Task-Specific Decision Support Systems in Multi-Level Production Systems based on the digital shadow

Pause, Daniel; Brauner, Philipp; Faber, Marco; Fischer, Markus; Hünnekes, Philipp; Petruck, Henning; Mertens, Alexander; Nitsch, Verena; Schuh, Günther; Stich, Volker; Ziefle, Martina

doi:10.1109/icphys.2019.8780186

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Since DS are task-specific, many different DS exist. [13,21] Additionally, DS can be used through the whole product life cycle. [20] DS support decision makers and operators by providing the right information at the right place in the right quality in a sufficient way.…”

Section: Characterization Of Digital Shadows In Manufacturingmentioning

confidence: 99%

“…[20] DS support decision makers and operators by providing the right information at the right place in the right quality in a sufficient way. [1,13] As DS provide only the taskspecific relevant data, the identification of necessary data, selection, aggregating and determination of granularity level of data are core part of designing DS. [22,23] Data of past and current conditions is analyzed, e.g.…”

Section: Characterization Of Digital Shadows In Manufacturingmentioning

confidence: 99%

“…PPC is chosen as an example because it is a core element in manufacturing companies. [12] Recent trends like volatile markets, increasing complexity and shortened product lifecycles also affect PPC [13]. Therefore, PPC must cope with increasing dynamic and complexity.…”

Section: Application Of Digital Shadows In Production Planning and Co...mentioning

confidence: 99%

“…As DS provide real-time data, one potential is the ability to react to dynamic changes. [13,14] Especially for scheduling of orders and production control DS enables task-specific decision support by data analyses. [31] One example for improving existing PPC systems is the lead time prediction as in [32].…”

Section: Potentials Of Digital Shadows For Production Planning and Co...mentioning

confidence: 99%

“…operation times deviating from the plan or machines break-downs. [13,14] Additionally, the necessary information is collected manually from different IT-systems causing delays, additional search efforts and missing information. [3,15] Often PPC is dependent on the expertise of the planner.…”

Section: Introductionmentioning

confidence: 99%

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Digital Shadows as an Enabler for the Internet of Production

Schuh

Gützlaff

Sauermann

et al. 2020

IFIP Advances in Information and Communication Technology

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Due to increasing atomization, manufacturing companies generate increasing amounts of production data. Most of this data is domain-specific, heterogeneous and unstructured. This complicates the access, interpretation, analysis and usage for efficiency improvement, faster reaction to change and weaknesses identification. To overcome this challenge, the idea of an "internet of production" is to link all kind of production relevant data by a data lake. Based on this data lake, digital shadows aggregate data for a specific purpose. For example, digital shadows in production planning and control help to manage the dynamic changes like delays in production or machine break-downs. This paper examines the existing research in the field of digital twins and digital shadows in manufacturing and gives a brief overview of the historical development. In particular, the potential and possible applications of digital shadows in production planning and control are analyzed. A top-down-bottom-up approach is developed to support the design of digital shadows in production planning and control.

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Section: Characterization Of Digital Shadows In Manufacturingmentioning

confidence: 99%

Section: Characterization Of Digital Shadows In Manufacturingmentioning

confidence: 99%

Section: Application Of Digital Shadows In Production Planning and Co...mentioning

confidence: 99%

Section: Potentials Of Digital Shadows For Production Planning and Co...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Digital Shadows as an Enabler for the Internet of Production

Schuh

Gützlaff

Sauermann

et al. 2020

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

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Actionable Artificial Intelligence for the Future of Production

Behery

Brauner

Zhou

et al. 2023

Interdisciplinary Excellence Accelerator Series

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The Internet of Production (IoP) promises to be the answer to major challenges facing the Industrial Internet of Things (IIoT) and Industry 4.0. The lack of inter-company communication channels and standards, the need for heightened safety in Human Robot Collaboration (HRC) scenarios, and the opacity of data-driven decision support systems are only a few of the challenges we tackle in this chapter. We outline the communication and data exchange within the World Wide Lab (WWL) and autonomous agents that query the WWL which is built on the Digital Shadows (DS). We categorize our approaches intomachine level, process level, and overarching principles. This chapter surveys the interdisciplinary work done in each category, presents different applications of the different approaches, and offers actionable items and guidelines for future work.The machine level handles the robots and machines used for production and their interactions with the human workers. It covers low-level robot control and optimization through gray-box models, task-specific motion planning, and optimization through reinforcement learning. In this level, we also examine quality assurance through nonintrusive real-time quality monitoring, defect recognition, and quality prediction. Work on this level also handles confidence, verification, and validation of re-configurable processes and reactive, modular, transparent process models. The process level handles the product life cycle, interoperability, and analysis and optimization of production processes, which is overall attained by analyzing process data and event logs to detect and eliminate bottlenecks and learn new process models. Moreover, this level presents a communication channel between human workers and processes by extracting and formalizing human knowledge into ontology and providing a decision support by reasoning over this information. Overarching principles present a toolbox of omnipresent approaches for data collection, analysis, augmentation, and management, as well as the visualization and explanation of black-box models.

show abstract

Actionable Artificial Intelligence for the Future of Production

Behery,

Brauner,

Zhou

et al. 2023

Interdisciplinary Excellence Accelerator Series

View full text Add to dashboard Cite

The Internet of Production (IoP) promises to be the answer to major challenges facing the Industrial Internet of Things (IIoT) and Industry 4.0. The lack of inter-company communication channels and standards, the need for heightened safety in Human Robot Collaboration (HRC) scenarios, and the opacity of data-driven decision support systems are only a few of the challenges we tackle in this chapter. We outline the communication and data exchange within the World Wide Lab (WWL) and autonomous agents that query the WWL which is built on the Digital Shadows (DS). We categorize our approaches into machine level, process level, and overarching principles. This chapter surveys the interdisciplinary work done in each category, presents different applications of the different approaches, and offers actionable items and guidelines for future work.The machine level handles the robots and machines used for production and their interactions with the human workers. It covers low-level robot control and optimization through gray-box models, task-specific motion planning, and optimization through reinforcement learning. In this level, we also examine quality assurance through nonintrusive real-time quality monitoring, defect recognition, and quality prediction. Work on this level also handles confidence, verification, and validation of re-configurable processes and reactive, modular, transparent process models. The process level handles the product life cycle, interoperability, and analysis and optimization of production processes, which is overall attained by analyzing process data and event logs to detect and eliminate bottlenecks and learn new process models. Moreover, this level presents a communication channel between human workers and processes by extracting and formalizing human knowledge into ontology and providing a decision support by reasoning over this information. Overarching principles present a toolbox of omnipresent approaches for data collection, analysis, augmentation, and management, as well as the visualization and explanation of black-box models.

show abstract

Task-Specific Decision Support Systems in Multi-Level Production Systems based on the digital shadow

Cited by 7 publications

References 25 publications

Digital Shadows as an Enabler for the Internet of Production

Digital Shadows as an Enabler for the Internet of Production

Actionable Artificial Intelligence for the Future of Production

Actionable Artificial Intelligence for the Future of Production

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