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Since imprecision, vagueness and ambiguity are often innate in human semantics, a flexible and tolerant method is needed to decode the voice of customer (VoC), so that the essential customer requirements can be identified and duly addressed. Quality function deployment (QFD) is a well‐known methodology for projecting the customer requirements onto the relevant design and production requirements and actions plan. This paper proposes an intelligent approach which extends the applications of QFD beyond its conventional boundary. The fuzzy inference technique is adopted to accommodate the possible imprecision and vagueness during VoC interpretation. The resulting model maps the customer requirements onto the relevant product attributes, taking into consideration their relationships and correlation during the inference process. The sub‐conclusions drawn from the fuzzy inference process are aggregated and defuzzified to yield the crisp design targets which can be used to guide the downstream manufacturing planning and control activities.

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Purpose -This paper proposes an infrastructure of a responsive supply chain network, focusing on the deployment of the m-commerce technology which transforms a traditional supply chain network to be more effective in coping with market changes. Design/methodology/approach -The proposed supply chain infrastructure embraces the concepts of distributed object technology, wireless markup language (WML), and extensible markup language (XML) schema to enable efficient data exchange among various data objects which reside in distributed platforms over geographically-isolated regions, thereby leveraging the responsiveness of the entire supply chain network. A case study is conducted to evaluate the feasibility of the proposed model. Findings -Recent studies have found that wireless technology, mobile computing and internet programming techniques drive the development of mobile solution in various industries. Apart from location tracking of goods as well as relevant services, m-commerce is able to play an important role to enhance the performance of a supply chain network, which is concerned with the proper monitoring of suppliers and production circles, encompassing a wide spectrum of value chain activities ranging from product design to after-sales services. Originality/value -The significance of this research is the demonstration of the synergy of using a combination of emerging technologies to form an integrated system that helps achieve flexibility and agility in supply chain network.

This paper investigates the modelling and forecasting method for non-stationary time series. Using wavelets, the authors propose a modelling procedure that decomposes the series as the sum of three separate components, namely trend, harmonic and irregular components. The estimates suggested in this paper are all consistent. This method has been used for the modelling of US dollar against DM exchange rate data, and ten steps ahead (2 weeks) forecasting are compared with several other methods. Under the Average Percentage of forecasting Error (APE) criterion, the wavelet approach is the best one. The results suggest that forecasting based on wavelets is a viable alternative to existing methods.

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