This book is a result of many years' interest in the economic theory of pro duction, first aroused by the reading of Professor ERICH SCHNEIDER'S classic Theorie der Produktion. A grant from the Danish-Norwegian Foundation made it possible for me to spend six months at the Institute of Economics, University of Oslo, where I became acquainted with Professor RAGNAR FRISCH'S penetrating pioneer works in this field and where the plan of writing the present book was conceived. Further studies as a Rockefeller fellow at several American univer sities, especially an eight months' stay at the Harvard Economic Research Pro ject, and a visit to the Unione Industriale di Torino have given valuable impulses. For these generous grants, and for the help and advice given by the various institutions I have visited, I am profoundly grateful. My sincere thanks are also due to the University of Copenhagen for the ex ceptionally favourable working conditions which I have enjoyed there, and to the Institute of Economics-especially its director, Professor P. N0RREGAARD RASMUSsEN-for patient and encouraging interest in my work. I also wish to thank the Institute's office staff, Miss G. SUENSON and Mrs. G. STEN0R, for their constant helpfulness, and Mrs. E. HAUGEBO for her efficient work in preparing the manuscript, which was completed in the spring of 1965.

Industrial production is one of the most basic human activities indispensable to the economic activity. Due to its complexity, production is not very well understood and modeled as opposed to traditional fields of inquiry such as physics. This book aims at enhancing rigorous understanding of a particular area of production, that of analysis and optimization of production lines and networks using discrete event models and simulation. To our knowledge, this is the first book treating this subject from the point of view mentioned above. We have arrived at the realization that discrete event models and simulation provide perhaps the best tools to model production lines and networks for a number of reasons. Analysis is precise but demands enormous computational resources, usually unavailable in practical situations. Brute force simulation is also precise but slow when quick decisions are to be made. Approximate analytical models are fast but often unreliable as far as accuracy is concerned. The approach of the book, on the other hand, combines speed and accuracy to an exceptional degree in most practical applications.

This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.

Comprehensive reference exploring the benefits and implementation of digital twins in industrial production and manufacturing Digital Twins in Industrial Production and Smart Manufacturing provides an overview of digital twin theoretical concepts, techniques, and recent trends used to meet the requirements and challenges of industrial production and smart manufacturing. The text describes how to achieve industrial excellence through virtual factory simulation and digital modeling innovations for next-generation manufacturing system design. The contributing authors address the many possible technical advantages of major Industry 5.0 technological advancements, using illustrations to aid readers in practical implementation of concepts, along with existing scenarios, potential research gaps, adoption difficulties, case studies, and future research objectives. The text also presents many applications and use cases of Industry 5.0 and digital twins in a variety of industries, including the aerospace industry, pharmaceutical manufacturing and biotech, augmented reality, virtual reality, edge computing and blockchain-based Internet of Things (IoT), cobots, intelligent logistics and supply chain management, and more. Edited by a group of highly qualified academics with significant experience in the field, Digital Twins in Industrial Production and Smart Manufacturing covers additional topics such as: Hyper-automation technology, including specialized workflow procedures and particular sectors of solicitations linked to hyper-automation Digital twins in the context of smart cities, with attempts to draw comparisons with the use of digital twins in industrial IoT Virtual factories based on digital twins and corresponding architecture to facilitate modeling, simulation, and assessment of manufacturing systems Cognitive, interactive, and standardization aspects of digital twins, and the proper implementation of digital twin technology for safety critical systems Digital Twins in Industrial Production and Smart Manufacturing is a must-have reference for researchers, scholars, and professionals in fields related to digital twins in industrial production and manufacturing. It is also suitable as a hands-on resource for students interested in the fields of digital twins and smart manufacturing.

On the one side, Industrial competitiveness today means shorter product lifecycles, increased product variety, and shorter times to market and customized tangible products and services. To face these challenges, the manufacturing industry is forced to move from traditional management, control, and automation approaches towards industrial cyber-physical systems. On the other side, several emergent engineering approaches and related Information‐Communication‐Control‐Technologies, such as Multi‐Agent-Systems, Service‐Oriented Architecture, Plug‐and‐Produce Systems, Cloud and Fog Technologies, Big Data and Analytics, among others, have been researched during the last years. The confluence of those results with the latest developments in Industrial Digitalization, Systems‐of‐Cyber-Physical-Systems Engineering, Internet‐of‐Things, Internet‐of‐Services, and Industry 4.0 is opening a new broad spectrum of innovation possibilities. The PERFoRM (Production-harmonizEd-Reconfiguration of Flexible Robots and Machinery) approach is one of them. It teaches the reader what it means when production machines and systems are digitalized and migrated into Industrial Cyber-Physical Systems and what happens when they are networked and start collaborating with each other and with the human, using the internet. After a Technology Trend Screening and beyond a comprehensive state-of-the-art analysis about Industrial Digitalization and Industry 4.0-compliant solutions, the book introduces methods, architectures, and technologies applicable in real industrial use cases, explained for a broad audience of researchers, practitioners, and industrialists.

This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.