Using Innovation Scorecards and Lossless Fuzzy Weighted Averaging in Multiple-criteria Multi-expert Innovation Evaluation
This chapter discusses the multiple-criteria evaluation of innovations and new concepts by multiple experts under uncertainty that causes imprecision regarding the evaluations. The tools used are scorecards and fuzzy logic. The chapter presents for the first time how the lossless fuzzy weighted average (LFWA) can be used in the context of innovation evaluation. The LFWA method is useful in combining expert evaluations given as scenarios and transformed into fuzzy numbers in a way that does not lose relevant information about the potential, or the downside of the evaluated innovations. The whole chain from collecting evaluations on a set of innovations to the ready aggregated evaluations and to the ranking of the evaluated innovations is presented and illustrated with a numerical example.
KeywordsInnovation management Evaluation Lossless fuzzy weighted average Ranking
This research would like to acknowledge the funding received from the Finnish Strategic Research Council, grant number 313396/MFG40 – Manufacturing 4.0.
- Bojadziev, G., & Bojadziev, M. (2007). Fuzzy logic for business, finance, and management (Vol. 23). Washington, DC: World Scientific.Google Scholar
- Bremser, W., & Barsky, N. (2004). Utilizing the balanced scorecard for R&D performance measurement. Research Technology Management, 47, 229–238.Google Scholar
- Collan, M. (2013). Fuzzy or linguistic input scorecard for IPR evaluation. Journal of Applied Operational Research, 5, 22–29.Google Scholar
- Collan, M., & Heikkilä, M. (2011). Enhancing patent valuation with the pay-off method. Journal of Intellectual Property Rights, 16(5), 377–384.Google Scholar
- Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard – Measures that drive performance. Harvard Business Review, 70, 71–80.Google Scholar
- Kaplan, R. S., & Norton, D. P. (1993). Putting the balanced scorecard to work. Harvard Business Review, 71, 134–147.Google Scholar
- Kaufmann, M., & Gupta, M. (1985). Introduction to fuzzy arithmetics: Theory and applications. New York: Van Nostrand Reinhold.Google Scholar
- Li, G., & Dalton, D. (2003). Balanced scorecard for R&D. Pharmaceutical executive, 23(10), 84–85.Google Scholar
- Luukka, P., Collan, M., Tam, F., & Lawryshyn, Y. (2018). Estimating one-off operational risk events with the lossless fuzzy weighed average method. In M. Collan & J. Kacprzyk (Eds.), Soft computing applications for group decision-making and consensus modeling (Vol. 237, pp. 227–236). Heidelberg: Springer International Publishing AG.CrossRefGoogle Scholar