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An integrated forecasting approach to hotel demand
Mathematical and Computer Modelling 46 (2007) 1063–1070 www.elsevier.com/locate/mcm
An integrated forecasting approach to hotel demand Sedat Y¨uksel ∗ Department of Tourism Management Education, The Faculty of Trade and Tourism Education, Gazi University, Ankara, 06380, Turkey Received 28 November 2006; accepted 14 March 2007
Abstract We aimed to forecast demand fluctuations in the hotel business that lead to crises and create a systematic and dynamic process that could be re-used. We forecasted demand for a five star hotel in Ankara using 149 monthly series of data and compared the results with those from MA, Simple, Holt’s, Winter’s Exponential Smoothing and ARIMA using error measures. Two Delphibased inquiry panels were used: The Variables Determination Panel and The Environmental Monitoring Panel. The opinions of the second group of panelists were used to adjust Winter’s Multiplicative forecasts with an AHP-based approach. We showed that if this forecasting and adjustment process is applied to a hotel monthly, it can be used to predict demand and help the management avoid crises arising from demand fluctuations in their business. The most important characteristic of the model is that it can accommodate change and be further refined in the future. c 2007 Elsevier Ltd. All rights reserved.
Keywords: Forecasting; Demand forecasting; Forecasting adjustment; AHP; Delphi; Hotel
1. Introduction In the early 1970s, the Analytical Hierarchy Process (AHP), which is not generally known as a forecasting method, was created by Thomas L. Saaty to solve multi-criteria decision making problems. The AHP can include intangibles and human judgment in additional to numerical data in decision-making in a way that could not have been done before. A hierarchy is a unique type of system that can be used to define the greater environment and the aims of the designer. This system cannot be understood without determining the function, the mission of every component, and the role of every mission with regard to the main aim. According to the assumptions underlying AHP, the components can be separately grouped and related to other components [1]. 1.1. AHP for forecasting One of the first AHP forecasting models was to predict the results of 1978 Karpov–Karchroi chess meet [2]. After that successful exercise, the AHP was adapted to predict competitive behavior [3]. Several theoretical examples were worked out by MacCormac [4] and Lewis and Levy [5]. AHP has been applied widely in other areas. Zahedi [6], Golden et al. [7] and Vargas [8] have compiled detailed overviews on AHP applications. ∗ Tel.: +90 312 485 1460/349; fax: +90 312 484 21 41.
E-mail address: [email protected]. c 2007 Elsevier Ltd. All rights reserved. 0895-7177/$ - see front matter doi:10.1016/j.mcm.2007.03.008
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Two groups have used AHP in forecasting. The first group used AHP directly for forecasting using the judgments of experts in areas such as inventory and logistics planning [9], demand forecasting of residents in Singapore [10], and the forecasting of $/DM parity in Turkey [11]. The second group used AHP to adjust forecasts. Wolfe and Flores [12] forecast earnings using ARIMA adjusted with AHP. In another example Flores and Olson [13] used and compared AHP and CENTROID for adjusting forecasting results obtained with quantitative methods. Belton and Goodwin had investigated AHP as a method of forecasting based on judgments. According to their work, AHP is not useful for forecasting when directly using judgments as the input. It could, however, be useful for adjusting forecasting results obtained with other methods. In this situation, AHP can provide new interpretations and offer a wider perspective [14]. Dyer and Forman [15] have recommended using AHP for forecasting in these ways: • To do forecasting with experts’ opinions. • To select the most suitable forecasting method. • To combine results from various forecasting methods. A three step process was offered to forecast demand using AHP by Korpela and Tuominen [9]: • Develop an AHP Hierarchy containing the relevant factors that are thought to affect the level and structure of the demand. • Determine the priorities of the factors in the hierarchy. • Synthesize the priorities to obtain overall priorities that estimate the combined demand, and test these priorities for consistency. AHP also has some advantages in group decision making, according to Dyer and Forman [16]: • All values, individual and/or group, tangible and/or intangible, can be included in a group decision process with AHP. • The discussion focuses on the goal instead of the alternatives. • AHP provides a framework for discussion which includes all the factors, and they can then be presented in a systematic and coherent way. • The discussion continues until the group reaches consensus, with all members being allowed to input their opinions. When using AHP in group decision making or forecasting processes, the participants are asked to compare the criteria pairwise. This can be done via questionnaires, or face to face. It is not obligatory that the participants be experts, but they must have some knowledge about the issues. If the decision is to be made jointly by the group, even though each participant gives his/her judgments, they need to agree that they should be respectful of others’ opinions during the process, and that they will support the group result that emerges at the end. There is usually no problem when the participants have reached a decision through making pairwise comparisons, debating their differences of opinion until they reach a common viewpoint, and changing the model if necessary so that it better reflects their understanding. If, however, the group cannot come to agreement on some judgment, then there is a process in the AHP to construct a group judgment from varying individual judgments using the geometric mean [15]. The geometric mean is the most suitable way to transform individual opinions to a group opinion for a given pairwise comparison [17,1]. 1.2. Forecasting in the hotel businesses The lodging industry, because of the structure of the business, is sensitive to fluctuations in demand. It often has crises because of unexpected reduced demand. Demand forecasting in the lodging industry has become relatively important because of the nature of the industry and its operational characteristics and difficulties. It is important to forecast demand, not only because of the wide fluctuations that may occur, but also because it is hard to track the success of efforts being engaged in to increase the occupancy rate. In hotels, forecasting differs for individuals and group customers. Management knows more about the quantitative and qualitative aspects of demand due to group customers. However, with individuals, it is more difficult to forecast demand. If hotels could repeat their forecasts during a month, the accuracy could increase [18]. There are many forecasting issues, such as bookings, arrivals, nights, duration of stay, and revenue in the hotels. These could
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be estimated separately (disaggregation) or combined (aggregation). Aggregate forecasting is called downward forecasting while upward forecasting is for each unit separately. In stationary demand periods, downward forecasting is less costly and more accurate. However, in nonstationary demand periods, upward forecasting is preferred [19]. Quantitative forecasting methods have been applied to both the entire lodging industry [20,21] and to forecasting for single hotels alone [19,22,23]. In the tourism sector, which is closely related to lodging, demand forecasting is also an important area. These studies on demand forecasting have generally used quantitative methods [24–27]. The use of econometrical models, especially, has been increasing [27]. However, Delphi, Executive Board Opinions, Cross Impact Analysis, Probability Forecasting [28], and Neural Networks [29] are among non-econometric advanced methods that are also being used. In the short term, when expert opinions are included in forecasting procedures, it is recommended that the forecasting algorithm contain or be combined with subjective experience, opinions, and knowledge aside from data [30]. Quantitative forecasting methods based on data alone do not get results accurate enough to justify their costs, the effort required, and the time invested in them. The criteria that have been used to evaluate forecasting methods (ease of application, cost, time and source, decision making support, etc.) show that qualitative methods alone are not suitable for forecasting in hotels [18,19,24–27,30]. Airlines and the lodging sector have similar characteristics that depend on demand. In a study on the impacts of being able to accurately forecast revenue for airlines, it is reported that a 10% adjustment due to forecasting generated increased revenue between 5%–20% [31]. 2. An integrated forecasting approach for hotels 2.1. Goal and importance By reducing uncertainty in future demand, there could be an increased occupancy rate and decreased costs depending on the idle capacity. In this study, we will be attempting to estimate demand fluctuations to avoid crises in the hotel business. Moreover, we also aim to create a systematic continual forecasting procedure for hotels. This procedure should be an open one that can be maintained and improved. When the existing studies on demand forecasting were analyzed, we saw that these studies focused either on national/regional tourism demand or quantitative market analysis. Therefore, we needed to combine the approaches at the micro level that were suitable for the hotel business with other quantitative and qualitative methods. 2.2. Assumptions and limitations The approach of this study is especially good for estimating demand, because the hotel/lodging business has ample sources of information with orderly past data, and a good deal of technical and intellectual capital. Even if this can be said to be more true generally of larger establishments, small and medium size hotels can also make use of the process we have developed. This study was carried out using real data from a five star hotel in Ankara. Even though every lodging business differs from every other, this study concentrated on common characteristics. Advance bookings and pre-sale contracts are common ways management has attempted to reduce uncertainty and risk. In this study, the following assumptions were considered: The hotel has no
advance booking
sales contracts
market segmentation
yield management. 2.3. Data sources The primary data sources were experts from sales/marketing, the room division, front offices, travel agencies, food & beverage professionals; tourism academicians, officers, journalists, counsellors, and NGO representatives in Ankara, the capital city of Turkey. Secondary data for quantitative forecasting were provided by the Local Tourism Directorate in Ankara. (Each hotel must send its arrivals, nights, and occupancy rates to the Local Tourism Directorate in Ankara monthly.)
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Table 1 Grouping the factors in the model Criteria
Factors
International criteria
Global economic crises Wars and other diplomatical crises International organisations/events Currency exchange rate
National criteria
Inflation rate Economic resession or crises Political mobility Terror, anarchy, strikes etc.
Social criteria
Education and or training activities Utilization of public/governmental services Activities/events/organizations in Ankara Rooms Hotel criteria
Marketing activities Sales prices New product/capacity facilities Likelihood of acts of God
Natural criteria
Climate Environmental/ecological issues Rooms
Competition criteria
Sales prices Marketing activities New product/capacity facilities Potential volume
Customer criteria
Travel frequency Service expectations
2.4. Operation The operation was carried out in three steps. First step: The panel of experts determined the variables The determination of the variables/factors to be used in the AHP model was done by a panel of experts. We specified that the variables should be related to demand forecasting in the lodging sector in Ankara and elsewhere. A list of 40 variables was put together and rated for importance by 21 academicians in the Department of Tourism/Hospitality using a Delphi process. The result of this work by the panel was a prioritized list by importance, from which we picked the top 25 variables as shown in Table 1. Second step: Forecasting with quantitative methods The past data used were for a five star hotel in Ankara and consisted of 148 monthly periods from 1990–2002. A night can be defined as one night spent by one person and the total number of nights was given each month for the hotel. The trends and seasonality of these series were not very pronounced and, in fact, were relatively stationary. When first differences were taken, the resulting series were completely stationary. The seasonality factors can be seen in Table 2. The quantitative methods used to smooth the data were Winters’, Decomposition, Moving Average, ARIMA and Regression, and were applied to the nights and arrivals series. The error measures determined by these methods are given in Tables 3 and 4.
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Table 2 The seasonality of series Period
Month
Arrivals
Nights
1 2 3 4 5 6 7 8 9 10 11 12
January February March April May June July August September October November December
0.773346 0.839606 0.874013 0.974557 1.20107 1.02045 0.934402 1.00506 1.17032 1.21761 1.11170 0.869521
0.781686 0.831445 0.885224 0.936058 1.11778 1.03631 0.956627 1.06967 1.16042 1.19584 1.08841 0.948865
Table 3 Comparison of forecasting methods for arrival series Method
MAPE
MAD
MSD
Decomposition Exponential smoothing Holt’s exposmooth. Winter’s season. add Winter’s season. multi. ARIMA (1, 1, 1)
22 20 21 17 17 25
850 726 834 645 631 937
1 319 621 1 041 545 1 283 950 826 375 788 910 –
Method
MAPE
MAD
MSD
Decomposition Exponential smoothing Holt’s exposmooth. Winter’s season. add Winter’s season. multi. ARIMA (1, 1, 1)
18 21 21 15 15 23
1176 1338 1408 982 988 1453
2 227 550 2 793 334 3 474 267 1 632 602 1 703 617 –
Table 4 Comparison of forecasting methods for night series
Table 5 Results of the forecasting process by Winter’s (Multiplicative) seasonality adjustment Period
Month
Year
Arrival
Night
149 150 151 152 153 154
May June July August September October
2002 2002 2002 2002 2002 2002
4468 4195 4346 4243 4672 4836
8180 7233 6556 6700 7882 8211
The highest accuracy was obtained from Winters’ Seasonality Adjustment. As the forecasting term was short, expo-smooth was more suitable and available. The forecast for the next six months according to Winters’ Method are given in Table 5. Third step: Forecasting adjustment The AHP hierarchy that was used for the forecasting adjustment was established in this step. In this hierarchy, we have the goal (level 1); variables that depend on the goal (level 2); scenarios that depend on the variables (level 3);
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Fig. 1. AHP hierarchy for forecasting adjustment. Table 6 Probability values of five-point scale in questionnaire Questionary scale
Value
1 Stronger probability 2 Stong probability 3 Fifty–fifty probability 4 Poor probability 5 Very poor probability Total
0.531 0.252 0.124 0.062 0.031 1.00
and the effects on hotel demand that depend on the scenarios (level 4) as shown in Fig. 1. At level 4, numbers were obtained from similar studies on hotel demand and from conversations with professionals. According to these, up to a ±50% change in expected demand could be accepted as a fluctuation. Higher changes than ±50% sign were ascribed to very extreme situations that could not be forecast. Moreover a change of ±50% is not an adjustment to forecasting results, but is a new forecast. So the five-point scale ranged between ±10% and ±50%. A Delphi based panel was organised and carried out with 30 panelists between May 19 and 26, 2002. Various questionnaires were tested in advance on individuals, and small groups that were not panellists, and the most available, understandable and easy-to-fill-out one was selected. The pairwise comparison matrix and nine-point scale of the AHP were used. There were three types of questions on the form. The first was to give relative weights as percentages. The second was to determine the probability of scenarios as percentage, and third was to give priorities on a five-point scale. The rates of adjustment depended on earlier studies and changes in the number of nights and arrivals at the hotel. The answers to this question were included in the hierarchy by assigning the probability values given in Table 6. In this inquiry, the panellists did not know each other and were located in different places as in Delphi. The questionnaires were mostly delivered by hand. The required explanations about how to fill out the questionnaires were stated face to face. The number of questionnaires that were returned included 13 from professionals and 14 from experts for a total of 27. 2.5. Results In the adjustment procedure, it was not required that there be repeated rounds for consensus; instead the geometric mean was used. The relative weights of the criteria and factors obtained from the panel are given in Table 7, and the adjustment gaps are given in Table 8. The panellists’ responses were normalized three times for all criteria and factors. At the end of the adjustment procedure, the panellists showed that they had not predicted an important change in expected demand. According to the panellists, with highest (37%) probability, the scenarios depending on factors would cause a ±10% deviation from quantitative methods’ results. This means that no important effect on demand at the hotel was expected the following month (June 2002).
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S. Y¨uksel / Mathematical and Computer Modelling 46 (2007) 1063–1070 Table 7 The weights of criteria and factors according to panellists 1 Criteria
2 Factors
1st normalization 1 × 2
International criteria 0.134
Global economic crises Wars and other diplomatical crises International organisations/events Currency exchange
0.395 0.48 0.125 0.331
0.0529 0.0643 0.0167 0.0516
National criteria 0.156
Inflation ratio Economic resession or crises Political mobility Terror, anarchy, strikes, etc.
0.214 0.455 0.366 0.152
0.0333 0.0709 0.0567 0.0235
Social criteria 0.155
Education and or training activities Utilization of public/governmental services Activities/events/organizations in Ankara Rooms
0.094 0.128 0.26 0.288
0.0145 0.0198 0.0403 0.0493
Hotel criteria 0.159
Marketing activities Sales prices New product/capacity facilities Act of God likelihood
0.256 0.31 0.146 0.53
0.0232 0.0493 0.0232 0.0387
Natural criteria 0.073
Climate Environmental/ecological issues Rooms
0.15 0.32 0.268
0.0109 0.0233 0.0536
Competition criteria 0.2
Sales prices Marketing activities New product/capacity facilities
0.299 0.244 0.189
0.0598 0.0488 0.0378
Customer criteria 0.123
Potential volume Travel frequency Service expectations
0.323 0.342 0.335
0.0397 0.0420 0.0414
Table 8 The results of adjustment panel Estimated probability
Verbal statement
Quantity gap(%)
0.3728 0.2240 0.1638 0.1281 0.1064
Not important effect Positive effect Negative effect Very positive effect Very negative effect
−10–10 11–30 −11–30 31–50 −31–50
The analysis was completed in June and the actual arrivals and nights that the hotel reported to the Local Tourism Officer after that were 3777 arrivals and 7455 nights for June. After making the adjustments, the arrivals had been expected to be between 3775–4614 and the nights between 6510–7956 in June for this hotel. So it would not be incorrect to say that the arrivals and nights were within the expected ranges. The forecasting error for arrivals was 3777−4195 = −418, and for nights was 7455−7233 = +222. Even though the adjustment panel seemed to succeed in getting close to the actual numbers, the process should be repeated several times using actual department managers as panellists. It was not possible to do this for this study. 3. Conclusion Quantitative forecasting methods are often used in hotels. Nevertheless, there are many factors which affect demand in hotels. As with any quantitative method, there was some difficulty in gaining access to information on all the factors. For hotels and other lodging businesses, accurate forecasting is one of the most important aspects of the business. This is because demand fluctuations have a large effect on their operations. The most suitable way to maximize
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forecasting accuracy is to integrate quantitative and qualitative methods for forecasting. The first consideration in selecting a quantitative method should be its accuracy. Forecasting adjustments with a qualitative method can then be used to overcome the limits and disadvantages of the quantitative methods currently being used in the lodging sector. AHP has some advantages, especially for use with groups, as it can accommodate many factors and including intangible ones. As it was not originally intended to be used as a forecasting method, some characteristics, like the pairwise comparisons using a nine point scale, present some difficulties in using with traditional forecasting adjustment concepts. However, the AHP-based adjustment procedure has some advantages, such as: flexibility, dynamism, openness, understandability, and ease of application. Acknowledgement I would like to acknowledge and thank Rozann Whitaker for her contributions. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]
T.L. Saaty, Multicriteria Decision Making: The Analytic Hierarchy Process, RWS Pub., Pittsburgh, 1989. T.L. Saaty, L. Vargas, Hierarchical analysis of behavior in competition: Prediction in chess, Behav. Sci. 25 (1980) 180–191. T.L. Saaty, L. Vargas, Modeling behavior in competition the analytic hierarchy process, Appl. Math. Comput. 16 (1) (1985) 49–92. E.R. MacCormac, Forecasting loads and designing rates for electric utilities, in: B.L. Golden, et al. (Eds.), The Analytic Hierarchy Process Applications and Studies, Springer Verlag, Berlin, 1989, pp. 138–154. R. Lewis, D.E. Levy, Predicting a national acid rain policy, in: B.L. Golden, et al. (Eds.), The Analytic Hierarchy Process Applications and Studies, Springer Verlag, Berlin, 1989, pp. 155–170. F. Zahedi, The analytic hierarchy process — A survey of the method and its applications, Interfaces (July–August) (1986) 96–108. B.L. Golden, et al., Applications of the analytic hierarchy process: A categorized, annotated bibliography, in: B.L. Golden, et al. (Eds.), The Analytic Hierarchy Process Applications and Studies, Springer Verlag, Berlin, 1989, pp. 37–58. L.G. Vargas, An overview of the analytic hierarchy process and its applications, European J. Oper. Res. 48 (1) (1990) 2–8. J. Korpela, M. Tuominen, Inventory forecasting with a multiple criteria decision tool, Int. J. Prod. Econ. 45 (1996) 159–168. S.E. Ong, T.I. Chew, Singapore residential market an expert judgmental forecast incorporating the analytical hierarchy process, J. Prop. Valuation Invest. 14 (1) (1996) 50–66. F. Ulengin, B. Ulengin, Forecasting foreign exhange rates: A comparative evaluation of AHP, Omega 22 (5) (1994) 505–519. C. Wolfe, B. Flores, Judgmental adjustment of earnings forecasts, J. Forecast. 9 (1990) 389–405. B.E. Flores, D.L. Olson, Judgmental adjustment of forecast: A comparison of methods, Int. J. Forecast. 7 (1992) 421–433. V. Belton, P. Goodwin, Remarks on the application of the analytic hierarchy process to judgmental forecasting, Int. J. Forecast. 12 (1996) 155–161. R.F. Dyer, E.H. Forman, An Analytic Approach to Marketing Decisions, Prentice Hall, Englewood Cliffs, 1991. R.F. Dyer, E.H. Forman, Group decision support with the analytic hierachy process, Decis. Support Syst. 8 (1992) 99–124. J. Aczel, T.L. Saaty, Procedures for synethesizing ratio judgements, J. Math. Psych. 27 (1983) 95–132. S.E. Kimes, Group forecasting accuracy in hotels, J. Oper. Res. Soc. 50 (1999) 1104–1110. L. Weatherford, et al., Forecasting for hotel revenue management testing aggregation againist dissaggreation, Cornell HRA Quarterly (August) (2001) 53–64. J.-G. Choi, et al., Forecasting industry turning points: The U.S. hotel industry cycle model, Hospitality Management 18 (1999) 159–170. W.S. Chow, J.-C. Shyu, K.-C. Wang, Developing a forecast system for hotel occupancy rate using integrated ARIMA models, J. Int. Hospitality, Leisure Tourism Management 1 (3) (1998) 55–80. P.E. Pfeifer, S.E. Bodily, A test of space–time ARMA modelling and forecasting of hotel data, J. Forecast. 9 (1990) 255–272. R.A. Smith, J.D. Lesure, Don’t shoot the messenger forecasting lodging performance, Cornell HRA Quarterly (February) (1996) 80–88. B. Archer, Forecasting demand quantitave and intuitive techniques, Int. J. Tourism Management (March) (1980) 5–12. M. Uysal, J.L. Crompton, An overview of approaches used to forecast tourism demand, J. Travel Res. 4 (1985) 7–15. C.A. Martin, S.F. Witt, Forecasting tourism demand: A comparison of the accuracy of several quantitatives methods, Int. J. Forecast. 5 (1989) 7–19. S.F. Witt, C.A. Witt, Forecasting tourism demand: A review of empirical researchs, Int. J. Forecast. 11 (1995) 447–475. S.F. Witt, L. Mountinho, Demand modeling and forecasting, in: L. Mountinho (Ed.), Strategic Management in Tourism, Oxon, Wallingford, 2000, pp. 293–314. R. Law, N. Au, A neural network model to forecast Japanese demand for travel to Hong Kong, Tourism Management 20 (1999) 89–97. M. Rajopadhye, et al., Forecasting uncertain hotel room demand, Inform. Sci. 132 (2001) 1–11. A. Lee, Airline reservations forecasting: Probabilistic and statistical models of the booking process, Flight Transportation Laboratory Report, R90-5, MIT, 1990.
An integrated forecasting approach to hotel demand Sedat Y¨uksel ∗ Department of Tourism Management Education, The Faculty of Trade and Tourism Education, Gazi University, Ankara, 06380, Turkey Received 28 November 2006; accepted 14 March 2007
Abstract We aimed to forecast demand fluctuations in the hotel business that lead to crises and create a systematic and dynamic process that could be re-used. We forecasted demand for a five star hotel in Ankara using 149 monthly series of data and compared the results with those from MA, Simple, Holt’s, Winter’s Exponential Smoothing and ARIMA using error measures. Two Delphibased inquiry panels were used: The Variables Determination Panel and The Environmental Monitoring Panel. The opinions of the second group of panelists were used to adjust Winter’s Multiplicative forecasts with an AHP-based approach. We showed that if this forecasting and adjustment process is applied to a hotel monthly, it can be used to predict demand and help the management avoid crises arising from demand fluctuations in their business. The most important characteristic of the model is that it can accommodate change and be further refined in the future. c 2007 Elsevier Ltd. All rights reserved.
Keywords: Forecasting; Demand forecasting; Forecasting adjustment; AHP; Delphi; Hotel
1. Introduction In the early 1970s, the Analytical Hierarchy Process (AHP), which is not generally known as a forecasting method, was created by Thomas L. Saaty to solve multi-criteria decision making problems. The AHP can include intangibles and human judgment in additional to numerical data in decision-making in a way that could not have been done before. A hierarchy is a unique type of system that can be used to define the greater environment and the aims of the designer. This system cannot be understood without determining the function, the mission of every component, and the role of every mission with regard to the main aim. According to the assumptions underlying AHP, the components can be separately grouped and related to other components [1]. 1.1. AHP for forecasting One of the first AHP forecasting models was to predict the results of 1978 Karpov–Karchroi chess meet [2]. After that successful exercise, the AHP was adapted to predict competitive behavior [3]. Several theoretical examples were worked out by MacCormac [4] and Lewis and Levy [5]. AHP has been applied widely in other areas. Zahedi [6], Golden et al. [7] and Vargas [8] have compiled detailed overviews on AHP applications. ∗ Tel.: +90 312 485 1460/349; fax: +90 312 484 21 41.
E-mail address: [email protected]. c 2007 Elsevier Ltd. All rights reserved. 0895-7177/$ - see front matter doi:10.1016/j.mcm.2007.03.008
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S. Y¨uksel / Mathematical and Computer Modelling 46 (2007) 1063–1070
Two groups have used AHP in forecasting. The first group used AHP directly for forecasting using the judgments of experts in areas such as inventory and logistics planning [9], demand forecasting of residents in Singapore [10], and the forecasting of $/DM parity in Turkey [11]. The second group used AHP to adjust forecasts. Wolfe and Flores [12] forecast earnings using ARIMA adjusted with AHP. In another example Flores and Olson [13] used and compared AHP and CENTROID for adjusting forecasting results obtained with quantitative methods. Belton and Goodwin had investigated AHP as a method of forecasting based on judgments. According to their work, AHP is not useful for forecasting when directly using judgments as the input. It could, however, be useful for adjusting forecasting results obtained with other methods. In this situation, AHP can provide new interpretations and offer a wider perspective [14]. Dyer and Forman [15] have recommended using AHP for forecasting in these ways: • To do forecasting with experts’ opinions. • To select the most suitable forecasting method. • To combine results from various forecasting methods. A three step process was offered to forecast demand using AHP by Korpela and Tuominen [9]: • Develop an AHP Hierarchy containing the relevant factors that are thought to affect the level and structure of the demand. • Determine the priorities of the factors in the hierarchy. • Synthesize the priorities to obtain overall priorities that estimate the combined demand, and test these priorities for consistency. AHP also has some advantages in group decision making, according to Dyer and Forman [16]: • All values, individual and/or group, tangible and/or intangible, can be included in a group decision process with AHP. • The discussion focuses on the goal instead of the alternatives. • AHP provides a framework for discussion which includes all the factors, and they can then be presented in a systematic and coherent way. • The discussion continues until the group reaches consensus, with all members being allowed to input their opinions. When using AHP in group decision making or forecasting processes, the participants are asked to compare the criteria pairwise. This can be done via questionnaires, or face to face. It is not obligatory that the participants be experts, but they must have some knowledge about the issues. If the decision is to be made jointly by the group, even though each participant gives his/her judgments, they need to agree that they should be respectful of others’ opinions during the process, and that they will support the group result that emerges at the end. There is usually no problem when the participants have reached a decision through making pairwise comparisons, debating their differences of opinion until they reach a common viewpoint, and changing the model if necessary so that it better reflects their understanding. If, however, the group cannot come to agreement on some judgment, then there is a process in the AHP to construct a group judgment from varying individual judgments using the geometric mean [15]. The geometric mean is the most suitable way to transform individual opinions to a group opinion for a given pairwise comparison [17,1]. 1.2. Forecasting in the hotel businesses The lodging industry, because of the structure of the business, is sensitive to fluctuations in demand. It often has crises because of unexpected reduced demand. Demand forecasting in the lodging industry has become relatively important because of the nature of the industry and its operational characteristics and difficulties. It is important to forecast demand, not only because of the wide fluctuations that may occur, but also because it is hard to track the success of efforts being engaged in to increase the occupancy rate. In hotels, forecasting differs for individuals and group customers. Management knows more about the quantitative and qualitative aspects of demand due to group customers. However, with individuals, it is more difficult to forecast demand. If hotels could repeat their forecasts during a month, the accuracy could increase [18]. There are many forecasting issues, such as bookings, arrivals, nights, duration of stay, and revenue in the hotels. These could
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be estimated separately (disaggregation) or combined (aggregation). Aggregate forecasting is called downward forecasting while upward forecasting is for each unit separately. In stationary demand periods, downward forecasting is less costly and more accurate. However, in nonstationary demand periods, upward forecasting is preferred [19]. Quantitative forecasting methods have been applied to both the entire lodging industry [20,21] and to forecasting for single hotels alone [19,22,23]. In the tourism sector, which is closely related to lodging, demand forecasting is also an important area. These studies on demand forecasting have generally used quantitative methods [24–27]. The use of econometrical models, especially, has been increasing [27]. However, Delphi, Executive Board Opinions, Cross Impact Analysis, Probability Forecasting [28], and Neural Networks [29] are among non-econometric advanced methods that are also being used. In the short term, when expert opinions are included in forecasting procedures, it is recommended that the forecasting algorithm contain or be combined with subjective experience, opinions, and knowledge aside from data [30]. Quantitative forecasting methods based on data alone do not get results accurate enough to justify their costs, the effort required, and the time invested in them. The criteria that have been used to evaluate forecasting methods (ease of application, cost, time and source, decision making support, etc.) show that qualitative methods alone are not suitable for forecasting in hotels [18,19,24–27,30]. Airlines and the lodging sector have similar characteristics that depend on demand. In a study on the impacts of being able to accurately forecast revenue for airlines, it is reported that a 10% adjustment due to forecasting generated increased revenue between 5%–20% [31]. 2. An integrated forecasting approach for hotels 2.1. Goal and importance By reducing uncertainty in future demand, there could be an increased occupancy rate and decreased costs depending on the idle capacity. In this study, we will be attempting to estimate demand fluctuations to avoid crises in the hotel business. Moreover, we also aim to create a systematic continual forecasting procedure for hotels. This procedure should be an open one that can be maintained and improved. When the existing studies on demand forecasting were analyzed, we saw that these studies focused either on national/regional tourism demand or quantitative market analysis. Therefore, we needed to combine the approaches at the micro level that were suitable for the hotel business with other quantitative and qualitative methods. 2.2. Assumptions and limitations The approach of this study is especially good for estimating demand, because the hotel/lodging business has ample sources of information with orderly past data, and a good deal of technical and intellectual capital. Even if this can be said to be more true generally of larger establishments, small and medium size hotels can also make use of the process we have developed. This study was carried out using real data from a five star hotel in Ankara. Even though every lodging business differs from every other, this study concentrated on common characteristics. Advance bookings and pre-sale contracts are common ways management has attempted to reduce uncertainty and risk. In this study, the following assumptions were considered: The hotel has no
advance booking
sales contracts
market segmentation
yield management. 2.3. Data sources The primary data sources were experts from sales/marketing, the room division, front offices, travel agencies, food & beverage professionals; tourism academicians, officers, journalists, counsellors, and NGO representatives in Ankara, the capital city of Turkey. Secondary data for quantitative forecasting were provided by the Local Tourism Directorate in Ankara. (Each hotel must send its arrivals, nights, and occupancy rates to the Local Tourism Directorate in Ankara monthly.)
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Table 1 Grouping the factors in the model Criteria
Factors
International criteria
Global economic crises Wars and other diplomatical crises International organisations/events Currency exchange rate
National criteria
Inflation rate Economic resession or crises Political mobility Terror, anarchy, strikes etc.
Social criteria
Education and or training activities Utilization of public/governmental services Activities/events/organizations in Ankara Rooms Hotel criteria
Marketing activities Sales prices New product/capacity facilities Likelihood of acts of God
Natural criteria
Climate Environmental/ecological issues Rooms
Competition criteria
Sales prices Marketing activities New product/capacity facilities Potential volume
Customer criteria
Travel frequency Service expectations
2.4. Operation The operation was carried out in three steps. First step: The panel of experts determined the variables The determination of the variables/factors to be used in the AHP model was done by a panel of experts. We specified that the variables should be related to demand forecasting in the lodging sector in Ankara and elsewhere. A list of 40 variables was put together and rated for importance by 21 academicians in the Department of Tourism/Hospitality using a Delphi process. The result of this work by the panel was a prioritized list by importance, from which we picked the top 25 variables as shown in Table 1. Second step: Forecasting with quantitative methods The past data used were for a five star hotel in Ankara and consisted of 148 monthly periods from 1990–2002. A night can be defined as one night spent by one person and the total number of nights was given each month for the hotel. The trends and seasonality of these series were not very pronounced and, in fact, were relatively stationary. When first differences were taken, the resulting series were completely stationary. The seasonality factors can be seen in Table 2. The quantitative methods used to smooth the data were Winters’, Decomposition, Moving Average, ARIMA and Regression, and were applied to the nights and arrivals series. The error measures determined by these methods are given in Tables 3 and 4.
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Table 2 The seasonality of series Period
Month
Arrivals
Nights
1 2 3 4 5 6 7 8 9 10 11 12
January February March April May June July August September October November December
0.773346 0.839606 0.874013 0.974557 1.20107 1.02045 0.934402 1.00506 1.17032 1.21761 1.11170 0.869521
0.781686 0.831445 0.885224 0.936058 1.11778 1.03631 0.956627 1.06967 1.16042 1.19584 1.08841 0.948865
Table 3 Comparison of forecasting methods for arrival series Method
MAPE
MAD
MSD
Decomposition Exponential smoothing Holt’s exposmooth. Winter’s season. add Winter’s season. multi. ARIMA (1, 1, 1)
22 20 21 17 17 25
850 726 834 645 631 937
1 319 621 1 041 545 1 283 950 826 375 788 910 –
Method
MAPE
MAD
MSD
Decomposition Exponential smoothing Holt’s exposmooth. Winter’s season. add Winter’s season. multi. ARIMA (1, 1, 1)
18 21 21 15 15 23
1176 1338 1408 982 988 1453
2 227 550 2 793 334 3 474 267 1 632 602 1 703 617 –
Table 4 Comparison of forecasting methods for night series
Table 5 Results of the forecasting process by Winter’s (Multiplicative) seasonality adjustment Period
Month
Year
Arrival
Night
149 150 151 152 153 154
May June July August September October
2002 2002 2002 2002 2002 2002
4468 4195 4346 4243 4672 4836
8180 7233 6556 6700 7882 8211
The highest accuracy was obtained from Winters’ Seasonality Adjustment. As the forecasting term was short, expo-smooth was more suitable and available. The forecast for the next six months according to Winters’ Method are given in Table 5. Third step: Forecasting adjustment The AHP hierarchy that was used for the forecasting adjustment was established in this step. In this hierarchy, we have the goal (level 1); variables that depend on the goal (level 2); scenarios that depend on the variables (level 3);
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Fig. 1. AHP hierarchy for forecasting adjustment. Table 6 Probability values of five-point scale in questionnaire Questionary scale
Value
1 Stronger probability 2 Stong probability 3 Fifty–fifty probability 4 Poor probability 5 Very poor probability Total
0.531 0.252 0.124 0.062 0.031 1.00
and the effects on hotel demand that depend on the scenarios (level 4) as shown in Fig. 1. At level 4, numbers were obtained from similar studies on hotel demand and from conversations with professionals. According to these, up to a ±50% change in expected demand could be accepted as a fluctuation. Higher changes than ±50% sign were ascribed to very extreme situations that could not be forecast. Moreover a change of ±50% is not an adjustment to forecasting results, but is a new forecast. So the five-point scale ranged between ±10% and ±50%. A Delphi based panel was organised and carried out with 30 panelists between May 19 and 26, 2002. Various questionnaires were tested in advance on individuals, and small groups that were not panellists, and the most available, understandable and easy-to-fill-out one was selected. The pairwise comparison matrix and nine-point scale of the AHP were used. There were three types of questions on the form. The first was to give relative weights as percentages. The second was to determine the probability of scenarios as percentage, and third was to give priorities on a five-point scale. The rates of adjustment depended on earlier studies and changes in the number of nights and arrivals at the hotel. The answers to this question were included in the hierarchy by assigning the probability values given in Table 6. In this inquiry, the panellists did not know each other and were located in different places as in Delphi. The questionnaires were mostly delivered by hand. The required explanations about how to fill out the questionnaires were stated face to face. The number of questionnaires that were returned included 13 from professionals and 14 from experts for a total of 27. 2.5. Results In the adjustment procedure, it was not required that there be repeated rounds for consensus; instead the geometric mean was used. The relative weights of the criteria and factors obtained from the panel are given in Table 7, and the adjustment gaps are given in Table 8. The panellists’ responses were normalized three times for all criteria and factors. At the end of the adjustment procedure, the panellists showed that they had not predicted an important change in expected demand. According to the panellists, with highest (37%) probability, the scenarios depending on factors would cause a ±10% deviation from quantitative methods’ results. This means that no important effect on demand at the hotel was expected the following month (June 2002).
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S. Y¨uksel / Mathematical and Computer Modelling 46 (2007) 1063–1070 Table 7 The weights of criteria and factors according to panellists 1 Criteria
2 Factors
1st normalization 1 × 2
International criteria 0.134
Global economic crises Wars and other diplomatical crises International organisations/events Currency exchange
0.395 0.48 0.125 0.331
0.0529 0.0643 0.0167 0.0516
National criteria 0.156
Inflation ratio Economic resession or crises Political mobility Terror, anarchy, strikes, etc.
0.214 0.455 0.366 0.152
0.0333 0.0709 0.0567 0.0235
Social criteria 0.155
Education and or training activities Utilization of public/governmental services Activities/events/organizations in Ankara Rooms
0.094 0.128 0.26 0.288
0.0145 0.0198 0.0403 0.0493
Hotel criteria 0.159
Marketing activities Sales prices New product/capacity facilities Act of God likelihood
0.256 0.31 0.146 0.53
0.0232 0.0493 0.0232 0.0387
Natural criteria 0.073
Climate Environmental/ecological issues Rooms
0.15 0.32 0.268
0.0109 0.0233 0.0536
Competition criteria 0.2
Sales prices Marketing activities New product/capacity facilities
0.299 0.244 0.189
0.0598 0.0488 0.0378
Customer criteria 0.123
Potential volume Travel frequency Service expectations
0.323 0.342 0.335
0.0397 0.0420 0.0414
Table 8 The results of adjustment panel Estimated probability
Verbal statement
Quantity gap(%)
0.3728 0.2240 0.1638 0.1281 0.1064
Not important effect Positive effect Negative effect Very positive effect Very negative effect
−10–10 11–30 −11–30 31–50 −31–50
The analysis was completed in June and the actual arrivals and nights that the hotel reported to the Local Tourism Officer after that were 3777 arrivals and 7455 nights for June. After making the adjustments, the arrivals had been expected to be between 3775–4614 and the nights between 6510–7956 in June for this hotel. So it would not be incorrect to say that the arrivals and nights were within the expected ranges. The forecasting error for arrivals was 3777−4195 = −418, and for nights was 7455−7233 = +222. Even though the adjustment panel seemed to succeed in getting close to the actual numbers, the process should be repeated several times using actual department managers as panellists. It was not possible to do this for this study. 3. Conclusion Quantitative forecasting methods are often used in hotels. Nevertheless, there are many factors which affect demand in hotels. As with any quantitative method, there was some difficulty in gaining access to information on all the factors. For hotels and other lodging businesses, accurate forecasting is one of the most important aspects of the business. This is because demand fluctuations have a large effect on their operations. The most suitable way to maximize
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forecasting accuracy is to integrate quantitative and qualitative methods for forecasting. The first consideration in selecting a quantitative method should be its accuracy. Forecasting adjustments with a qualitative method can then be used to overcome the limits and disadvantages of the quantitative methods currently being used in the lodging sector. AHP has some advantages, especially for use with groups, as it can accommodate many factors and including intangible ones. As it was not originally intended to be used as a forecasting method, some characteristics, like the pairwise comparisons using a nine point scale, present some difficulties in using with traditional forecasting adjustment concepts. However, the AHP-based adjustment procedure has some advantages, such as: flexibility, dynamism, openness, understandability, and ease of application. Acknowledgement I would like to acknowledge and thank Rozann Whitaker for her contributions. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]
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