Design model for digital shadows to support reconfiguration decisions in manufacturing

Abstract

Personalized production is the result of a paradigm shift in manufacturing driven by customers who are seeking increasingly individualized solutions. Consequently, product families with a high number of variants and short innovation cycles force companies to capture smaller windows of opportunity to stay competitive in a global market. Reconfigurable Manufacturing Systems can address this trend by enabling the manufacturing system to be reconfigured in response to changing environmental conditions. However, deciding when to execute a reconfiguration and selecting a suitable manufacturing system configuration for the future is a complex task that requires profound knowledge of the manufacturing system and potential environmental influences. Thus, this paper proposes utilizing Digital Shadows to substantiate reconfiguration decisions with a networked information base. To process this information into an objective decision support, Time-expanded Decision Networks evaluate a sequence of future reconfiguration decisions with regard to economic and environmental target dimensions.