Self-Optimizing Decision-Making in Production Control

Schuh, Günther; Potente, Till; Fuchs, Sascha; Thomas, Christina; Schmitz, Stephan; Hausberg, Carlo; Hauptvogel, Annika; Brambring, Felix

doi:10.1007/978-3-642-30749-2_32

Cited by 7 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only a few authors at all include DSS in their consideration. Those who do so, primarily deal with general challenges for decision support in production planning [20], the necessity of DSS in production planning [1] or how a DSS can make use of real-time data in PPC [21]. Two publication previously developed a CPS-based DSS for PPC.…”

Section: Basics and Related Researchmentioning

confidence: 99%

“…For example, presenting the current situation requires real-time data from the shop floor [1,31] and information from systems relevant to production (e.g., ERP) [32,33]. This mass of information must be collected, processed and presented in real-time in a form that is comprehensible for the decision maker [19,20,32]. One of the experts whom we interviewed summarized the situation of the representation of the current situation as follows: "Today's systems remained on MS DOS level, they spit out a few numbers and then you have to know exactly what those numbers mean.…”

Section: Problem Identificationmentioning

confidence: 99%

“…After the simulation is configured as described above the DSS must execute the simulations (FR rtd 7) [5,20,41]. Every simulation represents a combination of selected objectives and priority rules, applied measures, and considered events [5,41].…”

Section: Requirementmentioning

confidence: 99%

“…Every simulation represents a combination of selected objectives and priority rules, applied measures, and considered events [5,41]. The resulting alternative reactions must then be evaluated (FR rtd 8) and displayed (FR rtd 9) as well as sortable (FR rtd 10) and filterable (FR rtd 11) [19,20,41,42]. The evaluation should take into account the consequences of an alternative in the form of decisionrelevant factors such as costs, adherence to schedules, machine utilization, throughput times and risks, in order to make the alternatives and their effects comparable [41,42].…”

Section: Requirementmentioning

confidence: 99%

See 3 more Smart Citations

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Freier¹,

Schümann²

2020

WI2020 Zentrale Tracks

View full text Add to dashboard Cite

The use of cyber-physical systems in production promises great potential for production scheduling since a larger information base is available for the scheduling of production orders. However, the mere acquisition of realtime data does not inherently lead to improvements. On the contrary, a targeted preparation of the data is required in order to prevent an information overload. Decision support systems that support decision makers in production scheduling can perform this task. However, the design of such systems in combination with cyber-physical systems has hardly been investigated so far. In this paper, we therefore design and implement a corresponding decision support system in a design science approach. For this, we identify meta-requirements based on a literature analysis and an interview study. Finally, we evaluate the created meta-artifact in a laboratory setting in order to obtain generalizable knowledge about building such a decision support system.

show abstract

Section: Basics and Related Researchmentioning

confidence: 99%

Section: Problem Identificationmentioning

confidence: 99%

Section: Requirementmentioning

confidence: 99%

Section: Requirementmentioning

confidence: 99%

See 2 more Smart Citations

Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems

Freier¹,

Schümann²

2020

WI2020 Zentrale Tracks

View full text Add to dashboard Cite

show abstract

“…The mechanism of "Better performing than engineered" aims at the self-optimising capabilities of production systems which are already theoretically possible (Schuh et al 2013d). With the ongoing advancement of self-optimising production systems machines should be able to reach a productivity level which exceeds the previously determined maximum due to cybernetic effects (Schuh et al 2014a).…”

Section: Better Performing Than Engineeredmentioning

confidence: 99%

Hypotheses for a Theory of Production in the Context of Industrie 4.0

Schuh

Reuter

Hauptvogel

et al. 2014

Lecture Notes in Production Engineering

Self Cite

View full text Add to dashboard Cite

Significant increase in productivity of production systems has been an effect of all past industrial revolutions. In contrast to those industrial revolutions, which were driven by the production industry itself, Industrie 4.0 is pushed forward by an enormous change within the current society due to the invention and frequent usage of social networks in combination with smart devices. This new social behaviour and interaction now makes its presence felt in the industrial sector as companies use the interconnectivity in order to connect production systems and enhance collaboration. As employees bring their own smart devices to work the interconnectivity is brought into the companies as well and Industrie 4.0 is pushed into the companies rather than initiated by the companies themselves. On top of productivity improvement within production the fourth industrial revolution opens up new potentials in indirect departments such as engineering. This focus differentiates Industrie 4.0 from the first three industrial revolutions, which mainly focused on productivity increase by optimising the production process. Within the Cluster of Excellence "Integrative Production Technology for High-Wage Countries" of the RWTH Aachen University four mechanisms were developed which describe Industrie 4.0. The mechanisms "revolutionary product lifecycles", "virtual engineering of complete value chains", "better performing than engineered" and "revolutionary short value chains" can be achieved within an Industrie 4.0-environment. This environment is based on the four enablers "IT-Globalisation", "single source of truth", "automation" and "cooperation" and enhances collaboration productivity. Therefore the present paper examines and introduces hypotheses for a production theory in the context of Industrie 4.0. For each mechanism two hypotheses are presented which explain how the respective target state can be achieved. The transmission of these mechanisms into producing companies leads to an Industrie 4.0 capable environment strengthening competitiveness due to

show abstract