USG Uses Stochastic Optimization to Lower Distribution Costs

Amy David (), David Farr (), Ross Januszyk () and Urmila Diwekar ()
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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

Interfaces, 2015, vol. 45, issue 3, 216-227

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.

Keywords: supply chain planning; optimization; programming: probabilistic (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:45:y:2015:i:3:p:216-227

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