Advertisement

OR Spectrum

, Volume 37, Issue 4, pp 951–982 | Cite as

Revenue management approach to due date quoting and scheduling in an assemble-to-order production system

  • Hendrik GuhlichEmail author
  • Moritz Fleischmann
  • Raik Stolletz
Regular Article

Abstract

In this paper, we consider demand management decisions for an assemble-to-order production system in which both the availability of intermediate material and assembly capacity are limited. For each incoming order, the manufacturer must decide whether to accept it and what due date to quote for an accepted order. The actual assembly dates are still subject to change after these decisions, and a production schedule must be maintained to guarantee that the quoted due dates are met. Therefore, the decisions on accepting orders and quoting due dates must be made with incomplete knowledge of the actual resources used to fulfill the orders. To address these factors, we model this situation and develop a novel revenue management approach using bid prices. An extensive numerical study demonstrates the good performance of the proposed approach in comparison with benchmark algorithms and an ex-post optimal solution applied over a wide range of different supply and demand scenarios. Our results suggest that the consideration of assembly capacity constraints is more vital than the consideration of intermediate material constraints in our test cases.

Keywords

Revenue management Due date quoting Assemble-to-order Assembly capacity Scarce input materials 

References

  1. Adelman D (2007) Dynamic bid prices in revenue management. Oper Res 55(4):647–661CrossRefGoogle Scholar
  2. Agrawal N, Smith S (1996) Estimating negative binomial demand for retail inventory management with unobservable lost sales. Nav Res Logist 43:839–861CrossRefGoogle Scholar
  3. Barut M, Sridharan V (2005) Revenue management in order-driven production systems. Decis Sci 36(2):287–317CrossRefGoogle Scholar
  4. Benjaafar S, ElHafsi M (2006) Production and inventory control of a single product assemble-to-order system with multiple customer classes. Manag Sci 52(12):1896–1912CrossRefGoogle Scholar
  5. Brown A, Lee H, Petrakian R (2000) Xilinx improves its semiconductor supply chain using product and process postponement. Interfaces 10(5):65–80CrossRefGoogle Scholar
  6. Charnsirisakskul K, Griffin PM, Keskinocak P (2006) Pricing and scheduling decisions with leadtime flexibility. Eur J Oper Res 171(1):153–169CrossRefGoogle Scholar
  7. Chen C, Zhao Z, Ball M (2001) Quantity and due date quoting available to promise. Inf Syst Front 3:477–488CrossRefGoogle Scholar
  8. Chen CY, Zhao Z, Ball MO (2002) A model for batch advanced available-to-promise. Prod Oper Manag 11(4):424–440Google Scholar
  9. Cheng T, Gao C, Shen H (2011) Production planning and inventory allocation of a single-product assemble-to-order system with failure-prone machines. Int J Prod Econ 131(2):604–617CrossRefGoogle Scholar
  10. Ehrenberg ASC (1959) The pattern of consumer purchases. Appl Stat 8(1):26–41CrossRefGoogle Scholar
  11. ElHafsi M (2009) Optimal integrated production and inventory control of an assemble-to-order system with multiple non-unitary demand classes. Eur J Oper Res 194(1):127–142CrossRefGoogle Scholar
  12. Gallego G, Phillips R (2004) Revenue management of flexible products. Manuf Serv Oper Manag 6(4):321–337Google Scholar
  13. Gallego G, Iyengar G, Phillips R, Dubey A (2004) Managing flexible products on a network. Tech. Rep. TR-2004-01, Columbia UniversityGoogle Scholar
  14. Gallien J, Tallec YL, Schoenmeyr T (2004) A model for make-to-order revenue management. Working paper, MIT, CambridgeGoogle Scholar
  15. Gao L, Xu SH, Ball MO (2012) Managing an available-to-promise assembly system with dynamic short-term pseudo-order forecast. Manag Sci 58(4):770–790CrossRefGoogle Scholar
  16. Gönsch J, Koch S, Steinhardt C (2014) Revenue management with flexible products: the value of flexibility and its incorporation into DLP-based approaches. Int J Prod Econ 153:280–294CrossRefGoogle Scholar
  17. Harris Fd, Pinder J (1995) A revenue management approach to demand management and order booking in assemble-to-order manufacturing. J Oper Manag 13(4):299–309CrossRefGoogle Scholar
  18. Hintsches A, Spengler T, Volling T (2010) Revenue management in make-to-order manufacturing: case study of capacity control at ThyssenKrupp VDM. Bus Res 3(2):173–190CrossRefGoogle Scholar
  19. Klein R (2007) Network capacity control using self-adjusting bid-prices. OR Spectr 29(1):39–60CrossRefGoogle Scholar
  20. Kniker T, Burman M (2001) Applications of revenue management to manufacturing. In: Third International Conference on Design and Analysis of Manufacturing Systems, pp 299–308Google Scholar
  21. Kolisch R (2001) Make-to-order assembly management. Springer, BerlinGoogle Scholar
  22. Kuhn H, Defregger F (2005) Revenue management for a make-to-order company with limited inventory capacity. OR Spectr 29(1):137–156Google Scholar
  23. Lin JT, Hong IH, Wu CH, Wang KS (2010) A model for batch available-to-promise in order fulfillment processes for TFT-LCD production chains. Comput Ind Eng 59(4):720–729CrossRefGoogle Scholar
  24. Liu T (2005) Revenue management models in the manufacturing industry. Ph.D. thesis, MITGoogle Scholar
  25. Meyr H (2009) Customer segmentation, allocation planning and order promising in make-to-stock production. OR Spectr 31(1):229–256CrossRefGoogle Scholar
  26. Petrick A, Gönsch J, Steinhardt C, Klein R (2010) Dynamic control mechanisms for revenue management with flexible products. Comput Oper Res 37(11):2027–2039CrossRefGoogle Scholar
  27. Petrick A, Steinhardt C, Gönsch J, Klein R (2012) Using flexible products to cope with demand uncertainty in revenue management. OR Spectr 34(1):215–242CrossRefGoogle Scholar
  28. Pibernik R (2005) Advanced available-to-promise: classification, selected methods and requirements for operations and inventory management. Int J Prod Econ 93–94:239–252CrossRefGoogle Scholar
  29. Quante R, Fleischmann M, Meyr H (2009) A stochastic dynamic programming approach to revenue management in a make-to-stock production system. Erasmus University, Erasmus Research Institute of ManagementGoogle Scholar
  30. Simpson RW (1989) Using network flow techniques to find shadow prices for market and seat inventory control. Tech. repGoogle Scholar
  31. Song J, Zipkin P (2003) Supply chain operations: assemble-to-order systems. Handb Oper Res Manag Sci 11:561–596CrossRefGoogle Scholar
  32. Spengler T, Rehkopf S, Volling T (2007) Revenue management in make-to-order manufacturing—an application to the iron and steel industry. OR Spectr 29(1):157–171CrossRefGoogle Scholar
  33. Spengler TS, Volling T, Hintsches A (2008) Integration von Revenue Management Konzepten in die Auftragsannahme—konkretisiert für Unternehmen der eisen- und stahlerzeugenden Industrie. ZFB Special Issue 4:125–151Google Scholar
  34. Talluri K, van Ryzin G (1999) A randomized linear programming method for computing network bid prices. Transp Sci 33(2):207–216Google Scholar
  35. Talluri K, van Ryzin G (2004) The theory and practice of revenue management. Springer, New YorkGoogle Scholar
  36. Km Tsai, Sc Wang (2009) Multi-site available-to-promise modeling for assemble-to-order manufacturing: an illustration on TFT-LCD manufacturing. Int J Prod Econ 117(1):174–184CrossRefGoogle Scholar
  37. Volling T, Eren Akyol D, Wittek K, Spengler TS (2012) A two-stage bid-price control for make-to-order revenue management. Comput Oper Res 39(5):1021–1032CrossRefGoogle Scholar
  38. Williamson EL (1992) Airline network seat inventory control: Methodologies and revenue impacts. Ph.D. thesis, MIT, CambridgeGoogle Scholar
  39. Zhao Z, Ball M, Kotake M (2005) Optimization-based available-to-promise with multi-stage resource availability. Ann Oper Res 135:65–85CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Hendrik Guhlich
    • 1
    Email author
  • Moritz Fleischmann
    • 1
  • Raik Stolletz
    • 1
  1. 1.University of MannheimMannheimGermany

Personalised recommendations