Barco Implements Platform-Based Product Development in Its Healthcare Division

Robert N. Boute, Maud M. Van den Broeke, and Kristof A. Deneire, Barco Implements Platform-Based Product Development in Its Healthcare Division, Interfaces, Volume 48, Issue 1, January-February 2018, pp. 35–44.

In this article, we present how Barco, a global technology company, used an operations research optimization model, which was supported by an efficient solution method, to implement platforms—common structures from which sets of products could be made—for the design and production of its high-tech medical displays. Our optimization model captures all cost aspects related to the use of platforms; thus, it is an objective tool that considers the input from marketing, sales, research and development (R&D), operations, and the supply chain. This comprehensive view allowed Barco to avoid the excessive costs that may result from the implementation of an incorrect platform. Our model supported Barco in determining the elements that should comprise each platform, the number of platforms to develop, and the products to derive from each platform. The results of the project led to reductions in safety stock and increased flexibility due to the use of platforms: R&D can now introduce twice as many products using the same resources, thus increasing Barco’s earnings by more than five million euros annually and reducing product introduction time by nearly 50 percent.

Difficulty and Approach: 

Our assignment problem is nonlinear, because we optimize the buying quantities, and they include binary decision variables to assign a product to a platform. Moreover, the number of scenarios increases exponentially. For example, for the 17 diagnostic displays with 17 possible platforms, the number of possible scenarios for which we must evaluate the total costs is 17^17 = 8.27 * 10^20. That is because the assignment problem considers which platforms to produce and which products to build from which platforms. Because our assignment problem is NP-hard and binary nonlinear, we developed fathoming rules that can be implemented in a branch-and-bound solution procedure, or in heuristics, such as simulated annealing and genetic algorithms.