توسعه محصول ناب : به حداکثر رساندن ارزش درک شده مشتری از طریق تغییر طراحی (طراحی مجدد)
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|2729||2008||20 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Production Economics, Volume 114, Issue 1, July 2008, Pages 313–332
With the incremental product development, one of the main objectives is to make it more attractive and valuable for the customer, which leads to increased sales and higher profit. When existing product design is modified to improve its perceived value, each change needs resource commitment. It is important to identify and pursue only those changes, which give maximum improvement in the perceived value. In this paper a mathematical model is presented for perceived value and step-by-step methodology is provided to capture the optimized design changes with cost implications. With the help of a case study on automotive vehicle development, we show how proposed model and method can be used for highest value added change selection. Application of optimization model for perceived value and change trade-off in general is presented along with some special policy cases for different scenarios.
Any company can stay in market if and only if it makes profit and keeps its customer excited about its products. Therefore, product improvement by incremental development is the heart of any sustained business venture (Gilvan et al., 2004; Morgan et al., 2001). With product improvement, we here imply bringing the new features, technologies, attractive designs and improved quality to the market. However, this is possible generally by introducing changes in the existing product. In this study our scope is limited to the products being developed based on existing products such as automotives, computers, and printers, etc. This evolutionary product development for such products needs decision as, what to change and what to reuse. If changes are too many, product development may be too expensive and development time may be too long. On the other hand, if changes are too few, customer may not be able to see the differences from the last product and unable to get excited (Kosonen and Buhanist, 1995; Lindstedt and Burenius, 2003; Mallik and Chhajed, 2006; Rainey, 2005). However, with aggressive marketing strategy some gains can be made which do not last longer (McAdam and Leonard, 2004). Art of sharing the changes and change cost across several product line is outlined by number of researchers (Dahan and Hauser, 2002; Gilmore and Pine, 1997; Meyer and Lehnerd, 1997; Pine, 1999; Richards, 2000). The key to successful product development is to pursue the changes for which customer cares, while minimizing the overall changes to reduce development time, cost and risk involved due to these changes. Fisher et al. (1999) presented key costs related optimization model for component sharing and tradeoffs for variety and use of over designed parts. In their study, they did not consider impact of customer perception on sharing or reuse of the components across products. Samson and Wacker (1998) provided a broad and general understanding on maximizing the benefits per dollar to the customer. In their study on automobile industry, they provided a good comparison of various players in industry and emphasized the need for focusing more on the customer as a new organizational cultural, but did not give any theoretical model or step by step process to support their study. Value of a global product was analyzed and compared in cultural context by Boztepe (2007) and Matsui et al. (2007). However, their focus was on localization and standardization of the product. Marketing aspects of customer perceived value has been extensively reported in literature (Tam, 2004; Ulaga and Chacour, 2001), but during product development customer perceived value still needs more attention. Ulrich and Eppinger (2004) provided steps for identifying customer needs and establishing their relative importance, but did not provide method for selecting vital few needs based on their perceived value and practical constraints like budget, development time, risk due to change, etc. Several researchers (e.g. Hauser and Clausing, 1988; Ulrich and Eppinger, 2004; Wheelwright and Clark, 1992) emphasized the need to first establish critical customer attributes for the product using QFD approach. These attributes become the rows of the central matrix of the house of quality. Kansei Engineering (Nagamachi, 1989, Nagamachi, 1995 and Nagamachi, 2002) has been developed and successfully applied (Hsiao and Chen, 1997; Ishihara et al., 1995; Jindo and Hirasago, 1997; Lai et al., 2006; Yang et al., 1999) as ‘‘translating technology of a consumer's feeling (Kansei in Japanese) and image of a product into design elements’’. Han et al. (2004) prioritized customer attributes and engineering characteristics in QFD with incomplete information using linear partial ordering approach and Fung et al. (2006) helped in establishing functional relation using fuzzy regression. However, they did not study the cost implications and impact of changes required to implement the prioritized characteristics. Studies of Burke (1992) and Gilvan et al. (2004) were primarily based on optimization of development life cycle at the program level. In his study, Burke (1992) did not bring the analysis at the component level. Lin et al. (2008) proposed dynamic development process model, for managing overlapped iterative product development. Ozer (2007) emphasized the need of reducing demand uncertainties by predicting cluster-based methodologies for customer opinions. Whereas, Sobek (1997) and Sobek and Ford (2005) emphasized the need of knowledge-based product development for any effective and efficient design reuse, but did not discuss how reuse impact the customer perception. Ryan (2004) developed multiple models based on goal programming for generalized economies of scale. Ulrich (1995) presented the role of the product architecture by which function of a product is allocated to a physical component. He tried to conceptualize the linkages between product architecture and key areas like product change, product variety, component standardization, product performance and product development management. Krishnan and Ulrich (2001) presented an extensive review of literature on product development decisions. Sousa and Voss (2002) provided review and agenda for future research on multidimensional quality management and there is evidence of multidimensional quality construct (Stone-Romero et al., 1997) including customer perceived quality. However, not much has been reported on perceived value based product design change. Unlike the existing literature, in this paper we are proposing a method of feature selection during incremental product development to maximize the customer perceived value for the changes. In our analysis we included cost of change in the form of development cost, tooling cost, per piece cost and increased warranty cost due to increased risk. We also consider the role of coupled product architecture and undesired changes on customer perceived value and change cost. Based on binary integer optimization, we came out with maximized perceived value within the given budgetary constraints. Our results also provide analytical support that perceived value of the new product is largely depend on part/feature correlation and part complexity within the given budget, but it can be significantly improved with resource reallocation based on policies and product strategy.
نتیجه گیری انگلیسی
Developing an improved product and winning the heart of customer thought its new features has always been a big challenge in corporate world. Selecting new features for a product has been remained matter of intuition and instinct for top management and experts. With the given set of budgetary constraints, it is important to maximize the benefits of change by acquiring the highest possible customer perceived value in product. In this paper a mathematical model is presented for perceived value and step-by-step methodology is provided to capture the optimized design changes with cost implications. With the help of math model and logical binary integer optimization model, we presented the concepts and a systematic methodology for maximizing the benefits of the change in terms of the customer perceived value within the given set of constraints. In all practical scenarios, we have to make an early decision on what to change and what to keep in the next product development cycle. Through a case study on remote start feature in vehicle development, we provided step-by-step method to support decision making for choosing the functions while maximizing the customer perceived value. This approach can be easily modified and adopted for maximizing the value for other stakeholders in the enterprise i.e. “profit” for shareholders. We also provided method to take decisions on special situation based on specific focus like safety, system integrity, resources reallocation, etc. The proposed method requires decomposing of feature/function into physical parts. Therefore, all the limitations associated with function to physical world mapping are inherited here as well. Another challenge in using the proposed approach is upfront warranty cost calculation while implementing a new feature. The probability of failure assigned for changed component is difficult to be specified and can only be predicted based on past experience with the similar changes. Therefore, warranty cost for changes may not be accurate with new design. However, as more information is available from the field on failures, successive prediction for similar changes will become more and more accurate. Another limitation of this method is that when large number of parts participating in changes for a complex feature in a large product such as automobile, solution for step wise logical binary integer programming becomes too difficult to solve using simple tool like solver. In future, the same concept can be applied at product level when features are selected for the vehicle, where each column will represent a feature and a monster matrix of the participating parts can be created. However, there is need to find a solver tool which can apply solution from one stage and feed to the next stage in multiple iteration scenario. As an extension of this work, the ratio of the value added changes and non-value added change can be used as a measure of modularity in product architecture in future research. This ratio is also an enabler for the developing reusability index. Isolating the changes can be also used as another way of cutting down cost, time and risk in product development.