Predictive Production Planning Considering the Operative Risk in a Manufacturing System

J. Klöber-Koch, S. Braunreuther, G. Reinhart

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations

Abstract

Customers' ever more stringent quality requirements, continually shrinking product-life-cycle durations, and a rising number of variants confront manufacturing companies with new challenges. Reliable production is fundamentally important for any industrial company attempting to address these challenges. An effective risk management system helps to ensure such production. The ongoing digitization of production systems also yields new possibilities for evaluating production risks such as machine failures or delivery delays. Especially the growing number of sensors in production systems increases the availability of data for a manufacturing system. This data can be employed to more precisely recognize process related, operative risks during the production process. This offers the opportunity to act on possible risks during production planning and control (PPC). PPC-like sequencing or machine scheduling can hence be applied to reduce risks in a manufacturing system. We therefore present a new approach for a production planning system taking a production system's actual risk level into account. Risk identification in and modeling of a production system is therefore proposed. The evaluated risk then has to be integrated into the planning procedures to reduce the risk level in a manufacturing system. A prototypical application scenario is subsequently presented to demonstrate the approach's feasibility.

Original languageEnglish
Pages (from-to)360-365
Number of pages6
JournalProcedia CIRP
Volume63
DOIs
StatePublished - 2017
Externally publishedYes
Event50th CIRP Conference on Manufacturing Systems, CIRP CMS 2017 - Taichung, Taiwan, Province of China
Duration: 3 May 20175 May 2017

Keywords

  • manufacturing
  • methodolgy
  • modeling

Fingerprint

Dive into the research topics of 'Predictive Production Planning Considering the Operative Risk in a Manufacturing System'. Together they form a unique fingerprint.

Cite this