An Analysis of the Impact of Reputation on Supply Webs

Transcription

1 An Analyss of the Impact of Reputaton on Supply Webs Jochen Franke, Tm Stockhem Insttute for Informaton Systems Johann Wolfgang Goethe Unversty Mertonstr. 17, Frankfurt am Man, Germany Phone: +(11) Fax: +(11) emal: Abstract In long-term, recurrng contractual relatonshps, whch are common n the B2B-area, reputaton and trust play an outstandng role. The mpact of reputaton and prce-based assessment of supplers on the materal flow n the supply chan wll be nvestgated n ths analyss. Postve reputaton proves to be a key factor to reach a market domnatng poston. We observed n our smulaton, that the assessment of supplers towards a reputaton-based choce has a postve effect on supply chan stablty. In the worst case, a strong reputatonbased choce leads to the formaton of monopoles. The Bullwp-Effect, that could be observed as a second phenomenon n our smulaton settng, represents a countertendency to the reputaton-based monopoly effect. Ths countereffect s observed to be even stronger for members of ters wth a hgh fluctuaton of order rates. Keywords Supply Chan Management, reputaton, mult-agent-systems

2 1 Introducton 1.1 The Impact of Reputaton on Supply Chans Understandng nter-frm relatonshps n supply chans s becomng more mportant n modern economc theory. Intra-frm focussed enterprse resource plannng systems are well known, but they lack gazng at the supply chan as a whole. Understandng and controllng the dynamcs of supply chan processes s crucal to a company s success (Lambert, Cooper, Pagh 1998). Therefore, research n supply chan management has become a broad feld n recent years. In long-term and revolvng relatonshps, as often observed n B2B-relatons, reputaton and trust gan fundamental mportance. The mpact of reputaton and trust on supply chans wll be nvestgated n ths study by usng an agent-based smulaton. Supply chans or supply webs consst of relatonshps between several legal ndependent frms, appearng n one drecton as consumers buyng commodtes and selectng supplers, and n the other drecton as supplers by themselves sellng ther fnshed products to other frms or customers. Products and servces are sold n one drecton, drectly opposed by a fnancal stream of payments. The supply web may vary n depth by the number of ters as well as n broadness by the number of competng frms one ter conssts of. Decentralzed decson behavor s a characterstc attrbute of supply chans. Decson competence n frms s centralsed, wth the management servng as head of the system. In supply chans, frms are nteractng, tradng on markets or establshng contractual relatonshps wth partners, no central nstance s coordnatng and optmzng the whole supply chan. Supply chan management operates on strategcal, tactcal and operatonal levels. On the strategcal level, long-term plans on a hgh level of abstracton are made, whle the tactcal deals wth the mplementaton of these plans. Concrete realsaton s done on the operatonal level (Fox, Chonglo, Barbuceanu 1993). Ths smulaton focusses on the tactcal and operatonal levels. Knowledge of relevant data n the supply chan s generally decentralzed and not avalable to all members. In order to model decentralzed decson competence as well as decentralzed dstbuted knowledge, we used Agent Based Computatonal Economcs (ACE) as our smulaton paradgm, especally mult-agent smulatons (Tesfatson 2000). Frms n the supply chan are represented by economc agents, they only have lmted knowledge of the processes n the supply chan and ther decsons are based on ths lmted knowledge. The behavour of the system not only results as the sum of the ndvdual decsons, the supply chan as a whole shows emergng behavour (Goldsten 1999). The smulated agents buy a homogeneous, arbtrary and not further specfed good from ther supplers, process t, takng a specfed producton tme, and afterwards sell the transfomed convenence product to ther customers. The prce for the convenence good s set by the agent, based on n-formaton about past demand. In the assessment of a suppler, the agent

3 takes two factors nto account, the prce of the raw materal and the ndvdual reputaton, contanng nformaton about the relablty n past transactons wth ths suppler. Trust reduces the complexty of the decson process because the lkelhood of a faled order can be estmated wth lower varance and the qualty of a product need not be tested mmedately (Marsh 1992). Prce-focused decsons get less mportant and the trust of a suppler becomes the key factor. Reputaton reflects the past experences wth a certan suppler. A number of postve transactons wth a certan suppler ncrease hs reputaton, whle some faled transactons sgnfcantly reduce hs reputaton. 1.2 The smulaton model Every smulated agent ranks hs supplers by ther reputaton and ther clamed prce. Wth every transacton adequately completed, the suppler s reputaton s mproved and the lkelhood of a new contract wth ths suppler rses. On the other hand, f an order fals, the reputaton of the suppler goes down. Every agent mantans hs own lst of reputaton values for each of hs supplers. Two agents wth the same set of supplers may dffer n ther assessment of the supplers as there s no jont ratng between agents. If an agent s n the suppler role, then he can decde whether to accept or deny a specfed request by a customer. If the agent beleves, that he wll not be able to satsfy hs customer s needs, then he wll reject the request and no order wll be generated. Every agent generates a safety stock to buffer unexpected varaton n the demand of the succeedng ter. The amount of goods placed n ths safety stock s set by the agents themselves, based on nformaton about past demand. The last customer, the supply web s dp, s trggered exogenous to set a specfed demand quantty. Varous temporal paths for ths exogenous demand can be chosen before startng the smulaton. One may chose a constant demand quantty, a demand followng a random walk path as ntroduced by Hall n an extended verson of the permanent ncome theory (Hall 1978), a snusodal path to represent economc cycles or a constant demand quantty wth shocks at regular ntervals. The smulaton tself s mplemented usng the programmng language Java. 1.3 Related research Due to the specfc structure of the underlyng problem, supply chan management s an deal doman for the applcaton of mult-agent systems. Therefore, a lot of research has been contrbuted to ths area and there already exst several agent-based smulatons (Stockhem & Wendt 2002). Focussng on mult-agent-smulatons addressng the supply chan as a whole, the followng approaches should be mentoned: Swamnathan, Smth, Sadeh (1998) developed an agent-based smulaton platform to enable others to mplement own agents on top of ths platform. The system s ntended to support the development of decson support systems for specfc supply chan questons. Fox and Barbuceneau (1993) deal wth coordnaton ssues on the tactcal and operatonal level, ntroducng autonomous agents and coordnatng ther actvtes by usng cooperaton protocols.

4 Shen and Norre (1998) jon several systems for plannng and controllng producton processes n an open, dstrbuted envronment, smulatng nter-frm cooperatons. Kalakota, Stallaert, Whnston (1996) developed a real-tme nformaton system to control supply chan actvtes by usng a mathematcal model to represent the chan. Eymann and Padovan (2001) use genetc algorthms mplemented n ther agents to vary ther behavour. They analyze decentralzed coordnaton n supply chans. Ther agents take nto consderaton nformaton about the reputaton of a potental transacton partner and use ths to choose the approprate partner. The agents mplemented n our smulaton ought to be classfed as reactve agents. They possess sensors to observe ther envronment and track parameters lke prce or demandquanttes. They have nternal knowledge about the behavour of ther envronment and track past values of the nterestng parameters. By analysng ther sensoral nput and combnng ths nformaton wth nternal knowledge and defned rules, they deduce ther actons (Woolrdge 2000). Although decson rules are qute smple, a flexble and complex supply web system s bult. 2 Supply Chan Network Smulaton The followng secton descrbes the dynamc behavour of the smulaton and explans the core processes performed by the smulated agents. 2.1 The processes of a smulated perod Smulaton tme s dscrete and n every smulated perod a fxed number of processes s executed for all agents and processed step by step (see fgure 1). At the begnnng of every smulated perod, all outstandng orders are checked as to whether they can be fulflled or not. If the date agreed for delvery of a certan order has expred, the order s canceled, suppler and customer are nformed and the customer updates the reputaton value of the suppler. The reputaton value s updated by multplyng the old reputaton value wth a specfed reward or a specfed punshment level, whether the transacton was successful or not. Therefore, at the begnnng of a perod, there are only those orders outstandng whch can possbly be fulflled. In the next step, all agents calculate the prce of ther fnshed goods. Ths calculaton takes place n certan ntervals. It s not performed n every smulated perod and the regularty depends on the parameter settng of the agent hmself. All agents then check ther safety stocks as to whether the estmated quantty of fnshed goods for future perods satsfes the safety stock buffer. If there seem to be nsuffcent goods n a future perod, orders are generated to reach the mnmum acceptable stock quantty as specfed n the safety stock. At that pont all agents calculate the desred level of ther safety stock. Ths calculaton also takes place n certan ntervals, dependng on the parameter settng of the agent.

5 check order status [date of delvery has expred] [order vald] delete order update reputaton values set prce of fnshed goods check safety stock [estmated stock > desred safety stock] generate order set desred heght of safety stock check orders fallng due [actual stock > order quantty] [actual stock < order quantty] exchange goods, funds update reputaton values producton update agent mem ory save smulaton data Fgure 1:UML-actvty dagram of a smulated perod Afterwards, outstandng orders are checked and fulflled f there s a suffcent amount of fnshed goods n the agent s stock. Goods and funds are exchanged between suppler and customer. Fnally, producton s done and raw materal s tranformed one step further to fnshed goods. The length of the producton queue depends on the parameter settng of the agent. 2.2 Settng the prce of goods Settng the prce of the fnshed good s vtal for the economc success of the agent. The calculaton of the prce takes place n certan ntervals and t ncorporates data of recent smulaton perods. Frst of all the agent calculates the volume of sales of n recent perods stored n hs ndvdual memory. He compares the cumulated volume of sales of the frst n/2 perods wth the cumulated volume of sales of the second n/2 perods. If sales have grown, the agent concludes a growng demand and rases the prce of hs own fnshed goods. In contrast, f sales have dropped, he concludes that hs prce s not compettve and therefore needs to be reduced. If there are no sales at all, he sgnfcantly reduces hs prce to regan a compettve poston. Fnally, f sales are equal n both cases, the agent keeps up the actual prce. The value of n s set to 20 n ths smulaton.

6 2.3 Assortment of supplers Before the agent processes requests or buys raw materal, all m supplers of ths agent are assessed. The agents consder two factors to assess ther supplers. On one hand they consder the clamed prce, on the other hand, they assgn a reputaton value to every suppler they know. Ths reputaton value reflects past experences n transactons wth a specfed suppler. By consderng these two factors, a rankng of supplers can be calculated by the agent. 1 Dependng on the weghtng of the two factors, prce or reputaton have a dfferent mpact on the suppler rankng. The ndvdual suppler rankng value (SRV) of a suppler s calculated usng the followng formula: mn{ p 1,..., m} r (1) SRV ( ) = w* + (1 w) * p max{ r 1,..., m} SRV ( ) : Suppler Rankng Value of suppler. w : Weghtng of prce n relaton to reputaton, 0 w 1 p : Clamed prce of suppler. r : Reputaton value of suppler. The formula conssts of two components, one reflectng the clamed prce, the other reflectng the reputaton of the suppler. The prce component s calculated as the rato of the mnmal prce of all supplers known by the agent n relaton to the clamed prce of the suppler to be ranked. The second component, takng nto account the reputaton value of the suppler to be ranked, s calculated as the rato of the ndvdual suppler reputaton value n relaton to the maxmum reputaton value of all supplers known by the agent. Both components, weghted by w, result n the suppler rankng value of the evaluated suppler. The agent s now able to generate a lst of hs potental supplers sorted by the suppler rankng value. 2.4 Settng the safety stock The agents use the nformaton stored n ther ndvdual memory to predct the estmated demand n future perods. In order to be able to tolerate slght fluctuatons, they set up a safety stock buffer. Intally, the agent calculates the mean value of the quantty of goods for all accepted orders n hs memory. He adds the mean value of quantty of goods for all declned order requests. Ths takes care of a slght ncrease n demand level. If the agent declned a lot of order requests n the past because of nsuffcent stock, he was consstently understocked and therefore has to ncrease the safety buffer. 1 Cp. Padovan et al. (2001) for a smlar approach.

7 (2) stock s = s n n acc dec q q = = 1 * 1 can + + max{ q 1,..., n} n n s stock : Desred safety stock. acc q : Quantty of goods for the accepted order. dec q : Quantty of goods for the declned order. can q : Quantty of goods for the faled order. s : Scalng factor s, s > 0. To mnmze the loss of reputaton due to faled orders, the agent adds the maxmum quantty of goods for all faled orders n hs memory to avod beng short on goods n the future. In ths smulaton, hs memory ncorporates nformaton about the last 20 perods. The scalng factor s can be confgured ndvdually for every agent. By settng the scalng factor to values lower than one, agents wth an aggressve way of calculatng the desred stock can be modelled. By settng t to values greater than one, agents acceptng hgh store quanttes to avod reputaton loss can be generated. In ths smulaton, the scalng factor for all agents wll be set to one. 2.5 Processng an order Frst of all, the customer-agent sends a message to all hs supplers and requests the actual prces for ther respectve goods. Based on ths prce vector and the stored reputaton vector, the agent can calculate a vector contanng the suppler rankng values of all hs supplers. 2 Subsequently, he sorts ths vector descendng by value and sends an order request to the hghest ranked suppler, specfyng order quantty and delvery date. If the suppler confrms the request, an order s generated and both partners, suppler and customer get a reference to the object representng the order. They have agreed upon a contractual relatonshp. It s up to the suppler to delver the ordered goods on tme and up to the customer to pay upon delvery. In case there s no suppler able to fulfll the request by the customer, then he wll not meet hs requrements n ths perod. He wll wat untl the next perod and retry to fulfll hs requrements. 2.6 Checkng the stock Knowng the amount of goods on stock n a certan perod, the agent s able to calculate the estmated stock n the next perod usng the formula: 2 The calculaton of the suppler rankng value s shown n detal n secton 2.3.

8 s (3) stocke( t + 1) = stocke( t) + m( t + 1) + q ( t ptf ) n = 0 = 0 n c q ( t + 1) stock e (t) : Estmated quantty of fnshed goods n perod t. m ( t + x) : Raw materal, at present n the producton queue, completed n x perods. q s (t) : Quantty of goods for an order to be fulflled n perod t by a suppler. q c (t) : Quantty of goods for an order to be fulflled n perod t by the agent. ptf : Tme needed to transform raw materal nto fnshed goods. To calculate the estmated quantty of stock for a gven future perod, one must start wth the actual perod and teratvely evaluate the estmated quanttes for all succeedng perods. Based on nformaton about the estmated stock of a certan perod, the agents decdes whether to accept or declne an order request. 3 Analyss of the Results 3.1 Analyss setup Consderng comparable products, further factors affect the selecton of a suppler, for example product qualty, delvery relablty or corporate reputaton. In many cases, the customer s not able to check all relevant attrbutes of a product and reputaton of the suppler s playng a major role. It reflects all past experences wth a certan product or suppler whch the customer hmself or other customers have had. Intally we nvestgated the mpact of the reputaton component upon the formaton of stable supply chans out of the broad dstrbuted supply web and the overall performance of the supply chan. We focussed on comparng the market shares of the ndvdual agents and the number of faled orders n rato to the total number of generated orders. 3.2 Confguraton The smulated supply web conssts of four ters modellng an ndustral supply chan wth four component supplers, two manufacturers, four dstrbutors and eght retalers. 3 Ths confguraton s realstc n that the wdth of the supply chan often decreases n drecton of the manufacturer. Many raw materal supplers delver ther parts to component supplers, who assemble the parts and sell modules to the orgnal manufacturer. They, n turn, put the modules together and create the fnshed product. From ths pont, the supply web broadens for the physcal dstrbuton of the fnshed product, wth some dstrbutors and varous retalers nvolved. The smulated agents are dentcally confgured, dfferng only n the ndvdual amount of tme needed to transfer raw materal nto fnshed goods. It takes two perods for frst ter agents to transform ther raw materal to modules, second ter agents, called manufacturers, 3 Due to model reasons, two more agents are created; one actng as the frst suppler and always delverng raw materal, the other actng as the last customer provded wth and gven an exogenous demand level.

9 need four perods. The dstrbutors or thrd ter agents, take two perods for the regonal dstrbuton, and the agents n the fourth ter, the retalers, requre at least one perod to sell the goods to the last customer. The exogenous demand level of the last customer always requests delvery wth a delvery date lagged by fve perods. Demand follows a symmetrcal Random-Walk-Path wth a steppng of one unt and starts at a level of 40 unts. 3.3 Impact of reputaton to prce weghtng We start the nvestgaton and vary systematcally the weghtng of reputaton to prce. A weghtng of zero reflects a totally prce-based assortment of supplers, whle a weghtng of one leads to a pure reputaton-based selecton. 4 The reputaton value of an agent reflects the past experences n transactons wth a specfc agent. Improvng reputaton s a long-term process, whle reputaton loss can occur quckly followng repeated dsappontment (Padovan 2000). If an order s completed successfully, the customer agent ncreases the ndvdual reputaton value of the suppler. A faled order results n a decrease n the reputaton value. Every agent holds hs own lst of reputaton values for hs potental supplers. Ths may result n dfferent reputaton values by dfferent agents for the same suppler. Agents of the same ter do not share any nformaton about ther reputaton ratngs. In ths case, agents ncrease reputaon by 10% once an order s successfully completed, whlst they reduce reputaton by 25% for every faled order. The weghtng of the reputaton to prce rato s vared n steps by 0.1 startng wth 0.0 (a strctly prce-based assortment) and ncreasng up to 1.0 (a strctly reputaton-based assortment) of supplers. We conducted fve smulaton runs wth 1000 smulated perods each for every weghtng step. We observe the market share of each agent after 1000 smulated perods. Market share s calculated as the total amount of goods traded by a specfc agent n relaton to the total amount of traded goods by the ter the agent pertans to. Ths value represents the relatve quantty of goods traded by the agent and s a good ndcator for hs economc sze and weght. In fgure 2, the weghtng of prce to reputaton has been plotted aganst the respectve relatve market share of the largest agent. To assure comparatblty amongst all ters, the relatve sze s shown here. Assumng an equal dstrbuton, a frst ter agent should reach a market share of 25% because ter one conssts of four agents dvdng the total goods transfer among them. Ths dagram shows the deflecton of the largest agent to ths expected value. For example, consderng a weghtng of 0.2, the largest frst ter agent s on the average 11.57% larger than expected. The expectaton for second ter agents would be 50%, for thrd ter agents 25% and for fourth ter agents 12.5%. Ths dagram shows the respectve devatons only. 4 See paragraph 2.3 for further detals of the employed assessment process.

10 One can notce the tendency to large devatons and therefore domnatng agents for large weghtngs n favour of the reputaton component. Ths effect s most destnct for retalers. As one can see, f choosng a purely reputaton based assortment, the largest retaler s on average 68.90% larger than expected. Relatve Sze of largest Agent 80,00% 70,00% 60,00% 50,00% raw materal suppler (ter 1) manufacturer (ter 2) dstrbutor (ter 3) retaler (ter 4) 40,00% 30,00% 20,00% 10,00% 0,00% 0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70 0,80 0,90 1,00 weghtng reputaton/prce Fgure 2: Relatve Sze of largest Agent Wth a weghtng towards the reputaton component, stable supply chans emerge, carryng the man amount of goods transferred. Domnatng monopoly agents appear for every ter n the supply chan, handlng a vast amount of goods transferred. In contrast, f swtched to a prce-based assortment, market shares are approxmately equally dstrbuted. Wthout any mpact of reputaton (weghtng zero), the respectve largest agents of the dfferent ters are at most 12.75% larger than expected. Agents change ther supplers more frequently, always seekng the lowest prce. As soon as an agent clams a hgher prce than a compettor, he mmedately loses all demand, renderng hs products unnterestng for customers. For ths reason, none of the agents are able to establsh a monopoly or even domnant poston compared to a compettor. Consderng a hgher weghtng of the reputaton component, an agent wll not lose all order requests f clamng a hgher prce. If hs reputaton s hgh enough to offset the hgher prce, he wll contnue to attract customers further on and therefore be able to establsh a monopoly. A hgh mpact of the reputaton component n suppler assortment emphaszes the formaton of domnant monopoly agents. Ths effect s more dstnct for those ters close to the last customer. It s notcable that even wth a total dsregard of the prce component, agents may establsh domnant postons but not total control of ther respectve market. The larger manufacturer agent of ter two reaches a market share of 70.88% by complete dsregard of the prce component, leavng 29.12% of the market to the smaller one. The second manufacturer agent s able to provde goods f the larger one sn`t able to accept an order request. Therefore the smaller manufacturer acts as an alternatve source for rare products.

11 We notce ths phenomenon for all ters of the smulated supply web. The smaller agents act as a back-up source for goods, smoothng the goods crculaton through the supply web and, therefore, provdng a permanent supply. Consderng the performance of the supply web, measured by countng the number of faled orders n relaton to the total number of orders, a slghtly ncreasng contngent of faled orders correlates wth an ncreasng mportance of the reputaton component. The use of reputaton to assess supplers favors the formaton of stable supply chans and domnatng monopoly agents, whle lackng a dstnct mpact on the total performance of the supply web. Assgned to the real world, customers takng brands and reputaton nto account favor the formaton of stable supply chans wth domnatng monopoly agents. As a regresson analyss shows, a purely prce-based assortment of supplers would be favourable because the contngent of faled orders n relaton to total orders slghtly ncreases by rasng the weghtng of the reputaton component. 3.4 Impact of punshment level on faled orders Furthermore, we vary the amount of reputaton lost n a faled order and analyse the mpact on market shares and performance as we have done n the nvestgaton before. The weghtng of the reputaton to prce rato was set to 0.5, both components beng equally weghted. The loss of reputaton was globally set to 10%, 50%, 75%, 90% and 95% of the orgnal value. For ths reason, the 10% level denotes the hghest punshment because the agent loses 90% of hs former reputaton. On the 95% level, the agent loses 5% of hs former reputaton. We performed 15 smulaton runs, each conductng 1000 perods, for every respectve punshment level. As noted earler n the nvestgaton, stable supply chans and domnatng monopoly agents emerge for low punshment levels. Agan, ths effect s more dstnct for retalers or dstrbutors than t s for raw materal supplers or manufacturers. The followng dagams show the mean values of the market shares of retalers and raw materal supplers sorted by sze for the dfferent punshment levels.

12 Market Shares of Raw Materal Supplers 100% 90% 80% market shares 70% 60% 50% 40% 30% 20% 10% 0% 0,10 0,50 0,75 0,90 0,95 punshment level Suppler ranked 1 Suppler ranked 2 Suppler ranked 3 Suppler ranked 4 Fgure 3: Mean values of market shares of ter 1 supplers (raw materal supplers) Market Shares of Retalers 100% 90% market shares 80% 70% 60% 50% 40% 30% 20% 10% 0% 0,10 0,50 0,75 0,90 0,95 punshment level Retaler ranked 1 Retaler ranked 2 Retaler ranked 3 Retaler ranked 4 Retaler ranked 5 Retaler ranked 6 Retaler ranked 7 Retaler ranked 8 Fgure 4: Mean values of market shares of ter 4 supplers (retalers) As shown n fgure 3 and 4, at the 95% punshment level (the weakest level), the largest retaler occupes nearly 100% of the market. The largest raw materal suppler reaches 60% market share on the same punshment level. The monopoly effect s even stronger for weaker punshment levels and for ters n close contact to the end customer. Ths effect s due to the lower devaton of order quanttes for ters relatvely close to the end customer. Retalers have drect access to the actual demand of the last customer. Snce the exogenous demand of the last customer s confgured followng a symetrcal random walk wth a steppng of one, demand quantty wll vary lttle between two smulated perods and retalers are mostly able to accept a request for order.

13 Standard Devaton for 15 Smulaton Runs stdandard devaton max(raw materal suppler) max(manufacturer) max(dstrbutor) max(retaler) ter Fgure 5: Standard devaton of order quantty for 15 smulaton runs The nformaton about actual demand quantty reaches raw materal supplers ndrectly through the order quantty of the manufacturers, who themselves get ths nformaton from the dstrbutors and so on. Therefore, order quanttes vary more extremely for raw materal supplers than for retalers. Ths dagram llustrates the standard devatons of the order quantty for each ter takng the agent wth the maxmal varance for 15 smulaton runs. As one can see, there s a tendency toward hgher varaton n order quantty for ters far away from the actual source of demand, the end customer. Ths effect, n common lterature referred to as the Bullwhp Effect, mposes a countertendency on the reputaton effect. Actual demand varaton s often buffered by safety stock. Therefore, actual nformaton on the quantty of demand gets rregular and ncludes great devaton. The favoured raw materal suppler s often not able to serve a request for order and hs compettors are asked for delvery. Raw materal supplers would have to buffer large amounts of goods to absorb the greater devaton n demand quantty. In contrast, the favoured retaler s practcally always able to accept a request for order. On a low punshment level, he stays the favoured agent even f he ncreases hs prce. By usng a hgh punshment level, all agents are able to be ranked frst at some tme and, therefore, market shares are more regularly dstrbuted. 4 Summary As a result of our smulatons, a hgher weghthng n favour of the reputaton component at the expense of the prce component leads to the formaton of stable supply chans and domnant monopoly agents. Ths effect s more dstnct for ters close to the end customer. As a concluson, the buldng of postve reputaton represents a key factor for a company strvng to reach a domnant poston and gather a large market share n a supply chan. On

14 the other hand, customers assessng supplers by evaluatng nformaton about brands and reputaton favor the formaton of stable supply chans wth domnatng monopoly agents. For members of ters far away from nformaton about actual customer demand, there s a countertendency to the reputaton effect due to the Bullwhp Effect. The hgher devaton of demand quantty n ters far away from the end customer leads to augmented falure of orders and counteracts the monopoly tendency released by the reputaton effect. The amount of faled orders compared to the total amount of orders s lower f a selecton of supplers s done usng prce-based decsons rather than reputaton-based ones and, therefore, prce competton among the supplers s favoured. Ths behavour was already notced by Majahan/Ryzn, demonstratng n ther respectve model the correlaton between horzontal competton and supply chan effcency (Mahajan & Ryzn 1999). In our model, the structure of the supply chan s fxed and predetermned and all agents of a specfc ter are equpped wth the same resources. The extenson of the smulaton model towards dynamc supply web confguraton wll be subject to future research. 5 References Eymann, T, Padovan, B, Pppow, I & Sackmann, S (2001), A Prototype for an agentbased Secure Electronc Marketplace ncludng Reputaton Trackng Mechansms n Proceedngs of the 34 th Hawaan Internatonal Conference on Systems Scences, Hawa. Fox, M, Chonglo, J & Barbuceanu, M (1993), The Integrated Supply Chan Management, Techncal Report, Dept. of Industral Engneerng, Unversty of Toronto. Goldsten, J (1999), Emergence as a Construct: Hstory and Issues, Emergence, vol. 1, no. 1, p Hall, R (1978), Stochastc Implcatons of the Lfe Cycle-Permantent Income Hypothess: Theory and Evdence, The Journal of Poltcal Economy, vol. 86, no. 6, p Kalakota, R, Stallaert, J & Whnston, A (1996). Implementng real-tme supply chan optmzaton systems, Techncal report, Dept. of MSIS, Unversty of Texas at Austn. Lambert, D, Cooper, M & Pagh, J (1998), Supply Chan Management: Implementaton Issues and Research Opportuntes, The Internatonal Journal of Logstcs Management, vol. 9, no. 2. Lee, H, Padmanabhan, V & Whang, S (1997), Informaton Dstorton n a Supply Chan: The Bullwhp Effect, Management Scence, vol. 43, no. 4, p Mahajan, S & Ryzn, G (1999), Supply Chan Coordnaton Under Horzontal Competton, Workng Paper, Columba Unversty/Duke Unversty. Marsh, S (1992), Trust and Relance n Mult-Agent-Systems. A Prelmnary Report. Dept. of Computer Scence and Mathematcs, Unversty of Strlng. Padovan, B (2000), En Vertrauens- und Reputatonsmodell für Mult-Agenten-Systeme, PhD thess, Albert-Ludwgs Unverstät Freburg m Bresgau. Padovan, B, Sackmann, S, Eymann, T & Pppow, I (2001), A Prototype for an Agent-based Secure Electronc Marketplace ncludng Reputaton Tackng Mechansms n

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