Publication | Open Access
Cyber-Physical Production Systems Combined with Logistic Models – A Learning Factory Concept for an Improved Production Planning and Control
110
Citations
9
References
2015
Year
EngineeringIndustrial EngineeringLearning FactoryManufacturing Systems EngineeringIntelligent SystemsManufacturing ControlAutomated ManufacturingCyber-physical Production SystemsProduction EnterprisesLogistic ModelsSystems EngineeringIndustry 4.0Industrial InformaticsMachine SystemsManufacturing PlanningManufacturing SystemsComputer ScienceCyber ManufacturingProduction ControlCyber-physical Production SystemIndustrial DesignImproved Production PlanningCyber Physical SystemsAutomationBusinessIndustrial AutomationTechnology
Modern production enterprises face cost pressure, demand for customization, and rising logistic costs, prompting a need for advanced planning, control, and monitoring methods enabled by Industry 4.0 cyber‑physical systems and learning‑factory approaches. This paper clarifies how cyber‑physical systems enhance production planning, control, and monitoring. It demonstrates that IFA’s Learning Factory can integrate logistic models to streamline order processing.
Nowadays, production enterprises are faced with an array of challenges including increasing pressure regarding costs, demands for individualized products as well as the growing significance of logistic performance and costs, to name just a few. These in turn give rise to special requirements that the production planning, control and monitoring, among others, need to meet with suitable methods and techniques. Within this context, developments such as Industry 4.0 and cyber-physical production systems on the technology side, and approaches such as innovative learning factories for training employees hold great potential. This paper clarifies the advantages of cyber-physical systems in view of production planning, controlling and monitoring. Based on that, using the concept of IFA's Learning Factory, it describes how these can be specifically utilized in applying logistic models to improve order processing.
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