Janet K. Allen

"Design Engineering for Industry 4.0 (DE4.0) represents the 'human-cyber-physical view of the systems realization ecosystem “that is necessary to accommodate the drivers of Industry 4.0 (IoX) and provide an open ecosystem for the realization of complex systems. Seamless integration of digital threads and digital twins throughout the product design, the development and fulfillment lifecycle; the ability to accommodate diverse and rapidly changing technologies; and the mechanisms to facilitate the creation of new opportunities for the design of products, processes, services, and systems are some of the desired characteristics of DE4.0."Jiao, R., Commuri, S. Panchal, J., Milisavljevic-Syed, J, Allen, J.K., Mistree, F. and Schaefer, D., "Design Engineering in the Age of Industry 4.0," ASME Journal of Mechanical Design, 143(7), 070801, 25 pages.In keeping with the Design Engineering 4.0 construct the authors describe architecting a computer platform to support human designers make decisions associated with the realization of complex engineered systems. The platform is designed to facilitate end-to-end digital integration, customization and personalization, agile collaboration networks, open innovation, co-creation and crowdsourcing, product servitization and anything-as-a-service.Recognizing that simulation models are abstractions of reality the authors opt for a satisficing strategy instead of an optimization strategy. They include fundamentals and then describe tools for architecting a knowledge-based platforms for decision support. Challenges associated with developing a computational platform for decision support for the realization of complex engineered systems in the context of Design Engineering 4.0 are identified. Constructs for formulating design decisions (e.g., selection, compromise, and coupled decisions), knowledge modelling schemes (e.g., ontologies and modular templates), diagrams for designing decision workflows (e.g., the PEI-X diagram), and some analytical methods for robust design under uncertainty are presented. The authors describe integrating the knowledge-based platform to architect a cloud-based platform for decision support promoting co-design and cloud-based design communication essential for mass collaboration and open innovation for Design Engineering 4.0.This book is a valuable resource for researchers, design engineers, and others working on pushing the boundary of digitized manufacturing to include Design Engineering 4.0 principles in designing products, processes, and services.

"Design Engineering for Industry 4.0 (DE4.0) represents the 'human-cyber-physical view of the systems realization ecosystem “that is necessary to accommodate the drivers of Industry 4.0 (IoX) and provide an open ecosystem for the realization of complex systems. Seamless integration of digital threads and digital twins throughout the product design, the development and fulfillment lifecycle; the ability to accommodate diverse and rapidly changing technologies; and the mechanisms to facilitate the creation of new opportunities for the design of products, processes, services, and systems are some of the desired characteristics of DE4.0."Jiao, R., Commuri, S. Panchal, J., Milisavljevic-Syed, J, Allen, J.K., Mistree, F. and Schaefer, D., "Design Engineering in the Age of Industry 4.0," ASME Journal of Mechanical Design, 143(7), 070801, 25 pages.In keeping with the Design Engineering 4.0 construct the authors describe architecting a computer platform to support human designers make decisions associated with the realization of complex engineered systems. The platform is designed to facilitate end-to-end digital integration, customization and personalization, agile collaboration networks, open innovation, co-creation and crowdsourcing, product servitization and anything-as-a-service.Recognizing that simulation models are abstractions of reality the authors opt for a satisficing strategy instead of an optimization strategy. They include fundamentals and then describe tools for architecting a knowledge-based platforms for decision support. Challenges associated with developing a computational platform for decision support for the realization of complex engineered systems in the context of Design Engineering 4.0 are identified. Constructs for formulating design decisions (e.g., selection, compromise, and coupled decisions), knowledge modelling schemes (e.g., ontologies and modular templates), diagrams for designing decision workflows (e.g., the PEI-X diagram), and some analytical methods for robust design under uncertainty are presented. The authors describe integrating the knowledge-based platform to architect a cloud-based platform for decision support promoting co-design and cloud-based design communication essential for mass collaboration and open innovation for Design Engineering 4.0.This book is a valuable resource for researchers, design engineers, and others working on pushing the boundary of digitized manufacturing to include Design Engineering 4.0 principles in designing products, processes, and services.

This book explores systems-based, co-design, introducing a “Decision-Based, Co-Design” (DBCD) approach for the co-design of materials, products, and processes.In recent years there have been significant advances in modeling and simulation of material behavior, from the smallest atomic scale to the macro scale. However, the uncertainties associated with these approaches and models across different scales need to be addressed to enable decision-making resulting in designs that are robust, that is, relatively insensitive to uncertainties. An approach that facilitates co-design is needed across material, product design and manufacturing processes. This book describes a cloud-based platform to support decisions in the design of engineered systems (CB-PDSIDES), which feature an architecture that promotes co-design through the servitization of decision-making, knowledge capture and use templates that allow previous solutions to be reused. Placing the platform in the cloud aids masscollaboration and open innovation.A valuable reference resource reference on all areas related to the design of materials, products and processes, the book appeals to material scientists, design engineers and all those involved in the emerging interdisciplinary field of integrated computational materials engineering (ICME).

A fail-safe supply network is designed to mitigate the impact of variations and disruptions on people and corporations. This is achieved by (1) developing a network structure to mitigate the impact of disruptions that distort the network structure and (2) planning flow through the network to neutralize the effects of variations. In this monograph, we propose a framework, develop mathematical models and provide examples of fail-safe supply network design. We show that, contrary to current thinking as embodied in the supply network literature, disruption management decisions made at the strategic network design level are not independent from variation management decisions made at the operational level. Accordingly, we suggest that it is beneficial to manage disruptions and variations concurrently in supply networks. This is achieved by architecting fail-safe supply networks, which are characterized by the following elements: reliability, robustness, flexibility, structural controllability, and resilience. Organizations can use the framework presented in this monograph to manage variations and disruptions. Managers can select the best operational management strategies for their supply networks considering variations in supply and demand, and identify the best network restoration strategies including facility fortification, backup inventory, flexible production capacity, flexible inventory, and transportation route reconfiguration. The framework is generalizable to other complex engineered networks.

This book explores systems-based, co-design, introducing a “Decision-Based, Co-Design” (DBCD) approach for the co-design of materials, products, and processes.In recent years there have been significant advances in modeling and simulation of material behavior, from the smallest atomic scale to the macro scale. However, the uncertainties associated with these approaches and models across different scales need to be addressed to enable decision-making resulting in designs that are robust, that is, relatively insensitive to uncertainties. An approach that facilitates co-design is needed across material, product design and manufacturing processes. This book describes a cloud-based platform to support decisions in the design of engineered systems (CB-PDSIDES), which feature an architecture that promotes co-design through the servitization of decision-making, knowledge capture and use templates that allow previous solutions to be reused. Placing the platform in the cloud aids masscollaboration and open innovation.A valuable reference resource reference on all areas related to the design of materials, products and processes, the book appeals to material scientists, design engineers and all those involved in the emerging interdisciplinary field of integrated computational materials engineering (ICME).

In this monograph, the authors demonstrate how the integration of adaptability, operability, and re-configurability in the design of complex systems is indispensable for the further digitization of engineering systems in smart manufacturing. Globalization of the customer base has resulted in distributed and networked manufacturing systems. However, current design methods are not suitable to address variations in product design, changes in production scale, or variations in product quality necessitated by dynamic changes in the market. Adaptability, operability, and re-configurability are key characteristics that are necessary to address the limitations of the current methods used to design networked manufacturing systems. In recent years, the digital transformation driving Industry 4.0 has had an enormous impact on globally distributed manufacturing. Digitalisation, the integration of digital technology into networked engineered systems, is increasingly being adopted to respond to changes in the market. This is achieved by means of (a) the concurrent design of adaptable systems, (b) addressing flexibility in design parameters, (c) conducting an operability analysis, and (d) employing a reconfiguration strategy to address faults and variances in product quality and re-establish connectivity among the elements in the system. The design of manufacturing systems in the age of Industry 4.0 is addressed in this monograph. The authors introduce the concept of a ‘smart platform’ and a computational framework for the digitalization of networked manufacturing systems. They also suggest how the framework and techniques in this monograph are applicable beyond the manufacturing domain for architecting networked engineered systems in other industries such as chemical processes and health care, that are being transformed through the adoption of the Industry 4.0 construct.

A fail-safe supply network is designed to mitigate the impact of variations and disruptions on people and corporations. This is achieved by (1) developing a network structure to mitigate the impact of disruptions that distort the network structure and (2) planning flow through the network to neutralize the effects of variations. In this monograph, we propose a framework, develop mathematical models and provide examples of fail-safe supply network design. We show that, contrary to current thinking as embodied in the supply network literature, disruption management decisions made at the strategic network design level are not independent from variation management decisions made at the operational level. Accordingly, we suggest that it is beneficial to manage disruptions and variations concurrently in supply networks. This is achieved by architecting fail-safe supply networks, which are characterized by the following elements: reliability, robustness, flexibility, structural controllability, and resilience. Organizations can use the framework presented in this monograph to manage variations and disruptions. Managers can select the best operational management strategies for their supply networks considering variations in supply and demand, and identify the best network restoration strategies including facility fortification, backup inventory, flexible production capacity, flexible inventory, and transportation route reconfiguration. The framework is generalizable to other complex engineered networks.