A Prioritization Algorithm for Configuration Scheduling in a Mass Customization Environment

Ashok Kumar and Frank T. Piller
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Ashok Kumar: Department of Management, Grand Valley State University, USA
Frank T. Piller: Technology and Innovation Management Group, RWTH Aachen University, Germany

Chapter 24 in Handbook of Research in Mass Customization and Personalization:(In 2 Volumes), 2009, pp 487-512 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractMass customization (MC) as a business strategy seeks to deliver highly customized product to customers at affordable prices that are consistent with mass production efficiencies. Despite a significant volume of research in mass customization, a severe paucity of work in quantitative areas related to operations management, such as scheduling, inventory control, distribution systems, statistical process control, etc. has been well documented. In this chapter, we begin to fill this gap by proposing a methodology for scheduling the production of an arbitrary number of configurations of a product when the production budget and time are limited. A specific contribution of this chapter relates to the development of three measures of the value associated with each configuration of the product. These measures are more general than the profit motive usually employed in scheduling configurations. These measures depend on cost proportion, profit proportion, and a hybrid of these two measures. Using these value measures, we formulate a mixed integer linear programming model that would yield an optimal sequence of configurations to maximize the total value of the production over a given period. Dual constraints on budget and production time make the problem NP Hard in strong sense. These are reducible to 2-dimensional Bin packing or Knapsack problem after elimination of certain constraints. Given the dynamic nature of configuration demands and constantly evolving system state, an efficient heuristic solution with tight bounds that can be developed quickly is considered preferable over an optimal solution that takes long time and substantial computer resources to develop. Accordingly, two heuristic solutions are constructed that are quick as well as efficient.

Keywords: Mass Customization; Personalization; Engineer-to-Order; Open Innovation; User Co-Creation; Modularity; Platform Design; Customer Centricity (search for similar items in EconPapers)
Date: 2009
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