A Scorecard‐Markov model for new product screening decisions

Purpose – The paper aims to propose a novel strategic approach, named a Scorecard‐Markov model, combining an evaluation scorecard and a hidden Markov model (HMM) for new product idea screening (NPIS) decisions. Design/methodology/approach – A scorecard is constructed to evaluate new product ideas on several criteria, including customer needs, marketing strength, competency, manufacturing compatibility, and distribution channels, involving a consideration of risk buy. A HMM is then developed accordingly to predict the overall performance of new ideas in terms of success probability. To implement the model, it is trained and tested by the historical dataset of a world‐class, leading company in the power tools industry through a case study. Findings – The approach is proven to be encouraging and meaningful. The scorecard can serve as a guide for new product idea evaluation to convert experts' linguistic judgments to quantifiable and comparable data, whereas the HMM can determine the success probability of new product ideas to support NPIS decision making based on their computed evaluation performance. The optimal cut‐off value for making either a go or kill decision on each idea can thus be determined. Concerning the case company, a go decision should be made when the probability lies in the interval [0.53, 1]. Practical implications – The model can prevent companies from undertaking risky and failed new product development projects. Further, it is believed that this study can assist decision makers in choosing winning new product ideas towards commercialization in an effective and certain manner, thus enhancing the new product success rate in the innovation industry. Originality/value – The approach incorporating the scorecard method and HMM is novel. Illustrated by the case study, the application of this approach to NPIS decisions is confirmed to be effective.