A Systematic Literature Review of the Integration of Total Quality Management and Industry 4.0: Enhancing Sustainability Performance Through Dynamic Capabilities

Abstract

It is imperative to integrate advanced technologies and management practices to achieve operational excellence and sustainability in the contemporary manufacturing landscape, which is characterized by intense competitive pressures and stringent sustainability regulations. This systematic review of several databases from 1997 to 2024 was conducted in line with PRISMA guidelines to investigate how Industry 4.0 and Total Quality Management (TQM) integration can enhance sustainability performance in manufacturing organizations. Grounded within the dynamic capabilities framework, this research identifies the key drivers and barriers to adopting Industry 4.0 and examines their impact on the implementation of TQM. The findings reveal that technologies significantly enhance TQM by improving operational efficiency, reducing waste, and boosting product quality. However, barriers such as resource constraints, resistance to change, and data privacy concerns hinder this integration. This research suggests that dynamic capabilities—sensing, learning, coordinating, and integrating—are critical for successfully merging Industry 4.0 and TQM. This research enhances the academic dialogue on integrating Industry 4.0 and TQM by offering foundational insights into their theoretical and practical implications to achieve sustainability goals.

1. Introduction

In the contemporary realm of manufacturing, characterized by intense competitive pressures and stringent sustainability regulations, adopting the best practices in management offers enterprises a competitive advantage and the ability to overcome prevalent challenges [1]. Industry 4.0, often referred to as the fourth industrial revolution, represents the integration of advanced digital technologies, such as cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, into manufacturing processes. This revolution is transforming industries by enabling smarter and more autonomous systems. Both locally and globally, Industry 4.0 is revolutionizing manufacturing industries and production processes [2]. The urgency for businesses to capture larger market shares has driven them to enhance the sophistication of their operations by leveraging these emerging technologies. Technologies like cloud computing, big data analytics, robotic systems, and the IoT have significantly transformed global manufacturing, making processes more efficient, flexible, and responsive to market demands [3,4]. These technologies enhance the manufacturing industry’s ability to perform sustainably, contributing to advanced machinery, expedited communication, reduced lead times, improved work environments, and superior product quality. The three pivotal forces of Industry 4.0, connectivity, intelligence, and intelligent automation, bring transformative changes to business operations, affecting the entire product lifecycle [5].

Total Quality Management (TQM) is defined as a management approach that leads an organization to achieve a world-class competitive position by ensuring that its products and services satisfy customers by meeting and exceeding their requirements and expectations [6]. In this present manuscript, TQM is seen as “a comprehensive management philosophy which focuses on continuous improvement across all organizational processes, with the ultimate goal of optimizing quality and customer satisfaction”. The essence of TQM lies in its commitment to integrating quality into every aspect of the organization, from leadership commitment to employee involvement and customer interactions. It emphasizes a data-driven approach, teamwork, and a culture of continuous learning to improve processes. The successful implementation of TQM spans various industries, including manufacturing, healthcare, education, and finance [7]. It emphasizes the need to perform tasks correctly and adopt preemptive measures to prevent issues. TQM empowers employees through continuous process improvement for customer satisfaction and competitive cost management [6].

The integration of TQM and Industry 4.0 has recently garnered considerable interest in business research [8,9,10]. TQM, centered on continuous improvement and customer satisfaction, and Industry 4.0, with its advanced technology, are distinct yet complementary concepts that enhance organizational performance. Their integration holds the potential for substantial benefits, particularly in achieving sustainability performance, a critical determinant of competitive advantage in today’s business landscape [11]. Sustainability performance refers to the ability of an organization to achieve long-term economic success while simultaneously managing its environmental and social responsibilities. It encompasses the efficient use of resources, minimizing negative environmental impacts (e.g., waste, emissions, and energy consumption), and ensuring positive contributions to society, such as fair labor practices and community engagement. In manufacturing, sustainability performance often involves aligning production processes with sustainable practices to reduce ecological footprints while maintaining profitability and fostering social well-being [12]. This integration is expected to support sustainability performance from its economic, social, and environmental dimensions, creating long-term stakeholder value and competitive advantage for the organization [12].

A thorough review of the literature indicates significant academic interest in the relationships between TQM and sustainability performance [13,14,15], Industry 4.0 and TQM [14,16], and the dynamic interactions between Industry 4.0 technologies and TQM [17,18]. However, these studies often examine these aspects in isolation and do not consider the combined effects of TQM and Industry 4.0 on sustainability performance. This results in a gap in comprehensive models that integrate these critical dimensions to assess their joint impact on sustainability performance in manufacturing and services. The mechanisms through which Industry 4.0 and TQM enhance sustainability performance remain underexplored. This study aims to fill this gap by conducting a systematic literature review and developing a theoretical model elucidating the complex interrelations among TQM, Industry 4.0, and sustainability. Doing so demonstrates how integrating TQM and Industry 4.0 can significantly enhance sustainability performance.

However, integrating TQM with Industry 4.0 poses significant challenges due to the complex interplay of technical, organizational, and human factors [19]. Addressing these challenges requires a strategic approach to managing and adapting to the changing environment. To effectively navigate these challenges, adopting a dynamic capabilities perspective, which helps to understand the evolving factors that drive or impede this integration [20], is crucial. Dynamic capabilities (DCs) refer to an organization’s ability to integrate, build, and reconfigure internal and external resources and competencies in response to rapidly changing environments. They enable firms to adapt, innovate, and evolve their processes, products, or strategies to maintain a competitive advantage. Dynamic capabilities are crucial for organizations operating in volatile markets, as they involve sensing opportunities and threats, seizing opportunities, and transforming resources to meet new challenges and customer demands [21]. This research studies how the integration of Industry 4.0 and TQM can enhance sustainability performance. It develops a conceptual framework that addresses the challenges and opportunities associated with this integration. By applying the dynamic capabilities (DCs) perspective, this study highlights the processes involved in merging Industry 4.0 technologies with TQM practices to achieve sustainable outcomes while considering the shifting drivers and barriers. The findings of Felsberger et al. [21] suggest that the DCs perspective provides a robust theoretical foundation to guide the integration of technical, organizational, and human factors, offering practical insights to overcome the associated complexities.

This paper is structured in several key sections. It begins with an introduction that establishes the research’s background and rationale, followed by a detailed explanation of the methodology, encompassing a systematic literature review and data analysis. Next, this paper presents the findings, focusing on the challenges and benefits of integrating Industry 4.0 technologies with Total Quality Management practices. Based on these findings, this study constructs a theoretical framework to identify the main enablers and barriers to successful integration, with a focus on enhancing sustainability performance. This framework is grounded in the dynamic capabilities perspective, emphasizing the critical roles of sensing, learning, coordinating, and integrating capabilities. This paper concludes with recommendations and implications drawn from the findings and theoretical framework, offering insights for both academics and practitioners aiming to implement these strategies to achieve sustainability goals.

2. Research Methodology

This investigation employs a systematic literature review (SLR) as its methodology. This is defined as a technique for identifying, analyzing, and comprehending all pertinent research related to a specific question, topic, or phenomenon; an SLR is a recognized method for research on operations, TQM, and sustainability performance, offering reproducible, high-quality evidence. This approach involves a chain of meticulous and evident processes to extensively search for related research studies, thus providing a comprehensive review and addressing any potential limitations of individual research efforts. This study adheres to Denyer and Traffield’s five-step research technique to ensure scientific rigor [22]. In academic research, a panel of experts is often utilized to ensure the validity and robustness of methodologies and findings throughout systematic literature reviews (SLRs). In this research, the panel consisted of the paper’s authors, specializing in Total Quality Management, Operations Research, Industrial Engineering, and sustainability. The authors were directly involved in overseeing and validating every stage of the systematic literature review. Their expertise provided critical guidance for improving methodological rigor and minimizing biases that could arise during the selection and synthesis of the literature. This validation approach is widely recognized in research processes, as it ensures the quality and relevance of data collection instruments and review processes [23]. Data analysis was conducted using content analysis, similar to the method employed by Seuring and Gold [24], with results discussed in alignment with the research questions. The subsequent subsections detail each step of this methodology.

2.1. Formulation of Research Questions

The initial step entails formulating pertinent questions to guide the research. Through a systematic literature review, this study examines the drivers and barriers to incorporating Industry 4.0 and TQM and their influence on sustainability performance. The primary research questions (RQs) guiding this study are listed as follows:

• What are the drivers and barriers to the adoption of Industry 4.0 in manufacturing, and how do they impact the implementation of TQM?
• How do dynamic capabilities affect the interplay between the adoption of Industry 4.0, the implementation of TQM, and sustainability performance?

These RQs focus on concepts and variables such as adopting Industry 4.0 technologies, TQM implementation, sustainability performance, and the factors influencing these aspects. This study explores the synergistic relationship between the adoption of Industry 4.0, the implementation of TQM, and sustainability performance in manufacturing organizations, addressing two primary objectives: (i) to identify the drivers and barriers regarding adopting Industry 4.0 in manufacturing and their impact on the implementation of TQM and (ii) to investigate how integrating dynamic capabilities affects the interplay between adopting Industry 4.0, implementing TQM, and sustainability performance.

2.2. Research Studies Selection

The screening process was based on inclusion and exclusion criteria, focusing on elements like study design (e.g., empirical studies, case studies), publication date, and relevance. Data extraction involved analyzing the study’s characteristics, methodologies, and principal findings. Furthermore, a detailed analysis of the articles was conducted, enabling the researchers to assess the relevance of every single study [25]. The selection and evaluation process involved setting specific criteria for the inclusion and exclusion of studies, ensuring the selection of the most pertinent. For inclusion, the studies needed to be evaluated under the following criteria:

• Empirical Evidence: Studies must provide empirical evidence through surveys, case studies, or interviews, demonstrating the impact of the integration of Industry 4.0 and Total Quality Management (TQM) on sustainability performance.
• Theoretical or Conceptual Frameworks: Studies must present a theoretical or conceptual framework related to the integration of Industry 4.0 and TQM and its influence on sustainability performance.
• Context: Studies must focus on the integration of Industry 4.0 and TQM in the context of sustainability performance.
• Language: Studies must be written in English to avoid language barriers during analysis.
• Peer-Reviewed Journals: Studies must be published in peer-reviewed journals to ensure high-quality and rigorous academic standards.

These criteria were meticulously chosen to guarantee the thoroughness and relevance of the selected studies. The requirement for empirical evidence ensures that the findings would be credible and grounded in real-world data, demonstrating how Industry 4.0 and TQM contribute to sustainability. The focus on theoretical frameworks helped align the studies with the research objectives, allowing the integration of TQM and Industry 4.0 to be examined through a robust conceptual lens. Requiring studies to be in English removes potential language barriers, ensuring a comprehensive analysis. The mandate for studies to be published in peer-reviewed journals guarantees scientific rigor and reliability, which is crucial for drawing valid conclusions and formulating sound recommendations.

The data extraction process involved analyzing the study’s characteristics, methodologies, and principal findings, ensuring that the selected studies directly addressed the research objectives. Additionally, a list of search terms related to the research questions was developed, including keywords such as “total quality management”, “Industry 4.0”, “sustainability performance”, and “dynamic capabilities”. Boolean operators “AND” and “OR” were used to refine the search results (

Table 1. Search strings used for selecting the research papers.

Databases	Search Strings
ISI Web of Science	Topic Search (TS) = (“Total Quality Management” OR “TQM”) AND TS = (“Industry 4.0” OR “Industry Four” OR “Industry 4”) AND TS = (“Sustainability performance” OR “sustainability”) AND TS = (“dynamic capability” OR “dynamic capabilities”)
Scopus	TITLE-ABS-KEY (“Total Quality Management” AND “Industry 4.0” AND “Sustainability performance” AND “dynamic capability”)
Google Scholar	(“Total Quality Management” OR “TQM”) AND (“Industry 4.0” OR “Industry Four” OR “Industry 4”) AND (“Sustainability performance” OR “sustainability”) AND (“dynamic capability” OR “dynamic capabilities”)

outlines the specific search strings used). Searches were conducted in the ISI Web of Science, Scopus, and Google Scholar databases for studies published between 1997 and 2024.

The start year of 1997 was chosen because it coincides with the emergence of the dynamic capabilities (DCs) framework in organizational research, as highlighted by Teece, Pisano, and Shuen [26]. Recent reviews, such as Ortiz-Avram et al. (2022), also selected the late 1990s as a starting point for examining dynamic capabilities and sustainability performance [27]. Extending the review to 2024 ensures the inclusion of the most recent developments in Total Quality Management and Industry 4.0.

2.3. Selection Procedure

The selection process began with an initial screening of titles and abstracts to identify potentially relevant studies. Full-text articles were then obtained for further evaluation against the inclusion and exclusion criteria. The quality of each study was assessed using established criteria, and the final set of studies was included in the review based on relevance, quality, and contribution to the research questions. Figure 1 summarizes the selection process used in this study (PRISMA flow diagram).

2.4. Analysis

Following the selection and evaluation of the studies, the next phase entailed analyzing and synthesizing the data gathered. The articles selected underwent a thorough analysis and synthesis using a structured template to capture critical information from each study. The data from the chosen studies were organized into specific categories and themes, such as technological drivers, organizational barriers, sustainability outcomes, and dynamic capabilities, which were pertinent to the research questions and objectives [24]. A narrative synthesis approach was then employed to summarize the findings coherently, integrating the results from multiple studies into a comprehensive overview of the available data. This approach included developing tables to elucidate the primary conclusions and their interrelationships. Finally, a thematic analysis was conducted to qualitatively synthesize the findings from multiple studies, providing a detailed understanding of the effects of the intervention or predictor variables.

2.5. Reporting and Using the Results

This phase involved preparing a comprehensive report summarizing each study’s findings and drawing conclusions from the data analysis and synthesis. The report detailed research questions, search strategy, inclusion and exclusion criteria, data extraction process, and data analysis and synthesis. It also summarized the main findings and discussed the implications of the results for theory, practice, and future research.

3. Results

3.1. Descriptive Analysis

To provide an overview of the research landscape, this section presents the descriptive analysis of the selected literature on integrating Total Quality Management (TQM), Industry 4.0, and sustainability performance. It includes a year-wise distribution of publications, a journal-wise distribution, and the research methodologies adopted. Figure 2 presents a graphical representation of the yearly distribution of publications combining TQM, Industry 4.0, and sustainability performance from 1997 to 2024. These data, shown in frequency terms, provide insight into the relative volume of research output across these years. A notable increase in publications, especially from 2019 onwards, is evident. While the graph’s projection into 2024 suggests a potential continuation of this upward trend, such projections should be interpreted with caution. Factors influencing this trend include the accelerated adoption of Industry 4.0 technologies following the COVID-19 pandemic and the global push towards the Sustainable Development Goals by 2030. These factors are hypothesized to stimulate a surge in academic interest and research efforts, potentially maintaining the growth in publication volume. However, these projections are subject to change based on new global economic conditions or policy changes.

Based on the systematic review of 565 records screened, 50 studies were included in the review. Also, it is evident that the intersection of TQM and Industry 4.0 in the context of sustainability performance is a topic of widespread academic interest, as manifested by its coverage in thirty-two distinct journals. Predominant among these are the Journal of Cleaner Production, Production Planning & Control Journal, Sustainability, and the International Journal of Quality & Reliability Management, which have emerged as critical platforms and publish many pertinent studies. The multifaceted nature of TQM and Industry 4.0 in enhancing sustainability performance is underscored by its resonance across diverse fields such as economics, engineering, environmental protection, and technological innovation. This cross-disciplinary appeal underscores the complexity and interconnectivity of the topic.

In terms of research methodologies applied in this domain, each individual research article was categorized based on its primary approach, encompassing conceptual and theoretical frameworks and literature reviews, as defined in seminal works by Seuring and Müller [28] and Winter and Knemeyer [29]. The predominance of theoretical and conceptual articles, constituting 44% of the total, suggests a scholarly inclination towards developing novel ideas and theoretical constructs in this area. Surveys, comprising 28% of the research, highlight their utility in amassing empirical data on a large scale, particularly relevant to assessing the impact of Industry 4.0 and TQM practices on sustainability. Case studies and interviews, making up 20% of the literature, provide in-depth insights into specific instances of these practices, while literature reviews, at 8%, offer a holistic overview of existing knowledge. This diversity in research methods reflects the diverse nature of the inquiry, influenced by the research objectives, resource availability, and researcher’s preferences.

3.2. Drivers of and Barriers to the Integration from a Dynamic Capabilities Perspective

This investigation delves into the complexities of integrating Total Quality Management (TQM) and Industry 4.0, with the aim of uncovering practical strategies to enhance sustainability performance. Grounded in the dynamic capability framework proposed by Teece, Pisano, and Shuen [26], this study examines internal and external influences that facilitate or hinder the integration of Industry 4.0 and TQM. The theory of dynamic capabilities, introduced by Teece et al., explains how organizations achieve competitive advantage and superior performance in rapidly evolving markets. Dynamic capabilities refer to an organization’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments [26,30]. This study adopts a dynamic capabilities perspective, focusing on how organizations integrate Industry 4.0 technologies and TQM to adapt to rapid technological advancements. The framework provides a lens through which organizations’ abilities to sense, learn, and integrate new capabilities are evaluated. Key aspects of dynamic capabilities include their nature, role, context, genesis, outcomes, and variability, primarily characterized as an “ability” or “capacity” important for transforming a firm’s internal processes, routines, and resource allocations. Helfat [31] describes dynamic capabilities as an organization’s ability to purposefully adapt its resource base. These capabilities are increasingly seen as central to embedding social and environmental responsibilities into business operations [32]. The challenge of integrating Industry 4.0 and TQM, particularly in achieving sustainability performance, forms the core of this study. By employing a dynamic capabilities perspective, the study investigates the factors that drive or obstruct the practical integration of Industry 4.0 and TQM and examines how these factors contribute to the development of dynamic capabilities.

3.2.1. Drivers

The dynamic capabilities framework identifies four primary dynamic capabilities—sensing, learning, coordinating, and integrating—that are crucial in navigating the complexities of this integration [26]. Sensing capabilities involve identifying technological and market changes that can significantly impact operations. The integration of Industry 4.0 technologies enhances the core principles of TQM, such as continuous improvement and defect reduction, by enabling real-time process monitoring, predictive analytics, and automation. This allows organizations to proactively detect inefficiencies, reduce downtime, and improve overall quality. This capability enables organizations to detect and assess emerging Industry 4.0 and TQM trends, which is essential for proactive strategic planning. Learning capabilities refer to the organization’s ability to internalize and understand the information gathered through sensing. This involves assimilating new knowledge about Industry 4.0 technologies and TQM practices and understanding how these can be integrated to enhance operational and sustainability performance. Coordinating capabilities are needed to align and synchronize the activities and functions within the organization to support the integration of TQM and Industry 4.0. This includes managing resources, directing workflows, and ensuring that all parts of the organization work together to achieve the combined benefits of TQM and Industry 4.0. Integrating capabilities involves embedding the reconfigured processes and innovations into the existing systems. This capability focuses on integrating Industry 4.0 technologies into TQM practices seamlessly, thereby transforming organizational structure and operations to enhance competitiveness and sustainability.

As suggested by Shuaib, He, and Song [33], dynamic capabilities play a mediating role in enhancing innovation among manufacturing companies. This study extends its findings by applying the dynamic capabilities framework to the integration of TQM and Industry 4.0, proposing a model that enhances sustainability in manufacturing processes. Furthermore, it highlights how cultivating dynamic capabilities can elevate sustainability performance in a dynamic and complex environment [4].

Table 2. Drivers of integrating TQM and Industry 4.0 from a dynamic capability view.

Dynamic Capability	Sub-Component	Drivers	Description	Key Authors
Sensing	Opportunity/Threat Recognition	Technological advances	Industry 4.0 technologies, including the IoT, AI, and big data analytics, have enabled organizations to enhance sustainability performance. Adopting advanced technologies optimizes operations and improves practices, leading to better sustainability outcomes.	[10,34,35,36]
	Monitoring
Learning	Create Knowledge	Environmental concerns	Integrating Industry 4.0 and TQM has become essential for sustainability performance due to growing environmental concerns and resource depletion, as Industry 4.0 and TQM practices can reduce environmental impact, minimize waste, and optimize resource utilization.	[36,37,38,39]
	Acquire Knowledge
	Share Knowledge
Coordinating	Create Capabilities	Competitive advantage	Integrating Industry 4.0 and TQM can improve operational efficiency, reduce costs, enhance sustainability performance, improve product quality, minimize errors, and increase customer satisfaction and brand loyalty.	[3,40,41]
Integration	Integrate Capabilities

presents the drivers that facilitate the integration of TQM and Industry 4.0 in sustainability performance from a dynamic capability perspective. This Table provides a spectrum of interactions through which various elements of dynamic capabilities can engage with and impact the integration process.

Technological Advances

Innovative technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are revolutionizing organizational operations, fostering seamless integration with TQM principles. These technological advances are significant driving forces behind enhanced sustainability performance. For instance, IoT, with its network of interconnected devices equipped with sensors and software, allows for the real-time monitoring and control of operations, leading to the swift identification and resolution of problems. This real-time data collection and analysis are crucial for practical decision making, directly influencing sustainability outcomes by reducing waste and energy consumption [42]. AI, through its ability to perform visual perception and decision-making tasks, is essential to analyze vast data sets, yielding insights that optimize operations and reduce environmental impact. Similarly, big data analytics, with its capacity to process large and complex data sets, helps organizations uncover areas for improvement, enhance product quality, reduce costs, and improve customer satisfaction, all while contributing to environmental sustainability [35].

Industry 4.0 technologies are transforming TQM practices such as Six Sigma and continuous improvement by introducing data-driven methods for process control, defect prediction, and real-time optimization. AI tools now play a crucial role in identifying defects early in the production cycle, while the IoT ensures a smoother flow of information for quality assurance. TQM as a management technique is operationalized through Industry 4.0 technologies by leveraging the IoT for continuous process monitoring, AI for defect prediction, and big data analytics for process optimization. These tools help enforce TQM’s focus on process improvement, waste reduction, and customer satisfaction. The use of AI and big data analytics improves quality management by analyzing vast datasets to identify patterns and trends that would be impossible to detect manually. These technologies enable organizations to predict quality issues, adjust processes in real time, and maintain consistent product quality, reinforcing TQM’s focus on customer satisfaction and defect-free production. Integrating these technologies with TQM principles ensures a holistic approach to achieving sustainable organizational excellence.

Environmental Concerns

Environmental sustainability concerns have pressured organizations to minimize their ecological footprint and optimize resource use. In this context, integrating Industry 4.0 and TQM is essential to achieve sustainability performance. These practices not only optimize operational efficiency but also significantly reduce any environmental impact. Technologies like the IoT help to monitor and control energy consumption, thus enhancing energy efficiency. AI algorithms optimize production processes, minimize waste, and improve resource utilization. Big data analytics assists in identifying waste reduction opportunities, thereby enhancing environmental sustainability. Overall, adopting Industry 4.0 and TQM practices is instrumental in minimizing waste and improving environmental sustainability, as demonstrated by IoT’s role in monitoring waste generation and AI’s contribution to process optimization [9,43].

Competitive Advantage

Competitive advantage refers to the organization’s ability to outperform its competitors by offering superior value through unique resources, capabilities, or innovations that are difficult to replicate. This advantage allows the organization to maintain a leading position in the market, resulting in higher profitability, customer loyalty, and long-term success [26]. Research shows that competitive advantage can be achieved through cost leadership, differentiation, or a focused approach, where firms either deliver products or services at lower costs or provide distinct features that meet specific customer needs. The sustainability of competitive advantage is crucial, as it enables organizations to continuously capitalize on their strengths while remaining resilient to competitive pressures [26,44].

Integrating Industry 4.0 and TQM offers organizations a significant competitive advantage in the dynamic and competitive business landscape. This integration enhances operational efficiency, reduces costs, and boosts sustainability performance. It leads to improved product quality, shorter production times, higher customer satisfaction, and stronger brand loyalty, thereby strengthening the organization’s market position [45]. Organizations can use the IoT, AI, and big data analytics to identify opportunities for operational optimization, waste reduction, and environmental sustainability enhancement. This results in cost savings and revenue generation, bolstering the organization’s competitive advantage. As Ali and Johl [20] argue, adopting Industry 4.0 and TQM practices elevate an organization’s capability to meet customer needs and expectations, potentially increasing market share and profitability. Furthermore, integrating sustainability into operations attracts environmentally conscious consumers, enhancing brand recognition and loyalty [44].

3.3. Barriers

While integrating TQM and Industry 4.0 technologies offers substantial benefits, organizations face notable challenges, including resource limitations, resistance to change, and security concerns, as identified by Elkhairi et al. [46]. Effectively addressing these obstacles is essential to fully harness the advantages of these practices. Strategies to mitigate these barriers include promoting a culture of continuous improvement, investing in employee training, and enhancing data governance frameworks.

Table 3. Barriers to integrating TQM and Industry 4.0 from a dynamic capability view.

Dynamic Capability	Sub-Component	Barriers	Description	Key Authors
Sensing	Opportunity/Threat Recognition	Data Privacy and Security Concerns	Organizations face significant data privacy and security concerns when integrating advanced technologies like the IoT, AI, and big data analytics.	[47,48,49]
	Capability Monitoring	Lack of resources	Organizations that lack the necessary financial, technological, and human resources may struggle to implement these practices effectively.	[50,51]
Learning	Create Knowledge	Resistance to changes	Some employees may resist the significant changes accompanying the Integration of Industry 4.0 and TQM, and some organizations may lack the necessary awareness and expertise to implement them effectively.	[52,53]
	Acquire Knowledge
	Share Knowledge
Coordinating	Create Capabilities	Lack of awareness	Implementing Industry 4.0 and TQM practices requires a high level of technical knowledge and an understanding of the relevant business processes and sustainability issues.	[3,40,41]
Integrating	Integrate Capabilities

presents a comprehensive view of the barriers specifically related to the integration of dynamic capabilities such as sensing, learning, coordinating, and integrating with Industry 4.0 technologies to achieve enhanced sustainability performance. It is important to interpret this table as a complex matrix where each barrier is influenced by various aspects of dynamic capabilities rather than viewing them as having a simplistic direct correlation. This nuanced representation mirrors the organizations’ intricate challenges when adopting these integrated practices. This Table serves as a strategic tool to identify and address the multi-dimensional obstacles that impede the successful merging of Industry 4.0 and TQM, guiding organizations toward a more effective and sustainable operational model.

3.3.1. Lack of Resources

Integrating TQM and Industry 4.0 technologies holds immense promise to enhance sustainability performance. However, the scarcity of resources poses a significant challenge. Implementing Industry 4.0 and TQM requires substantial financial and technological investments and sufficient human resources. Organizations must allocate significant funds for advanced technologies like the IoT, cloud computing, and big data analytics, which are crucial for data management and analysis [51,54]. Additionally, such integration necessitates investment in new hardware and software. Training employees to use these technologies and adapt to new processes further adds to the financial burden. Beyond finances, technological resources are vital, with the IoT requiring sensors and devices and cloud computing demanding reliable internet and sufficient bandwidth. Human resources are equally critical; a skilled workforce proficient in technology, data analytics, and continuous improvement is essential for practical implementation. However, acquiring such talent, especially for small and resource-limited organizations, is a significant hurdle [50].

3.3.2. Resistance to Change

Another major obstacle to integrating Industry 4.0 and TQM is resistance to change. Adopting these practices requires substantial alterations to organizational processes, culture, and mindset, which can be disruptive and unsettling for employees. Resistance can be manifested as skepticism, anxiety, or a lack of motivation, often stemming from fears about job security, loss of autonomy, or the perceived impracticality of changes. Organizations with rigid and hierarchical cultures may find it particularly challenging to implement these practices, which require a culture of continuous improvement, collaboration, and experimentation. Additionally, shifting an employee’s mindset from tolerating waste and inefficiency to valuing innovation and data-driven decision making requires significant education, training, and time, particularly in resource-strained environments [52,53].

3.3.3. Lack of Awareness and Expertise

The lack of awareness and expertise is a significant barrier to integrating Industry 4.0 and TQM. Practical implementation demands a deep understanding of relevant technologies, business processes, and sustainability issues. Organizations lacking this knowledge may struggle to choose appropriate practices, develop practical implementation plans, or effectively utilize technologies and tools. This lack of awareness can result in suboptimal process optimization, increased costs, and potential adverse impacts on sustainability. Overcoming this challenge requires investments in training, workshops, and knowledge-sharing initiatives, along with recruiting personnel with the necessary expertise. Partnerships with external experts can also be instrumental in building awareness and expertise in Industry 4.0 and TQM [16,55].

3.3.4. Data Privacy and Security Concerns

Integrating Industry 4.0 and TQM entails utilizing advanced technologies like the IoT, AI, and big data analytics to collect and analyze vast amounts of data. While beneficial for monitoring and optimizing processes, this raises significant concerns regarding data privacy and security. Neglecting these issues can lead to legal repercussions, loss of customer trust, and reputational harm. To mitigate these risks, organizations must implement robust data privacy and security measures, comply with relevant regulations such as GDPR or PIPEDA, and train employees on best practices in data handling [47,48,49]. Robust cybersecurity measures and regular audits are essential to safeguard sensitive information and maintain trust among stakeholders.

4. Discussion

In the current landscape, the quest for sustainable development has become of paramount concern, particularly in light of the adverse effects that industrialization and globalization have had on the environment and society. This need is underscored by increased environmental challenges where the intensification of degradation and climate change demands swift and innovative responses from businesses to mitigate these impacts and ensure long-term viability and social license to operate [56]. TQM and Industry 4.0 stand at the forefront as concepts that can assist organizations in tackling this challenge by not only meeting regulatory and ethical demands but also offering economic benefits through improved efficiency and competitive advantage [57]. Furthermore, the growing expectations from consumers, investors, and governmental bodies for companies to demonstrate environmental and social responsibility significantly impel these companies to enhance their sustainable practices [58].

This section of our study delves into the convergence of Industry 4.0 and TQM and their influence on sustainability performance, positioning them as essential to achieve not just ecological balance but also economic gains. This multifaceted value creation is systematically presented in

Table 4. Integrating Industry 4.0 and TQM from a sustainability performance view.

Dimensions	Main Concept	Key Authors
Economic Sustainability
Operational Efficiency	Utilizing new technologies and data-driven approaches to optimize processes, reduce waste and costs, and enhance productivity.	[3,59,60,61]
Production Costs	Utilizing new technologies such as the IoT and AI can help organizations enhance their operational performance and achieve a more sustainable economic model, ensuring long-term profitability and success.
Increase Productivity	Automating processes, utilizing real-time data analytics, and improving quality management. This can lead to increased output and faster turnaround times, allowing organizations to better respond to the demands of the market and improve their bottom line.
Environmental Sustainability
Energy Consumption	By utilizing real-time data analytics and IoT-enabled devices, organizations can better monitor and control their energy usage, significantly reducing greenhouse gas emissions and environmental impact.	[1,20,62,63,64]
Optimization of Resources	By utilizing advanced technologies such as AI and the IoT, organizations can better track and manage their usage of resources, significantly reducing waste and environmental impact.
Social Sustainability
Working Conditions	Implementing data-driven approaches to optimize workflow, reduce repetitive tasks, and improve safety.	[56,65,66,67]
Employee Satisfaction	Integrating Industry 4.0 and TQM in social sustainability can help organizations improve employee satisfaction by creating a more supportive and inclusive work environment. Organizations can better understand employee needs and preferences by utilizing data-driven approaches and advanced technologies such as AI, leading to improved communication, collaboration, and work–life balance.

. By synergizing Industry 4.0 and TQM, organizations are not just elevating their performance to be more sustainable but are also contributing significantly to a more sustainable future. This integration facilitates a holistic approach where economic efficiency, environmental stewardship, and social responsibility are not separate entities but interconnected facets of a singular sustainability strategy.

The integration of advanced technologies such as the IoT, AI, and big data analytics supports TQM by enabling real-time monitoring, data-driven decision making, and process optimization. These technologies facilitate meticulous monitoring and analysis of production processes, allowing for early defect detection and identifying areas for improvement [10,68]. Industry 4.0, with its advanced technologies, provides the tools necessary to deepen the understanding of complex systems and improve the monitoring and control processes essential for applied TQM implementation.

This relationship simplifies the comprehension of dynamic systems and supports proactive quality management by leveraging real-time data and analytics to refine operational strategies and achieve sustainability performance. Smart sensors play a critical role in this integration by monitoring equipment and processes and providing real-time data to identify and rectify defects [12]. The IoT enhances this capability by connecting these sensors and other devices, facilitating centralized monitoring and control of production processes. This integration of TQM with Industry 4.0 technologies like the IoT and smart sensors enables organizations to significantly boost operational efficiency, minimize downtime, and enhance product quality, thereby attaining sustainability across economic, environmental, and social domains [43]. By integrating TQM with Industry 4.0 technologies, organizations can enhance their quality control procedures, reduce waste, and elevate both economic and environmental sustainability [37].

Proposition 1. 

Integrating Industry 4.0 technologies with Total Quality Management significantly improves an organization’s ability to identify and eliminate operational inefficiencies. This integration leads to enhancement in both short-term and long-term sustainability performance dimensions.

Delving deeper into the role of dynamic capabilities, we examine how these capabilities influence the relationship between the adoption of Industry 4.0 and the implementation of TQM, ultimately affecting sustainability performance. Dynamic capabilities, such as sensing, learning, coordinating, and integrating, play a crucial role in the practical integration of Industry 4.0 and TQM. These capabilities enable organizations to adapt to changing environments, innovate, and continuously improve their processes [26,30]. Specifically, the development of dynamic capabilities is both influenced by and essential for the integration of Industry 4.0 and TQM. Sensing capabilities allow organizations to detect and assess trends emerging from Industry 4.0 and TQM, essential for proactive strategic planning. Learning capabilities involve assimilating new knowledge about Industry 4.0 technologies and TQM practices to enhance operational and sustainability performance. Coordinating capabilities ensure that all parts of the organization work together to achieve the combined benefits of TQM and Industry 4.0. Integrating capabilities focus on embedding reconfigured processes and innovations into existing systems, transforming organizational structures and operations to enhance competitiveness and sustainability [4]. The integration of smart sensors, the IoT, and AI into TQM practices enables real-time monitoring and control of production processes, minimizing downtime and enhancing product quality. Data analytics supports proactive quality management by providing insights to refine operational strategies and achieving sustainability performance [12]. By effectively leveraging these technologies, organizations can significantly boost their operational efficiency, reduce waste, and elevate their economic, environmental, and social sustainability [43]. Therefore, we present the following theoretical proposition, which is simply illustrated in Figure 3.

Proposition 2. 

Integrating Industry 4.0 technologies with TQM catalyzes the development of dynamic capabilities, such as sensing, learning, coordinating, and integrating. These capabilities enable organizations to adapt continuously to technological advancements and market changes. This relationship enhances sustainability performance in the new industrial era.

In summary, the integration of Industry 4.0 technologies and TQM plays a pivotal role in enhancing sustainability performance across economic, environmental, and social domains. By leveraging advanced tools such as the IoT, AI, and big data analytics, organizations can monitor processes in real time, optimize operations, reduce waste, and improve product quality. This integration is further reinforced by the development of dynamic capabilities, which allow businesses to sense emerging trends, learn new technologies, coordinate efforts across functions, and integrate innovations into existing systems. The synergistic relationship between Industry 4.0 and TQM addresses operational inefficiencies and fosters continuous improvement and long-term sustainability.

5. Conclusions

The United Nations Industrial Development Organization (UNIDO) underscored the significance of Industry 4.0 as a catalyst for sustainable development in 2017. This study echoes that perspective, illuminating the profound connection between Industry 4.0 and TQM. It elucidates the potential benefits and inherent challenges of integrating these two approaches, where Industry 4.0 serves as a technological enabler, and TQM provides the structured framework, both working together to boost sustainability performance at the economic, environmental and social levels.

A key finding of this research is the identification of several barriers, including gaps in knowledge and resources, resistance to change, and concerns surrounding data privacy and security. For the successful integration of Industry 4.0 and TQM, organizations must craft a well-defined strategy that resonates with their unique goals and objectives. By “well-defined strategy”, the authors refer to the digital transformation strategy that the organization should adopt. This strategy outlines the comprehensive approach organizations need to take to align their adoption of Industry 4.0 technologies with their Total Quality Management (TQM) practices, ensuring that the two are integrated effectively. This strategy includes setting clear objectives, addressing technological infrastructure, building capabilities and employee skills, ensuring process optimization, and facilitating change management. This strategy aims to avoid fragmented initiatives and ensure a cohesive, organization-wide transformation that drives quality and sustainability performance.

Cultivating a culture of continuous improvement and innovation is essential to support this integration. Moreover, investment in employee training and building their capabilities is crucial to equip staff with the requisite skills for this transition. Collaborating with technology vendors and other stakeholders can also play a pivotal role in overcoming barriers and effectively implementing these technologies. This research proposes a conceptual framework that elucidates how integrating Industry 4.0 and TQM amplifies dynamic capabilities, fostering environmental, economic, and social sustainability.

This study shows that combining Industry 4.0 technologies with structured Total Quality Management processes helps organizations address key challenges and turn them into practical actions that continue to improve. Regular monitoring and evaluation are critical in ensuring that this integration adapts to evolving market conditions while meeting sustainability performance metrics. Continuous feedback mechanisms allow for the refinement of practices, ensuring alignment with both organizational objectives and sustainability outcomes. Given the emerging academic focus on this integration, this study aims to deepen the dialogue by exploring both the strategic and operational dimensions. Given the relatively recent emergence of the theme of the integration of Industry 4.0 and TQM in scholarly research, the aim of this study is to enhance the academic dialogue surrounding this integration.

The study’s theoretical contributions involve a comprehensive review of the drivers and barriers to integrating Industry 4.0 and TQM, informed by a dynamic capability perspective. This review provides a multifaceted viewpoint for further research into the practical application of Industry 4.0 and TQM, considering the identified drivers and barriers. For managers, the drivers and barriers outlined in this study may serve as a practical checklist for enhancing operational excellence in sustainability performance influenced by Industry 4.0 and TQM. From a managerial standpoint, the framework developed emphasizes the role of TQM in enhancing process effectiveness for organizational sustainability and positions Industry 4.0 as a strategic imperative to reduce process waste. The discussions on drivers and barriers also offer managerial insights for strategic investment decisions in Industry 4.0 and TQM, aligning with organizational capabilities and strategies.

6. Future Research

Despite its valuable insights, several limitations must be acknowledged. This research primarily focuses on manufacturing organizations, potentially limiting the generalizability of the findings to other sectors; future research should explore diverse sectors like healthcare, finance, and services. This study relies on the dynamic capabilities framework, which, while robust, may not capture all contextual factors influencing the integration of Industry 4.0 and TQM; other theoretical perspectives could offer additional insights. Additionally, empirical data were mainly gathered from developed economies, possibly introducing geographical bias, so future research should include diverse regions. The rapid pace of technological advances poses a challenge in capturing the most current practices, suggesting that longitudinal studies are necessary in order to track changes over time.

Future empirical research should explore the relationship between the adoption of Industry 4.0 and implementation of TQM across diverse sectors. Future studies should be aimed at developing and empirically evaluating a comprehensive framework to implement Industry 4.0 and TQM across various industries. Such studies could also investigate the mechanisms driving the integration of Industry 4.0 and TQM, thereby identifying practical strategies to surmount the obstacles identified. Such research endeavors will deepen the understanding of how the integration of Industry 4.0 and TQM may contribute to sustainability performance and help formulate practical approaches to achieve better sustainability performance.

As Industry 4.0 continues to drive automation and data exchange in manufacturing, the emergence of Industry 5.0 represents the next phase of industrial evolution, emphasizing human-centric approaches and collaboration between humans and machines. While Industry 4.0 focuses on smart technologies like the IoT, AI, and automation, Industry 5.0 introduces greater emphasis on personalization, ethical considerations, and human–machine synergy. This evolution could significantly impact the integration of Industry 4.0 with TQM by introducing more adaptive, personalized manufacturing processes and enhancing human decision making in sustainable practices. Future research should explore how Industry 5.0 technologies influence TQM, particularly in areas such as personalized production, human-centered innovation, and sustainability. Additionally, examining how dynamic capabilities evolve to support the transition from Industry 4.0 to Industry 5.0 and the role of human creativity in shaping more sustainable, efficient systems will be critical for advancing theory and practice.