Suchergebnisse

Purpose: Due to changing customer requirements and political regulations more and more companies strive to optimize their energy efficiency in regards to products and processes. The optimization of processes within supply chain design (SCD) is one lever in this regard. Since required data is often not available, this paper elaborates how data can be generated on a suitable level of aggregation. Methodology: In order to highlight the research gap, established energy measurement procedures as well as existing energy databases for procurement, production and transportation are analyzed and compared with data requirements for SCD tasks. Based on these findings, necessary methods and procedures for data preparation are presented. Findings: Firstly, it is shown that addressing energy efficiency within SCD leads to new challenges in regards to data availability and preparation. Secondly, this paper elaborates the requirements for necessary data usable in the context of SCD. The findings are the basis for a comprehensive approach combining collection, aggregation and clustering of energy and product related data. Originality: This paper works out the gap between usually available energy related information and the requirements of SCD. Since key conditions for optimizing energy efficiency are defined in strategic planning, the findings create a necessary prerequisite for realizing energy-optimized supply chains on a large scale in the future.

Um die Flexibilitätspotenziale einer Matrixproduktion zu nutzen, ist eine intelligente Steuerung und Anpassungsplanung und damit die Integration der Anlagen- und Produktionssystemebene erforderlich. Mit dem Ansatz einer Sandbox-Lösung als digitale Testumgebung lassen sich unterschiedliche simulationsbasierte KI-Module zur (Re-)Konfiguration einer Matrixproduktion sowie von Anlagenmodulen in Form eines digitalen Zwillings testen sowie zur iterativen Optimierung des realen Produktionssystems nutzen.

Exploiting the potential flexibility of a matrix production requires intelligent adjustment planning and control and thus to integrate the plant and production system level. The sandbox solution as a digital test environment allows for testing different simulation-based AI modules for (re-)configuration of a matrix production as well as plant modules in the form of a digital twin and using them for iterative optimization of the real production system.