A WEB-BASED DSS ARCHITECTURE AND ITS FORECASTING CORE IN SUPPLY CHAIN MANAGEMENT

98 Inernaional Journal of Elecronic Business Managemen, Vol. 7, No.,. 98- (009) A WEB-BASED DSS ARCHITECTURE AND ITS FORECASTING CORE IN SUPPLY CHAIN MANAGEMENT Tien-You Wang * and Din-Horng Yeh * Dearmen of Inernaional Business Managemen Tainan Universiy of Technology Tainan (70), Taiwan Dearmen of Business Adminisraion Naional Chung Cheng Universiy Chiayi (6), Taiwan ABSTRACT In a comeiive marke environmen, suly chain managemen (SCM) has been criical for comanies o survive. Demand lanning lays an imoran role in SCM, for i rovides accurae demand forecass which may achieve cusomer saisfacion by offering benefis such as low invenory level, shor lead ime, efficien resource allocaion, and quick resonse. To obain more accurae forecass, his sudy resens a web-based Decision Suor Sysem (DSS) archiecure and is forecasing core. The forecasing core, named Panel Funcion, conains hree modules: Segmenaion Module, Forecasing Module, and Coordinaion Module. The Segmenaion Module caegorizes cusomers ino hree segmens: Loyal Cusomer Segmen, Poenial Cusomer Segmen, and Swicher Segmen. Based on he hree segmens, he Forecasing Module emloys differen forecasing and analysis echnologies o make an inegraed forecas esimae: ime-series forecasing o caure he loyal cusomer demand rend, Bayesian inference o esimae he rediced value of swicher urchase quaniy, and quesionnaire analysis and brand choice models o unearh oenial cusomers. The resuls from hese hree rocesses are hen synhesized o obain he inegraed forecas, which is hen used in he Coordinaion Module as he base of disribuion lanning, and rovides a minimal sysem-wide oal cos soluion for all aries in he suly chain. As a whole, his DSS archiecure has been shown o rovide an efficien mechanism for collaboraive demand lanning and hel creae he maximum rofi for he suly chain. Keywords: Web-based DSS, Forecasing, Purchase Tendency, Inegraed Forecas, Daa Mining. INTRODUCTION Suly chain managemen (SCM), a ho business issue for he recen decade, has been graned as he soluion of survival in curren comeiive marke environmen. Many comanies recognize he imorance of SCM in order o remain comeiive, bu he imlemenaion of SCM seems no easy ask o be accomlished. According o a Deloie Consuling survey in 998, abou 9% of Norh American manufacurers rank SCM as very imoran or criical o heir comany's success. However, only % of hem in he same survey rank heir suly chains as world class [6]. The main reason for his exreme disariy migh be he comlexiy of inegraing logisics oeraions among firms as well as wihin firm boundaries while bringing o bear aroriae informaion echnologies []. Many researches oin ou ha firms were confroned wih various obsacles of emloying SCM, some of hem involve cos of communicaion wih and coordinaion among differen suliers [], high coss of he invesmen in emloying informaion sysems and develoing he informaion echnology (IT) skills of heir emloyees [5], and he comlex relaionshis in a suly chain, ec. The aricians in a suly chain form an alliance ha can quickly bring ogeher a se of core comeencies o ake advanage of a marke ooruniy. This leads o he advanages of adaabiliy, flexibiliy, agiliy, abiliy o globalize, * Corresonding auhor: 00@mail.u.edu.w

T. Y. Wang and D. H. Yeh: A Web-based DSS Archiecure and is Forecasing Core 99 and allowing arner firms o concenrae on heir core comeence [5]. This alliance requires imely availabiliy of informaion hroughou he suly chain o allow cooeraive and synchronized flow of maerial, roducs, and informaion among all aricians []. However, exhausive collaboraion is required in such an environmen, managers are ofen relucan o devoe hemselves o a virual eam because hey migh lose conrol of rorieary informaion and echnology, and hey also have o rus ousiders and do much more negoiaion as well as coordinaion [5]. These difficulies in SCM may ossibly accoun for he henomenon of he well-known bullwhi effec. The bullwhi effec haens when he demand informaion moving usream in a suly chain is disored and amlified. Lee e al. [] idenified one of he main causes o be demand forecas udaing. They conended ha demand signal rocessing (imlicaing informaion delays) was a maor conribuor o he bullwhi effec, which leads o serious invenory roblems. Lee e al. [] oined ou ha one remedy was o make demand daa a a downsream sie available o he usream sie, so ha boh sies could udae heir forecass wih he same raw daa. Neverheless, even hough he demand daa are shared wih boh usream and downsream sies, he inaccurae forecass led by limiaions of curren forecasing echnologies sill remain as a maor roblem of invenory in SCM. To minimize he bullwhi effec, demand forecasing lays an imoran role in SCM. Accurae demand forecass may achieve cusomer saisfacion by offering benefis such as low invenory level, shor lead ime, efficien resource allocaion, and quick resonse. However, he erformance of demand forecasing has been saggering due o he changing marke environmen and he limiaion of exising forecasing echnologies. This sudy aems o imrove forecasing erformance and o faciliae collaboraive forecasing in a suly chain by resening a web-based DSS archiecure wih an inegraed forecasing core in i. The forecasing core searaes cusomers ino hree segmens: Loyal Cusomer Segmen, Swicher Segmen, and Poenial Cusomer Segmen. Differen echnologies are emloyed in hese hree segmens according o heir characerisics. The resuls from hese hree segmens are inegraed o obain he forecas, which is aniciaed o aenuae he forecasing error. Secion illusraes hese echnologies and relaed researches, Secion deics he deails of he resened DSS archiecure, Secion describes he forecasing core, Secion 5 liss he imlemenaion resuls, and Secion 6 rovides he discussion and conclusion.. APPLICATIONS OF RELATED TECHNOLOGIES The echnologies involved in he resened DSS archiecure consis of differen domains, including decision suor sysem, ime-series forecasing, Bayesian inference, and brand choice models. These echnologies and some of heir alicaions are illusraed as follows.. Decision Suor Sysem (DSS) DSS echnology has been rising and flourishing since 970s. Shim e al. [] resened he evoluion and caegories of DSS, including daa warehousing, OLAP, daa mining, web-based DSS, collaboraive suor sysems, and oimizaionbased DSS. A web-based DSS refers o a comuerized sysem ha delivers decision suor informaion hrough a web browser o someone who needs i. When a user sends requess on a websie, his sysem asses he requess o a daabase server which generaes he query resul se and sends i back for viewing. The web-based DSS serves incessanly wih daa warehouses and OLAP, bu web daabase archiecure should be able o handle a large number of concurren requess when he number of users increases. The web environmen is emerging as a very imoran laform for DSS develomen, using a web infrasrucure for building DSS imroves he raid disseminaion of decision-making frameworks and romoes more consisen decision making on reeiive asks. This may hel faciliae collaboraion beween he suly chain members, so he web-based DSS is chosen as he DSS archiecure.. Forecasing Curren forecasing echnologies refer o quaniaive and qualiaive mehods. Among he quaniaive mehods, ime-series forecasing mehods are used o analyze ime-deenden series daa and redic he fuure values, brand choice models are used o calculae he robabiliy of choice o redic choice behavior, and Bayesian models are used o infer condiional robabiliy. Qualiaive forecasing echnology can be described by environmen scanning, scenarios, and Delhi. A review of recen advances in echnological forecasing can be found in Marino [9]. The forecasing echnologies in he resened archiecure focus on quaniaive mehods... Time-series Forecasing Time-series forecasing erforms linear analysis on demand daa o calculae forecass. Benchmark mehods in his domain include moving average, exonenial smoohing, and auoregressive inegraed moving average (ARIMA). Among hese models,

00 Inernaional Journal of Elecronic Business Managemen, Vol. 7, No. (009) ARIMA yields minimal mean square error [], his makes i he yical mehod for ime-series forecasing. In using ime-series forecasing mehods, forecasers assume ha he as of a ime series conains all he informaion needed o redic he fuure of ha ime series. An aroriae model is hen fied o he hisorical daa, and he roecion of ha model becomes he forecas. This assumion imlies he neglec of exloring he influences from environmenal changes and cusomer urchase reference, which induces a lagging forecas wih elusive residual error. Anoher limiaion of ime-series forecasing echnology is ha when i erforms on nonlinear ime series, he error caused by unknown facors remains o be solved. Recen researchers on ime-series forecasing ry o hel solve his roblem by emloying heurisic mehods such as daa mining o caure he demand aerns inheren in nonlinear ime series [,9,,,7]. A survey of ime-series daa mining was resened by Keogh & Kasey [0]. Alhough some accomlishmen have been achieved by he effor, in general difficulies sill remain because uncerain demand variaions do no follow he same aern all he ime due o consumer behavior, new echnologies or roducs, and oher environmenal facors. Exloring he comosie effecs of hese facors ha influence cusomer demand may caure he fuure rend and hel reduce he errors ha migh exis in classical ime-series forecasing models.. Segmenaion wih Daa Mining The diversiy of cusomer urchase behavior conribues o he demand variaion significanly in reailing indusry, so cusomer urchase endency is seleced for exloring a forecasing alernaive ha is disinguished from radiional ime-series echnologies. From he viewoin of resource allocaion, reail managemen should rioriize cusomers in he following order: () exising loyal cusomers, () new enrans o he marke, and () shoers who are oenially swichable from comeiors []. Based on his viewoin, cusomers are classified ino hree segmens: Loyal Cusomer Segmen, Poenial Cusomer Segmen, and Swicher Segmen in his sudy. The Loyal Cusomer urchases he roduc consanly; he Swicher urchases only a remium rices, and he "Poenial Cusomer" has never urchased he secific roduc bu here is a high ossibiliy of urchasing i in he fuure. Classical segmenaion mehods refer o qualiaive aroach such as Recency, Frequency, and Moneary (RFM) analysis, or quaniaive aroach such as K-means. In recen years daa mining has received more aenion from researchers who work on segmenaion. Some of hem emloy muli-sage rocedure by inegraing classical segmenaion mehods wih daa mining echnology, while ohers ake one of he daa mining mehods as he maor aroach. The mehods used in he lieraures referring o imlemenaion of daa mining on segmenaion are summarized in Table. Table : Mehods of daa mining alicaions on segmenaion in lieraure Single-sage Muli-sage Sage I Sage II Sage III An K-means [] RFM Analysis SOM [6] Associaion analysis [6] RFM Analysis SOM Associaion Analysis [7] SOM [7] LTV (Life Time Value) Decision Tree, Logisic Regression, SOM [8] DEA SOM Decision Tree [] MCFA (Muli-grou Confirmaory Facor Analysis) SOM [5] * Source: he auhor The abiliy of learning he relaionshis beween daa by self-organizing makes Self-organizing Ma (SOM) a oular mehod o work on clusering analysis. Some researchers emloy muli-sage aroaches wih classical segmenaion mehod such as RFM analysis or K-means, and daa mining mehod such as SOM. They claim ha he muli-sage aroaches ouerform classical mehods. In mos of hese researches, cusomer's RFM values are exracaed a he firs sage, and he RFM values are fed ino SOM as inu values a he second sage. The relaionshis beween hem are hus learned, and he clusers hen discriminaed [6,7,7]. The Ariori algorihm, a well-known daa mining echnology for associaion analysis, is also emloyed in hese researches o learn he associaion

T. Y. Wang and D. H. Yeh: A Web-based DSS Archiecure and is Forecasing Core 0 rules of demand daa. The loyal cusomer characerisics can be exraced by he Ariori algorihm o consruc loyal cusomer knowledge base. The ouside consumers and swichers are mached wih he knowledge base o sieve ou ossible loyal cusomer candidaes, or, o form he Poenial Cusomer Segmen.. Brand Choice Models A brand choice model reresens he underlying rocess by which an individual consumer inegraes informaion o selec a brand from a se of comeing brands. Consumers are exosed o various facors ha influence heir execaions and choices on roducs, and Logi models have long been he benchmark mehods of sudying consumers' brand choice behavior. Manrai [8] reviewed he develomens of brand choice models, and rovided he survey. In his survey, he choice models are broadly caegorized o hree grous: () muli-aribue choice models, () reference and choice maing models, and () conoin analysis. The muli-aribue choice models are he maor mehods for deerminaion of marke srucure, demand forecasing, roduc osiioning, buyer segmenaion, and redicion of consumer choice. There are wo fundamenal ways of classifying he muli-aribue choice models, which are driven by wo differen rinciles, namely, () he rincile of uiliy maximizaion founded in economic heory (also called brand-based rocessing ), and () he sychological rincile of feaure-based or aribue-based sequenial eliminaion or aribue-based rocessing. Maximum of uiliies model assumes ha cusomers selec only he roduc from which hey exec o gain he maximum uiliy value and reec all he res. Under his assumion, he uiliies are esimaed by maximum likelihood esimaion mehod, he calculaion of skewness and kurosis is based on hese uiliies, and he selecion of an aroriae brand choice model is decided by he skewness and kurosis rules in knowledge base [0]. The esimaion of urchase endency in Poenial Cusomer Segmen in subsecion.. is based on heir aroach.. THE DEMAND PLANNING DSS ARCHITECTURE The forecass in his secion are made on one secific roduc in reailing indusry. Figure deics he comonens in he resened archiecure. Three of hem are direcly linked o user inerface: Demand Udae Inerface, Panel Funcion, and Knowledge Base Inerface. Behind Panel Funcion is Demand Agen, which drives he main rocess of his DSS archiecure. The maor effor of his sudy focuses on Forecasing Module, which is he core of Panel Funcion.. Demand Udae Inerface Demand Udae Inerface conains DBMS module, dealing wih mainenance of SCM daabase, daa cleaning, daa rearaion, and informaion securiy. Users inu and udae demand daa hrough his inerface. Figure : Web-based archiecure for demand lanning. Demand Agen Demand Agen conrols Panel Funcion, Inference Engine, and Model Base. By he reques of Panel Funcion, Demand Agen reads he reques rofile and demand daa, and inacivaes Inference Engine. A reor is sen back o he user by Demand Agen wih he resul of rocess. Deails are described in he following secion... Panel Funcion Panel Funcion rovides he reques inerface for user. When a reques is roosed, Panel Funcion sends he reques rofile o Knowledge Base. If a mach is found, he resul is sen o Demand Agen; if no, Panel Funcion acivaes he corresonding module in i... Inference Engine The maor mission of Inference Engine involves maching he reques rofile wih knowledge base, and selecing an aroriae model in Model Base in resonse o he requiremen by he running module. Inference Engine also makes udgmen on which of he models o be chosen by reques, and sends i o he clien module... Model Base Model base conains models and algorihms referring o he sae of he ar echnology. These models and algorihms are groued as modules, and each module in hem will be udaed when an imroved version is released.

0 Inernaional Journal of Elecronic Business Managemen, Vol. 7, No. (009). Knowledge Base Inerface The knowledge base consiss of a fac base and a rule base. The fac base sores siuaions and variables, while he rule base sores obecives and crieria (e.g. wha-if rule). The Knowledge Base Inerface akes charge of he mainenance of he knowledge base.. THE ARCHITECTURE OF FORECASTING MODULE The Panel Funcion consiss of Segmenaion Module, Forecasing Module, and Coordinaion Module. As shown in Figure, he conex sars from he Segmenaion Module, goes on he Forecasing Module, and hen erminaes a he Coordinaion Module. This secion illusraes he Forecasing Module, which is he core conce of his sudy. Figure : Archiecure of anel funcion. Forecasing Module Cusomers in he hree segmens reveal differen urchase behaviors, herefore differen forecasing mehods are alied o each segmen based on he behavior aern. ARIMA models are used in he Loyal Cusomer Segmen for heir saionary characerisics. Bayesian inference is emloyed for calculaing each swicher's urchase robabiliy a every romoional aciviy, and esimaing he urchase quaniy. The unknown references and behaviors of oenial cusomers call for quesionnaire analysis and brand choice models o obain he urchase endency. This urchase endency is he key of inegraion funcion, which aims a synhesizing he analyical resuls from he hree segmens... Loyal Cusomer Segmen Loyal cusomers are defined o be hose who consanly urchase his roduc. Their urchase behavior is sable, long-erm and redicable, saisfying he saionary requiremen of ARIMA models. Therefore, an ARIMA model which fis he demand aern is aroriae o redic loyal cusomer demand. The oucome of he forecasing is FL, sanding for he forecas value a ime eriod for loyal cusomers... Poenial Cusomer Segmen The rocess of mining urchase endency includes wo sages: quesionnaire analysis for mining cusomer references, and quanificaion of hese references wih brand choice models. The reference of cusomers in his segmen is unknown, so quesionnaire analysis is needed o assis idenifying he cusomer reference. The resul of reference analysis decides wha aribues o be used for esimaion of uiliy. Wih seleced facors, i goes on assessmen of he uiliy funcion and calculaion of uiliies. The uiliies are he foundaion of esimaing skewness and kurosis, which decide he selecion of a fiing choice model. The robabiliy obained from he seleced choice model is T, he urchase endency. This esimaion rocess is based on he aroach resened by Masasinis and Samaras [0], and he imlemenaion of his rocess is illusraed in Subsecion 5.... Swicher Segmen Swichers refer o cusomers who only buy he secific roduc a romoional aciviies or remium rice. Bayesian inference mehod is emloyed o calculae he condiional robabiliy of cusomer's decision, and a oal urchase quaniy in he Swicher Segmen is esimaed. The esimaed oal quaniy is hen mulilied by he robabiliy o obain he Swicher forecas a ime eriod, FS. An imoran mission of Bayesian inference is o induce a beer aggregae forecas han a sand-alone one. The evaluaion of forecasing erformance in his segmen consiss of he aggregaion of Loyal Cusomer Segmen and Swicher Segmen... Inegraion Funcion of Forecasing One of he maor conribuions of his sudy is he inegraion funcion F, which inegraes he demand forecass FL, T, and FS. FL and FS are addiive quaniies, while T is a robabiliy reresening a endency. This calls for a ransformaion funcion, g ( T ), which is formulaed deending on he disribuion of he demand daa for ransforming T o he corresonding quaniy.

T. Y. Wang and D. H. Yeh: A Web-based DSS Archiecure and is Forecasing Core 0 The following liss he general form of inegraion funcion. T FS F f ( FL,T, FS ) FL g () 5. IMPLEMENTATION The roosed archiecure is designed o aly o cusomer daabase. Cusomers are searaed ino differen segmens based on heir demograhics and sales daa. To give an examle of how his archiecure works, osed daa from Monhly Reor on Tourism is used o illusrae he conces involved. 5. Daa In his research, inbound series of monhly ouris arrivals from Jaan is rerieved from Monhly Reor on Tourism (Tourism Bureau, R. O. C., 007) from January 00 o July 006 for imlemenaion in Forecasing Module. The series daa, rerieved from he official websie of Tourism Bureau, are ublic and rovided by he o-level auhoriy of ouris indusry in Taiwan, and are herefore considered auhenic and suffice for he urose. Figure draws he series line char. This series, named Series Jaan, liss differen uroses of visiing Taiwan including Business, Pleasure, Visiing Relaives, Conference, Sudy, Ohers, and Unsaed. The redominan wo caegories (Business and Pleasure, 88%) are seleced for analysis. flucuaes seasonally, which is associaed wih holidays, vacaions, and romoion aciviies, so Bayesian inference is emloyed for Swicher Forecasing for he even-driven behavior of he ouriss belonging o his segmen. In Poenial Cusomer Forecasing, he aniciaed oucome of quesionnaire analysis is shown by cusomer reference ables, a uiliy funcion is esimaed o obain uiliy values from hose reference ables, and a brand choice model is seleced based on he knowledge rules resened by Masasinis & Samaras [0]. ARIMA is also alied o Series Jaan for he urose of comaring erformance wih he inegraed forecas. In he imlemenaion, he raining samle eriod is chosen from January 00 o Augus 005, while he forecasing eriod is from Seember 005 o July 006. The comarison of erformance is based on he forecasing eriod, and he erformance measure used for comarison is mean absolue ercenage error (MAPE). 5. Loyal Cusomer Forecasing The ARIMA (, d, q ) model is defined as d ( B)( B) Y C ( B) [], where ( B)... q q B B B () q B B q B () ( B)... B is he backshif oeraor, i.e. BY Y, is he random disurbance called whie noise, indicaes he number of AR arameers of, d is he number of imes he daa should be differenced o induce a saionary series, q is he number of MA arameers of. The esimaed model for Series Business in his form is hen given by Log( Y ) Log Y A u Log Y 0 ( ) ( ) A () Figure : Inbound ouris series from Jaan wih is sub-series business and leasure As saed before, in he roosed forecasing archiecure differen forecasing echnologies are erformed in accordance wih he naure of he ouriss in each segmen. The sable naure of Series Business saisfies he saionary requiremen of ARIMA, so ARIMA is alied o Series Business o simulae Loyal Cusomer Forecasing. Swichers are hose who buy he roduc only a romoion aciviies, heir behavior is even-driven. The riori robabiliy can be calculaed from heir revious sales, so Bayesian inference is aroriae for redicing heir fuure sales. Series Pleasure where 5 5 ( B )( 5B ) u ( B)( 5B ), A is a dummy variable referring o he effec of SARS a ime eriod. For he urose of adusmen, if he number of ouris arrivals is affeced by SARS, hen A equals o ; oherwise, A equals o 0. The arameers of he model are esimaed by saisical sofware EViews. Thus, Equaion () can be rewrien as Log( Y ).86 0.7Log( Y 0.A 0.8A where ( 0.9B u )( 0.B ) 0.87Log( Y ) ) u ( 0.9B)( 0.859B (5) )

0 Inernaional Journal of Elecronic Business Managemen, Vol. 7, No. (009). This model fis he requiremens of no auocorrelaion, no serial correlaion, and no heeroskedasiciy. The R value of his model is 0.97, which leads o he MAPE of.9%. Figure shows he acual Series Business and esimaed series. x x 0., for,,..., 6 corresonding o years 00, 00,, 006, resecively. The esimaed oal number of arrivals in 006 is y e.56 77856, and monhly arrivals from January 006 o July 006 are calculaed by he raio o he esimaed oal as he forecass. The MAPE of Bayesian aroach is 7.%, which is much beer han ha of ARIMA model. The aggregae forecasing erformance of Series Business and Series Pleasure is also comared beween Model RR and Model RB, in which Model RR is he summaion of Business ARIMA forecass and Pleasure ARIMA forecass, and Model RB refers o he summaion of Business ARIMA forecass and Pleasure Bayesian forecass. The MAPE calculaion is based on equaion (). I is shown in Table ha Model RB ouerforms Model RR significanly. Figure : Acual series business and he esimaed series Jaan ( EsimaedBusiness EsimaedPleasure ) (6) 5. Swicher Forecasing An ARIMA model of Series Pleasure is esimaed for he urose of comarison beween ARIMA and Bayesian inference, so he swicher forecasing succeeds he loyal cusomer forecasing in his secion. The R value of his model is 0.9667 and he MAPE is 0.9%, revealing he need of a beer fiing model for he Swicher Segmen. For his reason, monhly ouris arrival raio from Seember 005 o July 006 is calculaed as he rior robabiliy. The duraion of each even refers o each year from 00 o 006, while he "ime eriod" refers o each monh from January o December. The logarihm of he oal arrivals in x 006 is esimaed by he equaion ln y b m, he arameers b., m. 00, and Table : Comarison erformance beween Mode RR and Model RB Model RR Model RB MAPE.99 6.88 5. Poenial Cusomer Choice Probabiliy Poenial cusomer forecasing involves comlicaed analysis skills. According o he rocess resened by Masasinis & Samaras [0], cusomer reference able is he saring oin. Based on he cusomer reference ables, a uiliy funcion is evaluaed o obain uiliy values, and he arameers are assessed wih hese uiliy values. Table liss wo reference ables and heir esimaed uiliy values, in which equaion (5) and equaion (6) saes heir uiliy funcions. Figure 5 deics he line chars of he sored uiliies for resonden A and B. ln( g ) ln( ) ln( ) ln( ) max( 5) g g g g g ua ( g) 0.07 0.6 0. 0.86 0.08 ln( 5) ln( 5) ln( 5) ln( 5) max( g ) ln( g max( ) ) ln( ) ln( ) ln( ) g5 g g g g i ub ( g) 0.0 0.05 0.068 0.669 0.089 ln( 5) ln( 5) ln( 5) ln( 5) max( g ) i 5 5 5 5 (7) (8) The assessmen of arameers involves he calculaion of skewness, kurosis, and he udgmen of. By definiion, m and ( m m ), where n i n f x i i f i i of arrival, and m m n i i i r n r f ( x ) and f i i. is he mean value of he frequencies fi ni n is he frequency of arrivals in inerval x i. The value of is deermined by he range R U max U as follows: if min 0 R 0. ; if 0. R 0., if 0. R 0.6, and if 0.6 R. Table shows he variables of esimaion of and. A choice model is hen seleced from Table 5 as a resul of maing he arameers wih he knowledge rules in Aendix A. The choice robabiliy is calculaed from he seleced choice model. When he robabiliies for all desinaions

T. Y. Wang and D. H. Yeh: A Web-based DSS Archiecure and is Forecasing Core 05 are yielded, he averaged robabiliy for a secific desinaion is calculaed o be he arameer of esimaing he number of oenial cusomers in inegraion funcion. Table 6 liss he obained arameers, he seleced choice model, he robabiliies for each cusomer, and he averaged choice robabiliy. Table : Two mulicrieria reference ables and uiliies of overseas ravelers from Jaan Cusomer A g g g g g 5 Uiliy China 0 0.79 Korea 0.69 Hong Kong 65 0.56 Thailand 8 0.97 Taiwan 7 0.857 Indonesia 8 0.560 Singaore 5 0.65 Philiine 6 0.8 Malaysia 55 0.0 Vienam 5 0.90 Macau 58 0.5 Cusomer B g g g g g 5 Uiliy China 5 0 0.850 Korea 0.809 Hong Kong 5 5 65 0.505 Thailand 8 0.5575 Taiwan 7 0.857 Indonesia 8 0.809 Singaore 5 5 0.5608 Philiine 5 5 6 0.55 Malaysia 55 0.8850 Vienam 5 5 0.6 Macau 58 0.56 0.65 0.6 0.60 0.57 0.5 0.5 0.8 0.6 0. 0.0 0.7 0. x x Uiliy A Hong Kong Philiine Malaysia Singaore Vienam China Taiwan Thailand Macau Indonesia Korea Figure 5a: Line char of he sored uiliies from resonden A 0.86 0.8 0.75 0.70 0.65 0.59 0.5 0.9 0. 0.8 0. Uiliy B Philiine Hong Kong Thailand Singaore Macau Vienam Indonesia Korea China Taiwan Malaysia Figure 5b: Line char of he sored uiliies from resonden B Table : Necessary arameers for assessing skewness and kurosis coefficiens A B A B U max 0.69 0.8850 x 0.987 0.6 U min 0.56 0.55 x 0.7 0.876 R 0.8 0.599 x 0.55 0.506 δ x 0.87 0.596 m 0.006 0.00 x 5 0.50 0.666 m 0.000-0.000 x 6 0.50 0.6996 m 0.000 0.00 x 7 0.5686 0.756 μ 0.5068 0.788 x 8 0.5969 0.8056 x 9 0.65 0.8585 x 0 0.656 0.95 A B f 0.0909 0.0909 f 0.0000 0.0000 f 0.77 0.0909 f 0.0909 0.77 f 5 0.77 0.0909 f 6 0.0909 0.0000 f 7 0.0000 0.0000 f 8 0.0909 0.0000 f 9 0.0000 0.0909 f 0 0.0909 0.66 5.5 Inegraed Forecasing The Inegraion funcion is he ulimae decision mechanism of he inegraed forecas. I consiss of he aggregae forecas by Model RB, and he ransformaion funcion of he choice robabiliy. The general form of inegraion funcion is reresened by equaion F FL FS gt, where F is he inegraed forecas, FL is he loyal cusomer forecas, FS is he swicher forecas, and g ( T ) he ransformaion funcion of choice robabiliy T. FL FS refers o he aggregae forecas by Model RB, g ( T ) ransforms he choice robabiliy o a rediced oenial ouris arrivals by equaion

06 Inernaional Journal of Elecronic Business Managemen, Vol. 7, No. (009) g( T ) T B, where B is he oenial cusomer base. The grou of Jaanese overseas ravelers who visied Asian counries exce Taiwan is he oulaion of oenial cusomers. A roorion is assumed ha some of hese ravelers ranked Taiwan as heir firs rioriy, bu he our is no carried ou for some reason. Thus B is esimaed by equaion TW B Asia TW, where Asia is he oal Asia ravelers o all Asian counries a ime eriod, and TW is he number of ravelers o Taiwan a ime eriod. The deailed inegraion funcion can be reresened by equaion (7), which is called Model RBP. F Fl Fs T Asia TW (9) Asia TW Table 5: Formula of brand choice models Brand Choice Model Formula Luce U i P i C U Lesourne Mulinomial Logi Model (McFadden-) Slighly Reinforced (McFadden-) Widh of Uiliies- Widh of Uiliies- Maximum of Uiliies Equal Probabiliies k C U ik i Pi C k U C ik P P i C C U i e U ik k e C e Ui i Uik k e C U max U min Ui Pi C U max U min k U C ik ( U max U min ) U i e P i C ( U max U min ) U ik k e C if Umax U Umax i, P C m i 0 oherwise, where P, m U i U n max where U max min U min 0. * Source: Masasinis and Samaras [0] Table 6: Parameers, seleced choice models, and averaged choice robabiliy Model Probabiliy Average A 0.65-0.090 0.095 B -0.06 -.5907 0.09 0.099 For he urose of comaring he erformance of differen aroaches, ARIMA is alied o Series Jaan, reresened by Model AJ. The comarison summarized in Table 7 shows ha Model RB ouerforms Model AJ, while Model RBP ouerforms Model RB. This resul verifies he effeciveness of he inegraed forecasing aroach. Table 7: Comarison of erformance beween inegraed forecasing and ohers Model AJ Model RB Model RBP MAPE 8.0 6.88.6 6. DISCUSSION AND CONCLUSION Ineffecive demand lanning in SCM leads o serious roblems such as he bullwhi effec and severe invenory roblems, a web-based DSS archiecure is roosed in his sudy for roviding more effecive and accurae forecass which may imrove he demand lanning erformance, as well as faciliae collaboraive forecasing in a suly chain wih efficien collaboraion mechanism. 6. Inegraed Forecasing Sraegy In he roosed archiecure, cusomers are divided ino hree differen grous: Loyal Cusomer Segmen, Poenial Cusomer Segmen, and Swicher Segmen. ARIMA models are alied o he Loyal Cusomer Segmen for esimaing a ime-series forecas, quesionnaire and brand choice models are erformed in he Poenial Cusomer Segmen o obain cusomer urchase endency, and Bayesian inference is emloyed in he Swicher Segmen o evaluae he urchase quaniies a romoional aciviies. The inegraion funcion inegraes hese analyical resuls o obain an inegraed forecas, which is aniciaed o miigae he forecasing error. 6. Research Imlicaions As deiced in Figure, he resened archiecure rovides a web mechanism for collaboraion beween suly chain members, i involves inerne informaion echnology. This sudy emhasizes on he forecasing core, namely Panel Funcion. Among he modules in Panel Funcion, Forecasing Module is he core of his sudy. In he following subsecions 6.. o 6.., he imlicaions of each segmen and he inegraion funcion are discussed. 6.. Loyal Cusomer Forecasing In he Loyal Cusomer Segmen, he MAPE of he esimaed model for Business is.9%, while ha for Pleasure is 0.9%, indicaing he excellen work of he esimaed ARIMA model wih he dummy variable A. The ARIMA model erforms

T. Y. Wang and D. H. Yeh: A Web-based DSS Archiecure and is Forecasing Core 07 ousandingly on saionary series, and A hels caure he ouliers formed by SARS effecively. 6.. Poenial Cusomer Forecasing Poenial cusomer forecasing is unique for is feaure of mining. The rocess illusraed in Subsecion 5. is designed for uncovering cusomer endency. The urchase endency reresens cusomer inclinaion on he secific roduc, i also servers as he base of oenial cusomer forecasing, oining ou a direcion for demand lanning. The imoran imlicaion of his urchase endency is ha i quanifies wha is inside a cusomer's mind and indicaes a rend of cusomer reference, which rovides a leading view of demand lanning, his may hel raciioners avoid mislead invesmen. This is he maor difference of his resened forecasing mehod from radiional forecasing models. 6.. Swicher Forecasing Swichers urchase only a romoional aciviies or remium rices, making he swicher demand a non-saionary series, which exlains ar of he forecasing error. Bayesian inference ouerforms ARIMA in he Swicher Segmen on boh sand-alone Series Pleasure and aggregae series of Business and Pleasure, which roves Bayesian inference a more effecive aroach o redic even-driven urchase behavior. 6.. Inegraion Funcion The Forecasing Module resened in his sudy sars from erforming differen analysis and forecasing rocesses in each segmen, and ends u a inegraion funcion of inegraing he analyical resuls from he hree segmens. The execed conribuion of inegraion funcion is a more accurae forecas o romoe demand lanning erformance. The imlemenaion resul lised in Table 7 reveals marvelous imrovemen of inegraed forecasing in comarison wih he sraigh ARIMA aroach, and he hybrid model of ARIMA and Bayesian inference. The imlicaion of his resul oins ou a differen direcion from radiional linear forecasing hinking: differeniaion in differen cusomer segmens should be considered in forecasing analysis. I is aniciaed working effecively on romoing demand forecasing accuracy. Furhermore, he influence from qualiaive facors such as cusomer urchase endency should be dug ou and accommodaed in he forecasing model o reduce he unredicable error. migh ake years of exer eamwork effor, alhough he ouu will rove i worhwhile in he long run. 6. Conclusion and Fuure Research Demand lanning is he key facor of lowering down invenory level and gaining maximum rofi in SCM. In he as, ime-series forecasing models, he classical mehods for demand forecasing, assume ime series as a linear series conaining all he informaion needed o forecas he fuure, and his makes a roblem when hey are alied o a nonlinear series. Forecasing Module, he core of his archiecure, is designed o work ou his roblem by mining cusomer urchase endency wih heir reference. The roosed web-based DSS archiecure is designed o hel unloose he obsrucion of informaion caused by informaion sharing deficiencies. This archiecure is quie comlicaed bu worh imlemenaion for is effeciveness, i faciliaes collaboraive forecasing and benefis quick resonse in a suly chain. The roosed web-based DSS is a firs sone of web-based inegraed forecasing sysem, oining ou an oimisic direcion of imroving forecasing echnology. Boundless develomen may be done by fuure effor in his archiecure. Some ossible fuure researches are: () alying Fuzzy o seize cusomer reference in comarison wih brand choice models () analyzing swichers' choice behavior wih brand choice models. This migh lead o a deeer behavioral analysis. () esimaing oenial cusomer base wih differen aroaches and variables. For insance, economic roseriy migh be an imoran index of oenial cusomer base. In his sudy he resul of inegraion funcion rovides a managerial imlicaion of lanning cos: a rough segmenaion of sub-series may achieve a saisfacory erformance, indicaing beer erformance wih recise segmenaion. I is a rade-off beween beer erformance and lower lanning cos. Also, i is ineresing o deciher wha influences he decision rocess behind urchase behavior and how o measure i in a managerial archiecure. The Poenial Cusomer Forecasing is an aem o quanify he qualiaive inside endency, and rovides i as a foundaion of quaniaive inegraion forecasing. The imlemenaion resuls show ha he roosed inegraed forecasing core effecively miigaes forecasing error, indicaing an oimisic direcion. Furher research oward his direcion could make he inegraed forecasing echnology fruiful. 6. Research Limiaions A echnological limiaion is ha he imlemenaion of he resened web-based DSS

08 Inernaional Journal of Elecronic Business Managemen, Vol. 7, No. (009) REFERENCES. Box, G. E. P., Jenkins, G. M. and Reinsel, G. C., 99, Time Series Analysis: Forecasing & Conrol, Prenice Hall, New Jersey.. Fredenhall, L. and Hill, E., 00, Basics of Suly Chain Managemen, S. Lucie Press, New York.. Ghiassi, M. and Sera, C., 00, Defining he inerne-based suly chain sysem for mass cusomized markes, Comuers & Indusrial Engineering, Vol. 5, No.,. 7-.. Ghiassi, M., Saidane, H. and Zimbra, D. K., 005, A dynamic arificial neural nework model for forecasing ime series evens, Inernaional Journal of Forecasing, Vol., No.,. -6. 5. Gunasekaran, A. and Ngai, E. W. T., 00, Informaion sysems in suly chain inegraion & managemen, Euroean Journal of Oeraional Research, Vol. 59, No.,. 69-95. 6. Ha, S. H., Bae, S. M. and Park, S. C., 00, Cusomer s ime-varian urchase behavior and corresonding markeing sraegies: An online reailer s case, Comuers & Indusrial Engineering, Vol., No.,. 80-80. 7. Hsieh, N. C., 00, An inegraed daa mining and behavioral scoring model for analyzing bank cusomers, Exer Sysems wih Alicaions, Vol. 7, No.,. 6-6. 8. Hwang, H. S., Jung. T. S. and Suh, E. H., 00, A LTV model and cusomer segmenaion based on cusomer value: A case sudy on he wireless elecommunicaion indusry, Exer Sysems wih Alicaions, Vol. 6, No.,. 8-88. 9. Jeong, B., Jung, H. S. and Park, N. K., 00, A comuerized causal forecasing sysem using geneic algorihms in suly chain managemen, The Journal of Sysems and Sofware, Vol. 60, No.,. -7. 0. Keogh, E. and Kasey, S., 00, On he need for ime series daa mining benchmarks: A survey and emirical demonsraion, The 8h ACM SIGKDD Inernaional Conference on Knowledge Discovery and Daa Mining, h://cieseer.is.su.edu/keogh0need.hml.. Kuo, R. J., Wang, H. S., Hu, T. L. and Chou, S. H., 005, Alicaion of an K-means on clusering analysis, Comuers and Mahemaics wih Alicaions, Vol. 50, No. 0-,. 709-7.. Las, M., Klein, Y. and Kandel, A., 00, Knowledge discovery in ime series daabases, Proceedings of IEEE Transacions on Sysem, Man, and Cyberneics Par B: Cyberneics, Vol., No.,. 60-69.. Lee, H. L., Padmanabhan, V. and Whang, S., 997, The bullwhi effec in suly chains, Sloan Managemen Review, Vol. 8, No.,. 9-0.. Lee, J. H. and Park, S. C., 005, Inelligen rofiable cusomers segmenaion sysem based on business inelligence ools, Exer Sysems wih Alicaions, Vol. 9, No.,. 5-5. 5. Lee, S. C., Suh, Y. H., Kim, J. K. and Lee, K. J., 00, A cross-naional marke segmenaion of online game indusry using SOM, Exer Sysems wih Alicaions, Vol. 7, No.,. 559-570. 6. Liao, S. H. and Chen, Y. J., 00, Mining cusomer knowledge for elecronic caalog markeing, Exer Sysems wih Alicaions, Vol. 7, No.,. 5-5. 7. Lingras, P., Hogo, M., Snorek, M. and Wes, C., 005, Temoral analysis of clusers of suermarke cusomers: Convenional versus inerval se aroach, Informaion Science, Vol. 7, No. -,. 5-0. 8. Manrai, A. K., 995, Mahemaical models of brand choice behavior, Euroean Journal of Oeraional Research, Vol. 8, No.,. -7. 9. Marino, J. P., 00, A review of seleced recen advances in echnological forecasing, Technological Forecasing and Social Change, Vol. 70, No. 8,. 79-7. 0. Masasinis, N. F. and Samaras, A. P., 000, Brand choice model selecion based on consumers mulicrieria references and exers knowledge, Comuers & Oeraions Research, Vol. 7, No. 7-8,. 689-707.. Paerson, K. A., Grimm, C. M. and Corsi, T. M., 00, Adoing new echnologies for suly chain managemen, Transoraion Research, Vol. 9, No.,. 95-.. Peng, Y. Q., Zhang, Y. and Tian, H. S., 00, Research of ime series aern finding based on arificial neural nework, The Second Inernaional Conference on Machine Learning and Cyberneics,. 85-88.. Rhee, H. and Bell, D. R., 00, The iner-sore mobiliy of suermarke shoers, Journal of Reailing, Vol. 78, No.,. 5-7.. Shim, J. P., Warkenin, M., Courney, J. F., Power, D. J., Sharda, R. and Carlsson, C., 00, Pas, resen, and fuure of decision suor echnology, Decision Suor Sysems, Vol., No.,. -6. 5. Srader, T. J., Lin, F. R. and Shaw, M. J., 998, Informaion infrasrucure for elecronic virual organizaion managemen, Decision Suor Sysems, Vol., No.,. 75-9. 6. Thomas, J., 999, Why your suly chain

T. Y. Wang and D. H. Yeh: A Web-based DSS Archiecure and is Forecasing Core 09 doesn work, Logisics Managemen and Disribuion Reor, Vol. 8, No. 6,. -. 7. Wang, X. Y. and Wang, Z. O., 00, Sock marke ime series daa mining based on regularized neural nework and rough se, The Firs Inernaional Conference on Machine Learning and Cybernecics, Beiing, China,. 5-8. ABOUT THE AUTHORS Tien-You Wang is an Associae Professor in he Dearmen of Inernaional Business Managemen a Tainan Universiy of Technology (TUT), Taiwan. She received her Ph.D. degree in Business Adminisraion a Naional Chung Cheng Universiy in 006. Her research ineress are inegraing IT and demand lanning relaed echnologies in Suly Chain Managemen. In aricular, she is ineresed in daa mining, neural nework, and ime series forecasing. Din-Horng Yeh is an Associae Professor in he Dearmen of Business Adminisraion a Naional Chung Cheng Universiy (CCU), Taiwan. His research ineress are Queuing and Simulaion. (Received November 007, revised March 008, acceed May 008) Aendix A The Rule Base of Choice Model Selecion Noaion:,, : caegory of uiliy, skewness, kurosis. Model: he model number in Table 5. Rule if = hen Model = 8 Rule if = and ( -0.5 and 0.5) and < -0.5 hen Model = Rule if = and ( -0.5 and 0.5) and ( -0.5 and 0.5) hen Model = Rule if = and ( -0.5 and 0.5) and > 0.5 hen Model = Rule 5 if = and > 0.5 and < -0.5 hen Model = Rule 6 if = and > 0.5 and ( -0.5 and 0.5) hen Model = Rule 7 if = and > 0.5 and > 0.5 hen Model = Rule 8 if = and < -0.5 and < -0.5 hen Model = Rule 9 if = and < -0.5 and ( -0.5 and 0.5) hen Model = Rule 0 if = and < -0.5 and > 0.5 hen Model = 5 Rule if = and ( -0.5 and 0.5) and < -0.5 hen Model = Rule if = and ( -0.5 and 0.5) and ( -0.5 and 0.5) hen Model = Rule if = and ( -0.5 and 0.5) and > 0.5 hen Model = 5 Rule if = and > 0.5 and < -0.5 hen Model = Rule 5 if = and > 0.5 and ( -0.5 and 0.5) hen Model = Rule 6 if = and > 0.5 and > 0.5 hen Model = Rule 7 if = and < -0.5 and < -0.5 hen Model = Rule 8 if = and < -0.5 and ( -0.5 and 0.5) hen Model = 5 Rule 9 if = and < -0.5 and > 0.5 hen Model = 6 Rule 0 if = and ( -0.5 and 0.5) and < -0.5 hen Model = Rule if = and ( -0.5 and 0.5) and ( -0.5 and 0.5) hen Model = 5 Rule if = and ( -0.5 and 0.5) and > 0.5 hen Model =

0 Inernaional Journal of Elecronic Business Managemen, Vol. 7, No. (009) Rule if = and > 0.5 and < -0.5 hen Model = 5 Rule if = and > 0.5 and ( -0.5 and 0.5) hen Model = 6 Rule 5 if = and > 0.5 and > 0.5 hen Model = 7 Rule 6 if = and < -0.5 and < -0.5 hen Model = 6 Rule 7 if = and < -0.5 and ( -0.5 and 0.5) hen Model = 7 Rule 8 if = and < -0.5 and > 0.5 hen Model = 7 * Source: Revised from Masasinis and Samaras (000).

T. Y. Wang and D. H. Yeh: A Web-based DSS Archiecure and is Forecasing Core 供應鏈需求預測之網際決策支援系統架構王天祐 * 葉丁鴻 * 台南科技大學國際企業經營系台南縣永康市中正路 59 號國立中正大學企業管理系嘉義縣民雄鄉大學路 68 號摘要在現今競爭激烈的市場環境下, 供應鏈管理已成為企業生存的關鍵因素而在供應鏈管理之中, 需求規劃又扮演了極重要的角色, 因為正確的需求預測能提供降低存貨水準縮短前置時間進行有效的資源配置, 以及迅速回應顧客需求的諸多益處, 來讓顧客滿意為了尋求更精確的需求預測結果, 本研究提出了一套內含預測核心的網際決策支援系統, 期待藉由針對不同特性顧客族群個別預測再整合族群預測結果的方式, 來達到提昇預測正確率的功效該預測核心先以市場區隔技術, 將顧客分為三項群組 : 忠誠顧客, 潛在顧客, 以及投機顧客 ; 在這三項族群中分別以適當的預測技術進行預測, 再將個別預測結果加以整合, 以達到最佳成效忠誠顧客購買行為有一定規律, 適合以時間數列預測 ; 投機顧客指有優惠活動時才出手購買的消費者, 對於消費行為的預測須借重其歷史資料中的購買行為模式, 因此採用貝氏推論法由事前機率來估計潛在顧客需靠問卷及品牌選擇模式來分析消費者心中的購買傾向, 來預測其購買機率, 再整合潛在顧客基底轉換成預測量這三項群組的預測完成後, 整合函數便將這些預測結果加以整合實證結果證明, 此預測核心比傳統需求預測技術的績效更好, 明顯降低了預測誤差率 ; 而此決策支援系統架構則提供了供應鏈管理中有效的協作機制, 使供應鏈成員能透過該機制迅速進行溝通協調, 並以預測核心所提供的高效能預測結果, 有效達成協同預測的目的關鍵詞 : 網際決策支援系統預測購買傾向整合預測資料探勘 (* 聯絡人 :00@mail.u.edu.w)