A Linguistic Screening Evaluation Model in New Product Development

Abstract

The screening of new product ideas is critically very important in new product development (NPD). Due to the incompleteness of information available and the qualitative nature of most evaluation criteria regarding NPD process, a fuzzy linguistic approach may be necessary for new-product screening, making use of linguistic assessments and the fuzzy-set-based computation. However, an inherent limitation of such a fuzzy linguistic approach is the loss of information caused by approximation processes, which eventually implies a lack of precision in the final results. This limitation even becomes more critical when applying the approach to new product screening. This paper proposes an approach to new product go/stop evaluation at the front end in NPD, based on the 2-tuple linguistic representation and the so-called preference-preserving transformation. It is shown that the proposed approach always yields a consistent result, while maintaining the flexibility for managers in making their decisions as in the fuzzy-set-based approach. Ultimately, this approach enhances the fuzzy-logic-based screening model proposed in the previous studies by overcoming the mentioned limitation. A case study taken from the literature is used to illuminate the proposed technique and to compare with the previous technique based on fuzzy computation.