A comprehensive framework for complexity resolution in product development

New Product Development has become extremely complex due to ever changing customer tastes and demands, legal requirements, technological advancements. Complexity in Product Development derives from three major aspects---variety, variability and vulnerability. Organizations need to manage the complexity from different perspectives. Non-value added complexity is transparent to the customer and functionally there is no distinction. This can be reduced by strategically developing components and sub systems that can be used for a wide range of products. Least variety is the logical alternative here. A second type of complexity is the functional value added but still transparent to the customer. More variety becomes necessary here to cater to the functional needs. The third category is the value added complexity or the customer driven complexity. Variety becomes the key here and the organization manages variety to satisfy the customers and maximize its profits.; This research proposes ways of managing each type of complexity from an organization wide perspective. Total Cost of Complexity equation proposed by the research helps quantify the impact of non-value added complexity on organization's health and proposes DFSS as a means to achieve complexity reduction. In this context, DFSS growth and evolution in an organization is studied and a DFSS evolution model is proposed. As a last stage in the evolution process organizations can leverage DFSS for organization wide complexity reduction. A business case for this is established using TCC and a case study illustrates the application of DFSS methodology to achieve non-value added complexity reduction.; The second aspect is to manage complexity in a knowledge-based environment and a Dynamic Programming model illustrates how this is different from the popular concept selection methodologies. Thirdly, a comprehensive rule based heuristics is proposed to manage value added complexity. Issues like quantifying customer perceptions, technology maturity, life time cost, and integration costs are addressed in the integer linear programming models, developed during this research study, for maximizing value and or minimizing Total Cost of Complexity. All three cases are illustrated using automotive examples. Additionally, application areas of DFSS in these case examples are highlighted. Parsimony indices were developed for each case as simple measures of complexity. Additionally, DFLSS methodology was proposed to address the process complexity associated with DFSS itself.