A Queuing Network Based Framework for PCS Engineering

  • Christoph Karrer
Part of the Management for Professionals book series (MANAGPROF)


In order to develop a framework for PCS engineering, the relevant drivers that influence PCS design need to be elicited first. Two main driver categories can be identified: structure-based drivers and variability-based drivers. Structure-based drivers (1) stem from the static production setup, whereas variability-based drivers (2) stem from the dynamic behavior of the production system. Variability-based drivers can be further separated according to the source of the variability into drivers resulting from production system variability (2a) and drivers resulting from demand variability (2b). Drivers based on production system variability have their origin within the production system or its inputs. Drivers based on demand variability have their origin at the customer. Table 3.1 illustrates this split and gives concrete examples for each driver category.


Forecast Error Demand Model Customer Order Forecast Period Production Order 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Alicke K (2005) Planung und betrieb von logistiknetzwerken: unternehmensübergreifendes supply chain management (VDI-Buch), 2nd edn. Springer, Berlin (in German)Google Scholar
  2. Berger JO (1993) Statistical decision theory and Bayesian analysis, 2nd edn. Springer, BerlinGoogle Scholar
  3. Bosch K (1998) Statistik-Taschenbuch, 3rd edn. Oldenbourg, München (in German)Google Scholar
  4. Dallery Y, Liberopoulos G (2000) Extended kanban control system: combining kanban and base stock. IIE Trans 32:369–386Google Scholar
  5. Hopp WJ, Spearman ML (2008) Factory physics, 3rd edn. McGraw-Hill, BostonGoogle Scholar
  6. Howard RA, Matheson JE (1984) Influence diagrams. In: Howard RA, Matheson JE (eds) Readings on the principles and applications of decision analysis. Strategic Decisions Group, Menlo ParkGoogle Scholar
  7. Law AM, Kelton WD (2008) Simulation modeling and analysis, 4th edn. McGraw-Hill, BostonGoogle Scholar
  8. Liberopoulos G, Dallery Y (2000) A unified framework for pull control mechanisms in multi-stage manufacturing systems. Ann Oper Res 93:325–355CrossRefGoogle Scholar
  9. Muchiri P, Pintelon L (2008) Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion. Int J Prod Res 46:1–19CrossRefGoogle Scholar
  10. Nyhuis P, Wiendahl HP (1999) Logistische kennlinien. Springer, Berlin (in German)Google Scholar
  11. Simchi-Levi D, Kaminsky P, Simchi-Levi E (2007) Designing and managing the suppy chain, 3rd edn. McGraw-Hill, BostonGoogle Scholar
  12. Taguchi G (1986) Introduction to quality engineering: designing quality into products and processes. Quality Resources, New YorkGoogle Scholar
  13. Womack JP, Jones DT, Roos D (2007) The machine that changed the world: the story of lean production. Free Press, New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.McKinsey & Company, Inc.MünchenGermany

Personalised recommendations