Assessing Critical Success Factors for Supply Chain 4.0 Implementation Using a Hybrid MCDM Framework

Abstract

Heightened environmental policies along with the necessity for a resilient supply chain (SC) network have driven companies to adopt circular economy (CE) strategies. Although CE initiatives have shown significant effects on SC operations, the advent of digital technologies is encouraging businesses to digitize their SCs. However, the relationship connecting SC digitalization with CE practices remains underexplored. This study presents a novel framework that bridges the gap between CE principles and SC digitalization by identifying and prioritizing critical success factors (CSFs) for implementing SC4.0 in a circular economy context. We conducted a comprehensive literature review to determine CSFs and approaches relevant to Supply Chain 4.0 (SC4.0), and expert insights were gathered using the Delphi method for final validation. To capture the complex interrelationships among these factors, the study employed a combined approach using Intuitionistic Fuzzy Set (IFS), Analytic Network Process (ANP), decision-making trial and evaluation laboratory, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) techniques to assess the CSFs and strategies. The findings highlight that an intelligent work environment, performance tracking, and data accuracy and pertinence are the top three critical CSFs for SC digitalization. Furthermore, enhancing analytical capabilities, optimizing processes through data-driven methods, and developing a unified digital platform were identified as key strategies for transitioning to SC4.0. By embedding CE principles into the evaluation of digital SC transformation, this research contributes a novel interdisciplinary perspective and offers practical guidance for industries aiming to achieve both digital resilience and environmental sustainability. The study delivers a comprehensive evaluation of CSFs for SC4.0, applicable to a variety of sectors aiming for digital and sustainable transformation.

1. Introduction

Due to increasing sustainability demands and rapid technological advancements, the industrial sector is now required to adopt responsible consumption practices and focus on producing added-value products [1,2]. Furthermore, disruptive innovations are accelerating and strengthening supply chain (SC) operations while simultaneously reducing carbon emissions. In addition, changing customer preferences, the increasing demand for fast deliveries, and the growing necessity for immediate access to product information call for a more agile SC network [3]. Moreover, globalization—serving to accelerate business-to-business (B2B) interactions—has led to the transformation of SC operations into complex networks, presenting numerous challenges to companies and stakeholders [4,5]. To meet these market requirements, surmount the obstacles, and adhere to environmental laws, the industrial sector must establish a circular SC network [6,7].

This necessitates a significant overhaul of organizational structures, and leadership must be prepared for a deep transformation. SC4.0, which incorporates advanced disruptive technologies, enhances the operational efficiency of the SC network. Digital innovations greatly influence B2B operations by increasing efficiency and offering a competitive advantage [8,9]. As Abdelwahed [10] observes, digital innovations have improved the coordination and flexibility of SC processes within the automotive industry. This has facilitated seamless communication throughout global SC activities, leading to more effective SC operations [11]. By enhancing stakeholder transparency and optimizing SC processes, the agility along with the resilience of the SC network are reinforced. Therefore, in SC4.0, in contrast to traditional SC management, where data moves sequentially from the manufacturer to the wholesaler and then to the consumer, information is shared concurrently with multiple stakeholders [12,13]. To stay aligned with the evolving business landscape, transitioning away from conventional SC operations towards digital SC practices is unavoidable; thus, organizations must prepare for SC4.0 advancements.

Organizations can significantly reduce their resource consumption and waste generation by adopting circular SC networks that emphasize extensive recycling throughout the value chain, thereby minimizing their environmental impact [12,14]. SC4.0 furthers this objective through the enhancement of SC operations to reduce pollution and ecological damage, in harmony with the global transition toward eco-friendly methods [15,16]. The integration of digital technologies not only boosts sustainable performance in the industrial sector but also aids in establishing circular SC networks [17,18].

To meet customer demands while adhering to environmental standards, businesses require agile SC networks. SC4.0 addresses this need by utilizing advanced data analytics to anticipate customer behavior and preferences, making SCs more customer-centric [12,15]. In an era marked by frequent disruptions and uncertainties, SC4.0 enhances the resilience and agility of SCs through real-time data analysis, enabling swift and informed decision-making [13,16].

Technologies like blockchain play a crucial role in SC4.0 by ensuring comprehensive product traceability and authenticity, which are essential for circular SC networks. Additionally, SC4.0 contributes to reducing costs through process simplification and improving resource allocation efficiency. The incorporation of 5G technology allows organizations to manage global SCs more effectively, providing instant oversight, multi-site management capabilities, and the flexibility to meet changing market needs [19,20].

Consequently, SC4.0 signifies a transformative shift in supply chain management, harnessing technology to enhance efficiency, environmental sustainability, and robustness within modern corporate contexts. However, despite these significant advantages, research highlights a gap between the potential benefits and the actual adoption of digital technologies in business-to-business settings [14,17].

Adapting to the evolving dynamics of modern business is essential, particularly for B2B enterprises across various sectors known for rapid product turnover. Incorporating principles of the circular economy (CE) within SC4.0 is crucial to minimize negative environmental impacts. The CE focuses on the recycling and reusing of products to reduce waste while optimizing resource utilization [9,21].

Within the context of SC4.0, the CE is supported by advanced digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and blockchain, which enable the real-time tracking of materials, predictive maintenance, waste reduction, and optimized reverse logistics. These technologies not only help monitor environmental performance but also facilitate closed-loop supply chains where products and components are returned, refurbished, and reintegrated into production cycles [22].

Implementing CE principles in SC4.0 enhances operational efficiency, supports compliance with environmental regulations, and improves a firm’s corporate social responsibility profile. Moreover, it provides significant cost savings by reducing raw material consumption and waste disposal expenses. As such, CE-driven SC4.0 initiatives contribute to building agile, adaptive, and low-carbon industrial ecosystems aligned with national and international sustainability goals.

Despite numerous MCDM-based studies addressing SC4.0 adoption, few have explicitly integrated circular economy principles within the prioritization of critical success factors. This study aims to fill this gap by presenting a novel hybrid framework that combines Intuitionistic Fuzzy Sets (IFSs), the Analytic Network Process (ANP), the DEMATEL, and the TOPSIS. This integrative approach enables a more holistic evaluation of the interdependencies and causal relationships among CSFs, especially within a sustainability-focused supply chain transformation. Furthermore, the research contributes original insights by contextualizing the model for developing economies—specifically, within the scope of Saudi Arabia’s Vision 2030 logistics goals—thus extending the existing literature beyond its current boundaries.

This study aims to facilitate sustainable growth in industries within developing nations by focusing on critical success factors (CSFs) and developing approaches for transitions. The importance lies in identifying and examining CSFs that enable industries to concentrate on vital aspects for successful SC4.0 adoption. Yadav S, Singh [22] investigated blockchain-associated CSFs to achieve sustainability throughout SC connections, pinpointing 12 essential factors like robust systems, secure data, regulatory compliance, and reliable systems.

Academic research underscores a gap in evaluating key success factors (KSFs) and formulating approaches for SC4.0 implementation in developing nations. While previous studies highlight the numerous benefits of SC4.0, its adoption faces various challenges. As an emerging economy, Saudi Arabia is aiming to reduce logistics expenses through its Vision 2030 initiative, with a target to decrease costs from 14% to below 10% of GDP. Implementing digital transformation in logistics is crucial for achieving these objectives. Therefore, the research aims are as follows:

RQ1: To investigate the CSFs and strategies for adopting SC4.0 in the context of emerging economies.

RQ2: To establish causal relationships among the identified CSFs.

RQ3: To prioritize the CSFs and strategies that are crucial for SC4.0 adoption.

The structure of the paper is as follows: Section 2 reviews the literature; Section 3 details the research methodology; Section 4 delivers a current case analysis; Section 5 discusses the findings and analysis. The second-to-last section, Section 6, delves into the theoretical and managerial implications of the research. Finally, Section 7 concludes the paper, acknowledges its limitations, and suggests avenues for future research.

2. Review of the Literature

2.1. The Necessity of SC4.0

In today’s intensely competitive business environment, adopting SC4.0 is not just an option but a strategic necessity [8,21,23]. The drive to enhance productivity and efficiency remains paramount, and SC4.0 addresses this by utilizing state-of-the-art technologies and automation. By eliminating bottlenecks, minimizing human errors, and optimizing resource allocation, SC4.0 significantly boosts operational efficiency [8,9]. Furthermore, the dynamic nature of modern business demands real-time decision-making capabilities. SC4.0 meets this demand by offering immediate data analysis and actionable insights, allowing businesses to respond quickly and make decisive moves to stay ahead of competitors [19,20].

Contemporary consumers also expect outstanding customer service, personalized products, and rapid deliveries [24,25]. SC4.0 caters to these evolving customer expectations by streamlining SC processes to improve customer satisfaction and guarantee the prompt delivery of offerings. As businesses expand globally, SC networks involving a diverse array of suppliers, producers, and distributors spanning multiple regions are becoming increasingly intricate. SC4.0 tackles these issues by equipping management and professionals with comprehensive visibility of the entire worldwide SC and enabling effective oversight of business activities [25].

The implementation of SC4.0 has significantly improved B2B operations by transforming conventional processes into streamlined and cooperative ecosystems by integrating cutting-edge technologies, and companies can enhance SC efficiency, effectively mitigate risks, and deliver increased benefits for their business-to-business partners. For example, Amazon uses cloud computing via Amazon Web Services in order to generate revenue. However, the CMO survey indicates that almost 40% of B2B firms hesitate to embrace digital tools [26].

Effective risk management and resilience are essential during times of uncertainty like international conflicts, environmental catastrophes, and global health crises. SC4.0 supports organizations in navigating these unknown challenges by improving risk evaluation abilities via digital tools. It aids in transforming, reshaping, and improving organization in B2B events. Technologies like the Industrial Internet of Things (IoT) contribute to creating value in B2B by relying on accessible and flexible knowledge and skills, thus boosting the company’s capacity to handle risks [27].

SC4.0 addresses challenges by enhancing inventory optimization, reducing holding costs, and boosting operational efficiency, leading to substantial cost reductions and improved economic outcomes [28,29,30]. With stricter environmental regulations, the importance of sustainability is increasing, and SC4.0 serves a vital role in developing a digital circular supply chain network [31]. Such a network enables organizations to achieve their sustainability objectives by focusing on optimal resource usage, improving stakeholder communication, and reducing waste production. Therefore, digital technologies enhance the capabilities and strategies for demand, supply, and collaboration in B2B operations. As societal environmental awareness grows, companies must embrace eco-friendly practices, and implementing a digital circular supply chain provides a competitive edge in the market.

To fully leverage SC4.0’s benefits, leadership should focus on key areas such as data precision, data integration, and the necessary technological infrastructure [32]. Additionally, SC companies must adopt strategies like comprehensive employee development, improving data management, and creating a unified digital platform. As a result, restructuring B2B processes among business partners and key stakeholders becomes essential.

2.2. Essential Success Factors and Strategies for SC4.0

The successful implementation of SC4.0 relies on several critical factors and strategic approaches. A foundational element is fostering a cooperative setting for all parties involved in product creation inside the SC. This collaborative atmosphere is pivotal for integrated and collaborative product design, as emphasized by Farooque et al. [33].

An indispensable component for SC4.0 adoption is the establishment of an information platform that acts as a central hub for data collection, organization, analysis, and dissemination across the SC. This platform leverages smart devices to enable instantaneous information acquisition from various SC links, such as vendors, manufacturing plants, logistics providers, as well as end-users [34].

Building consensus among different organizational departments and stakeholders is also crucial. Aligning the vision, goals, and strategies related to SC4.0 ensures a unified approach to its adoption [22,35]. The integration of advanced tools and data analysis methods necessitates that staff develop new competencies to function efficiently within SC4.0’s digital landscape. Upskilling the workforce is therefore essential, allowing them to fully leverage SC4.0’s advantages and contribute to its successful deployment [36,37].

Data serves as the cornerstone of SC4.0 and is often referred to as the new oil of industries. The immense volume of data generated from business operations must be effectively managed to optimize the SC. Data collected from various equipment through smart devices might be unprocessed, disorganized, or partially organized. To organize this data and extract meaningful insights, artificial intelligence (AI) and machine learning (ML) techniques are employed [38,39].

The commitment of top management is a critical factor in adopting SC4.0, as their support and leadership drive the necessary changes [40,41]. Monitoring key performance indicators such as dwell time, SC efficiency, and inventory turnover rates is essential to evaluate the effectiveness of SC4.0 initiatives [42,43].

In addition to internal enablers, recent studies highlight the role of policy mechanisms and digital technologies in advancing SC4.0 strategies. Regulatory tools such as carbon tax, carbon cap, and carbon trading are increasingly influencing how companies approach green supply chain initiatives [44]. These mechanisms promote CE alignment within SC4.0 implementation, especially when integrated with decision-support systems. A fuzzy multi-objective model proposed by recent research helps organizations select suppliers based on environmental and time-based criteria, minimizing emissions from transportation and optimizing delivery schedules [45].

Furthermore, technologies such as blockchain, the IoT, and AI are being integrated into SC4.0 frameworks to improve transparency, traceability, and decision-making. Blockchain ensures data integrity and trust across distributed supply networks, while IoT devices enable real-time monitoring of assets and inventory flows. AI-powered analytics offer predictive capabilities that support proactive SC management and sustainability-driven optimization [44,45]. These technologies not only enhance operational efficiency but also reinforce CE principles by enabling resource circularity and closed-loop supply chain practices.

3. Methodology

This study employs a combined methodology integrating an Intuitionistic Fuzzy Set (IFS), a Analytic Network Process (ANP), Structural Equation Modeling (SEM), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the CSFs in SC4.0 and to formulate corresponding strategies. The IFS represents the latest advancement in fuzzy set theory, while ANP and SEM are utilized to rank and analyze the causal relationships among the CSFs. Subsequently, the TOPSIS method is applied to prioritize the strategies.

The sequence of ANP → SEM → TOPSIS was intentionally selected to ensure methodological coherence and decision-making depth. First, the ANP is used to determine the relative importance of the CSFs, accounting for interdependencies between factors. SEM is then applied to explore and validate the causal relationships among these CSFs. Finally, the TOPSIS is employed to prioritize strategic actions based on the insights gained from the previous stages. This structured flow—from weighting to causal analysis to strategy ranking—provides a comprehensive approach for guiding SC4.0 transformation in alignment with circular economy principles.

The choice of the IFS, ANP, SEM, and TOPSIS was guided by their ability to address key methodological needs of the study. The IFS was chosen for its strength in handling uncertainty and hesitation in expert judgment, which is common in strategic decision-making. The ANP was selected for its ability to capture interdependencies among CSFs, unlike the traditional AHP. SEM was employed to uncover and validate causal relationships among CSFs, providing structural clarity. Lastly, the TOPSIS was used for its effectiveness in ranking strategic alternatives based on proximity to the ideal solution, making it suitable for prioritizing SC4.0 transition strategies.

3.1. Intuitionistic Fuzzy Set

Multi-criteria decision-making (MCDM) approaches are essential when addressing problems influenced by various factors. Typically, MCDM methods evaluate these factors using precise, real numbers [44,45,46]. However, relying solely on exact numbers makes it challenging to account for ambiguity and uncertainty that are inherent in many problems. To overcome this limitation, Pettigrew [47] introduced fuzzy set theory, which allows for the analysis of vagueness and uncertainty, thereby enhancing the evaluation’s robustness. Building on fuzzy set theory, several extensions have been developed, including rough fuzzy sets, Bayesian fuzzy sets, Pythagorean fuzzy sets, and Intuitionistic Fuzzy Sets, all of which have been applied in various evaluation contexts [48,49]. The most recent extension, IFSs, was proposed by Seikh M, Mandal [50]. Compared to its predecessors, the IFS provides greater flexibility and more effectively manages uncertainty [51]. The process for implementing IFSs involves the following steps:

Consider a universal set $U$ and the general form of the IFS, $f$ in $U$, as follows:

$$f=\{(u,{\mu }_f^F(u),v_f^F(u)):u\in U\}$$

(1)

where ${\mu }_f^F(u):X\to [0,1]$ and $v_f^F(u):X\to [0,1]$. Here, $u$ stands for a member of the overall set $U$, while ${\mu }_f^F(u)$ and $v_f^F(u)$ denote the membership and non-membership degrees of element $U$ with respect to $f$, respectively.

The level of uncertainty $({\pi }_f^{\hspace{0.17em}\mathrm{F}})$ is calculated by employing Equation (2):

$${\pi }_f^{\hspace{0.17em}\mathrm{F}}(u)=\sqrt[3]{1-{({\mu }_f^F(u))}^3-{(v_f^F(u))}^3}$$

(2)

To compute the Fermatean fuzzy weighted average (FFWA), we use Equation (3):

$$FFWA(f_1,f_2,\dots ,f_n)=({\sum }_{i=1}^n{w}_i{\mu }_{f_i}^F,{\sum }_{i=1}^n{w}_i{v}_{f_i}^F)$$

(3)

where $w_i$ represents the corresponding weight vector.

The value for the IFS is determined by Equation (4):

$$S^F(f)={\displaystyle \frac{1}{2}}[({({\mu }_f^F)}^3-{(v_f^F)}^3-\mathrm{l}\mathrm{n}\hspace{0.17em}(1+{({\pi }_f^F)}^3))]$$

(4)

where $S^F:f\to R.$

3.2. Integration of Analytic Network Process with Fuzzy Systems

The ANP, a MCDM method created by Saaty in 1980 [52], is used to ascertain the relative importance of various factors by conducting pairwise comparisons between them. In addition, the ANP ranks these factors based on their calculated weights. This method involves dividing the problem into multiple categories and constructing a hierarchical framework. The ANP is commonly employed in research due to its straightforward computational procedures and its ability to provide a structured hierarchy of the problem at hand [53,54]. To improve result precision, the ANP is often combined with fuzzy logic and gray system theories. In the present study, the ANP is integrated with a Fuzzy System. The following sections outline the steps involved in the ANP methodology:

Step 1

Construct the Pairwise Comparison Matrix (D):

The matrix $D$ is structured as follows:

$$D=[\begin{array}{cccc}{d}_{11}& \dots & \dots & d_{1n}\\ ⋮& \ddots & ⋰& ⋮\\ ⋮& ⋰& \ddots & ⋮\\ d_{n1}& \dots & \dots & d_{nn}\end{array}]$$

(5)

where $dij$ indicates the extent to which criterion $(i)$ influences criterion $(j)$.

Step 2

Verify that the matrix is consistent:

To ensure consistency, the value $W^{\prime }$ must initially be calculated. This requires the initial computation of the criteria weights, represented by $W$, where

$$W=[\begin{array}{c}{W}_1\\ W_2\\ ⋮\\ W_n\end{array}]\mathrm{and}{W}_i={\displaystyle \frac{{\sum }_{i-1}^n{d}_{ij}}{n}}$$

(6)

Step 3

Determine the comparative weights of the CSFs:

The weights $W$ for the CSFs are calculated as:

$$W=\left(w_1,\dots ,w_n\right)$$

(7)

where $W$ stands for the weight vector associated with the difficulties, while n represents the overall count of CSFs.

3.3. Structural Equation Modeling (SEM)

The SEM acts as a visual methodology used to categorize the factors under analysis into groups of causes and effects through systematic pairwise assessments [55,56]. Using the SEM approach improves the understanding of the interconnections between various factors and emphasizes the relative importance of each factor compared to others [57,58,59]. In this research, the SEM method is combined with the IFS to identify and analyze the causal relationships between the factors. The SEM process includes the following steps:

Step 1

Develop a Direct-Relation Matrix (D)

A direct-relation matrix is created by performing pairwise comparisons between each of the CSFs.

Step 2

Normalize the Direct-Relation Matrix (D)

The direct-relation matrix is normalized using Equations (8) and (9), resulting in the normalized matrix ($M$):

$$M=c^{-1}D$$

(8)

$$c=\mathrm{m}\mathrm{a}\mathrm{x}\hspace{0.17em}(\underset{1\le i\le n}{\mathrm{m}\mathrm{a}\mathrm{x}\hspace{0.17em}}{\sum }_{j=1}^n{x}_{ij},\underset{1\le i\le n}{\mathrm{m}\mathrm{a}\mathrm{x}\hspace{0.17em}}{\sum }_{i=1}^n{x}_{ij})$$

(9)

Here, $xij$ represents the components of the direct-relation matrix (D).

Step 3

Construct the Total-Relation Matrix (n)

The total-relation matrix (n) is considered using the subsequent equation: $N=(M-I)\times M-1$; where $I$ represents the identity matrix.

Step 4

Categorize Factors into Cause-and-Effect Groups

Factors are grouped into cause-and-effect categories using Equations (10) and (11):

$$P_i={\left({\sum }_{i=1}^n{n}_{ij}\right)}_{n\times 1}={\left(n_i\right)}_{n\times 1}$$

(10)

$$Q_i={({\sum }_{j=1}^n{n}_{ij})}_{1\times n}={(n_i)}_{1\times n}$$

(11)

where $P_i$ represents the total sum of the rows, $Q_i$ represents the total sum of the columns, and $n_{ij}$ are the elements of the overall-relation matrix.

3.4. Integrated Compromise Approach

Ecer and Pamucar [60] introduced the TOPSIS, which combines three distinct techniques: the Simple Additive Weighting (SAW) method, the Weighted Aggregated Sum Product Assessment (WASPAS), and the Exponentially Weighted Product Model. By integrating these approaches, the TOPSIS produces more dependable outcomes than any individual method. The following sections outline the steps involved in the TOPSIS:

Step 1

Create the Initial Decision-Making Matrix (E):

The decision matrix is represented as:

$$E_{ij}=[\begin{array}{ccc}{e}_{11}& \dots & e_{1n}\\ ⋮& \ddots & ⋮\\ e_{m1}& \dots & e_{mn}\end{array}];i=\mathrm{1,2},\dots ,m;j=\mathrm{1,2},\dots ,n.$$

(12)

Step 2

Normalize the Initial Decision-Making Matrix:

For benefit-related factors:

$$r_{ij}={\displaystyle \frac{e_{ij}-\underset{i}{\mathrm{m}\mathrm{i}\mathrm{n}\hspace{0.17em}}{e}_{ij}}{\underset{i}{\mathrm{m}\mathrm{a}\mathrm{x}\hspace{0.17em}}{e}_{ij}-\underset{i}{\mathrm{m}\mathrm{i}\mathrm{n}\hspace{0.17em}}{e}_{ij}}},\mathrm{for}\mathrm{benefit}\mathrm{factors}$$

(13)

For cost-related factors:

$$r_{ij}={\displaystyle \frac{\underset{i}{\mathrm{m}\mathrm{a}\mathrm{x}\hspace{0.17em}}{e}_{ij}-e_{ij}}{\underset{i}{\mathrm{m}\mathrm{a}\mathrm{x}\hspace{0.17em}}{e}_{ij}-\underset{i}{\mathrm{m}\mathrm{i}\mathrm{n}\hspace{0.17em}}{e}_{ij}}},\mathrm{for}\mathrm{cost}\mathrm{factors}$$

(14)

Step 3

Calculate the Weighted Sum for Alternatives:

The total weighted sum for each alternative is calculated as follows:

$$X_i={\sum }_{j=1}^m(w_j{r}_{ij})$$

(15)

$$Y_i={\sum }_{j=1}^n{(r_{ij})}^{w_j}$$

(16)

where $X_i$ and $Y_i$ represent the weighted sums for the alternatives.

Step 4

Compute the Relative Weights of the Alternatives:

The significance of the alternatives is determined using the following equations:

$$K_a={\displaystyle \frac{X_i+Y_i}{{\sum }_{i=1}^m(X_i+Y_i)}}$$

(17)

$$K_b={\displaystyle \frac{Y_i}{\underset{i}{\mathrm{m}\mathrm{i}\mathrm{n}\hspace{0.17em}}{Y}_i}}+{\displaystyle \frac{X_i}{\underset{i}{\mathrm{m}\mathrm{i}\mathrm{n}\hspace{0.17em}}{X}_i}},$$

(18)

$$K_c={\displaystyle \frac{\lambda (Y_i)+(1-\lambda )X_i}{\lambda \underset{i}{\mathrm{m}\mathrm{a}\mathrm{x}\hspace{0.17em}}{Y}_i+(1-\lambda )\underset{i}{\mathrm{m}\mathrm{a}\mathrm{x}\hspace{0.17em}}{X}_i}},0\le \lambda \le 1$$

(19)

Here, $K_a$ represents the arithmetic mean of the weighted sum and product models, $K_b$ indicates the sum of the relative scores of the weighted sum and product models, and $K_c$ provides a balanced score between them. The parameter λ controls the adaptability and consistency of the TOPSIS method.

Step 5

Rank the Alternatives:

Finally, the overall ranking of the alternatives is calculated as:

$$K={(K_a{K}_b{K}_c)}^{1/3}+{\displaystyle \frac{1}{3}}(K_a+K_b+K_c)$$

(20)

4. A Contemporary Case Analysis

This segment highlights the use of the comprehensive framework created in this research through a modern example in Saudi Arabia’s manufacturing sector. The surge in digitalization has increased the demand for various components. Despite this rising demand, manufacturers across industries face challenges such as intense international competition, changing customer requirements, and the need to comply with eco-friendly practices. The intricate design of the SC network complicates the ability of these manufacturers to meet customer needs efficiently. Digitalization has significantly improved various industrial processes, demonstrating its ability to enhance the flexibility of SC operations [61,62]. Digitalization supports companies in areas such as route optimization, managing inventory, and forecasting demand. Additionally, digital tools have shown their capacity to integrate CE practices into SC processes. As a result, digitalization of the SC has become vital for businesses.

Saudi Arabia is positioning itself as an emerging player in global manufacturing. According to recent Vision 2030 reports, Saudi Arabia has focused on increasing its contribution to global production, especially as it aims to solidify its position as a key regional manufacturing hub. Therefore, Saudi Arabia is expected to assume a pivotal role in the global manufacturing industry. It is evident that the SC networks of Saudi manufacturers must be agile, resilient, and adaptable, with digitalization anticipated to enhance SC performance. However, the shift in SC companies towards SC4.0 in Saudi Arabia is still in its early phases. While SC digitalization provides numerous advantages, Saudi SC companies face challenges such as the need for substantial capital investment, low awareness, restricted customer acceptance, and technological under-development [13,50]. Another significant challenge for Saudi SC firms is the lack of alignment among stakeholders. Therefore, it is crucial to explore the CSFs and strategies that companies should adopt for long-term success. In line with this objective, the current research aims to identify and assess the CSFs and approaches using an integrated methodology that incorporates the IFS, ANP, SEM, and TOPSIS techniques.

4.1. Selection and Profiling of Experts

This research adopts a mixed-methods approach, integrating both qualitative and quantitative techniques to evaluate CSFs and tactics within SC4.0. The procedure for determining and confirming these CSFs and tactics was informed by an extensive review of the literature and enriched by insights from industry professionals. The experts were selected through purposive sampling, ensuring that they met particular eligibility criteria, including at least ten years of professional experience and holding a postgraduate degree at minimum. Additional selection criteria included the expert’s current or prior involvement in digital transformation initiatives, specifically those aligned with supply chain innovation, Industry 4.0 technologies, and/or circular economy practices.

As a result, a panel of 33 experts was assembled, ensuring a broad range of expertise in various domains relevant to SC4.0. These experts represent diverse sectors and functions within SC management, ranging from data management to logistics and transportation. Specifically, the panel includes professionals from key industrial sectors in Saudi Arabia—such as manufacturing, logistics, and electronics—making the study particularly relevant to business-to-business (B2B) industrial supply chains undergoing digital transformation.

To ensure the reliability and consistency of expert input, several validation measures were applied. During the ANP stage, the consistency of pairwise comparison matrices was verified using the Consistency Ratio (CR), and all values remained below the standard threshold of 0.1. Additionally, during SEM analysis, causal relationships among the CSFs were validated through structural assessment techniques to ensure the conceptual soundness and robustness of results. The panel also reflects a balance in terms of gender, education, and experience levels. The demographic characteristics of these experts are detailed in

Table 1. Demographic characteristics of experts (n = 33).

Details	n	%
Gender
Female	14	42.4
Male	19	57.6
Education
Post-graduate	14	42.4
Doctorate	11	33.3
Post-doc	8	24.3
Field of Expertise
Information systems	7	21.2
Stock control	6	18.2
Forecasting	6	18.2
Purchase department	5	15.2
Transportation department	4	12.1
SC management	3	9.1
Logistics and transportation	2	6.1
Designation
Operations supervisor	6	18.2
Transport specialist	8	24.3
Procurement officer	7	21.2
Storage and distribution manager	5	15.2
SC Director	4	12.1
Head of Logistics	3	9.1
Experience
Up to 10 years	9	27.3
10–15 years	11	33.3
Above 15 years	13	39.4
Age Group
25–34	13	39.4
35–44	12	36.4
45–54	8	24.3
Industry Experience
Manufacturing	11	33.3
Retail	12	36.4
Technology	10	30.3

.

4.2. Determining and Finalizing Key Success Factors and Strategies in SC4.0

In the initial phase of the research, an exhaustive literature review was conducted to identify CSFs relevant to SC4.0. Utilizing the Web of Science database as the primary scholarly repository, a systematic search strategy was employed using a combination of keywords such as “Supply chain” intersected with “digitalization,” “Supply chain digitalization,” “SC4.0,” and additional terms like “Enablers,” “Critical Success Factors,” and “Antecedents.” This comprehensive search yielded a total of 97 scholarly articles. Each article underwent a rigorous screening process to assess its relevance, which involved excluding studies not centrally focused on SC4.0, non-English publications, and any duplicates. This meticulous curation narrowed the selection to 39 articles suitable for detailed analysis. From the synthesized insights of these selected studies, a consolidated list of 23 CSFs was formulated, as presented in Table 1.

To enhance the list, the Delphi method was applied by engaging industry experts who assessed the relevance of each CSF. Based on their input, five CSFs were eliminated: “implementation consensus, training and emerging” competencies, and adherence to “government regulations, data modularization and prioritization, and technology infrastructure”. This process led to a final list of 20 CSFs, which are coded and outlined in

Table 2. Finalized CSFs for SC4.0 e.g., [22,42,43].

Code	Critical Success Factor	Description
CSF1	Collaborative and integrated product design	Enhancing product development through cross-functional teamwork and integration across the SC.
CSF2	Information platform	Implementing robust IT systems to facilitate seamless data exchange and real-time communication.
CSF3	Data reliability and relevance	Ensuring accuracy and pertinence of data used for decision-making processes.
CSF4	Data integration	Combining data from diverse sources to create a unified and coherent dataset for analysis.
CSF5	Top management support	Securing commitment and active involvement from senior leadership to drive SC4.0 initiatives.
CSF6	Performance monitoring	Continuously tracking key performance indicators to assess and improve SC operations.
CSF7	Research environment	Promoting a culture that encourages innovation and supports research and development activities.
CSF8	Trust and collaboration	Building strong, trust-based relationships among SC partners to enhance cooperation.
CSF9	Establishment of a smart work environment	Creating workplaces equipped with advanced technologies like the IoT and AI to improve efficiency.
CSF10	Aligning SC4.0 initiatives with organizational strategy	Ensuring that SC4.0 efforts are in harmony with the company’s overall strategic objectives.
CSF11	Reliance on knowledge transfer	Facilitating the sharing and dissemination of knowledge within and between organizations.
CSF12	Organizational agility	Developing the capability to quickly adapt to market changes and new technological advancements.
CSF13	Data security	Implementing measures to protect data from cyber threats and unauthorized access.
CSF14	Adaptability of software innovations	Being open and able to integrate new software solutions effectively into existing systems.
CSF15	Managing and analyzing data quality	Maintaining high standards for data accuracy, completeness, and consistency, and employing effective analytical methods.
CSF16	Ownership and control of data	Clearly defining data ownership rights and control mechanisms within the SC network.

below.

An analogous procedure was followed to finalize the strategies. All proposed strategies received endorsement from the experts, as detailed in

Table 3. Fuzzy IFS values for linguistic variables and their corresponding influence scores.

Linguistic Variables	Influence Score	Membership Value	Non-Membership Value
No Impact (NI)	0	1.0	0.0
Very Low (VL)	1	0.15	0.70
Low (L)	2	0.45	0.55
High (H)	3	0.72	0.05
Very High (VH)	4	0.92	0.12

.

4.3. Evaluation of CSFs and Strategy Prioritization Using Advanced Analytical Methods

In this section, experts conducted pairwise comparisons (Equation (5)) among the CSFs based on the impact scores outlined in Table 3.

The influence scores were substituted with their corresponding IFS values, as determined by the linguistic variables and their associated membership and non-membership values. Using Equations (1)–(4), the IFS values were converted into exact numerical values, taking into account both the membership and non-membership degrees. The weights for the CSFs were then calculated using Equation (6). Following this, the normalized weights were determined through Equation (7). The process then involved estimating λmax using Equation (8). Equations (10) and (11) were applied to compute the Consistency Index (CI) and Consistency Ratio (CR), respectively. Lastly, the comparative weights of the CSFs were computed using Equation (12) and are shown in

Table 4. Weights and classification of CSFs in SC4.0.

CSF	P	QP	Q	P × Q	Category	Rank P × Q	Weight	Rank Weight
CSF1	0.75	1.18	1.92	0.425	Effect	21	0.187	20
CSF2	0.85	1.16	1.99	0.310	Effect	20	0.209	11
CSF3	2.07	1.12	3.18	0.965	Cause	7	0.543	3
CSF4	0.76	1.00	1.75	0.245	Effect	19	0.191	18
CSF5	0.78	0.97	1.73	0.195	Effect	18	0.198	15
CSF6	2.09	0.94	3.00	1.150	Cause	6	0.545	2
CSF7	0.79	0.81	1.60	0.035	Effect	17	0.199	14
CSF8	0.82	0.77	1.58	0.040	Cause	15	0.205	12
CSF9	2.14	0.75	2.87	1.400	Cause	2	0.561	1
CSF10	2.00	0.62	2.60	1.380	Cause	3	0.522	5
CSF11	0.73	0.53	1.24	0.210	Cause	14	0.187	19
CSF12	1.17	0.48	1.65	0.690	Cause	10	0.305	8
CSF13	1.18	0.44	1.60	0.730	Cause	9	0.302	9
CSF14	2.03	0.39	2.40	1.660	Cause	1	0.532	4
CSF15	0.74	0.26	1.00	0.490	Cause	13	0.190	17
CSF16	1.25	0.23	1.48	1.030	Cause	5	0.324	6

Note: The “Cause” and “Effect” categories are determined based on the value of (P − Q). If (P − Q) > 0, the CSF is classified as a Cause factor (i.e., it has a stronger influence on other factors). If (P − Q) < 0, it is classified as an Effect factor (i.e., it is more influenced by other factors).

.

In the FSS-SEM methodology, the analytical process initiates with the preliminary pairwise comparison matrix D derived from the Intuitionistic IFS-ANP, serving as the foundational starting point. This matrix D is standardized using Equations (13) and (14), resulting in the normalized matrix M. Following standardization, Equation (15) is applied to M to generate the total relation matrix n. Subsequently, Equations (16) and (17) are employed to compute the datasets (P + Q) and (P − Q), respectively, which are presented in Table 4.

In Table 4, the CSFs are systematically ranked based on their (P + Q) values, (P − Q) values, and corresponding weights. CSFs categorized as causes necessitate focused attention due to their potential to influence other CSFs, whereas those categorized as effects are more susceptible to being influenced by others. Specifically, a CSF is classified as a cause when its influencing degree P exceeds its degree of being influenced Q. Conversely, if a CSF’s influencing degree P is less than its degree of being influenced Q, it is categorized as an effect.

Within the IFS-TOPSIS framework, the analytical process begins with the construction of the initial decision matrix E using Equation (18), which establishes the relationship between various approaches and CSFs. This matrix E is subsequently standardized through Equation (19), treating all CSFs as advantageous standards.

The weighting of each strategy is then determined using Equations (16) and (19). Following this, the relative weights, denoted as Ka, Kb, and Kc, are calculated via Equations (17) and (18). The final K values are derived using Equation (20) and are presented in Table 4. These K values are utilized to prioritize the strategies based on their respective scores.

As illustrated in

Table 5. Prioritization of strategies using IFS- TOPSIS Method.

Strategies	Ka	Kb	Kc	K	Rank
S1	0.18	6.65	0.46	3.30	3
S2	0.37	30.12	1.03	13.00	1
S3	0.06	2.15	0.15	1.05	5
S4	0.10	3.24	0.28	1.62	4
S5	0.35	28.08	0.97	12.00	2

, strategy S2 ranks highest with a K value of 13.00, followed by S5 with a K value of 12.00, and S1 with 3.30. Strategies S4 and S3 are ranked fourth and fifth, respectively. This prioritization facilitates the identification of the most effective strategies in relation to the established CSFs.

In this study, when computing the Kc values with Equation (19), a λ value of 0.5 is selected, placing it centrally between 0 and 1 [60]. To assess the impact of the λ parameter on the ultimate rankings, λ is adjusted within the range of 0 to 1. This adjustment aims to balance the trade-offs among CSFs, manage uncertainties, and guarantee the robustness of the outcomes. The impact of varying λ values on the K values is shown in

Table 6. Influence of λ on the ranking of strategies.

Strategies	λ = 0 K	λ = 0 K Rank	λ = 0.1 K	λ = 0.1 K Rank	λ = 0.2 K	λ = 0.2 K Rank	λ = 0.3 K	λ = 0.3 K Rank	λ = 0.4 K	λ = 0.4 K Rank
S1	3.34	3	3.27	3	3.26	3	3.29	3	3.28	3
S2	12.80	1	12.68	1	12.72	1	12.82	1	12.84	1
S3	1.00	5	0.98	5	0.97	5	0.98	5	0.97	5
S4	1.67	4	1.63	4	1.62	4	1.63	4	1.64	4
S5	11.82	2	11.75	2	11.77	2	11.86	2	11.85	2

. Changing λ from 0 to 1 has minimal effect on the strategy values, yet the overall appraisal score (K) for the approaches stays consistent.

5. Findings and Analysis

Based on the weightings derived from the IFS-ANP method (Table 4), the CSFs have been ranked in order of importance. Figure 1 illustrates these rankings in a bar chart format for improved clarity.

The analysis identifies a smart work environment (CSF9) as the top priority. In the context of SC4.0, it is crucial to digitize every node within the SC network to facilitate seamless and instantaneous data exchange. This necessity underscores the importance of digitalizing all network nodes. Additionally, establishing a smart work environment significantly aids employees in adapting to digital technologies [57,59]. A smart work environment leverages cutting-edge technologies to develop an intelligent workplace, and it also enables comprehensive training programs for employees. Nonetheless, many manufacturing companies encounter challenges in digitizing their SC operations due to financial limitations and the lack of advanced technological infrastructure [63,64,65]. Therefore, prioritizing the development of a smart work environment is essential for advancing towards SC4.0.

The second most crucial CSF in SC4.0 is performance monitoring (CSF6). Assessing the effects of digitalization on SC activities is vital. Regular monitoring allows for the evaluation of digitalization efficiency within these activities and helps pinpoint areas that require additional enhancements [48,49]. Consequently, performance monitoring plays an indispensable role in the success of SC4.0.

Within the framework of SC4.0, the third critical success factor (CSF3) pertains to the dependability and relevance of data. The intricate network of stakeholders involved in the SC necessitates managing a substantial volume of data during digital transformation initiatives. For SC4.0 to operate effectively, the data utilized must be both consistent and pertinent, ensuring high levels of accuracy and significance [9,21]. Issues related to data quality can diminish the SC’s flexibility and resilience. Inaccurate data or data that has been tampered with may introduce unnecessary complexities, leading to delays in the delivery of products. On the contrary, dependable and pertinent data improves transparency among stakeholders and maximizes resource utilization [66,67].

The fourth essential success factor in SC4.0 is the capacity to adapt to software innovations (CSF14). As technology continuously evolves, organizations aiming to digitize their SCs must strengthen their technological capabilities to incorporate the latest advancements. However, because of several expected difficulties, only a subset of stakeholders within the SC network may successfully adopt these new technologies [68,69]. To fully capitalize upon the advantages offered by digitalization and improve the efficiency of SC operations, it is crucial for all stakeholders to enhance their ability to adopt software innovations. According to Agrawal et al. [63], the financial resources of stakeholders play a significant role in determining their readiness to implement digital technologies.

Additionally, aligning SC4.0 initiatives with the organization’s strategic objectives (CSF10) is a pivotal success factor. The SC network includes multiple entities, each with distinct roles, making it essential to integrate SC4.0 into the overall organizational strategy carefully. Corporate strategies and guidelines are fundamental in guiding the company toward implementing SC4.0. These strategies must reflect the commitment and interest of top leadership in the digital transformation of SC processes. The company’s strategic plan influences investments in advanced technologies capable of improving SC operations. Research by Ardra and Baru [65] supports the significance of organizational strategy in SC digitalization and highlights that inadequate strategies can hinder the digitalization process.

After utilizing the (P − Q) metrics detailed in Table 4 across columns 4, 5, and 6, the CSFs have been classified into causal and resultant categories. CSFs exhibiting positive (P − Q) values are placed in the causal category, indicating they have a higher influence on other factors. Conversely, those with negative (P − Q) values are assigned to the resultant category, meaning they are more influenced by other factors.

Within the cause category, CSF14, CSF9, CSF10, and CSF6 rank first, second, third, and fifth, respectively, as previously discussed. The ownership and control of data (CSF16) holds the fourth position. This facility signifies an organization’s technical ability to assess the performance of new technologies or software intended for SC operations. Before these systems are implemented commercially, it is essential to verify their reliability and validity [70,71,72,73]. Hence, having a robust troubleshooting facility is crucial for the transition to SC4.0.

Regarding the key critical success factor (CSF16) of the ownership and control of data, traditional SC activities have faced challenges due to inadequate data transparency and failures in transmitting data in real-time. To address these issues, there is an increasing digitization of SC activities. The use of blockchain technology enhances data transparency among various stakeholders by decentralizing data flows, thereby improving the efficiency of SC operations [74,75].

Data security (CSF13) is also a pivotal CSF in SC4.0. A major concern in the digitalization of SCs is maintaining data security. The extensive and sensitive nature of data involved in SC activities means that any data breach at a single node can significantly disrupt operations [76,77]. Ensuring robust data security measures is therefore essential to protect SC activities from potential threats.

Within the effect category, the CSFs are ordered as CSF1, CSF2, CSF4, CSF5, and CSF7, from highest to lowest priority. CSF1, which involves collaborative and integrated product design, is influenced by CSF9. Establishing a smart work environment enables seamless information exchange among all participants in the SC network, thereby fostering integrated product development. Research by Shakur et al. [45] supports that collaborative product design enhances mutual comprehension and confidence among participants in the SC network.

Following this, CSF2, the unified information platform, is affected by CSF14. In SC operations, sharing of information between stakeholders is essential, and with the advancement to SC4.0, having a secure and unified information platform is crucial. This facilitates effective information sharing among all parties involved. Recently, stakeholders have been utilizing blockchain technology for data exchange and real-time product tracking of products [78,79]. As it is a decentralized ledger, blockchain allows every member of the SC network to obtain information regarding the items being shipped.

Data integration, known as CSF4, is also a crucial element. Proper coordination is essential for the resilience and efficiency of SC activities. To carry out SC activities effectively, it is necessary to collect and integrate data from all stakeholders accurately. Data from various sources are gathered and unified into a consistent dataset. With this consolidated data, SC companies can optimize their operations, oversee stock levels, and forecast demand utilizing data analytics tools like artificial intelligence (AI) and machine learning (ML) [68,69].

The methods supporting SC companies in the context of SC4.0 have been systematically ranked using the IFS-TOPSIS framework (Table 5). At the forefront of these methods is the strategy aimed at enhancing analytical capabilities (S2). To maintain competitiveness and achieve success in today’s dynamic business environment, organizations must accurately predict demand and develop strategic plans to effectively meet this demand. Consequently, predicting demand and managing inventory become fundamental components. To further enhance their analytical capabilities, companies engaged in supply chain operations must integrate big data analytics. The use of big data tools allows organizations to concentrate on tailoring customer experiences, forecasting market trends, reinforcing robustness within supply chain operations, and improving performance efficiency [80,81,82]. The application of big data analytics fosters greater transparency, improves the accuracy of demand forecasting, optimizes routing, increases operational efficiency, and reduces costs.

As a result, data-driven process optimization (S5) stands out as a pivotal strategy in SC4.0. Due to the significant importance of data in supply chain operations, companies should focus on enhancing their data processes to boost both data quality and reliability. The implementation of data-driven process optimization also helps SC companies enhance their analytical capabilities. The creation of an integrated digital platform (S1) is identified as the third-highest priority for SC4.0. To reduce the risks posed by cyber threats and ensure data privacy and security, it is advised that SC companies develop unified digital platforms for secure data sharing. While blockchain technology is widely used, it still faces certain risks [83]. Hence, companies need to create digital platforms that enable secure information exchange across all stakeholders. To validate the robustness of the IFS-TOPSIS ranking results, a sensitivity analysis was conducted by gradually varying the λ parameter from 0 to 1, as shown in Table 6. This λ parameter controls the hesitation degree in the intuitionistic fuzzy environment. Although the K values exhibited slight changes, the overall ranking order of the strategies remained consistent across all tested values. This consistency indicates that the prioritization results are stable and not overly sensitive to small changes in λ. Such robustness confirms the reliability of the decision-support framework and reinforces confidence in the recommended strategies for SC4.0 adoption.

6. Implications of the Study

6.1. Theoretical Implications

The research differentiates itself from previous studies on SC digitalization by focusing specifically on the CSFs that organizations need to prioritize and evaluating possible approaches that could help the manufacturing industry transition towards SC 4.0. It adds value to the B2B sector by improving stakeholder communication, real-time oversight, transparency, effective inventory management, and operational flexibility. While previous research primarily examined the advantages and obstacles of SC digitalization, the present work emphasizes the significance of CSFs, boosting confidence in companies preparing for SC 4.0. Gaining a comprehensive understanding of CSFs will allow firms to be better equipped for the transition.

The theoretical contribution of this study lies in its integrative perspective—bringing together circular economy (CE) principles with the digital transformation of supply chains (SC4.0)—a linkage that remains underdeveloped in the current literature. This interdisciplinary lens allows for a deeper exploration of how sustainability and digitalization intersect in shaping future-ready SC networks. Additionally, this study prioritizes the CSFs based on their relative importance and investigates the causal relationships among them. We utilized a combined approach incorporating IFS-ANP-SEM-TOPSIS to evaluate both the importance of the CSFs and their causal links.

To enable this evaluation, a novel hybrid multi-criteria decision-making (MCDM) framework was applied, combining Intuitionistic Fuzzy Sets (IFSs), the Analytic Network Process (ANP), the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This is among the few studies to use the IFS in tandem with causal mapping techniques to assess interdependencies among CSFs in the SC4.0-CE integration context, thus offering methodological advancement over conventional models. Earlier studies on SC digitalization have not used the IFS for such assessments.

By comprehending these causal relationships, industrial management obtains deeper insights into which CSFs are most impactful. Concentrating on the key CSFs will help achieve other related CSFs. The ranking of the CSFs enables management to more effectively prioritize their preparation efforts. Moreover, the strategies to support SC transition were evaluated and can be implemented by companies according to their technological capabilities.

In summary, this research advances the theoretical understanding of digital supply chain transformation by uniquely framing it within the CE paradigm and employing a sophisticated, underutilized MCDM methodology that captures both the importance and interdependence of success factors. This dual contribution helps distinguish the study from the conventional SC4.0 literature.

6.2. Implications for Management and Society

The shift towards SC4.0 is inevitable, and the industrial sector has already begun to adopt it. However, many organizations are uncertain about where to begin and what elements need special attention during this transition. The challenge lies in digitalizing the SC network, especially as technology is constantly advancing. Despite this ongoing evolution, certain critical factors remain relevant across all technologies. This study aims to assist the industrial community by identifying the CSFs that are essential for SC4.0. Understanding these CSFs will help firms prepare for the digital transformation of their SCs.

In today’s digital landscape, organizations cannot avoid adopting digital technologies. From both managerial and societal standpoints, this research emphasizes the importance of CSFs in the SC4.0 transition. Ranking these factors provides the industrial sector with better insight into the key aspects of SC4.0, and prioritizing strategies can ensure a smoother transition. Additionally, understanding the cause–effect relationships between the CSFs enables firms to see how improving one factor can positively impact others.

What sets this study apart is its practical guidance tailored specifically for industrial managers aiming to align digital transformation (SC4.0) with circular economy (CE) goals. While many prior studies have treated SC4.0 and the CE as separate domains, this work offers a unified managerial roadmap that simultaneously advances operational efficiency and sustainability—an increasingly strategic requirement under tightening regulatory and environmental standards.

Moreover, the application of a hybrid IFS-ANP-DEMATEL-TOPSIS framework enables a much deeper and interconnected assessment of digital transformation enablers than traditional linear or single-method analyses. This integrated decision-making model provides managers with a tool to identify high-impact CSFs and assess their mutual influence, ensuring more informed and context-sensitive decisions in digital supply chain planning.

Based on these findings, the study offers several critical recommendations for a successful transition to SC4.0:

• Engage stakeholders in SC digitalization and ensure alignment: In an SC network, the involvement of all stakeholders is crucial. For successful digitalization, every part of the network must undergo this transformation. However, many stakeholders are hesitant due to the high investment costs associated with SC digitalization. The proposed strategies in this work rely heavily on stakeholder agreement. Full digitalization is essential for seamless data transmission through an integrated platform, as well as for data optimization and enhancing analytical capabilities. Raising awareness among stakeholders about the importance of SC4.0 is therefore necessary.
• Develop organizational policies focused on SC digitalization: To remain competitive, companies must regularly update their policies to reflect ongoing technological advancements. Keeping pace with global trends is critical for the long-term success of the industrial sector. Governments should recognize the positive impact digital technologies can have on industry and economic growth, providing both financial and technical support to help organizations implement SC4.0. Consequently, the industrial community should adopt SC4.0 and expedite the digital transformation with support from governmental agencies.

From a societal perspective, governments have tightened environmental regulations to support the achievement of the Sustainable Development Goals (SDGs). Only organizations that meet these regulations are qualified for government subsidies. To obtain these funds and improve their sustainability performance, many companies are prioritizing the integration of CE practices into their supply chain operations. In various industries, especially manufacturing, sustainability performance is evaluated based on criteria such as carbon emissions throughout production, recycling initiatives, the amount of waste collected, the recovery of valuable materials, the use of reclaimed materials in product development, and supply chain transparency.

Many companies are identifying the importance of corporate social accountability and are adopting CE practices to improve their public image. Implementing CE strategies also contributes to job creation. Organizations that utilize CE methods often experience cost savings, as raw materials are retained in the production cycle for as long as possible, significantly reducing resource consumption. CE practices also help minimize health and safety risks by reducing waste generation.

Across industries, many firms are leveraging Internet of Things (IoT) devices to monitor SC operations, allowing for real-time oversight and optimization. The use of 3D printing, a form of additive manufacturing, allows for just-in-time production, dropping inventory costs, main times, and waste. These companies are also employing big data analytics to more accurately predict demand and optimize inventory management. Collaboration among stakeholders is critical for ensuring the robustness and reliability of SC operations, particularly when incorporating CE methods.

To foster the adoption of CE practices, governments have introduced policies such as extended producer responsibility and product stewardship frameworks, holding producers accountable for the entire lifecycle of their products. Reports from industry associations advocate for greater collaboration between public and private sectors in the area of waste recycling. These reports emphasize the importance of establishing transparent, auditable databases to monitor materials collected through CE initiatives and recommend the creation of geographical clusters for waste collection and dismantling.

Educational initiatives, including webinars and other events, are being conducted to increase awareness of circular SC practices. Furthermore, SC companies are increasingly leveraging blockchain technology for real-time tracking and monitoring of waste across stakeholders. However, in spite of growing organizational interest in CE practices, the amount of consumer requests for recycled products remains low due to persistent misconceptions about their quality and reliability. This lack of awareness poses a significant challenge for companies attempting to implement CE practices.

By integrating circular economy thinking into the strategic prioritization of digital SC success factors—supported by a robust, underused decision-making methodology—this study delivers practical insights not only for operational improvement but also for broader societal goals, including sustainability, economic development, and resource efficiency.

By addressing both managerial and societal implications, organizations can better navigate the transition to SC4.0, ensuring not only a competitive edge but also contributing to environmental sustainability and social responsibility. This dual-impact approach—targeting both operational efficiency and sustainability integration—is what distinguishes this research from conventional SC4.0 implementation studies. This holistic approach is essential for the long-term success of the industrial sector and aligns with global efforts to achieve sustainable development.

7. Conclusions, Limitations, and Directions for Future Research

For industries to maintain a competitive edge in the current business landscape, they must embrace technological advancements. Digital technologies have proven their effectiveness in enhancing the agility of SC operations. However, interest in SC 4.0 remains comparatively lower than the enthusiasm shown toward other areas of digital transformation across industries. Nevertheless, digitizing SC processes is essential for improving both agility and resilience, particularly in B2B contexts. Given the present circumstances, this research explores the CSFs necessary for transitioning to SC 4.0. Saudi’s significant role in global manufacturing makes its position especially critical on the international stage. This research identifies the CSFs and outlines strategies for SC 4.0 implementation by combining a literature review with expert insights. The evaluation process employs an integrated MCDM approach using IFS-ANP-SEM-TOPSIS. The findings highlight five major CSFs: the creation of a smart working ecosystem, the monitoring of performance metrics, the accuracy and relevance of data, adaptability to software innovation, and aligning SC 4.0 initiatives with organizational strategy. Additionally, developing analytical capabilities, optimizing processes through data-driven decision-making, and building an integrated digital platform are key strategies for SC 4.0. These results will assist SC professionals in preparing for the SC 4.0 transition, ensuring compliance with regulatory requirements and emerging laws governing digital operations.

By focusing on CSFs, this study emphasizes the importance of transitioning to SC 4.0 and outlines potential strategies to facilitate that shift. The insights gained will be invaluable for SC professionals as they navigate this transition. The CSFs were prioritized using IFS-ANP, providing SC managers with a clearer understanding of each factor’s importance. The causal relationships among these CSFs were explored using IFS-SEM, revealing a hierarchical structure. Finally, the strategies for SC 4.0 transition were ranked through the IFS-TOPSIS method.

Several limitations in this study point to opportunities for future research. First, the findings are based on input from experts in Saudi’s SC network, limiting the generalizability of the results to other countries. Future research should include cross-country comparisons to broaden applicability. Second, while this work integrated multiple MCDM techniques to enhance robustness, it was limited to expert-derived qualitative input; future studies could extend the framework using quantitative industry data or simulation models. Moreover, this study only examines hierarchical and causal relationships among CSFs, leaving room for future exploration of structural relationships using structural equation modeling (SEM). Another limitation lies in the current method for assessing result robustness, which only involves varying λ values. Since expert opinions may differ, future studies should perform sensitivity analysis by assigning different weights to expert inputs.

In conclusion, this research offers a comprehensive, methodologically novel, and sustainability-driven approach to SC4.0 implementation, bridging a gap in both theory and practice, and provides a scalable decision-support model for industrial organizations seeking to digitally transform while aligning with CE goals.