The Data-driven Factory - Leveraging Big Industrial Data for Agile, Learning and Human-centric Manufacturing

Gröger, Christoph; Kassner, Laura; Hoos, Eva; Königsberger, Jan; Kiefer, Cornelia; Silcher, Stefan; Mitschang, Bernhard

doi:10.5220/0005831500400052

Cited by 16 publications

(15 citation statements)

References 16 publications

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“…An important prerequisite for the Social Factory is a data-driven factory which provides flexible access to all IT systems and data along the product lifecycle [2]. It exploits this data to keep human workers in the loop.…”

Section: Foundation and Related Workmentioning

confidence: 99%

“…The vision is a smart, connected and analytics-or data-driven factory [2] which combines a high degree of automatization with enormous flexibility on the basis of data-derived insights. Unlike the 1990s vision of Computer-integrated Manufacturing (CIM) [3], the smart factories of the fourth industrial revolution do not exclude the human worker.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, human workers are more important than ever because they remain the best at making quick decisions and figuring out problem solutions based on possibly incomplete or conflicting data. One important goal of the fourth industrial revolution is therefore the integration of human workers into a very complex and datarich environment [2].…”

Section: Introductionmentioning

confidence: 99%

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The Social Factory: Connecting People, Machines and Data in Manufacturing for Context-Aware Exception Escalation

Kassner¹,

Hirmer²,

Wieland³

et al. 2017

Proceedings of the Annual Hawaii International Conference on System Sciences

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Abstract-Manufacturing environments are socio-technical systems where people have to interact with machines to achieve a common goal. The goal of the fourth industrial revolution is to improve their flexibility for mass customization and rapidly changing production conditions. As a contribution towards this goal, we introduce the Social Factory: a social network with a powerful analytics backend to improve the connection between the persons working in the production environment, the manufacturing machines, and the data that is created in the process. We represent machines, people and chatbots for information provisioning as abstract users in the social network. We enable natural language based communication between them and provide a rich knowledge base and automated problem solution suggestions. Access to complex production environments thus becomes intuitive, cooperation among users improves and problems are resolved more easily.

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Section: Foundation and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Social Factory: Connecting People, Machines and Data in Manufacturing for Context-Aware Exception Escalation

Kassner¹,

Hirmer²,

Wieland³

et al. 2017

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

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“…Workers are and will remain a major and indeed indispensable resource in businesses [1,5,7,[11][12][13][14]. Real-time control of the entire socio-technical system holds the promise of allowing workers to focus on value-adding activities (e.g.…”

Section: Literaturementioning

confidence: 99%

“…For example, assembly decisions will have software support, and simulations of machine operation in interaction with humans will be integrated through real-time digital visualization [19,20]. The Internet of things and services, the web of things, big industrial data and smart data are game changers [1,2,12]. Some observers are forecasting that businesses will compile employee personal data including whereabouts, position, vital signs and work quality [2].…”

Section: Literaturementioning

confidence: 99%

Towards Dynamic and Adaptive Allocation of Staff in a Digital-organized Production Context: An Innovative Perspective from Work Science

Nadeau¹,

Landau²

2017

J Ergonomics

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The increasingly complex socio-technical systems in modern businesses are forcing us to adapt our risk management models to a context of digital-organized production. This new phase of industrial digitization is bringing new opportunities and new challenges for managers and workers. A new work organization is emerging, along with new models for optimizing the allocation of labor to production activities. Three approaches are proposed in the literature: Mathematical optimization, models from work sciences, and integration of these two. Integrated analytical models are limited both by the number of variables considered and knowledge of the associated uncertainties. These models are static, not always focused on reducing risks at the source, and based on expert elicitation by unspecified protocols. Advances in this field are raising several questions regarding ethics, legality and safety. A model that allows systematic reduction of occupational health and safety risks and dynamic integration of operational considerations is needed. Successful businesses in the future will rely on managerial approaches that are interdisciplinary, focused on human wellbeing and draw on smart technologies and artificial intelligence.

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Toward Data‐Driven Applications in Lithium‐Ion Battery Cell Manufacturing

et al. 2019

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Production chain of lithium‐ion battery cells is a highly complicated system with manifold process–product interdependencies and high sensitivity to ambient conditions. This complexity makes it harder to control and regulate economic and environmental target criteria (e.g., product quality, cost, and energy demand). Therefore, it is necessary to develop a holistic system understanding and to identify and evaluate the interactions between the process steps within the production chain of battery cells and their effects on the relevant cell properties. In this study, a data‐driven concept to acquire relevant data along the production line, including technical building services and cell diagnostics, is presented. Furthermore, it describes the combination of automated and manual data acquisition as well as merging of data from different sources, of different communicational protocols, and of different formats toward accessibility, convenient data management, and visualization.

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The Data-driven Factory - Leveraging Big Industrial Data for Agile, Learning and Human-centric Manufacturing

Cited by 16 publications

References 16 publications

The Social Factory: Connecting People, Machines and Data in Manufacturing for Context-Aware Exception Escalation

The Social Factory: Connecting People, Machines and Data in Manufacturing for Context-Aware Exception Escalation

Towards Dynamic and Adaptive Allocation of Staff in a Digital-organized Production Context: An Innovative Perspective from Work Science

Toward Data‐Driven Applications in Lithium‐Ion Battery Cell Manufacturing

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