From concept to application: implementation of a capability roadmap towards Quality 4.0

Universidade do Minho Escola de Engenharia Ana Rita Silva Dias From Concept to Application: Implementation of a Capability Roadmap towards Quality 4.0 outubro de 2023 From Concept to Application: Implementation of a Capability Roadmap towards Quality 4.0 Ana Rita Silva Dias UMinho | 2023 outubro de 2023 Universidade do Minho Escola de Engenharia Ana Rita Silva Dias From concept to application: Implementation of a Capability Roadmap towards Quality 4.0 Dissertação de Mestrado Mestrado em Engenharia e Gestão da Qualidade Trabalho efetuado sob a orientação do Professor Doutor Paulo Alexandre Costa Araújo Sampaio iv Despacho RT - 31 /2019 - Anexo 3 DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Licença concedida aos utilizadores deste trabalho Atribuição-NãoComercial-SemDerivações CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/ v AGRADECIMENTOS Agradeço ao Centro de investigação ALGORITMI, ao Departamento de Produção e Sistemas, pelo apoio logístico. Agradeço ao meu orientador, Professor Doutor Paulo Sampaio, pela disponibilidade total para ajudar, pelas horas despendidas, pelo incentivo, por tudo o que me ensinou, pelo sorriso fácil e por me sempre fazer sentir como parte da equipa. Agradeço também a todos os membros do grupo QOE que partilharam comigo esta jornada e com quem sempre pude contar para partilhar experiências, saber e até desabafos. À minha família, por serem os meus constantes ouvintes e por me apoiarem sempre ao longo de todo o processo. Aos meus amigos e colegas com quem sempre pude contar para ajudar, para conversar e trocar experiências. vi DECLARAÇÃO DE INTEGRIDADE Declaro ter atuado com integridade na elaboração do presente trabalho académico e confirmo que não recorri à prática de plágio nem a qualquer forma de utilização indevida ou falsificação de informações ou resultados em nenhuma das etapas conducente à sua elaboração. Mais declaro que conheço e que respeitei o Código de Conduta Ética da Universidade do Minho. vii DO CONCEITO À APLICAÇÃO: IMPLEMENTAÇÃO DE UM CAPABILITY ROADMAP PARA A QUALIDADE 4.0 RESUMO A ligação entre os domínios físicos e digitais surgiu com a Quarta Revolução Industrial, com o aumento da automação, da realidade aumentada e a integração de pessoas, máquinas e processos. Com o foco na Transformação Digital emerge também o conceito de Qualidade 4.0. A Qualidade 4.0 não substitui os métodos tradicionais, mas adapta-os para que o conceito de Qualidade inclua a nova tecnologia como forma de maximizar valor. A combinação das práticas de gestão da qualidade com as ferramentas tecnológicas pode tornar-se um fator crítico para o sucesso organizacional, permitindo às organizações tirar partido de dados em tempo real e análise de Big Data , implementar produtos e processos inovadores e identificar de forma eficiente como corresponder às necessidades e requisitos dos seus stakeholders e redefini-las. A gestão da qualidade também beneficia de processos de produção mais digitais e automatizados pois tornam a inspeção da qualidade mais fácil e mais fiável. Ao utilizar as novas tecnologias disponíveis torna-se possível antecipar e eliminar não-conformidades, e os trabalhadores ou até mesmo as próprias máquinas, poderão ter a capacidade de realizar quaisquer ações preventivas ou corretivas que possam ser necessárias. Face a esta transição, o desenvolvimento e implementação de capability roadmaps tem gerado interesse. Ao providenciar uma avaliação da maturidade para a Qualidade 4.0, através da análise de cada dimensão ou campo estrutural do modelo em profundidade, estes modelos mostram às organizações qual o seu estado atual de preparação, permitindo-lhes também perceber o que lhes falta para atingirem o nível de maturidade que desejam. Como resultado, as organizações podem acabar por ter um roadmap personalizado para as ajudar a atingir os seus objetivos estratégicos. No entanto, como o desenvolvimento de roadmaps para a implementação da Qualidade 4.0 ainda é um esforço relativamente recente, faltam evidências empíricas consistentes e casos de estudo suficientes. Este trabalho apresenta a implementação e validação empírica do modelo proposto por Dias (2021). Este modelo é constituído por 3 dimensões – “Value Chain and Operations”, “Strategy and Organization” e “People and Culture” – cada uma com 3 subdimensões. Os níveis estão divididos em níveis de preparação, de 1 a 3, e de maturidade, de 4 a 6, onde as metodologias e ferramentas tradicionais da qualidade são integradas com a utilização de tecnologia. PALAVRAS-CHAVE: Capability Roadmap , Gestão da Qualidade, Qualidade 4.0, Maturidade viii FROM CONCEPT TO APPLICATION: IMPLEMENTATION OF A CAPABILITY ROADMAP TOWARDS QUALITY 4.0 ABSTRACT The connection between the digital and physical domains was brought to the forefront with the Fourth Industrial Revolution, with increased automation, augmentation, and the integration of people, devices, and processes. With the focus on the Digital Transformation, the concept of Quality 4.0 also emerges. While Quality 4.0 does not replace traditional quality methods, it adapts them so that the concept of quality embraces new technology to maximize value. The combination of quality management practices with technological tools can become a critical factor for organizational success, allowing organizations to take advantage of real-time data and Big Data analytics, implement innovative products and processes, and efficiently identify how to fulfil the needs and requirements of stakeholders and redefine them. Quality management also benefits from automated and digital production processes that make quality inspection easier and more reliable. By using new and emerging technologies it becomes possible to anticipate and eliminate non-conformances, and the workers, or even the machines themselves, could have the capacity to perform any corrective or preventive measures that may be needed. In the face of this transition, the development and implementation of capability roadmaps have gained interest. By providing an assessment of maturity for Quality 4.0, models show organizations their current state of readiness for the digital transition allowing them to see what they lack to meet the desired level of maturity through an analysis of each dimension or structural field of the model in depth. As a result, organizations can end up with a customized roadmap to help them achieve their strategic goals. However, since the development of Quality 4.0 implementation roadmaps is still a relatively recent effort, it lacks validation through sufficient case studies and consistent empirical evidence. This work presents the efforts to implement and empirically validate the model proposed by Dias (2021). This capability roadmap looks at 3 dimensions – “value chain and operations”, “strategy and organization” and “people and culture” – each with 3 sub-dimensions. The capability levels are divided into readiness, for levels 1 to 3, and maturity, for levels 4 to 6, where traditional quality tools and approaches are integrated with the use of technology. KEYWORDS: Capability Roadmap, Quality 4.0, Quality management, Maturity, ix TABLE OF CONTENTS 1| INTRODUCTION .......................................................................................................................... 1 2| METHODOLOGY ......................................................................................................................... 4 2.1| ORGANIZATION SELECTION ................................................................................................... 4 2.2| DATA COLLECTION ................................................................................................................ 5 2.2.1| LITERATURE REVIEW ....................................................................................................... 5 2.2.2| INTERVIEWS .................................................................................................................... 6 2.2.3| DIRECT OBSERVATION .................................................................................................... 6 2.3| DATA ANALYSIS ..................................................................................................................... 6 2.3.1| ANALYSING THE INTERVIEW TRANSCRIPTS AND OBSERVATION NOTES ......................... 6 2.3.2| APPLICATION OF THE CAPABILITY ROADMAP ................................................................. 7 2.3.3| STRATEGIC GUIDELINES DEVELOPMENT ........................................................................ 7 3| LITERATURE REVIEW .................................................................................................................. 8 3.1| QUALITY 4.0 .......................................................................................................................... 8 3.2| POTENTIAL BENEFITS .......................................................................................................... 11 3.3| POSSIBLE CHALLENGES ...................................................................................................... 13 3.4| APPLICATION EXAMPLES ..................................................................................................... 14 3.5| THE CAPABILITY ROADMAP TOWARDS QUALITY 4.0 ............................................................ 16 4| RESULTS .................................................................................................................................. 18 4.1| VALUE CHAIN AND OPERATIONS ......................................................................................... 18 4.1.1| BDO .............................................................................................................................. 18 4.1.2| Becri ............................................................................................................................. 20 4.1.3| SNA Europe ................................................................................................................... 20 4.2| STRATEGY AND ORGANIZATION ........................................................................................... 21 4.2.1| BDO .............................................................................................................................. 21 4.2.2| Becri ............................................................................................................................. 22 4.2.3| SNA Europe ................................................................................................................... 22 4.3| PEOPLE AND CULTURE ....................................................................................................... 23 4.3.1| BDO .............................................................................................................................. 23 4.3.2| Becri ............................................................................................................................. 24 4.3.3| SNA Europe ................................................................................................................... 24 5| DISCUSSION ............................................................................................................................ 26 6| STRATEGIC GUIDELINES .......................................................................................................... 31 5 BDO is one of the five biggest global networks of auditing and consulting services, working in 164 countries through more than 111000 professionals distributed over more than 1800 offices worldwide. In Portugal, BDO has seven offices and more than 300 professionals and 18 partners. BDO Portugal offers auditing and consulting services, with the office located in Braga, which constitutes our case study, focusing mainly on financial and IT advisory services, including business analytics. Becri was founded in 1983 and currently, the Becri Group has 10 units, encompassing several commercial identities, spread across Portugal with close to 800 employees, being a textile company focussed on international markets. Becri, specifically, is the central company of the group and offers services focused on the textile cutting process and sample manufacturing. SNA Europe, with its 170 years of history, is part of the larger industrial group Snap-on, present in 130 countries and counting with 128000 employees. SNA Europe is constituted by 10 production facilities, 2 distribution centres and 30 sales units, employing more than 2000 people. Its main activity is the manufacture of tools like wrenches, cutting tools and others, having invented the adjustable wrench in 1892. The case study was applied to the branch SNA Europe Vila do Conde, which is mainly a production facility that does not deal with sales to the final customer. Confidentiality and anonymity were maintained by adapting the data collection protocol to not include any confidential information, with a signed informative leaflet being delivered to all participating organizations, and authorization from the participants was obtained to proceed with the data collection whereas one participant in each organization signed a collaboration agreement (Appendix I). There were no major confidentiality concerns with the information collected during the whole process and so, the information presented here has not been censored or doctored besides the elimination of personal information of the people participating. 2.2| DATA COLLECTION 2.2.1| LITERATURE REVIEW For a better understanding of the subject of Quality 4.0, a review of recent and available articles was conducted. This review was based, mainly on the literature review by Dias et al. (2022) and supplemented with a search in the Scopus database where articles, conference proceedings, books and reports were considered for review, but limited to those published after 2019. The main focus was on documents related to the practical application of Quality 4.0 tools and techniques, with documents pertaining mainly to the definition of Quality 4.0 being excluded. This literature review consisted of three 6 basic steps: screening, analysis, and writing. In the first step, the output from the Scopus database consisted of 131 documents, whose titles, keywords, and abstracts were screened to determine relevance for our literature review. The first screening resulted in 78 documents that were then analysed in full. The final selection consisted of 51 documents, obtained both by the database search and by analysing the references of selected papers. This search allowed a deeper understanding of how Quality 4.0 can be applied to the organizations selected for this study and what benefits and challenges they might face. 2.2.2| INTERVIEWS The main source of data was semi-structured interviews, while having a set of questions prepared and reviewed, follow-up questions can come up organically during the interview both for clarification and to cover possible gaps in the data needed. At least two people, within the selected organizations who had the necessary knowledge and experience to answer the questions, were the main participants in the interviews, with the option of being accompanied by a coworker who had more specialized knowledge and could share specific inputs. The interviews were conducted in the organizations’ facilities, having a duration of approximately 30 to 150 minutes. The questions were asked in Portuguese since that was the native language of all the participants and to ensure that there was no misinterpretation of information or miscommunication due to the language barrier. All the interviews were recorded and then transcribed word for word in the following days. 2.2.3| DIRECT OBSERVATION In order to adhere to the underlying principle of triangulation, and in an effort to increase the validity and reliability of the data collected, the interviews will be backed up by direct observation of processes and/or technologies chosen by the organizations to share. During this observation process, we verified if the statements of the interview were translated into the organizations' reality and took written notes of new information. 2.3| DATA ANALYSIS 2.3.1| ANALYSING THE INTERVIEW TRANSCRIPTS AND OBSERVATION NOTES The interview transcripts were analysed according to the methodology described in Sheppard (2020), with a deductive coding approach since we had a specific set of interest categories from the beginning. Since the main purpose of the interviews and observation was to support the application of the Capability Roadmap towards Q4.0 (Dias, 2021) the categories created reflected a combination of its 7 dimensions and levels. Finally, a data table was created reflecting the model that allowed us to sort through affirmations/sentences from the transcript of the interviews and place them in the corresponding category. 2.3.2| APPLICATION OF THE CAPABILITY ROADMAP Following the information collected from the interviews and direct observation, a score from 1 to 6 is attributed to each dimension of the capability roadmap, with the first three levels representing the organization’s readiness and the last three representing its maturity towards Quality 4.0. The attribution of a level in each dimension was supported with specific affirmations of the interview transcripts that showed the necessary conditions were applicable in the organization. Since the model has a strong emphasis on progression, the nest level was only attributed once all the conditions of each dimension were present. 2.3.3| STRATEGIC GUIDELINES DEVELOPMENT The strategic guidelines for the implementation of Q4.0 projects were developed based on a gap analysis, considering the results obtained by the application of the Capability Roadmap. Taking into account the levels of readiness or maturity achieved by each organization where the roadmap was tested, we evaluated the current state, the strengths – where the scores were higher – and weaknesses – where the scores were lower – of each organization. A priority list of strategic guidelines was then created based on those strengths and weaknesses, aiming to help the participating organizations increase their scores and achieve the readiness or maturity level they desire. 8 3| LITERATURE REVIEW Traditional quality methods and tools need to be improved so the connection between quality excellence and new-era technologies can be made, new business approaches are necessary to deal with new economic, human, and technological aspects, so reshaping risk and overall business management is essential. Industry 4.0 (I4.0), based on connected and intelligent ecosystems at both intra and interorganizational levels, was proposed as a large-scale industrial revolution that blurred the boundaries between physical and digital systems. The term digital transformation was proposed to refer to the use of disruptive and digital technology to alter the entire value chain, business model, management, and organizational methods to meet the organization’s strategic objectives, build capabilities and enhance its agility (Cots, 2018; Herceg et al., 2020). A core concept of I4.0 is the smart factory consisting of a highly digitalized, connected, autonomous, intelligent, and dynamic environment where machinery and equipment are data-driven, incorporate smart technology, and thus are able to improve processes through automation and selfoptimization, converging information technologies with operational ones (Pollak et al., 2020). The connectivity and communication between people, machines, and objects, allows for customer-specific production, creating a dynamic distribution of value chain activities and achieving higher efficiency and productivity levels at a lower cost (Armani et al., 2021; Sanchez et al., 2020; Xu et al., 2018). I4.0 systems can self-plan and self-adapt, which increases operational efficiency, providing greater flexibility and adaptability, allowing the fulfilment of customer’s requirements along with the development of new business models, products, and services (Pollak et al., 2020; Salkin et al., 2018). Industry 4.0 brought forth the production of increasingly sophisticated and complex products which, in turn, required more complex quality control and assurance systems, including more sophisticated and accurate measurement and correction tools (Sader et al., 2022). To make the connection between quality excellence and new-era technologies, the traditional quality tools and methods need to be improved and adapted and to achieve a successful transition to Q4.0 organizations must understand the concept and its importance (Dias, 2021). 3.1| QUALITY 4.0 Quality 4.0 (Q4.0) combines the use of traditional quality methods with digital tools to accomplish quality objectives and pursue performance excellence. According to Radziwill (2020), Q4.0 can be 9 considered an approach that prioritizes quality and performance goals, focusing on how individuals, systems, and emerging technologies interact to improve connectedness, intelligence, and automation. Salimova et al. (2020) define Q4.0 as a product’s adaptative capacity to meet the needs of customers, at any stage of its life cycle, while also considering the interests of other stakeholders along the value chain. Sony et al. (2020) add that Q4.0 can be seen as a new method for digital tools to drive improvements across the value chain. Q4.0 is a large-scale transformation with implications on culture, leadership, collaboration, and compliance (Jacob, 2017) where new-era technologies are used as catalysts of people, products, and processes efficiency, performance, and innovation (Radziwill, 2018). Antony et al. (2022) define Quality 4.0 as “the use of advanced technologies – such as Internet of things (IOT), Cyber-physical Systems (CPS), and cloud computing – to design, operate, and maintain adaptive, predictive, self-corrective, automated quality systems along with improved human interaction through quality planning, assurance, control, and improvement to achieve new optimums in performance and operational excellence”, while Dias et al. (2022) see Quality 4.0 as the “delivery of superior quality, using modern technology to augment the capabilities of both people and quality tools and methods”. Since Quality 4.0 is still a relatively new concept there is not a single universal agreed-upon definition, but all the proposed definitions seem to refer to the synergy between people and emerging technology – with a focus on its predictive and auto-corrective capacities - to achieve quality and performance goals and pursue organizational excellence. Some authors also highlight similarities between Q4.0 and Lean Six Sigma, including the fact that both bring breakdown improvement in performance but require investment, management commitment, training, system integration, need for project management and culture change while also being oriented towards data and analytics (Yadav et al., 2021). Q4.0 will shift focus from the operationally oriented task of creating and executing a quality strategy to holistically applying quality as a strategy across the entire organization (Antony & Sony, 2023), not by replacing the traditional quality methods, but by adapting them so that the concept of quality embraces the new technology and its users and processes to maximize value (Armani et al., 2020). We can go further and say that the basic premise remains the same – how can organizations meet the needs of their customers and surpass their expectations (Antony, Sony, et al., 2022). The combination of quality management practices with technological tools can become a critical factor for organizational success, allowing organizations to take advantage of real-time data and Big Data analytics, implement innovative products and processes successfully by incorporating emerging technologies and materials and, 10 efficiently identify how to fulfil the needs and requirements of stakeholders and redefine them as needed (Carvalho et al., 2021), while, due to the resulting products’ and services’ adaptive capacity, ensuring superior quality and performance and, consequently, increasing customer satisfaction and stakeholder interest along the value network (Dias et al., 2022; Salimova et al., 2020). Antony, McDermott, Sony, et al. (2022) surveyed a global panel of quality management, and operational excellence professionals from leading companies deploying Q4.0 on their views of Q4.0 and their organizations’ transition. Based on respondents' definitions and inputs Q4.0 was defined as “the systematic integration of I4.0 technologies with existing quality tools and methods using a meaningful framework and also incorporating human factors, to achieve superior quality performance and higher levels of operational excellence”. To achieve a successful transition, organizations must acquire some essential technical and soft skills – like data science, analytics, teamwork, communication, decisionmaking, problem-solving and enhanced management skills – potentially by investing in the reskilling and upskilling of their employees (Antony, McDermott, Sony, et al., 2022), since shortages of digital skills and challenges with technology and data are significant barriers to Q4.0 implementation (Santos et al., 2021; Sony et al., 2021). It’s important for an organization to determine their readiness state for Q4.0 implementation and their ability to take effective advantage of I4.0 technologies is an essential factor (Antony, McDermott, Sony, et al., 2022). There are 8 factors described as critical for the effective implementation of Q4.0 (Antony, Sony, et al., 2022): 1. The organization’s capacity to handle Big Data 2. Their improvement of prescriptive analysis 3. The effective vertical, horizontal, and end-to-end integration of Q4.0 4. How the organization uses Q4.0 for strategic advantage 5. Leadership 6. Training 7. Organizational culture 8. Top management support Quality is much more than technology; it is characterized by the synergies established between humans and technology in a goal-oriented manner to meet the quality strategy and objectives of the organization (Sony et al., 2020). Previously, Quality Management was focused on the detection and diagnosis of processes, and failures and anomalies were dominated by in-process controls. However, because of advanced sensing technologies and analytical capabilities, the paradigm shifted to the 11 prediction and diagnosis of process conditions and understanding of the performance of quality characteristics, which are critical to both customers and organizations (Antony, Sony, et al., 2022). This shift allows organizations to measure and predict the quality of systems and products much sooner than what was possible by traditional preventive approaches and is possible by utilizing technologies like Big Data, automation, and data analytics (Zonnenshain & Kenett, 2020). 3.2| POTENTIAL BENEFITS A sustainable environment is key for continuous development. A sustainable development based on Q4.0 is aided by a connected, transparent, preventive, and innovative environment. Connecting allows a holistic view of the organization, transparency enables one to easily understand the organization’s activities and responsibilities, and preventive activities show an understanding of the future. One of the foundations of organizational development in today’s environment must be innovation (Dias, 2021). Q4.0, incorporating the principle of customer focus, can utilize technology to create new communication channels and incorporate the customer through the value chain. Technologies that support 24/7 Voice of the Customer services present an interaction pathway, including but not limited to call centres, automated online assistants or chatbots, and sensors built into the product (Dias, 2021), whereas interaction is not limited to after-sales but also allows for a new concept of industry customer experience including participation in the design and development of the products and services (N. Radziwill, 2018). Virtual Customer Integration, for example, is a collaborative system that allows customers to provide insights on new product design. The customer feedback obtained in the design phase reduces the risks associated with the launch of a new product as it helps their needs to be recognized more precisely (Bartl et al., 2012; Dias, 2021). Additional reported benefits of emerging technologies for quality management in general, include a much smaller margin of error, and the capability to transmit, process and analyse a large amount of data in no time, reducing the dependence on human intervention, among others (Yadav et al., 2021). Utilizing these emerging technologies means that most products, services, and processes can be controlled in an automated way and throughout the value chain (Sader et al., 2019). Considering the increasingly customized products and processes, it becomes extremely important to maintain a high process quality. This complexity of products and processes entails an equally complex analysis, particularly when aligning causal relationships between problems and their unseen root causes (Sader et al., 2022). Automation of inspection facilitates lean quality methods where the ultimate goal is to identify future problems instead of reaching process stability. Smart machines, factories, and operators armed 12 with augmented reality technologies can communicate and collaborate to identify and remove the root causes of production defects, take immediate measures to prevent defects and avoid production and product failures. Furthermore, predictive analysis of mass data can provide early insights into the expected defects, enabling organizations to avoid them before they occur (Sader et al., 2019, 2022). These links between people, machines, and data offer new opportunities to support quality operations, eliminate rework and scraping, increase productivity, and reduce the time and effort required to address quality issues (Sader et al., 2019). Quality 4.0, supporting dynamic data-based decisions, can reduce the turnaround time to launch a product or service, leading to similarly reduced costs of redesign and rework, empowering internal and external customers with effective collaboration, connectivity, and co-creation (Dias et al., 2022). Quality management also benefits from digital and automated production processes where the work becomes automatic, capable, safe, and error-free, enabling organizations to increase conformance, whereas corrective and preventive measures may be done by the workers or by the machines themselves (Antony, Sony, et al., 2022; Dias, 2021). By automating inspection, the time between detecting a defect and the response to adjust the process is minimized. This automation can be done by employing sensors and IOT devices that control quality gates preventing defective elements from passing through in production, and providing accurate information aligned with operational instructions to production operators and supervisors, possibly resulting in zero defects in that production station (Sader et al., 2022). Because of the improved information flow, organizations can monitor a product throughout all the stages of the process and adjust the process when needed according to the quality analysis results. Information sharing is one of the main challenges in supply chain design and the use of technology such as digital twins or CPS can help information dissemination in all elements across the supply chain, enabling the product value chain to adjust immediately responding to quality issues detected during inspection (Chen & Huang, 2021; Sader et al., 2022). Moreover, Big Data generated in real-time within the organization and across the value chain can be streamed back to involve all parties allowing efficient resource utilization, to track and resolve quality issues, standardize quality practices, and improve performance throughout. Big Data can also provide the flexibility to reduce non-value-added activities and will enable dynamic resource scheduling in product manufacturing (Antony, Sony, et al., 2022; Sader et al., 2022). Information accessibility also allows for a real-time perspective of the entire process which, in turn, allows for more flexibility in product and service development, and real-time process data collection assists in the early identification of failures and hazards, allowing for appropriate corrective and preventive actions (Dias, 2021). Besides, accurate customer needs assessment and improved responsiveness to customer 13 needs are also possible because of increased automation of all production-related activities and through the utilization of Big Data analytics. The improved quality of products and services will improve customer satisfaction, and this will result in enhanced revenue and competitive advantage (Antony, Sony, et al., 2022). With the ever-changing customer preferences, volatile markets and high stakeholder expectations, the potential benefits of Quality 4.0 become even more attractive. Besides, associated with these, there are also other possible organizational motivators, like the current society’s focus on circular economy and sustainability, and the expectation’s shift from continuous quality improvement and sustainment to dynamic product and service development (Antony, McDermott, Sony, et al., 2022; Sony et al., 2021). 3.3| POSSIBLE CHALLENGES Despite its many potential benefits, the implementation of Quality 4.0 also has its challenges. The lack of resources but also a lack of awareness of the potential benefits were reported in a survey of managers across several industries (Antony, McDermott, Sony, et al., 2022). The high initial investment required and the mismatch of Quality 4.0 with the existing corporate strategy were also mentioned. The implementation of Q4.0 requires substantial investment in the deployment, provisioning, operation, and management of a network of interconnected technologies (Hanifa et al., 2018; Saihi et al., 2023). In addition to this, organizations need to have effective change management so that new technologies and ways of work are accepted but also need to be careful with the interoperability of all different systems or software they intend to implement (Sony et al., 2020). Data is essential in Q4.0. Through technology, organizations can collect massive amounts of data but the lack of processing capacity, delays in feeding real-time data, and effective management of massive amounts of data that need to be analysed to obtain useful insights, can be a challenge (Godina & Matias, 2019; Saihi et al., 2023). Also, with these massive amounts of digital data emerge concerns about cybersecurity and data protection (Sader et al., 2019). Moreover, the already effective continual improvement strategies implemented by organizations limited the adoption of Quality 4.0 (Antony, McDermott, Sony, et al., 2022). Since digital empowerment, digitalisation, and dynamic quality enhancement seem to be essential for Quality 4.0 implementation along with the need for cultural transformation, the people involved could become the greatest challenge - either due to the lack of specific skills or due to their resistance to change– along with the need and 14 capacity to acquire the experience of Q4.0 in action at the factory level (Balouei et al., 2022; Ramezani & Jassbi, 2020). 3.4| APPLICATION EXAMPLES The overarching consensus amongst participants in a survey conducted by Antony, McDermott, Sony, et al. (2022) was that the Q4.0 projects they were involved in were considerably advantageous to their organisations and brought numerous benefits. For example, in one of the participant’s organizations, using automated inspection with enhanced vision systems led to an increase in the accuracy of the inspection process, and the elimination of non-value human inspection and the associated costs. In another, going paperless with centralized documentation systems led to a reduction in document signoffs from an average of 30 days to an average of 3 days on the electronic system. Data visualization of key process data and KPIs allowed the prioritization of quality issues and tracking of root causes and corrective actions in real-time all in the same place at another organization. One organization used technology to replace manual and repetitive interventions in processes by introducing autonomous mobile robots into single lines to reduce handling, increase floor space and improve quality (Antony, McDermott, Sony, et al., 2022). Improvement programs and digital technologies seem to be most often linked through continuous improvement (CI) tools like Lean Six Sigma and technologies like Big Data, IoT and Machine Learning (ML) (Santos & Martins, 2020). An empirical study, with data collected from organizations like Accenture, Capgemini, McKinsey, BBC, BDO, and PwC, evaluated process monitoring and estimated that through intelligence and automation, achieved with technologies like IOT and CPS, increased adjustability which in turn enhanced the products’ quality facilitated by the superior and real-time process monitoring in I4.0 systems (Gray-Hawkins et al., 2019). Automated ML has been used for predicting quality in production and, when compared to a manual implementation, has been useful to predict earlier whether a final product will meet the specifications, especially for cases where the process chain takes several days (Krauß et al., 2020). These predictions of machine failure or product defects allow organizations to make the necessary corrections and instantly analyse possible root causes and their recommended solutions which, in turn, enhances their ability to anticipate risks (Krauß et al., 2020; Sader et al., 2019). A case study in a food packaging business that supplies fruit to major UK retailers optimized quality control checks to reduce physical waste and resource usage through business process engineering where the paper-based quality checks were replaced by tablet-based checks with specific software. This led to a reduction in the time needed to conduct the checks, an increase in the time the quality team 21 of tablets at the production cells for data collection and analysis through Power BI. By digitizing data collection and centralizing data, they want to reduce the time it takes to detect and react to any fluctuation in the process, leading to optimized processes and a decrease in defects, at the point of fluctuation and those that continue in the production process. The operators at this phase will introduce the data they already collect on paper in the tablets and through Power BI will receive a report of what is happening in real-time, predominantly through graphics. More strategic and management data are collected, centralized, and analysed in their ERP, with some data collection being made through barcode pistols. Nonetheless, in their experience these two data streams rarely intersect. SNA does not deal with the final customer in the Vila do Conde facility, shipping to distribution centres in great quantities. This allied to the fact that the product that reaches the final customer is relatively cheap and so not worth the trouble of issuing a formal complaint, which means that they tend not to receive feedback from the final customer. The feedback they get is from the distribution centres and only if an order of several thousand products is all defective or has any problems with the packing or transport, which, by their accounts, rarely happens. Therefore, their “customer interaction” happens inside the SNA group, and the evolution, not in the product but in the processes, is due to the need to continuously improve to survive in a very competitive market and not necessarily due to customer feedback. 4.2| STRATEGY AND ORGANIZATION 4.2.1| BDO In the strategy and organization dimension, BDO also achieves the maturity level 4 – Automation – where the appropriate levels of automation are defined, the information flow is also automated, and the human interaction and autonomous improvement level in the organization's processes are defined. BDO has a defined strategy for the transition even if they don’t have specific Key Process Indicators (KPIs). A platform was created to function like a suggestion box where employees can suggest improvement opportunities, there are periodical meetings, not only for each team but also for the Business Unit and Service Line where managers are invited to provide feedback and participate in the strategy definition. BDO understands that their clients value an individualized approach and that projects differ from each other not allowing full automation of processes, having created a team focused specifically on this transition to Quality 4.0, identifying opportunities for digitization and automation, and identifying where human intervention is essential, and either procuring or creating the technological tools needed to achieve it. Their systems belong to a network with employees having access to their digital workspace anywhere 22 – as long as there is internet access. This digitization of the workspace facilitates remote work – vital during the COVID-19 pandemic – and allows for several people working on the same document simultaneously as well as allowing clients immediate access to their project documentation without the need for back-and-forth communication. This led to great time savings, minimization of human error, facilitated the standardization of the services provided and increased the unity and synergies in and between teams. 4.2.2| Becri In the “Strategy and Organization” dimension, Becri also reaches level 2 – Process Integration – although in its very early stages in all subdimensions. The organization has a perception of their readiness for the introduction of new technologies and ways of work since management has taken some time to attend events focused on Q4.0 initiatives, like colloquiums and presentations which gave them a perception of what the future of the textile industry might look like and ideas on how Becri might achieve it. This allowed them to develop a strategy and consider several investments. Some, due to the costbenefit ratio or the available technology being considered inadequate, were put on hold or rejected until new developments arise. Nonetheless, there has been significant investment in IT infrastructure, like new servers, dedicated Wi-Fi, UPS systems and other failsafes to prevent the complete stop of the whole infrastructure in case of, for example, an energy cut or network failure. They have adapted procedures for Q4.0, namely, in the cutting process, which was the first process they decided to start the digitization and possibly automation. Becri and its leadership seem open to innovation and continuous improvement, seeking new knowledge and experiences when it is possible, pioneering some projects and coordinating some developments with the consortium they integrate. This partnership with the consortium also allows Becri to exchange experiences with other similar organizations and somewhat benchmark their own Quality 4.0 transition. 4.2.3| SNA Europe SNA is also at readiness level 1 – Stakeholders Interaction – in the “Strategy and Organization” dimension. SNA’s management is aware of I4.0 and Q4.0 concepts and the potential benefits of their application, being at an embryonic stage of its implementation, as mentioned in the previous section, with the testing of the use of tablets in the production cells for data collection and automatic data analysis, in real-time. There is some interaction for defining and implementing quality initiatives, but it happens mainly between organizational branches of Snap-on, the group SNA Europe integrates, where different initiatives might be tested in each facility, the results are shared among the group and, if the 23 implementation is considered successful and beneficial, these initiatives are replicated at other locations with similar operational processes. Rapid continuous improvement is one of the values of Snap-on, mirrored in SNA and visible to all throughout their facilities. At present, they are focused on the Quality and Security dimensions of their operations, investing in training their workforce to improve these areas. Since they lack the customer interaction that would enable them to receive feedback, SNA’s improvements and innovation initiatives are not so much a result of corrective actions due to customers’ feedback but preventive ones and the ever-present need to accompany a ferociously competitive market in order to survive. 4.3| PEOPLE AND CULTURE 4.3.1| BDO Like in the previous dimensions, BDO achieves a maturity level 4 in the People and Culture dimension with the sub-dimensions “Role Transition” and “Leadership” scoring above at a maturity level 5 – Connectivity. This dimension’s level 4 conditions include the use of technology to augment people's capabilities while adapting to new technologies and ways of work and supporting the decision-making process with automation and reliable information. The “Role Transition” and “Leadership” subdimensions go a little further by having an active value network connected for knowledge sharing, decentralizing leadership and creating a sense of shared ownership of Quality. BDO corresponds by investing in technological solutions, some already described in the previous sections, that automate repetitive processes allowing for reduced human intervention and leading to significant time savings and reduced human errors. This means that employees complete the same amount of work in less time, reducing overtime, delays and increasing compliance to set deadlines and employee satisfaction. There has been a marked investment in the upskilling and reskilling of the employees with frequent training sessions when a new technology is adopted. This training needs can be a result of performance evaluations and suggested by the organization but also can result from a request from the employee, with training being financed by BDO, which is an additional incentive to pursue further education that brings added value to their work. A shared knowledge repository has also been created where training and education materials are shared and can be accessed by anyone who wishes to do so, whereas the use of manuals with process instructions is also encouraged. Policies and procedures have also been updated to reflect the growing concern with cybersecurity and data protection, with the acquisition of technology that allows sharing of digital data in a safer manner. BDO leadership seems to be a big driver of this transition towards Quality 4.0. They have established a strategy, created space for employees to 24 participate in the innovation and improvement processes and adapt to new ways of work, invested in technological solutions that brought numerous benefits to the organization and, due to the way the company is structured, decentralized leadership – with each team, business unit and service line having a leader as well as an executive council – and created a culture where Quality is everyone's concern. 4.3.2| Becri Becri, once again, achieves a tentative level 2 – Process Integration – in the “People and Culture” dimension. “Leadership” in particular seems to be the most advanced subdimension because, according to the participants, Becri’s leaders are participative and involved not only in the day-to-day activities of the organization but also in any new initiatives and approaches that might appear, giving room and encouraging all employees to bring forth their improvement ideas and potential learning needs. Training and learning happen mainly inside the organization, since, when recruiting, it is hard to find candidates that already have all the necessary skills to perform the job. Due to this, Becri has an extensive onboarding training plan where new employees learn by doing and are introduced to the new procedures. Even so, Becri is part of a larger group, and the work is still heavily reliant on manual work, so this training is still very focused on the manual skills needed and not so much on the digital skills that might potentially be needed as they advance in their transition towards Q4.0. Furthermore, with the adoption of new ways of work and technologies, like tablets on the shopfloor, management felt resistance to the changes from employees, but, with the involvement of leadership and middle management in the process, as well as some time to adapt, this resistance seems to have lessened if not disappeared. With this decrease in resistance and adoption of new ways of work, employees were also described to have become more participative in the organization’s meetings, offering their improvement ideas, either in those meetings or by email or suggestion box, more readily and showing enthusiasm for the evolution of Q4.0. 4.3.3| SNA Europe In the “People and Culture” dimension, SNA is also at a readiness level 1 – Stakeholders Interaction. The conditions for this level include the analysis of competencies and skills in order to understand which are needed, the associates’ engagement and empowerment whose fostering is a responsibility of the leadership. At SNA, a recurring practice is the use of proficiency matrixes, where employee skills, knowledge and experience are recorded. These matrixes are also used to determine training plans and to assign workers in another position to cover for an absentee, for example, or to teach more junior personnel. Their training is mainly offered internally due to the specificity of both the product and processes. 25 Through, regular meetings and assemblies, including the daily meetings to discuss the previous day, there is space for employees to come forward with their ideas and to discuss improvement opportunities and solutions. Leadership is almost always present at these meetings as well as at least one-third of the employees of the intervened area. Leadership has noticed some resistance to the changes, mainly from older employees, but they are still at the beginning of the testing for the introduction of tablets, and on the opposite side, they have some more interventive workers who are proactive in sharing their ideas for management to evaluate. Leadership has a policy of considering all suggestions, applying the ones they consider feasible at that moment and offering explanations to the staff about the ones that are either not possible to implement or not a priority at that particular time. This makes the employees feel heard and appreciated and encourages them to continue to come forward in the future. 26 5| DISCUSSION Most published models are oriented towards I4.0 maturity or readiness assessment and review, with few models focusing primarily on Q4.0 assessment, and very few case studies of their implementation, nonetheless, some principles and fundamentals of these models can be adapted to Q4.0 maturity assessment methodology (Nenadál et al., 2022). By providing an assessment of maturity for I4.0 or Q4.0, models show organizations their current state of readiness for the digital transition allowing them to see what they lack to meet the desired level of maturity through an analysis of each dimension of the model in depth. As a result, organizations can end up with a customized roadmap to help them achieve their strategic goals (Dias, 2021). Furthermore, most published models include a technology dimension, either specifically or extended through other dimensions like operations or product lifecycle. Dimensions related to people and organizational management also appear often, as well as ones related to organizational culture and strategy. Customer focus and leadership-related dimensions also seem to have gained importance in the most recent models (Dias, 2021; Hizam-Hanafiah et al., 2020). However, these models differ in terms of content, quantity, definition and in the methods of assessing maturity (e.g., Dias, 2021; Hizam-Hanafiah et al., 2020; Lichtblau et al., 2015; Nenadál et al., 2022; Santos & Martinho, 2020). Zulfiqar et al. (2023) developed a readiness model for Q4.0 meant to be applied in manufacturing organizations. This model takes the form of a questionnaire, with the dimensions – “Top management commitment and support”, “Leadership”, “Organizational Culture”, “Employee competency” and “ISO QMS Standard Implementation”. A five-point Likert scale was used to assess each of the dimensions and readiness was divided into 5 levels. They implemented the model at 6 packaging companies in Pakistan that already had some Q4.0 initiatives in place, having placed three of the companies at level 3 and the other three at level 4, marking these organizations as mature (Zulfiqar et al., 2023). This model has a big focus on leadership, similar to the Capability Roadmap (Dias, 2021) we tested, which is understandable since leadership should be the driving force behind a sustainable transition towards Q4.0. However, it does not appear to place a strong focus on technology, which is a big theme in other models like the one developed by Jacob, 2017, which encompasses 11 dimensions including “Connectivity” and “App development”, and our own Capability Roadmap that even without a specific dimension has technology diluted across all the others, especially in higher readiness and maturity levels. Quality 4.0 is not about the acquisition and application of technology, but about the synergies this technology can create with people, systems, and processes, however, it is undeniable the role technology can have on the transition. 27 Technology can enable organizations to collect and analyse massive amounts of data from their processes, while ML and AI can provide insights that allow prescriptive analytics to predict loss and assist in clarifying what steps to take to improve results. Smart devices, like sensors connected to an organization’s information systems, like their ERP, can provide information in real time, whereas rapid and efficient data collection from different sources can lead to a more agile decision-making process. EQMS technologies can lead to optimized quality systems that improve compliance and efficiency. AI and ML systems as well as AR and VR can be used to upskill employees, enhance their capabilities, and facilitate and potentiate training while improved systems autonomy frees workers from repetitive lowvalue-added tasks to perform more innovative and high-value tasks (Alzahrani et al., 2021; Jacob, 2017; Tambare et al., 2022). Tambare et al. (2022) report the case study of Rolls-Royce, where advanced technology, like Big Data, is used to manage aircraft engines and generate massive amounts of data, while ICT technologies are used for data analysis, looking at operational strategies in order to reduce losses by preventing errors or failure during the design phase. Rolls-Royce used robots for predictive maintenance and inspections in the production plant, in situations where inspection is dangerous or inaccessible to humans. They provide an after-sales service that combines thousands of hours of operation information of the engines, collected through various sensors (like vibration, temperature, velocity, or flow sensors), and environmental information like the weather and air traffic to provide information to airline maintenance crews allowing for predictive maintenance before any system failure happens. And this is just one example that showcases the potential benefits, across a variety of metrics - from customer and employee satisfaction to safety, performance, and compliance - of an effective I4.0 and Q4.0 transition, where technology is employed to potentiate an organization’s capabilities leading to operational excellence and innovation to realize its vision, mission and goals. Another recently published model aimed specifically for the assessment of maturity towards Q4.0 was developed and implemented in 121 Czech production companies (Nenadál et al., 2022). The model has four main dimensions – “strategic direction”, “people and culture”, “processes” and “methods and tools” - and 22 partial items, creating 7 levels of maturity. The assessment was made using electronic questionnaires sent to representatives of each company, most being quality managers. The results showed that nearly 60% of the participating companies had a below average maturity score, between levels 1 and 3, while only 6,3% had already achieved the highest levels of maturity, levels 6 and 7. The companies from the automotive sector also had an average maturity level higher than the organizations from other industrial sectors. The main limitation identified was the fact that the study happened during 28 the pandemic which prevented the researchers from validating the data collected from the questionnaires with personal interviews and observations (Nenadál et al., 2022). However, there was no research made towards the development of a strategy for the implementation of Q4.0 projects based on the companies’ results even if a severe lack of awareness related to Q4.0 was registered by the authors. The Capability Roadmap towards Q4.0 was created with the assumption that it would be applied in organizations that already have some culture for quality, like for example, effective ISO 9001 certifications, so the first level – Stakeholders Interaction – already presupposes the existence of some practices like the collection of customer feedback and the implementation of improvement and innovation actions based on that feedback, the involvement of the organization’s leadership in those actions as well as some awareness to the Digital Transformation, I4.0 and Q4.0 and their potential impact. This was observed in all three of the case studies, with all three being certified not only for ISO quality standards but also for other standards, like environmental management, health and safety management or IT security. In a study by Antony et al. (2023), Q4.0 seemed to be adopted mainly by large enterprises from manufacturing sectors in the European continent. The main factor for manufacturing industries to proceed with the transition towards Q4.0 was the ability to handle Bid Data in quality management followed closely by the reliability of the data collected and the increase in productivity. On the other hand, service industries listed their main benefit as improved customer satisfaction, and while they also placed handling Big Data at the top, the increased accuracy that leads to fewer errors and the improved decision-making process were also mentioned. In large enterprises, in addition to handling Big Data, the other top benefit was the long run time and cost savings, whereas, in small enterprises, exclusively, faster, and more transparent processes appear in the top 3. Understandably, the ability to handle Big Data is considered a main benefit across industries, big or small, since reliable data can be a significant factor in the quality of design, conformance, and performance (Antony et al., 2023). When moved to the topic of challenges, manufacturing industries list organizational culture in the top 5 whereas service industries list the training. Antony et al. (2023) reason that since service industries are people-dominated organizations, intensive training of the workforce takes on more importance compared to organizational culture, and manufacturing industries tend to have a more mature organizational culture for general quality management than service ones. This is clearly not the case with the case study where the Capability Roadmap was tested, since the BDO, a service organization, had a strong organizational culture not only for general quality management but also for innovation and 29 continuous improvement, where the training and upskilling of employees was seen not as a great challenge but as an opportunity that should be encouraged in order to maximize the potential benefits obtained through the use of technology. This awareness and the fact that the technological tools and software needed for digitization and automation, and further to reach intelligence, lack a high degree of specificity, consisting mainly of computers and Power BI tools already in the market, that are relatively simple to adapt to what they do, means that BDO has a solid transition strategy and is making effective and sustainable progress towards “Intelligence”. Becri, a manufacturing industry, highlighted the existence of technological barriers to their transition towards Q4.0, with the available machines, robots and technologies representing, at present, a very big investment for a very specific task, lacking the adaptive capacity necessary to justify their acquisition facing the predicted added value. A similar situation was reported at SNA Europe, where, even though they are still in an initial stage of transition, the machines used in their production processes are very specific to what they do, with most being approximately 80 years into their lifecycle, and right now there is simply no viable alternative in the market that could replace them. This makes automating the production processes difficult, leading to both organizations focusing on the digitization of data collection and analysis first. These two organizations also have a relatively older workforce, especially in production, which created some resistance to the changes, including the introduction of technology. Nonetheless, in Becri, after some time during the testing phase, and with the encouragement of middle management and leadership, this resistance seems to have lessened and workers see the benefits these changes bring to the organization and how technology can make some tasks easier, faster, and less repetitive. The Capability Roadmap towards Quality 4.0 developed by Dias (2021) was only validated theoretically by a group of experts and a public session. Through our attempt of empirically validating the model on three different organizations – one service and two manufacturing - we found the model adequate to evaluate the readiness or maturity of each organization, with the three dimensions and nine subdimensions covering the essential changes or conditions an organization needs to undergo, across all of its the structure - from processes to strategy and organizational culture - in order to transition. Nevertheless, due to each of the case studies unique characteristics and the fact that we want the model to be applicable to all types of organizations, be they manufacturing or service, there were some conditions of the model that need refining. For the model to be fully applicable to a service industry like BDO, where the value network is BDO itself, having no suppliers (except for general services like electricity, water and telecommunications) or permanent partners, and the client, some conditions, particularly in levels 3 – “Digitization” – and 5 – “Connectivity” – need to be reworked since they focus on aspects like 30 suppliers, logistics, wearables or inventory management, none applicable in BDO’s case. Another particularity of one of the case studies, SNA Europe, is that the plant where we tested the model has no external customer interaction, their customer would be SNA’s distribution centres or sales points, which would mean they only have internal customers. One of the subdimensions of the “Value Chain and Operations” is the “Customer”, focusing on the organization’s interaction with the customer, their integration in the product or service lifecycle and even after-sale services, which, once again, is not really applicable. Ultimately, despite the successful application in the three case studies, the Capability Roadmap towards Quality 4.0 was found to have a strong focus on the typical functioning of a manufacturing industry, needing some refining if it is to be adopted, in general, by any kind of industry, including services. 37 Lichtblau, K., Stich, V., Bertenrath, R., Blum, M., Bleider, M., Millack, A., Schmitt, K., Schmitz, E., & Schröter, M. (2015). INDUSTRIE 4.0 READINESS . Nenadál, J., Vykydal, D., Halfarová, P., & Tylečková, E. (2022). Quality 4.0 Maturity Assessment in Light of the Current Situation in the Czech Republic. 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Quality Engineering , 32 (4), 614–626. https://doi.org/10.1080/08982112.2019.1706744 Zulfiqar, M., Antony, J., Swarnakar, V., Sony, M., Jayaraman, R., & McDermott, O. (2023). A readiness assessment of Quality 4.0 in packaging companies: an empirical investigation. Total Quality Management and Business Excellence , 34 (11–12), 1334–1352. https://doi.org/10.1080/14783363.2023.2170223 39 Appendix I – Informative Leaflet and signed Collaboration 40 Agreements 41 42 43 APPENDIX II Table 2| The Capability Roadmap towards Quality 4.0 44 Table 3| The Capability Roadmap towards Quality 4.0 (cont.)