سیستم پشتیبانی از تصمیم گیری برای مدیریت ریسک تدارکات در حضور بازار نقدی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|5871||2013||12 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 8896 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 55, Issue 1, April 2013, Pages 67–78
In the presence of spot market, this paper presents a decision support system to model risks for procurement processes and to design a robust purchasing plan, including supplier selection and order allocation. Taking advantages of contract supplier and spot market, the buyer can better meet business requirements in this dynamic business environment. However, there are limitations of existing methods for modeling multiple correlated risks to support decision makers for allocating orders among multiple suppliers in the presence of spot market. Therefore, Monte Carlo simulation algorithm termed as Expected Profit–Supply at Risk (A-EPSaR) is proposed to quantify each supplier's risk so as to let decision maker realize the trade-off between profit and risk. The goal programming model helps to allocate orders among the supplier pool and the contract-spot allocation model can assign orders between the spot market and the supplier pool, respectively. The significance of this paper is to propose a novel decision support framework which helps the buyer to make optimal and robust procurement decision including supplier selection and order allocation among multiple supplier sources in the existence of correlated demand, yield and spot price uncertainties. A case study is used to illustrate the performance of the proposed framework and the proposed methods show the promising result.
Procurement risk management is of great importance and is crucial to the success of supply management  and . With the popularity of outsourcing from 1980s, many companies outsource the business which is not their core competency . For example, Dell outsources the manufacturing of computer components to other companies and focuses on assembling each ordered unit according to a selection of custom options. Outsourcing becomes a business strategy for Dell and other business partners, so enterprises can focus on its core technology and enhance their competency. But this brings a great challenge for procurement. Indeed, procurement is becoming more and more demanding in this dynamic market environment in terms of supplier risk management ,  and . Nowadays the majority of raw materials and components are manufactured in countries where costs are lower. Many of the business partners and component producers are overseas and difficult to be monitored. Therefore, supply from these contract suppliers is hard to be controlled and has different extents of uncertainty. In fact, there are many uncertainties that existed in procurement ,  and , such as variable lead time and uncertain demand. Since the lead times of these contracts are usually quite long, the buyer doesn't have enough time to place a second order when the uncertain demand or uncertain yield is realized. Or when the supply is affected by nature disasters, part or all of suppliers' production capability is halted. More and more companies have already realized the importance of managing procurement risk. For example, HP has formed a Procurement Risk Management (PRM) team and enabled $100 million dollars in accumulative savings over the past 5 years according to . The incorporation of a reactive supply channel in procurement is advocated by many scholars. These reactive supply channels can be spot market , option contracts  or a back up supplier . Utilizing the short lead time advantage of spot market, Seifert et al.  figure out the optimal order allocation among the single contract supplier and the spot market under demand uncertainty. Haksöz and Kadam  provide a tool to assess the effects of contract breaches in the presence of demand and spot price risks. A new term Supply at Risk (SaR), which returns the worst loss that will not be exceeded with a given level of confidence, is introduced by Haksöz and Kadam  to evaluate supply risk. Indeed, SaR is a similar concept akin to the Value at Risk (VaR) statistic in finance . A portfolio of contracts are evaluated and SaR is obtained to find out the optimal supplier portfolio according to . As the shortcomings from contract supplier can be made up and compensated by the reactive supply, therefore, to deal with the procurement risk management, it is essential to utilize both supply sources and take related risk factors into consideration. At the beginning of every procurement period, the buyer will place orders on the supplier pool of long term contract suppliers. Because of the uncertain yield from these contract suppliers and unstable demand, spot market with negligible lead time is adopted as the reactive supply source to meet unexpected demand or sell extra stock. The price from spot market changes continuously and is higher compared with the long term contract suppliers. Indeed, these uncertain factors are not independent and can affect one another. In this paper, the most general case of a completely correlated demand, spot price and uncertain supply is considered. The risk attitude of a buyer is also taken into consideration as it affects the procurement decision. In addition, the following factors are also studied: procurement cost, minimal order and maximal order proportion assigned to a single supplier and fixed cost of adding one more supplier. In order to assist decision making in formulating the management plan, a novel PRM framework is proposed. The proposed framework helps to generate a procurement plan which includes 1) the selection of appropriate suppliers; 2) the order allocation for the respective supplier; 3) the aggregate order to be purchased from the selected supplier pool; and 4) the total order amount purchased from spot market. This paper is organized as follows: Section 2 describes the related research work and the gaps in these areas. Section 3 presents the integrated framework from supply risk identification to risk monitoring. Section 4 illustrates the framework with a case study. In Section 5, conclusions are drawn and future research directions are stated.
نتیجه گیری انگلیسی
In this paper, a novel PRM framework is proposed to support decision making of order allocation among multiple suppliers in the presence of spot market. Unlike the majority of previous research on procurement risk management, the effects of correlated demand, yield and price uncertainties are considered in the proposed model. To our knowledge, there is a lack of complete and quantitative procurement decision support system with the existence of spot market. This model provides a solution to address the limitations of the current gap, i.e. the lack of method to model multiple correlated procurement risks, and providing decision support for utilizing multiple contract suppliers and the spot market for procurement from the perspective of risk management. The performance of the proposed model is illustrated by a real case of purchasing flash memory. Compared with the conventional method of procurement plan formulation which is merely based on estimation and experience, the proposed PRM framework supports purchasers to make the decision about the order quantity to which supplier and determine the supplier portfolio by pair wise comparison. The risk of supply is reduced and a sustainable procurement plan is formulated. The DSS based on PRM framework are designed and developed. The buyer can identify its specific risks in their procurement and build their own profit model. Supply risk assessment is supported by the Monte Carlo simulation with the two comparison indicators: expected profit and SaR. These two unified risk indexes resolve the complexity of multi dimensions risks assessment. After conducting the pairwise comparisons of performance among the potential suppliers, only those who are favorable are still kept in the pool. For assigning orders in the supplier pool, the buyer can adjust the weight of the two objectives (increase profit and mitigate risks) in line with their needs. Once the optimal portfolio with order share is determined, the buyer can figure out the average portfolio yield and wholesale price. The buyer can then obtain the optimal total quantity from contract suppliers, while the rest of uncertain demand can be met through the reactive supply channel (spot market). There are some limitations and potential future works of our study. One issue is that the general applicability of our model to purchase all commodities and products. Currently, purchasers may not purchase all the raw materials and commodities from spot markets. Indeed, only some of the items are commonly sourced from spot markets. For example, spot markets are quite common for purchasing memory chips, such as dynamic random access memory and flash memory. One of the popular B2B platforms is DRAMeXchange. Other products available for spot market trading include grains, livestock, steel, oil and chemicals. To generalize our model into the procurement of other commodities, it may need to consider other reactive channels, such as local supplier or option and futures contracts. The other future work is to consider inventory in the multiple periods setting. In our study, we focus on some of the time sensitive components such as memory chips. With the rapid development of technology, it is very risky to keep excess inventory in the warehouse. Instead, companies are recommended to meet the extra demand from spot market, and sell surplus items into the spot market too. Therefore, using the platform of DRAMeXchange, buyers are active in both buying and selling memory chips. Companies may choose to sell all their additional reserves in the spot market. However, it is suitable to keep inventory for some kinds of commodities, for instance, grains, oil and steels. In order to enable our model to be also applicable to these commodities, inventory should be considered in the future model.