An e-procurement Model with Specification Description Improvement

Reiko Hishiyama and Tom Ishida Department of Social Informatics

Kyoto University, Japan Email: hishiyama@kuis.kyoto-u.ac.jp, ishida@i.kyoto-u.ac.jp

Abstract-Some procurement negotiations are difficult to con- clude smoothly using existing information processing systems became they need to first develop fuller buyingkupplying speci- fications through bilateral negotiations between the buyer and supplier. In this paper, we propose an e-procurement model that provides a specification improvement mechanism, whereby a buyer can improve hisher procurement specifications using the supplier’s knowledge. We conducted a simulation based on a real procurement case study to establish the effectiveness of our e-procurement model. Simultaneous procurements using multi-agent based technology can help buyers improve their own specifications. In addition, the e-procurement model helps suppliers to enhance the order-acceptance potential.

, I. INTRODUCTION

Over the past several years, we have had more and more opportunities to carry out procurement activities globally via auction or e-procurement on the Internet. Procurement activi- ties on the Internet encourage a dramatic decrease in procure- men t costs, making skill- independent procurement negotiation possible and increasing supplier availability. However, some goodskervices are still procured via face-to-face negotiation or private negotiation, because most procurements accompany the process of creative specification design or complex negotiation about multiple attributes. Therefore, the procurement process between buyers and suppliers is considered to be a ”Creative collaboration” [ 141.

We need to construct a new information processing model that can account for and thus support the collaborative pro- curement processes seen in the real business world.

To address these issues, we analyzed the distinctive charac- teristics of the procurement activities discovered in the process of specification improvement, and proposed a new information processing model to support such procurement activities on the Internet. As the computational environment considered, we describe a procurement scenario using the scenario description language Q [7], which can be used for interactions between agents and users on the Internet. We conducted a simulation experiment based on a real procurement example in order to verify the agreement of our e-procurement model with the experimental procurement activities. The simulation provided us with a point of view into the interaction between a buyer and suppliers, as well as the design of e-procurement systems.

The remainder of this paper is organized as follows. Sec- tion 2 provides the background to e-procurement modeling. Section 3 outlines our e-procurement model and compares

it to other e-commerce models. In Section 4 we discuss in detail the design of an e-procurement base. Section 5 covers the simulation model and the results that validate our model. Section 6 investigates the procurement interaction and shows our findings on this topic. Also, we show how incremental specification improvement occurs within our model, and how the e-procurement system itself is designed. Our conclusions are found in Section 7.

11. BACKGROUND This section highlights some of the characteristics and issues

related to procurement involving specification improvement. In the real world, there are at least two characteristics that

must be considered with regard to creative col!aboration and that specification improvement. First, the buyer’s procurement activities can be considered as ”online information retrieval”, which incrementally retrieve goods/services that reinfose her value position. At first, when the buyer wants to procure goods or services, her specification is imperfect, because she has insufficient knowledge about the goodstservices. So she dynamically improves the specifications using supplier knowledge via incremental information retrievals. In other words, the buyer continues to improve her ”Request For Pro- posal (RFP)” during the procurement process while reducing the number of suppIy candidates [6]. Second, procurement activities involve, not only the collaboration process between buyers and suppliers, of course, but the competitive selection of bidders via trading negotiation. The buyer is expected to sew up a deal with the best supplier under the best terms.

As suggested above, the goals in this paper are twofold: First, by taking advantage of e-procurement on the Internet,

we propose an e-procurement model of the specification design process, and a suitable means of reaIizing the model.

Second, we investigate whether our new e-procurement system can well reproduce a real procurement case study. That is, we examine the interaction between buyers and suppliers, as weH as the advantages of specification modification according to the buyer’s and supplier’s decision-making on the Internet.

111. COMPARISON OF EXISTING E-COMMERCE MODELS

The e-commerce models that are widely used as means to realize procurement on the Internet are the auction, the e- marketplace, and the Intemet-based private supply network ( P W [21, [41, [51, P I .

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In auctions, including reverse-auctions and PSN, price com- petition is established as a primary goal. Specifications of goodslservices are fixed and do not change once the bidding starts. Also, in relatively new models of e-commerce, such as multiunit auctions, multiround auctions 181, and combinational auctions [13], the primary goal is to find the best price for goods/services specified by the buyer. The Federal Communi- cations Commission (FCC) in the US. has conducted auctions of licenses for electromagnetic spectrum [3]. In this auction, the combination of items changed, however, the modification of combinations depended on the buyer’s decision-making, not a collaborative design process between buyer and supplier.

Auctions are mainly used to determine the final bidder using a winner determination algorithm. However, in the real business world, it is used not only to determine a finalist, but also as a selection process to cut the list of candidates. The buyer often uses the auction as a part of the continuous procurement process by integrating auction into face-to-face competition.

The procurement of some multi-attribute goods/services is based on the multi-attribute utility theory [ 11. Previous models assume a homogeneous supplier community but this does not reflect the diverse needs of buyers or suppliers. The e- marketplace addresses the diversity of participants and brings in more buyers and suppliers. However, the specifications are fixed throughout the procurement process.

We define a supplier’s proposal as making a concerted effort to achieve the solution of the problem, sort of like a ”Rosetta stone” (121 as a critical key to a process of a difficult problem. Our information processing model for conducting e- procurement is defined as follows:

1) The model can deal with goods/services that have multi constraints or multi attributes, for example, price, quan- tity and brand, simultaneously.

2) The information is appropriately collected from a sup- plier’s specifications based on heterogeneous utilities on the Internet.

3) The procurement process is conducted by incremental negotiations, a process in which the specification are not initially fixed. As needed, they are dynamically improved to better reflect the buyer’s needs.

4) The model is designed to correspond not only with a direct ”negotiation phase” against each constraint, but also a creative ”incremental information retrieval phase” through specification refinement.

Fig. 1. Auction model IS] (left) and e-Procurement model (right)

Fig. 2. e-Procurement base for the model

Figure 1 compares the existing typical auction model [XI with our e-procurement model.

Iv. BASE DESIGN

The e-procurement model which we propose accompanies the process of specification design. Therefore, the intelligent support by a software agent is efficient for this model. We have adapted a multiagent framework for our e-procurement model and have designed an e-procurement base that enables communication to be established between buyers and suppliers on the Internet.

In recent years, the application of software agents to e- commerce has been studied intensively and many articles have been published. According to these studies, centralized processing systems are characterized by bottlenecks at the e- market server, so decentralized e-market systems using dis- tributed agent technology have been proposed [6], [ 101. How- ever, the agent interaction for distributed systems is designed by computer engineers, and it is not easy for end-users to describe their own interactions using their terms. To avoid this problem, we adopted the scenaria description language Q [7] and designed a user-centered e-procurement base (Figure 2). A description of a typical interaction pattern can be specialized for each application domain easily. This enables a buyer to monitor supplier’s behavior in the procurement process, or to trace the history of supplier’s decision-making. The buyer can thus view their strategic moves as business logic.

A. Scenario description for specification design We first described a scenario that observes the behavior

of a human buyer and supplier agents. Figure 3 shows one part of the scenario. The scenario is described assuming that the human buyer negotiates price, delivery period, brand, model number, etc. We designed a procurement context where the buyer refines the specifications via online incremental negotiation of each attribute and in doing so narrows down the supply candidates. The negotiation patterns are as follows:

We consider the example of the attribute price. When the human buyer wants to lower the price, her agent who has been delegated to achieve price reductions, negotiates the price directly. The human buyer can then focus on suppliers who have price elasticity.

Taking the price example again, when the human buyer wants to get the price down, the agent negotiates the price

1. Direct negotiation pattern for each attribute

2. Indirect negotiation pattern for each attribute

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(defscenario scenario-buyer () (idle ((?item-selected)( !caIl-for-proposal)(go negotiation)))

(negotiation ((?changecondition-brand)( !propose-brand) (!request-start-negotiation)(go negotiation))

((?c hangecondition-price)(!propose-price) (!request-start-negotiation)(go negotiation))

((changecondition-delivery)( ! proposedelivery ) (!request-start-negotiation)(go negotiation)) ((?ok-selected)(!continue)(go negotiation)) ((?qui t-selected)( ! stop-negotiation)(go check))) .....

Fig. 3. An example for the scenario description (e-buyers)

indirectly, thus, the negotiation considers attributes other than price. If the buyer changes the brand of the item, she will be able to have another chance to search for a potential deal in price.

In above case l,, the attribute being negotiated is the one that the human buyer wants to negotiate. In above case Z., this is not true. The human buyer should concentrate on procurement strategy. Once Q receives a negotiation request from the buyer, the request is sent to the suppliers and the supplier’s responses appear as counter proposals. The buyer uses the responses to improve the specification. The specifications are individualized to meet the needs of the buyer by the incremental presentation of the improved specifications.

B. Negotiation by an e-procurement agent

We also introduce the ”Scope for e-procurement” as a negotiation support tool. It covers two perspectives: 1) the buyer wants to find the specifications that best meet her own needs and 2) the supplier, who wants to propose the beneficial proposals for the buyer, make an attractive counter proposal based on speculation of the buyer’s needs. The view of the scope, which is just a form of spotlighting, is used for nar- rowing down the supply candidates. Unlike an auction, in this method, buying specifications do not necessarily equal supply specifications. If a specification stands in the scope area, it can be proposed back to a buyer as a common specification. The buyer’s RFP is improved and, in accordance with this improvement, the scope on which the spotlight focuses is moved (see Figure 4).

v. EVALUATION EXPERlMENT

In order to evaluate our e-procurement model, we simulated an interaction between a buyer and supplier agents. A real procurement case study exhibiting specification improvement was used as the source material. The simulation was executed based on the scenario shown in Section 4.

This experiment has two purposes. One is to explain a real procurement case study using our e-procurement model. The other is to extract the views of buyers and suppliers via the decision-making process in specification improvement. This

Attr ibute 1

Fig. 4. Specification retrieval and selection using ”Scope”

evaluation allowed us to verify the adequacy of our model throughout the process.

A. Real procurement example and the foremost task

A real-world procurement case study in the real business world for model verification is described below: In August, 2000, a buyer made a procurement request for a

router (1924C-EN), manufactured by Company C. On the same day he decided to open a Call for Proposal to his supplier’s network. At the same time, he individually sent a request for a quotation to a supplier who had a strong association, ”a gold sales partner” with Company C. Howevec the replies received one after another were that they could not deliver by the next

According to one suppiiel; the product that the buyer wanted was in short supply because I ) the production line had not been working well since the factory moved to Mexico in Spring 2000, and 2) the Mexican export procedures were so cumber- some that it would take a long time to obtairl permission. The supplier informed the buyer that if the specifcation were changed to the latest model, 2924, of the same product series, the buyer may find some suppliers who could meet the delivery date. The buyer ckanged the RFP to the latest model, 2924, resulting in the buyer receiving a proposal agreeing tu prompt delivety.

Company G makes two types of proposal. One is the price of delivery only, and the other is the price stated in the maintenance contract. The latter price is lower than the forme5 so the buyer accepts the latter proposal from Company G.

a%.

The key features of this case study are as follows: 1. Delivery date and price were the key factors for the

buyer in decision-making. 2. With the assistance of suppliers, the buyer autonomously

rewrote and improved the specification. 3. The supplier could learn from the RFP that the delivery

circumstances were the key point for the buyer. 4. The supplier could provide useful information to the

buyer, in this case that the latest model was an alternative and may satisfy the buyer’s needs.

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0 node 81 (e-buyer) I' /

V i ewer w i ndow

E H Scope

. ..

Fig. 5. Experimental Q architecture

5. The result of the buyer implementing the supplier's suggestion was that the buyer obtained better proposals, including faster delivery and a lower price.

We verified that our e-procurement model could provide a good explanation of the above five features. In the simulation, we implemented the above example as follows:

Items: Router Model: 1924C-EN Selection process: After 2 suppliers were selected via network procurement, the final selection was conducted via face-to-face competition. Delivery: On or before the buyer's specified date - a late delivery offer is rejected. Budget: Under $250 per 1 router.

The human buyer who set the above specifications in- teracted with the supplier agents and improved the speci- fication through their decision-making. The experimental Q architecture is shown in Figure S. The human cognitive faculty is generally limited to 2- or 3-dimensional space, so the experimental system provided a window to analyze the proposals and allows the two most important features to be focused on (see Figure 6). The screen-displayed proposal data was updated simultaneously as the search progressed. The improvements to the specification were input via the user interface window. The buyer's specifications were sent to all suppIier's agents via the Internet, and the buyer and suppliers negotiated simultaneously. In addition, the behavioral context of the e-procurement interaction was recorded.

B. Experimental results

Figures 7(a) to 7(f) show the experimental results of the pro- cess of adjusting specifications. The two important attributes,

Fig. 6. An implementation for the e-procurement experiments

which are delivery and price, can be selected on the viewer- window by the buyer. The vertical axis shows the delivery date, with an earlier date appearing closer to the coordinate origin. The prices are plotted on the horizontal axis, with a lower price appearing closer to the coordinate origin. The mid-point of both axes is measured with reference to the first call for proposal by the buyer. That is, a proposal that shows quick delivery and low price approaches the coordinate origin. The "r" means RFP, the "s + number" indicates the supplier's id.

Table I shows the progress of specification improvement. The buyer could not obtain a satisfactory supply specifica-

tion (delivery date) in response to the first CFP (Figure 7(b)). He then rewrote the specification in terms of price (which was raised) and reissued the proposal because the delivery date was not negotiable. This behavior indicated to the suppliers that price was not the problem and the buyer was not satisfied by any conditions except the price.

In response to the second CFP, he received a counter proposal that was satisfactory in terms of the delivery date, but it was not the expected product model (see Figure 7(c), the lower right screen) since the counter proposal received from suppliet S4 indicated product 2924.

After noticing that the supplier was indicating the existence of an alternative with a satisfactory delivery date (Figure 7(c), Figure 7(d)), the buyer altered both the model and price and reissued the proposal. This behavior indicated that the buyer was willing to accept a lower-priced alternative. As a result, the buyer's updated specifications yielded a lower price and an earlier delivery date (Figure 7(e)). Furthermore, by using the RFP to send a message, the buyer succeeded in price negotiation (Figure 7(f)).

VI. DISCUSSION

Considering the opinions of the experimental subject, we discuss the findings and implications of our experiment.

A. Evaluarion based on the features of the e-procurement model shown in Section 5

1. The viewer-window allows the buyer to focus on the key attributes, which are delivery date and price. The

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(a) 1st round CFP

I

pnct , . , I

(d) 3rd round CFP

I pncc

(b) 1st round Proposal

( e ) 4th round CFP

Fig. 7. Simulation results

TABLE I CONTEXT OF THE BUYER’S E-PROCUREMENT DECISION

1st round CFP 2nd round CFP 3rd round CFP 2924 On time 4th round CFP 2924 20 days early 5th round CFP 2924 10 days early

buyer can make hisher decision by checking this data during the e-procurement process.

2. Based on information from the suppliers, the buyer changed his mind and improved the specifications, and leading to another model 2924 with a satisfactory delivery date (Figure 7(c), Figure 7(d)).

3. According to the buyer’s call for proposal, which is altered only in price, the supplier can assume that the buyer’s needs might be satisfied by rewriting attributes other than price. That the buyer does not wish to compro- mise price can be understood from the CFP (Figure 7(d)). Thus, by improving their specification in the attributes other than price, and proposing it to the buyer again, the

(c) 2nd round CFP

(4 5tb round CFP

supplier can identify the buyer’s needs. If the suppliers keep track of the procurement context and improve their counter proposal appropriately, they can determine the buyer’s needs.

4. Due to the effect of the Scope, the buyer retrieved related specifications, revealing the existence of another model of the same product series. Our e-procurement model need not necessarily exactly match the buyer’s specifications to the supplier’s specifications. It is impor- tant to match a buyer’s needs and a supplier’s seeds. A look at a range of proposals helps to increase a buyer’s possibility of procurement. 5. The buyer can obtain a new supplier’s specification through hisher own specification improvement. We con- sider that ifthe buyer rewrites the spec$cation, the buyer is nos satis-ed b y one or more attributes. Therefore, if the buyer continues to send rewritten specifications to the market, they will eventually find an opportunity for specification improvement. Specification improvement, incremental proposition process and scrutinizing the ex- pected specification results in a good procurement in this e-procurement model, as well as it is important for buyers

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to search for and gather information by foot in the real business world,

B. Evaluation based on the view of supplkrs

It is expected that the supplier deals in a variety of gooddservices for proposed specifications based on a diverse range of attribute data. A supplier who can propose adaptable specifications using diverse proposal capability will provide beneficial information to the buyer. To ensure proposal vari- ation, a large number of suppliers is better for the buyer. Furthermore, regardless of the contents of an RFP, the supplier, who can read the procurement context and the buyer’s needs from the buyer’s behavior, can increase the chance of order acceptance. In other words, the supplier’s specifications do not need to exactly match the buyer’s specifications in providing benefit to the buyer. The diverse specifications, which are selected according to the similarity or relativity between the needs and the seeds, provide a chance to secure order acceptance in line with the proposal advantage.

C. Key to e-procurement system design

The scenario description language Q can provide an in- terface on cues and actions [7], which enables efficient cooperation on the distributed computing environment. There- fore, we expect an e-procurement system to cooperate with other distributed application systems which are related to the procurement procedure, for example, a contract administration system or a fixed assets management system.

The procurement base is designed as a multiagent system. Human buyerskuppliers are able to delegate their decision- making throughout the procurement process to an intemet- based agent acting on their behalf.

In any e-procurement system, we have to consider that the disclosure of information provided by each supplier would profoundly influence their negotiation strategy. In this sim- ulation, the viewer-window is experimentally hidden from the suppliers. However, if the suppliers know the contents of each other’s proposals, the specification assumes a competitive character. In fact, the relationship between the share of the product market and product rollout has been studied in a multi- alternative decision-making domain [ 1 11. We will address this issue in refining our e-procurement model in the future.

VII. CONCLUSION

To realize an information processing model for e- procurement dealing that offers specification improvement, we addressed the following issues:

We propose the implementation of an information pro- cessing model for e-procurement with specification im- provement.: Analyzing the procurement behavior in the real business world and finding the auction model inad- equate, we propose an e-procurement model. The base design for our e-procurement model uses a multiagent scenario.: We apply the scenario description language Q as an e-procurement base archetecture. It

can reflect the change of the e-procurement context dynamically.

3 ) The validation of our e-procurement model is based on a real example.: Using a real case in the business world, we then conducted a simulation experiment which was ultimately effective in providing an explanation.

In the e-procurement base, the buyer delegates hisher decision-making and negotiates with multiple suppliers simul- taneously. We can explain this case by defining the features of the case and conducting a simulation. Additionally, we extract the decision-making point via analyzing the behavior of buyershppliers, and discuss the design of the application system for our model.

Now we start to verify further cases of e-procurement behavior. Furthermore, from the viewpoint of practical use, the policy of information disclosure, computer interface design, coordination design with product database, and operation policy have to be considered and presented as a future work.

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