A Taxonomy of Industry 4.0 and Related Technologies

Industry 4.0 and related technologies will remain at the top agenda of manufacturing systems until respective digital transformation is completed. In order to increase the speed of the transformation and the respective performance, a taxonomy of industry 4.0 is proposed in this chapter. The taxonomy is defined through four aspects including strategic understanding, managerial practices, technological infrastructure and developments, as well as human intervention with respective skills and competencies. Each aspect of these is defined and further sub-categorized in order to reveal the real dynamics of industry 4.0 and respective implementations. Generating the taxonomy would also easy the categorization of the respective efforts and make the assessment processes to be carried out more effectively. It is also believed that the proposed taxonomy will be the source of generating a maturity model of industry 4.0. Note that the proposed taxonomy and respective components are defined by reviewing 620 papers, maturity models, and industry 4.0 projects

A methodology to measure the degre of managerial innovation

Purpose: The main objective of this study is to introduce the concept of managerial innovation and to propose a quantitative methodology to measure the degree of managerial innovation capability by analyzing the evolution of the techniques used for management functions. Design/methodology/approach: The methodology mainly focuses on the different techniques used for each management functions namely; Planning, Organizing, Leading, Controlling and Coordinating. These functions are studied and the different techniques used for them are listed. Since the techniques used for these management functions evolve in time due to technological and social changes, a methodology is required to measure the degree of managerial innovation capability. This competency is measured through an analysis performed to point out which techniques used for each of these functions. Findings: To check the validity and applicability of this methodology, it is implemented to a manufacturing company. Depending on the results of the implementation, enhancements are suggested to the company for each function to survive in the changing managerial conditions Research limitations/implications: The primary limitation of this study is the implementation area. Although the study is implemented in just a single manufacturing company, it is welcomed to apply the same methodology to measure the managerial innovation capabilities of other manufacturing companies. Moreover, the model is ready to be adapted to different sectors although it is mainly prepared for manufacturing sector. Originality/value: Although innovation management is widely studied, managerial innovation is a new concept and introduced to measure the capability to challenge the changes occur in managerial functions. As a brief this methodology aims to be a pioneer in the field of managerial innovation regarding the evolution of management functions. Therefore it is expected to lead more studies to inspect the progress of change throughout the history and the future trends.Peer Reviewe

Introduction to Intelligent Quality Management

Intelligent manufacturing is becoming more and more attractive for industrial societies especially after the introduction of industry 4.0 where most of industrial operations are to be carried by robots equipped with intelligent capabilities. This explicitly implies that the manufacturing systems will entirely be integrated and all manufacturing functions including quality control and management will have to be made as much intelligent as possible in operating with minimum human intervention. This Chapter will present a brief overview of some implications about intelligent quality systems. It intends to provide the readers of the book to understand how the concept of artificial intelligence is to be embedded into quality functions. It is known that the interoperability is the rapid transformation requirement of industry specific operations. This requires the integration of quality functions to other manufacturing functions for sharing the quality related knowledge with other manufacturing functions in order to sustain total intelligent collaboration. Achieving this, on the other hand, ensures the improvement of manufacturing processes for better performance in an integrated manner. Note that, although some general information about intelligent manufacturing systems are given, this chapter is particularly focused on discussing intelligent quality related issues

Smart Manufacturing and Intelligent Manufacturing:A Comparative Review

The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world. However, different terminologies, namely smart manufacturing (SM) and intelligent manufacturing (IM), have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners. While SM and IM are similar, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, thought, connotation, and technical development of the concepts of SM or IM are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM. A bibliometric analysis of publication sources, annual publication numbers, keyword frequency, and top regions of research and development establishes the scope and trends of the currently presented research. Critical topics discussed include origin, definitions, evolutionary path, and key technologies of SM and IM. The implementation architecture, standards, and national focus are also discussed. In this work, a basis to understand SM and IM is provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems

Integrating expert systems and neural networks for on-line statistical process control

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