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

Abstract

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.

Design and Implementation of Hierarchical Digital Twins in Industrial Production Environments

“…In the process, industrial production environments have become increasingly modernized and digitized. CPS, machine-to-machine communication, and extensive networking have now become part of industrial production and are covered by the term "Industry 4.0" (Freier & Schumann, 2020;Lasi et al, 2014). CPS are embedded, physical systems that integrate physical components into their environment with the help of sensors and actuators and can interact with them.…”

“…Accordingly, the data generated by the machines is aggregated and processed on-site, and appropriate responses are initiated on this as a result. This is suitable for monitoring and controlling physical processes within machines of a production site (Freier & Schumann, 2020). The sensor data collected at the lowest level of production and processed on-site is particularly important because they allow a real-time response to unforeseen events (Vermesan & Friess, 2011).…”

“…CPS-based processing of data directly on the object, e.g., the machine, allows for faster response times in the case of unforeseen events (Section 2.1). Furthermore, the resulting information can be transferred into a DT and made available to the responsible persons and systems (Freier & Schumann, 2020;Müller et al, 2021). However, this usually only happens on a low hierarchical level, e.g., machines, of the industrial production environment.…”

“…For this purpose, production environments, especially machines, have been equipped with cyber-physical systems (CPS) to make information from the manufacturing environment available in real-time which potentially intensifies the existing problems of an adequate data presentation in industrial productions (Schuh et al, 2021). However, the mere availability of real-time data from production does not result in an appropriate presentation to the relevant groups of people, e.g., shop floor managers (Freier & Schumann, 2020;Krüger & Borsato, 2019). Digital Twins (DTs) offer an approach that has emerged in recent years in research and practice to manage, process, and aggregate the generated data, e.g., by CPS.…”

Decision Support Systems in the Context of Cyber-Physical Systems

“…In order to address these limitations and further investigate our findings, we are currently developing a prototypical DSS to support production scheduling in the context of CPS. Thereby, we want, on the one hand, to verify whether the identified and presented results occur in practice and to generate further insights on, for example, design science research knowledge like functional requirements or generalizable design principles and potential effects on the other hand (Freier & Schumann, 2020;Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007).…”