Research on Pricing Strategy of Online Reverse Auction Based on Complete Information

[a] Finance Economics Department， Shandong University of Science Technology， Jinan City， Shandong Province， China.

*Corresponding author.

Supported by National Natural Science Foundation Program （China） of 2013， NO.71240003.

Received 17 March 2013； accepted 12 May 2013

Abstract

Aiming at the problem of reverse auction which involves one buyer and multiple sellers in procurement market， this paper studies about online reverse auction via internet during which different sellers arrive at different time and bid， and the buyer makes decision whether to purchase after receiving each bid. And then， the random pricing strategy of online reverse auction is researched. After the compare with single pricing strategy， it shows that the random pricing strategy using the market information to make a procurement price can avoid the waste of cost and incomplete procurement， and a case test is provided in the end.

Key words： Online reverse auction； Random pricing strategy； Competitive analysis

INTRODUCTION

How to use the new procurement technology and operational mode to transform and manage the supply chain and procurement processes， then reduce the procurement costs and improve efficiency is becoming more and more concerned and paid attention. The FreeMarkets Company which was established in 1995 by Glen Meakem is the earliest one who used online reverse auction， which is innovative for traditional procurement mode. Reverse auction is one kind of procurement which makes a decision after the end of bidding. With the intensification of the time effect on the procurement cost， reverse auction participants are not willing to wait for the results for a long time. Waiting means that the time cost increases， as well as loss new purchases and sales opportunities， so online reverse auction was proposed. Online reverse auction is that sellers arrive at different time and bid， the decision whether to buy the bidders’goods needs to be made immediately after the buyer receives each bid. Reverse auction in application process gradually gets into transparent equalization. Buyers make public supplier’s information and bidding to change the incomplete information into complete information. The supplier’s competition is more intense under the complete information， which will bring lower prices， higher quality suppliers， and reduce the procurement costs. In practice， the online reverse auction becomes prevalent in Europe and the U.S. since 2000， which has a reverse bid process via internet. Compared with traditional negotiation， this kind of auction can save costs up to 11%-12% for buyers. In this paper， we will mainly study the random pricing strategy of online reverse auction， with comparative analysis to the single pricing strategy.

1. LITERATURE REVIEW

With the development of E-business， it is common to buy and sell goods via internet. The researches on online reverse auctions began from auction research in the first deriving from McAfee， R. P. and J. McMillan’s （1986） agent competitive strategy analysis and government procurement mathematical model in the commissioned agency theory. Subsequently， Dasgupta. S and D. F. Spulber （1989） analyze the reverse auction strategy problem of the procurement management. CHEN （1999） summarizes the quantities of the reverse auction and strategic equilibrium of price auction on the basis of the aforementioned studies， and comparatively analyzes the efficiency of the different auction mechanisms. The research of online reverse auction model and algorithm mostly focus on the online algorithms and competitive analysis methods growing up in computer science. Ron Lavi and Noam Nisan （2000） study the online auctions that after bidders arriving at different time the auction mechanism must make decision immediately after receipt of each bid， they also take advantage of the competition analysis in the worst case to identify the auctioneer optimal supply curve， based on which the online auction mechanism is very competitive. Alan Smart and Alan Harrison （2003） study the role in buyersupplier relationships of online reverse auctions. Avrim Blum， Tuomas Sandholm and Martin Zinkevich （2006） put online learning applications in digital goods online auction， get a new digital goods auction model and a constant competition ratio on the optimal auction revenue under fixed price， and apply the technology applications in the design of online auction mechanism.

The goods purchased through reverse auction are generally standardized goods， of which the true valuation of the supplier is generally converge， so the majority of the bids usually fluctuate within a relatively small range. Buyers provide complete information to suppliers， and then a phenomenon appear that all bids of suppliers are concentrated in a range because of the fierce competition to get the object with smaller advantage. Because buyers can not understand this small range， their valuations of the commodity usually belong to a large range， in which a single pricing is likely to be too high to cause waste of cost， or be set so low that reverse auction comes to nothing， so that the procurement tasks can not be completed. With the help of researches on online algorithms and competitive analysis methods by many other scholars （XU， XU， LU， 2005； DING XU， 2007； XIN， XU， YI， 2007； XU XU； 2008）， this paper studies the random pricing strategy of online reverse auction in which all bids in competition are more concentrated， and comparative analysis with a single pricing proves that when a random pricing strategy of online reverse auction takes advantage of the market price information， the disadvantages of single pricing caused such as the waste of cost or unable to complete the task of procurement can be overcome.

2. PROBLEM DESCRIPTION

Assuming buyers want to buy a certain type of standardized non-market exclusive products， and select a purchase items from a number of suppliers， and suppliers can meet the number of purchasing items of buyers. According to the complete information provided by the buyer， in each stage of the bidding process of online reverse auction， suppliers give different bids in different time， and change the bids in accordance with the changes of other suppliers. Suppliers bid on the one hand are based on their own reservation price， on the other hand are based on the bids of other suppliers at any time to adjust， and maximize their expected utility with lower prices. When the expected utility falls to the minimum margin， the price will no longer be reduced. According to the predetermined procurement strategy， the buyer can analysis the bids of suppliers， determine the most suitable purchase price， and close a deal with a supplier. Overall， in this process， the buyer always expects that the price is more and more lower to achieve the maximum expected utility by reducing procurement costs.

3. MODELING AND ANALYSIS

4.1 Case Description

An assembly production enterprise wants to procure a number of parts from multiple suppliers. Because assembled commodities parts has the characteristics such as purchasing large quantities and high standardization， the enterprise decides to use multi-stage online reverse auction by using bulk procurement of multi-channel. During online reverse auction， the buyer will firstly audit and evaluate the suppliers who applied online to determine

The detailed procurement process of this enterprise is described as follows：

（1） The number attributes of supplier

Because of bulk purchasing， the buyer needs to divide an order into multi orders to respectively procure. According to the number attributes of the procurement goods and each batch procurement number range， in this case， 3 suppliers are needed， and at most one supplier is selected in each procurement stage， so at least there are 3 stages of the bidding. For the simplicity of test， assuming that one supplier is chosen in each stage and the procurement needs 3 stages to be finished.

（2） Assuming that that buyers’ understanding of this product price is limited to a certain price range [48， 72]， the initial price is set at b0=60， because the supplier’s bid would be more concentrated in the less bid interval， the buyer will put every 10 bids into a stage.

To easily establish the model， we assume that the bidding sequence number is the serial number of suppliers， which is shown in Table 1， 2， 3.

Obviously， it is better than the cost of offline purchasing by using online reverse auction RP strategy.

Although using the RP strategy can not make a deal soon， it is able to reflect the supplier’s bidding information from the prices， and the probability of which the transaction price closes to the bidding prices of most suppliers is the largest. The buyer can conduct the transaction price and decide whether or not to choose the supplier.

CONCLUSION

Aiming at the procurement problem that using online reverse auction to select a supplier， this paper studies the optimal single pricing strategy and random pricing strategy， using which the price made for the supplier is independent of has bid price. The latter can take advantage of market price information， correct the defects caused by static single pricing， besides， it has many other advantages such as full market information， low cost of transaction management， and excellent operability， etc. However， this strategy is applicable to the online reverse auction for standard commodities with bid prices more concentrated. It still needs further studies on the problem of more dispersive bid prices and big competitive ratio.

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