Kostenoptimale Zuverlässigkeit produktiver Systeme

Opfermann, Klaus

doi:10.1007/978-3-663-02639-6

Cited by 8 publications

(1 citation statement)

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“…prefabricated decisions that intervene through the system and define measures to eliminate the disruption) are required. These are control processes that eliminate operational interruptions or occur as preventive measures to prevent malfunctions [9]. Dealing with disruptions in an organized way has several advantages, since the time it takes to react to a disturbance can be shortened noticeably.…”

Section: Motivationmentioning

confidence: 99%

Development of a Methodology for the Analysis and Evaluation of Alternative Actions in Disruption Management in Production

Luetkehoff¹,

Stich²,

Schroeter³

et al. 2019

Collaborative Networks and Digital Transformation

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Due to Digital Transformation, also called Industry 4.0 or the Industrial Internet of Things, the barrier for implementing data collecting technology on the shop floor has decreased dramatically in the past yearsleading to an increasingly growing amount of data from a multitude of IT systems in production companies worldwide. Despite that, the production controller still relies heavily on intrinsic knowledge and intuition for the management of disruptions in production. Thanks to advances in the fields of production control and artificial intelligence, potentials for the collected data for disruption management arise. However, in order to transform data into usable information and allow drawing conclusions for disruption management in production, the relevant data-objects, disturbances and alternative actions must be known. Thus, the decision-making can be supported, reducing the decision latency and increasing benefit of alternative actions. Therefore, the goal of this paper is to discuss the prerequisites necessary to perform a data based disruption management and the methodology itself, serving as an approach to allow companies to build a data basis, classify disruptions and alternative actions in order to improve decision making in the future.

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Section: Motivationmentioning

confidence: 99%