Production

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This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today's world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.

This book systematically analyzes the applicability of big data analytics and Industry 4.0 from the perspective of semiconductor manufacturing management. It reports in real examples and presents case studies as supporting evidence. In recent years, technologies of big data analytics and Industry 4.0 have been frequently applied to the management of semiconductor manufacturing. However, related research results are mostly scattered in various journal issues or conference proceedings, and there is an urgent need for a systematic integration of these results. In addition, many related discussions have placed too much emphasis on the theoretical framework of information systems rather than on the needs of semiconductor manufacturing management. This book addresses these issues.

This edited book addresses the challenges in managing the operations and supply chain of organizations in the era of internet of things and Industry 4.0. It presents cutting edge research on real world operations related problems, in-depth analyses, and relevant managerial implications. Wide variety of solution approaches such as quantitative, quantitative, and simulations are presented in the context of managing the operations and supply chains. Consisting of selected papers from the XXIII Annual International Conference of Society of Operations Management, this volume is part of a two volume series with the other book consisting of chapters on quantitative decision making. This edited book covers various quantitative models on operations and supply chain management such as inventory optimization, machine learning-operations research integrated model for healthcare systems, game-theoretic analysis of review strategies in truthful information sharing, design of contracts insupply chains, supply chain optimization, inventory routing, and shop floor scheduling. In addition to the quantitative models, several innovative heuristics are proposed for different problems. This book explores qualitative models on improving the performance of small and medium enterprises and petroleum industries and a simulation model for staff allocation in the information technology industry. Finally, this book provides review articles on vaccine supply chains and behavioral operations management.The book throws light on the emerging trends in the use of analytics, optimization, and simulation tools and empirical analysis to improve the performance of operations and supply chains of organizations. It will serve as an essential resource for practitioners, students, faculty members and scholars in operations management and related areas to gain knowledge and pursue high quality research on developments in areas such as managing the resource management andthe solution methodology---innovative tools employed in addressing the real world problems and the different optimization techniques.

This book addresses reliability, maintenance, risk, and safety issues of industrial systems with applications of the latest decision-making techniques. Thus, this book presents chapters that apply advanced tools, techniques, and computing models for optimizing the performance of industrial and manufacturing systems, along with other complex engineering equipment. Computing techniques like data analytics, failure mode and effects analysis, fuzzy set theory, petri-net, multi-criteria decision-making (MCDM), and soft computing are used for solving problems of reliability, risk, and safety related issues.

Erfolgreiche Unternehmen verfolgen eine nachhaltige Innovationsstrategie und setzen diese konsequent um. Innovationspotenziale richtig zu erkennen, einzuschätzen und zielgenau umzusetzen ist eine Stärke, die Wettbewerbsvorteile und damit hohe Profitabilität ermöglicht. Dabei sind die Randbedingungen, welche zusätzlich durch die VUCA-Welt verstärkt werden, bei der Gestaltung des Innovationsmanagements vielfältig: erhöhter Kostendruck, rasche Technologiewandel und schwer abzuschätzende Marktpotenziale stehen limitierten Entwicklungsbudgets und ambitionierten Zeitplänen gegenüber. Hinzu kommen Anforderungen aus dem Megatrend Sustainability. Hierbei müssen neben ökonomischen auch ökologische und soziale Aspekte berücksichtigt werden. Um diesen spezifischen Herausforderungen systematisch begegnen zu können, stellen die Autoren einen ganzheitlichen Ansatz zur Neuausrichtung des Innovationsmanagements vor. Der Innovationsprozess wird dabei verstanden als iterativer Zyklus von der Identifizierung von Marktpotenzialen über die Strukturierung von Innovations- und Entwicklungsprojekten, die Realisierung sowie Industrialisierung von Produkten bis hin zur Markteinführung. Im Zeitalter des digitalen Wandels besteht darüber eine zentrale Maßgabe für das Innovationsmanagement darin, Innovationspotenziale während der Nutzungsphase zu identifizieren und mit Hilfe von Informationssystemen zielgerichtet in neue Produktkonzepte zu überführen.

Die Gestaltung des Anlaufs für neue Produkte gewinnt für die Unternehmen zunehmend an Bedeutung. Risiken und Störfaktoren nehmen Einfluss auf das Erreichen geplanter Zielstellungen. Insbesondere für Entscheidungsträger mittelständischer Unternehmen besteht vor diesem Hintergrund der Bedarf, eine strukturierte Vorgehensweise zur Unterstützung der Zielerreichung zu erhalten. Jörg Nagel untersucht entsprechend des zeitlichen Horizonts strategische und operative Inhalte eines risikoorientierten Anlaufmanagements. Er entwickelt eine Methodik zur Unterstützung der Planung und Steuerung des Anlaufs unter Risikogesichtspunkten.

Aufbauend auf einer umfassenden und durch vielfältige Praxisbeispiele angereicherten Typologie liegt der Schwerpunkt des Buchs in der produktionstheoretischen Modellierung und Analyse verschiedener Kuppelproduktions-Typen. Den Rahmen liefert die Aktivitätsanalyse. Ziel ist es, eine einheitliche theoretische Grundlage zu schaffen, die der Handhabung einer Vielzahl spezieller Problemstellungen dienen kann. So lassen sich Aussagen für die Kalkulation oder die Programmplanung ableiten.

Dem wissenschaftlich interessierten Leser bietet dieses Buch eine ausführliche Auseinandersetzung mit der Theorie der Entsorgung; dem Praktiker dient es als Hilfestellung zur konstruktiven Modellierung und Optimierung des Entsorgungsprozesses. Neben grundlegenden produktionswirtschaftlichen Überlegungen werden auf der Basis der Aktivitätsanalyse Reduktions- bzw. Entsorgungprozesse produktionstheoretisch analysiert. Durch die neuartige Modellierung der Objektkomponenten lassen sich qualitative Unterschiede der Objekte erfassen und Entsorgungsprozesse realitätsnäher beschreiben. Für verschiedene, in der Arbeit herausgearbeitete Prozeßtypen werden allgemeine Überlegungen zur Modellierung und Optimierung angestellt, die an vielen Stellen für praktische Entsorgungsprozesse verdeutlicht werden.

Unternehmensinterne Denkfabriken mit dem kreativen Spirit von Start-ups zur Entwicklung von Strategien und bahnbrechenden Innovationen stellen den Kern dieses Buches dar. Sogenannte Corporate Think Tanks sind Foren, Projektgruppen oder Unternehmensbereiche, in denen interdisziplinäre Teams zukunftsgerichtete Themen, wie zum Beispiel Digitalisierung, bearbeiten. Spannende Praxisbeispiele von namhaften Unternehmen illustrieren den Einsatz von Corporate Think Tanks in der Praxis. Ein umfassender Toolkasten zeigt auf, wie die konkrete Durchführung im eigenen Unternehmen realisiert werden kann. Neben klassischen Methoden werden auch innovative Tools wie ThinkX, Design Thinking, LEGO®-SERIOUS-PLAY® oder der Business Model Canvas anschaulich erläutert. Die vierte Auflage wurde um Inhalte zum Thema Generative AI sowie weitere Formatbeispiele ergänzt.

This book explores how the network sustainable business model is being built in response to the significant changes that are increasing strategic effectiveness and operating efficiency. Incorporating the new post-COVID19 digital landscape, it synthesizes the outputs of practitioner oriented publications and integrates these with classic concepts in operations strategy to provide a unique perspective on value generally, and the value chain network as a part of the business model in the Industry 4.0/5.0 environment specifically. Including illustrative case examples and pursuing a unique workbook approach, each chapter is built around a set of diagrams, making the concepts more accessible for graduate business students and practitioners alike.

Soft computing and Industry 5.0 are two distinct concepts that, when combined, can have a significant impact on sustainability initiatives within various industries. Soft computing is a subfield of artificial intelligence (AI) that aims to address problems characterized by uncertainty, imprecision, and partial truth. It encompasses various computational techniques, such as fuzzy logic, neural networks, genetic algorithms, and machine learning, which enable machines to deal with complex and uncertain data in a more human-like manner. Soft computing techniques are particularly valuable in sustainability efforts because they can handle non-linear relationships and uncertain data that often arise in environmental and social contexts. For example, they can be used to optimize energy consumption, waste management, and resource allocation in industries by considering various factors and trade-offs.The book highlights the latest innovations in intelligent systems in classical machine learning, deep learning, Internet of Things (IoT), Industrial Internet of Things (IIoT), blockchain, knowledge representation, knowledge management, big data, and natural language processing. (NLP). The book contains many contemporary articles from both scientists and practitioners working in many fields where soft computing, intelligent systems and the IIoT can break new ground. Intelligent systems and the Internet of Things are now essential technologies in almost every field. From agriculture to industry to healthcare, the scope of smart systems and IIoT is as wide as the horizon. Nowadays, these technologies are extensively used in developed countries, but they are still at an early stage in emerging countries.The primary market of this book is senior undergraduate students, post graduate students, practitioners, researchers, academicians, industrialists, and professionals working in areas of core computer science, electrical engineering, mechanical engineering, environmental engineering and agricultural engineering. The secondary audience of this book is individuals working in the areas of manufacturing, agriculture, remote sensing, environmental engineering, health care, smart cities, smart farming, remote sensing, supply chain management and hydrology.

This book analyses the possible regionalization of global value chains, particularly in manufacturing. Amidst calls for an industrial renaissance in Europe and a resurgence of manufacturing jobs in the United States, the dynamics of global value chains have been reshaped by global pandemics, geopolitical tensions, and increased automation. Employing a multi-regional input-output model, the book scrutinizes the decline in offshoring afer the mid-2010s. It sheds light on regionalization as a pivotal force redefining global markets, and focuses on Europe and North America, revealing the strengthening of regional ties. Discussing future challenges, the book will appeal to scholars, students, and researchers, as well as practitioners and policy-makers interested in a better understanding of (de)globalization nuances connected to the evolving role of manufacturing in global value chains.

The objective of the book is to adopt the application of data analytics to enhance the sustainability and resilience of the green supply chain networks. To demonstrate the applicability and usefulness of the method, the book adopts different data analytic models and approaches against the backdrop of case studies. In summary, this book attempts to address the question of methods, tools, and techniques that can be used to create resilient, anti-fragile, reliable, and invulnerable green supply chain networks.

This book critically examines the concept of 'supply chain agility' (SCA) in a novel context, shedding light on its implications for executives and professionals. While much literature has explored SCA in manufacturing, this book pioneers a comprehensive exploration of its application in the service sector, an often-overlooked domain in operations management, supply chain management, and innovation literature. The analysis, based on data from 245 service firms in Australia, employs a unique approach, revealing a positive relationship between firm innovativeness and SCA, with SCA in turn positively impacting firm performance.By bridging gaps in previous research, this book offers conceptual, methodological, and empirical contributions, emphasizing the critical role of innovation in achieving SCA, particularly in uncertain contexts. It provides valuable insights for firms navigating the service economy, offering a holistic examination of the benefits of SCA and empirically validating its role in managing market changes cost-effectively for a competitive advantage. The findings encourage a re-evaluation of firm performance, urging business professionals and managers to adopt a comprehensive perspective. The insights presented serve as a valuable tool for measurement, estimation, assessment, and benchmarking-key drivers for improving and predicting performance in dynamic supply chain environments. Overall, this book is a timely and essential resource for those seeking to understand and enhance supply chain agility in the evolving landscape of the service industry.

This book presents a guide to digital twin technologies and their applications within manufacturing. It examines key technological advances in the area of Industry 4.0, including numerical and experimental models and the Internet of Things (IoT), and explores their potential technical benefits through real-world application examples. This book presents digital models of advanced manufacturing processes dynamics that enable to control the cutting processes including experimental and simulation studies for brittle-ductile transition of ultra-precision machining materials assuring product quality. Innovative electrical power harvesting solutions from tool vibrations and wireless data transmission from confined and heavily cooled environment are also included. It explains the benefits of virtual and physical twins adapted to real systems, including the ability to shorten the product's path to the market, and enabling the transition to higher value-added manufacturing processes. Including numerous illustrations and clear solved problems, this book will be of interest to researchers and industry professionals in the fields of mechatronics, manufacturing engineering, computational mechanics.

Part 1: Artificial Intelligence.- 1. Application of Artificial Intelligence and Big Data for Fighting COVID-19 Pandemic.- 2. An IOT-Based COVID-19 Detector Using K-Nearest Neighbour.- 3. Predictive Analytics for Early Detection of COVID-19 by Fuzzy Logic.- 4. Role of Artificial Intelligence in Diagnosis of Covid-19 Using CT-Scan.- 5. Predicting the Pandemic Effect of COVID 19 on the Nigeria Economic, Crude Oil as a Measure Parameter Using Machine Learning.- Part II: Forecasting Techniques.- 6. Time Series Analysis and Forecast of COVID19 Pandemic.- 7. Forecasting COVID-19 pandemic with machine learning models in Nigeria.- 8. Nigerian COVID-19 Incidence Modelling and Forecasting with Univariate Time Model.- 9. Predicting the Spread of COVID-19 in Africa Using Facebook Prophet, Polynomial Regression and SIR Model.- 10. Comparing Predictive Accuracy of COVID-19 Prediction Models: A Case Study.- Part 3: Social Sciences.- 11. Akaike's Information Criteria (AIC) Algorithm for the Student Entrepreneurial Intention in Covid-19 Pandemic.- 12. Sentiment Analysis for COVID vaccinations using Twitter - Text Clustering of Positive and Negative Sentiments.- 13. Participation and Active Contribution of Private Universities in the Prevention of the COVID-19 Pandemic Transmission.- 14. Effects on Mental Health by the Coronavirus Disease 2019 (COVID-19) Pandemic Outbreak.- 15. Multimodal Analysis of Cognitive and Social Psychology Effects of COVID 19 Victims.- 16. Public Transport Passenger's Density Estimation Tool for Supporting Policy Responses for COVID-19.- Part 4: Optimization Techniques.- 17. A Generalized Model for Scheduling Multi-Objective Multiple Shuttle Ambulance Vehicles to Evacuate COVID-19 Quarantine Cases.- 18. Hyper-Parameters Optimization of Deep Convolutional Neural Network for Detecting COVID-19 Using Differential Evolution.- Part 5: Data Science.- 19. Quality Design for COVID-19 Pandemic: Web Scraping Technique on Text Comments and Quality Ratings Using MultipleOnline Sources.- 20. Data Science Models for Short-Term Forecast of Covid-19 Spread in Nigeria.- Part 6: COVID-19 Detection.- 21. Attention based Residual Learning Network for COVID-19 Detection Using Chest CT Images.- 22. COVID-19 Face Mask Detection Using CNN and Transfer Learning.- 23. LASSO-DT Based Classification Technique for Discovery of COVID-19 Disease Using Chest X-Ray Images.- Part 7: Economy.- 24. Economic Policies for the COVID-19 Pandemic: Lessons from the Great Recession.- 25. QOL Barometer for the Wellbeing of Citizens: Leverages during Critical Emergencies & Pandemic Disasters.

This book comprises high-quality refereed research papers presented at the 3rd International Conference on Artificial Intelligence and Logistics Engineering (ICAILE2023), held in Wuhan, China, on March 11-12, 2023, organized jointly by Wuhan University of Technology, Nanning University, the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Huazhong University of Science and Technology, the Polish Operational and Systems Society, Wuhan Technology and Business University, and the International Research Association of Modern Education and Computer Science. The topics discussed in the book include state-of-the-art papers in artificial intelligence and logistics engineering. It is an excellent source of references for researchers, graduate students, engineers, management practitioners, and undergraduate students interested in artificial intelligence and its applications in logistics engineering.

This book presents a novel approach for studying the impact of leadership styles on supply chain resilience using a combination of transactional and transformational leadership. The book consists of two main sections: The first introduces key concepts and provides a framework for understanding the research problem and methodology. The second section presents five structural equation models developed and validated using data collected from a survey of managers and engineers in the Mexican Maquiladora Industry. These models explore the relationships between key variables, such as agility, flexibility, alertness, and efficiency, and how they are affected by different leadership styles. The findings of this study suggest that a combination of transactional and transformational leadership is highly effective in enhancing resilience and achieving better results during disruptive events. Written in a clear and accessible style, this book is an essential resource for scholars, researchers, and practitioners interested in supply chain management, leadership, and resilience. It contributes to the existing literature on these topics and provides practical insights for improving supply chain performance in today's complex and dynamic business environments.

This second edition systematically presents the underlying theory, model development, and applications of network Data Envelopment Analysis (DEA). It discusses the models used to measure the efficiency of systems in specific network structures and introduces readers to the latest applications. It demonstrates how the "network DEA" approach helps identify and manage the specific components that cause inefficiencies in the overall system. The existing models for measuring the efficiency of systems in specific network structures are also discussed, and the relationships between system efficiency and component efficiency are explored. Moreover, the book provides an advanced exposition on performance evaluation of systems with network structures. It explores the networked nature of most production and operation systems, and explains why network analyses are necessary. Accordingly, the book will inspire new research and applications based on the state of the art. In this new edition, the latest research advances and discoveries are discussed. Two new chapters on Linkage Efficiency and External and Internal Evaluations have been added. This book is mainly aimed at researchers and graduate students who are interested in performance evaluation, DEA, and multi-criteria decision analysis. Practitioners who want to measure the performance of production, operation, or any type of decision-making units will also find it useful.