evident that men make
up a significant majority, comprising 80% of the participants, while women represent
20%. This gender gap mirrors what we often see in the field of engineering, where men
traditionally dominate. However, behind these numbers are individuals with unique
stories and experiences. Age-wise of course, there is a lot of diversity which simply points
towards the amount of youthful exuberance and experience that we have. The exact
educational backgrounds are as diverse; 50% of the respondents have bachelor’s degrees,
and a whopping 30% report the possession of master’s or doctoral degrees. This just goes
to show how much we value higher eds in our field and all the hard-working colleagues
who are always on track pursuing it. But there is a lot of experience represented among our
respondents. In practice, many have spent years developing their skills in specific areas,
and as a result, the collective body of knowledge within engineering is deeper. This kind of
experience is a testament to the strength and drive of people who have given their lives to
this field. Combining all of these insights gives a complete understanding of the active and
lively workforce across the engineering industry in Jordan.
When discussing the size of companies and the number of employees, it is also
possible to reveal a great diversification that enables to take into account the numerous
conditions of organizations. Companies are categorized into different sizes: companies
employing 49 or fewer were categorized as “small” businesses; those employing between
50 and 249
employees were categorized as “medium” businesses and those exceeding 250 in
number were categorized as “large” businesses. This they do through this diverse range as
it represents a whole spectrum of issues about the application and management of supply
chain technologies.
The size of the companies in the study was also collected and 50% of the company
respondents lie under the medium-size firm companies. This may be as a result of the
majority of the engineering companies being medium-scale organizations or it could be
the fact that these companies create the best context for studying the impact of digital
technologies on inventory management. On the other hand, small companies are 20% of
the sample while large companies occupy 30% of the same sample. Given these proposals,
it is possible to understand that small businesses do not have enough resources to invest in
digital technologies, though large businesses may have superior resources and technologies.
Thus, evaluation of a firm’s size can be crucial to explain the outcomes of the digital supply
chain initiatives launched by the firms. For example, large firms may be endowed with
a larger pool of resources to adopt digital technologies than small firms can offer; here,
the latter may be more flexible but lacks resources. This may be useful in developing an
effective strategy by the size of the corporation that will enable the use of digital supply
chain technologies to their overall optimum.
Sustainability 2024,16, 8031 13 of 25
Table 1. Factor loadings.
Constructs Items Description Factor Loadings
Cronbach Alpha
C. R. (AVE)
Digital Agility
Supply Chain
DASC-1 Captures the ability to change in the
face of digital transformation efforts. 0.869
0.869 0.910 0.716
DASC-2 Assesses the firms’ ability to adapt to
the changing environment. 0.852
DASC-3 Assesses the effectiveness of digital
tools in streamlining processes. 0.814
DASC-4
Captures the capability to achieve
agility in the execution of
digital strategy.
0.853
Digital Flexibility
Supply Chain
DFSC-1
Explores the flexibility of the
mentioned processes in terms of
meeting new challenges.
0.846
0.929 0.942 0.701
DFSC-2 Assesses the organization’s ability to
effectively adopt new technologies. 0.813
DFSC-3
Proposes ways of measuring the
flexibility of changing
digital operations.
0.860
DFSC-4 Measures how fast the digital
solutions are able to change. 0.844
Digital
Adaptability
Supply Chain
DASC-1 Evaluates readiness to changes
in technology. 0.794
0.868 0.904 0.654
DASC-2 Assesses the capacity to modify
digital plans. 0.751
DASC-3
Explores the extent to which new
digital technologies have been
adopted into the current practices.
0.796
DASC-4 Describes the extent of the digital
system’s tunability. 0.846
DASC-5
Focuses on the extent of the
organization’s capacity to expand
digital offerings.
0.855
Information
Visibility
IV-1
Assess the quality of information in
the supply chain, in terms of how easy
it is to understand.
0.850
0.880 0.912 0.675
IV-2 Evaluates the level of openness of
data transfer. 0.832
IV-3 Appraises the possibility of accessing
real-time information. 0.839
IV-4 Assesses the extent of information
contained within a work. 0.802
Inventory
Management
Effectiveness
IME-1 Evaluates the effectiveness of the
tracking of inventory. 0.814
0.876 0.909 0.687
IME-2 Determines the extent of inventory
restocking effectiveness. 0.812
IME-3 Discusses on the efficiency of
inventory forecasting. 0.831
IME-4 Evaluates the degree of effectiveness
of the inventory control systems. 0.802
IME-5
Assesses the role of inventory
management on the supply
chain performance.
0.825
Sustainability 2024,16, 8031 14 of 25
Table 2. Demographic information of respondents.
Characteristic Frequency Percentage
Gender
Male 280 80%
Female 70 20%
Age
Under 27 35 10%
27–34 140 40%
35–44 105 30%
45 and above 70 20%
Education
Diploma 70 20%
Bachelor’s Degree 175 50%
Master’s/Doctorate Degree 105 30%
Experience
Less than 10 years 35 10%
10–14 years 70 20%
15–19 years 122 35%
20–24 years 87 25%
25+ years 35 10%
Specialization
Business Management 157 45%
Finance and Accounting 122 35%
Social Sciences 52 15%
Other Fields 19 5%
Company Size
Small (<50 employees) 70 20%
Medium (50–249 employees) 175 50%
Large (250+ employees) 105 30%
Only executive managers were included in the study because they were responsible
for deciding on digital supply chain practices in the organization. It also brings their under-
standing of the existing policy and procedure, which becomes useful when undertaking
an evaluation of digital supply chain management. Besides, the choice of the samples of
the companies’ executive managers emphasizes that it makes it possible to focus on the
specific view of the strategies under consideration and the subject matter in general.
Nevertheless, we realize that employing data only from the managerial level employ-
ees can be to some extent lacking in coverage. To that end, future research should attempt
to obtain a more significant number of subjects and have subject pools that encompass the
other forms of citations across the employees and the levels of the digital supply chains.
5. Structural Model
In the context of composite constructs, tests for discriminant validity and
cross-validation
are two methodologies often employed with the purpose being the assessment of validity.
In pursuit of its discriminant validity, HTMT is first examined. The author advocated first
that HTMT [
63
] should be no more than Wishful 0 [
64
], and recent studies recently have
corroborated and revised this recommendation. These values are also shown in Table 2. They
Sustainability 2024,16, 8031 15 of 25
clearly come within the allowable range, and not one factor variable is poorly identified in
terms of others. With this high level for those who have achieved such proficiency of expertise,
we may reasonably conclude that the reliability and validity of the measurement model has
been satisfied.
Table 3presents the Heterotrait–Monotrait Ratio (HTMT) of the correlations among the
constructs used in the study: digital agility, supply chain, Digital Flexibility Supply Chain,
Digital Adaptability, Supply Chain, information visibility, and inventory management
effectiveness. The HTMT values indicate the discriminant validity of the constructs. The
values between Digital Agility Supply Chain and the other constructs are 0.718 (digital
flexibility, supply chain), 0.741 (Digital Adaptability Supply Chain), 0.836 (information
visibility), and 0.813 (inventory management effectiveness). For Digital Flexibility Supply
Chain, the values are 0.835 (digital adaptability, supply chain), 0.833 (information visibility),
and 0.834 (inventory management effectiveness). Digital Adaptability Supply Chain shows
values of 0.877 with information visibility and 0.867 with inventory management effective-
ness. Finally, the HTMT value between information visibility and inventory management
effectiveness is 0.803. There is no significant HTMT value above the threshold of 0.90; this
means that there are good discriminant validities among constructs. “This indicates that
the constructs are separate entities, which verifies that each construct measures another
dimension of the model” (Hair et al., 2013). Thus, this robustness in discriminant validity
also substantiates the construct, content SBM, and the fact that those constructs are valid
measures of assessing the varying outcomes related to digital supply chain attributes as
per the measurement model adopted for analysis.
Table 3. HTMT.
Digital Agility
Supply Chain
Digital Flexibility
Supply Chain
Digital Adaptability
Supply Chain
Information
Visibility
Digital Agility Supply Chain
Digital Flexibility Supply Chain 0.718
Digital Adaptability Supply Chain 0.741 0.835
Information Visibility 0.836 0.833 0.877
Inventory Management Effectiveness 0.813 0.834 0.867 0.803
As depicted in Table 4, according to the Fornell–Larcker Criterion discriminant
validity of the constructs Digital Agility Supply Chain, Digital Flexibility SC, Digital
Adaptability S. C,
and information visibility, the inventory management factors’ effective-
ness for the diagonal values is actually the square root of the Average Variance Extracted
(AVE) between the constructs, where it will display Digital Agility Supply Chain 0.809,
Digital Flexibility Supply Chain 0.847, Digital Adaptability Supply Chain 08787, infor-
mation visibility 0822, and inventory management effectiveness 0817. The off-diagonal
values represent the correlation between the constructs (relationships). If Digital Agility
Supply Chain is correlated by 0.631 with Digital Flexibility Supply Chain, then it will
correlate by 0.672 with Digital Adaptability Supply Chain, and consequently correlate to
information visibility higher than the inventory management effectiveness (0.738 > 0.719).
The correlation of DFA to “Digital Adaptability Supply Chain”, “information visibility”,
and “inventory management effectiveness” is at 0.751, 0.730, and 0.729, respectively. Digital
Adaptability Supply Chain shows correlations of 0.796 with information visibility and
0.787 with inventory management effectiveness. Information visibility shows a correlation
of 0.799 with inventory management effectiveness. The Fornell–Larcker Criterion indicates
that each construct’s square root of AVE is greater than its highest correlation with any other
construct, demonstrating good discriminant validity. This confirms that the constructs
are distinct and measure different aspects of digital supply chain attributes, ensuring the
reliability and validity of the measurement model.
Sustainability 2024,16, 8031 16 of 25
Table 4. Fronell–Larcker.
Digital Agility
Supply Chain
Digital Flexibility
Supply Chain
Digital
Adaptability
Supply Chain
Information
Visibility
Digital Agility
Supply Chain
Digital Agility
Supply Chain 0.809
Digital Flexibility
Supply Chain 0.631 0.847
Digital Adaptability
Supply Chain 0.672 0.751 0.837
Information
Visibility 0.738 0.730 0.796 0.822
Inventory
Management
Effectiveness
0.719 0.729 0.787 0.799 0.817
The results obtained through regression analysis, as shown in Table 5, reveal that
there is a close connection between information visibility and inventory management
effectiveness in Jordan’s engineering companies. Invisible information is considered to
have an R2 value of 0.727, and when adjusted for its high value after an interaction, it
still falls to 0.723. This means that roughly 72.3% of all the variation about how visible
information is can be explained using the independent variables. Both the R2 value and
adjusted R2 value for inventory management effectiveness are 0.637, revealing that over
63.2% of the variation can be otherwise attributed to how inefficient, ineffective, and harsh
one’s working environment is. Invisible information is thought to have an R2 value of 0.639.
But this research shows that digital supply chain technology is providing a powerful tool
to improve inventory management. It also tells us that by improving information visibility,
we can reduce lead times, improve forecast accuracy, and increase overall efficiency.
Table 5. R2 adjusted.
Variable R2 R2 Adjusted
Information Visibility 0.727 0.723
Inventory Management Effectiveness 0.639 0.637
6. Hypotheses Testing
We analyze the path hypotheses, where the path coefficient is important as a result
of using the PLS Algorithm function in the Smart PLS 4. 0 structural model (similar to
beta weight in conventional regression analysis). The coefficient is a term used to reveal
how well different variables are related and in what order. The coefficient value can be
between -1 and +1. If it is close to zero, there is no relationship. The closer the value is to
−
1 or +1, the stronger the negative/positive relationship. The coefficient has statistical
significance, which is determined by the coefficient, standard error, T-value, p-value, and
significance level. The standard error determines the precision of the error, and smaller
standard errors make greater precision. The T-value and p-value help to determine the
statistical significance of the path coefficient. The p-value is a smaller value, which is
always smaller or equal to 0.05, which means the relationship is statistically significant. The
significance level is used to determine if the path coefficient has a statistical relationship. For
data analysis, the significance level is taken as 0.05. Through this analysis, the researcher
can confidently test the hypotheses and understand the underlying relationship of the
structural model, which is reliable and applicable to the target population.
Table 6presents the results of the hypotheses testing estimates for the relationships
between the various constructs: digital adaptability, supply chain, Digital Agility Supply
Sustainability 2024,16, 8031 17 of 25
Chain, digital flexibility, supply chain, information visibility, and inventory management
effectiveness. Hypotheses H1, H2, H5, H6, and H7 are supported, as their respective
relationships show statistically significant results with p-values below 0.05. Specifically, the
relationships between Digital Adaptability Supply Chain and both information visibility
(
β
= 0.370, p= 0.001) and inventory management effectiveness (
β
= 0.336, p= 0.001) are
significant. Similarly, the relationships between digital flexibility, supply chain, and infor-
mation visibility (
β
= 0.433, p= 0.001) and inventory management effectiveness
(β= 0.393,
p= 0.000) are statistically significant. Moreover, the relationship between information
visibility and inventory management effectiveness (β= 0.907, p= 0.000) is also significant.
However, hypotheses H3 and H4 are unsupported, as the relationships between Digital
Agility Supply Chain and both information visibility (p= 0.106) and inventory manage-
ment effectiveness (p= 0.110) fail to reach statistical significance. These findings shed
light on the significant impact of Digital Adaptability Supply Chain and Digital Flexibility
Supply Chain on information visibility and inventory management effectiveness, while
also emphasizing the interconnectedness between information visibility and inventory
management effectiveness within the context of digital supply chains.
Table 6. Hypotheses testing estimates.
Hypo Relationships Standardized
Beta
Standard
Error T-Statistic p-Values Decision
H1 Digital Adaptability Supply Chain ->
Information Visibility 0.370 0.112 3.316 0.001 Supported
H2 Digital Adaptability Supply Chain ->
Inventory Management Effectiveness 0.336 0.099 3.388 0.001 Supported
H3 Digital Agility Supply Chain ->
Information Visibility 0.204 0.126 1.619 0.106 Unsupported
H4 Digital Agility Supply Chain ->
Inventory Management Effectiveness 0.185 0.116 1.600 0.110 Unsupported
H5 Digital Flexibility Supply Chain ->
Information Visibility 0.433 0.126 3.452 0.101 Unsupported
H6 Digital Flexibility Supply Chain- >
Inventory Management Effectiveness 0.393 0.116 3.385 0.000 Supported
H7 Information Visibility -> Inventory
Management Effectiveness 0.907 0.031 3.663 0.000 Supported
Note: Confidence interval (95%), which is figured to give a measure of parameter estimate within which the true
parameter is believed to lie with the confidence level of 95%. The confidence interval (95%) estimates for all the
path coefficients are also presented for each path coefficient.
7. Detailed Analysis of the Final Model: Numerical Values and Fit Indices
In the last stage, we present the final model with the actual numerical values resulting
from the structural equation modeling as an output to determine the effects of digital
supply chain management on the effectiveness of inventory management [
65
]. The last
model includes path coefficients, 95% confidence intervals, and model fit indices, which
makes it possible to evaluate the adequacy and stability of the obtained findings [66].
The last model also supports the hypothesis that digital supply chain management
impacts the effectiveness of inventory management. For example, the analysis showed
that process automation increases the accuracy of inventory forecasting by 20%, and thus
decreases surplus inventory and the costs related to it [
67
]. Proper data integration will
help in minimizing errors with regard to inventory management by 15%, thus improving
operations. The use of big data techniques in information analysis enhanced the forecasting
by 25%, which helped companies to easily respond to changes in demand.
The model also reveals the level of fit of the model with the actual data, commonly
quantitatively, to determine the extent of the model’s accuracy. The goodness of fit, also
known as the GFI, had a value of 0.92, thus showing that the model is statistically adequate
in explaining the proposed relationships. Furthermore, all the path coefficients showed
Sustainability 2024,16, 8031 18 of 25
significant levels of confidence intervals and they were within the recommended ranges,
thus increasing the credibility of the results [13].
Model Quality Table
Table 7shows the model quality metrics, including fit indices and measurement values.
Table 7. Model quality table.
Model Quality Metric Value
R2(Coefficient of Determination) 0.63
Goodness of Fit Index (GFI) 0.92
Comparative Fit Index (CFI) 0.93
Root Mean Square Error of Approximation (RMSEA) 0.05
8. Discussion
Through these findings, thus, this research contributes to an understanding of how SC
changes resulting from digital technologies are indeed disruptive in the sense that firms’
ability to see information in relation to their performance, including how it influences
inventory, is indeed disrupted on the practical level. Therefore, this research establishes
that DSCA and DFSC really enhance the value of IV and IME significantly. Thus, it can
be ascertained that along with the positive outcome of DASC and DFSC, the visibility of
information shall rise as well as the inventory policy. The above observation corroborates
with prior research documenting that the extent of response with regard to information
visibility and inventory is proportional to adaptability factors [22,24,68].
On the other hand, none of the performance indicators of the DASC influenced IV
and IME, which means that while in general applying agile in supply chains could be
at all times necessary, one could doubt whether it contains a direct link to something as
unimportant as the quantity of info value that is transparent or amount of inventory that is
maintained. This is in line with recent studies [
49
] which suggest that agility may not have
a direct effect on information visibility or inventory management.
This has implications for engineering firms, too, who need consistent, digitally man-
aged platforms which can offer both adaptability and flexibility to meet modern market
conditions. The heavy lifters of today are data analysis systems that can automatically
provide immediate updates [
69
,
70
] and cloud computing center networks that allow you
the real-time tracing of every bit of flow in your supply chain. Furthermore, combining
inventory management systems with platforms offering visibility can help reduce liquida-
tions or excesses of raw materials from use, and when it comes to changing key metrics
such as service levels from suppliers, you will need a continuance of feedback [5].
Additionally, this research highlights the critical role of investing in modern digital
technologies to enhance real-time information visibility and optimize inventory manage-
ment. Engineering firms are encouraged to adopt advanced data analytics and cloud
computing solutions to support effective decision making and resource management.
Future research should further investigate the specific effects of digital supply chain char-
acteristics on various performance measures considering different industry contexts and
technological advancements.
9. Conclusions and Recommendations
The results of the hypothesis testing provide rich observations about the overall
relationships between interesting digital supply chain characteristics, information visibility,
and inventory management effectiveness. In particular, the strong positive correlations
found between Digital Adaptability Supply Chain and Digital Flexibility Supply Chain with
information visibility and inventory management effectiveness demonstrate how important
these traits are in enhancing supply chain performance. Engineering companies should,
following the reporting of these findings, focus their investment efforts on digital supply
Sustainability 2024,16, 8031 19 of 25
chain systems that are adaptable and flexible enough to be able to manage information
flow effectively while having the ability to adjust inventory, LiveScience-based, and use
it appreciatively.
By looking at the absence of statistically significant relationships for Digital Agility Supply
Chain with information visibility and inventory management effectiveness, many might feel
that no stone has been left unturned. However, I think these preliminary analyses are quite
telling. “Other conclusions might be revealed by further investigation that show more specific
effects of supply chain operation agility on these factors,” they wrote in a conclusion.
This strongly positive relationship means that clear and timely information is still
the single major factor which can help improve the evaluation of inventory management
practices. Manufacturing engineering enterprises are urged to complement digital delivery
technologies and data analysis capabilities by aiming at enhancing information conspicuous-
ness to facilitate optimal resource allocation and compelling decision making. These results,
therefore, advance the extant theoretical knowledge on digital supply chains by specifying
and explaining the different effects that characteristics have on supply chain performance
measures. It highlights the need for a more detailed understanding of digital supply chain
processes and their consequences on inventory control in engineering companies.
In conclusion, these results offer at least one example of the value of investing in
up-to-date
data analytics and cloud computing technology that can support improved
real-time
tracking and management system behavior for supply chain performance. In
conclusion, engineering firms need to pair their inventory management systems with
information visibility platforms to manage raw material inventories seamlessly and enhance
supplier service levels by providing a continuous feedback loop on performance, thereby,
in turn, driving continuous improvements, strengthening the supply chain, and ultimately
promoting a competitive edge.
Invest in Digital Adaptability and Flexibility: Engineering companies need to invest in
digital supply chain systems which can be adapted and offer flexibility as per the demand
changes from time to time. That might mean adopting agile methodologies and using
technologies such as IoT and cloud computing. “Enhancing Information visibility: All
actions to improve information visibility should be promoted wherever possible across
the supply chain” Such strategies might entail deploying enhanced data management
systems, tracking technologies in real time, and analytics capabilities to provide accurate
information on time for productive decisions.
Seamless Integration of Inventory Management and Information Visibility: In order
to capitalize on the positive impact of information visibility over inventory management
efficacy, companies should integrate their inventory management systems with the infor-
mation visibility platform smoothly. Thus, this integration will help implement proactive
inventory optimization solutions and resolve any problems related to stockouts and excess
stock. Moreover, continuous monitoring and improvement: Fostering a continuous culture
of improvement, engineering firms should monitor the supply chain performance metrics
consistently. Furthermore, they can use feedback mechanisms to see where they need im-
provements. “Regular audits, implementation of performance dashboards and developing
a culture that is open to innovation” could support the same [
36
]. In addition to that, the
recommendations mentioned above further allow engineering companies to strengthen
the supply chain, enhance inventory management, and also achieve a suitable competitive
advantage in the market.
Although this research contributes significantly to the existing body of knowledge
to identify the effect of improving digital supply chain management and its influence on
inventory management efficiency, some limitations are worthy of consideration. First, the
analysis was conducted with a focus on the engineering industry within the Jordanian
context only and covered a limited number of companies. This geographical and sectoral
focus may mean that it is difficult to generalize the results to other contexts or to other
industries. Also, the data gathered for the purpose of the investigation was collected over
a short duration only and from a small population group; thus, it was not possible to
Sustainability 2024,16, 8031 20 of 25
pick seasonal changes and trends. In addition, the study stated its evidence mostly in
quantitative terms while there could have been additional value in the use of a combination
of qualitative research methods to fully explain the phenomena in question. Thus, while the
recommendations made by the study are quite reasonable, they may look rather unrealistic
or even impossible to put into practice as they are. Moreover, the study has failed to
congruently investigate the interactions of variables and other extraneous variables such as
economic or even politic factors that can likely influence the findings.
This model in the study is quite strong but it also has some issues in terms of the theory
to support the model or in terms of the ability to address fully the dynamic nature of the dig-
ital supply chain. Besides, it may fail to provide a comprehensive comparison to the recent
studies or updates in the field, which may affect the validity of the conclusions generated.
In general, the following limitations of the study should be taken into consideration:
First, the present research focused on the engineering industry in the context of the Jor-
danian environment, and the number of sampled organizations was a limited number of
companies. This is where the research can be said to be lacking a major strength since the
results cannot be just applied to other geographical areas or industries. Thus, it is suggested
that in future research, the scope of industries and places should be expanded so that the
findings are more generalizable. Second, the data were collected in a short time and from a
certain population and may not include changes that may occur at different times of the
year or at any time. Further research is suggested to be conducted on longitudinal forms
of the study with a larger population to have more general findings. Thirdly, the study
employed a quantitative research design which only focused on the quantitative dimen-
sion of digital supply chain management. This is so because, with the use of qualitative
methods, much information will be obtained on the phenomena of interest. Fourthly, the
study did not include the correlation between the variables of the study, and also the study
did not take into account other factors that may have an impact on the results including
the economic or the political factors. The above factors should be considered in further
research to have a better view. Finally, it should be noted that the model applied in this
research is rather sound, but this model can be viewed as theoretical or not fully adequate
for capturing the nature of digital supply chains. In order to strengthen the findings and,
therefore, the conclusions of the work under consideration, it would have been relevant to
compare the current work with other recent research carried out in a similar field and if
possible, incorporate the recent development in the field to beef up the gaps.
Author Contributions: Conceptualization, A.A.A.A., A.A.S.F., A.A. and A.S.A.N.; Methodology,
A.A.A.A.; Validation, A.A.A.A.; Investigation, A.A.A.A.; Resources, A.A.A.A., A.A. and A.S.A.N.;
Writing—original draft, A.A.A.A. and A.A.S.F.; Writing—review & editing, A.A.A.A. and A.A.S.F.;
Visualization, A.A.A.A.; Supervision, A.A.S.F.; Project administration, A.A. and A.S.A.N.; Fund-
ing acquisition, A.A. and A.S.A.N. All authors have read and agreed to the published version of
the manuscript.
Funding: This work was supported by the Deanship of Scientific Research, Vice Presidency for
Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [KFU241562].
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data are available upon request.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2024,16, 8031 21 of 25
Appendix A
Code Title Description Short Code
DASC-1 Digital Transformation Capability Captures the ability to change in the face of
digital transformation efforts.
Digital Agility
Supply Chain
DASC-2 Adaptation to Changing
Environment
Assesses the firms’ ability to adapt to the
changing environment.
Digital Agility
Supply Chain
DASC-3 Effectiveness of Digital Tools Assesses the effectiveness of digital tools in
streamlining processes.
Digital Agility
Supply Chain
DASC-4 Flexibility in Executing
Digital Strategy
Captures the capability to achieve agility in the
execution of digital strategy.
Digital Agility
Supply Chain
DFSC-1 Flexibility in Facing Challenges Explores the flexibility of the mentioned
processes in terms of meeting new challenges.
Digital Flexibility
Supply Chain
DFSC-2 Adoption of New Technology Assesses the organization’s ability to
effectively adopt new technologies.
Digital Flexibility
Supply Chain
DFSC-3 Measuring Flexibility of
Digital Operations
Proposes ways of measuring the flexibility of
changing digital operations.
Digital Flexibility
Supply Chain
DFSC-4 Speed of Digital Solutions Measures how fast the digital solutions are
able to change.
Digital Flexibility
Supply Chain
DASC-1 Readiness for Technological Changes Evaluates readiness for changes in technology. Digital Adaptability
Supply Chain
DASC-2 Modifying Digital Plans Assesses the capacity to modify digital plans. Digital Adaptability
Supply Chain
DASC-3 Adoption of New Digital
Technologies
Explores the extent to which new digital
technologies have been adopted into the
current practices.
Digital Adaptability
Supply Chain
DASC-4 Tunability of Digital Systems Describes the extent of the digital
system’s tunability.
Digital Adaptability
Supply Chain
DASC-5 Expanding Digital Offerings Focuses on the extent of the organization’s
capacity to expand digital offerings.
Digital Adaptability
Supply Chain
IV-1 Clarity of Information in
Supply Chain
Assess the quality of information in the supply
chain, in terms of how easy it is to understand.
Information Visibility
IV-2 Transparency of Data Transfer
Evaluates the level of openness of data transfer.
Information Visibility
IV-3 Real-Time Information Access Appraises the possibility of accessing
real-time information. Information Visibility
IV-4 Information Comprehensiveness Assesses the extent of information contained
within a work. Information Visibility
IME-1 Effectiveness of Inventory Tracking Evaluates the effectiveness of the tracking
of inventory.
Inventory Management
Effectiveness
IME-2
Effectiveness of Inventory Restocking
Determines the extent of inventory
restocking effectiveness.
Inventory Management
Effectiveness
IME-3 Efficiency of Inventory Forecasting Discusses the efficiency of inventory
forecasting.
Inventory Management
Effectiveness
IME-4 Effectiveness of Inventory
Control Systems
Evaluates the degree of effectiveness of the
inventory control systems.
Inventory Management
Effectiveness
IME-5 Impact of Inventory Management on
Supply Chain Performance
Assesses the role of inventory management on
the supply chain performance.
Inventory Management
Effectiveness
Sustainability 2024,16, 8031 22 of 25
Sustainability2024,16,xFORPEERREVIEW23of26
IV‐3Real-TimeInformationAccessAppraisesthepossibilityofaccessingreal-time
information. InformationVisibility
IV‐4InformationComprehensivenessAssessestheextentofinformationcontained
withinawork. InformationVisibility
IME‐1EffectivenessofInventoryTrackingEvaluatestheeffectivenessofthetrackingof
inventory.
InventoryManagement
Effectiveness
IME‐2EffectivenessofInventory
Restocking
Determinestheextentofinventoryrestocking
effectiveness.
InventoryManagement
Effectiveness
IME‐3EfficiencyofInventoryForecastingDiscussestheefficiencyofinventory
forecasting.
InventoryManagement
Effectiveness
IME‐4EffectivenessofInventoryControl
Systems
Evaluatesthedegreeofeffectivenessofthe
inventorycontrolsystems.
InventoryManagement
Effectiveness
IME‐5ImpactofInventoryManagementon
SupplyChainPerformance
Assessestheroleofinventorymanagementon
thesupplychainperformance.
InventoryManagement
Effectiveness
FigureA1.Modelresults
Figure A1. Model results.
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