تصمیم گیری برون سپاری تحت ریسک اختلالات رویداد فاجعه بار
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|19320||2011||17 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 9290 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Transportation Research Part E: Logistics and Transportation Review, Volume 47, Issue 6, November 2011, Pages 1058–1074
In this paper, a supplier selection problem is studied under risks of supplier failure due to the catastrophic events disruption. An analytical model is developed to determine the optimal number of suppliers considering different failure probability, capacity, and compensation. An algorithm is designed to find the optimal solution and numerical study is carried out to illustrate the model. Results of numerical study and sensitivity analysis provide useful guidelines for managers to select the optimal number of suppliers under the risks of supply disruption.
Supplier selection is one of the most important activities of purchasing department of any organization. Selection of right suppliers significantly reduces the purchasing cost and improves the corporate competitiveness. Earlier literature and business practices revealed that the business performance of organizations largely depends upon the suppliers. Therefore, supplier selection is becoming one of the most critical issues for purchasing managers. In the present competitive business environment companies are focusing on integrating their supply chains in order to reduce costs, shorten production lead time, increase quality and improve the relationship with suppliers (Moritz and Pibernik, 2008). Recently, due to the pressure of cost reduction and development of various logistics theories and practices like just-in-time philosophy, lean production, etc., the trend of supply base reduction has increased. The major reason behind this supply base reduction is diminution of administrative and transaction costs and cost savings from concentrating greater purchase volumes with fewer suppliers (Trent and Monczka, 1998). Choi and Krause (2006) have defined supply base as a group of suppliers from whom buyer purchases the parts, materials, and services. Reduction of supply base can have many advantages in costs and management such as cost-effectiveness, higher quality of coordination, improved delivery performance, continuous improvement and innovation (Carbone, 1999, Burt et al., 2004 and Nam et al., in press). On the other hand, reduced supply base might not always be beneficial for a buying firm as it has some negative consequences also. A reduced supply base increases the risks of supply disruption and suppliers opportunism for the buying organization (Trevelen and Schweikhart, 1988, Norrman and Jansson, 2004 and Manuj and Mentzer, 2008). Failure of a single supplier to supply the negotiated order quantity (e.g. raw materials) can badly affect the performance of the entire supply chain. A well known example that highlights the shortcomings of the single sourcing option is the case of Ericsson. A fire at the manufacturing plant of Ericsson supplier (Philips microchip) located at Albuquerque, New Mexico in 2000 caused Ericsson to incur a loss of about 400 million Euros. Similarly, there are many other examples such as, the insolvency of one of the Land Rover suppliers in 2001 causing the company to lay off 14,000 workers; reduction of $900 million in the quarterly earnings of General Motors (GM) is observed in 1996 due to the labor strike at one of the brake supplier factory that idled workers for 18 days at 26 assembly plants of GM and in 1997 Boeing incurred a loss of $2.6 billion due to the failure of its two key suppliers to deliver critical parts, etc. The above examples highlight the importance of determining the right number of suppliers that optimally trades off between potential risks of having fewer suppliers and benefits. Recent high-profile catastrophic events like 9/11, the hurricane Katrina and Rita in 2005, and the tsunami in 2004, etc. have motivated researchers to include risks of catastrophic events disruption into procurement and supply chain problems (Oke and Gopalakrishnan, 2009, Knemeyer et al., 2009, Yu et al., 2009, Chopra and Sodhi, 2004, Tang, 2006 and Kleindorfer and Saad, 2005). Numerous other types of catastrophic events like, snowstorms, heavy rain, excessive wind, fire, industrial and road accidents, strikes, and changes in government regulations (Ellis et al., 2010 and Stecke and Kumar, 2009) regularly interrupt business operations suggesting that the possibility of supply disruption should not be overlooked by purchasing managers while taking sourcing decisions. Hou et al. (2010) have defined supply disruption as the sudden non-availability of supplies due occurrence of an unexpected event making one or more supply sources totally unavailable. More than half of the respondents of an Indian Business Continuity Survey of 95 organizations have reported that they have faced at least one significant business disruption in the year 2008. The survey stated that the average number of significant disruptions per organization is increased to 1.8 in 2008 compared to the 1.6 in year 2007. The survey also mentioned that the average loss per disruption in 2008 has increased significantly to Rs. 205 million compared to Rs. 77 million in 2007, which is an increase of more than 200%. Hence, today’s business environment, one cannot ignore the risks of disruption. In the real world, disruptions do and will occur and the best business plans are those that anticipate and prepare for this inevitability (Handfield and McCormack, 2008). Tomlin (2006) and Linthorst and Telgen (2007) have suggested multiple sourcing as one of the efficient strategies to cope with risks of supply disruption. Though multiple sourcing option is more reliable, but it increases the management cost which includes cost of negotiation, managing a supplier contract, and monitoring the quality, etc. (Moritz and Pibernik, 2008). Therefore, in today’s competitive and uncertain business environment, the task before a buying firm is to find the optimal number of suppliers that tradeoff between supplier management cost and the costs due to supply disruption. Nevertheless, research pertaining to sourcing decisions or supplier selection under risks of supply disruption is limited. The motivation of the current study emanates from this limitation. In this study, we have made an attempt to develop an analytical model for determining the optimal number of suppliers to minimize the total cost considering the risks of catastrophic events. Furthermore, the concept of service level (SL) is also incorporated into the model to measure the performance of supply network. Service level is defined here as the probability that a buyer/manufacturer will not face loss due to complete disruption of all supply sources during the cycle and receive the negotiated ordered quantity. Intuitively, the service level will be more when the buyer/manufacturer engages more number of suppliers. However, this will increase the management or operating cost. Therefore, the challenge is to balance between the total cost and service level. Two constraint based optimization models are developed considering service level for determining the optimal number of suppliers. The first model deals with maximizing the service level within total budget constraint and in the second model, minimum cost required to achieve a target service level is determined. The paper is organized as follows. A brief review of literature is included in Section 2. In Section 3, an analytical model is developed for minimizing the total expected cost of manufacturer. Solution for determining the optimal number of suppliers is provided in Section 4. In Section 5, a numerical study is carried out to illustrate the model and sensitivity analysis is performed to determine the optimal number of suppliers. The optimization models of service level and their sensitivity analysis is carried out in Section 6. Finally, conclusions and future scope of the work are presented in Section 7.
نتیجه گیری انگلیسی
This paper deals with a sourcing decision problem encountered by purchase managers regarding determination of the optimal number of suppliers in presence of supply disruption risks. This paper contributes to the literature by developing a mathematical model for determining the optimal number of suppliers under the risks of supply disruption. The main contributions of this work are as follow. First, this study considers three elements, namely, different failure probability, capacity and capacity specific compensation potential for each supplier together, and to the best of our knowledge, no study has considered all these elements together. Incorporation of these elements into the model have made the problem too complex and the solution was too cumbersome to handle with the decision-tree approach as suggested by earlier authors. We have proposed a simple algorithm to solve this problem. Moreover, the loss incurred by the manufacturer due to supplier failure is determined endogenously whereas; the previous studies have considered this loss exogenously. Further, we have incorporated service level into the model as a supply network performance measure. In literature, only a few studies have considered the service level in the problem of supplier selection under the risks of supply disruption. We have developed two constraint based optimization models for imposing the service level in the determination of optimal number of suppliers. The first model deals with the determination of the optimal number of suppliers considering maximization of service level under the manufacturer’s budget constraint, whereas, the second model determines minimum ETC required for achieving the specific target service level. A numerical study is conducted to show the proposed solution procedure for determining the optimal number of suppliers. Sensitivity analysis is performed to check the effect of various parameters on the optimal solution. Similar to the studies of Yang and Qian, 2008 and Berger et al., 2004, Ruizz-torres and Mahmoodi (2006) and Sarkar and Mohapatra (2009), our results also reveal that the optimal the number of suppliers decreases as the cost of supplier management increases. On the other hand, the optimal number of suppliers decreases with the increasing value of super-event probability; but as can be seen from the sensitivity analysis, this doesn’t have much impact on optimal number of suppliers. Furthermore, another numerical study is conducted for the two models related to incorporation of the service level in optimal number of suppliers problem. The sensitivity analysis of these two models revealed that as the optimal number of suppliers increases, the service level also increases. Furthermore, we have observed that as the values of supplier management cost and super-event probability increases, the optimal number of suppliers and service level decreases and after a certain values of supplier management cost and super-event probability, the solution become infeasible due to the budget constraint. Incorporation of service level in supplier selection under supplier failure risks has increased the optimal number of suppliers significantly. Moreover, results suggest that under service level consideration, the multi-sourcing is a superior strategy as compared to single and dual sourcing. We believe that the models proposed in this paper will be much more helpful for managers in selecting the optimal number of suppliers in the presence of supply disruption risks compared. In future, there are several ways to extend this work. First, in this study we have considered the deterministic demand of buyer over a cycle and assumed that the management cost has liner relationship with increasing number of supplier. Therefore, a model can be build-up considering stochastic demand of the manufacturer and different management cost for each supplier in order to determine the optimal number of suppliers under the risks of supply disruption. Second, we considered failure probabilities of two events only such as, super-event and unique events that causing the supplier to fail. Therefore, in future a study can be taken up considering the risks of super-event, semi-super events and unique events probabilities similar to the study of Sarkar and Mohapatra (2009). Third, a study regarding the optimal allocation of manufacturer demand among selected optimal number of suppliers under the risks of supply disruption will be an interesting future direction.