Technical service platform planning based on a company’s competitive advantage and future market trends: A case study of an IC foundry
Introduction
Service-oriented manufacturing had emerged as a new business paradigm, which consists of consumer services, producer services, and manufacturing industries (Gao, Yao, Zhu, Sun, & Lin, 2011). At present, more than one-third of all large manufacturing firms have positioned themselves as service providers (Neely, 2008). The servitization of manufacturing means that enterprises are able to offer multiple services to meet different customer requirements (Hobo, Watanabe, & Chen, 2006). Servitization can also enable enterprises to achieve greater profits and to increase customer satisfaction.
In the competitive globalized market, enterprises must distinguish themselves to better generate services for downstream partners. Technical service platform planning can take different market demands and underlying technologies into account. For a more comprehensive market analysis, crucial factors such as the core competitiveness of enterprises, future markets, and technology trends must also be considered. This study integrates these elements within a new platform planning methodology. In conjunction with platform development, we also consider forecasting as an important part of the study. Technology forecasting can help businesses determine the direction of needed service development. Future technology trends prediction considers the market, products, and technology itself (Phaal, Farrukh, & Probert, 2004). It enables enterprises to concentrate their resources in the right strategic directions. However, the technology forecasting and assessment methods with an aim to sustain competitive advantage for uncertain situations have rarely been investigated in previous researches (Chiou and Chiu, 2014, Martino, 2003). Therefore, this study addresses that gap and combines technical service platform planning and technology forecasting to generate a new planning tool for enterprises.
Section snippets
Literature review
Core competence is an enterprise’s fundamental capability that provides a competitive advantage in the market place. After the core competence of company is identified, enterprise is keen to sustain its strength by predicting future technology trends. Based on the technology forecasting, company could plan the technical service platform. Therefore, this section sequentially reviews related literature on core competence, technology forecasting, and technical service platform planning.
Proposed methodology
This study proposes a three-phase approach to develop a technical service platform. Phase I addresses technical service platform planning. It includes supply chain positioning, technology forecasting, and technical service platform identification. This phase can identify different market entry points and service composition.
Phase II involves technical service platform evaluation. In this phase, both RBF neural networks and fuzzy SMARTER are selected to evaluate a set of alternatives. RBF is a
Case study
This research worked with company A as a case study to illustrate the proposed method. Company A is a well-known wafer foundry in Taiwan. The foundry market share reached 49.5% in 2012, ranking it number one in performance (Gartner.com, 2013). Company A operates three advanced 12-in. wafer fabrication units, four 8-in. wafer fabs, and one 6-in. wafer fab in Taiwan. The total managed capacity of company A reached 15.1 million 8-in. equivalent wafers in 2012, ranking it one of top foundries (
Conclusion
A systematic technical service platform planning method involves current core competence, technology forecasting, and scenario analysis under uncertainties, concurrently achieved in this study. First, innovative and diverse service alternatives are generated. These alternatives are then evaluated according to technical benefits, commercial benefits, industrial chain completeness and risk attributes. Using our proposed methodology, a foundry services company was tested to validate the advantage
Acknowledgements
The authors would like to thank the Ministry of Science and Technology, Taiwan, for partially financially supporting this research under contract number MOST 103-2221-E-007-051-MY3. This paper was also supported by the Advanced Manufacturing and Service Management Research Center (AMSMRC), National Tsing Hua University, Taiwan.
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