Proceedings of he Asia Pacific Indusrial Engineering & Managemen Sysems Conference 0 V. Kachivichyanuul, H.T. Luong, and R. Piaaso Eds. Hoel Room Demand Forecasing via Observed Reservaion Informaion aragain Phumchusri, Prachya Mongolul, Deparmen of Indusrial Engineering, Faculy of Engineering, Chulalongorn Universiy, Bango 0330, Thailand E-mail: naragain.p@chula.ac.h, prachya.m@suden.chula.ac.h Absrac. There is a significan increase in hoel compeiion due o he ravel indusry expansion and he rapid advances in informaion echnology in his era. Revenue Managemen has become an imporan ool for hoel managemen o effecively conrol cusomers demand via is opimal room raes suggesions. In a decision suppor sysem, effecive decision maings require effecive inpus. A ey ingredien for a successful Revenue Managemen sysem is he inpu accuracy, i.e., he hoel room demand forecasing. While a number of previous wors proposed forecasing mehods o help predic he hoel room demand, less published researches verify resuls wih he acual hoel booing daa. The goal of his paper is o develop a forecasing model ha can ae advanage of observed booing informaion in predicing final room demand. Our model resuls are compared wih differen exising forecasing models (i.e., ime series, addiive and muliplicaive pic up mehods) using acual hisorical daa from a large hoel chain in Thailand. We observe model performance under differen scenarios such as forecasing a differen days prior o he dae of say. We find ha he proposed model generally ouperforms oher models, especially for a shor-erm forecasing. Keywords: Forecasing, Revenue Managemen, Hoel Demand. ITRODUCTIO Revenue managemen (RM) has been a significan sraegy o maximize revenue for indusries wih limied resources. RM research was pioneered in he airline indusry and has been widely praciced in hoels wih he objecive o deermine room prices and mare secor allocaions o maximize hoel revenue. These decisions are made prior o he rooms dae of say and need a good esimae of fuure room demand. Thus, an essenial componen for a successful hoel RM is an accurae room demand forecasing, which is he opimizaion inpu (Weaherford and Kimes 003). A descripive flow of he revenue managemen process is illusraed in Figure. Alhough several sudies in he lieraure focus on he hoel opimizaion par (e.g., Biran and Gilber 996, Biran, and Mondschein 995), less wor has done on developing room demand forecasing models (Rajopadhye e al. 00). Demand forecasing mehods in revenue managemen can be grouped ino hree main caegories (Zahary e al. 008): () Hisorical booing models forecas he final demand by considering he recorded pas daa o predic cusomers behavior in ime series. () Advanced booing models use he observed reservaion in forecasing he final number of demand. The models esimae he rae of increased reservaion (pic up) in each booing period and hen aggregae all fuure increased reservaion o obain he final forecas of demand. (3) Combined models inegrae hisorical and advanced booing models via eiher regression or weighed average mehods (Weaherford and Kimes 003). In hoel booing behavior, reservaion usually occurs wees or monhs in advance. Even hough, here is uncerainy in he final room demand, his observed booing : Corresponding Auhor 978
wih a summary of insighs from he resuls and indicaes ineresing fuure exensions of his research.. FORECASTIG MODELS I HOTEL REVEUE MAAGEMET Several forecasing mehods were proposed in revenue managemen lieraure. In he conex of forecasing hoel room demand, hey can be summarized as follow.. Same Day Las Year Figure : Hoel revenue managemen process informaion can be essenial for predicing wha will happen in he fuure. The main differences beween models () and () are ha he laer use updaed informaion abou he reservaion occurred by he forecasing ime o esimae he fuure booing o come in he fuure periods, while he firs groups rely only on he hisorical informaion on he daily rooms sold. Combined models ry o capure he usefulness of boh hisorical informaion and observed demand by inegraing hem in regression or weigh average mehod. Fildes and Ord (00) found in heir experimens ha combine models provide higher forecas accuracy compared o he firs wo groups of models. Alhough here have been several papers ha develop forecasing models for hoel room demand, only few of hem used acual daa obained from a hisorical hoel booing record o verify heir resuls. In his paper, we propose a hoel forecasing mehod ha employs observed reservaion daa and he characerisics of dae of say o esimae final room demand via regression model. The model performance is evaluaed, using he deailed ransacions of hoel booing, ranging from year January, 007 o July 3, 0, obained from a major hoel chain in Thailand. This paper is organized as follows. We begin by presening a summary of forecasing models used in revenue managemen in he nex secion. In Secion 3, we explain characerisics of daa obained from a large hoel chain in Thailand. In Secion 4, we presen he model esimaion and forecasing resuls. Comparaive performances among oher exising models are explored. Finally, Secion 5 concludes Same-Day-Las-Year mehod uses he previous-year hisorical booing daa o forecas he final room booing his year. The model assumes similar behavior of demand if hey are he same day and he same order of he year. For example, he forecas of room booing of Monday (he firs wee) of year 0 is equal o he acual booing happened on Monday (he firs wee) of year 0. Similar idea applies for oher cases. Table shows an example of forecas resuls obained from his mehod.. Moving Average Forecasing wih Moving average mehod is performed by using he average of all previous value and finding he mos suiable parameer, ha give he lowes forecasing error. The forecas is compued by: Where Table : An example of forecas resuls by Same Year Las Year mehod F + x i F + = i= + x, () is he forecas of room demand a ime +. is he ne number of rooms sold in period i. is he number of pas periods used in forecasing. Weaherford and Kimes (003) applied moving average i 979
mehods in hoel demand forecasing wih he number of period in he average varying beween o 8. They found his mehod provides robus forecasing resuls. In heir experimen, moving average wih n=8 provides he lowes error..3 Single Exponenial Smoohing. Fuure room demand is forecased using he previous observaions ha are discouned wih is weigh. This weigh exponenially decreases as he observaions are from furher in he pas. The forecas is compued as follows:. () where α is he exponenial smoohing parameer, and 0< α<. The higher he α, he more responsive o he mos recen value. Weaherford and Kimes (003) employed hoel booing daa in exploring model accuracy, using α values beween 0.05 and 0.95, and found ha exponenial smoohing is one of he mos robus models in heir experimen. Chen and Kachani (007) presened a framewor for forecasing and opimizaion for hoel revenue managemen found ha exponenial smoohing mehods wih eigh wees of pas daa provided he lowes forecasing errors..4 Time Series wih Seasonal Facors This mehod exends exponenial smoohing concep o capure rend and seasonaliy when hey exis. While rend indenifies he direcion of daa ha are moving hrough ime, seasonaliy deecs impacs of differen season on he ineresed value. The models assume similar behaviors of daa exis in he same season in ime series. There are wo main mehods applied in hoel revenue managemen lieraure..4. Addiive Model esimaes he seasonal componen by considering he seasonal deviaion from he cycle average, and his deviaion for each season does no depend on he magniude of he average value. The forecas is compued by: where is he ne number of rooms sold in cycle i. is he number of seasons in each cycle. s is he seasonal facor of season, noe ha: and y i F = αx + ( + α) F, F = y + s, i i = i= s = 0, (3) (4).4. Muliplicaive Model assumes he seasonal deviaion from he cycle average depends on he magniude of he average value. The forecas is esimaed as follows: where is he seasonal facor of season, and in his case, noe ha: and s s = x x. i i = F = yi s, i = i This mehod is usually called a Hol-Winers procedure and is popular in a ime series modeling as i is simple and generally gives accurae resuls. Rajopadhye e al. (00) explored he performance of his mehod in forecasing uncerain room demand wih boh shor-erm and long-erm forecasing. They obained iniialized model parameer using he hisorical daa from an acual hoel, and model performance is invesigaed by a simulaed daa..5 Advanced Booing Model s s =, = = i = A major advanced booing model is called Picup forecasing model which idenifies he esimaed increase of booing in each period and hen accumulae all daa ino a oal demand ha is arriving in he fuure. (Zahary e.al 008). There are wo main ypes of he picup mehod..5. Addiive Picup Model assumes independency beween observed reservaion a any day before he cusomer arrivals and he final number of room successfully sold. I esimaes he fuure booing of rooms by averaging of incremen demand from he same period (in he pas daa). Table shows an example of he number of room reserved prior o he acual dae of say. For insance, on dae of say ( Day ) he number of rooms reserved hree days prior o day is and he number of oal room reserved is 0 (shown in he firs column and he row ). x x i. (5) (6) (7) (8) 980
Table : An example of he number of rooms reserved prior o he dae of say To compue demand forecas by his mehod, le be he number of reservaion already booed by ime j for he dae of say i, and A i be he number of increased reservaion, j occurred during ime j- o ime j for he dae of say i. Thus, Ai, j = xi, j xi, j.table shows he calculaion of A i, j which is he resuling increased addiive booing from daa in Table. x ij M ij Le be he rae of increased reservaion during ime j- o ime j for dae of say i. Thus, M The forecas of reservaion booed by ime T for dae of say i is compued by: T FiT = M j xij j= ij = xi, j (0) () where M j is he average rae of increased demand during ime j- o ime j and T is he booing horizon. Table 3 shows he resuling rae of increased demand ( M ij ) from he daa in Table. Table 3: The rae of increased demand from he booing daa shown in Table x ij., Table : An example of he number of rooms reserved prior o he dae of say F it Le be he forecas of reservaion booed by ime T for dae of say i, where T is he booing horizon, and A j be he average increased reservaion occurred during ime j- o ime j. The forecas of reservaion booed by ime T for dae of say i is compued by: T FiT = A j + xij j=.5. Muliplicaive Picup Model esimaes he fuure booing by averaging he rae of increased demand from he same period (in he pas daa). This Model assumes he observed reservaion affecs he rae of increased demand in he fuure periods.. (9) Wicham (995) found in his experimen on airline booing daa ha pic-up mehod gives more accurae forecasing resul, as compared o he hisorical mehod lie moving average models. Weaherford (998) performed a comparaive esing on forecasing airline demand and concluded ha addiive picup models provide lower error han muliplicaive picup models. Weaherford and Kimes (003) sudied forecasing performances of various models in predicing hoel demand and found ha exponenial smoohing, picup, and moving average models were he mos robus. Zahary e.al (008) used simulaed hoel reservaion daa o compare various ypes of picup mehods and showed ha he classical picup ouperformed he advanced picup models. In he nex secion, we summarize characerisics of he booing daa used in his sudy and idenify major parameers obained from he described daa. 3. DATA We use deailed ransacions of hoel booing, ranging from year January, 007 o July 3, 0, obained from a major hoel chain in Thailand. The deailed ransacional daa 98
ha we sudy conains he following informaion: reservaion dae, arrival dae, deparure dae, cancelaion dae and room ype of each booing occurred in he oal period of 54 monhs. and residual plos are shown in Figure. In esing forecasing models accuracy, we divide daa ino wo groups. The firs se conains booing informaion from January, 007 o December 3, 00 and is used o obain model parameers. The second se conains daa from January, 0 o July 3, 0 and is used o es forecasing models accuracy. The daa is hen summarized o he following informaion: oal number of rooms sold (and uilized) each day, he number of rooms reserved 60, 30, 4 and 7 days prior o he dae of say, respecively, and dummy variable indicaing he dae of say (e.g., Monday, Tuesday,, Sunday). 4. MODEL ESTIMATIO AD FORECASTIG RESULTS The model specificaions of facors affecing he final number of rooms sold each day is as follows: Residual Plos for Room igh = β0+ β + β + β3 Y Booed Booed Mon +... + β Sa+ β Booed Mon 9 0 +... + β Booed Sun, 6 () Percen 99.99 99 90 50 0 0.0-0 ormal Probabiliy Plo -0 0 0 Residual 0 Residual 0 0 0-0 0 Versus Fis 40 60 Fied Value 80 where Y is he forecas of he oal number of rooms sold (or he number of paid rooms) on day i. Booed is he number of rooms reserved days prior o he dae of say. Mon, Tue,..., Sun are he dummy variables which are equal o if he dae of say is on Monday, Tuesday,, or Sunday, respecively. β 0 is he consan erm, and β are he coefficiens of he described,..., β6 parameers, respecively. The model parameers are obained from he firs se of daa (dae of say from January, 007 o December 3, 00). The independen variables are seleced by forward and bacward sepwise process and he resuling coefficiens for forecasing 7 days in advance are as follows: Y =.46+.3Booed7 0.0044Booed7 + 9.06Thu+.7Fri+ 40.6Sa 0.30Booed Thu 0.76Booed Fri 0.493Booed Sa 7 7 (3) The esimaion deails wih he analysis of variance 7 Frequency 60 0 80 40 0 - -8 Hisogram -4 0 4 Residual 8 6 Residual 00 00 300 400 500 700 800 900 000 00 00 300 Figure : The esimaion deails wih he analysis of variance and residual plos of he models described in equaion (3). When forecasing is performed furher in advance, he resuling coefficiens for 4, 30 and 60 days prior o he dae of say are shown in equaion (4), (5) and (6), respecively: + 8.6Thu+.Fri+ 36.8Sa 0.7Booed4 Thu 0.69Booed Fri 0.438Booed Sa (4) 0 0 0-0 Versus Order 600 Observaion Order Y = 6.78+.3Booed4 0.00554Booed4 4 4 Y = 6.5+ 0.99Booed30 0.00373Booed30 + 6.5Thu + 9.99Fri+ 30.38Sa 0.09Booed Thu 0.68Booed Fri 0.37Booed Sa 30 30 30 (5) 98
Y = 38.8+ 0.899Booed60 0.00495Booed 60 + 5.35Thu+ 7.8Fri+ 5.9Sa 0.093Booed Thu 0.97Booed Fri 0.38Booed Sa 60 60 (6) From he resuls, he final number of rooms sold can be explained by he number of observed reservaions, he characerisics of he dae of say (Thursday, Friday, Saurdays or ohers) and heir ineracions. The model resuls show ha he informaion of observed reservaion (up o he forecasing dae) is useful in predicing he final demand. The implicaion of he negaive coefficien of he variable, Booed, is ha alhough we may observe a large number of reservaion in advance, some of hem may be cancelled before he dae of say. The final number of rooms sold is also affeced by he dae of say (from he significance of he day dummy variable). Overall, room forecasing on Saurday is he highes, while Sunday o Wednesday are similarly lower han oher days. In addiion, he coefficien of he observed reservaion, Booed is impaced by he dae of say as well (from he significance of he ineracion erms). Similar model srucures are found for forecasing furher in advance, i.e., 4, 30 and 60 days wih differen resuling model coefficiens. 5. COMPARIG FORECAST RESULTS The forecasing performance is measured by applying he models o he pos-daa se, (dae of say from January, 0 o July 3, 0). We compare our models resul o he following forecasing mehods:. Same Day Las Year. Moving Average 3. Single Exponenial Smoohing wih α ϵ (0,) 4. Time Series wih Seasonal Facors- Addiive model 5. Time Series wih Seasonal Facors- Muliplicaive model 6. Advanced Booing Model- Addiive picup model 7. Advanced Booing Model- Muliplicaive picup model In addiion o comparing he forecasing performance of our model o oher mehods, we es he impac of differen forecasing ime periods. In paricular, we capure model errors when he forecass are made 7, 4, 30 and 60 days in advance o idenify which mehod performs well in 60 each case. The following error measures are calculaed: ) Mean Absolue Deviaion (MAD) capures he average of forecas errors absolue values; MAD= F x = ) Mean Absolue Percenage Error (MAPE) measures he average absolue percenage deviaions beween forecass and acual booings; F x MAPE= x = 3) Median Absolue Percenage Error (MdAPE) capures he median of absolue percenage deviaions beween forecass and acual booings; MdAPE = Median x The summary resuls for all 8 forecasing models are shown in Figure 3. I can be noiced ha forecasing resul from regression models, explained by equaion (3) o (6) give he lowes error for all cases (i.e., forecasing 7, 4, 30 and 60 days in advance). I indicaes our regression model ouperforms oher exising mehods for boh long-erm and shor-erm forecasing. Comparing he difference of forecasing ime, we observe ha bes performance occurs when forecasing is performed 7 days prior o he dae of say. The model performance is poorer as he forecas is performed furher in advance. 6. COCLUSIOS AD FUTURE WORK In his paper, we presened a muliple regression model for hoel room demand forecasing. We found ha he observed booing informaion and he dae of say are essenial in predicing final room demand. We used acual hisorical daa from a large hoel chain in Thailand o evaluae he model performance. Compared wih various exising forecasing models (such as ime series, addiive and muliplicaive pic up mehod), he developed regression model yields he lowes forecas error, especially for he shor-erm forecasing. In fuure wor, i can be ineresing o compare resuls wih oher advanced ime series mehods such as ARIMA F x 983
Figure 3: Summary of forecasing errors obained from each forecasing mehod models. I is also possible o invesigae if here are oher independen variables can be added o improve he model forecasing performance. The impacs of disaggregaion version aggregaion of room ypes can also be essenial for fuure model improvemen. In addiion, yield managemen sudy ha employs our presened forecasing mehod as he sysem inpu can be invesigaed as a research exension o explore possible effecive revenue managemen opporuniy. REFERECES Andrew, W., Cranage, D., Lee, C. (990) Forecasing hoel occupancy raes wih ime series models: an empirical analysis. Hospialiy, Research Journal 4, 73 8. Armsrong, J.S. and Collopy, F. (99) Error Measures for Generalizing abou Forecasing Mehods: Empirical Comparisons, Inernaional Journal of Forecasing, 8, 69-80. Bing Pan, Doris Chenguang Wu, Haiyan Song, (0) Forecasing hoel room demand using search engine daa, Journal of Hospialiy and Tourism Technology, 3, 96 0. Biran, G.R. and Gilber, S.M. (996) Managing Hoel Reservaions wih Uncerain Arrivals, Operaion. Research. 44, 35 49 Biran, G.R. and Mondschein, S.V. (995) An Applicaion of Yield Managemen o he Hoel Indusry Biran, G.R. and Gilber, S.M. (996) Managing Hoel Reservaions wih Uncerain Arrivals, Operaion. Research. 44, 35 49. Biran, G.R. and Mondschein, S.V. (995) An Applicaion of Yield Managemen o he Hoel Indusry Considering Muliple Day Says, Operaion. Research, 43, 47 443. Chen, C. and Kachani, S. (007). Forecasing and opimisaion for hoel revenue managemen. Journal of Revenue and Pricing Managemen, 6, 63 74. Fildes, R., and Ord, K. (00). Forecasing compeiions heir role airline in improving forecasing 984
pracice and research. In Clemens, improved M., & Hendry, D. (Eds.), Kimes, S. E. (999). Group forecasing accuracy for hoels. Journal of he Operaional Research Sociey, 50, 04 0. Lee, A.O. (990) Airline Reservaions Forecasing: Probabilisic and Saisical Models of he Booing Process. Massachuses Insiue of Technology MIT Ph.D. Thesis. Liu, S., Lai, K. K., Dong, J., Wang, S.Y. (006) A sochasic approach o hoel revenue managemen considering muliple-day says. Inernaional Journal of Informaion Technology & Decision Maing, 5, 545 556. McCarney, S. (000) Airlines Find a Bag of High- Tech Trics o Keep Income Alof, Wall Sree Journal (January), A. Rajopadhye, M., Ghalia, M.B. and Wang, P.P. (00) Forecasing Uncerain Hoel Room Demand, Informaion Sciences, 3,. Varini, K. (0) Revenue managemen for he hospialiy indusry, Journal of Revenue and Pricing Managemen,, 479 480. Zahary, A., El Gayar,., Aiya, A. F. (008) A comparaive sudy of he picup mehod and is variaions using a simulaed hoel reservaion daa. ICGST Inernaional Journal on Arificial Inelligence and Machine Learning, 8, 5. AUTHOR BIOGRAPHIES aragain Phumchusri is a lecurer in Deparmen of Indusrial Engineering, Faculy of Engineering, Chulalongorn Universiy, Bango, Thailand. She received maser s and docoral degrees in Indusrial Engineering from The H. Milon Sewar School of Indusrial and Sysems Engineering, Georgia Insiue of Technology, Georgia, USA in 00. Her research ineress include Operaion Research, Revenue Managemen, Applied Saisics, Sochasic Opimizaion, and Supply Chain & Logisics Managemen. Her email address is <naragain.p@chula.ac.h> Prachya Mongolul is a suden in Deparmen of Indusrial Engineering, Faculy of Engineering, Chulalongorn Universiy, Bango, Thailand. His research ineress include Revenue Managemen, Operaion Managemen and Financial Engineering. His email address is <prachya.m@suden.chula.ac.h> 985