New Product Development Process (NPD) is a key aspect of launching new and innovative products in the market. Many products fail in the market because of technical risks, financial risks and product development time risks. It is very important to understand the overall risk factors associated with different stages of product development so that risks can be mitigated effectively. This paper presents a methodology to understand the risk associated with the initial stages of NPD. Design flexibility is higher in initial design stages requiring minimum redesign efforts and costs. It is a great opportunity to deal with risk factors and uncertainties in initial design stages than the later design stages. Product development costs in initial stages are around 5 to 10 percent but impact is 70 to 80 percent so exploration assessment in initial stages of NPD can be hugely beneficial. Stage-wise risk assessment will also provide the details of risk associated with each stage, which will be helpful in implementing appropriate mitigation strategies. Since transition from one stage to another stage of NPD is independent of the previous stage, different NPD stages can be easily expressed by the transition state of the Markov process. In this paper, the Markov process has been used for the risk assessment of initial stages of NPD, keeping mitigation strategies in mind. The three initial stages of NPD considered in this study include the concept design, detailed design and integration & testing stages. This paper also explores a method by integration of quality function deployment (QFD) and Markov process, to understand risk patterns associated with several complete design solutions (CFDs). By using QFD, the mapping between customer requirements can be reflected into risk assessment of complete design solutions (CFDs). This methodology has been demonstrated by a case study on Coffee Maker.

In the present work, a recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of fabric finishing system of a textile industry. Fabric Finishing System has four main subsystems, arranged in hybrid configuration. For performance modeling and analysis of availability, a performance evaluating model of fabric finishing system has been developed with the help of mathematical formulation based on Markov-Birth-Death process using Probabilistic Approach. Then, the overall performance of the concerned system has first analyzed and then, optimized by using teaching–learning-based optimization (TLBO). The results of optimization using the proposed algorithm are validated by comparing with those obtained by using the genetic algorithm (GA) on the same system. Improvement in the results is obtained by the proposed algorithm. The results of effect of variation of the algorithm parameters on fitness values of the objective function are reported.