برون سپاری تدارکات استراتژیک: رویکرد یکپارچه گسترش کارکرد کیفیت و تحلیل سلسله مراتبی فازی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|7202||2012||10 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 12, 15 September 2012, Pages 10841–10850
This paper develops an integrated approach, combining quality function deployment (QFD), fuzzy set theory, and analytic hierarchy process (AHP) approach, to evaluate and select the optimal third-party logistics service providers (3PLs). In the approach, multiple evaluating criteria are derived from the requirements of company stakeholders using a series of house of quality (HOQ). The importance of evaluating criteria is prioritized with respect to the degree of achieving the stakeholder requirements using fuzzy AHP. Based on the ranked criteria, alternative 3PLs are evaluated and compared with each other using fuzzy AHP again to make an optimal selection. The effectiveness of proposed approach is demonstrated by applying it to a Hong Kong based enterprise that supplies hard disk components. The proposed integrated approach outperforms the existing approaches because the outsourcing strategy and 3PLs selection are derived from the corporate/business strategy.
Logistics outsourcing or third-party logistics is regarded as using external companies to perform some or all logistics functions, including transportation, distribution, warehousing, inventory management, order processing, and material handling, that have traditionally been performed within an outsourcing firm (Işıklar et al., 2007, Razzaque and Sheng, 1998 and Sink and Langley, 1997). Those logistics functions can be treated as non value-added activities because they are critical to the smooth running of the business, but not a unique ingredient of the overall product (Sink & Langley, 1997). Because of this reason, firms tend to outsource those activities to the external companies or 3PLs, and focus on value-added activities to develop sustainable competitive advantage. Evaluation and selection of 3PL is a critical step in the logistics outsourcing process because an appropriate 3PL will help the outsourcing firms to reduce capital investment in facilities, equipment, information technology and manpower, increase the flexibility of outsourcing firms in adapting to changes in the market, reduce inventory and improve inventory turnover rate, improve on-time delivery, reduce the transportation cost, and so on (Liu and Wang, 2009 and Razzaque and Sheng, 1998). Choosing the right 3PLs involves much more than scanning a series of price list, and choices will depend on a wide range of factors which involve both quantitative and qualitative. Various individual and integrated multi-criteria decision making approaches have been proposed for the 3PL selection, such as AHP, analytic network process (ANP), artificial neural networks (ANN), case-based reasoning (CBR), data envelopment analysis (DEA), rule-based reasoning (RBR), technique for order preference by similarity to ideal solution (TOPSIS), and so on. Although these approaches can deal with multiple and conflicting criteria, they have not taken into consideration the impact of business objectives and requirements of company stakeholders on the evaluating criteria. In reality, the weightings of 3PL evaluating criteria depend a lot on business priorities and strategies. In cases where the weightings are assigned arbitrarily and subjectively without considering the “voice” of company stakeholders, the selected 3PL cannot provide what the company exactly wants. To enable the “voice” of company stakeholders is considered, this paper develops an integrated approach, combining QFD, fuzzy set theory, and AHP, for selecting 3PL strategically. HOQ, a technique of QFD, is responsible for translating the requirements of company stakeholders into evaluating criteria. Since multiple evaluating criteria are proposed, and some of them are qualitative and uncertain, the fuzzy set theory is therefore incorporated into the traditional AHP to enable company stakeholders to express their linguistic preferences, and to transform those preferences into the quantitative form for comparison. Fuzzy AHP is responsible for the assignment of importance ratings and relationship weightings in the HOQs so that inconsistencies due to subjective judgments can be avoided. Based on the ranked criteria, alternative 3PLs are evaluated and compared with each other using fuzzy AHP again to make an optimal selection.
نتیجه گیری انگلیسی
This paper developed an integrated multiple criteria fuzzy decision-making approach to measure the performance of alternative 3PLs. A case study was given to demonstrate how it works. In the approach, QFD was used to translate the hard disk components manufacturing company stakeholder requirements into 20 evaluating factors, which were used to benchmark the 3PLs. Fuzzy AHP was used to determine both importance ratings and relationship weightings in HOQs consistently. The major advantage of this integrated approach is that the evaluating factors are of interest to the stakeholders. This ensures that the selected 3PL will achieve the business objectives and satisfy the stakeholders most. Another advantage is that the approach can guarantee the benchmarking to be consistent and reliable. Furthermore, the integrated approach involves a team of people representing various functional departments that have involvement in 3PL selection: finance, logistics/transportation, manufacturing, and marketing. The active involvement of these departments can lead to a balanced consideration of the requirements or “what’s” at each stage of this translation process, and provide a mechanism to communicate implicit knowledge – knowledge that is known by one individual or department but may not otherwise be communicated through the company. Therefore, the proposed approach outranks the conventional approaches to strategic logistics outsourcing. In the immediate future, a sensitivity analysis should be carried out in order to check the effect of changes in the importance levels of various factors on final outcome.