This study mainly focuses on the risk evaluation of customer integration in new product development. Customer integration in product innovation projects has been widely recognized a best practice to enhance innovation success rate and reduces the development cycle time, but it also has many potential risks including loss of know-how, much dependence on customer, and limitation to incremental innovations, etc. Unfortunately, there are few researches about risk evaluation for customer integration which is important to the risk management of the co-innovation process. Further, evaluating customer integration risk involves much subjectivity and vagueness. To manipulate this problem, a novel evaluation approach for assessing customer integration risk under uncertainty is proposed. The novel approach integrates the merit of rough set theory in handling vagueness and the strength of group analytic hierarchy process (GAHP) in modeling hierarchy evaluation. Finally, an application in a project of mobile phone development is provided to demonstrate the application and potential of the methodology.
New product development is becoming an important competitive advantage in current industrial market (Wang & Lin, 2009). In times of decreasing R&D budgets and shorter innovation cycles, companies have to rethink the fundamental ways of managing their product development activities. It is increasingly more important for company to open up organization boundaries to utilize external innovation resources, and customers are frequently seen as an important source of product innovation (Chesbrough, 2003, Gassmann and Enkel, 2004 and Von Hippel, 2005). It was a very attractive proposal to take measures to encourage users’ involvement in product innovation because it increases a company’s potential for innovation. Customer integration in company innovation activities can increase the amount of diversified and customer based knowledge in companies which helps to well understand the customer, enhance the customer relationships with company, and reduce the innovation failure rate. Meanwhile, customer integration also helps to create innovative ideas and feedbacks regarding concepts or prototypes for new products. Other benefits of customer integration includes better satisfying the current and future market, improving product performance (Prahalad & Ramaswamy, 2004), enabling product innovation more sustainable, constituting reliable buyer potential, etc. Therefore, customer integration into the innovation process is about to become best practice.
Although customer integration in new product development brings many opportunities, it also has considerable risks. These risks include loss of know-how due to disloyal integrated customer, too much dependence on customer, and limitation to incremental innovations, wrong product investment, etc. However, as Enkel, Kausch, and Gassman (2005) state, the risks of not integrating customers are greater than the risks of integration. Because company would neglect important source of ideas, increase R&D costs, develop less market-driven products (service). Therefore, it is necessary for companies to evaluate the customer integration risk, maximize the possible benefits and simultaneously minimize the risks involved. Unfortunately, the risk assessment of customer integration has so far met with little attention. There is little theory or practice providing systematic evaluation framework and recommendations on how to assess or manage undesired risks of customer integration. In fact, lack of much prior information, subjective and vague judgments always make it difficult to conduct risk assessment of customer integration accurately. This is particularly true in the early product development phase (Raharjo, Brombacher, & Xie, 2008). Thus, in this work, the authors propose a novel risk evaluation method based on rough set theory and group AHP approach to evaluate the risk factors in customer integration quantitatively and intelligently. The proposed method provides a more rational risk evaluation framework without requiring much prior information. Besides, the risk evaluation approach has a good mechanism to deal with subjectivity and vagueness of judgments using rough logic under uncertain environment. To our knowledge, there is no method, or integrated method, in the literature until now.
After the introduction, the remainder of the paper is structured as follows: Relevant previous research and basis of the proposed method are presented in Section 2. The proposed rough group AHP approach is explained in Section 3. Then, the proposed method is illustrated with a case study of the mobile phone development in Section 4. In Section 5, conclusion and future research directions are remarked.
This paper explores the possibility to design a novel risk evaluation method based on rough set theory for customer integration in product innovation projects. The presented approach makes use of strength of both AHP and rough set theory to manipulate subjective risk assessments under uncertain environments. The proposed approach allows individual experts to present judgments in conventional manner. The validation of the proposed method in the case of customer integration in a mobile phone company shows that it can be used as an effective risk evaluation tool. To sum up, the new approach reveals the following features:
The rough set theory based-AHP structure brings a general description of expert’s opinion, and presents a more holistic judgment on the risk factors.
Customer integration risk evaluation using rough group AHP has a flexible boundary that well reflects expert’s subjective and vague judgment.
The proposed risk evaluation method can avoid relying much on priori information (assumptions or pre-set membership functions in fuzzy methods, etc.).
The rough group method can discern the change of decision makers’ preferences, and manipulates the inconsistency of experts’ judgment in risk evaluation process.
The optimistic indicator λ can help risk managers to obtain flexible and reliable risk priorities according to their risk propensities.
The approach provides a simple and effective mechanism for customer integration risk evaluation involving subjective judgment in group decision environment. The proposed method also has its limitations of little consideration of the relationship between risk factors, which will be further improved by rough group analytic network process (ANP) approach in future. Besides, the performance of the risk evaluation approach would be verified by application to other real industries.
