A non-linear possibilistic regression approach to model functional relationships in product planning

Product planning is one of four important processes in new product development (NPD) using quality function deployment (QFD). In order to model the process of product planning, the first problem to be solved is how to incorporate both qualitative and quantitative information regarding relationships between customer requirements and engineering characteristics, as well as among engineering characteristics, into the problem formulation. The inherent fuzziness of functional relationships in product planning makes the use of possibilistic regression justifiable. However, when linear programming in possibilistic regression analysis is used, some coefficients tend to become crisp because of the characteristic of linear programming. To tackle the problem, a non-linear programming based possibilistic regression approach is proposed, by which more diverse spread coefficients can be obtained than from a linear programming approach. An emulsification dynamite packing-machine design is used to illustrate the performance of the proposed approach.