The Quality 4.0 Roadmap: Designing a capability roadmap toward quality management in Industry 4.0

Abstract

Literature shows that the path toward Quality 4.0 is unclear and that there is no roadmap to assist organizations in preparing or navigating this transition. This article aims to understand such a path, design, and develop a roadmap to guide organizations. The “Quality 4.0 Roadmap” allows organizations to position themselves and smoothly transition to Quality 4.0. It consolidates fundamental Quality Management practices to promote sustained progress toward stages of advanced technological integration. In each stage, the “Quality 4.0 Roadmap” highlights the necessary capabilities organizations need to develop and alerts them of potential pitfalls. This roadmap offers an original tool to assist organizations in achieving and developing the required capabilities for pursuing the digital transition with an operational and human-centered perspective. It also defines vital stages in the Quality 4.0 transition, identifying the evolving requirements and marking boundaries and touchpoints between Technology and Quality management.

Keywords:

• Digital transformation
• maturity
• Quality 4.0
• quality management
• roadmap

Introduction

Industry 4.0 (I4.0) has blurred the boundaries between physical and digital systems (Kagermann, Wahlster, and Helbig 2013), promoting economic, social, and industrial transitions and transforming entire value chains (Cots 2018). Of particular interest to this work is that it has been shown that I4.0 affects how quality is deployed, measured, and managed (Carvalho et al. 2020). The term “Quality 4.0” (Q4.0) is born from these changes and the need to frame Quality engineering and management activities in the scope of I4.0 (Dias, Carvalho, and Sampaio 2021).

Although some discussion around the concept of Q4.0, practical Quality Engineering and Management approaches in the scope of I4.0 have lagged in comparison with other I4.0 advancements (Carvalho et al. 2020; Gunasekaran, Subramanian, and Ngai 2019). Accordingly, Q4.0 has seen most research focused on its foundations and definitions (Chiarini and Kumar 2022; Dias, Carvalho, and Sampaio 2021), on international studies on perceptions and challenges (Antony, McDermott, and Sony 2022), and in the reporting of early applications cases (Singh et al. 2022). There have also been efforts to define the dimensions (Dias, Carvalho & Sampaio 2021; Sureshchandar 2022) and the motivators, building blocks, and challenges for Quality 4.0 (Ranjith Kumar, Ganesh, and Rajendran 2022). However, when looking at the literature, one of the most significant gaps at the present moment is the lack of proper tools to guide the activities of Quality-driven organizations to navigate the I4.0 transition (Dias 2021). While several Industry 4.0 roadmaps and maturity models are available (Armani et al. 2021; Santos and Martinho 2019), dedicated tools to guide a Quality-oriented digital transformation are missing, creating a clear research opportunity.

Having identified the absence of a clear transition plan toward Q4.0, this article presents the design and development process of a roadmap that assists organizations in achieving and developing the required capabilities for pursuing the digital transition with a Qualiy-oriented perspective. This investigation supporting the roadmap development follows two research questions: (i) “What dimensions of Quality 4.0 need to be considered for an organization-wide transition toward Quality 4.0?” and (ii) “What structure best supports an iterative structure transition path?”

The “Quality 4.0 Capability Roadmap” aims to serve academic researchers and professionals who wish to explore and promote Q4.0 capabilities in organizations that are (or aspire to be) involved in a human-oriented digital transformation journey. Initially, the framework can be viewed as a diagnostic tool of the organizational capability stage in the transition for Q4.0; later, it assists organizations in improving and pursuing greater readiness and progress stages for the digital transition. Besides the primary outcome of this design exercise—the roadmap itself—this work identifies the levels, dimensions, and pitfalls associated with the transition.

The structure of this paper is as follows: first, the methodological approach followed throughout this research project is outlined (Section “Methodology”). The results of the literature review are presented next in Section “Literature review.” Next, Section “Quality 4.0 capability roadmap” describes the design and development process of the model, including its validation and review. Finally, Section “Discussion” presents the conclusions of this work, including its contributions to science and practice.

Methodology

Literature review

A comprehensive literature review of Industry 4.0 (I4.0) and Quality 4.0 (Q4.0) was conducted in the first stage. A narrative literature review was developed to allow a broad understanding of the Q4.0 subject through the research and review of the published body of knowledge (Green, Johnson, and Adams 2006; Paré et al. 2015). Journal articles, conference proceedings, books, reports, and materials from significant Quality societies were considered for review. Selected databases include Scopus and Web of Science, complemented by Google Scholar. The references of selected articles were also used to expand the selection process. For this work, only sources in English and Portuguese were considered. Since few articles identify Q4.0 per se, the literature search was carried out based on a broad search string specified in Table 1. All sources with substantively relevant content were included in the review after an initial screening analysis of 203 sources. The final content results from the study of 84 sources gave us a comprehensive understanding of the topic and the ability to group information by categories.

Table 1. Search string definition.

"Quality 4.0"	–	"Quality 4.0"
"Quality" OR "Quality Management"	AND	"Digital" OR "Digital Transformation" OR "Fourth Industrial Revolution" OR "Industrial Revolution" OR "Technology"
"Industry 4.0" OR "Industry 5.0"	AND	"Digital Quality" OR "Quality" OR "Quality Approaches" OR "Quality Assurance" OR "Quality Control" OR "Quality Culture" OR "Quality Management" OR "Quality Principles" OR "Quality Tools"
"Industry 4.0" OR "Quality 4.0"	AND	"Maturity Model" OR "Maturity Assessment" OR "Readiness Assessment"

This literature review resulted in the publication of an article that helps define the concepts of Quality 4.0 and identifies its key areas and functions - dimensions and subdimensions (authors removed for review 2021). Following this literature review and based on the elements that support the Q4.0 transition, the dimensions used in the roadmap were established—“Value Chain and Operations,” “Strategy and Organization,” and “People and Culture.” Each was further broken down into three subdimensions. The “Value Chain and Operations” dimension includes “Customers,” “Products and Services,” and “Processes,” looking at the integration of operations and technology to deliver greater value—from customization to swifter deliveries (Skapinyecz, Illés, and Bányai 2018; Zhang et al. 2019). The dimension “Strategy and Organization” features “Strategy,” “Integration,” and “Innovation and Improvement,” highlighting the importance of assessing strategic decisions and initiatives to align them with the broader Quality/Industry 4.0 drive (Brusa 2018; Jacob 2017). At the same time, this dimension looks at the integration and qualitative management of virtual organizations (Alomaim, Zihni Tunca, and Zairi 2003; Boström and Olsson 2006). Finally, the “People and Culture” dimensions open into subdimensions “Role Transition,” “Organizational Culture,” and “Leadership” (Carvalho et al. 2019; Domingues et al. 2020; Sony, Antony, and Douglas 2020; Zonnenshain and Kenett 2020). Dimensions and subdimensions are presented and discussed in greater detail in Section “Quality 4.0 capability roadmap.”

Having identified the key aspects supporting the transition and operationalization of Q4.0, we addressed the structure of the model. For that, we investigated existing models’ architectural design and presentation style, the number of dimensions they considered, and their number of subdimensions, maturity items, and maturity levels. Without Q4.0-dedicated models, we studied mostly I4.0 transition models and roadmaps.

Model structure

There is no one-size-fits-all approach to creating a capability roadmap. However, detailed resources are frequently described in a taxonomy structure that helps further map and explain the link between capabilities and practical actions to access them (Eagar, Ross, and Kolk 2013). The roadmap development was done as a design exercise (Maier, Moultrie, and Clarkson 2012) and followed the development process for capability maturity models and roadmaps (Kumar, Antony, and Tiwari 2011; Maier, Moultrie, and Clarkson 2012; Schumacher, Erol, and Sihn 2016). Capability models provide the means for evaluating the ability to perform or achieve specific actions or outcomes, considering previously stipulated criteria (Maier, Moultrie, and Clarkson 2012). Roadmaps consist of defining paths to meet objectives. The notion of a roadmap is closely linked to technology, and they are essential tools to manage and sustain change (Kumar, Antony, and Tiwari 2011). Accordingly, roadmaps are widely used to help transformation and implementation processes, aligning technology needs and articulating the steps needed to meet them (Schumacher, Nemeth, and Sihn 2019; Zhang et al. 2019). Capability roadmaps detail organizational needs and how they can be developed, rather than just defining which routes the goals will be achieved. They provide a rational strategy and actions to ensure that capabilities are adequate to meet general ambitions and goals (Schumacher, Nemeth, and Sihn 2019).

The methodology used to develop the “Quality 4.0 Capability Roadmap” is based on the method used by Schumacher et al. (2016) and Armani et al. (2021), encompassing three distinct stages. The first endorses a broad understanding of the subject; the second comprises the framework design, and the third corresponds to the model validation. The first stage was built on a broad literature review, the summary of which can be found in Section “Literature review.” This review addressed not only the topic of Q4.0 but also any relevant models and roadmaps related to I4.0 or the Digital Transformation. The second stage encompassed the building of the roadmap. This stage was split into two parts: (1) the definition of the roadmap structure and (2) the detailing of the dimensions and subdimensions to be considered in the model. The roadmap structure is inspired both by existing models and the scientific literature on the development of maturity models and roadmaps (Maier, Moultrie, and Clarkson 2012; Schumacher, Erol, and Sihn 2016). The dimensions and sub-dimensions identified in our previous works (authors removed for review 2021) were explained and translated into actions, practices, and capabilities. The definition of the roadmap structure is described in Section “Design and development.” The complete model is presented on Page 9.

Once the model development was completed, an expert panel approach was used to validate the model. An expert panel’s purpose is to provide a reliable method for obtaining impartial and scientific-based opinions from a domain expert (Nan, Hall, and Barker 2008). As a result, based on the expert’s know-how, it is feasible to evaluate the model’s scientific rigor, coverage, representativeness as well as usefulness, and usability (Wagire et al. 2020). In this way, it will be possible to understand the critical aspects of the model while also determining what should be reviewed or reconsidered. The validation process is described in Section “Validation process.”

Literature review

Quality 4.0 insights

Q4.0 marks a shift from conventional Quality and can be considered an I4.0 approach that prioritizes Quality and performance goals by examining how individuals, systems, and emerging technology interact (Radziwill 2020). With this approach, Quality becomes a leading force in the I4.0 transition (Aldag and Eker 2018; Küpper et al. 2019; Radziwill 2018; Zonnenshain and Kenett 2020). Quality Management is an excellent foundation for every organization, and in a transition context, it may serve as a facilitator of change (Carvalho et al. 2020). Furthermore, Q4.0 can impact an organization’s financial and social performance, environmental sustainability, and external business growth (Antony, McDermott, and Sony 2022, Antony et al. 2023).

The new era of Quality is marked by the use of technology to augment people’s capabilities and quality tools and approaches (Dias, Carvalho, and Sampaio 2021). Using technology and adopting different approaches is critical to getting closer to the customer and managing the development of products, services, and processes (Cots 2018). Quality control, for example, becomes more reliable, effective, and automated—and as it shifts from sampling to total (automatic) control, work becomes increasingly capable, safe, and error-free (Mendling et al. 2018). This reality also reduces the time spent dealing with non-conformances, as it becomes easier to anticipate and eliminate them (Radziwill 2020). Technology makes it easier to promote knowledge exchange, collective decision-making, and collaborative problem-solving (Armani et al. 2021). Connecting people (customers, stakeholders, and employees), machines, devices, products, services, and processes allows the organization to adapt quickly to market trends. Connectivity and integration reduce failures and rework, increase productivity, optimize the devices’ reliability, and drive innovation (Armani et al. 2021; Ralea et al. 2019; Verhoef et al. 2017).

A social view is also pointed out as a foundation for Q4.0 (Gunasekaran, Subramanian, and Ngai 2019). Q4.0. Effective pursuit of Q4.0 requires actions to upskill people to adapt to emerging technology and to adapt work and technology to people, their needs, and their practices (Breque, Nul, and Petridis 2021). A transformed industry will also have a socio-economical effect (Radziwill 2018). Human-oriented perspectives are becoming increasingly important and are on the foundations of Quality 4.0 (Dias, Carvalho, and Sampaio 2021). As a result, the rise of customized production has triggered new business models, and future businesses based on customer experience will be profitable (Hyun Park et al. 2017). Moreover, rethinking and redesigning business models and ways of work are necessary to ensure that organizations and the workforce benefit from the digital transition. Customer service and employee integration cannot be underestimated in the Q4.0 transition.

The shift to Q4.0, if to be sustainable, entails developing, maintaining, and assessing a strategy. There can be no improvement without knowing what to improve. As such, Quality 4.0 needs a basis of traditional Quality (Dias, Carvalho, and Sampaio 2021).

Models related to quality in the context of Industry 4.0

To master the Digital Transformation, the scientific and professional literature has offered some examples of attempts to build and redevelop self-assessment models. Identifying these models is critical as it allows organizations to assess their standing in the digital transformation (Hizam-Hanafiah, Soomro, and Abdullah 2020). While there is still a lack of maturity and readiness models or roadmaps focused on Q4.0 that combine technological and human perspectives, analyzing models in the broader I4.0 context is valuable. The literature was explored to collect various maturity and readiness assessment models related to this subject. The analysis of twenty-one models was performed, allowing the collection of relevant information for developing the Quality 4.0 Capability Roadmap (Appendix A).

These models were developed in the aim of allowing organizations to assess their maturity in the transition toward I4.0. In addition to the overall assessment, the models provide an assessment of maturity by different structural areas. The most common aspect in such models is to enable organizations to see what they lack to meet a desired stage of maturity. As a result, it allows organizations to create a customized path to pursue their goals strategically and directly.

Technology-related dimensions in the models are unavoidable since the main force of digital transformation is technology. Therefore, most models have a Technology dimension that extends through operational and product lifecycle dimensions. Nonetheless, organizational culture and strategy are also present in some of these models. Customer focus is one of the dimensions most often noticed, a dimension that aligns with the pillars of Quality management. Leadership-related dimensions were also frequently noted.

Quality 4.0 capability roadmap

Design and development

Based on the broader literature review (authors removed for review 2021), we have identified the main dimensions—and subdimensions that support a stable transition to Quality 4.0. While new, technology-enabled advances affect all aspects of organizational life, technology is not an isolated element in this broader transformation (Kane et al. 2015; Vial 2019). As such, we have focused our review on the operational, organizational, and cultural aspects that allow the effective adoption and integration of these new technology. Quality 4.0 is not about implementing new technology to control Quality but instead seamlessly integrating innovative solutions with Quality principles, practices, tools, and methods (Carvalho et al. 2023; Dias, Carvalho, and Sampaio 2021).

The first dimension, “Value Chain and Operations,” looks at value generation from product to delivery. It focuses on the integration of Quality Management practices and technology to enhance the added value an organization intends to deliver to its customers - from the incorporation of customer insights data to improve products and services (Wagire et al. 2020) to the availability and optimization of the development and delivery processes (Radziwill 2020). This dimension includes the sub-dimensions “Customers,” “Products and Services,” and “Processes.” “Customers” refers to managing customers’ needs and expectations—from “must-haves” to “exciting” features. This sub-dimension supports the development of an organization to an “Intelligent” state where technology and data are used in real-time to predict customers’ needs and expectations and improve or develop new products and services accordingly (Küpper et al. 2019). As such, “Customers” links to another subdimension - “Products and services.” The roadmap intends to help companies manage requirements and product performance throughout their lifecycle (Carvalho et al. 2020). It departs from using development models where requirements are defined and evolves toward increased automation and customization. At the higher levels of maturity, product management relies on the use of technology for customer assistance, prediction in case of failure (Küpper et al. 2019), digitalized project management, and simulation (Santos and Martinho 2019). Last, “Processes” looks at how processes are managed, emphasizing their definition and the analysis of how the data to support them is collected. This sub-dimension underlines how process data is collected and the reasons behind process definition. Along the different levels, organizations evolve from defining processes due to customer demand or regulatory purposes (Dennis et al. 2017) to a quality-minded continuous improvement perspective where the processes can be seen in real-time and changes can be made immediately (Bibby and Dehe 2018; Wagire et al. 2020). At the higher level of Quality 4.0 maturity, the process data acquisition is also highlighted, with emphasis on the use of intelligent infrastructure for real-time process optimization and automated event handling, including machine failure (Agca et al. 2017; Reinhard, Jesper, and Stefan 2016; Santos and Martinho 2019).

The following dimension, “Strategy and Organization,” features “Strategy,” “Integration,” and “Innovation and Improvement.” The importance of the sub-dimension “Strategy” is highlighted by studies that show that one major issue impacting the readiness of organizations in I4.0 is the lack of its precise definition within the strategy of an organization (Machado et al. 2019; McDermott et al. 2023). To support the pursuit of Quality 4.0 the perception of the organizational readiness for the transition is essential. As the transition evolves, understanding Digital Transformation metrics and plans gains importance and frames all actions focused on Q4.0 objectives (Horvat et al. 2018; Santos and Martinho 2019). The integration of new practices, knowledge, and technology is essential. The “Integration” sub-dimension emphasizes the introduction of new technology and ways of work (Wagire et al. 2020); upholds the participation of the organization’s different stakeholders in the Quality 4.0 transition, and supports the development of Quality management programs and tools based on digital technology (Mayakova 2019). At the same time, the “Innovation” subdimension fosters an organization’s practices to encourage innovation and pursue continuous improvement in developing new products, services, and processes. The “Innovation” subdimension is critical to support a growing emphasis on technology and data, often mentioned as the biggest challenge in the shift toward Quality 4.0 (Zonnenshain and Kenett 2020). It supports an organizational shift from a reactive approach to problems and opportunities, increasingly integrating data and using new technology to predict responses to changes in the market environment and individual customer requirements (Agca et al. 2017).

Finally, the “People and Culture” dimension integrates technology into the daily routine of the people in any organization (Dias, Carvalho, and Sampaio 2021; Gunasekaran, Subramanian, and Ngai 2019). The “People and Culture” dimension considers three subdimensions: “Role transition,” Organizational Culture,” and “Leadership.” “Role transition” looks at how to skill and reskill people in the organization, helping them to adapt to new working methods, novel technology, or to address individual limitations or motivation challenges connected with the digital transition (Domingues et al. 2020). In every organization, and especially in a more advanced phase of transformation, the awareness of the “Organizational Culture” is critical, as it contributes as a facilitator in many aspects. This subdimension is, in fact, central to the “People and Culture” dimension. Several authors have supported the idea that enduring Quality orientation may only be attained via the transformation of the Organizational Culture (Carvalho et al. 2023). However, in a digital world, this transformation is constant. As new technology empowers customers to demand more complex products and services, the margin for error of organizations decreases, and the likelihood of error rises. Organizations need an environment in which employees follow quality guidelines and consistently see others taking quality-focused actions to address new challenges (Srinivasan and Kurey 2014); a balance between the use of new technology and a human-centered approach should be promoted (Breque, Nul, and Petridis 2021). Finally, “Leadership” is mainly responsible for all this transformation and is prominent in this model. It profoundly impacts role transition (Schumacher, Erol, and Sihn 2016), culture transformation (Carvalho et al. 2023), and decision-making processes. At the same time, “Leadership” evolves along the roadmap from promoting data-based decision-making and managing information (Agca et al. 2017; Schumacher, Erol, and Sihn 2016) to a scenario where reliable data collection and analysis are consistently used to support decision-making or exploration of future scenarios (Anderson and Ellerby 2018; Wagire et al. 2020).

Structure

To define the structure of our model, we looked at existing roadmaps and readiness or maturity models, the majority of which were related to I4.0. We aimed to identify how technical, social, and technological integration could be done. We also investigated how models are designed to be applicable in different organizations and searched for how best to convey the iterative nature of a technology-driven transition process. These efforts allowed us to imprint, in the roadmap structure, an incremental path toward higher stages of technological and digital performance. This path is sustained by increasingly developed tech competencies and the integration of organizational practices and systems in light of technological demands.

Effective transitions occur in stages (Gökalp, Şener, and Eren 2017), based on a series of capability levels, each built on the preceding one. The roadmap was built on the examples of several capability maturity models (see Appendix A), where authors have highlighted—and established—that readiness is critical for the sustained development of new capabilities (Hizam-Hanafiah, Soomro, and Abdullah 2020; Schumacher, Erol, and Sihn 2016; Singapore Economic Development Board 2017). As a result, it was decided to define the first three stages as readiness stages and the final three as maturity stages. These readiness and maturity stages were designed to further imprint in the Q4.0 roadmap a sense of progression. The main goal is to convey to organizations that further advancements will not be sustainable without the basic foundations of Quality.

The first stage, “Stakeholders Interaction,” is the foundation that all organizations must build, as stakeholders are fundamental to the activity of any Quality-minded organization (International Organization for Standardization 2015). Stakeholders’ interaction - within an organization or outreaching - is critical for success (Foster and Jonker 2007; Olkiewicz 2020), emphasizing the importance of establishing and maintaining communication (and, later, data-sharing) channels along the value chain. By the same logic, the ensuing stage - “Process Integration”—supports the development of communication channels promoted in the previous level to connect and integrate processes better (Berente, Vandenbosch, and Aubert 2009). It builds on the process orientation native to Quality Management (International Organization for Standardization 2015) and further promotes integration across functional units. “Process Integration” is also intended to encourage the integration of Q4.0 approaches into the organization’s various processes and supply chains (Marcinkevicius and Vilkas 2023; Rajaguru and Matanda 2019). Next, the “Digitization” stage - still within readiness status - is set to bridge the gap between the fundamentals and what begins to be an advanced stage in the transition to Quality 4.0. This stage aims to transfer organizational data to a digital format and, as a result, establish effective communication channels to assist in day-to-day management (Vasilev et al. 2020). It separates the basic principle of digitizing—creating a digital document, process, or tool—from truly digitalizing, meaning a broader capability to digitally manage a process or operation across the board (Frenzel et al. 2021). This stage sets the first steps for allowing a transformation that, while technological, will lead to a broad transformation that impacts the entire organization’s business model (Ritter and Pedersen 2020).

At a higher stage, in “maturity” levels, the “Automation” stage aims to employ technology to automate touchpoints in different organizational processes, facilitating data collection and analysis and augmenting human capabilities and decision-making (Carvalho et al. 2020; Radziwill 2020). While digitization and automation are not new nor exclusive to I4.0, they will be essential to integrate seamlessly and digitalize entire operations and Quality Management solutions. Following this logic, we find next the “Connectivity” stage. “Connectivity” supports effective communication between people, machines, and systems, resulting in the simplification of information and knowledge exchange along the active value network, facilitated by the creation of information loops, which allow new functionalities based on the collaboration between systems (Colombo et al. 2017). Connectivity goes beyond the simple use of platforms and technological tools (Leischnig et al. 2019), adapting them to enhance productivity and value generation for the customers (Lele 2019; Skapinyecz, Illés, and Bányai 2018). Finally, the use of state-of-the-art technology allows organizations to reach the stage of “Intelligence.” At this stage, organizations should be able to predict various process parameters and market changes and progress toward a more resilient, sustainable, and human-centered organization in alignment with the principles of Industry 5.0 (Breque, Nul, and Petridis 2021).

The readiness and maturity stages were developed from the literature review. They yielded a conceptual model that can be used to understand capability stages better when traditional Quality tools and approaches are integrated with technology. The outcome is a six-stage model that guides users from the physical, traditional quality tools and practices through the integration with digital tools until a full and seamless incorporation into cyber-physical systems (Figure 1).

Figure 1. Capability levels used in the conceptual model.

Validation process

Expert selection and feedback

As mentioned in Section “Model structure,” an expert panel approach was used to validate the model. The selection of experts considered a balance between academia and industry perspectives to promote scientific rigor, coverage, representativeness, usefulness, and applicability. Experts selection was based on practical expertise and/or research contributions in Quality 4.0. To identify the experts, we selected two profiles: (1) academics with publications in Quality 4.0 or Quality in I4.0 and (2) industry professionals with roles related to those same areas. Academics were selected from our literature review source database (authors removed for review 2021). Only the first or corresponding authors in each article were contacted and invited to participate. For industry professionals, LinkedIn searches were used to identify suitable experts.

Since Q4.0 is a recent concept, finding experts in this domain was no easy task. Twenty-nine profiles were initially identified; twenty-one profiles remained after review and elimination of duplicates. As a result of this selection, invitations to participate were sent via email or LinkedIn messaging. Twenty-one experts were contacted, thirteen responded, and nine agreed to participate. However, only six responded actively or fully. While this constitutes a small panel, the balance and complementarity of the two types of experts (three from industry and three from academia) offered some strength. Furthermore, small panels are not unheard of in several scientific fields. Ma, Hall, and Barker (2008) use ten panelists, and Allahyari et al. (2011) use a panel of eight experts - although, in their research review, they consider panels as small as having five members. According to the latter authors, the requirement related to small panels is the need to achieve high levels of consensus. In this sense, we worked to reach a complete consensus in our final version of the roadmap.

Data collection methods

Questionnaires were sent to all panel members in the first iteration, and the model was made available for individual and impartial evaluation. The questionnaire included a combination of open-ended and closed questions (these were measured on a five-point Likert scale). The questionnaires focused on aspects about (i) the structure, (ii) the dimensions and subdimensions, and (iii) the description of maturity/readiness levels in each subdimension. Questions on the structure assessed the levels and progression path, including the split between “readiness” and “maturity” levels and the increasing incorporation of technology. Questions on the dimensions and submissions focused on understanding if experts considered the selected dimensions comprehending enough, thus being able to represent the multitude of sides that compose Q4.0. The last set of questions evaluated if the description for each level made sense and was easy to understand, if these descriptions were aligned, at the same level, across subdimensions; and if levels correctly offered a sense of progression along each subdimension. Finally, an open-ended question allowed panelists to add other suggestions, concerns, or praise on the roadmap.

Data analysis and model update

Once the questionnaires were returned, the results were individually analyzed and then compared. While no statistical treatment was possible given the low number of respondents, it was possible to quickly compare the feedback provided by the different panelists and list their suggestions.

The experts in the panel had only received the roadmap as presented in Table 2. As such, one common feedback was that the roadmap was offered only with a high-level view. Considering that the model is designed to be used as a roadmap, most panelists suggested that a second level, with greater detail, be prepared and published.

Table 2. The Quality 4.0 capability roadmap—2nd layer.

Workshops and model update

To allow a better understanding of the panelists’ feedback, individual interviews were prepared (in person and online) to discuss and clarify opinions. All feedback was then gathered, and a presentation with suggested changes was prepared. Subsequently, a group workshop was promoted to align the different feedback and achieve complete consensus on the model structure, content, and applicability. While addressing architecture and content, the following topics were discussed: (i) dimensions and subdimensions, (ii) readiness and maturity stages adequacy, (iii) model features fit between dimensions and stages, content and representativeness, (iv) interpretation, and (v) innovation. Three consensual suggestions were given concerning the model’s overall structure and content: (1) to promote a stronger emphasis on the value chain due to its central role in the management of organizational processes, thus ensuring a more balanced structure; (2) to add stronger emphasis on education and training in the “People and Culture” dimension; and (3) to reflect on the user-centric approach of the model, reframing it as a human-oriented approach.

Table 3. The Quality 4.0 capability roadmap—2nd layer.

The applicability, usefulness, and usability were also debated but found strong agreement amongst the panel. Nevertheless, a few suggestions resulted from the discussion, leading to the identification of potential challenges and mistakes that companies could fall into while implementing the model. Together with the panelists, we identified five possible pitfalls organizations could fall into when using this model and for which they should be alerted. In this sense, it was first suggested that (#1) some organizations may not recognize their current stages of capabilities concerning the Q4.0 transition, thus being unable to place themselves in the model. Due to this, they may (#2) initiate efforts from the wrong stage or hurry the Quality 4.0 implementation, thus (#3) starting their journey from the outermost stages. These pitfalls are further complicated when there is no model adaptation to the organization’s working methods—leading us to pitfall #4, where the transition is supported by incomplete knowledge and skills, often not fully acquired by the organization or dependent on external knowledge. Organizations, thus (#5), start at a new stage without stabilizing or sustaining the previous one, spending resources to develop unsustainable skills and expertise.

Model update and final validation

As a significant result of this discussion, a second, more detailed roadmap (Table 3) was developed. To create a new level of detail for the roadmap, complete descriptions that had been defined for each level were used. These had not been previously shared and were initially kept as notes. An update to the literature review was also promoted during these efforts. In this second layer, the concepts, actions, and requirements for the transitions are further detailed. As a result, the roadmap becomes more transparent and straightforward so that organizations can turn it into an easy-to-use assessment tool for self-assessing their Quality 4.0 readiness and positioning themselves to define a strategy for moving up the model’s stages.

The new and refined version of the model was presented to the expert panel for final validation. There was complete agreement that the second layer of detail could provide users with a simple understanding of the path to take and the expected outcome and increase the roadmap’s understanding and applicability.

Discussion

There was, to date, no specific roadmap to assist organizations in migrating to Q4.0, a clear gap existed regarding practical tools to guide and support organizations in this transition. While a limited number of “Quality 4.0”/” Quality in Industry 4.0” capability models (Armani et al. 2021; Sader, Husti, and Daróczi 2019) or assessment frameworks exist (Sureshchandar 2023), a comprehensive step-by-step roadmap to guide the transition was still missing by the date we started to develop this roadmap. This gap is now curtailed with the publication of this article.

The model presented in this article works as a descriptive tool to assist organizations in better understanding Q4.0. It integrates several organizational dimensions essential to an organization-wide, sustained pursuit of Q4.0 that had not been integrated so far.

The Roadmap looks at the different functions (dimensions) of organizations that, being critical to Quality Management, need to be addressed to lead the change toward Q4.0. As such, it departs from the “Value Chain and Operations” dimension to consider all operations management activities, highlighting value creation through the digital transformation to offer increased customer value. This considers how processes are managed, not only in terms of product and service development and delivery, but also in gathering feedback and improving performance levels, which have been argued to be critical in driving Quality and performance in increasingly digital supply chains (Lim et al. 2022). Similarly, organizational dimensions related to “Strategy and Organization” are addressed, from strategy formulation to innovation and improvement initiatives, as are integration and communication efforts within the organization. This dimension is critical as it links across the organization’s hierarchy, ensuring communication and information feedback between the top management, where strategies are defined, and the workforce that will deliver them. It is only by ensuring this alignment—between market needs and strategies and between strategies and current practices—that a Quality orientation and the digital transformation may be recognized homogeneously across an organization (Carvalho et al. 2023). Finally, the roadmap highlights the social and cultural side of organizations (“People and Culture”), a dimension that has been pointed as missing from most initiatives aiming to bring Digital Transformation into the realm of Quality Management (Carvalho et al. 2020; Gunasekaran, Subramanian, and Ngai 2019).

For each of these dimensions, the roadmap offers a clear view of the steps to promote a sustained implementation of actions conducting to a “Quality 4.0” practicing organization. Although through different practices, we found in the literature that the promotion of the digital transformation is sustained in a set of incremental capabilities useful to all dimensions. As a diagnostic and roadmap tool, the model allows organizations to position themselves and understand what they lack to make a smooth and sustainable transition to Q4.0. This model shows how there can be no full achievement of Q4.0 without technology, but, more importantly, it demonstrates that there will be no Q4.0 without sustained quality practices that are later incorporated with different levels of technology.

Unlike most of the models analyzed, the “Quality 4.0 Roadmap” is focused on giving a holistic overview of Q4.0 and meeting the demands of real organizations. Further, it highlights that organizations must establish a Quality foundation before using more advanced technical approaches. Therefore, before moving on to state-of-the-art technology, the most fundamental aspects of the model must be addressed.

As evidenced in the analyzed models (Appendix A), model validation can be performed in several ways. Although practical validation is often used, theoretical validation of models through a survey or a panel of experts is notably prevalent, and several authors (such as Maqsood, Sandhu, and Shamsuzzoha 2016; Carolis et al. 2017; Carvalho et al. 2019) have published their conceptual models before empirical validation. This work also allows for validating the Q4.0 Capability Roadmap with a broader scope. Using this publication to make the model available to the scientific community might be considered future validation, gaining broader acceptance from academics and practitioners. In addition, a case study can be carried out for the model’s applicability validation in practice. Furthermore, the model can be a basis for developing other models in this context.

Conclusions

This work addresses the lack of a structured guiding tool to support organizations pursuing a Quality 4.0 Transition. The Q4.0 Capability Roadmap is the primary outcome of this project and provides well-founded, human-centered, and comprehensive guidance in the transition toward Q4.0. It allowed the identification and integration of critical organizational dimensions necessary to address a successful Q4.0 transition while offering a step-by-step guide to navigate the path from an increasingly Quality-inclined organization to a digitally driven, Quality-performing one. Supporting this path, six levels are presented in our roadmap: three levels of readiness, where Quality principles and practices are sustained and integrated; and three maturity levels, where technology and digital literacy are increasingly implemented and integrated into processes, practices, and principles to sustain a quality-oriented digital transformation. While building the roadmap, this work allowed us to gather and summarize the knowledge related to Q4.0, providing an overview and precise definition of the subject. As such, not only is a roadmap toward Q4.0 presented, but grounds for a better understanding of Q4.0 and future research are offered.

Limitations

Naturally, some limitations and future work opportunities remain and must be addressed. One of the possible future actions is to consider the development of a model application pathway to enable organizations to develop a plan that identifies which dimension to begin working on in this transition, for example. Another future action may be a study of the implementation of weighted means to support a (self-)assessment point system that helps organizations understand how they score in the transition while comparing their performance to others. These systems, often used in performance excellence or agility models, can open up the wait for the development of broader assessment systems in the scope of Quality 4.0. Finally, there may be some bias toward manufacturing organizations in the Roadmap. While authors and academic experts involved in the model development have experience in working with the service industry, industry experts worked in manufacturing organizations. As we move on to future work, this possibility needs to be taken into consideration.

Future work

Looking toward the future, the model is expected to suffer updates over time. As technology evolves and society changes, industrial paradigms also do—as the emergence of perspectives such as Industry/Quality 5.0 has already shown us. In line with this evolutive perspective, the model was built on a capability stages basis and can be refined and improved, resulting in increased fit of the developed structures and overall resilience.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

Additional information

Funding

The authors acknowledge Fundação para a Ciência e a Tecnologia (FCT-MCTES) for its financial support via projects [UIDB/00667/2020 and UIDP/00667/2020 (UNIDEMI) and UIDB/00319/2020 (ALGORITMI)].

Notes on contributors

André M. Carvalho

André M. Carvalho is an Assistant Professor of Industrial Engineering at NOVA School of Science and Technology, NOVA University Lisbon, Portugal. He holds a PhD (University of Minho, 2020) in Engineering Design and Advanced Manufacturing, resulting from a joint program between Portuguese Engineering Schools and the Massachusetts Institute of Technology. He has been a Visiting Student and Research Affiliate at the Sociotechnical Systems Research Center at MIT (2018–2020), a Visiting Scholar at Northeastern University (2019), and a Postdoctoral Researcher at the Technical University of Denmark (Engineering Systems Design group, 2020). His research focuses on engineering management, exploring how technology, people and processes intermingle in the ongoing business transitions. Looking at subjects such as quality and performance management, organizational cultures, technology use, and organizational agility, he has sought to identify how organizations can best adapt to respond to the challenges of the world around us. His research has been recognized by organizations such as the Industrial Engineering and Operations Management (IEOM) Society, the International Academy for Quality (IAQ) and American Society for Quality (ASQ).

Ana Rita Dias

Ana Rita Dias is an MSc student in Engineering and Quality Management in the Department of Production and Systems of the University of Minho and is also affiliated with the ALGORITMI Research Center. Ana Rita is also a representative of the ASQ Student Branch at the University of Minho.

Ana Margarida Dias

Ana Margarida Dias was an MSc student in Engineering and Quality Management in the Department of Production and Systems of the University of Minho.

Paulo Sampaio

Paulo Sampaio graduated in Industrial Engineering and Management in 2002 and completed his PhD in Industrial Engineering in 2008 at the University of Minho. He has been lecturing courses in the fields of Quality and Organizational Excellence, whereas his research activities are developed under the Industrial Engineering and Management Research Line of the ALGORITMI Research Center. Always privileging research and development for industrial applications, he has been involved in several R&D projects supported by Portuguese Institutions and under European funding programs. He has coauthored or authored more than 200 publications, 160 of them ISI/Scopus indexed papers.

Appendix A:

Models related to quality in the context of Industry 4.0

Table

		Structural areas
Model	Research area and source	Technology	Product and Services Lifecycle	Operations	Customer Focus	Organization	Leadership	Culture	Strategy	People	QM approaches	Maturity Scale	Validation method	Contribution and limitations
IMPULS: Industrie 4.0 readiness (Lichtblau et al. 2015)	Industry 4.0 transition; Consultancy firm.	x	x	x		x			x	x	x	Six-scale (0–5)	Survey	Assessment of organizational readiness for the I4.0 transition and offers insights into improvement; Online self-assessment tool; The model’s primary focus is on operational excellence, not organizational excellence.
Industry 4.0 Maturity Model (Schumacher et al. 2016)	Industry 4.0; Academic.	x	x	x	x	x	x	x	x	x	x	Five-scale (1–5)	Case-study in industrial enterprise	Maturity items developed through experts survey, and the results serve as a basis for automatically calculating the maturity items’ weights by a software tool; Transversal to an extended number of organizational capabilities; Lack of details regarding maturity items, survey assessment, and maturity stages.
PwC maturity model (Reinhard, Jesper, and Stefan 2016)	Digital maturity; Consultancy Firm.	x	x		x	x		x			x	Four-scale (1–4)	Survey	Assessment of organizational readiness for the I4.0 transition and serve as a roadmap; Focus on the digitalization of industrial capabilities across the organization; Online self-assessment tool; Lack of details regarding maturity assessment and identification of maturity stage.
Asset Performance Management Maturity Model (APM) (Dennis et al. 2017)	Asset performance and Industry 4.0; Consultancy Firm.	x		x		x		x		x	x	Five-scale (1–5) of maturity for each dimension	–	Assessment of the organization’s maturity in the I4.0 transition and offers insights to build a roadmap strategy; Only offers a detailed view of asset performance.
DREAMY- Digital Readiness Assessment Maturity Model (Carolis et al. 2017)	Process-based Industry 4.0; Consultancy Firm.	x	x	x		x					x	Five-scale (1–5)	Survey	Focus on a process-based approach; Encompasses quality practices; Lack of details regarding maturity items and assessment.
Industry 4 readiness assessment tool (Agca et al. 2017)	Industry 4.0; Consultancy Firm.	x	x	x	x	x	x	x	x	x	x	Four-scale (1–4)	Survey	Assessment of the organization’s maturity in the I4.0 transition and offers insights to build a roadmap strategy; Transversal to an extended number of organizational capabilities; Lack of details regarding maturity assessment and identification of maturity stage.
Industry 4.0-MM (Gökalp, Şener, and Eren 2017)	Industry 4.0; Academic.	x	x	x		x		x	x	x	x	Six–scale (0–5)	Not validated	Assessment of the organization’s maturity in the I4.0 transition and offers insights to build a roadmap strategy; Transversal to a vast number of organizational capabilities.
Quality 4.0 Impact And Strategy Handbook (Jacob 2017)	Quality 4.0; Consultancy Firm.	x	x	x	x	x	x	x		x	x	–	Partial validation by use cases	Assessment of the organization’s maturity in the Q4.0 transition; Centered on quality methods; Transversal to an extended number of organizational capabilities; Lack of a detailed roadmap to the Q4.0 transition.
The Singapore Smart Industry Readiness Index (Singapore Smart Industry Readiness EDB 2017)	Industry 4.0; Consultancy Firm.	x	x	x	x	x	x	x	x	x		Five-scale (0–5) of maturity for each dimension	Expert panel and case study in a group of industrial organizations.	Assessment of the organization’s readiness in the I4.0 transition and insights on how to start, how to improve, and how to sustain growth; Online self-assessment tool; The Index does not offer a specific evaluation scale.
Deloitte Digital Maturity Model (Anderson and Ellerby 2018)	Industry 4.0; Consultancy Firm.	x		x	x	x	x	x	x	x		Not Provided	Case-study	Design took into account key contributions from Industry and subject matter experts; Focused on technology and organizational capabilities; - Lack of details regarding sub-dimensions, individual criteria, maturity assessment process, and scale.
Industry 4.0 maturity framework (Bibby and Dehe 2018)	Implementation of Industry 4.0 technology; Academic.	x	x	x		x		x	x	x		Four-scale (1–4)	Case study on defense manufacturing organization	-Focused on the implementation of I4.0 technology at the operational stage; Lack of details regarding the maturity scale.
Readiness for Industry 4.0 (Horvat et al. 2018)	Industry 4.0 in emerging economies; Academic.	x	x	x	x	x	x	x	x	x		Not Provided	A survey in the manufacturing sector	Assessment of the organization’s readiness in the I4.0 transition; Focused on automation and digitization of processes throughout the organization; Lack of details regarding the assessment of the readiness stage.
Smart Industry Roadmap Model (The Industry Working Group (IWG) 2018)	Human-touch aspects of I4.0; Consultancy Firm.	x	x	x	x	x	x	x	x	x	x	–	Not specified	Transversal to an extended number of organizational capabilities; Aims to contribute to the human-touch aspects of I4.0; Although it is labeled as a roadmap, the model was designed to contribute to the definition of strategic research directions for the coming 10-15 years.
Maturity and Readiness Model for Industry 4.0 Strategy (Akdil et al. 2018)	Industry 4.0; Academic.	x	x	x					x	x		Four-scale (0–3)	Survey	Assessment of the organization’s readiness and maturity in the I4.0 transition and offers insights to build a roadmap strategy; Transversal to an extended number of organizational capabilities.
The Strategic Roadmap for Industry 4.0 transition (Morteza 2018)	Industry 4.0 strategies; Academic.	x	x	x	x	x			x	x		–	Not specified	Offers a general view that can be used as a starting point to define detailed strategies for organizational excellence; Transversal to an extended number of organizational capabilities.
Quality 4.0 maturity framework (Armani et al. 2021)	Quality 4.0 maturity assessment; Academic.	x	x	x	x	x	x	x		x	x	Five-scale (1–5)	Case-studies	Assessment of the organization’s maturity in the Q4.0 transition; Transversal to an extended number of organizational capabilities; Based on the 11 Axes of Quality 4.0 from (Jacob 2017).
Industry maturity model (Santos and Martinho 2019)	Industry 4.0; Academic.	x	x	x	x	x			x	x		Six-scale (0–5)	Survey and case-study in the automotive industry	Assessment of the organization’s maturity in the I4.0 transition and offers insights to build a Roadmap strategy; Transversal to an extended number of organizational capabilities.
Integrated Industry 4.0 - Total Quality Management framework (Sader, Husti, and Daróczi 2019)	TQM and Industry 4.0; Academic.	x		x	x		x	x	x	x	x	–	Not specified	Transversal to an extended number of organizational capabilities; A quantitative method for maturity evaluation is pointed as future work.
Industry 4.0 maturity assessment (Schumacher, Nemeth, and Sihn 2019)	Industry 4.0 roadmap assessment; Academic.	x	x	x	x	x	x	x	x	x		Not Provided	Survey and workshop	Assessment of organization’s maturity in the I4.0 transition and offers a step-by-step approach to a roadmap definition; Maturity items developed through experts survey, and the results serve as a basis for automatically calculating the maturity items’ weights by a software tool; Lack of details regarding survey assessment and maturity stages.
Industrie 4.0 Maturity Index—Update (Schuh et al. 2020)	Industry 4.0; Consultancy Firm.	x	x	x		x		x		x		Five-scale (1–6) of maturity for each dimension	Survey and case study in different industries organizations	Assessment of the organization’s maturity in the I4.0 transition and offers insights to build a roadmap strategy; Lack of details regarding the survey, which is used to address the capabilities.
Industry 4.0 maturity model (Wagire et al. 2020)	Industry 4.0; Academic.	x	x	x	x	x	x	x	x	x	x	Four-scale (1–4)	A case study in the area of mobility solutions, consumer goods, and technology	Assessment of the organization’s maturity in the I4.0 transition and offers insights to build a roadmap strategy; Technology-focused; Maturity items have different weights.