طراحی مکانیزم برای مزایده الکترونیکی تأمین تجهیزات: درباره تأثیرات مذاکرات پس از مزایده و مشوق های فعالیت های کیفی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|19319||2011||23 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Electronic Commerce Research and Applications, Volume 10, Issue 6, November–December 2011, Pages 650–672
Practical mechanisms for procurement involve bidding, negotiation, transfer payments and subsidies, and the possibility of verification of unobservable product and service quality. We model two proposed multi-stage procurement mechanisms. One focuses on the auction price that is established, and the other emphasizes price negotiation. Both also emphasize quality and offer incentives for the unobservable level of a supplier’s effort, while addressing the buyer’s satisfaction. Our results show that, with the appropriate incentive, which we will refer to as a qualityeffort bonus, the supplier will exert more effort to supply higher quality goods or services after winning the procurement auction. We also find that a mechanism incorporating price and quality negotiation improves the supply chain’s surplus and generates the possibility of Pareto optimal improvement in comparison to a mechanism that emphasizes the auction price only. From the buyer’s perspective though, either mechanism can dominate the other, depending on the circumstances of procurement. Thus, post-auction negotiation may not always be optimal for the buyer, although it always produces first-best goods or service quality outcomes. The buyer’s choice between mechanisms will be influenced by different values of the quality effort bonus. For managers in practice, our analysis shows that it is possible to simplify the optimization procedure by using a new approach for selecting the appropriate mechanism and determining what value of the incentive for the supplier makes sense.
The use of electronic auctions in supply chain procurement has grown dramatically in the past 15 years with the advent of the Internet in support of electronic commerce, putting new demands on economists and supply chain managers to blend the capabilities of economics and engineering (Roth, 2002 and Varian, 2002). It also requires technologists, behavioralists and methodologists to build a shared base of knowledge from analytical modeling and experimental work, computational analysis and simulation, and algorithm development for agent-based systems and artificial intelligence (Gimpel et al., 2008, Kersten et al., 2008, Jennings et al., 2001, Parkes and Kalagnanam, 2005 and Smith, 1982). In 2007, Aberdeen Group reported that the enterprises they studied used e-procurement to: improve requisition-to-pay process efficiency; achieve better procurement contract compliance; improve spending visibility, lower procurement transaction costs; and exert more control on spending management (Gupta 2007). Aberdeen’s research analyst, Amit Gupta, has stated: “The procurement department is no longer just a transaction center for placing orders, but can also be a source of competitive advantage by acting as an information hub supporting business planning and decision-making. There is more to an e-procurement solution than cost savings; it is now a tool that removes manual error-prone repetitive tasks and promotes compliance with business controls allowing procurement resources to focus on more strategic tasks” (Selko 2007). In this context, computer science researchers have made a number of notable efforts to develop agent-based systems that support electronic procurement with different structures and supporting technical approaches to enable solutions to the problem of winner determination, while reflecting buyer and seller constraints and preferences. An outstanding example of this kind of research is iBundler, which is described in Cerquides et al. (2007), Giovannucci et al., 2004, Giovannucci et al., 2008 and Rodriguez-Aguilar et al., 2003. The authors describe their intelligent system as an agent-based negotiation service for buying agents and as a winner determination service for reverse combinatorial auctions with constraints on the attributes of individual items and multiple items in bundles. Another well-known proposed system is iAuctionMaker by Reyes-Moro and Rodriguez-Aguilar (2005), who developed and analyzed its performance. This second proposed system supports the work of an auctioneer who wishes to separate a superset of auction demand items into “promising bundles” that are likely to be more easily bid upon by suppliers who can deliver them in a competitive procurement market. The authors’ approach involves the capture and encoding of expert knowledge from sourcing specialists, as a basis for creating algorithms that optimize buyer satisfaction with the supplied bundles based on multiattribute utility theory.1 Davenport and Kalagnanam (2002) point out that the procurement of direct inputs that are used in the manufacturing of a firm’s primary products represent as much as 90% of its procurement spending. This dollar volume is large, and such procurement transactions occur with a high frequency. In their work at a large food manufacturing company, the authors note: “As a result there is considerable room for negotiations. However, a fundamental concern in such sourcing decisions is related to the reliability of suppliers, since defaulting suppliers might have considerable impact on the firm’s ability to satisfy demand obligations. As a result, these negotiations are generally confined to a restricted number of pre-certified suppliers having established relationships with the company” (Davenport and Kalagnanam 2002, p. 27). Early procurement auction models in economics tended to focus on the price of goods with fixed-quality bidding or quality-price pair bidding. Rothkopf and Whinston (2007) have noted that procurement auctions that are entirely based on non-negotiable supply quality and prices are not sufficient. If quality is not observable, or the buyer’s profit based on the supplier’s effort to deliver a quality product cannot be measured easily, then a buyer will benefit from offering an incentive contract to the supplier to compensate and stimulate effort so the transaction will yield greater value (Laffont and Tirole 1993).2 Informational asymmetries naturally arise between buyers and sellers, when sellers have private information that cannot easily be obtained by buyers about what they are selling. This makes it difficult for them to agree upon a fair price for exchange (Akerlof 1970), which creates a need for minimum standards to be established in different settings (Leland 1979). The present article is motivated by the business problem that arises in practical situations related to procurement, where the buyer initiates an auction for goods or services with a specific quality requirement, and solicits suppliers, who act as bidders. When a winning supplier emerges, the buyer may choose to either negotiate with the supplier to ensure an appropriate level of quality and price, or accept the auction price without negotiation.3 In either case, because of moral hazard and adverse selection that may occur in the auction setting (Rothkopf and Whinston 2007), the buyer will benefit from being able to establish incentives to encourage an acceptable outcome and prevent an inappropriate level of effort on the part of the supplier to deliver quality goods or services ( Bajari and Tadelis 2001). In practice, some sort of bonus payment to the supplier making more effort to deliver a quality product or service to the buyer and transaction completion is quite attractive ( Parkes and Kalagnanam 2005). 4 For example, many firms in China hire meal preparation service suppliers to prepare lunchboxes and dinners for their employees. Usually, the firms will invite bids from several suppliers and then negotiate with one supplier or just a few candidates, and set a monthly bonus for the final supplier, based on its performance. Performance can be assessed in various ways. For example, it might be proposed that a bonus be transferred to the supplier only if some fixed percentage of total employees (say 85%) rates the lunchbox service as “satisfactory.” Another possibility is that evaluative scores on the service from the employees are all above some fixed level (say 80 out of 100 points). It might be hard to pre-specify employee satisfaction in the auction phase, however, thereafter the employees’ satisfaction with the service will become common knowledge, and this should play an important role in supporting the two parties’ decision about whether a bonus should be given. The business problem arises because procurement managers and their supplier still lack sufficiently refined knowledge to fully understand how the inner workings of quality effort bonuses, the procurement auction mechanism and the negotiation process are interrelated in the creation of supply chain surplus. In this research, we will address this issue by analyzing two proposed procurement mechanisms involving economic exchange. We will focus on a setting in which the buyer chooses a winning supplier in a modified second-price sealed-bid auction, which hosts suppliers who make bids on delivering goods or services that meet a specific requirement for quality. When this is the case, there are two different possibilities. One possibility is that the buyer will buy the goods or services from the winning supplier at an appropriate and pre-determined level of quality, and a price for this supply will be established in auction. A second possibility is that the buyer may decide to negotiate with the winning supplier to obtain goods or services at a negotiated level of quality at a mutually agreeable price. In both cases, the buyer offers the supplier an effort bonus to encourage the supplier to make an appropriate though unobservable effort to deliver the goods or services in order to satisfy the buyer demand for quality. The dimensions of quality that are required may be non-contractible, so that observing pre-transaction quality is difficult or costly. Our goal in this research is to provide more refined theoretical knowledge to support managerial decision-making for e-procurement mechanism selection. This will permit us to establish a clearer understanding of the quantitative relationship among the bid price in the procurement auction, the post-procurement auction negotiation quality and price, and the size of the bonus that is needed to engender the right effort level on the part of the supplier to deliver what the buyer wants. It will also allow us to recommend to the buyer how to implement an optimal strategy when it uses an auction mechanism for procurement. We obtained a number of key results. Based on the modeling approach that we formulated, we find that the optimal bonus will be based on the form of the incentive that is used, and also on the auction or negotiation outcomes. We also find that there are two different kinds of bonus structures that are optimal for each of the mechanisms that we evaluate. We further show how to stimulate the supplier’s optimal effort to deliver goods and services of acceptable quality, as well as determine the value flows associated with the different types of bonuses for the different procurement auction mechanisms. We also present a decision-making procedure for selecting an effective procurement auction mechanism and the optimal bonuses associated with the mechanisms. Finally, we consider supply chain coordination as a problem from a social planner’s perspective, where the goal is to identify the transaction-making mechanism that maximizes social welfare and yields Pareto-improving value for the buyer and winning supplier, who act as partners in the transaction. We also show that the mechanism which incorporates the establishment of an auction price through the completion of the procurement auction, combined with a post-auction bonus, offers less surplus for the supply chain than the alternative mechanism that includes negotiation does. The latter mechanism creates the possibility for Pareto improvement. The remainder of this article is organized as follows. Section 2 offers some of the theoretical background of this research. Section 3 describes the basic models that pertain to the procurement mechanisms that we propose. Section 4 analyzes the properties of the models and compares them with bonuses are included. Section 5 presents the optimization procedure for the buyer to choose the optimal sourcing policy and optimal procurement mechanism for supply chain’s surplus. Section 6 offers some additional interpretation of our primary results, to bring out their managerial relevance. Section 7 concludes, and discusses limitations and directions for future work.
نتیجه گیری انگلیسی
We have explored a problem related to the selection of an e-procurement mechanism for an organizational buyer in supply chain management, which has been discussed recently in decision support system terms by Block and Neumann (2008). We compared two multi-stage mechanisms for choosing an appropriate supplier in a setting that involves a procurement auction, and optional price and quality negotiation with an effort bonus for the supplier. The mechanisms both involve an initial second-price sealed-bid procurement auction, and the possibility of offering the winning supplier a bonus for supplying a product or service so that the buyer is satisfied. The mechanisms also differ. The fixed-quality mechanism does not permit supply price or quality negotiation, while our negotiable-quality mechanism allows this. From our review of the prior research, we concluded there are no standard or preferred ways for designing multi-stage e-procurement processes. The inclusion of buyer–supplier negotiation and supplier bonuses are observed in the real world, although there are numerous different mechanisms that have been explored. Yet there is no research that has analyzed the additional aspects of the mechanism that we have focused on to our satisfaction. In particular, we have noted the potential agency problems on the supplier’s side. This is why we decided to include the possibility of a bonus in the mechanism; it treats the issue of moral hazard directly, by permitting the supplier to make a chosen level of effort to produce a product that makes the buyer satisfied. Our results suggest that an option for buyer–supplier negotiation improves the supply chain’s surplus, by encouraging a transaction that includes the most desired price-quality combination by the buyer. This result only will hold so long as the negotiation results differ from the results that the auction produces in terms of the winning supplier, price and quality though. Our results further suggest that negotiation is not always the preferred option for the buyer due to the effects of other characteristics of the transaction. They include the auction outcome, the discount factors on value that the buyer and suppliers ascribe to the duration of the negotiation, and the impacts of the bonus. We note two key limitations to this research that we would like to share with the reader. First, we have boiled down the e-procurement process to a relatively basic form, in order to make it possible to build up greater complexity with post-auction negotiations on price and quality, and the possibility that the supplier can obtain a performance bonus. It is natural to recognize that many e-procurement transactions occur in combinatorial auction form, rather than as one-off acquisitions of individual products or services. This doesn’t invalidate the modeling work that we have done, nor does it blunt the main intuition of our findings. Still, it points out that it is important to see whether it is possible, in advance of building more complex models, to see what sort of intuition might apply in our setting. Thus, it will be necessary to ask questions that include: What issues could arise with the offering of a supplier effort bonus in a combinatorial auction setting that would diminish the logic of our conclusion that bonuses are important in deterring suppliers actions that are founded on moral hazard or adverse selection? How might such a chance in the pre-negotiation mechanism affect the conclusion we drew that post-auction mechanism will lead to first-best supply quality delivery and the most surplus from the supply chain operations? Nevertheless, it will be necessary to spend more time to evaluate whether the general structure of the trade-offs that we described (if not the functional forms or parameter values) will continue to characterize combinatorial e-procurement with negotiation and bonuses. Second, another simplification that we made in this research is that there is always one winning supplier and that the supplier only supplies a unit of the demanded supply. In many real-world e-procurement contexts, it is the case that multiple suppliers provide a buyer with the amount of supplies that are needed to meet the buyer’s manufacturing input and later sales demand. We made a series of explicit choices in the development of our market mechanism – again, to keep things fairly simple, so we could bring out the intuition of our findings – with only one winning supplier, and the winning supplier supplying only a unit of the supply that is required. In terms of future work, this model can be extended in a number of different ways to bring it more in line with the problems in e-procurement practice that we have not considered. First, we now assume only two values for the supplier’s effort, high and low effort, to yield satisfaction for the buyer. A meaningful extension is to consider supplier effort as a continuous variable, which may give rise to somewhat different conclusions regarding the various choice variables for e-procurement mechanism design. Second, recall that our model included a parameter to represent the common knowledge that the buyer and the suppliers have about the cost associated with a supplier’s effort to produce an acceptable quality product. In real-world supply chain operations, it may be the case that this parameter will be a supplier’s private information. The result is that it will be harder for the buyer to gauge what the suppliers’ cost for producing supply of acceptable quality will be. As a result, it may be necessary to incorporate a more sophisticated bonus mechanism that dissuades a supplier from engaging in adverse selection when it wins the right to supply the product. Third, as pointed out by the forward-looking works of Davenport and Kalagnanam (2002) and Rothkopf and Whinston (2007), there will be many settings in which making mechanism design choices involving auctions, bonuses, and price and quality negotiation, whereas we noted that our assumption that procurement occurs on a unit-by-unit basis is too constraining. It will be interesting to further analyze combinatorial supply procurement auctions, and extend them to include supplier effort bonuses and a buyer’s option to include price and quality negotiation. Finally, there is analytical complexity associated with the assessment of the mechanism design choices for e-procurement that we have explored. It will be appropriate to further extend our present efforts with new decision support system capabilities that will permit managers to leverage automated assistance that will produce the right kinds of information.