Implementation roadmap guiding manufacturing SMEs transition towards Quality 4.0

The advent of Industry 4.0 has ushered in a transformative era for the manufacturing sector, characterized by the increasing digitalization of processes and the utilization of Big Data and Artificial Intelligence technologies. This revolution has far-reaching implications, not just for operational efficiencies, but also for aspects such as quality management (Dias et al., 2022). The concept of Quality 4.0 emerges at this intersection, representing the digitalization of quality management systems and offering a pathway for organizations to elevate performance on multiple fronts, ranging from process and product quality to safety, reliability and environmental considerations.

While substantial research has been devoted to understanding the facets of Industry 4.0, the discussion around Quality 4.0 is still in its infancy, and concrete applications are still limited and heterogeneous to date (Sader et al., 2022). Many studies on the impact of the fourth industrial revolution on production systems have been conducted, and numerous adoption frameworks of the new technologies have been proposed, but few are specific to the field of Quality management. Fertile ground thus exists for academic inquiry, particularly concerning the convergence of traditional quality management systems with emerging digital technologies (Javaid et al., 2021).

This paper aims to develop a Quality 4.0 guiding roadmap for manufacturing SMEs interested in exploiting the digital transition to transform their quality management system into a strategic asset that could strengthen the competitive position of the organization. Although traditional quality management systems have broader applications, this analysis is tailored to the manufacturing sector to focus the literature review and leverage the authors' expertise. The discussion is built upon an in-depth literature analysis covering the manufacturing domain that sheds light on the main aspects that need to be coordinated to support a smooth transition and that provides a critical summary of the Quality 4.0 value propositions, the related pillars and the current gaps. The roadmap, developed on top of the literature findings, is tailored to the needs of SMEs. In fact, in the transition process towards higher levels of digitalization, SMEs face more challenges than their larger counterparts due to the lack of internal resources. The framework, organized around five consecutive phases, has a strategic relevance: it is intended for the company's management, and it considers both technological, organizational and cultural aspects to develop and deploy an efficient quality management system rooted in the Industry 4.0 paradigm. The conceptual framework is validated in a real scenario to demonstrate its potential in guiding decision makers. The rationale behind the framework development makes it easy to extend its use, with little adjustments, to any organization in the manufacturing domain. Findings of the research can be analysed through the lens of other contexts, like services, as a further step, starting from the analysis of the actual needs of organizations.

By addressing this objective, the paper aims to offer a comprehensive guide for practitioners in the manufacturing sector, looking to embrace Quality 4.0 as a strategic lever towards operational excellence. The paper thus adds a significant layer of empirical and theoretical understanding to the ongoing academic and industrial conversations around Quality 4.0. In the following sections (§ 2), the adopted methodology is presented before moving to the critical analysis of the literature findings (§ 3). The central part of the paper is devoted to the presentation of the proposed framework (§ 4) and its validation in a Swiss SME producing material handling solutions (§ 5). Conclusions are eventually drawn discussing the main contribution of the paper to the Quality 4.0 debate, its limitations and future extensions (§ 6).

The aim of the research has been achieved by addressing the following research questions:

From a methodological point of view, the answer to these questions had to pass through different steps. Firstly, an extensive literature review has been conducted on the Quality 4.0 topic, which, due to its recent scientific attention, is dealt with in a fragmented way. The purpose of the literature review was to acquire insight into the main antecedents, components and benefits of Quality 4.0 as discussed by the main contributions. These findings, along with the identified research gaps, guided the development of the research framework.

The development of the research framework drew upon a combination of theoretical insights from the literature and practical experience from industry projects. The literature review provided a comprehensive view of the existing knowledge on Quality 4.0, highlighting key concepts, challenges and adoption models. This review helped to identify gaps in the current understanding, particularly concerning small and medium-sized enterprises (SMEs), which are often underrepresented in the Quality 4.0 discussion.

The insights gained from the literature were not applied in isolation but were critically evaluated in the context of real-world challenges faced by SMEs. The researchers' industry experience played a crucial role in this process. Over the course of various industry engagements, the researchers observed common obstacles SMEs face, such as limited resources, lower levels of digitalization and the need for a phased approach to adopting new technologies. This practical understanding helped refine the theoretical concepts from the literature and ensure they were aligned with the operational realities of SMEs.

For example, while the literature emphasized the importance of interconnected systems and data-driven decision-making, the researchers' industry experience demonstrated the need for scalable, cost-effective solutions that SMEs could gradually integrate. Additionally, real-world cases revealed specific bottlenecks in supplier collaboration and employee engagement that are often overlooked in theoretical models. These industry insights were instrumental in shaping the framework's phased approach, which balances technological advancements with organizational readiness.

The empirical validation followed a step-by-step analysis of the proposed framework, using a case study conducted in a Swiss SME producing material handling solutions. This company was selected for its ongoing digitalization efforts, which aligned with the research objectives focused on Quality 4.0. The case study involved key participants, including senior management, quality control personnel and IT staff, all directly engaged in the project to digitize the company's quality management system.

Data for the case study were collected through unstructured interviews with project stakeholders, direct observations of the digital transformation processes and an analysis of internal company documents such as reports and performance metrics. This multi-source approach ensured a comprehensive understanding of the challenges and opportunities presented by the digital transformation.

The focus of the research was to identify relationships between the company's process of implementing digital technologies to improve quality management and the five steps outlined in the proposed framework. The use of the framework as a structured reference provided coherence and methodological rigor, supporting a qualitative analysis that identified both similarities and differences between the framework and the company's real-world implementation on a step-by-step basis.

The literature review research has been conducted consulting Google Scholar, Emerald, Taylor & Francis, Web of Science and Science Direct as main databases. The searches were based on the keywords: “Quality 4.0”, “Smart Manufacturing and Quality”, “AI for Quality”, “Smart factory and Quality”, “CPPS and Quality”, “Quality 4.0 adoption framework”. No restrictions were placed on year of publication, type of publication or rankings and additional known relevant studies were included outside of this initial search. Therefore, reports and materials from major quality societies, such as the American Society for Quality (ASQ), were considered for review.

To ensure methodological rigor and replicability, a set of inclusion criteria was applied, resulting in the selection of 86 studies for detailed analysis. The criteria required that studies be focused on Quality 4.0 within industrial manufacturing environments, particularly in the context of Industry 4.0. Additionally, research is needed to address the impact of Quality 4.0 transitions, including value propositions, adoption frameworks and implementation outcomes. Furthermore, empirical studies were sought that provided evidence through use cases, testimonials or data-driven evaluations related to Quality 4.0 adoption.

Titles and abstracts were then screened to assess relevance according to the above criteria, followed by a full-text analysis of selected papers. This process ensured a focused review centred on adoption strategies, technological and organizational impacts and practical frameworks for Quality 4.0. After analysing the full papers, the results of the literature review have been discussed, identifying three macro themes that provide a detailed description of the key concepts surrounding Q4.0 in the manufacturing sector, namely the emergence of Quality 4.0; the value propositions and the pillars of Quality 4.0 adoption. The discussion on the last two topics provides an answer to RQ1 and RQ2. As a prerequisite to achieve RQ3, Q4.0 implementation frameworks have been compared against each other to identify gaps and pave the way for the proposed framework.

The evolution of quality management through industrial revolutions has involved extensive research and method development in production. However, over the past decade, there has been a decline in innovative models and waning managerial interest in quality, resulting in stagnation in the discipline (Dias et al., 2022; Zonnenshain and Kenett, 2020). On the other hand, the manufacturing sector faces disruptive revolutions demanding an evolution in quality management systems (Schmidt et al., 2015; Bongomin et al., 2020). Companies struggle with quality, as evidenced by recent recall campaigns by major companies like General Motors and Toyota (Sony et al., 2021), with estimates of poor-quality costs ranging from 5% to 30% of gross sales. Managers increasingly realize the need to advance their quality management procedures (Gunasekaran et al., 2019). A survey of senior quality professionals revealed a need for systems capable of handling large, reliable data for efficient quality management activities. Today's market demands highly adaptable production lines (Galindo-Salcedo et al., 2022) to deliver top-quality products efficiently while meeting stringent social and environmental regulations (European Commission, n.d.). This necessitates an evolution of traditional quality management systems by integrating the latest Industry 4.0 technologies without abandoning established Quality Control (QC) and Quality Assurance (QA) methods (Aleksandrova et al., 2019; Zulqarnain et al., 2022).

In this context, the convergence of Quality management with Cyber-Physical Production Systems (CPPS) and digital manufacturing platforms is reshaping quality and organizational excellence under the paradigms of Industry 4.0. It involves a shift, which, rather than excluding traditional approaches like Total Quality Management (TQM) and Six Sigma, amplifies and strengthens them through integrated approaches like Cyber-Physical Digital Quality Management (DQM) (Christou et al., 2022; Escobar et al., 2024). The link between Q4.0 and Industry 4.0 is clearly highlighted in most of the research (Sisodia and Villegas Forero, 2019; Sony et al., 2020; Radziwill, 2018; Dror, 2022; Singh et al., 2022). Q4.0 represents the culmination of technology's influence on TQM by digitalizing quality technologies, processes and human resources (Mtotywa, 2022; Küpper et al., 2019). It aligns with Industry 4.0 by integrating emerging technologies to achieve quality-related goals, serving as a framework and a model for assessing a company's quality management system maturity in the digital age (Dias et al., 2022). More concretely, Q4.0 is also defined as a framework for the quality discipline supporting the fourth industrial revolution (Zonnenshain and Kenett, 2020; Nenadál, 2020; Galindo-Salcedo et al., 2022) as well as a general model useful in assessing the maturity level of a company's quality management system against the digital age (Glogovac et al., 2022). Giving a comprehensive definition, Q4.0 is the term used to describe the paradigm shift in quality management systems within manufacturing companies, triggered by the application of digital innovations brought by the fourth industrial revolution.

Q4.0 is not an isolated initiative but rather an integrated approach that spans the entire value chain and is applicable across diverse sectors.

Currently, Q4.0 remains primarily a theoretical concept, lacking substantial evidence of extensive implementation within the manufacturing sector. Based on a BCG survey (Küpper et al., 2019), only 16% of over 220 surveyed companies have initiated Q4.0 adoption programs. Nevertheless, four Israeli companies claim to have successfully implemented parts of their Q4.0 framework (Zonnenshain and Kenett, 2020). The BCG survey also indicates that 20% of companies are in the planning phase, expecting implementation in the coming years.

The value proposition of Q4.0, drawn from literature, encompasses five key categories in terms of perceived importance:

1. Augmentation of human intelligence. Q4.0 initiatives broaden operators' oversight, enabling remote monitoring and management of diverse operations, thereby improving productivity and ethics (Radziwill, 2018). Data models facilitate simulations, reducing manufacturing engineering time and preventing errors in the manufacturing and integration process, while aiding in creating operational and training materials (Dias et al., 2022; Zonnenshain and Kenett, 2020; Montini et al., 2021). Digital Twins (DT) and Extended Reality (XR) technologies exemplify advanced simulation capabilities synchronized with real systems (Bodi, 2020).

2. Increase in the speed and quality of decision-making. The core of any structured quality management system lies in precise data (Saraph et al., 1989). Initiating Q4.0 projects primarily aims to obtain dependable information, providing choices for managerial decisions within the enterprise (Aleksandrova et al., 2019). This decision-making knowledge and predictive insights help reduce uncertainty, enabling businesses to mitigate risks and maximize gains (Petropoulos et al., 2022).

3. Improvement of performance, traceability and auditability. Q4.0 optimizes both social and technical systems to boost overall productivity and oversee the traceability of products and services within the organization (Saraph et al., 1989). Q4.0, utilizing information technology (IT) enables cost-effective total process inspection, real-time equipment monitoring and predictive maintenance (Sony et al., 2021). Digitalization, with automated data storage and easy access, significantly aids certification and process auditing.

4. Evolution of external relationships, create new business models. The Q4.0 can promote significant improvements in relationships with organizations' stakeholders and, particularly, increase customer satisfaction. As Industry 4.0 shifts from automation to intelligent manufacturing, Q4.0 pivots from process-centric to customer-centric approaches. Q4.0 embodies a product's adaptive capacity throughout its lifecycle to meet customer needs, considering the interests of various stakeholders along the value chain (Salimova et al., 2020). Simultaneously, this transition gives rise to new business models that prioritize individualization and closer customer relationships (Dias et al., 2022).

5. Savings in cost and time in the long run. One of the most feared challenges is certainly bearing the upfront costs of investments in technology, automation and training (Taguchi, 2002). However, in the long run, Q4.0, improving the efficiency, the transparency and reliability of the systems, will reduce the internal and external failure costs. Contributions, such as Laciok et al. (2021), Fahle et al. (2020) or Gittler et al. (2019), showcase examples where AI technologies like statistical modelling, decision trees, neural and Bayesian networks drive the reduction of quality costs and enhance process line quality.

In recent years, in-depth research, expert interviews, field trials and global surveys have aimed to define the interaction between quality management systems and Industry 4.0, identifying the fundamental aspects of this new paradigm in production. Being a new topic, the time is not yet ripe to identify specific streams of research in the field of Q4.0 (Rashid and Taibb, 2016; Anil and Satish, 2016) or to find a validated framework for its adoption in companies, but valuable considerations, challenges and “winning moves” have already emerged. For instance, Zulqarnain et al. (2022) utilized a Q4.0 maturity framework in a survey to assess both advanced and underdeveloped aspects among participants commencing Q4.0 implementation. Similarly, Mtotywa and Dube (2023) identified gaps and areas for improvement in Q4.0 within the mining sector through surveys and interviews, while Mittal et al. (2024) and Rogala et al. (2024) explore and validate Q4.0 readiness factors and essential organizational variables with senior quality professionals to create a methodical roadmap for Q4.0 implementation. Other research, such as Maganga and Taifa (2023), Antony et al. (2023), Bousdekis et al. (2023) and Rogala et al. (2024), delves into organizational benefits, motivations, ongoing initiatives, crucial skills, challenges and success factors related to implementing Q4.0. With a more human-centric focus (Hattinger and Stylidis, 2023), the gaps and key elements focused on Operator 5.0 need to be included in Q.4. In a more specialized approach, Glogovac et al. (2023) examine the impact of leadership on the quality management system within the context of Industry 4.0. Adopting Q4.0 presents several challenges, including aligning new technologies with existing regulatory frameworks, Standard Operation Procedures (SOPs) and internal processes (QUALITY 4, n.d.).

For a thorough comprehension, the results of literature research on Q4.0 adoption have been classified into three distinct pillars encompassing Q4.0 strategic, technological and non-technological dimensions.

The digitalization of quality management offers substantial benefits in improving customer experience, engagement, efficiency, decision-making and innovation within organizations (Gunasekaran et al., 2019). However, transitioning to Q4.0 poses a range of challenges whose complexity varies industry by industry. In large-scale production, estimating ROI is complex, requiring a focus on reducing failure costs before investing in new methodologies. This issue is particularly pronounced in smaller enterprises, which often contend with constrained resources. A comprehensive review of the literature (Sarker and Dunston, 2025) identifies several key barriers within a set of probable root causes with an impact on the Q4.0 implementation. These include deficits in critical skillsets, the absence of a robust quality culture and a limited focus on emerging technologies. Other critical barriers include the lack of a solid foundation in dynamic operational contexts, insufficient traceability at the micro-level within supply chains and financial constraints. Establishing a formalized, well-defined strategy, which aligns the organization's business goals and vision (Sony et al., 2021), is crucial for a smooth adoption of Q4.0 (Zonnenshain and Kenett, 2020). A digitalization project in the quality management field requires significant funding, rapid investment return, organizational restructuring and the identification of suitable external partners while fostering a quality-focused culture within the organization (Corti et al., 2021). Strong support from top management throughout the project's phases is pivotal, as it facilitates technology integration into the business strategy and mitigates resistance to change within the organization (Sony et al., 2020; By, 2005). Moreover, top management plays a vital role in fostering a new quality culture and encouraging employee adoption (Rogala et al., 2024). Since no universally validated reference frameworks exist, developing a well-structured strategy that accommodates both technological, organizational and cultural aspects is challenging.

Literature suggests beginning with the identification, prioritization and resolution of quality-related pain points, emphasizing resolving issues that unlock value and reduce risks (Singh et al., 2022; Bongomin et al., 2020). A multidisciplinary team should conduct the identification and prioritization of these pain points, evaluating them from various perspectives, including business, operational and technological aspects. Proof-of-concept pilots focusing on high-value cases should precede full-scale implementation to address the identified pain points. The BCG research (Küpper et al., 2019) pioneers this approach by detailing it in seven steps, encompassing identification of pain points, testing and scaling use cases, vision development, technology and data enablement, skills development, change management and fostering a quality culture. This method is recognized as highly comprehensive, comparable to the model proposed by the LNS research institute (QUALITY 4, n.d.), which emphasizes 11 axes to assess digital maturity and achieve Q4.0, focusing on cultural, management, collaboration and leadership aspects (Sisodia and Villegas Forero, 2019; Sony and Naik, 2020). A comprehensive strategy that merges organizational, human and economic factors is crucial for the successful implementation of Q4.0 projects (Sisodia and Villegas Forero, 2019; Sony and Naik, 2020).

Q4.0, as the culmination of technological impact on TQM, involves the digitalization of quality technologies, processes and human resources, driven by the advancements within Industry 4.0 digital technologies (Mtotywa, 2022; LNS Research, n.d.; Radziwill, 2018). Q4.0 indeed does not seek to replace traditional methods but aims to enhance them with digital technologies (Daniel et al., 2017; Leng et al., 2020). The literature highlights six different key digital technologies that hold the potential to influence and revolutionize industries' quality management systems (Escobar et al., 2024; By, 2005). These technologies are briefly introduced from a technical perspective (QUALITY 4, n.d.; Bongomin et al., 2020; Zulqarnain et al., 2022; Sony, 2020).

1. Big data. Modern factories use advanced information technology like Hadoop, Hive and NoSQL databases to handle extensive data from machines, IoT devices and sensors (Kolajo et al., 2019). This data infrastructure grants almost real-time access to information, supporting quality management resources and generating valuable useable data for AI modules and early failure detection, thereby boosting productivity in Industry 4.0 (Goetsch and Davis, 2016; Lee et al., 2014).

2. Artificial Intelligence. AI, known for intuitive problem-solving, is compared to the “new electricity” for its broad applications, aiding in decision-making and human tasks (Carvalho et al., 2021; Jewell and Ng, 2019). The AI modules can assist humans in cognitive and physical activities, providing autonomous decisions based on processing conditions and outputs (Fahle et al., 2020; Bettoni et al., 2021).

3. Machine learning. ML enables systems to self-learn from experience without explicit programming (Daniel et al., 2017), applied effectively in process optimization, manufacturing monitoring and predictive maintenance over recent decades (LNS Research, n.d.; Wuest et al., 2016; Gönen and Alpaydin, 2010; Pham and Afify, 2005; Susto et al., 2015). In quality management, ML techniques exhibit potential for enhancing quality (Chen et al., 2023; Carvalho et al., 2021).

4. Deep learning. DL, a form of ML, involves multi-layered models that transform and abstract data in successive layers. Inspired by how the brain learns, it is used for image classification, pattern recognition, forecasting and creating various content (Apte et al., 1993).

5. Blockchain. As an innovative computing paradigm, it revolutionizes the digital world by enhancing system security, traceability and efficiency (Daniel et al., 2017), offering transparent and decentralized transaction mechanisms through distributed ledgers (Leng et al., 2020).

6. Data science. Data science involves amalgamating various datasets to extract meaningful insights. Various tools like SAS, MATLAB, Apache Spark, Excel and Tableau help in statistical operations, numerical computing, batch and data stream processing, data analysis and visualization, offering diverse functionalities for different industry needs (Wang et al., 2018).

The development and deployment of new technologies in manufacturing pose significant challenges that can hinder initial digitalization projects. A comprehensive framework in Gunasekaran et al. (2019) outlines technical barriers in Q4.0 adoption, encompassing integration of diverse data sources, data processing, system architecture maintenance and the establishment of flexible technology ecosystems. A list of challenges is also listed in Corti et al. (2021), while Sarker and Dunston (2025) explore their underlying causes, including a lack of knowledge about newly updated software and hardware and inadequate integration of data integrity with tools like IoT, ML and AI. A BCG survey (Sisodia and Villegas Forero, 2019) reveals contrasting perceptions between companies at different stages of Q4.0 implementation. “Followers” emphasize barriers like digital skill shortage, unclear strategy and a lack of quality culture, while “frontrunners” face technical challenges such as outdated systems, data fragmentation and integrity issues. These challenges arise from the management of new technologies, their integration with existing systems (Gunasekaran et al., 2019; Antony et al., 2023) and the adaptation to industrial environments differing from laboratory ones. Although no definitive framework for addressing technological issues in transitioning to Q4.0 exists, some suggested “winning moves” include developing robust infrastructure for handling diverse data sources and implementing prescriptive analytics alongside extensive integration.

The extensive potential for digital technologies to transform manufacturing quality management is vast, contingent not only on technological advancements but also on non-technological elements like assets, people and processes, which actively drive the digital transformation (Montini et al., 2023). Considering the strategic approach required to implement Q4.0, the literature emphasizes integrating modern digital technologies into existing quality management methods and activities, emphasizing two crucial non-technological aspects for quality managers' attention, namely training for building competences and stakeholder collaboration and communication (Daniel et al., 2017; Leng et al., 2020; Bortolini et al., 2017; Swarnakar et al., 2025).

1. Training for building competency. The true value of digitalization projects lies not only in deploying new technologies but in their continuous and effective utilization, which requires ongoing training of operators and technicians. A major obstacle is the shortage of digital skills (Gunasekaran et al., 2019; Sisodia and Villegas Forero, 2019; Antony et al., 2023), which impedes the effective use of advanced systems like AI, IoT and ML (Agrawal et al., 2019). The skills needed for Q4.0 include technical abilities such as IT systems operation, big data analysis and RFID usage, along with transformative skills like adaptability and critical thinking or creativity (Sitek et al., 2008). Learning factories play a pivotal role in imparting both theoretical and practical knowledge to operators, practitioners and students within this realm of continual learning (Daniele et al., 2021).

2. Stakeholders’ collaboration and communication. Collaboration and communication are crucial for quality management but are complex due to their cross-functional nature. In the digital age, successful quality management requires secure data-sharing strategies, multi-plant supervision and auditability (Sisodia and Villegas Forero, 2019). Digitalization affects relationships within organizations, among employees, suppliers and customers (Jokovic et al., 2023; Qu et al., 2023). Employees may perceive digital technology as a threat or a punishment, necessitating managerial communication regarding the benefits to reduce stress and enhance efficiency (Spencer, 2018). Additionally, weak leadership and insufficient stakeholder engagement can result in low employee motivation, resistance to change and siloed operations. To address these challenges, it is crucial to implement robust Q4.0 leadership programs (Virmani et al., 2024), promote cross-departmental collaboration and establish effective indicators to monitor the deployment and impact of new digital systems (Escobar et al., 2024).

3. Suppliers play a crucial role in implementing digital technology, providing essential knowledge and support (Bag et al., 2021; Ponsignon et al., 2019). Some customers, particularly those with significant contractual power, may drive the need for digitalization to meet their demands for certified documents or centralized data.

From the literature review, 14 studies emerged related to the implementation of Q4.0 by means of frameworks, models or guidelines. Table 1 summarizes them, highlighting some features of interest for this research: the focus on manufacturing; the focus on SMEs; the application scope aligned with that of TQM and the integration with an empirical validation in real-world scenarios.

From the results, it is evident that the application of Q4.0 lacks maturity and still requires in-depth exploration. In this line, a review from Liu et al. (2023) focusing on 46 journal articles related to Q4.0 models and applications found only 10 studies between 2017 and 2022 that proposed models or addressed Q4.0 technology applications. Of these, three studies stand out for contributing insights (Sader et al., 2019): presented a theoretical framework combining Industry 4.0 characteristics with TQM principles (Sariyer et al., 2021); proposed a multi-stage model using big data analytics for quality management (Christou et al., 2022); addressed architecture, design, implementation and evaluation of an end-to-end platform for Q4.0 applications.

Zulqarnain et al. (2022) developed a Q4.0 framework to aid companies in transitioning from traditional to advanced quality management systems. However, this framework, based on ASQ's 11 dimensions of Q4.0, requires modifications for specific industry needs, particularly SMEs. Other studies, like QUALITY 4 (n.d.) and Cassoli et al. (2022), report frameworks for Quality Management Systems, offering differing perspectives: QUALITY 4 (n.d.) suggests a bottom-up approach focusing on “quality hot spots”, while Singh et al. (2022) presents a comprehensive roadmap for companies, leaving the application decisions to management. Similarly, Carvalho et al. (2024a) propose a roadmap that consolidates key quality management practices to promote sustained progress towards advanced technology integration phases, including a human-centred perspective. A different approach is used in Sarker and Dunston (2025) and Virmani et al. (2024), where they show Q4.0 models that reflect the main Q4.0 implementation problems and barriers and present possible solutions. Instead, Khourshed and Gohar (2023) and Mittal et al. (2024) propose implementation roadmaps, in which the steps for the transition are based on the Q4.0 readiness factors and the essential organizational variables. Yet, both lack detailed implementation specifications, remaining high-level and suitable for companies of any nature, sector or size. Thus, according to Liu et al. (2023) and Bousdekis et al. (2023) considerations and the review findings, while numerous theoretical frameworks and models exist for optimizing quality management, their practical application is limited and underexplored. These studies often lack integration into corporate quality practices or are limited to conceptual studies and specific cases. Furthermore, it is clear from the results that only Sariyer et al. (2021) propose a model adapted to SME manufacturing, while the others are designed for larger companies. The reason may be related to their level of readiness, as they are more likely to have already acquired a suitable level of maturity to make a transition to a Q4.0 system, compared to small businesses. Moreover, most of the manufacturing SMEs do not even understand or do not have the resources and capabilities to assess their Q4.0 maturity level, which is the first step in any wide-ranging digitalization program. This is the direct consequence of the lack of consensus, definitions and standard measurement tools (Ramingwong and Manopiniwes, 2019).

The roadmap described in this chapter is designed as a practical tool to assist the management of manufacturing SMEs in strengthening and enhancing their Quality Management System through digitalization. The framework integrates both technological and organizational aspects into a comprehensive model, combining the advantages of existing adoption models found in literature with the needs of SMEs. From a conceptual point of view, the development of the framework embraces the fundamental principles of socio-technical systems theory, sharing the idea that technology and human factors must be integrated to achieve better outcomes in complex systems. As with Sony and Naik (2020), which propose an Industry 4.0 framework based on this theory, this paper approaches the integration of Quality 4.0 in manufacturing systems by ensuring strategic alignment between technologies and human factors, considering the reciprocal influences and their impact on the resulting system's effectiveness.

While the framework has been deliberately developed for manufacturing SMEs with lower levels of digitalization and automation, it also has the potential to be adapted to other contexts. For example, with slight adjustments, it could be applied to SMEs in different industries or sectors that are undergoing similar digital transformation processes. These industries may include those that are process-driven but less dependent on sophisticated digital systems, allowing for flexibility in the roadmap's application. By broadening its scope, in future iterations or use cases, the framework could provide valuable guidance to a diverse range of SMEs, even in the service sector, navigating the complexities of Quality 4.0 adoption.

Although the framework has been primarily designed for SMEs, it holds potential applicability for larger enterprises or more mature organizations. The core structure of the roadmap remains valid across different organizational scales and sectors; however, the level of emphasis placed on each phase may vary depending on company size, digital maturity and strategic positioning. For SMEs, the initial phases of the roadmap (e.g. self-assessment, strategic goal setting) are often of paramount importance. These organizations frequently face challenges related to limited internal capabilities, a lack of clarity on digital value creation and insufficient resources. Consequently, structured guidance is essential to support them in evaluating their current state, prioritizing digital quality initiatives and setting realistic and context-specific goals. In contrast, large enterprises often have dedicated quality departments and greater familiarity with Industry 4.0, making the framework more useful for validating existing strategies, facilitating cross-departmental alignment and enhancing structured decision-making. In such contexts, the framework could help guide more advanced objectives, such as integration into digital ecosystems, networked value chains or continuous data-driven improvement, beyond internal process optimization.

Accordingly, although the roadmap was conceived with SMEs in mind, it provides a flexible structure in which the depth, focus and function of each phase can be adapted to suit diverse organizational contexts.

This adaptability is particularly relevant for manufacturing companies where I4.0 technologies are already adopted in several use cases, and their extension to quality management is unavoidable.

The practical use of the framework is expected to have a multi-fold impact. Improvements in the quality control performance are the direct effect, but the strategic relevance of the move towards the new approach also paves the way for a renewed way of training and upskilling operators and a more structured approach of a strategic nature to align technical and non-technical aspects that could also be expanded to the management of other processes. In the medium-long term, lower costs and more satisfied operators contribute to creating value for the whole organization.

The roadmap, shown in Figure 1, is organized around three concentric circles. The outermost circle is composed of five implementation phases, while the innermost circle represents traditional quality management systems. The middle circle is characterized by independent pilots, which serves as a link between the traditional quality management systems and the roadmap towards Quality 4.0.

The external circle, in the form of a cycle in line with the continuous improvement idea and with TQM tools such as the PDCA, is achievable from the traditional quality management systems through practical use cases. This structure of the framework highlights a key element: towards a smooth Quality 4.0 adoption, the separation of areas of activities and implementation of independent, relatively small use cases proved to be the best approach within the digitalization road for SMEs that have limited resources. Launching, in parallel with the main digitalization project, small implementation pilots (e.g. regarding quality control or quality inspection) allow the company to immediately gain experience, start creating know-how, experience real benefits and identify the most important constraints. In this way, it is possible to achieve quick wins that generate first results and increase the level of motivation. The implementation pilots follow the same logic of the overall approach, passing through all the phases of the roadmap, from the formalization of processes and definition of objectives to the value generation. Moreover, this type of approach allows SMEs to overcome the so-called “pilot purgatory”: the situation in which SMEs do not know, due to hurdles related to a lack of clarity regarding business value, limited resources and an overwhelming number of applications, how to proceed and get benefits (Schmitz et al., 2019).

The initial focus on pilots in different areas is a good practice to overcome the initial deadlock caused by a lack of experience and skills, but it is not the ultimate result to aim for. Q4.0 is a comprehensive solution in which all the digital systems deployed in the organization are capable of communicating and sharing their status and information. For this reason, the proposed framework aims at a state in which all the independent pilots are integrated into a unique standard digital system.

“Who are you?”

Before initiating any major transformation, it is crucial for SMEs to thoroughly understand their starting position, especially when considering a digitalization project. This understanding encompasses the role of quality in the organization, what distinguishes the company, its market strategies, the motivation for change and the anticipated outcomes, benefits and risks of adopting Quality 4.0 and digital transformation. Documenting these insights, though it may seem mundane, is vital for laying the groundwork for Quality 4.0 transition and offers a stable reference point during times of change. This preliminary phase, focused on business and strategy, should be conducted by the organization's management in conjunction with department’ heads. They should evaluate various aspects such as product offerings, mission, vision, values, workforce, assets, regulatory environment, organizational structure and customer and stakeholder profiles.

Moreover, measuring the Industry 4.0 maturity level within the organization and benchmarking it against competitors is critical. This helps in identifying specific deficiencies and forming an action plan that includes necessary support systems, technologies and competences. Skills like data analysis, process thinking and customer perspective are key for transitioning to Quality 4.0. It is also important to recognize internal needs for competence development to enhance the Industry 4.0 maturity level.

During this phase, the company must continually monitor local, regional and international regulations and standards impacting digitalization, such as IEC 61499 related to the Cyber Physical Production System (CPPS). This also includes ensuring compliance with regulations regarding worker safety when introducing automated systems like cobots in production lines.

Evaluating processes is a central activity in this phase. Processes need to be clearly understood, stabilized and documented before considering major changes. This helps the organization compare its performance with competitors, identify processes for improvement, assess digital maturity, estimate resources needed for Quality 4.0, explore new business opportunities through digitalization and document reasons for embarking on digital transformation.

At the end of this phase, the SME should have a documented organizational profile as a baseline for setting objectives and strategies for the Quality 4.0 transition and a formalization of main processes with performance measurements through numerical indicators, aiding in decision-making for practical use case investments.

To enhance the practical applicability of this phase for SMEs, a structured checklist can be employed to assess readiness. This includes: (1) Evaluating digital maturity using established models such as the Quality 4.0 maturity framework proposed by Zulqarnain et al. (2022), adapted for SMEs by focusing on key dimensions like data infrastructure and process digitalization; (2) Conducting a SWOT analysis tailored to quality management, identifying strengths (e.g. existing ISO 9001 certification), weaknesses (e.g. limited IT resources), opportunities (e.g. predictive maintenance via IoT) and threats (e.g. regulatory compliance gaps); and (3) Benchmarking against sector peers using tools like the European Foundation for Quality Management (EFQM) self-assessment questionnaire, which provides diagnostic criteria for organizational excellence in a digital contex. These tools enable SMEs to quantify readiness with scores (e.g. on a 1–5 scale per dimension) and prioritize gaps, such as skill deficiencies or process instabilities, before proceeding.

“Why transform? What?”

In the critical second phase of an SME's journey towards digital transformation, the focus shifts to articulating the motivations and specific objectives for this change. Following the groundwork laid in the first phase, management must delve deeper into the “why” and “what” of the transformation. The precision in defining the reasons for digitalization and setting quantifiable goals greatly influences the formulation of a detailed strategy, thereby reducing ambiguity about the direction in which the organization should move towards achieving Quality 4.0.

The motivations for adopting Industry 4.0 technologies are diverse and unique to each organization. These can range from tangible benefits like enhanced quality, increased productivity and reduced costs, to strategic advantages such as heightened competitiveness, improved profit margins and the opportunity to be a first-mover in the market. In aligning with these motivations, the objectives set by an SME must not only resonate with the broader business strategy but also fortify or open avenues for new business lines.

However, defining these objectives is not straightforward. The rapidly evolving business context and the typically limited initial knowledge about Industry 4.0 and Quality 4.0 make this a complex, ongoing process. As such, objectives undergo continual recalibration in response to new insights and evolving understanding of the associated benefits and risks. Implementing small-scale digital pilots plays a crucial role in this dynamic environment, allowing the organization to experiment with and refine its goal-setting processes. These pilots should consider the availability of resources such as time, expertise and budget, ensuring that the objectives are realistic and achievable.

A pivotal element in the journey towards these goals is the development and incremental enhancement of a data management platform within the organization (see Figure 2). This platform is central to orchestrating and integrating the various digital systems adopted in the individual pilots. It evolves to support the achievement of quality objectives, categorized into three progressive stages:

1. Internal Process Optimization: the first category involves incremental innovation within the existing quality management system. The aim is to optimize current processes using enabling technologies like Big Data, the Industrial Internet of Things (IIoT), collaborative robots and cloud computing. This stage focuses on enhancing process efficiency and reducing waste, thereby improving quality and lowering costs.

2. Improving External Relationships: the second category expands the focus to the SME's interactions with customers and suppliers. By employing technologies such as data science, artificial intelligence, virtual reality and blockchain, the organization seeks to enhance communication and collaboration. This stage aims at improving customer satisfaction and engagement, streamlining supply chain processes and fostering more efficient and effective external relationships.

3. Creating New Ecosystems and Value Networks: the final category represents a significant leap, targeting a transformation in the SME's business model. It involves leveraging the data and insights gained from the first two stages to innovate and diversify the organization's offerings. This could include introducing smart products and services, refining servitisation strategies based on performance data and expanding the customer base.

Throughout this phase, the SME must document and refine its list of objectives, which are influenced by the insights gained from the digital pilots and the evolving industry context. These objectives should be predominantly quantitative, allowing for effective measurement and monitoring through Key Performance Indicators (KPIs). This approach ensures that the management can track the organization's progress in digitalization accurately and make informed decisions to steer the transformation journey effectively.

For SMEs, defining objectives can be streamlined using SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound) tailored to Quality 4.0. For example, under Internal Process Optimization: “Reduce defect rates by 20% within 12 months via IoT-enabled monitoring, measured by OEE and FPY”. Prioritization can be supported by a decision matrix scoring pilots by ROI, resources and strategic fit. Tools like the LNS Quality 4.0 Maturity Model offer benchmarks, from basic data use to AI integration, enabling SMEs to track progress and refine goals through quarterly reviews.

“How can you transform?”

In the third phase of digital transformation, an SME defines and plans its strategy for upgrading the quality management system. This phase involves integrating both non-technological and technological aspects, underpinned by solid data management (Figure 3). Organizations with established structures, such as those conforming to ISO 9001, Baldrige or lean management principles, often find it easier to innovate due to their existing frameworks for action plans and clear roles.

The strategy development should consider ten key elements, as illustrated in Figure 3, that are divided into non-technological aspects (employee training, cultural aspects, stakeholder collaboration, compliance) and technological aspects (digital platform, data management, digital systems and technology providers), with resources and use-cases being common to many business projects.

The non-technological aspects encompass several critical areas that require careful attention. Training represents a fundamental challenge, as employees often lack experience with new digital systems. Management must develop educational pathways to enhance technical expertise and appreciation for these technologies. Cultural aspects are equally important, requiring employees to understand and value the digital evolution of quality in their work. This can be achieved through training, incentivizing and integrating a quality culture throughout the organization. Stakeholder impact must also be considered, as digitalization affects relationships with stakeholders and necessitates a digital platform that compartmentalizes information based on responsibilities and tasks. Finally, compliance remains essential, as digital systems must align with regulatory standards. Consulting with technicians and employees familiar with existing procedures is crucial, especially for ISO 9001-certified organizations.

On the technological front, management must address two primary considerations. Digital systems selection requires careful evaluation of appropriate technologies and providers based on the organization's digital maturity level and objectives outlined in the second phase. The data management system serves as the core of digital transformation and must interface with a digital platform for exchanging quality-related data.

To effectively complete the third phase of digital transformation, an organization must take several key steps to ensure a seamless transition to Quality 4.0. Initially, it is essential to define the available resources, including financial, human and technological. A realistic assessment of these resources is crucial for planning and allocation.

The same approach has to be carried out for each identified pilot.

The fourth phase of the digital transformation roadmap focuses on implementing the strategies planned in the previous phase. This involves three sub-phases: preparation, development and deployment of digital systems. These subphases are often executed in parallel, influencing each other and are coordinated through management. This phase also includes a coordination activity to ensure alignment with the overall Quality 4.0 strategy (Figure 4).

The preparatory phase sets the groundwork for implementing the Quality 4.0 vision. It involves mapping and analysing the procedures and processes involved in each selected use case. This analysis helps define the specifications and requirements for the digital systems, such as retrofitting machines with sensors for data collection, which is crucial for developing and programming digital systems.

In the development of digital systems, based on the preparatory analysis, digital systems are designed and developed. This often involves selecting and programming algorithms, such as Machine Learning (ML) systems, according to the requirements gathered. This development is typically outsourced, but an internal team should oversee the process and ensure that operators are trained and prepared to use and champion these new systems.

After development and testing, digital systems are deployed into existing processes. This phase requires careful planning to minimize disruptions and inefficiencies. It involves risk assessment, precautionary measures and ensuring that the new system integrates well, delivering added value as per the objectives. The management of non-technological aspects like training, competency, stakeholder collaboration, compliance and measurements is critical during this phase.

To sum up, to successfully implement the digital systems, the following list of tasks needs to be completed:

1. In-depth mapping of the involved process/procedure;

2. Defining the specification and requirements of the envisaged quality digital system;

3. Managing the development activities given in outsourcing;

4. Training the employee who should use the new technologies;

5. Managing all the deployment aspects, both technological and organizational.

The final phase of the framework is dedicated to the collection and analysis of the results and feedback arising from the implemented strategy. Specifically, the results achieved are compared with the expected ones, identifying the causes of deviation. It is worth identifying, for each deviation found, its causes. The categorisation of causes is a useful tool for understanding which aspects of the strategy should be modified to achieve better results. Wrong choices may have been made in choosing technologies or, in contrast, non-technological factors may have been the critical ones. Therefore, it is also important that the management collect feedback from operators or those who are supposed to use the digital systems on an ongoing basis. In most cases, deviations from the expected results are caused by both technical and organisational factors binding the management to collect heterogeneous information and take corrective action of a different nature. It is worth specifying that the management should analyse the result of each Quality 4.0 pilot completed or ongoing, considering their status and results in the overall Quality 4.0 strategy. Since the Quality 4.0 pilots have different implementation timelines, the management should review the status of all of them at fixed intervals.

The fifth phase addresses, as well as refining the strategy for the next iteration of the cycle, the distribution of the value created among the stakeholders. Specifically, the leaders are expected to extrapolate and multiply the value, in terms of experience and skills acquired, that emerged from each pilot.

In the last phase of the framework, the management of the organization is expected to conduct a depth analysis of the results achieved, comparing them with the goals set previously. In particular, the activities to be accomplished are:

1. Collection of the results and feedback arising from the implemented strategy;

2. Gap analysis;

3. Identification of causes of deviation and refining of the refining the strategy for the next iteration of the cycle; and

4. Distribution, also with dissemination activities, of the values among organization stakeholders.

The proposed framework has been validated with a case study in a Swiss plant of a company working in the injection moulding sector and producing most of the plastic parts needed by the group, which specializes in material handling solutions. The SME began the digitalization of its quality management system in the first half of 2019.3 main reasons led the management to embark on such a major project:

1. Availability of a large amount of data, thanks to recent machines with proprietary data collection systems that are not exploited to extract valuable information;

2. Receipt, in mid-2019, of an expensive recall for a faulty supply due to a defective component;

3. Increased customers' demand to carry out quality control tests and to share the results effectively.

As a first step of the digitalization process, the quality inspection of ball bearings and shafts was taken into consideration. Before the project, the quality inspection was based on the use of two different stand-alone testing machines, each one able to carry out tests on the same family of products. Operators in charge of this phase had to take care of different activities, beginning with setting up the quality control procedures by downloading and printing a standard test report and manually completing it with the test specifications. They were also responsible for loading and programming the automated machines, taking care of the setup of the machines and of the different controls in the process. Additionally, operators had to trace testing results and upload the test report in the company's management software application.

With the digitalization project, the aim of the company was to improve the efficiency of the quality control process of incoming components using a completely new solution based on a smart, digital and modular testing cell driven by Industry 4.0 principles.

The testing cell is composed of three testing machines designed and developed purposefully based on the company's needs and the two machines already in use have been retrofitted through a pervasive use of digital technologies. All the machines are equipped with multiple sensors (e.g. accelerometers, torque meters, thermocouples, microphones, etc.), enabling in-depth monitoring of all relevant parameters. Data coming from these machines is coherently aggregated in the developed platform, which has been designed in compliance with all Industry 4.0 standards to be fully scalable and adaptable to integrate any additional machine. Specifically, during test execution, the sensors mounted on testing equipment generate different streams of data, which are regulated by a single gateway solution and saved in a database. The platform fetches, cleans and aggregates data from the database, enabling, also remotely, the pattern-tracking, the significant KPIs displaying and the comprehensive system behaviour monitoring. The platform functions also as an HMI for the operator, who can monitor test performances and schedule new test campaigns, setting machine test parameters.

In addition to setting up the cell, a bearing test rig capable of executing a non-destructive check of articles by analysing the frequency spectra generated by bearing rotation has been developed. The identification of defective bearings is automatically performed by a Machine Learning algorithm (i.e. the random forest) that aims at discerning damaged bearings from the uncorrupted ones. The ML algorithm is fed by a set of statistical measures extracted from the raw data gathered by the sensors (i.e. the vibration generated by the bearing in three directions) and returns the class of the tested bearing (defective vs. good). The approach also aims to continuously learning from the tests performed with the cell. Indeed, to provide an adaptive model, the algorithm is tuned and adjusted every time a new set of experiments is executed.

The performance of the quality control process improved as reported in Table 2: it is foreseen that there will be an 80% increase in the testing capacity for both items using 20% of the workforce hours. The results are due to the combined effect of changes implemented with the new digital logic acting at different levels:

1. Transition from a manual enslavement of stand-alone machines to a fully automatic cell;

2. Optimization of the asset utilization with a decrease of the total machine downtime and a 20% improvement of throughput rate;

3. Halving the time needed for analysis and decision making is thanks to the automatic centralized data aggregation in a standardized format;

4. Reduction of setup time is thanks to the plug-and-produce modules and virtual commissioning tools;

5. Guarantee of complete traceability on tested components, leading to improved management of potential claims and the overall quality department;

6. Deepening quantitative knowledge on incoming batches of bearings and shafts, through the implementation of big data analysis and the resulting adaptive behaviour of the cell towards an optimized sampling strategy.

In this section, the digitalization of the testing cell is analysed through the lens of the proposed roadmap with a twofold aim: on the one hand, to validate the appropriateness of the roadmap itself to support the digitalization process; on the other hand, to identify how the roadmap could have supported the process in this company.

With these intentions, the analysis is carried out on a dual layer. Firstly, the deliverables of the project are mapped to the expected outputs of each phase of the proposed implementation roadmap. The criticalities encountered during activities of each phase, e.g. in terms of delays, shortage of tools or methods, are then re-evaluated considering the framework, trying to understand, in a logic of continuous improvement, if they could have been avoided and how the effects could have been alleviated. Table 3 provides an overview of what has been done in the project compared to the expected outcomes of each phase of the framework.

The main emerging gap between the actual deliverables and the expected outcomes, considering the whole framework, is the focus on the specific use cases due to a lack of a more comprehensive strategic plan, where the new quality control cell should have represented one building block. An analysis of the as-is situation has been carried out before launching the project (phase 1), but it was carried out without a focus on quality management and the reasoning was kept at a more general level, including several processes.

The lack of high-level objectives, requested in Phase 2, has generated both management and operational inefficiencies during the development of the testing cell, including the waste of resources and the choice of improper digital systems. To compensate for this lack of global vision, the company had to make changes to the digital systems deployed during the project to integrate them into a comprehensive system.

Phase 3 has been approached with an operational perspective, even though the company developed a risk plan for the use case whose need was not explicit in the framework.

The main trigger for the project was the need to overcome a specific problem, namely an expensive recall due to defective components supplied by external partners. This event highlighted the critical role that supplier collaboration plays in maintaining quality control throughout the production process. However, despite the urgency created by the recall, some suppliers were reluctant to cooperate fully with the digitalization project. Their reluctance to share information and materials posed a significant obstacle, delaying the integration of quality control systems across the supply chain. Suppliers cited concerns over data security and the cost of upgrading their systems as key reasons for their hesitation.

The lack of supplier engagement exacerbated existing inefficiencies in the overall process and highlighted the importance of establishing clearer communication and collaboration strategies with external stakeholders. A more proactive approach to managing supplier relationships, such as incentivizing participation or offering technical support, could have mitigated these challenges and accelerated the digitalization process.

In addition to the challenges with suppliers, the project also faced internal inefficiencies, including a lack of quality culture fostering within the organization. This issue hindered the adoption of the digital systems and limited employee engagement. Only one employee was formally trained in using the new digital systems, which was insufficient for embedding the digitalization process across the organization. While video tutorials were produced to support future training efforts, this limited initial training did not adequately equip the workforce to engage with the new systems fully.

Employees, who are key non-technological factors in the success of digitalization projects, were not given the comprehensive training needed to understand and embrace the transformation. The lack of employee involvement and broader engagement strategies contributed to slower adoption rates, as many staff members felt disconnected from the digital transition. A more structured and inclusive training program would have likely accelerated the implementation and improved the overall success of the digitalization effort by fostering a stronger digital culture.

Furthermore, there were difficulties in defining overall indicators to measure process performance, which made it harder to evaluate the progress of the digitalization initiative. Establishing clearer performance metrics from the outset could have provided a more structured approach to tracking the impact of Quality 4.0 implementation.

Adopting a high-level strategy would have allowed addressing the non-technological aspects that not only caused delays and inefficiencies during the project, but would have forced management to make an incremental effort shortly.

Moving to Phase 4, the deployment of the digital systems and the development of the quality platform did not give rise to any particular and unexpected problems. It should be specified that, given the lack of a global vision of Quality 4.0, no coordination activities with other (even planned) use cases and no alignment with high-level objectives were carried out. The lack of a global vision led the company to skip completely Phase 5, since it prevented the company from creating value in terms of knowledge about digital systems and developing a plan to refine the strategy with a continuous improvement approach.

On the other hand, the application of the framework to the case study allowed us to understand how to improve its usability and some areas of improvement emerged. Phase 1 and Phase 3 resulted the ones that would need some more details. It is indeed advisable to better specify what the reasons can be why a company can undertake the digitisation of its quality management system and, according to different categories of reasons, to detail how to outline both objectives and strategy at a high level. The implementation phase would benefit from the addition of details about the cost and risks of undertaking a certain strategy would be beneficial. The development of a risk management plan should be added as an outcome. In general, it is worth adding an implementation guide that clarifies in detail how to perform the envisaged activities.

On the one hand, the use case proved the validity of the framework to guide the implementation process of a company. Indeed, neglecting activities included in the framework led the company to some inefficiencies. On the other hand, the empirical test was useful to identify what is missing to increase the perceived value by users.

The quality management in manufacturing companies is witnessing a period of changes triggered by the application of the digital advances brought by the fourth industrial revolution. This shift has been named Quality 4.0 and a recent stream of research has emerged to explore it. This paper, by carrying out an extensive literature review, first contributes to the literature debate by summarizing the value proposition behind the adoption of a Quality 4.0 approach and by detailing the main pillars supporting the transition. Literature on the topic is still fragmented: the conceptual framework is built on top of the literature findings, providing a structured summary that could also be exploited by practitioners. A second contribution in this direction is the development of a roadmap for implementation tailored to the manufacturing SMEs. The roadmap fills the identified gaps that emerged during the analysis of existing frameworks that pointed to the need for supporting frameworks to guide practitioners. Indeed, it is developed based on the literature review, but it has been conceptualized as a practical tool. From this point of view, the framework acts as a bridge between the direction that has been identified by theoretical works to succeed and the challenges that still hinder a more widespread Quality 4.0 approach in the manufacturing world. Since quality is a priority area for AI implementation in manufacturing, the focus on SMEs is meant to facilitate the undertaking of a strategic approach for their transition towards digital transformation. Despite their relevance in the industrial ecosystem, SMEs often lack the necessary resources to embark on a successful digital journey that could increase their competitiveness. At the same time, the roadmap, promoting the (re)upskilling in manufacturing and the diffusion of a more aware adoption of new technologies, contributes, in a more general way, to the digital transformation of the manufacturing ecosystem and to the tackling of societal challenges such as the lack of skills, the need to create a more inclusive environment for operators or the impact of technologies. The roadmap, thanks to its structured approach, can be used as an example of how to create a quality culture within an organization, even in a teaching context.

So far, the empirical validation of the framework has been limited to a single use case. The use of unstructured interviews could limit the replicability of the study: a protocol of data collection is being designed based on this experience to guide further validation. A wider campaign is indeed needed to further prove its applicability and to fine-tune its structure. The single use case has been useful in identifying some possible improvements and extensions. The development of guidelines and the integration of more detailed explanations in each step are needed as the next step to support practitioners in their use. When more empirical evidence is available, it could be interesting to develop a version tailored to single sectors and to allow companies to compare their position through benchmark analysis. Possible extensions to other contexts, including both non-manufacturing sectors and larger enterprises, need to be explored more concretely. While the roadmap is designed for SMEs, some phases, particularly the early ones, could be adjusted to suit the strategic and organizational characteristics of larger firms. Such adaptations would enhance the framework's generalizability and relevance across different industrial scenarios.

Finally, to provide a more robust theoretical basis, it is deemed interesting to further explore the link between the proposed framework and socio-technical systems theory.