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1) The document discusses how quality management models may not be fully aligned with Industry 4.0 developments. It evaluates quality models against five dimensions: mindful quality management, intellectual capital management, quality predictions from big data, lean structures, and managing networked firms. 2) While quality models and Industry 4.0 both aim to improve process performance, their approaches differ. Quality models rely on standardized processes while Industry 4.0 utilizes new technologies. 3) The document concludes that quality models do not adequately address aspects of Industry 4.0 like cognitive engagement, intellectual capital management, customization, lean structures, and managing business ecosystems. Quality models need to be updated to better align with current technological advances.

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FDRE Technical and Vocational Training Institution GARMENT TECHNOLOGY TOTAL QUALITY MANAGEMENT SEMINAR GROUP PRESENTATION SUBMITTED TO: DR. SENAY YACOB TITLE:- Are Quality Management Models Aligned with Industry 4.0? A perspective on current practices BY SIYOUM NEGASH
Abstract The latest technological developments epitomized in Industry 4.0 have created a disruptive effect on the production/service systems and value chains. Industry 4.0, building on the integration of information and communication technologies, Internet of things, robotics, additive manufacturing, and artificial intelligence, aims for developing autonomous and dynamic operations to enable the mass production of highly customized products Industry 4.0 and quality management share the same objective that is, improving process performance, yet through different trajectories. However, notwithstanding these developments, quality management models have remained stagnant and failed to keep abreast of industry 4.0. This paper evaluates the alignment of quality management models with Industry 4.0. The paper shows that quality models are not congruent with Industry 4.0.
INTRODUCTION Firms strive to excel in technology, operations, information systems, and support processes. These resources and capabilities are leveraged to create new competitive advantages. The developments in the realms of technology, information systems, knowledge management, organizational design, and business ecosystems are now shaping the modern business. The latest developments in these fields are epitomized in Industry 4.0 Industry 4.0 builds on the integration of information technology, industrial robots, artificial intelligence (AI), and robotics to give rise to what is called as smart manufacturing
Cont Quality models comprise a set of best practices that are established to improve quality and process performance. Industry 4.0 and QM share the same objectives of improving quality, productivity, and flexibility, yet their modus operandi are entirely different. QM models proceed through the implementation of best practices aimed at streamlining processes, reducing variation, and adopting customer-oriented policies and procedures. While Industry 4.0 improves process performance through the integrated use of the latest technologies.
Methodology First, to qualify for selection for the review process, the paper had to discuss quality models meant for the whole business. Second, papers had to discuss a quality model as the main theoretical framework of the paper. Any paper that mentioned quality models tangentially or in passing were excluded as such papers cannot generate rich insights needed for this study. Third, as an extension of the second criteria, the selected paper had to discuss constituent practices of the relevant quality models. Finally, the selected papers had to discuss at least one additional theoretical concept or management practice, such as industry 4.0, innovation, knowledge management, intellectual capital management, buyer-supplier relationships, operational performance, healthcare, technology management, supply chain management, or service performance, among many others
QM Models QM concerns how the personal, organizational, and societal resources and processes are steered to Achieve quality. American Society for quality defines QM as managing activities and resources of an organization to achieve Objectives and prevent non conformances; and a QM system as a formalized system for documenting processes, procedures, and responsibilities for achieving quality policies and objectives.
TQM represents another approach to quality management. It comprises three components: Values tools and methodologies The purpose of which is to increase internal and external customer satisfaction using a reduced amount of resources. The common values in TQM are: a focus on customers, continuous improvement, focus on processes and fact-based decision making. Tools are specific matrices or diagrams (such as fishbone diagram, control charts, and process mapping), and methodologies are the ways to work which consist of a sequence of activities. TQM represents a popular model for implementing QM.
Industry 4.0 The usage of intelligent processes and products supported by autonomous data collection and analysis, and with end-to- end integration to result in smart, intelligent, and efficient processes. These developments have reached the current form through a long evolutionary process spread over decades. The first industrial revolution introduced mechanical production and steam-powered machines, hence called mechanization. The second industrial revolution was characterized by key developments such as mass production and assembly line powered by electricity, hence called electrification.
The third industrial revolution was characterized by further advancements in autonomous production through the use of electronics and IT, hence called digitization. The latest Industry 4.0 developments make use of cyber physical systems, i.e., a fusion of the physical and virtual worlds, and builds upon technological breakthroughs in various fields, including robotics, quantum computing, AI, the blockchain, Internet of Things, and nanotechnology, among many others .
ARE QM MODELS ALIGNED WITH INDUSTRY 4.0? There is a plethora of literature reporting inadequacy of QM models in addressing quality objectives, hampering innovations, and a lack of alignment of QM with other organizational. As discussed in the methodology section, this paper builds upon the inductive-deductive approach. An example of the application of the inductive approach was acquiring the knowledge of Industry 4.0 and related developments. The inductive approach helped to identify a number of Industry 4.0 dimensions useful to evaluate QM models.
The review process revealed that Industry 4.0 builds upon technological, human, information, and knowledge resources, and complementarities among these resources and capitals (Tortorella and Fettermann, 2018). Building on the inductive-deductive approach, this paper developed a five-dimensions framework to assess the alignment of QM models against industry 4.0. Each of these dimensions represents a well-established management idea, though lacking research on connections with industry 4.0 and quality models.
Some representative examples of the theoretical support of these dimensions are :- Mindful QM Intellectual capital management Making quality predictions from big data Lean organizational structure Managing networked firms
The Table summarizes the transition desired to make QM models more relevant. The desired transition of quality models: Dimensions From To Mindful QM 1. Automaticity 2. Standardized routines 3. Compliance with rules and procedures 1. Cognitive engagement 2. Mindful task execution 3. The direction of attention towards ones ongoing experience 4. Evaluating and questioning the value of a routine Intellectual capital management 1. Managing employees 2. Managing human resource 1. Managing human, social, and intellectual capital Making quality predictions from big data 1. Anticipating customer requirements and addressing them. 1. Making accurate prediction using big data. 2. Using big data to determine changing customer preferences, and to enable agility, flexibility, and responsiveness, to create delightful customer experiences.
Cont Lean structures 1. Developing formal systems through manuals, procedures, work instructions, and records 2. Establishing documented evidence for quality processes 1. Coexistence of technology and human-based simplicity 2. Alignment of human-side with new lean structures Managing networked firms in business ecosystems 1. Defined boundaries and scope of operations 2. Management of a relatively stable set of partners and suppliers 3. Supplier management 1. Management of networked firms operating in business ecosystems 2. Managing collective value creation 3. Going beyond supplier management to integration with other firms for strategic advantage.
Human capital Social capital Intellectual capital Definition Knowledge, skills, capabilities, and experiences of people. It is a key source of quality innovations. The network and working relationships of people both within and outside an organization. Social capital creates opportunities for quality innovation The value of assets such as reputation, employee loyalty, customer relationships, company values, brand image, and experience and skills of employees. It is an approximation of the knowledge assets of employees. The focus of the current QM models QM models mainly discuss human resources management but lack an explicit focus on developing and leveraging human capital Lack of an explicit focus Lack of an explicit focus The focus of QM models on human, social, and intellectual capital
Discussion QM has a long history and has been applied worldwide to improve quality performance. Industry 4.0 represents the latest developments that bring fundamental changes in organizational processes, the role of employees, and the overall workplace. QM models and Industry 4.0 share a common objective, that is, improving organizational performance However, this paper shows that QM models are not aligned with industry 4.0 and need to be updated. Some of the key aspects of lack of alignment are as follows.
First Industry 4.0 developments have taken place on the technical side of the organization. Automation, intelligent systems, and AI dwindle the human role in the workplace. which means they promote efficiencies and streamline processes An imbalanced emphasis of Industry 4.0 on the technical side only, reduces employee cognitive engagement and perceived meaningfulness of job, which can entail boredom, errors, and burnout. This, in turn, reduces the quality of work and adversely affects job satisfaction. Bringing mindfulness to modern jobs to make people more involved and improve their perceived value of job needs to be addressed in future quality models.
Second, Industry 4.0 comprises the latest developments in engineering, IT, nanotechnology, cloud computing, and AI These developments make some types of skills obsolete and others more relevant. Managing the required repertoire of skills, knowledge, and capabilities is essential to harvesting benefits These knowledge bases exist not only in explicit and tacit resources but also in social networks Traditional QM models focus on the development of human resources which is a subset of intellectual capital and an insufficient requirement for managing knowledge intensive Industry 4.0. The need, therefore, arises to manage the human, social, and intellectual capital of firms on a systematic basis. QM models need to be updated for this pivotal feature underpinning industry 4.0.
Third building on big data, AI, and advanced analytical approaches, the modern concept of quality is about creating unique customer experiences (cf. meeting a specified set of customer requirements) Virtual reality facilitates product customization that appeals to the customer. Defining and managing quality for fluid and impermanent customer requirements need better consideration in QM models. Building on the analysis of big data, a challenge for managers is to make accurate predictions about what actually matters to the customer Making accurate predictions based on big data is the new face of addressing customer requirements and needs to addressed in future QM models
Fourth Traditional QM models, in order to create a systematic approach to the management of quality, develop structures which usually take the form of policy documents, procedures, instructions, records, and traceability mechanisms all of which make management systems bulky All these features are made redundant by industry 4.0 which uses real-time data and AI to determine the most efficient way to task execution, and eliminates structures inherent in traditional QM system Therefore, QM models need to be updated to align with these developments
Finally Industry 4.0 develops new business models, such as complementary and business ecosystems where multiple firms co-create value These firms work in an integrated and seamless manner to create product or services for the customer The management of operations of networked firms is different from supplier management required in traditional QM models. Future QM models need to address this transition. It should be noted that although traditional quality models do not prevent the adoption of industry 4.0, they don't discuss it either. Quality models are mute on these aspects, and the reason for this indifference is also apparent: the quality models have gradually evolved through incremental improvements over several decades; Industry 4.0, on the other hand, represents the latest developments surfaced after 2011. Since Industry 4.0 is emerging as the foundation block of modern businesses, ignoring these developments and leaving them to the discretion of managers can lead to sub-optimal performance.
Conclusions QM models build upon the paradigm of economic efficiencies, control, and systematic management. Industry 4.0 brings fundamental changes in processes and makes several features of QM redundant. Consequently, firms will find the current quality models irrelevant to their unique context. Industry 4.0 is an emerging topic and the nature of these developments is still poorly understood. As research on industry 4.0 is growing and theoretical models are being developed, alignment of these developments with QM and social and behavioral side of the firm emerges as the key issue.
Presentation_ON_QUALITY__MODEL[1].pptx

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Presentation_ON_QUALITY__MODEL[1].pptx

  • 1. FDRE Technical and Vocational Training Institution GARMENT TECHNOLOGY TOTAL QUALITY MANAGEMENT SEMINAR GROUP PRESENTATION SUBMITTED TO: DR. SENAY YACOB TITLE:- Are Quality Management Models Aligned with Industry 4.0? A perspective on current practices BY SIYOUM NEGASH
  • 2. Abstract The latest technological developments epitomized in Industry 4.0 have created a disruptive effect on the production/service systems and value chains. Industry 4.0, building on the integration of information and communication technologies, Internet of things, robotics, additive manufacturing, and artificial intelligence, aims for developing autonomous and dynamic operations to enable the mass production of highly customized products Industry 4.0 and quality management share the same objective that is, improving process performance, yet through different trajectories. However, notwithstanding these developments, quality management models have remained stagnant and failed to keep abreast of industry 4.0. This paper evaluates the alignment of quality management models with Industry 4.0. The paper shows that quality models are not congruent with Industry 4.0.
  • 3. INTRODUCTION Firms strive to excel in technology, operations, information systems, and support processes. These resources and capabilities are leveraged to create new competitive advantages. The developments in the realms of technology, information systems, knowledge management, organizational design, and business ecosystems are now shaping the modern business. The latest developments in these fields are epitomized in Industry 4.0 Industry 4.0 builds on the integration of information technology, industrial robots, artificial intelligence (AI), and robotics to give rise to what is called as smart manufacturing
  • 4. Cont Quality models comprise a set of best practices that are established to improve quality and process performance. Industry 4.0 and QM share the same objectives of improving quality, productivity, and flexibility, yet their modus operandi are entirely different. QM models proceed through the implementation of best practices aimed at streamlining processes, reducing variation, and adopting customer-oriented policies and procedures. While Industry 4.0 improves process performance through the integrated use of the latest technologies.
  • 5. Methodology First, to qualify for selection for the review process, the paper had to discuss quality models meant for the whole business. Second, papers had to discuss a quality model as the main theoretical framework of the paper. Any paper that mentioned quality models tangentially or in passing were excluded as such papers cannot generate rich insights needed for this study. Third, as an extension of the second criteria, the selected paper had to discuss constituent practices of the relevant quality models. Finally, the selected papers had to discuss at least one additional theoretical concept or management practice, such as industry 4.0, innovation, knowledge management, intellectual capital management, buyer-supplier relationships, operational performance, healthcare, technology management, supply chain management, or service performance, among many others
  • 6. QM Models QM concerns how the personal, organizational, and societal resources and processes are steered to Achieve quality. American Society for quality defines QM as managing activities and resources of an organization to achieve Objectives and prevent non conformances; and a QM system as a formalized system for documenting processes, procedures, and responsibilities for achieving quality policies and objectives.
  • 7. TQM represents another approach to quality management. It comprises three components: Values tools and methodologies The purpose of which is to increase internal and external customer satisfaction using a reduced amount of resources. The common values in TQM are: a focus on customers, continuous improvement, focus on processes and fact-based decision making. Tools are specific matrices or diagrams (such as fishbone diagram, control charts, and process mapping), and methodologies are the ways to work which consist of a sequence of activities. TQM represents a popular model for implementing QM.
  • 8. Industry 4.0 The usage of intelligent processes and products supported by autonomous data collection and analysis, and with end-to- end integration to result in smart, intelligent, and efficient processes. These developments have reached the current form through a long evolutionary process spread over decades. The first industrial revolution introduced mechanical production and steam-powered machines, hence called mechanization. The second industrial revolution was characterized by key developments such as mass production and assembly line powered by electricity, hence called electrification.
  • 9. The third industrial revolution was characterized by further advancements in autonomous production through the use of electronics and IT, hence called digitization. The latest Industry 4.0 developments make use of cyber physical systems, i.e., a fusion of the physical and virtual worlds, and builds upon technological breakthroughs in various fields, including robotics, quantum computing, AI, the blockchain, Internet of Things, and nanotechnology, among many others .
  • 10. ARE QM MODELS ALIGNED WITH INDUSTRY 4.0? There is a plethora of literature reporting inadequacy of QM models in addressing quality objectives, hampering innovations, and a lack of alignment of QM with other organizational. As discussed in the methodology section, this paper builds upon the inductive-deductive approach. An example of the application of the inductive approach was acquiring the knowledge of Industry 4.0 and related developments. The inductive approach helped to identify a number of Industry 4.0 dimensions useful to evaluate QM models.
  • 11. The review process revealed that Industry 4.0 builds upon technological, human, information, and knowledge resources, and complementarities among these resources and capitals (Tortorella and Fettermann, 2018). Building on the inductive-deductive approach, this paper developed a five-dimensions framework to assess the alignment of QM models against industry 4.0. Each of these dimensions represents a well-established management idea, though lacking research on connections with industry 4.0 and quality models.
  • 12. Some representative examples of the theoretical support of these dimensions are :- Mindful QM Intellectual capital management Making quality predictions from big data Lean organizational structure Managing networked firms
  • 13. The Table summarizes the transition desired to make QM models more relevant. The desired transition of quality models: Dimensions From To Mindful QM 1. Automaticity 2. Standardized routines 3. Compliance with rules and procedures 1. Cognitive engagement 2. Mindful task execution 3. The direction of attention towards ones ongoing experience 4. Evaluating and questioning the value of a routine Intellectual capital management 1. Managing employees 2. Managing human resource 1. Managing human, social, and intellectual capital Making quality predictions from big data 1. Anticipating customer requirements and addressing them. 1. Making accurate prediction using big data. 2. Using big data to determine changing customer preferences, and to enable agility, flexibility, and responsiveness, to create delightful customer experiences.
  • 14. Cont Lean structures 1. Developing formal systems through manuals, procedures, work instructions, and records 2. Establishing documented evidence for quality processes 1. Coexistence of technology and human-based simplicity 2. Alignment of human-side with new lean structures Managing networked firms in business ecosystems 1. Defined boundaries and scope of operations 2. Management of a relatively stable set of partners and suppliers 3. Supplier management 1. Management of networked firms operating in business ecosystems 2. Managing collective value creation 3. Going beyond supplier management to integration with other firms for strategic advantage.
  • 15. Human capital Social capital Intellectual capital Definition Knowledge, skills, capabilities, and experiences of people. It is a key source of quality innovations. The network and working relationships of people both within and outside an organization. Social capital creates opportunities for quality innovation The value of assets such as reputation, employee loyalty, customer relationships, company values, brand image, and experience and skills of employees. It is an approximation of the knowledge assets of employees. The focus of the current QM models QM models mainly discuss human resources management but lack an explicit focus on developing and leveraging human capital Lack of an explicit focus Lack of an explicit focus The focus of QM models on human, social, and intellectual capital
  • 16. Discussion QM has a long history and has been applied worldwide to improve quality performance. Industry 4.0 represents the latest developments that bring fundamental changes in organizational processes, the role of employees, and the overall workplace. QM models and Industry 4.0 share a common objective, that is, improving organizational performance However, this paper shows that QM models are not aligned with industry 4.0 and need to be updated. Some of the key aspects of lack of alignment are as follows.
  • 17. First Industry 4.0 developments have taken place on the technical side of the organization. Automation, intelligent systems, and AI dwindle the human role in the workplace. which means they promote efficiencies and streamline processes An imbalanced emphasis of Industry 4.0 on the technical side only, reduces employee cognitive engagement and perceived meaningfulness of job, which can entail boredom, errors, and burnout. This, in turn, reduces the quality of work and adversely affects job satisfaction. Bringing mindfulness to modern jobs to make people more involved and improve their perceived value of job needs to be addressed in future quality models.
  • 18. Second, Industry 4.0 comprises the latest developments in engineering, IT, nanotechnology, cloud computing, and AI These developments make some types of skills obsolete and others more relevant. Managing the required repertoire of skills, knowledge, and capabilities is essential to harvesting benefits These knowledge bases exist not only in explicit and tacit resources but also in social networks Traditional QM models focus on the development of human resources which is a subset of intellectual capital and an insufficient requirement for managing knowledge intensive Industry 4.0. The need, therefore, arises to manage the human, social, and intellectual capital of firms on a systematic basis. QM models need to be updated for this pivotal feature underpinning industry 4.0.
  • 19. Third building on big data, AI, and advanced analytical approaches, the modern concept of quality is about creating unique customer experiences (cf. meeting a specified set of customer requirements) Virtual reality facilitates product customization that appeals to the customer. Defining and managing quality for fluid and impermanent customer requirements need better consideration in QM models. Building on the analysis of big data, a challenge for managers is to make accurate predictions about what actually matters to the customer Making accurate predictions based on big data is the new face of addressing customer requirements and needs to addressed in future QM models
  • 20. Fourth Traditional QM models, in order to create a systematic approach to the management of quality, develop structures which usually take the form of policy documents, procedures, instructions, records, and traceability mechanisms all of which make management systems bulky All these features are made redundant by industry 4.0 which uses real-time data and AI to determine the most efficient way to task execution, and eliminates structures inherent in traditional QM system Therefore, QM models need to be updated to align with these developments
  • 21. Finally Industry 4.0 develops new business models, such as complementary and business ecosystems where multiple firms co-create value These firms work in an integrated and seamless manner to create product or services for the customer The management of operations of networked firms is different from supplier management required in traditional QM models. Future QM models need to address this transition. It should be noted that although traditional quality models do not prevent the adoption of industry 4.0, they don't discuss it either. Quality models are mute on these aspects, and the reason for this indifference is also apparent: the quality models have gradually evolved through incremental improvements over several decades; Industry 4.0, on the other hand, represents the latest developments surfaced after 2011. Since Industry 4.0 is emerging as the foundation block of modern businesses, ignoring these developments and leaving them to the discretion of managers can lead to sub-optimal performance.
  • 22. Conclusions QM models build upon the paradigm of economic efficiencies, control, and systematic management. Industry 4.0 brings fundamental changes in processes and makes several features of QM redundant. Consequently, firms will find the current quality models irrelevant to their unique context. Industry 4.0 is an emerging topic and the nature of these developments is still poorly understood. As research on industry 4.0 is growing and theoretical models are being developed, alignment of these developments with QM and social and behavioral side of the firm emerges as the key issue.