Design Engineering in the Age of Industry 4.0

Jiao, Roger J.; Commuri, Sesh; Panchal, Jitesh H.; Milisavljevic-Syed, Jelena; Allen, Janet K.; Mistree, Farrokh; Schaefer, Dirk

doi:10.1115/1.4051041

Cited by 62 publications

(32 citation statements)

References 226 publications

(253 reference statements)

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“…In manufacturing, the IoS role is not totally clear and there are challenges in addressing an IoS-based business model. Jiao et al [73] question "how to customize products and services to user requirements while producing products of "zero lot size" and "mass production costs". Despite the relevance of the IoS as a pillar for the Industry 4.0 [1,6], the relationship between IoS and business model is not deeply covered by the current scientific research.…”

Section: Exploring the Ios' Application Fieldsmentioning

confidence: 99%

“…[32,72] How to expand the business model in manufacturing sector for an IoS ecosystem perspective, shaping the firm-centric models as servitization and digitalization? [29,35,67,73,74] What is being applied for innovations toward IoS-based business model in agri-business sector? [68,71] In agri-business, Lezoche et al [71] highlight the diversity of stakeholders integrating agri-food chains, and suggest that an integrated platform could support the several players in delivering new valuable services.…”

Section: Research Streammentioning

confidence: 99%

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Business Models for the Internet of Services: State of the Art and Research Agenda

et al. 2022

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The relevance of the Internet of Services (IoS) comes from the global reach of the Internet into everyone’s home and daily activities and from the move from a manufacturing-based economy to a service-based economy. The IoS is seen as a new ecosystem where service providers and consumers explore their business networks for service provision and consumption. The scientific literature refers to IoS as an important cornerstone for Industry 4.0 and Future Internet; thus, it becomes relevant to study how IoS interacts with business models. Nevertheless, there is a lack of clarity on such an intersection. Moreover, a systematic review of IoS-based business models is still missing. This paper aims to make a systematic review of IoS-based business models and their application fields. We included studies from Scopus and Web of Science databases, we excluded duplicated papers and short conference versions of the later full paper journal publications. Twenty-three different studies are presented, categorized in the sub-areas of IoS, and then by the fields of applications. The main finding highlights the opportunities of IoS applications in different fields, offering directions for future research on this new arena.

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Section: Exploring the Ios' Application Fieldsmentioning

confidence: 99%

Section: Research Streammentioning

confidence: 99%

Business Models for the Internet of Services: State of the Art and Research Agenda

et al. 2022

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“…Smart Welding Quality Management systems must incorporate and make provisions for welding quality in environments in which co-designed, unique products are to be manufactured. Design is not part of a welding quality standard per se, but its activities can now be digitized [85] and, therefore, measured and made part of the welding quality system. Codesign involves the direct participation and involvement of customers and final users in the design, manufacturing, inspecting, and testing of their equipment.…”

Section: End To End Integration: Co-design Servitization and Business Models Supportmentioning

confidence: 99%

Development of an Intelligent Quality Management System for Micro Laser Welding: An Innovative Framework and Its Implementation Perspectives

Hoz

Cheng

2021

Machines

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Laser micro-welding manufacturers face substantial challenges in verifying weldment quality, as the industry and applications are requiring increasingly the miniaturization and compactness of products. The problem is compounded by new stringent demands for personalized products at competitive, low costs and the highest quality levels. High-pressure equipment manufacturers, in particular, rely on ISO 3834:2021 to assure and demonstrate best welding practices but also to manage risks associated with liability issues. ISO 3834:2021, like all conventional quality management systems, offers a one-dimensional, quasi-static overview of welding quality that may fail to deal with these new challenges and underlying complexities required to deal effectively with process variability. This paper presents a framework for welding companies to integrate horizontally their suppliers and customers with their processes and products, which are also integrated vertically in the context of Smart Manufacturing or Industry 4.0. It is focused on the development of a smart quality management system for intelligent digitization of all company manufacturing and business processes. Furthermore, an innovative data-based welding quality management framework is described for laser micro-welding applications and their implementation perspectives. The research is driven by an inductive methodology and based on a seamless integration of engineering-oriented heuristic and empirical approaches that is appropriate for intelligent and autonomous quality management, given the lack of research in this niche, but increasingly important topic area.

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“…This places constraints on what types of analytical approaches are usable (i.e., those requiring more participants) for studying design activities and may limit some design research and education progress. Second, a technology and training challenge stems from rapid and widespread advances in smart technologies and their impact on industry and training new engineers (National Science Foundation, 2020;Jiao et al, 2021). As Jiao et al (2021) explain, we are moving into industry 4.0 where advances in AI, cyber-physical systems, and human-technology collaboration are transforming our industry, manufacturing, and design, making it imperative to incorporate these technologies into training future engineers.…”

mentioning

confidence: 99%

“…Second, a technology and training challenge stems from rapid and widespread advances in smart technologies and their impact on industry and training new engineers (National Science Foundation, 2020;Jiao et al, 2021). As Jiao et al (2021) explain, we are moving into industry 4.0 where advances in AI, cyber-physical systems, and human-technology collaboration are transforming our industry, manufacturing, and design, making it imperative to incorporate these technologies into training future engineers. Moreover, this change opens new opportunities to deploy these smart technologies to study and support practicing designers and support the learning of new generations of engineers.…”

mentioning

confidence: 99%

Large data for design research: An educational technology framework for studying design activity using a big data approach

Schimpf¹,

Goldstein²

2022

Front. Manuf. Technol.

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The complexity of design problems compels the collection of rich process data to understand designers. While some methods exist for capturing detailed process data (e.g., protocol studies), design research focused on design activities still faces challenges, including the scalability of these methods and technology transformations in industry that require new training. This work proposes the Large Data for Design Research (LaDDR) framework, which seeks to integrate big data properties into platforms dedicated to studying design practice and design learning to offer a new approach for capturing process data. This technological framework has three design principles for transforming design platforms: broad simulation scope, unobtrusive logging and support for creation and analysis actions. The case is made that LaDDR platforms will lead to three affordances for research and education: capturing design activities, context setting and operationalization, and research design scalability. Big data and design expertise are reviewed to show how this approach builds on past work. Next, the framework and affordances are presented. Three previously published studies are presented as cases to illustrate the ways in which a LaDDR platform’s affordances manifest. The discussion covers how LaDDR platforms can address the aforementioned challenges, including advancing human-technology collaboration and how this approach can be extended to other design platforms.

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Design Engineering in the Age of Industry 4.0

Cited by 62 publications

References 226 publications

Business Models for the Internet of Services: State of the Art and Research Agenda

Business Models for the Internet of Services: State of the Art and Research Agenda

Development of an Intelligent Quality Management System for Micro Laser Welding: An Innovative Framework and Its Implementation Perspectives

Large data for design research: An educational technology framework for studying design activity using a big data approach

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