Digital Transformation, Supply Chain Resilience, and Sustainability: A Comprehensive Review with Implications for Saudi Arabian Manufacturing

Abstract

This systematic review examines the critical intersection of digital transformation, supply chain resilience, and sustainability within manufacturing contexts, with specific implications for Saudi Arabian industries. Through a comprehensive analysis of 124 peer-reviewed articles published between 2018 and 2024, we identify how emerging technologies—including Internet of Things (IoT), artificial intelligence, blockchain, and big data analytics—transform traditional supply chains into dynamic ecosystems capable of withstanding disruptions while advancing sustainability goals. Our findings reveal that digital transformation positively influences both resilience and sustainability outcomes. Still, these relationships are significantly moderated by three key factors: supply chain dynamism, regulatory uncertainty, and integration of innovative technologies. The study demonstrates that while high supply chain dynamism amplifies the positive effects of digital technologies on resilience capabilities, regulatory uncertainty creates implementation barriers that potentially diminish these benefits. Moreover, successfully integrating innovative technologies is a critical mediating mechanism translating digital initiatives into tangible sustainability improvements. The review synthesises these findings into an integrated conceptual framework that captures the complex interrelationships between these domains and provides specific strategic recommendations for Saudi Arabian manufacturing organisations. By addressing the identified research gaps—particularly the lack of industry-specific investigations in emerging economies—this review offers valuable insights for researchers and practitioners seeking to leverage digital transformation for simultaneously efficient, resilient, and sustainable supply chain operations in rapidly evolving business environments.

1. Introduction

1.1. Background and Significance

The global business landscape has witnessed unprecedented changes over the past decade, with supply chains experiencing significant transformation due to technological advancements, increasing market volatility, and growing sustainability concerns [1,2]. Today, organisations from various industries are concluding that these traditional supply chain models, i.e., linear processes, limited sharing of information, and operational silos, are no longer sufficient in confronting current complicated business needs [3]. Realising this has accelerated efforts to digitalise the supply chains, make the supply chains more resilient, and adopt sustainable practices [4]. Digital transformation in supply chains represents a paradigm shift from conventional approaches to technology-enabled, data-driven operations. As Ivanov et al. noted, this evolution has completely transformed conventional models, enabling supply chains to integrate unprecedentedly with greater visibility and responsiveness [5]. The integration of technologies like the Internet of Things (IoT), artificial intelligence (AI), blockchain, cloud computing, and big data analytics has enabled the shift from static, reactive structures to dynamic, predictive ecosystems that can optimise in real-time and self-correct [6].

Recent bibliometric studies by Chen et al. have demonstrated a significant trend toward interdisciplinary approaches in technological innovation, particularly those centred on sustainability and efficiency, highlighting the growing importance of integrated technological frameworks in modern supply chain operations [7]. This integration trend is further reinforced by Hu et al., whose visualisation analyses reveal that emerging supply chain technologies enable unprecedented levels of monitoring and coordination, particularly in temperature-sensitive domains like cold chain logistics, with applications that directly parallel digital supply chain transformation challenges in manufacturing [8].

As digital transformation reshapes supply chains, we are witnessing the emergence of Industry 5.0, which represents the next evolutionary step beyond Industry 4.0 [9]. While Industry 4.0 focused primarily on automation, interconnectivity, and data exchange, Industry 5.0 introduces a more balanced approach defined by three fundamental pillars: sustainability, resilience, and human centricity. This paradigm shift recognises that technological advancement must be balanced with environmental responsibility, system robustness, and human welfare. Consequently, Supply Chain 5.0 extends these principles to supply chain management, emphasising sustainability-driven objectives, built-in resilience against disruptions, and collaboration between humans and technology rather than replacement [10].

The integration of these three pillars fundamentally distinguishes Industry 5.0 and Supply Chain 5.0 from their predecessors [11]. While Industry 4.0 primarily focused on efficiency and productivity through automation and connectivity, Industry 5.0 reintroduces the human dimension while simultaneously building environmental sustainability and operational resilience into the core of industrial systems. This evolution is particularly relevant in manufacturing contexts, where balancing technological advancement with ecological impact and human welfare has become increasingly critical [12].

1.2. Research Gaps

Despite the growing literature on digital supply chains, resilience, and sustainability, several significant research gaps remain. While each domain—digital transformation, resilience, and sustainability—has been explored individually, limited research examines their interconnections and potential synergies or trade-offs. Few studies have adopted a holistic approach to understanding how digital technologies simultaneously influence resilience and sustainability outcomes.

The role of moderating factors such as supply chain dynamism and regulatory uncertainty in shaping the effectiveness of digital transformation initiatives remains underexplored. Previous studies have suggested that supply chain dynamism can significantly influence the relationship between digital technologies and organisational outcomes [13,14]. Similarly, regulatory uncertainty has been identified as a critical contextual factor affecting technology implementation and performance [15,16]. However, comprehensive empirical investigations examining how these factors moderate the relationships between digital transformation, resilience, and sustainability are limited, particularly in manufacturing contexts.

Even though many cases document the potential rewards of digital technologies for creating resilience, the practical challenges associated with implementing digital technologies, such as technological complexity, integration complexity, workforce transformation, and project feasibility, need to be examined. Comprehensive frameworks for measuring the effects of digital transformation on resilience and sustainability outcomes do not exist [17]. Most existing metrics are based on a single dimension rather than being multifaceted.

Digital technologies can be applied to enhance resilience and sustainability in various industries because of differences in supply chain structure, regulatory environment and technological requirements. However, industry research is relatively scarce in the industrial sector, especially for the emerging industries such as new energy vehicle (NEV) manufacturing. Today, most researchers pay attention to the immediate or short-term impacts of digital transformation, and less is considered about the long-term effects on supply chain resilience and sustainability, although with potential unintended consequences [18].

Based on the identified research gaps, this study addresses the following key research questions: (1) How do digital technologies transform traditional supply chains? What are the primary implementation challenges and opportunities in manufacturing contexts? (2) What are the critical dimensions of supply chain resilience, and how can digital technologies enhance these capabilities? (3) How do digital transformation initiatives influence supply chain environmental, social, and economic sustainability outcomes? (4) What role do moderating factors such as supply chain dynamism, regulatory uncertainty, and integration of innovative technologies play in shaping the relationships between digital transformation, resilience, and sustainability? (5) What are the specific implications for Saudi Arabian manufacturing organisations operating in a rapidly evolving regulatory and economic landscape? By addressing these questions, this review aims to develop an integrated understanding of how organisations can leverage digital technologies to create simultaneously resilient and sustainable supply chains.

1.3. Review Objectives and Scope

To fill these gaps, this paper systematically reviews the interrelationships between digital transformation, supply chain resilience, and sustainability. A review of digital supply chain transformation, resilience, and sustainability is required, synthesising what is already known about digital supply chain transformation, resilience, and sustainability, using multi-layered theoretical perspectives and empirical evidence. It intends to suggest a general framework that incorporates the intricate relationships between these domains and suitable moderating factors. The review will pull best practices and successful strategies for exploiting the use of digital technologies as a means to improve both resilience and sustainability outcomes in supply chains. Finally, this study assesses current measurement frameworks of resilience and sustainability to determine their ability to gauge the effect of digital transformation on resilience and sustainability, as well as their strengths and weaknesses in accomplishing this goal in many situations. It describes potential avenues of research for future work that fill knowledge gaps regarding the nexus in the relationship between digital transformation, resilience and sustainability. The scope of this review encompasses three primary domains. First, it covers digital supply chain transformation with the journey from traditional to digital supply chains, technologies enabling the transformation, implementation challenges to the digital supply chain, and opportunities ahead. Second, supply chain resilience is discussed from the perspective of multi-dimensionality of resilience, its antecedents and consequences, and ways to measure and improve supply chain resilience capabilities. Thirdly, it covers supply chain sustainability, including the environmental, social, and economic aspects of sustainability and how to increase the sustainability practices through digital transformation.

The review also examines three major moderating factors, namely supply chain dynamism, regulatory uncertainty, and innovative technology integration, to explain the relationships among digital transformation, resilience, and sustainability. This review helps understand how organisations can use digital technologies to create resilient and sustainable supply chains by addressing these objectives in the defined scope.

1.4. Methodology and Review Framework

This paper presents a comprehensive systematic literature review synthesising existing knowledge on digital transformation, supply chain resilience, and sustainability in manufacturing contexts, with specific implications for Saudi Arabian industries. The methodology followed established systematic review guidelines to ensure methodological rigour and transparency in the literature synthesis process.

1.4.1. Literature Search and Selection Strategy

This study systematically searched scholarly literature published between January 2018 and February 2024 across major academic databases, including Scopus, Web of Science, IEEE Xplore, Science Direct, and Emerald Insight. Our search strategy was developed through a rigorous process that involved identifying key domains, defining relevant keywords within each domain, and constructing comprehensive search strings using appropriate Boolean operators.

Search Keywords and Boolean Operators

The systematic search was structured around three primary conceptual domains, with specific keywords for each domain:

Digital Transformation Domain: “digital transformation” OR “digital supply chain” OR “supply chain digitalisation” OR “Industry 4.0” OR “Industry 5.0” OR “smart supply chain” OR “digital technologies” OR “IoT” OR “Internet of Things” OR “artificial intelligence” OR “blockchain” OR “big data analytics” OR “cloud computing” OR “automation” OR “robotics”.

Resilience Domain: “supply chain resilience” OR “resilient supply chain” OR “supply chain risk” OR “disruption management” OR “supply chain recovery” OR “supply chain adaptability” OR “supply chain flexibility” OR “supply chain robustness” OR “business continuity” OR “risk mitigation”.

Sustainability Domain: “sustainable supply chain” OR “supply chain sustainability” OR “green supply chain” OR “environmental performance” OR “social sustainability” OR “economic sustainability” OR “triple bottom line” OR “circular economy” OR “sustainable operations” OR “corporate social responsibility” OR “ESG”.

Saudi Arabian Context: “Saudi Arabia” OR “KSA” OR “GCC” OR “Gulf Cooperation Council” OR “Middle East manufacturing” OR “Vision 2030” OR “Saudi manufacturing” OR “MENA region” OR “emerging economy”.

We constructed our search strings by combining these domain-specific keywords using Boolean operators in the following structure:

(Digital Transformation Domain) AND (Resilience Domain OR Sustainability Domain) AND (Manufacturing OR Industry OR Production OR Factory).

For searches specifically related to the Saudi Arabian context, we used:

(Digital Transformation Domain) AND (Resilience Domain OR Sustainability Domain) AND (Saudi Arabian Context).

Database-Specific Search Implementation

For each database, we adapted our search strings to conform to the specific syntax and search capabilities of that database. For example:

Scopus:

TITLE-ABS-KEY((“digital transformation” OR “digital supply chain” OR “Industry 4.0”) AND (“supply chain resilience” OR “sustainable supply chain”) AND (“manufacturing” OR “industry”)) AND PUBYEAR > 2017.

Web of Science:

TS=((“digital transformation” OR “digital supply chain” OR “Industry 4.0”) AND (“supply chain resilience” OR “sustainable supply chain”) AND (“manufacturing” OR “industry”)) AND PY=(2018–2024).

Additional targeted searches were conducted to ensure comprehensive coverage of research specific to Saudi Arabian manufacturing contexts:

TITLE-ABS-KEY((“digital transformation” OR “digital supply chain”) AND (“Saudi Arabia” OR “Vision 2030”) AND (“manufacturing” OR “industry”)) AND PUBYEAR > 2017.

Literature Selection Process

The initial search yielded 743 potentially relevant articles. After removing 157 duplicates, 586 articles underwent screening, resulting in 241 articles for full-text review. We established inclusion criteria requiring peer-reviewed publications addressing at least two domains relevant to manufacturing contexts. We excluded non-scholarly sources, single-domain studies, and papers without manufacturing relevance.

To specifically address the Saudi Arabian manufacturing focus, we employed a three-tiered approach:

• Studies conducted within Saudi Arabian manufacturing organisations (27 papers)
• Research from Gulf Cooperation Council countries with similar contexts (43 papers)
• Studies from comparable emerging economies with transferable insights (54 papers)

This resulted in 124 articles that met all criteria, forming our core literature base. Figure 1 presents the PRISMA flow diagram illustrating this selection process.

The systematic implementation of this comprehensive search strategy ensured that our review captured the breadth and depth of relevant literature across the intersecting domains of digital transformation, supply chain resilience, and sustainability, with specific attention to the Saudi Arabian manufacturing context.

1.4.2. Analysis and Synthesis Framework

The review employed a structured analytical framework to examine and synthesise the selected literature. This framework facilitated extracting and organising key information from each publication, including theoretical foundations, digital technologies examined, resilience dimensions addressed, sustainability aspects covered, moderating factors identified, key findings, and contextual factors relevant to manufacturing industries.

The analytical approach combined several complementary methods to synthesise the findings. Descriptive analysis identified publication trends, geographic distribution of studies, predominant theoretical approaches, and technology focus areas. Content analysis enabled systematic categorisation of the qualitative conclusions to identify recurring themes, concepts, and relationships reported across studies. Thematic synthesis involved identifying cross-cutting themes and conceptual linkages between digital transformation, resilience, and sustainability through iterative coding and theme development.

We conducted the comparative analysis to systematically examine findings across different industrial contexts, geographic regions, technological implementations, and theoretical frameworks. This comparative work informed the development of an integrated conceptual framework mapping the relationships between key constructs, identifying moderating factors, and highlighting contextual influences relevant to the Saudi Arabian manufacturing sector.

1.4.3. Quality Assessment of Literature

The selected literature was evaluated for methodological quality and theoretical contribution using established criteria for literature reviews. This assessment considered the clarity of research questions, theoretical foundations, methodological rigour, findings validity, and contribution significance. Articles were classified into three quality tiers (high, medium, or acceptable) based on these criteria and among the 124 included articles, 47 (38%) were rated high quality, 62 (50%) medium quality, and 15 (12%) acceptable quality, ensuring that our review was based on methodologically sound literature.

1.4.4. Contextual Focus on Saudi Arabian Manufacturing

To address the specific implications for Saudi Arabian manufacturing, we identified and analysed a subset of literature focused on Saudi Arabian manufacturing contexts, comparative studies including Saudi Arabia and other Gulf Cooperation Council countries, and studies from similar emerging economies with transferable insights. This focused analysis examined contextual factors influencing digital transformation in Saudi Arabian manufacturing, including Vision 2030 policy implications, regulatory environment characteristics, industrial diversification initiatives, workforce transformation challenges, and industry-specific adoption patterns. This targeted examination allowed us to develop contextually appropriate recommendations that account for the unique economic, regulatory, and industrial landscape of Saudi Arabia.

1.4.5. Limitations of the Review Methodology

This systematic review has several methodological limitations that should be acknowledged. Despite the comprehensive search strategy, some relevant literature may have been missed, particularly works in non-indexed journals or languages other than English. The rapid evolution of digital technologies means that recent innovations may be underrepresented in the published literature. There is limited research specifically focused on the Saudi Arabian manufacturing context, necessitating careful extrapolation from similar contexts. These limitations were addressed through careful documentation of the review methodology, transparent reporting of inclusion criteria, and contextual interpretation of findings.

2. Digital Supply Chain Transformation

2.1. Evolution from Traditional to Digital Supply Chains

Effective digital supply chain management significantly changes how businesses run operations throughout the value chain, from a traditional to a digital supply chain. This existed within conventional supply chains, characterised by linear, sequential processes with little information sharing among stakeholders that resulted in operational silos and inefficiencies. The digital evolution of supply chains has completely transformed these conventional models, meaning supply chains can now integrate in ways never seen before, having greater visibility with higher responsiveness [19]. The early adoption of basic information technologies to automate the individual supply chain functions marked the beginning of the journey towards the digital supply chain. In the first phase, internal processes were digitised mainly to improve efficiency. According to Nasiri et al., the following evolution consisted of assimilating these individual digital solutions into more global systems capable of linking different parts of an organisation [20]. In the 1990s, the advent of enterprise resource planning (ERP) systems aided in this integration phase as it triggered companies to integrate different business processes into a single information system. When viewed as a part of Industry 4.0, today’s digital supply chain is a quantum leap over the preceding stages. Zhang et al. underscore that this evolution is not just technological but also fundamental in the evolution of the organisational culture, business model and collaborative relations [14]. Using the former approach, the traditional supplier-customer relationships were based on transactional exchanges. At the same time, they later transformed into strategic partners through deep collaboration, resulting in mutually shared digital capabilities. For instance, the researchers say that the digital supply chain transition in new energy vehicle (NEV) manufacturing has greatly permitted unprecedented supplier integration to improve supply chain resilience. The further characterised transition is from periodic batch-oriented information flows to continuous real-time data streams, enabling dynamic decision making. Historically, manufacturing companies have had to respond to market changes and disruptions quickly, as this has reduced information latency. Backhaus and Nadarajah see that this shift has dramatically reduced information latency. The companies that have succeeded with digital supply chain technologies could reduce lead times and perform better in operations [21]. The evolution of digital supply chain technologies is listed in

Table 1. Evolution of digital supply chain technologies.

Era	Key Technologies	Characteristics	Supply Chain Impact
Traditional Supply Chain (Pre-1990s)	Basic information systems	Linear, sequential processes; Limited information sharing; Operational silos	Functional optimisation; Limited visibility, Batch-oriented information flows
Early Digital Integration (1990s–2000s)	ERP systems, EDI, and Basic automation	Integration of individual functions; Internal process digitisation	Improved internal efficiency, Better coordination, Reduced manual processes
Connected Supply Chain (2000s–2010s)	Cloud computing; Early IoT; Mobile technologies	Connected processes; External integration; Real-time data collection	Enhanced visibility, Improved collaboration, and more responsive operations
Industry 4.0 (2010s–Present)	IoT; AI/ML; Blockchain; Big data analytics; Advanced robotics; 5G	Dynamic, predictive ecosystems; Autonomous capabilities; Real-time optimisation	End-to-end visibility; Self-correction; Predictive capabilities; Strategic partnerships
Future Trends (Emerging)	Digital twins, Quantum computing, Extended reality (AR/VR/MR), Advanced AI	Immersive environments; Cognitive capabilities; Hyper-personalisation	Resilient by design; Circular economy enablement; Autonomous networks

.

2.2. Key Components and Technologies

Digital supply chain transformation is driven by a range of technologies that together enable new capabilities and business models (

Table 2. Key smart technologies in digital supply chains.

Technology	Primary Function	Resilience Contribution	Sustainability Contribution	Implementation Challenges
Internet of Things (IoT)	Real-time monitoring of assets, inventory, and environmental conditions	Enhanced visibility, Early warning systems, and Rapid response	Resource usage monitoring, Environmental parameter tracking, Waste reduction	Connectivity issues, Data management, Device security
Artificial Intelligence (AI) and Machine Learning	Pattern recognition, Prediction, Autonomous decision-making	Predictive maintenance, Demand forecasting, Disruption prediction	Optimisation of resource usage; Emissions reduction; Waste minimisation	Algorithm transparency, Data quality, Skills requirements
Blockchain	Secure, immutable record-keeping; Smart contracts	Supply chain traceability; Secure information sharing	Ethical sourcing verification; Environmental compliance tracking	Scalability, Integration complexity, Energy consumption
Cloud Computing	Scalable infrastructure, Remote access, and Collaboration platforms	Information sharing, Process integration, Distributed operations	Reduced resource requirements; Efficient computing usage	Data security, Regulatory compliance, Dependency risks
Big Data Analytics	Processing large, complex datasets; Pattern recognition	Risk identification, Performance monitoring, Scenario analysis	Impact measurement, Resource optimisation, Compliance monitoring	Data quality, Analytical capabilities, and Integration with existing systems
Advanced Robotics and Automation	Physical task execution; Process automation	Labour continuity, Consistent operations, Reduced human intervention	Precision in resource usage, Energy efficiency, and Waste reduction	Cost, Integration with legacy systems, Workforce impacts
5G and Advanced Connectivity	High-bandwidth, low-latency communication	Real-time coordination, Remote operations, Fast data exchange	Optimisation of transportation, Energy management, Remote monitoring	Infrastructure requirements, Coverage limitations, Security concerns

). The research conducted by Belhadi et al. classifies these technologies as a set of key components that constitute the digital supply chain [22]. The Internet of Things is the sensory system of the digital supply chains through which connected devices and sensors make physical processes visible in real time. The progress in IoT deployments in retail supply chains, as reported by De Vass et al., has been in having better visibility about the movement of a product, storage environment, and transport status. This continuous monitoring of assets and inventory on the supply chain creates a digital twin of physical operations through these technologies. The IoT has laterally generated massive data streams which, when effectively analysed, give actionable insight into the opportunity of optimisation and risk mitigation [23]. Artificial Intelligence (AI) and Machine Learning (ML) provide the cognitive functions of digital supply chains. These technologies translate raw data to make sense of things and autonomously act.

Nwagwu et al. state that AI accomplished through manufacturing and logistics organisations enables predictive maintenance, demand forecasting, and intelligent routing, significantly impacting supply chain performance. AI algorithms can detect patterns invisible to human analysts, predict future trends with remarkable accuracy, and continually learn from new data to enhance their performance [24]. Blockchain technology solved trust and transparency challenges in the digital supply chain. Blockchain’s role is to give sustainable supply chains, as noted by Rejeb et al., the ability to keep records immutably and to share information securely. Blockchain removes the need for intermediaries and verifies records at a lower cost by creating secure, tamper-proof records of transactions and events. This technology is very valuable because such supply networks and the necessity for such strict regulatory requirements are so common in the pharmaceutical and food industry [25]. The proper infrastructure for digital supply chain functions comes from cloud computing. Perano et al. state that cloud-based supply chain management solutions improve performance by enabling information sharing and process integration across organisational boundaries. The democratisation of digital transformation on the cloud platforms lets smaller organisations access more advanced supply chain features and accelerate their response times without making a huge capital outlay [26]. Big data analytics is the process of transforming massive structured as well as unstructured data into actionable insights. According to Bahrami et al., big data analytics capabilities strengthen the resilience and innovation levels of the supply chain by enabling better information processing. Our analytical tools help organisations uncover hidden patterns, predict the next trend, make operations run well and build a data-driven strategy. This further integrates big data analytics with AI, which can make autonomous decisions in a complex supply chain environment [27].

Physical automation is now provided by advanced robotics and automation in digital supply chains. As Bashar et al. described, robotics and automation technologies are increasingly infused with digital to establish a perfect blend of physical and digital workflows [28]. Operating with precision, Autonomous guided vehicles (AGVs), warehouse robots, and Collaborative robots (cobots) perform physical tasks to aid in continuous optimisation through the data feed. Advanced connectivity technologies such as 5G make up the communication infrastructure of the digital supply chains. Lundgren et al. studied 5G technology’s influence on manufacturing performance and found that it enhances operational efficiency and responsiveness. Real-time, high bandwidth and low latency networks allow communication between the devices, systems and stakeholders in a way that supports real-time data communication [29]. AR/VR enable human capability in a digital supply chain. Particularly for these technologies, visualisation tools that facilitate decision-making, training and operational execution were developed. Resources in the field of warehouse and logistics show that implementing AR warehouse apps has reduced picking errors and increased worker productivity, which indicates the importance of augmented human capabilities in digital supply chains [30]. Integration of these technologies yields digital supply chain platforms that orchestrate entire end-to-end processes with unparalleled efficiency and resilience. Metwally et al. have emphasised that the synergistic effects of these technologies simultaneously on supply chain sustainability and operations sustainability instead make the most significant improvement on supply chain sustainability and operational performance [31].

2.3. Challenges and Opportunities

The transformation to digital supply chains presents significant challenges and extraordinary opportunities for organisations across industries. Understanding these factors is crucial for developing effective implementation strategies and realising the full potential of digital technologies in supply chain management.

2.3.1. Challenges

Technological complexity and integration issues are significant barriers to adopting the digital supply chain. Hizam-Hanafiah and Soomro write that many manufacturing companies lack knowledge of the scope of the different innovative technologies available and their suitability in particular organisational contexts. It further compounds the complexity due to requirements to run new technologies in an ecosystem with or for legacy systems that necessitate significant investments in infrastructure and people expertise [32]. As the demand for digitising the supply chain requires organisations to transform their workforce and address skills gaps, there is a growing need to adopt new digital technologies. Reza et al. highlight that manufacturers’ poor ability to adapt to the transformation in technology development at an out-of-control speed is partly due to a poor understanding of innovative technology implementation [33]. In this knowledge gap, we have the technical skills to operate new technology gaps and the strategic capabilities to reimagine supply chain processes in a digital context. Because supply chains are becoming more digital, data security and privacy become increasingly important. In their study, Sulaiman et al. acknowledge that cybersecurity risks pose significant dangers to the digital supply chain operations, as they may expose sensitive information and disrupt critical functions. Data ownership, access rights, and regulatory compliance become more challenging with increasing data sharing across organisational boundaries [34]. Financial constraints and ROI uncertainty can inhibit digital transformation initiatives for smaller organisations. The cost of undertaking the comprehensive digital supply chain implementations is also substantial, and combined with the inability to quantify their benefits, they are also a financial challenge. As pointed out by Nasiri et al., organisations are unable to develop convincing business cases for digital investments because they are uncertain about long-term returns and implementation costs [35]. Often, organisational resistance to change undermines a digital transformation’s effort. The fundamental changes that need to occur in traditional organisational structures, processes and cultures may not be welcomed by conventional structures, processes and cultures. Therefore, due to entrenched operational practices and organisational silos, companies with limited digital supply chain integration often find it challenging to improve the sustainable supply chain performance [36].

2.3.2. Opportunities

The first significant benefits of introducing the digital transformation include unprecedented visibility and transparency at every step within the supply chain. Büyüközkan et al. define digital supply chains as digital places that enable firms to identify where products, materials and information flow to the granular level. It enhances visibility, improving project risk management and stakeholder communication [6]. The digital supply chain’s optimisation capabilities lead to improved operational efficiency and cost reduction. According to Soomro et al., applying innovative technologies solves the above challenges in digital supply chains. It could help significantly reduce costs through more optimal utilisation of resources, less waste and faster processes. Based on their research, they found that digital supply chain transformation has a positive impact on the cost performance in manufacturing operations [37]. Critical advantages of digital supply chains include increased market changes and disruptions, and increased agility and responsiveness. The fact that artificial intelligence-driven innovation can improve supply chain resilience by enhancing organisations’ ability to detect and respond to disruptions is shown by Belhadi et al. Characterising the value of this agility, it is beneficial for environments of very high dynamism, characterised by persistent changes in customer preferences, competitive landscape, and regulatory requirements [38]. The capabilities enabled by digital supply chains create new business models for distribution and new revenue streams. Ivanov holds that digital supply chain management technologies can contribute to the innovation of service offerings, personalised products, and platform-based business models that introduce new revenue opportunities. These business model innovations often go beyond the conventional product-centric approach towards data-oriented services and collaborative value creation [39]. Due to the increasingly important opportunity of enhanced sustainability performance, digital supply chains are becoming increasingly important. By improving resource efficiency through IoT technologies, reducing waste and monitoring the environmental perspective, De Vass et al. emphasise the role of IoT technologies in promoting sustainable supply chain practices [23]. Digital technologies make it possible to increasingly accurately measure and manage environmental impacts, which assists organisations with achieving their sustainability goals and meeting regulatory requirements. Digital supply chains enable an ecosystem to collaboratively and seamlessly integrate their supply chains. Digital technologies’ enabled supplier integration will have the most favourable effects on supply chain resilience in NEV manufacturing enterprises [14]. Digital platforms make it easier to break organisational boundaries and facilitate smoother collaboration for joint innovation, resource sharing, and collective solutions to problems. However, depending on the industry, region and organisational context, a different mix of these challenges and opportunities will be considered. The social transformation of the digital supply chain may be successfully achieved only through long-term planning, optimisation strategies and change management tools based on the technological and organisational aspects of change, as approached by Scholz et al. [40].

3. Supply Chain Resilience

3.1. Definitions and Dimensions

The need for supply chain resilience (SCR) as a critical capability at an organisation’s disposal in an increasingly volatile, uncertain, complex, and ambiguous environment represents its significance in the modern supply chain. Since the initial formulation, the concept has developed substantially in new ways, with different dimensions and interpretations amongst academic and practitioner communities. The essence of supply chain resilience lies in having the supply chain prepared and ready to tackle, survive and recover from disruptions to continuously maintain operations (Table 3). However, different aspects of this capability have been noted in scholarly definitions. In their paper, Rice and Caniato define SCR as “the ability of a company to respond quickly and to recover from unexpected disruptions.” Such a definition shows a reactive dimension of resilience, offering how quickly it is to respond and recover [41]. According to de Carvalho Gomes, SCR can be characterised as the capability of a system to return promptly to its original state after disruptions. These perspectives point towards a more holistic view of resilience as a property of the network supply chain, implying that resilience is a property of the overall supply chain and not individual organisations [42]. The scope of SCR has been revised to be anticipatory and adaptive.

Zouari, D, characterises SCR as the capacity to survive, adapt, and grow in the face of changing disturbances, introducing a developmental aspect that points to sustainable supply chains that can survive and thrive in the face of disturbances [43]. The concept of resilience, as it relates to definition, comes with an evolution resulting from growing acknowledgement that resilience is not simply the ability of something to ‘bounce back’ but to ‘bounce forward’ utilising the knowledge resulting from any disruption. Ponomarov and Holcomb give one of the most complete definitions regarding how the definition includes three stages of preparation, response and recovery and how the supply chain’s capacity to respond to unforeseen events and disruption, maintaining structural and functional stability through three stages of preparation, response, and recovery [44]. The dimensions of supply chain resilience are similarly diverse, reflecting the multifaceted nature of this capability. Based on the literature review, several key dimensions emerge: Anticipatory capability refers to identifying future interruptions before they happen. This dimension involves risk identification, environmental scanning and early warning systems to make good use of risk management. This capacity permits organisations to attend and react quickly to changes brought about by supply chain interruptions [45]. A supply chain’s adaptive capacity is the ability to reconfigure supply chain configurations in response to changing conditions. This dimension involves flexibility of sourcing, production, distribution and business models which permit the organisation to continue working in the face of disturbances. Acquaah et al. stress its role in facilitating organisations to deal with changes in the external environment and overcome the associated disruption events [46]. Recovery capacity refers to how well operations can be returned to normal, if not better, conditions following disruptions. The dimensions of this include redundancy and restoration protocol in the critical resources and planned business continuity planning to maintain business function and minimise the days of disruption. Blackhurst et al. refer to the ability to recover from disruption to return to operational stability [47].

The collaborative capacity includes the capability to work effectively with the supply chain partners in times of disruptions. Information sharing and coordinated problem solving, along with coordinated response efforts that utilise the combined network capacity of the supply chain network, are included in this dimension. Yao and Fabbe-Costes refer to this as a complex adaptive capability in supply networks to maintain dynamic balance in the face of disruptive events [48]. Cavadi, G., further expands these dimensions by adding technological aspects of resilience in artificial intelligence for gaining anticipatory, adaptive and recovery capacities. This research shows that AI-inspired innovation is critical to the supply chain’s resilience, especially in high-dynamic environments where conventional resilience methods may not be sufficient [49]. Together, these dimensions constitute a multifaceted supply chain resilience capability bundled with technological and organisational elements and relational dimensions. Operating from this holistic perspective is essential, as it will enable the development of effective resilience strategies that tackle the various challenges in the contemporary supply chain.

Table 3. Dimensions of supply chain resilience.

Dimension	Definition	Key Components	Digital Enablers	References
Anticipatory Capability	Ability to identify potential disruptions before they occur	Risk identification, Environmental scanning, Early warning systems	Predictive analytics, IoT sensors, AI/ML algorithms	[44]
Adaptive Capacity	Ability to reconfigure operations in response to changing conditions	Flexible sourcing, Production flexibility, Alternative logistics	Real-time analytics, Digital twins, Cloud-based platforms	[50]
Recovery Capacity	Ability to return to normal operations following disruptions	Redundancy, Restoration protocols, Business continuity planning	Automation, Cloud backups, Distributed systems	[41,47]
Collaborative Capacity	Ability to work effectively with partners during disruptions	Information sharing, Coordinated problem-solving, and Joint response	Collaborative platforms, Blockchain, Shared dashboards	[48]
Technological Resilience	Technology-enabled capabilities to enhance other resilience dimensions	AI-driven disruption management; Digital disruption detection	AI innovation, Digital platforms, Advanced analytics	[51]
Operational Resilience	Practical capabilities to maintain operations during disruptions	Supplier diversity, Inventorybuffers, Flexible manufacturing	Digital supply networks, Smart factories, Autonomous logistics	[14]

Table 3. Dimensions of supply chain resilience.

Dimension	Definition	Key Components	Digital Enablers	References
Anticipatory Capability	Ability to identify potential disruptions before they occur	Risk identification, Environmental scanning, Early warning systems	Predictive analytics, IoT sensors, AI/ML algorithms	[44]
Adaptive Capacity	Ability to reconfigure operations in response to changing conditions	Flexible sourcing, Production flexibility, Alternative logistics	Real-time analytics, Digital twins, Cloud-based platforms	[50]
Recovery Capacity	Ability to return to normal operations following disruptions	Redundancy, Restoration protocols, Business continuity planning	Automation, Cloud backups, Distributed systems	[41,47]
Collaborative Capacity	Ability to work effectively with partners during disruptions	Information sharing, Coordinated problem-solving, and Joint response	Collaborative platforms, Blockchain, Shared dashboards	[48]
Technological Resilience	Technology-enabled capabilities to enhance other resilience dimensions	AI-driven disruption management; Digital disruption detection	AI innovation, Digital platforms, Advanced analytics	[51]
Operational Resilience	Practical capabilities to maintain operations during disruptions	Supplier diversity, Inventorybuffers, Flexible manufacturing	Digital supply networks, Smart factories, Autonomous logistics	[14]

3.2. Antecedents and Outcomes

The development of supply chain resilience is influenced by various antecedents, categorised into organisational factors, supply chain characteristics, and environmental conditions. Understanding these antecedents is crucial for identifying leverage points to enhance resilience capabilities (

Table 4. Antecedents and outcomes of supply chain resilience.

Category	Factor	Description	Digital Transformation Linkage	Key References
Organisational Antecedents	Organisational Culture and Leadership	Learning-oriented culture; Leadership commitment to resilience	Digital leadership; Technology adoption culture	[52,53]
	Resource Availability	Financial reserves, Technological infrastructure, Human expertise	Digital capability investments; Technology resource allocation	[14,54]
	Risk Management Capabilities	Risk identification; Assessment; Mitigation; Monitoring	AI-enabled risk analytics; Digital risk management platforms	[37,38]
Supply Chain Antecedents	Supply Chain Integration	Internal, supplier, and customer integration	Digital integration platforms; Information sharing systems	[6,14]
	Network Structure and Complexity	Network topology, Connectivity patterns, Structural redundancy	Digital network modelling; Network optimisation tools	[5,39]
	Technological Capabilities	Digital infrastructure, Analytical capabilities, and Automation	Smart technologies; IoT; AI; Blockchain	[22,51]
Environmental Antecedents	Supply Chain Dynamism	Market volatility; Rate of innovation; Demand fluctuations	Real-time monitoring, Adaptive algorithms, Predictive analytics	[55]
	Regulatory Uncertainty	Policy changes; Compliance requirements; Standard evolution	Regulatory intelligence systems; Compliance platforms	[56,57]
	Industry Characteristics	Technological intensity; Market structure; Competitive dynamics	Industry-specific digital solutions; Sector technology platforms	[14,17]
Resilience Outcomes	Operational Continuity	Maintained functionality, Process stability, Service levels	Automated responses; Digital continuity planning	[41,47]
	Financial Performance	Cost management, Revenue protection, Profitability	Digital cost optimisation; Revenue analytics	[58,59]
	Competitive Advantage	Reputation; Reliability; Opportunity exploitation	Digital differentiation; Customer experience enhancement	[53,60]
	Sustainability Performance	Resource efficiency, Stakeholder relationships, Risk reduction	Integrated sustainability-resilience digital platforms	[13,61]
	Organisational Learning	Knowledge retention, Process improvement, Capability development	Digital knowledge management; AI-enabled learning systems	[44,45]

).

3.2.1. Organisational Antecedents

Supply chain resilience development is strongly related to organisational culture and leadership [52]. As with resource allocation, strategic prioritisation and personal contribution, leadership commitment to resilience positively impacts the development of resilience capabilities. Supply chain resilience is dependent on resource availability and resource allocation. Zhang et al. employed the resource-based view theory, in which it is argued that the capacity of organisations to develop and deploy valuable, rare and difficult to imitate resources is highly crucial in determining a firm’s resilience capability. These resources are financial reserves, technological infrastructure, human expertise and information systems that support the management of the act of disruption [54]. The so-called risk management capabilities are essential precedents toward supply chain resilience. According to Belhadi et al. [38], supply chain risk management practices mediate the relationship between supply chain integration and resilience. Better capable organisations that identify, assess, mitigate, and monitor risks will be better positioned to implement effective resilience strategies more geared to their profiles of vulnerabilities.

3.2.2. Supply Chain Antecedents

In current research, supply chain integration is a large antecedent to resilience. Empirical evidence is given by Zhang et al. that internal integration, supplier integration and customer integration all positively impact the supply chain resilience in NEV manufacturing enterprises. Information sharing, collaborative planning, and coordinated response efforts are promoted through these integration mechanisms, enabling the aggregation of reactance capacity in the form of the organisation’s collective resilience capabilities [14]. The supply chain resilience of an organisation is related to the capacity of the network and its complexity for development and maintenance. Ivanov argues that relevance and resilience in intertwined supply networks depend on topology characteristics, such as node centrality, connectivity patterns, and structural redundancy. More complex networks may also suggest better flexibility and alternative pathways, while seemingly more straightforward network structures may be easier to monitor and manage during disruptions [39]. More often, technological capabilities are used as antecedents to supply chain resilience, particularly digital technological capabilities.

3.2.3. Environmental Antecedents

Belhadi et al. reported that supply chain dynamism is a key moderator of the relationship between AI-driven innovation and supply chain resilience. Technological innovations significantly positively affect the emergence (resilience) of high-dynamic environments. This indicates that emergence (resilience) depends on context [55]. Regulatory conditions determine resilience strategies and capabilities. This implies that the effort of building resilience can be adversely affected by unpredictable regulatory environments and, thus, the approach to resilience development has to be context-specific. Similarly, the development of resilience patterns is affected by industry characteristics. Since the NEV industry is a high-tech industry, Zhang et al. indicate that the NEV industry has characteristics in technological innovation, market volatility, and supply chain complexity, which are all essential elements to its resilience needs and capabilities. These industry factors merit tailoring resilience strategies to the particular sector context [14].

3.2.4. Outcomes of Supply Chain Resilience

Supply chain resilience outcomes have an operational, financial and strategic dimension covering the integrated effect of resilience capabilities on organisational performance. Operational continuity and recovery represent primary outcomes of supply chain resilience. It is beneficial for financial performance to increase supply chain resilience. According to Y Han et al., when the issue of supply chain resilience is considered, a positive relationship exists between financial performance and supply chain resilience, reflected in the ability to manage disruptions and lower recovery costs. In terms of financial benefits, besides cost avoidance during disturbance, retained customer service levels lead to enhanced revenue generation [58]. Delivering superior supply chain resilience capabilities provides the source of the competitive advantage. In a resource-based view theory that Sun et al. apply, this gives the sustainable competitive advantage, resilience capabilities that are valuable, rare, inimitable and non-substitutable. The advantages come out here in the form of reliability, reputation for dependability and ability to take advantage of opportunities created by disruptions. Supply chain resilience outcomes are becoming increasingly linked to sustainability performance. Hervani, A. A et al. also develop viable supply chain models in which resilience capabilities support environmental and social sustainability objectives by integrating resilience, agility, and sustainability perspectives. These contributions include reduced waste from disruptions, better use of resources, and better stakeholder relationships [61]. Organisational learning and adaptation represent developmental outcomes of effective resilience practices. Ponomarov and Holcomb suggest that resilient supply chains not only recover from disruptions but also learn from these disruptions for more efficient future dealings. In this way, the learning process continues with continuously improving resilience strategies and being more flexible as an organisation [44]. The relationship among these antecedents and outcomes is neither straightforward nor straightforward. Still, the relationships are complex, with interactions and feedback loops amongst these antecedents and outcomes.

3.3. Measurement Frameworks

Supply chain resilience is a multidimensional and contextual variable, which adds to the challenges of measuring supply chain resilience. Researchers and practitioners have developed several frameworks to quantify resilience capabilities from different perspectives and methodologies. Qualitative Assessment Frameworks analyse how resilience capabilities can be accessed via structured evaluation of those who comprise an organisation, its processes and relationships. Based on the literature review, Francesco and Asha, A.A. et al., identified the key factors affecting supply chain resilience: flexibility, agility, speed, visibility, and redundancy. Typically, these qualitative frameworks use expert assessments, case studies, and semi-structured interviews to build a comprehensive understanding of the resilience capacities of their components. Scholten and Schilder used qualitative methods to understand how collaborative activities positively affect supply chain resilience. Their research focuses significantly on the importance of sharing information, cooperative relationships and joint learning processes for developing collective resilience capabilities [62]. Likewise, Tukamuhabwa et al. use interviews to establish the complex interrelations between threats, strategies, outcomes, and network embeddedness in supply chain resilience development [63]. Quantitative Measurement Approaches are numerical methods of determining resilience capabilities through statistical analysis of operational data, survey responses and simulation results. Applying graphical and interpretive structural modelling (ISM), Soni et al. determined the key factors influencing supply chain resilience and their interrelated relationships. These approaches make it easier to compare organisations and periods more precisely and with data-driven decisions in resilience investments [64]. Liao et al. used the supply chain resilience theory to build mathematical models for analysing supplier relationships in demand disruptions. They revealed that supplier relationships strongly correlate with supply resilience during disruptions and that partnering with the resilient supplier increases supply chain resilience [65]. The applicability of these quantitative approaches enables us to use them to understand specific resilience mechanisms and how they are operational. Composite Measurement Frameworks combine qualitative and quantitative components to qualify all aspects of resilience. Alenezi et al. illuminate how AI marketing tools contribute to Industry 5.0 implementation through three critical pillars: sustainability, resilience, and customer engagement. Their research demonstrates that AI-driven technologies enhance digital transformation processes and create a more comprehensive framework for businesses to develop adaptive capabilities that simultaneously strengthen operational resilience and sustainability outcomes [66]. Time-based resilience metrics are the ones that pay attention to having metrics on the temporal aspects of the disruption Management process, like detection time, response time, recovery time, and stabilisation time. They are practical metrics of the efficiency of resilience capability available in actual disruption cases. Ponomarov and Holcomb expanded on this temporal aspect of measurement by incorporating the impacts metric, both magnitude and duration of disruption impacts, in their definition of supply chain resilience [67].

Resilience Indices and Benchmarking tools consolidate indicators into standardised metrics to compare resilience by organisations, industries and periods. Building on this, Pettit et al. proposed a comprehensive framework to ensure supply chain resilience. They discussed the assessment tools that can be used to balance vulnerability factors against capability factors to optimise resilience positions. Usually, these indices combine both the capability measure (leading indicators) and performance during actual disruption (lagging indicators) to form a holistic assessment [68]. Using simulation and modelling approaches, resilience capabilities can be predicted through computational experimentation with disruption scenarios. Spieske and Birkel et al. discuss approaches to strengthening supply chain resilience through Industry 4.0 [69]. The simulation is a suitable approach to evaluate a potential resilience-enhancing intervention. Organisations can test resilience strategies without the need to embed them in place, thus, making the investment decision more informed.

Different measurement framework choices are based on specific organisational contexts, resilience objectives, and available data resources. In alignment with the challenges to specific industrial supply chain configurations, one of the everyday challenges of the measurement frameworks lies in segregating the potential resilience (capabilities developed regularly before disruptions) from the realised resilience (actual performance during disruptions). Then, comprehensive measurement approaches typically cover both dimensions, as there must still be a correspondence between resilience capabilities and value through effective disruption management.

4. Supply Chain Sustainability

4.1. Environmental Dimensions

In recent years, organisations have increasingly recognised the importance of environmental sustainability in their supply chains as they strive to minimise their ecological footprint throughout their entire value chain. As per Umar, M. et al., environmental sustainability and green supply chain performance are indivisibly interdependent, and supply chain operations majorly impact various environmental indicators [70]. Primary ecological concerns in the supply chain management are the Carbon footprint and greenhouse gas emissions. Raw materials, components, and finished products are being transported, resulting in many carbon emissions that are responsible for causing climate change. According to de Vass et al., IoT technologies in the retail supply chain can develop monitoring and optimise transportation routes and reduce fuel consumption and its resulting emissions [71]. Environmental sustainability is influenced mainly by energy consumption and the efficiency of operations throughout the supply chain. Ünal, B.B. et. al., suggested that the energy efficiency in supply chains can be improved by the optimised processes, an intelligent energy management system, and renewable energy integration through digital transformation. Innovative technologies and digital monitors for various data centres, manufacturing, warehouses, transportation networks and facilities create opportunities for energy optimisation [72].

The circular economy principles for resource utilisation include using materials within a supply chain. As stated by Bag et al., Industry 4.0 technologies create opportunities for a more precise tracking and management of the resources throughout their lifecycle, supporting the closed-loop supply chains, eliminating waste and generating as much value recovery as possible [73]. Typically, these circular approaches are replacing traditional linear supply chains, which necessitate new measurement systems and performance indicators linked to resource productivity rather than throughput efficiency. Waste generation and management covers supply chains’ by-products, packaging materials and end-of-life products. Wang, Q. et al. suggest that supply chain integration can harm the environmental performance if not aligned with sustainability objectives. Their research emphasises the significance of including waste reduction targets into supply chain design and operation to prevent unintended environmental results from integration initiatives resulting in efficiency [74]. Water utilisation and quality impacts have become increasingly prominent environmental dimensions, especially in watersheds where such impacts are water-intensive and in areas of water scarcity. Supply chain operations influence water consumption patterns and pollution levels through manufacturing processes, agricultural practices, and waste management methods. Digital technologies allow for more precise water resource monitoring and management in the supply chain, helping to contribute to conservation efforts and compliance with water regulations. Direct resource consumption has a biodiversity and ecosystem impact and a broader impact on natural habitats and species diversity. Raw material (or land), extraction, use conversion, and pollution can majorly impact biodiversity through supply chain activities. Biodiversity considerations are increasingly included in sustainable supply chain decisions on how to source, what to source, and how to assess environmental impacts associated with particular suppliers. Integrating digital technologies into supply chain management provides new opportunities for dealing with the environmental dimensions of this problem. According to Atieh Ali et al., using digital supply chains to increase monitoring capabilities, optimisation algorithms and decision support systems can reduce the need for resource consumption and environmental impacts. Their research underscores the role of innovative technologies in mediating the relationship between the use of innovative technologies in digital supply chain transformation and sustainability outcomes [75]. Similar to Belhadi et al. [22], AI-driven innovation in supply chains can optimise transport routes, predict future breakdowns that break resources to prevent them in artificial ways, and identify opportunities to reduce waste that might be invisible to human analysts [66]. However, digital supply chains are not an automatic or specific solution to environmental problems. Su et.al. (2021) note that digital technologies require high energy and material resource consumption and may contribute a new set of environmental issues, even as some were addressed [76]. For sustainable digital supply chains, these tradeoffs must be carefully considered, and the life cycle environmental impact balancing must be done based on life cycle thinking and accounting for direct and indirect environmental impact.

4.2. Social Dimensions

The social dimensions of supply chain sustainability encompass the impacts of supply chain operations on people and communities throughout the value network. These dimensions have become highly relevant over the past few years as stakeholders have become more demanding and want to be more transparent and responsible about labour practices, community relations, and social impacts at the ends of global supply chains. Acting under supply chain considerations concerns labour conditions and human rights, which are fundamental social considerations. As Zhang et al. pointed out, manufacturing businesses—especially those in emerging economies—seek to address supply network issues about labour and human rights compliance [14]. Digital supply chain technologies provide more effective monitoring and verification of labour conditions, ultimately improving compliance with international standards, corporate social responsibility conformances, underlying supply chain relationships, and market outcomes. In hazardous production processes or under working conditions, worker health and safety are essential social performance indicators for the industry. Huma et al. show that supply chain quality integration positively impacts sustainability performance, including performance outcomes in workplace safety. To augment the capabilities of supply chain safety risk identification and reduction, digital technologies, including IoT sensors, wearable devices, and AI-based risk analysis systems, are brought into use [77].

4.3. Economic Dimensions

From an economic perspective, supply chain sustainability centres on delivering and distributing financial value over the long term while providing value over time. This shows that these dimensions tie economic performance to environmental and social aspects. Thus, no sustainable supply chains would be economically sound without being environmentally and socially efficient. Financial performance and value creation represent foundational economic considerations in sustainable supply chain management. As per AlMulhim, innovative supply chain technologies secure better firm performance through improved operational efficiency, lower costs and support for innovation. Digital supply chains lead to more precise optimisation of the use of resources, inventory levels, logistics operations, and financial, environmental and social objectives [59]. The long-term viability and risk management part has something to do with the capacity of supply chains to keep going economically in the face of disruptions and changing market conditions. These capabilities are enabled by digital technologies as they help in the identification and analysis of risks, scenarios, and adaptive response mechanisms that sustain economic performance during turbulent times. They have the capabilities of innovation and adaptability, thus, allowing them to be continuously present in rapidly changing markets and remain economically relevant and competitive. Belhadi et al. show that AI-driven innovation improves the resilience and performance of the supply chain, especially in a highly dynamic environment, and several researchers suggest additional future research on how AI could be incorporated successfully in the supply chain operations [66]. The digital supply chains enable continuous innovation through improved information processing, collaborative development platforms and rapid experimentation capabilities that will allow quick adaptation to changing customer preferences and market dynamics. A value distribution and economic inclusivity determine how the financial benefits are shared with supply chain participants. Distribution of equitable value among all participants, from raw material suppliers to the last end user service providers, increases economic sustainability by ensuring the financial viability of all. Blockchain and innovative contract technologies provide more transparent and automated procedures for value distribution, which offer fair compensation for actions and performance metrics. The total cost and life cycle economics take a deeper view and apply the traditional cost accounting beyond the product lifecycle, including the externalities cost. Liu et al. argue that sustainable supply chains consider direct operational costs and broader economic impacts such as future cost of environmental remediation or social conflicts [78]. This expanded economic dimension is supported by digital technologies that improve capabilities such as life cycle assessment, valuation of externality, and predictive analytics that quantify the long-term economic impacts. Introduction of digital technology into supply chain management brings new economic opportunities, but at the same time, it requires a high level of investment. In fact, with Zhang calling to keep a close and thoughtful watch on the business case for digital investment savings (both in short-term tangible financial returns and in longer-term intangible benefits like resilience, reputation, and risk management), there is a lot to be considered here [79]. With this comprehensive economic assessment, more strategic decisions concerning digital supply chain investments and prioritisation of implementation are also given.

Table 5. Digital supply chain sustainability dimensions.

Sustainability Dimension	Key Indicators	Digital Enablers	Measurement Approaches	Key References
Environmental	Carbon footprint and GHG emissions	IoT monitoring, Route optimisation; Digital twins	Carbon accounting software; Environmental management systems	[70,71]
	Energy consumption and efficiency	Intelligent energy management; Process optimisation	Energy monitoring platforms; Efficiency analytics	[72,75]
	Circular economy implementation	Resource tracking, Lifecycle management	Blockchain for circular materials tracking; Digital product passports	[73,78]
	Waste generation and management	Innovative waste management; Predictive maintenance	IoT-enabled waste monitoring; AI waste reduction	[18,74]
	Water utilisation and quality	Water usage monitoring, Quality sensors	Digital water management systems; Real-time quality monitoring	[31,71]
	Biodiversity and ecosystem impacts	Habitat monitoring; Supply chain mapping	Remote sensing technologies; Geospatial analytics	[56,78]
Social	Labour conditions and human rights	Supply chain transparency; Compliance verification	Blockchain verification; Digital auditing platforms	[14,25]
	Worker health and safety	IoT safety monitoring; Hazard prediction	Wearable safety technologies; AI risk prediction	[38,77]
	Diversity, equity and inclusion	Workforce analytics; Supplier diversity tracking	Digital diversity tracking platforms; Inclusive sourcing systems	[61,80]
	Community engagement	Stakeholder platforms; Impact monitoring	Digital engagement tools, Social impact analytics	[63,81]
	Fair trade and ethical sourcing	Provenance tracking; Value distribution	Blockchain verification, Smart contracts for fair payments	[1,5]
	Cultural heritage and indigenous rights	Digital mapping; Community consultation	Digital cultural mapping; Consultation platforms	[82,83]
Economic	Financial performance and value creation	Cost optimisation; Revenue analytics	Digital financial management: Performance dashboards	[37,59]
	Long-term viability and risk management	Predictive risk analytics; Scenario planning	Digital risk intelligence platforms; Simulation tools	[27,51]
	Innovation and adaptability	Digital R and D platforms; Market intelligence	Digital innovation management: Collaborative platforms	[84,85]
	Value distribution and economic inclusivity	Value chain analysis; Equity monitoring	Smart contracts, Digital value tracking	[6,26]
	Total cost and lifecycle economics	Lifecycle analysis; Externality valuation	Digital lifecycle assessment tools; Impact valuation platforms	[76,78]
	Investment efficiency and capital allocation	Investment analytics; ROI modelling	Digital investment management; Portfolio optimisation tools	[20,35]

lists the key indicators, digital enablers and measurement approaches in digital supply chain sustainability dimensions.

5. Moderating Factors

This section examines three important moderating factors: supply chain dynamism, regulatory uncertainty, and innovative technology integration. It details the complexities in the mix between digital transformation inputs and resultant resilience efforts to achieve a sustainable supply chain.

5.1. Supply Chain Dynamism

Supply chain dynamism refers to the speed and degree of unpredictability of change within supply chain settings governed by rapid and transformative shifts of supply chain processes, goods, corporations, and technologies [75].

5.1.1. Conceptualisation of Supply Chain Dynamism

By employing three key indicators earlier defined by Zhang et al. earnings volatility from products and services as a measure of variability in revenue streams that call for proactive supply chain processes; rate of process innovation as the pace with which new operational processes are made and implemented; and product innovation as an indication of how frequently and quickly products are changed in ways that alter supply chain activities, the dynamism of a supply chain can be metered [14]. For instance, manufacturing companies in Saudi Arabia must consider their exposure to supply chain dynamics to reduce performance fluctuations and make suitable strategic decisions. The Open Innovation and Participatory Theory (OIPT) presents a theory for understanding the dynamics of a supply chain that constrains the processes of information sharing and supply chain management.

5.1.2. Impact on Digital Transformation and Resilience Relationship

Digital transformation has a substantially weaker relationship with supply chain resilience in the presence of supply chain dynamism. For Belhadi et al., dynamism of the supply chain leads to better efficiency of its components and further increases the positive effect of digital technologies on the capabilities of resilience. The authors found that artificial intelligence-driven innovation had a stronger link to supply chain resilience in highly dynamic supply chains [66]. This moderation effect can be explained from the perspective of dynamic capability theory. Organisations are operating in very dynamic environments, bringing greater uncertainty and disturbances. It becomes increasingly valuable to possess digital capabilities that detect, respond to, and recover from these disruptions. Real-time monitoring of environmental changes through digital technologies and the capacity to make quick decisions and respond agilely to disturbances is crucial in dynamic situations. This implies that Saudi Arabian manufacturing organisations will gain greater resilience benefits if they invest in digital transformation initiatives within more dynamic industry segments or market conditions. Such digital investments may yield subtle ‘ripples’ or more significant ‘waves’ of resilience increase for companies operating in stable and predictable environments.

5.1.3. Sustainability: Key Challenges and Promising Opportunities

For enterprises, it is imperative to grasp the total extent of supply chain dynamism within their specific context to develop appropriate strategies that support resilience and performance. Research gaps still exist, while some knowledge has been gained about building dynamic capabilities in supply chain management. These capabilities should be further studied regarding their development processes, implementation, and contributions to organisational performance and supply chain adaptability in the Saudi Arabian manufacturing. Furthermore, organisational trade-offs between adaptation and stability warrant more attention in dynamic supply chains due to a potential reliance on reliability and efficiency rather than adaptation and responsiveness.

5.2. Regulatory Uncertainty

In line with previous literature in supply chain management, regulatory uncertainty (i.e., the unpredictability of changes in government regulations and policies as defined by Wang et al.) is recognised as a critical moderating factor in the relationship between the digital transformation of Saudi Arabia’s manufacturing sector and a combination of supply chain resilience and sustainability [56].

5.2.1. Nature of Regulatory Uncertainty

Environmental uncertainty focuses on regulatory uncertainty, significantly impacting strategic decision-making and operational effectiveness. In the Saudi Arabian context, regulatory uncertainty emerges from several sources: economic diversification initiatives, where the government’s efforts to reduce dependence on oil revenues through Vision 2030 have led to rapid changes in industrial policies, subsidies, and incentives; localisation requirements, with evolving regulations regarding local content and employment (such as Saudization policies) creating uncertainty for manufacturing operations; environmental regulations, as an increasing focus on sustainability has resulted in evolving ecological standards and compliance requirements; and trade policies, where changes in import/export regulations, tariffs, and international trade agreements affect supply chain design and operations.

5.2.2. Moderating Effect on Digital Transformation and Resilience

Such a moderating effect may be explained by several mechanisms. High levels of regulatory uncertainty increase information processing complexity, as seen in the case where information processing requirements first increase [86]. In such conditions, the contribution of integrating information resources (through digital transformation) to supply chain resilience may diminish, since changes in policies and regulations affect normal business operations [86]. Second, due to the higher costs and development time associated with emerging technology, as uncertainty in government regulations rises, technology adoption encounters barriers to digital transformation aimed at boosting resilience [87]. Third, strategic planning challenges emerge amid regulatory uncertainty, which complicates long-term planning for organisations, hindering digital transformation initiatives to achieve resilience establishment goals [88].

5.2.3. Industry-Specific Implications

As shown by Hoffman et al., even if industry-induced regulatory uncertainty differences are not large, geographical variations in regulatory uncertainty exist [57]. This indicates that Saudi Arabia’s manufacturing sector is regulated differently depending on the location and across various industrial zones. To effectively achieve digital transformation and build resilience in manufacturing organisations in Saudi Arabia, it is essential to understand and implement strategies for navigating regulatory uncertainty. Companies must learn to monitor regulatory changes, assess their implications, and adapt their approach accordingly. This may include utilising regulatory intelligence systems to leverage digital tools for monitoring and analysing regulatory developments; employing scenario planning to create multiple strategic scenarios based on various potential regulatory outcomes; designing flexible implementation approaches to ensure digital transformation initiatives have the adaptability necessary to accommodate regulatory changes; and fostering stakeholder engagement to proactively interact with regulatory authorities and industry associations to anticipate and potentially influence regulatory developments.

5.3. Smart Technologies Integration

The integration of innovative technologies plays a critical moderating role in the relationship between digital transformation, supply chain resilience, and sustainability outcomes. Innovative technologies, including Internet of Things (IoT), artificial intelligence (AI), blockchain, cloud computing, and big data analytics, represent the technological infrastructure that enables digital transformation initiatives to enhance resilience capabilities and drive sustainability performance.

5.3.1. Moderating Role in Digital Transformation and Resilience

Innovative technologies integration reduces the impact of digital transformation initiatives on supply chain resilience outcomes. The research by Belhadi et al. demonstrates that artificial intelligence-driven innovation enhances supply chain resilience during high environmental dynamism. The extent to which digital transformation can foster resilience depends on successfully integrating suitable innovative technologies. Several mechanisms facilitate this moderating effect. Real-time visibility into supply chain operations is achieved through innovative technologies, where IoT sensors and big data analytics provide enhanced visibility and situational awareness for rapidly identifying disruptions and improved response planning. AI-driven predictions are achievable through machine learning systems, which evaluate historical operational data and current datasets to anticipate events. This capability allows organisations to prepare for emergency recoveries proactively. Decision support systems and automated processes operate more efficiently, as innovative technologies supply relevant information for decision-making and may execute routine choices independently to reduce response times during disruptions. Integrated innovative technology systems endow supply chains with adaptable designs that swiftly tailor operations in response to changing market demands and recovery situations.

5.3.2. Impact on Sustainability Performance

Integrating innovative technologies in operations diminishes the connection between digital transformation activities and developing supply chain resilience and sustainable results. Initiating smart technology implementation within digital supply chains positively affects both environmental efficiency and cost outcomes for manufacturing operations, according to Atieh Ali et al. [75]. This moderating effect is manifested through resource optimisation, as innovative technologies enable more efficient utilisation of resources, reducing waste and environmental impact while enhancing economic performance; environmental monitoring and compliance, where IoT sensors and analytics tools facilitate monitoring of ecological parameters and regulatory compliance, supporting improved environmental sustainability; supply chain transparency, through blockchain and other transparency-enhancing technologies, which improve visibility into social and environmental practices across the supply chain, supporting both social and ecological sustainability dimensions; and operational efficiency, where the integration of innovative technologies improves process efficiency, reducing energy consumption, emissions, and costs, thus, supporting both environmental and economic sustainability.

5.3.3. Challenges and Implementation Considerations

The successful implementation of innovative technology encounters multiple barriers when applied to Saudi Arabian manufacturing industry operations. Technological complexity exists because manufacturing facilities face difficulties analysing the numerous innovative technologies available to choose suitable solutions (Hizam-Hanafiah and Soomro) [57]. Many manufacturing organisations struggle to integrate innovative technologies due to their reliance on established legacy systems, which prove challenging when uniting with new smart technology systems. Successful implementation demands innovative technical capabilities, often challenging to find within the local workforce (Reza et al.) [89]. Data security and privacy risks increase due to innovative technology connectivity, which creates potential security threats and protection challenges (Sulaiman et al.) [90]. Organisations face significant challenges in determining investments because large innovative technology implementation expenses require transparent business cases and strong return on investment projections. Saudi Arabian manufacturing organisations need to implement a strategic plan for innovative technology integration by assessing organisational preparedness, selecting impactful applications, and developing support and governance structures.

5.3.4. Research Gaps and Future Directions

The Saudi Arabian supply chain context demands greater research to clarify innovative technology applications that boost supply chain resilience and sustainability [91]. Future research should explore technology selection frameworks to guide the choice of appropriate innovative technologies based on specific resilience and sustainability objectives; implementation roadmaps to create structured approaches for adopting innovative technologies in manufacturing supply chains, considering the unique characteristics of the Saudi Arabian business environment; performance measurement to develop metrics and assessment methods to evaluate the effectiveness of innovative technologies in enhancing resilience and sustainability outcomes; and contextual factors to investigate the organisational, cultural, and environmental aspects that influence the successful integration of innovative technologies in Saudi Arabian manufacturing organisations [60,82,92,93,94].

5.4. Integration of Moderating Factors

Supply chain dynamism, regulatory uncertainty, and the integration of innovative technologies influence the relationships among digital transformation, supply chain resilience, and sustainability. These three factors do not operate in isolation; they affect and influence one another. Developing successful strategies within Saudi Arabian manufacturing depends on understanding these interacting factors.

5.4.1. Interactions Between Moderating Factors

Supply Chain Dynamism and Regulatory Uncertainty

When supply chain dynamism meets regulatory uncertainty, it creates effects that enhance or reduce the separate impacts of each factor one by one [95]. Organisations operating in supply chains characterised by high dynamics and significant regulatory uncertainty encounter multiplied difficulties when attempting to establish digital transformation and resilience measures. Businesses in volatile market situations with unclear regulatory circumstances must employ innovative tactics to address their digital transformation needs and resilience challenges [96,97]. Organisations in stable supply chain environments under low regulatory uncertainty have the opportunity to develop structured digital transformation plans that enhance supply chain resilience over time. Systematic implementation and planning become feasible because the market and regulatory requirements exhibit predictable patterns [98,99].

Supply Chain Dynamism and Smart Technologies Integration

The level of supply chain dynamism determines which innovative technologies deliver optimal benefits to sustainability and resilience enhancement in the supply chain [84]. Supplied environments benefit most from technologies that combine real-time monitoring with rapid decision-making and agile response capabilities, including IoT, AI, and advanced analytics. The hardware integration of innovative technologies produces a more pronounced impact on the resilience outcomes of digital transformation at dynamic supply chain locations [100]. The strategic choice for Saudi Arabian manufacturing organisations in dynamic market segments is to invest in innovative technologies that focus on agility before efficiency-maximising technologies, which will create better sustainability and resilience benefits [101,102,103].

Regulatory Uncertainty and Smart Technologies Integration

The extent of supply chain dynamism directly determines which innovative technologies bring the most value when improving resilience and sustainability [104]. Dynamic supply chain environments make technologies with real-time monitoring, rapid decision-making, and agile responses, such as IoT, AI, and advanced analytics, especially beneficial [105]. The level of supply chain dynamism determines how much innovative technology integration enhances the relationship between digital transformation and resilience results (Figure 2). Manufacturing organisations in Saudi Arabian dynamic market segments should direct investments toward innovative technologies that facilitate agility, as such technologies offer stronger sustainability compared to efficiency optimisation technologies suitable for stable industries [106].

5.4.2. Comprehensive Moderation Framework

The complete framework that combines these three moderating factors enhances the understanding of digital transformation’s effects on resilience and sustainability across various business environments. This framework suggests that the effectiveness of digital transformation in improving supply chain resilience and sustainability depends on the environmental context, characterised by the level of supply chain dynamism and regulatory uncertainty, which collectively define the complexity and predictability of the operating environment; the technological infrastructure, reflected in the selection and integration of appropriate innovative technologies that align with both the environmental context and organisational objectives; and managerial capabilities, including the ability to monitor and respond to environmental changes, select and implement suitable technologies, and align digital transformation initiatives with resilience and sustainability goals. For Saudi Arabian manufacturing organisations, applying this comprehensive framework involves context assessment by evaluating the specific levels of supply chain dynamism and regulatory uncertainty facing the organisation, taking into account both industry-specific and geographical factors; technology strategy through developing an innovative technology integration strategy that aligns with the identified context, prioritising technologies that address the most significant challenges and opportunities; capability development by building the organisational competencies needed to effectively implement digital transformation initiatives and enhance resilience and sustainability in the specific operating context; and adaptive implementation by establishing flexible approaches that can adapt to changes in the moderating factors over time, ensuring the sustained effectiveness of digital transformation efforts [81,83,107,108,109,110,111]. Organisations must utilise a comprehensive framework because no universal method exists to improve supply chain resilience and sustainability through digital transformation. Organisations must create tailored strategies for their specific circumstances when addressing their moderating factors.

5.4.3. Practical Implications

Saudi Arabian manufacturing organisations that aim to enhance supply chain resilience through digital transformation should consider these moderating factors, as they impact practical outcomes [112]. Organisations must comprehensively examine specific supply chain dynamics and regulatory risks before initiating digital transformation to ensure initiatives align with operational requirements [85,113,114]. Scenario planning is essential since changes may occur in moderating factors; by developing various implementation scenarios, organisations can identify triggering points to adjust their strategies when conditions change. Technology portfolio management takes precedence over singular technology emphasis, as organisations need to create coherent groups of innovative technologies to address operational risks and environmental requirements in diverse operational scenarios [115,116,117]. Integrating diverse expertise under one governance structure facilitates effective management of interconnected moderating factors. Organisations should collaborate with industry associations, technology providers, and academic institutions to develop collaborative ecosystems to tackle common challenges related to these moderating factors, potentially leading to industry-wide standards [118,119,120].

5.4.4. Research Agenda

The research needs further exploration to understand how different moderating factors function together in a Saudi Arabian manufacturing environment. A future research agenda should include empirical studies with quantitative research examining how different combinations of supply chain dynamism and regulatory uncertainty influence the effectiveness of digital transformation initiatives in enhancing resilience and sustainability; longitudinal analyses tracking how changes in these moderating factors over time affect the sustainability of digital transformation benefits, particularly as Saudi Arabia’s regulatory environment evolves under Vision 2030; technology-specific investigations exploring how various innovative technologies perform under differing levels of supply chain dynamism and regulatory uncertainty, providing guidance for technology selection in specific contexts; capability development models identifying the organisational capabilities needed to effectively manage these moderating factors and successfully implement digital transformation initiatives in different operating contexts; and cross-industry comparisons with comparative analyses of how these moderating factors influence digital transformation outcomes across different manufacturing subsectors in Saudi Arabia, identifying industry-specific patterns and best practices [121,122,123,124]. Strategies to research supply chain resilience and sustainability through digital transformation initiatives in Saudi Arabian manufacturing will lead to a deeper understanding of digital transformation’s practical impacts on the continually evolving Saudi Arabian manufacturing context.

6. Discussion and Implications

6.1. Interpretation of Key Findings

This systematic review reveals significant interconnections between digital transformation, supply chain resilience, and sustainability in manufacturing contexts. Our analysis indicates that digital technologies substantially enhance resilience capabilities through improved visibility, predictive analytics, and autonomous decision-making. IoT and AI technologies demonstrate robust associations with anticipatory resilience capabilities. In the Saudi Arabian manufacturing context, where Industry 4.0 adoption aligns with Vision 2030 goals, the varying maturity of digital infrastructure across sectors presents both implementation challenges and opportunities. The relationship between digital transformation and sustainability outcomes is nuanced. While technologies enable improvements in environmental performance through resource optimisation, their relationship with social sustainability is more complex. Studies from emerging economies like Saudi Arabia indicate that technological implementation without concurrent workforce development may create unintended social impacts, underscoring the importance of human-centred approaches to digital transformation in the Saudi context. Supply chain dynamism emerged as a significant moderating factor, with digital transformation yielding greater resilience benefits in highly dynamic environments. This finding is particularly relevant for Saudi Arabian manufacturers in volatile global markets like petrochemicals. Similarly, regulatory uncertainty can diminish the effectiveness of digital transformation, a critical consideration in Saudi Arabia’s rapidly evolving regulatory landscape, where economic diversification efforts are creating new compliance requirements and industrial policies.

6.2. Theoretical and Practical Implications

Our findings extend resource orchestration theory by demonstrating how digital capabilities interact with contextual factors to create resilience and sustainability outcomes. They contribute to contingency theory by identifying moderating factors influencing digital transformation effectiveness. They also advance dynamic capabilities theory by clarifying how digital technologies enable adaptive capacities under varying environmental conditions. Our integrated framework offers Saudi Arabian manufacturers a strategic roadmap for digital transformation that balances efficiency, resilience, and sustainability. Organisations should adopt phased implementation approaches, prioritising foundational visibility capabilities before advancing to predictive and autonomous capabilities. Success requires integrating technological changes with organisational transformation, particularly in the traditional manufacturing environments prevalent in Saudi Arabia. For Saudi Arabia’s industrial development initiatives, our findings suggest that regulatory frameworks should evolve to support digital adoption while addressing emergent risks. Industrial policy should facilitate technology transfer while building local capabilities, and education policies should address the digital skills gap that constrains transformation efforts.

6.3. Future Research Directions

Several promising research avenues emerge from this review. Industry-specific studies examining the effects of digital transformation in key Saudi Arabian manufacturing subsectors would provide valuable contextual insights. Future research should explore the socio-technical dimensions of digital transformation, particularly analysing how workforce transformation and organisational culture influence implementation success.

Further research is needed on integrated measurement frameworks that effectively quantify the contributions of digital technologies to both resilience and sustainability outcomes. Comparative studies examining different approaches to digital transformation across Gulf Cooperation Council countries would identify transferable best practices while acknowledging unique national contexts. Research on emerging technologies that enable circular economy business models represents another promising direction as Saudi Arabia diversifies its economy and addresses environmental challenges.

6.4. Limitations and Methodological Reflections

This review has several limitations. The recent emergence of digital supply chain literature means empirical evidence of long-term impacts remains limited, with many studies presenting theoretical frameworks or short-term case studies rather than longitudinal investigations. Publication bias may influence our findings, as studies reporting positive relationships are more likely to be published than those finding neutral or harmful relationships.

Limited research specifically addressing Saudi Arabian manufacturing necessitated extrapolation from similar economies, which may not fully account for the unique characteristics of the Saudi industrial landscape. Additionally, the rapid evolution of digital technologies means recent publications may not reflect current technological capabilities. Despite these limitations, this review provides a comprehensive synthesis of current knowledge regarding the relationships between digital transformation, resilience, and sustainability in manufacturing contexts, offering a foundation for theoretical advancement and practical application in Saudi Arabia.

6.5. Theoretical Propositions

Based on the comprehensive literature review and analysis conducted in this study, we propose the following theoretical propositions that encapsulate the relationships between digital transformation, supply chain resilience, sustainability, and their moderating factors in manufacturing contexts:

Proposition 1.

Digital transformation positively influences supply chain resilience through enhanced visibility, predictive capabilities, and autonomous decision-making, with the strength of this relationship increasing as the integration of innovative technologies increases.

Proposition 2.

Digital transformation positively influences sustainability outcomes in manufacturing supply chains, with environmental sustainability benefits realised more directly than social sustainability benefits.

Proposition 3.

Supply chain resilience is a mediating mechanism between digital transformation and sustainability performance, particularly during periods of disruption.

Proposition 4.

The positive relationship between digital transformation and supply chain resilience is moderated by supply chain dynamism and is stronger under high dynamism.

Proposition 5.

Regulatory uncertainty negatively moderates the relationship between digital transformation and supply chain resilience, weakening it under high regulatory uncertainty.

Proposition 6.

Integrating innovative technologies moderates the relationship between digital transformation and resilience and sustainability outcomes, and more comprehensive and strategic integration strengthens these relationships.

Proposition 7.

In the Saudi Arabian manufacturing context, the effectiveness of digital transformation initiatives is contingent upon the alignment between technological implementation, organisational capabilities, and the regulatory environment shaped by Vision 2030 policies.

Proposition 8.

The simultaneous pursuit of resilience and sustainability through digital transformation requires balancing sometimes competing objectives, with this balance best achieved through integrated measurement frameworks and governance structures.

These propositions offer a theoretical foundation for future empirical research examining the complex interrelationships between digital transformation, resilience, and sustainability in manufacturing contexts. They also provide a basis for developing more specific hypotheses tailored to particular industry contexts within the Saudi Arabian manufacturing sector.

7. Conclusions

This systematic review has examined the intersection of digital transformation, supply chain resilience, and sustainability within manufacturing contexts, with implications for Saudi Arabian industries. Our analysis revealed that digital transformation positively influences both resilience and sustainability outcomes, significantly moderated by supply chain dynamism, regulatory uncertainty, and the integration of innovative technologies. Our integrated framework for Saudi Arabian manufacturing organisations pursuing Vision 2030 objectives guides balancing operational efficiency, resilience, and sustainability through strategic digital transformation.

Several limitations should be acknowledged. The rapidly evolving nature of digital technologies means recent innovations may not be fully captured in the literature reviewed. Limited empirical research specifically focused on Saudi manufacturing necessitated extrapolation from similar economies. Our methodology’s focus on peer-reviewed publications potentially excluded valuable insights from industry reports and practitioner knowledge. The complex interactions between moderating factors in different industry subsectors require further investigation. Finally, as many organisations are in the early stages of digital transformation, longitudinal evidence of sustained benefits remains limited.

Despite these limitations, this review offers a synthesis of current knowledge that provides a theoretical foundation for future research and practical guidance for Saudi Arabian manufacturing organisations. Future research should address these limitations through industry-specific investigations and longitudinal studies examining contextual factors influencing digital transformation outcomes in Saudi Arabian manufacturing.