Manufacturing strategy is part of a manufacturing company’s total strategy. It contains the pattern of strategic decisions and actions which set the role, objectives and activities of the manufacturing in a manufacturing company. Just as with any type of strategy, we can consider its content and process separately. The content of manufacturing strategy comprises the specific decisions and actions which set the manufacturing role, objectives and activities. The process of manufacturing strategy refers the procedures which can be used to develop manufacturing strategies (Slack, Chambers, & Johnston, 2004).
Within strategy research, a clear distinction between research on the content of strategy and research on the process of strategy has been presented for a long time (Hallgren & Olhager, 2006). At present, most research focuses on strategy content, however, research on manufacturing strategy development is relatively limited (Dangayach & Deshmukh, 2001).
Manufacturing strategy comprises a series of structural and infrastructural decisions which provide the necessary support for the relevant order winners and qualifiers of the different market segments of a company. From Hill’s point of view, manufacturing strategy should be supportive to the achievement of a company’s competitive priorities. Hill proposes a five-step procedure to link manufacturing strategy to order winners in order to achieve the congruence between them (Hill, 1995). This procedure is an iterative process, in which the identification of competitive factors is seen as critical. At this stage, any mismatches between the requirements of organization’s strategy and the capability of its manufacturing become evident. So far, different analysis models have been developed to describe the congruence between various aspects of manufacturing strategy and competitive priorities. Hayes and Wheelwright provide a tool for the assessment of manufacturing’s strategic role, and introduce product/process matrix (Hayes & Wheelwright, 1984). Voss, 1990 and Marucheck et al., 1990 have made empirical observation of the strategy formulation and implementation process, and find that the process is essentially hierarchical, which is consistent with Skinner’s approach. Skinner’s approach have led to a predominant hierarchical process model starting from corporate strategy forming the context for the business strategy which in turn forms the context for each functional strategy including manufacturing (Skinner, 1969). Miltenburg proposes an overall framework with three steps for performing an analysis of a company’s manufacturing strategy in terms of congruence with the production system, its products, and its capabilities (Miltenburg, 1995). Safsten and Winroth investigate the usability of Miltenburg’s framework in small and medium sized manufacturing companies, and further suggest some changes of the model (Safsten & Winroth, 2002). Lee, Jeong, Park, and Park (2002) propose a framework for a decision-support system to support the formulation of a manufacturing strategy which consists of manufacturing system modeling and analyzing performance measures. The proposed decision-support system enables the formulation of manufacturing strategy using what-if analysis against dynamic manufacturing environments. Quezada, Cordova, and O’Brien (2003) develop a methodology for the development of a manufacturing strategy by means of exploiting the concepts of the analytic hierarchy process. In terms of this methodology, a manufacturing strategy can be formulated by creating a five level hierarchy: focus, company objectives, strategic business units, critical success factors and manufacturing decision areas. This methodology also allows a strategic diagnosis of the current manufacturing system and the generation and evaluation of action plans to improve the company competitiveness. Slack et al. give some indications on how to assess the support from the operations function (Slack et al., 2004). Platts and Gregory propose a three-stage procedure of developing manufacturing strategy. The procedure uses profiles of market requirements and achieved performance to show up the gaps which the manufacturing strategy must address (Platts & Gregory, 2004). Karacapilidis, Adamides, and Evangelou (2006) develop a computerized knowledge management system for the collaborative development of manufacturing strategy. The system is used to capture the strategists’ rationale and stimulates knowledge elicitation, and it can support the social and knowledge processes of collaborative strategy development by integrating a domain specific modeling formalism.
In summary, the majority research related to manufacturing strategy development has specified and described strategy development process, and as a result, many different methodologies related to strategy development have been suggested. Most literature has proposed many prescriptive processes, and the manufacturing strategy domain has being dominated by conceptual models (Hallgren & Olhager, 2006).
The quality function deployment (QFD) originated in 1972 in Japan as a methodology to be adopted to improve products quality in some Japanese firms (Hauser & Clausing, 1988). QFD methodology has introduced a twofold innovation in traditional product development processes. First, the application of QFD requires the careful consideration of customer during the development process (Akao, 1990). Second, the QFD approach has introduced the collaboration among different business areas as a prerequisite for product design.
Many authors have published papers discussing how to exploit QFD to enhance the quality of product or service design. Karsak, Sozer, and Alptekin (2002) present a systematic decision procedure to be used in QFD product planning. The proposed approach combined analytic network process and 0–1 goal programming approach to incorporate the customer needs and the product technical requirements systematically into the product design phase in QFD. Luo, Tang, and Wang (2008) put forward an optimization method for components selection based on QFD to minimize the difference between the customer’s expectation and the selected product. The model is converted into an equivalent linear integer programming model to facilitate the solving approach, and Fuzzy customer requirements are also considered to deal with the uncertainties of human subjective judgment on customer requirements. Chaudhuria and Bhattacharyya (2009) link QFD with Conjoint Analysis through an integer programming based framework to determine the appropriate technical characteristics and consequently the right attribute levels. It is also proposed to measure the elements of the relationship matrix in QFD in a way so that the right levels of the attributes can be generated from the integer programming solution. Chen (2009) integrates QFD with process management techniques to optimize product design investment and process improvement. Process management is used to construct an integrated product and process development model to promote the effectiveness and benefits of applying QFD techniques. Deros, Rahman, Rahman, Ismail, and Said (2009) propose a method based on QFD to measure the service quality performance and identify critical service quality characteristics. In this method, QFD is used as a tool to improve quality in service industry by helping the firms involved to have clearer picture of quality requirements that could improve their customers’ satisfaction.
In addition, some authors have also integrated QFD with other methods to improve QFD approach or to propose new approaches based on QFD. Bouchereau and Rowlands (2000) present an approach to incorporated QFD and fuzzy logic, and integrate artificial neural networks and the Taguchi method to produce an intelligent systems approach to QFD. Raharjo, Brombacher, and Xie (2008) propose generic ANP-based network model, which improves the QFD results’ accuracy and flexibility. The proposed network model takes into account the crucial factors in new product design simultaneously. Chen and Ngai (2008) propose a novel fuzzy-QFD program modeling approach to complex product planning which integrates fuzzy set theory and QFD framework to optimize the values of engineering characteristics by taking the design uncertainty and financial considerations into account. In the proposed methodology, fuzzy set theory is used to account for design uncertainty, and the method of imprecision is employed to perform multiple-attribute synthesis to generate a family of synthesis strategies. Lee, Sheu, and Tsou (2008) presents an integrative approach by incorporating the Kano model with Fuzzy mode into the matrix of QFD to provide a new way to optimize the product design and enhance customer satisfaction. QFD matrix is used to assure that most critical needs of customers’ are translated into the next phases of product development, and Fuzzy mode is used to improve subjective linguistic scale in Kano’s two dimensional quality elements. Delice and Zülal (2009) propose a new QFD optimization approach combining mixed integer linear programming model and Kano model to acquire the optimized solution from a limited number of alternative the design requirements. The proposed model can be used to optimize the product development and in other applications of QFD such as quality management, planning, design, engineering and decision-making. Liang (2010) develops an approach of fuzzy-QFD to identify service management requirements for customer quality needs. This approach provides a method to construct a fuzzy relation matrix to link service management requirements and customer quality needs based on cross-functional expertise.
Some authors have also conducted categorical analysis about QFD’s functional fields, applied industries and methodological development (Carnevalli and Miguel, 2008 and Chan and Wu, 2002), and their findings have shown that QFD can be used as a tool to be applied in the development of strategies.
Therefore, QFD is a technique used to convert ‘voice of the customer’ into design, engineering, manufacturing and production in order to ensure product meeting the needs of the customers. It tries to capture what the customer needs and how it might be achieved through the effort of relevant functional areas. With these characteristics, QFD can be an effective tool to organize and carry out the manufacturing strategy development.
In recent years, the QFD methodology has been applied in the development of business or manufacturing strategies. Jugulum and Sefik (1998) realize that QFD can help organizations develop manufacturing strategies, and it can be incorporated into the classic steps of corporate planning to make strategy more effectively. Crowe and Cheng (1996) propose a methodology by using QFD in manufacturing strategic planning. The methodology comprises four stages called functional strategies, manufacturing priorities, action plans and detail tasks respectively. The proposed methodology provides a systematic tool to facilitate strategy development, and manufacturing strategy and action plans can be realized through the QFD process. Olhager and West (2002) use QFD for linking manufacturing flexibility to marketing requirements. Bottani and Rizzi (2006) suggest that QFD can be applied effectively to various issues such as business strategies and performance assessment.
Barad and Gien (2001) utilize QFD to deploy manufacturing strategies into improvement activities. They develop a structured two phased model for connecting the improvement actions of a company with its strategic and operating improvement needs. Dror and Barad (2002) use QFD to construct a performance measurement system based on the balanced scorecard map. Further, they develop a House of Strategy by using QFD matrix for translating the improvement needs of a company’s business objectives into relative importance of its competitive priorities (Dror & Brard, 2006), and also suggest a mean square error (MSE) criterion to supporting the selection of vital competitive priorities to be improved. As the extension of their previous work, Barad and Dror (2008) utilize the QFD methodology for building the strategy map to extract the desired improvement needs in the company’s objectives and to translate them into required improvement in its competitive priorities, required improvement in its core processes and, finally into the required improvement in the components of its organizational profile. Chuang, Yang, and Lin (2009) use the relationship matrix in QFD method to provide a tool for integrating the market trends, competitive and operational strategies, as well as manufacturing attributes. Bottani (2009) presents an approach based on QFD to develop agile strategy of enterprises. This approach aims at identifying the most appropriate enablers to be implemented by companies starting from competitive characteristics of the related market by linking competitive bases, agile attributes and agile enablers. The approach also exploits fuzzy logic to translate linguistics judgements required for relationships and correlations matrixes into numerical values.
However, there are still some limitations in the existing research and therefore further research is needed, e.g. (1) there still lacks formal mechanisms for translating qualitative “whats” into quantitative “hows” in QFD matrix, and the values of a certain alternative concerning a given attribute often cannot be precisely defined. To deal with this type of uncertainty, mathematical tools such as group decision-making and fuzzy set theory could be applied during the process. (2) Links between business strategy and manufacturing strategy is obscure, and the supportive degree of manufacturing strategy to competitive priorities cannot be determined. Thus, there is a need to develop a process which is able to derive manufacturing strategy from business strategy. (3) Low level of detail in current methodologies causes discontinuity in the process of manufacturing strategy development, and mutual influence between “hows” is usually ignored. Therefore, the methodology of manufacturing strategy development with systematic and detailed process is still needed. (4) Many methodologies proposed are too academic and complicated to be grasped and used by practicers in practice.
In this paper, we will present a manufacturing strategy development model based on QFD. The main objective of this paper is to introduce a methodology based on QFD that could develop the manufacturing strategy quantitatively. The main contributions of this paper include: (1) the methodology uses HOQ as a transforming device to link business strategy with manufacturing decision categories such as structural decision categories and infrastructural categories; (2) the methodology provides a platform for multiple decision-makers to identify competitive factors and to determine their relative importance to avoid the bias and minimize the partiality in the decision process, and group decision-making and fuzzy set theory are integrated with HOQ to provide a structured tool to capture the inaccurate decision-relevant inputs and to facilitate to analyze decision-relevant QFD information; (3) the methodology provides a detailed stepwise process for manufacturing strategy development and use HOQ as a main tool in different stages of manufacturing strategy development to ensure consistency of strategic manufacturing decisions; (4) the methodology is easy to be understood and grasped by practicers, and it provides a participating platform for stakeholders relating to manufacturing strategy development, which allows them to play their roles in the process of manufacturing strategy development.
The remainder of the paper is organized as follows. In the next paragraph, manufacturing strategy is first analyzed in term of strategy contents and process, and then the QFD methodology is briefly described. Following these discussions, a process of manufacturing strategy development based on QFD is proposed, and a fuzzy approach is introduced. Finally, a case is given which is able to show how to apply this methodology in practice, and concluding remarks are presented.
