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USG Uses Stochastic Optimization to Lower Distribution Costs

Author

Listed:
  • Amy David

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47904)

  • David Farr

    (USG, Chicago, Illinois 60661)

  • Ross Januszyk

    (USG, Chicago, Illinois 60661)

  • Urmila Diwekar

    (Vishwamitra Research Institute, Crystal Lake, Illinois 60012)

Abstract

We present a case study of a large-scale stochastic optimization problem for USG, a building supply manufacturer with plants and customers throughout North America. USG seeks to minimize total delivered cost (including production and freight costs) of products in its Durock ® product line, subject to capacity constraints and uncertainties in both demand and production costs. We first demonstrate that demand uncertainty, rather than production-cost uncertainty, is the main cause of month-to-month variations in total cost. We then use the chance constraint method to optimize the network, and propagate uncertainty through the cost models, applying a penalty cost for unfulfilled constraints. We show that we can reduce theoretical costs by approximately 4.8 percent by optimizing the network for the 50th percentile of demand, as compared to the base case that uses demand and cost data for a single month. We implemented the new network plan via sourcing rules in both USG’s order fulfillment system and Oracle’s advanced supply-chain planning module. Several practical delivery concerns limit the benefits realized to an amount less than the theoretical cost reductions, but savings are still considered to be substantial.

Suggested Citation

  • Amy David & David Farr & Ross Januszyk & Urmila Diwekar, 2015. "USG Uses Stochastic Optimization to Lower Distribution Costs," Interfaces, INFORMS, vol. 45(3), pages 216-227, June.
  • Handle: RePEc:inm:orinte:v:45:y:2015:i:3:p:216-227
    DOI: 10.1287/inte.2014.0786
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    References listed on IDEAS

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    1. Mete, Huseyin Onur & Zabinsky, Zelda B., 2010. "Stochastic optimization of medical supply location and distribution in disaster management," International Journal of Production Economics, Elsevier, vol. 126(1), pages 76-84, July.
    2. Klose, Andreas & Drexl, Andreas, 2005. "Facility location models for distribution system design," European Journal of Operational Research, Elsevier, vol. 162(1), pages 4-29, April.
    3. Vidal, Carlos J. & Goetschalckx, Marc, 1997. "Strategic production-distribution models: A critical review with emphasis on global supply chain models," European Journal of Operational Research, Elsevier, vol. 98(1), pages 1-18, April.
    4. Grace Lin & Markus Ettl & Steve Buckley & Sugato Bagchi & David D. Yao & Bret L. Naccarato & Rob Allan & Kerry Kim & Lisa Koenig, 2000. "Extended-Enterprise Supply-Chain Management at IBM Personal Systems Group and Other Divisions," Interfaces, INFORMS, vol. 30(1), pages 7-25, February.
    5. Brian T. Denton & John Forrest & R. John Milne, 2006. "IBM Solves a Mixed-Integer Program to Optimize Its Semiconductor Supply Chain," Interfaces, INFORMS, vol. 36(5), pages 386-399, October.
    6. A. Charnes & W. W. Cooper & G. H. Symonds, 1958. "Cost Horizons and Certainty Equivalents: An Approach to Stochastic Programming of Heating Oil," Management Science, INFORMS, vol. 4(3), pages 235-263, April.
    7. A. M. Geoffrion & G. W. Graves, 1974. "Multicommodity Distribution System Design by Benders Decomposition," Management Science, INFORMS, vol. 20(5), pages 822-844, January.
    8. Suvrajeet Sen & Julia L. Higle, 1999. "An Introductory Tutorial on Stochastic Linear Programming Models," Interfaces, INFORMS, vol. 29(2), pages 33-61, April.
    9. Alper Atamtürk & Muhong Zhang, 2007. "Two-Stage Robust Network Flow and Design Under Demand Uncertainty," Operations Research, INFORMS, vol. 55(4), pages 662-673, August.
    10. G Barbarosoǧlu & Y Arda, 2004. "A two-stage stochastic programming framework for transportation planning in disaster response," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(1), pages 43-53, January.
    11. Alfred Degbotse & Brian T. Denton & Kenneth Fordyce & R. John Milne & Robert Orzell & Chi-Tai Wang, 2013. "IBM Blends Heuristics and Optimization to Plan Its Semiconductor Supply Chain," Interfaces, INFORMS, vol. 43(2), pages 130-141, April.
    12. Chang, Mei-Shiang & Tseng, Ya-Ling & Chen, Jing-Wen, 2007. "A scenario planning approach for the flood emergency logistics preparation problem under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 737-754, November.
    13. George Dikos & Stavroula Spyropoulou, 2013. "Supply Chain Optimization and Planning in Heracles General Cement Company," Interfaces, INFORMS, vol. 43(4), pages 297-312, August.
    14. Haghani, Ali & Oh, Sei-Chang, 1996. "Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(3), pages 231-250, May.
    15. Thomas, Douglas J. & Griffin, Paul M., 1996. "Coordinated supply chain management," European Journal of Operational Research, Elsevier, vol. 94(1), pages 1-15, October.
    16. Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
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