Koray Altun

Multi-issue negotiation in quality function deployment

A multi-issue negotiation based QFD methodology is proposed.Complicated design trade-offs can be handled.Customer requirements are included in real-time.A mode for negotiation of decentralized NPD teams is provided.Customers play a more active role in NPD. Quality function deployment (QFD) is one of the most powerful methodologies in new product development (NPD) because of its systematic support of knowledge creation and sharing. However, greater product complexity, increasing dynamic customer requirements and further decentralization of innovation teams undermine applicability of QFD in modern NPD. Modifying QFD methodology with a multi-issue negotiation mechanism promises to overcome these obstacles because this negotiation-based structure (i) enables QFD to define a large number of interdependent decision parameters while handling complicated design trade-offs, (ii) includes changing customer requirements in real-time rather than a cycle-time determined by length of the product development process, (iii) allows decentralized NPD teams to negotiate with each other and customers concurrently. This study presents a multi-issue negotiation based QFD methodology by using a recently developed mechanism for multi-issue negotiation (namely, Modified Even-Swaps) and illustrates strengths and weaknesses along with practical insights with a case study of the development of a washing machine. The QFD methodology proposed in this study provides new options to assess new product alternatives by allowing customer co-creation which is essential in complex and often tacit-knowledge dependent comparisons and also intrinsically helpful for fine-tuning dynamically evolving customer requirements. Moreover, it supports competition-based interactions of decentralized NPD teams.

Imovator’s Dilemma: How to Decide When to be Offensive and When to be Defensive?

Although being an innovator has some merits, being an imitator has its own merits as well. Staying competitive therefore requires consideration of imovation—both innovation and imitation simultaneously. An imovator should play role of offensive imovator (near to being pure innovator) when innovation is worthwhile. In case of otherwise, being a defensive imovator (near to being early imitator) can be compatible strategy. How to decide and when to be offensive/defensive greatly depends on innovation potentials of industries assessed by consideration of both their technological and market conditions. This chapter introduces a unique framework called as quick innovation intelligence process for assessment of innovation potential. It makes use of databases of patent and publication and some marketing indicators/determinants in order to take technological and market conditions into account. The framework uses interval type-2 fuzzy sets and systems (IT2FSSs) and a data-fusion methodology to combine above-mentioned databases in order to infer about innovation potential. An imovator can be aware of which strategy is well suited and can go through calculations about when innovation is worthwhile by taking likelihood of being imitated into account as well through the presented assessment process. For that reason, the process presented in this chapter can contribute to solution of the imovator’s dilemma.