Cost-Benefit Analysis to Hedge with Third-Party Producers in Demand-Driven Production

Part of the Springer Optimization and Its Applications book series (SOIA, volume 41)

Summary

One of the characteristics of Demand-Driven Production is that goods should be manufactured and delivered to customers within the specified period of time. Manufacturers achieve this by utilizing various efficient production planning, scheduling tools and techniques. But situations may arise where the manufacturer, despite such techniques, may not be able to meet the required demand. So strategies need to be developed by which situations like these are countered and the financial loss from them alleviated. One such strategy is to hedge the production of goods from third-party producers. But before doing so, the manufacturer has to carry out a cost-benefit analysis that will determine the feasibility and viability of considering this option. In this chapter, we propose a methodology by which the manufacturer does the cost-benefit analysis and then makes an informed decision about whether to hedge with third-party producers.

Keywords

Convolution Hedging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. B. Berndt: Accurate planning in demand-driven world. Quality Control: Manufacturing & Distribution (2006) 56–58 Google Scholar
  2. E. Chang, T. Dillon, F. K. Hussain: Trust and Reputation for Service-Oriented Environments. Wiley, West Sussex (2006) CrossRefGoogle Scholar
  3. M. Chen, R. Dubrawski, S. P. Meyn: Management of demand-driven production systems. IEEE Transactions on Automatic Control 49 (2004) 686–698 CrossRefMathSciNetGoogle Scholar
  4. R. Lebovitz, M. Graban: The journey toward demand driven manufacturing. 2nd International Workshop on Engineering Management for Applied Technology (2001) 29–35 Google Scholar
  5. E. Mohebbi, F. Choobineh, A. Pattanayak: Capacity-driven vs. demand-driven material procurement systems. International Journal of Production Economics 107 (2007) 451–466 CrossRefGoogle Scholar
  6. M. M. Qiu, E. E. Burch: Hierarchical production planning and scheduling in a multiproduct, multi-machine environment. International Journal of Production Research 35 (1997) 3023–3042 MATHCrossRefGoogle Scholar
  7. S. Sharma: Revisiting the shelf life constrained multi-product manufacturing problem. European Journal of Operational Research 193 (2009) 129–139 MATHCrossRefGoogle Scholar
  8. S. Simkins, M. Maier: Using just-in-time teaching techniques in the principles of economics course. Social Science Computer Review 22 (2004) 444–456 CrossRefGoogle Scholar
  9. B. Tan, S. B. Gershwin: Production and subcontracting strategies for manufacturers with limited capacity and volatile demand. Annals of Operations Research 125 (2004) 205–232 MATHCrossRefMathSciNetGoogle Scholar
  10. C.-W. Wu: Decision-making in testing process performance with fuzzy data. European Journal of Operational Research 193 (2009) 499–509 MATHCrossRefMathSciNetGoogle Scholar
  11. I. Yıldırım, B. Tan, F. Karaesmen: A multiperiod stochastic production planning and sourcing problem with service level constraints. OR Spectrum 27 (2005) 471–489 MATHCrossRefGoogle Scholar
  12. G. Zäpfel: Customer-order-driven production: an economical concept for responding to demand uncertainty? International Journal of Production Economics 56–57 (1998) 699–709 CrossRefGoogle Scholar
  13. Q. Zhang, M. A. Vonderembse, M. Cao: Product concept and prototype flexibility in manufacturing: Implications for customer satisfaction. European Journal of Operational Research 194 (2009) 143–154 MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Digital Ecosystems and Business Intelligence InstituteCurtin University of TechnologyPerthAustralia

Personalised recommendations