Managing Value Added Complexity to Optimize Reliability of Product Configuration Offerings

While managing product complexity it is essential to proactively consider product reliability in almost all categories of complexity especially for the Value-Added complexity class. Many organizations tend to have unnecessarily complex offerings including all possible configurations to have differentiation. Since it is considered value added, a deeper look at the non-value add within this category itself is non-existent. While the economics of complexity generally has been addressed in the RAMS paper “Understanding the economic impact of Complexity and Reliability interactions in Product Development” (Thomas, 2017), the detailed methodology and modeling approach for Value Added Complexity was not discussed adequately. This paper focuses on modeling the Value Added (VA) Complexity to optimize value, reliability, and cost, and demonstrates the effectiveness of the approach using a case example. Value Added Product Configuration Management Strategy (VAPCMS) employs the concepts of utility, parsimony and Total Cost of Complexity (TCC) to comprehensively capture the different dimensions of value added complexity. Before applying the TCC, utility and unit costs need to be established for each competing offering. This paper proposes using squared Mahalanobis distance as a measure to exploit the dissimilarity among configurations. Squared Mahalanobis distances between Utility and Unit Cost for configurations help eliminate many configurations that are identical. The TCC model considers the Cost of the configuration itself, Cost of Poor Quality within warranty or an estimate as a surrogate for Reliability, Service Cost of the configuration, the probability of a recall associated with a configuration, the number of features in a configuration, Technology Maturity of the configuration, costs associated with integrating the features into a base configuration, Assembly cost associated with incremental features etc. A base model as a reference model helps establish incremental costs. Configuration assignment is done using an Integer Linear Programming (ILP) model that utilizes as its objective function a quadratic loss or gain function. If an offering produces utility gain, it is assigned to a product. Multiple strategies have been assessed using the VAPCMS model for an illustrative case example. An aggressive strategy to maximize utility / utility gain is found to be effective in addressing Customer Value Added complexity.