Characterization of Autonomous Production by a Stage Model

Bauer, Dennis; Schumacher, Simon; Gust, Alexander; Seidelmann, Joachim; Bauernhansl, Thomas

doi:10.1016/j.procir.2019.03.034

Cited by 21 publications

(9 citation statements)

References 8 publications

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“…However, human intelligence and intervention remain a key role because of the safety, security, social aspects and uncertainties posed by such autonomous systems (Fosch-Villaronga et al, 2020 ; Gil et al, 2019 ; Nahavandi, 2017 ; Santoni de Sio & van den Hoven 2018 ; Weichhart et al, 2019 ). Along with advanced technologies in such smart systems, the role of humans has changed and shifted from low-level operations—which can be dangerous, dirty, difficult, and dull tasks—to high expertise and safe tasks (Bauer et al, 2019 ; Campbell 2021 ; Nahavandi, 2017 ; Zhang et al, 2017 ). This phenomenon highlights two different concepts of HCD: human-in-the-loop and human-on-the-loop systems (HioTL).…”

Section: Human-centred Design In Industry 40mentioning

confidence: 99%

“…This context reconfigures the interrelationship among human and non-human actors: people and organizations, technologies and manufacturing systems, and production and consumption. Smartness expresses a new relationship between society and technology in the name of Industry 4.0 (Bauer et al, 2019 ; Bednar & Welch, 2020 ; Mazali, 2018 ; Rogers et al, 2019 ; Rossi & Di Nicolantonio, 2020 ; Yao et al, 2019 ).…”

Section: Human-centred Design In Industry 40mentioning

confidence: 99%

“…They have evolved and enlarged the scope of design: manufacturability fosters the collaboration of design and manufacturing operations, taking the perspectives of efficiency, effectiveness and economics into account (Chen et al, 1995 ; Venkatachalam et al, 1993 ); social sustainability addresses design for quality of human life by considering transdisciplinary relationships with human diversity (Demirel & Duffy, 2013 ; Martin et al, 2013 ; Papetti et al, 2020 ). These new requirements have impacted the factories themselves, but they affect the entire value chain, from the product design and development process through market segmentation to manufacturing and product disposal management (Bauer et al, 2019 ; Kong et al, 2019 ; Pereira Pessôa & Jauregui Becker, 2020 ). In this sense, for transitioning to sustainable manufacturing processes and consumption, human-centred factors play a core role in the achievement of sustainability-oriented operations throughout the supply chain (Bednar & Welch, 2020 ; Ceccacci et al, 2019 ; Grandi et al, 2020 ; Gualtieri et al, 2020 ; Lin, 2018 ; Rossi & Di Nicolantonio, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Human-centred design in industry 4.0: case study review and opportunities for future research

2021

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The transition to industry 4.0 has impacted factories, but it also affects the entire value chain. In this sense, human-centred factors play a core role in transitioning to sustainable manufacturing processes and consumption. The awareness of human roles in Industry 4.0 is increasing, as evidenced by active work in developing methods, exploring influencing factors, and proving the effectiveness of design oriented to humans. However, numerous studies have been brought into existence but then disconnected from other studies. As a consequence, these studies in industry and research alike are not regularly adopted, and the network of studies is seemingly broad and expands without forming a coherent structure. This study is a unique attempt to bridge the gap through the literature characteristics and lessons learnt derived from a collection of case studies regarding human-centred design (HCD) in the context of Industry 4.0. This objective is achieved by a well-rounded systematic literature review whose special unit of analysis is given to the case studies, delivering contributions in three ways: (1) providing an insight into how the literature has evolved through the cross-disciplinary lens; (2) identifying what research themes associated with design methods are emerging in the field; (3) and setting the research agenda in the context of HCD in Industry 4.0, taking into account the lessons learnt, as uncovered by the in-depth review of case studies.

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Section: Human-centred Design In Industry 40mentioning

confidence: 99%

Section: Human-centred Design In Industry 40mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human-centred design in industry 4.0: case study review and opportunities for future research

2021

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“…The taxonomy consists of six levels on two dimensions, which are the role of the human and the scope of automated tasks. Bauer, Schumacher, Gust, Seidelmann and Bauernhansl (2019) presented a model to describe and characterize autonomous production by five stages focusing on 12 features, such as manufacturing cell, material, and information flow. According to Bahrin, Othman, Azli and Talib (2016), autonomous systems are built in a greater range of capabilities that focus on intelligence, safety, flexibility, versatility, and collaboration.…”

Section: Nine Pillars Of Industry 40mentioning

confidence: 99%

Surveying the sense of urgency of the tactical-level management to adopt Industry 4.0 technologies: Ranking of three sister plants based on BWM-CRITIC-TOPSIS

Stark

Wan

Feng

2022

JIEM

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Purpose: Although the decision to adopt Industry 4.0 is commonly strategical, the selection and implementation of technology are the responsibilities of the tactical level management. The tactical level management will also directly experience the impact of adopting the technology towards the organizational performances in their functional areas. The comparative survey study aims to measure the tactical level management’s sense of urgency of the nine pillars in three plants of a single manufacturing organization.Design/methodology/approach: The research methodology starts with a literature review to collect the criteria appertaining to the pillars. Based on the 95 constituting criteria, the second step prepares and conducts a questionnaire survey with 32 participants on three sister plants. Next, rough BWM-CRITIC-TOPSIS ranks these plants at the pillar and criteria levels. The ranking method integrates Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (CRITIC), and technique for order performance by similarity to ideal solution (TOPSIS). The top management discussed and rendered insights into the results.Findings: Results show that the high-mix and labor-intensive plant (Plant 1) has the highest urgency, whereas the largely automated plant (Plant 3) has the lowest urgency to adopt the nine pillars. The findings provide empirical evidence of the effect of the recent Industry 4.0 awareness programs in Plant 1 and advanced infrastructure would lead to organization inertia (Plant 3) to aggressively pursue technological change. The most urgent pillar is cybersecurity, and the least urgent pillar is additive manufacturing (AM), outlining the concern over cyber threats when product information is increasingly integrated into the supply chain and technology immaturity of AM in production.Research limitations/implications: A limitation of this study is that the comparative survey only focused on three plants and the tactical level management of an organization.Originality/value: This study contributes to the knowledge of Industry 4.0 readiness by being the first to show different levels in the sense of urgency of the tactical level managements on the relevant technologies, which potentially affect the direction and the pace of Industry 4.0 adoption.

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“…Fig. 6 Features and maturity levels of Autonomous Production [73] each feature (see Figure 6), and each line represents the corresponding maturity 258 level of the features in different stages of the manufacturing system [73].…”

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confidence: 99%

An Evaluation Model of Autonomy Levels in Manufacturing and its Features

Monetti²,

Torayev³

et al. 2022

Preprint

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Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalising the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy -- associated with maturity levels for the features -- to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model.

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Characterization of Autonomous Production by a Stage Model

Cited by 21 publications

References 8 publications

Human-centred design in industry 4.0: case study review and opportunities for future research

Human-centred design in industry 4.0: case study review and opportunities for future research

Surveying the sense of urgency of the tactical-level management to adopt Industry 4.0 technologies: Ranking of three sister plants based on BWM-CRITIC-TOPSIS

An Evaluation Model of Autonomy Levels in Manufacturing and its Features

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