The Role of Digital Supply Chain on Inventory Management Effectiveness within Engineering Companies in Jordan

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.

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 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 transformation provides both opportunities and challenges for design organizations in Jordan. Through mastery in these areas, they can position themselves as market leaders, establishing 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 straightforward 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 achievement [10]. Through the selection of cutting-edge computerized innovations, designing organizations in Jordan have the opportunity to redefine their methodology to stock administration, 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 management 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 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 solutions 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 [14,15,16]. 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 reliability. 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]. 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 agile 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 organizations can change their operations and strategy, utilizing digital technologies that facilitate rapid changes in the business environment and will constantly redefine firm capabilities [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, 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 organization [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 inventory, 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, 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 comprises 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]. Innovation 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 Processing Theory (OIPT), the following section explains how the research model represented in Figure 1 is 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 technologies and manage assets. It also depicts the usefulness of information systems in the management of inventory through the improvement of information flow and decision making. 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, 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 digitization 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.

2.8. Digital Twin Technology

As mentioned above, Digital Twin technology is the development of a virtual representation 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]. 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 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 organization 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 indispensable aspect of competitive advantage” [56]. Digital adaptability relates to the exploitation of digital technologies to make supply chains responsive and flexible. These involve advanced 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 encountered [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 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 2 for details.

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 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

provides a comprehensive analysis of the constructs used in the study, including Digital Agility Supply Chain (DASC), Digital Flexibility Supply Chain (DFSC), Digital 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 measurement 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 standard 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 A for more details.

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%

after 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.

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 understanding 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 employees 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 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 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

presents 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 effectiveness. 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.

As depicted in

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

, 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’ effectiveness 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, information 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.

The results obtained through regression analysis, as shown in

Table 5. R2 adjusted.

Variable	R2	R2 Adjusted
Information Visibility	0.727	0.723
Inventory Management Effectiveness	0.639	0.637

, 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.

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 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.

presents the results of the hypotheses testing estimates for the relationships between the various constructs: digital adaptability, supply chain, Digital Agility Supply 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 information 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 management 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.

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 significant levels of confidence intervals and they were within the recommended ranges, thus increasing the credibility of the results [13].

Model Quality Table

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

shows the model quality metrics, including fit indices and measurement values.

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 managed 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 liquidations 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 management. 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 characteristics 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 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 conspicuousness 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 information 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 improvements. “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 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 digital 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 Jordanian 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 dimension 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.