1. Introduction

1.1. Research Background

In recent years, global supply chains have faced unprecedented disruptions. The COVID-19 pandemic severely disrupted production, logistics, and demand, while trade wars have introduced uncertainties in international trade policies. These disruptions have highlighted the vulnerability of traditional supply chain models. Digital technologies have emerged as a crucial solution for modernizing supply chains. Technologies like the Internet of Things (IoT), big data analytics, and cloud computing enable real-time monitoring, data-driven decision-making, and seamless communication across supply chain nodes.

The Yangtze River Delta (YRD) region in China is a vital manufacturing hub. SMEs in the YRD manufacturing sector play a significant role in driving economic growth, creating jobs, and contributing to innovation. However, they are particularly susceptible to supply chain disruptions due to their limited resources and capabilities. Therefore, exploring how digital transformation can enhance their supply chain resilience is of great practical significance.

1.2. Research Objectives

The primary objectives of this study are as follows. First, to assess the impact of digital transformation on supply chain resilience in SMEs based in the YRD. Second, to identify the barriers and enablers that affect the adoption of digital transformation in these enterprises. Finally, to propose effective strategies for enhancing supply chain resilience through digital transformation, aiming to provide practical guidance for SMEs in the region.

1.3. Significance of the Study

Despite the growing interest in digital transformation and supply chain resilience, there is a lack of empirical studies specifically focusing on the relationship between the two in SMEs, especially in emerging economies like China. This study fills this gap by conducting in-depth research in the YRD manufacturing sector. The findings can inform regional industrial policies, helping policymakers design more targeted support frameworks for SMEs. For SME managers, the study provides actionable insights that can guide their digital transformation strategies and improve supply chain resilience.

2. Literature Review and Theoretical Framework

2.1. Digital Transformation in Supply Chains

Digital transformation (DT) in supply chains represents a comprehensive reconfiguration of traditional operational models through the integration of digital technologies. At its core, DT encompasses multiple dimensions. Automation is a fundamental aspect, where tasks that were once labor-intensive and error-prone are automated using technologies such as robotic process automation (RPA) and intelligent software systems. This not only increases operational efficiency but also reduces the margin of human error in supply chain processes, from order processing to inventory management.

Data-driven decision-making is another pivotal dimension. With the explosion of data in the digital age, supply chains generate vast amounts of information at every stage. Big data analytics, machine learning algorithms, and artificial intelligence are employed to extract meaningful insights from this data. For example, analyzing historical sales data, customer behavior patterns, and market trends can enable more accurate demand forecasting, allowing enterprises to optimize production schedules and inventory levels, thereby reducing costs and enhancing responsiveness.

Connectivity, enabled by the Internet of Things (IoT) and cloud computing, is also integral to DT. IoT devices embedded in products, vehicles, and warehouses collect real-time data on location, condition, and usage, providing seamless visibility across the supply chain. Cloud computing, on the other hand, offers scalable storage and computing power, facilitating data sharing and collaboration among supply chain partners regardless of geographical location.

In the manufacturing sector, numerous case studies illustrate the successful application of DT. Industry 4.0, often seen as the epitome of digital transformation in manufacturing, has transformed factories into smart, interconnected ecosystems. For instance, in German automotive manufacturing, factories use IoT sensors to monitor the performance of production equipment in real-time. Predictive maintenance algorithms analyze the sensor data to predict when a machine is likely to fail, enabling maintenance teams to schedule repairs proactively, minimizing downtime, and improving overall production efficiency. Similarly, in the electronics manufacturing industry, companies leverage big data analytics to optimize the supply chain, from sourcing raw materials to delivering finished products. By analyzing data on component availability, production lead times, and transportation costs, they can make informed decisions to streamline operations and enhance competitiveness.

2.2. Supply Chain Resilience: Concepts and Metrics

Supply chain resilience (SCR) is the ability of a supply chain to withstand disruptions, adapt to changing circumstances, and recover quickly to maintain normal operations. It comprises several key components. Visibility refers to the ability to track the movement of goods, information, and finances throughout the supply chain in real-time. With high visibility, enterprises can detect potential disruptions early, such as delays in raw material shipments or bottlenecks in production, and take proactive measures to mitigate their impact.

Flexibility allows supply chains to adjust rapidly to changes in demand, supply, or external factors. This could involve quickly reconfiguring production lines to produce different products, switching suppliers in case of shortages, or modifying distribution routes in response to transportation disruptions. Redundancy is another crucial element, which involves having backup options for critical resources, processes, or suppliers. For example, maintaining multiple suppliers for key raw materials or having excess production capacity in reserve can ensure continuity of operations during disruptions.

Collaboration among supply chain partners is essential for building resilience. Sharing information, resources, and risks can enhance the collective ability of the supply chain to respond to challenges. By working together, partners can coordinate efforts to address disruptions, such as jointly developing contingency plans or sharing inventory during shortages.

The Ponomarov & Holcomb’s model is a widely recognized framework for understanding SCR. It defines resilience as the capacity of a supply chain to absorb, adapt, and recover from disruptions. Metrics used to measure SCR within this model include the time taken to recover from a disruption, the percentage of orders fulfilled on time during a disruption, and the ability to maintain service levels despite external shocks. Other frameworks also incorporate factors such as the supply chain's agility in responding to changes and its overall robustness in the face of uncertainties.

2.3. DT-SCR Nexus in SMEs

The relationship between digital transformation and supply chain resilience in SMEs can be theoretically explained by the Resource-Based View (RBV) and Dynamic Capabilities Theory. According to the RBV, firms possess a set of resources, both tangible and intangible, that can be a source of competitive advantage. Digital technologies, when effectively integrated into SMEs' supply chain operations, can be considered valuable, rare, inimitable, and non-substitutable resources. For example, a unique digital platform developed by an SME for managing its supply chain relationships can give it an edge over competitors, as it is difficult for others to replicate quickly.

Dynamic Capabilities Theory emphasizes an organization's ability to integrate, build, and reconfigure internal and external resources to address rapidly changing environments. Digital transformation enhances SMEs' dynamic capabilities by providing them with the tools and capabilities to sense changes in the market and supply chain, seize new opportunities, and respond effectively to disruptions. For instance, SMEs that adopt digital technologies can use real-time data analytics to sense emerging market trends and adjust their supply chain strategies accordingly, demonstrating greater adaptability and resilience.

Empirical evidence from emerging economies supports the positive link between DT and SCR in SMEs. In China, studies have shown that SMEs that have invested in digital transformation, such as implementing e-commerce platforms for sales and digital communication tools for supplier management, have been better able to withstand the impacts of disruptions like the COVID-19 pandemic. They were more agile in adjusting to changes in customer demand, finding alternative suppliers, and maintaining supply chain operations compared to their less digitally mature counterparts.

2.4. Contextualizing the YRD Manufacturing Sector

The Yangtze River Delta (YRD) region in China is one of the most economically developed and manufacturing-intensive areas in the country. It has a diverse and sophisticated industrial ecosystem, with a large number of SMEs playing a vital role in driving economic growth. However, these SMEs face several challenges. Intense competition both domestically and internationally puts pressure on them to improve efficiency and innovation continuously. Rising labor costs, due to factors such as an aging population and increasing living standards, erode their cost competitiveness. Additionally, environmental regulations are becoming more stringent, forcing SMEs to invest in cleaner production technologies and sustainable practices, which can be a significant financial burden.

The Chinese government has launched several initiatives to support the development of the manufacturing industry in the YRD and across the country, with "Made in China 2025" being a prominent one. This initiative aims to transform China from a manufacturing giant into a manufacturing powerhouse by promoting digitalization, intelligence, and innovation in the industry. It provides policy support, including financial incentives, research and development subsidies, and industry standards, to encourage SMEs in the YRD to embrace digital transformation. These policies create opportunities for SMEs to enhance their competitiveness, improve supply chain resilience, and contribute to the region's economic upgrade. However, the implementation of these initiatives also requires SMEs to overcome challenges related to technology adoption, skill development, and organizational change.

3. Methodology and Empirical Analysis

3.1. Research Design

This study employs a mixed-methods approach, combining quantitative and qualitative research techniques to comprehensively explore the relationship between digital transformation (DT) and supply chain resilience (SCR) in small and medium-sized enterprises (SMEs) within the manufacturing sector of the Yangtze River Delta (YRD). The quantitative component involves a survey of 150 SMEs, which provides numerical data for statistical analysis. Meanwhile, the qualitative component comprises five in-depth case studies, offering rich, detailed insights into the real-world implementation and impact of DT on SCR.

The independent variable in this study is the DT adoption level, which is measured by evaluating the extent to which SMEs have integrated digital technologies such as the Internet of Things (IoT), big data analytics, and cloud computing into their supply chain operations. This includes assessing the use of digital tools for tasks like inventory management, production scheduling, and supplier communication. On the other hand, the dependent variable, SCR indicators, encompasses multiple dimensions of supply chain resilience, such as adaptability, agility, and risk mitigation capabilities. These indicators are evaluated through a series of questions and metrics that capture how well SMEs can respond to disruptions, adjust to changing market conditions, and maintain stable operations.

3.2. Data Collection

The primary data collection method for the quantitative part of the study is a survey questionnaire distributed to SME managers in the YRD manufacturing sector. The questionnaire was carefully designed to ensure validity and reliability. It began with basic questions about the enterprise, including its size, industry, and annual revenue, to provide context for the subsequent analysis. Then, it delved into specific aspects of DT adoption, such as the types of digital technologies used, the level of investment in digital transformation, and the perceived benefits and challenges. For SCR indicators, questions were formulated to assess the enterprise's ability to handle various disruptions, its flexibility in supply chain operations, and the effectiveness of its risk management strategies.

To supplement the survey data, semi-structured interviews were conducted with industry experts, including academics specializing in supply chain management and digital transformation, government officials involved in industrial policies, and consultants with practical experience in helping SMEs implement digital technologies. These interviews aimed to gather in-depth perspectives on the current state of DT and SCR in the YRD manufacturing sector, as well as insights into the barriers and enablers of digital transformation. The semi-structured format allowed for open-ended discussions, enabling the experts to share detailed and nuanced information that might not be captured in the survey.

3.3. Analytical Tools

For the quantitative analysis, Structural Equation Modeling (SEM) was employed. SEM is a powerful statistical technique that can simultaneously analyze multiple relationships among variables, making it suitable for testing complex hypotheses about the relationship between DT adoption level and SCR indicators. In this study, SEM was used to examine both the direct and indirect effects of DT on SCR. By estimating the parameters of the structural model, we could determine the strength and significance of these relationships, providing empirical evidence for the hypothesized associations.

On the qualitative side, thematic analysis was applied to the data collected from the case studies. Thematic analysis involves systematically identifying, organizing, and offering insights into patterns of meaning (themes) across a dataset. First, the interview transcripts and other case study materials were transcribed and carefully read to gain an overall understanding. Then, codes were assigned to relevant segments of the text to categorize the data. Through an iterative process of comparing and contrasting these codes, overarching themes emerged, such as the key strategies SMEs used to implement digital transformation, the specific ways DT enhanced their supply chain resilience, and the common challenges they faced during the process.

3.4. Findings

The analysis revealed a positive association between DT maturity and SCR performance. SMEs that had achieved a higher level of digital transformation, characterized by more extensive use of digital technologies and greater integration of digital systems into their supply chain processes, showed stronger adaptability, agility, and better risk mitigation capabilities. For example, those that utilized IoT sensors for real-time inventory tracking and big data analytics for demand forecasting were able to respond more quickly to changes in market demand and supply chain disruptions, maintaining stable operations even in challenging circumstances.

However, several critical barriers to digital transformation were also identified. High implementation costs were a major concern for SMEs. The expenses associated with purchasing digital technologies, upgrading IT infrastructure, and training employees were often beyond the financial capabilities of many small and medium-sized enterprises. Additionally, a lack of technical expertise was prevalent. Many SMEs struggled to find and retain skilled professionals who could effectively implement and manage digital transformation projects, which hindered their progress in adopting and leveraging digital technologies.

On a positive note, the study highlighted the important role of government subsidies and industry-academia collaboration. SMEs that received government financial support for digital transformation projects were more likely to overcome the financial barriers and successfully implement digital technologies. Moreover, partnerships between SMEs, academic institutions, and industry associations provided valuable resources, knowledge, and technical support. For instance, through joint research projects and training programs, SMEs could access the latest technological advancements and acquire the necessary skills to enhance their digital capabilities and supply chain resilience.

4. Discussion and Implications

4.1. Interpretation of Results

Digital transformation (DT) enhances supply chain resilience (SCR) primarily through real-time data sharing and predictive analytics. In the context of the Yangtze River Delta (YRD) manufacturing SMEs, the integration of Internet of Things (IoT) devices and cloud computing platforms enables seamless data collection from various supply chain touchpoints, including raw material procurement, production lines, and distribution networks. For instance, a case study of a YRD-based electronics SME revealed that by deploying IoT sensors on its production equipment, it could monitor machine performance in real-time, detect early signs of potential breakdowns, and schedule maintenance proactively. This not only minimized production downtime but also ensured timely product delivery, thereby enhancing the adaptability of the supply chain.

Predictive analytics, powered by big data algorithms, further empowers SMEs to anticipate market fluctuations and supply chain disruptions. By analyzing historical sales data, market trends, and external factors such as weather conditions and geopolitical events, these enterprises can adjust production plans, optimize inventory levels, and reconfigure supply routes in advance. A textile SME in the YRD used predictive analytics to forecast demand surges during peak seasons and adjusted its raw material procurement and production schedules accordingly, reducing the risk of stockouts and improving overall supply chain agility.

SMEs with cross-functional digital teams demonstrate higher levels of supply chain resilience. These teams typically comprise members from diverse departments, such as information technology, operations, marketing, and finance. Their collaborative nature breaks down traditional departmental silos, enabling more efficient communication and faster decision-making. For example, when faced with sudden changes in customer demand, a cross-functional team can quickly assess the impact on production capacity, inventory, and logistics. The IT members can leverage digital tools to analyze relevant data, while the operations team can make immediate adjustments to production schedules, and the marketing team can communicate with customers to manage expectations. This integrated approach allows SMEs to respond promptly to disruptions and maintain supply chain stability.

4.2. Theoretical Contributions

This study makes significant theoretical contributions by extending the Resource-Based View (RBV) theory. Traditionally, RBV emphasizes the role of tangible and intangible resources in creating competitive advantages. This research integrates digital transformation as a strategic resource, highlighting its potential to enhance supply chain resilience. Digital technologies, including advanced analytics platforms, cloud-based management systems, and digital communication tools, are not only valuable but also rare and difficult to imitate, especially for SMEs in emerging economies. By effectively leveraging these digital resources, SMEs can build sustainable competitive advantages in the face of global supply chain challenges. For example, the ability to quickly adapt to changing market demands through digital-driven supply chain optimization becomes a unique resource that sets successful SMEs apart from their competitors.

Furthermore, the study refines the supply chain resilience measurement frameworks for SMEs. Existing frameworks often focus on large enterprises, overlooking the specific characteristics and constraints of SMEs. This research incorporates factors such as digital technology adoption levels, the capacity for cross-functional collaboration, and the impact of government support policies into the measurement of SCR. For instance, the new framework takes into account the limited financial and human resources of SMEs, evaluating how they can achieve resilience with scaled-down digital solutions. By doing so, it provides a more accurate and practical tool for assessing and improving supply chain resilience in the SME context, filling an important gap in the existing literature.

4.3. Practical Implications

For policymakers, several actionable recommendations can be derived from this study. First, tax incentives for digital transformation investments should be strengthened. SMEs often face significant financial barriers when implementing digital technologies, and tax breaks can effectively reduce their costs. For example, offering a percentage-based tax reduction on expenditures related to the purchase and implementation of digital systems, such as enterprise resource planning (ERP) software or data analytics platforms, can encourage more SMEs to embark on digital transformation. Second, comprehensive skill development programs should be established. Given the shortage of technical expertise in SMEs, training initiatives can focus on both basic digital literacy for employees at all levels and specialized skills for digital transformation leaders. These programs can be jointly organized by government agencies, educational institutions, and industry associations, providing practical courses on topics like digital supply chain management, data analysis, and IoT implementation.

From a managerial perspective, SMEs should prioritize the adoption of scalable digital transformation tools. Instead of investing in large, complex systems that may exceed their capabilities, SMEs can start with modular and flexible digital solutions that can be gradually expanded as the business grows. For example, cloud-based inventory management systems or mobile-based order tracking applications can be easily integrated into existing operations without significant upfront costs. Additionally, fostering partnerships is crucial. SMEs can collaborate with technology providers to access the latest digital technologies at reduced costs, and form alliances with other enterprises in the supply chain to share resources and risks. A group of YRD-based SMEs formed a consortium to jointly invest in a shared digital platform for supply chain visibility, enabling them to collectively monitor and manage their supply chains more effectively.

4.4. Limitations and Future Research

This study has several limitations. The primary limitation is its geographic focus on the manufacturing SMEs in the Yangtze River Delta region. While the YRD is a significant economic hub in China, the unique economic, social, and policy environments in this region may lead to results that are not directly generalizable to other areas. Different regions may have varying levels of digital infrastructure, government support, and market characteristics, which can influence the relationship between digital transformation and supply chain resilience.

Future research can address these limitations by expanding the sample size to include SMEs from diverse regions, industries, and economic backgrounds. Longitudinal studies are also highly recommended. By tracking the digital transformation processes and supply chain resilience levels of SMEs over an extended period, researchers can gain a more comprehensive understanding of the long-term impacts of digital transformation. Such studies can explore how SMEs’ resilience capabilities evolve as they continuously upgrade their digital systems, adapt to changing market conditions, and respond to new technological advancements. Additionally, future research could delve deeper into the specific mechanisms through which digital transformation affects supply chain resilience, providing more detailed insights for both theoretical development and practical applications.