Journal of Revenue and Pricing Management

, Volume 10, Issue 4, pp 306–324 | Cite as

Forecasting for cruise line revenue management

  • Xiaodong Sun
  • Dinesh K Gauri
  • Scott Webster
Research Article

Abstract

In recent years, the cruise line industry has become an exciting growth category in the leisure travel market. Like airlines and hotels, it reports all characteristics of revenue management (RM). Although RM has attracted widespread research interest in airline and hotel contexts, studies of cruise line revenue management are very limited. Using data from a major North American cruise company, we apply a variety of (24) forecasting methods, which are divided into three categories (non-pickup methods, classical pickup (CP) methods and advanced pickup (AP) methods), to generate forecasts of final bookings for the cruises that have not yet departed at a particular reading point. We use a two-stage framework to test alternative forecasting methods and compare their performance. We found the performance of multiplicative methods to be significantly worse. Among the additive methods, we find that classical methods perform the best, followed by AP and non-pickup methods. All CP methods with the exception of exponential smoothing with trend perform fairly well. Among AP methods, Autoregressive integrated moving average, linear regression and moving average (MA) produce the most accurate forecasts. Within non-pickup methods, MA is the most effective method.

Keywords

cruise lines cruise line industry forecasting revenue management cruise line revenue management (CLRM) 

References

  1. Ahmed, Z.U., Johnson, J.P., Ling, C.P., Fang, T.W. and Hui, A.K. (2002) Country-of-origin and brand effects on consumers’ evaluations of cruise lines. International Marketing Review 19 (2/3): 279–302.CrossRefGoogle Scholar
  2. Biehn, N. (2006) A cruise ship is not a floating hotel. Journal of Revenue and Pricing Management 5 (2): 135–142.CrossRefGoogle Scholar
  3. Box, G.E.P. and Jenkins, G.M. (1970) Time Series Analysis, Forecasting and Control. San Francisco, CA: Holden Day.Google Scholar
  4. Chen, C. and Kachani, S. (2007) Forecasting and optimisation for hotel revenue management. Journal of Revenue and Pricing Management 6 (3): 163–174.CrossRefGoogle Scholar
  5. Duman, T. and Mattila, A.S. (2005) The role of affective factors on perceived cruise vacation value. Tourism Management 26: 311–323.CrossRefGoogle Scholar
  6. Dwyer, L. and Forsyth, P. (1998) Economic significance of cruise tourism. Annals of Tourism Research 25 (2): 393–415.CrossRefGoogle Scholar
  7. Gorin, T.O. (2000) Airline revenue management: Sell-up and forecasting algorithms. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  8. Ji, L. and Mazzarella, J. (2007) Application of modified nested and dynamic class allocation models for cruise line revenue management. Journal of Revenue and Pricing Management 6 (1): 19–32.CrossRefGoogle Scholar
  9. Kimes, S.E. (1989) Yield management: A tool for capacity-considered service firms. Journal of Operations Management 8 (4): 348–363.CrossRefGoogle Scholar
  10. Ladany, S.P. and Arbel, A. (1991) Optimal cruise-liner passenger cabin pricing policy. European Journal of Operational Research 55 (2): 136–147.CrossRefGoogle Scholar
  11. Lee, A.O. (1990) Airline reservations forecasting: Probabilistic and statistical models of the booking process. PhD dissertation, Massachusetts Institute of Technology.Google Scholar
  12. Lieberman, W.H. and Dieck, T. (2002) Expanding the revenue management frontier: Optimal air planning in the cruise industry. Journal of Revenue and Pricing Management 1 (1): 7–24.CrossRefGoogle Scholar
  13. Littlewood, K. (1972) Forecasting and control of passenger bookings. Proceedings of the 12th Annual AGIFORS Symposium; Nathanya, Israel, Vol. 12, pp. 95–117, reprinted in Journal of Revenue and Pricing Management, 4 (2): 111–123.Google Scholar
  14. Marti, B.E. (2004) Trends in world and extended-length cruising (1985–2002). Marine Policy 28 (3): 199–211.CrossRefGoogle Scholar
  15. McGill, J.I. and Van Ryzin, G.J. (1999) Revenue management: Research overview and prospects. Transportation Science 33 (2): 233–256.CrossRefGoogle Scholar
  16. Qu, H. and Ping, E.W.Y. (1999) A service performance model of Hong Kong cruise travelers’ motivation factors and satisfaction. Tourism Management 20: 237–244.CrossRefGoogle Scholar
  17. Petrick, J.F. (2004) Are loyal visitors desired visitors? Tourism Management 25: 463–470.CrossRefGoogle Scholar
  18. Petrick, J.F. (2005) Segmenting cruise passengers with price sensitivity. Tourism Management 26: 753–762.CrossRefGoogle Scholar
  19. Reyes, M.H. (2006) Hybrid forecasting for airline yield management in semi-restricted fare structures. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  20. Sa, J. (1987) Reservation forecasting in airline yield management. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  21. Skwarek, D.K. (1996) Competitive impacts of yield management system components: forecasting and sell-up models. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  22. Talluri, K. and van Ryzin, J. (2004) The Theory and Practice of Revenue Management. Boston, MA: Kluwer Academic.CrossRefGoogle Scholar
  23. Teye, V.B. and Leclerc, D. (1998) Product and service delivery satisfaction among North American cruise passengers. Tourism Management 19 (2): 153–160.CrossRefGoogle Scholar
  24. Toh, R.S., Rivers, M.J. and Ling, T.W. (2005) Room occupancies: Cruise lines out-do the hotels. International Journal of Hospitality Management 24 (1): 121–135.CrossRefGoogle Scholar
  25. Weatherford, L.R. (1998) Forecasting issues in revenue management. In: Presentation at Spring INFORMS Conference, Montreal, Canada.Google Scholar
  26. Weatherford, L.R. and Kimes, S.E. (2003) A comparison of forecasting methods for hotel revenue management. International Journal of Forecasting 19 (3): 401–415.CrossRefGoogle Scholar
  27. Weatherford, L.R., Kimes, S.E. and Scott, D.A. (2001) Forecasting for hotel revenue management: Testing aggregation against disaggregation. Cornell Hotel and Restaurant Administration Quarterly 42: 53–64.CrossRefGoogle Scholar
  28. Wickham, R.R. (1995) Evaluation of forecasting techniques for short-term demand of air transportation. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  29. Wie, B.-W. (2004) Open-loop and closed-loop models of dynamic oligopoly in the cruise line industry. Asia-Pacific Journal of Operational Research 21 (4): 517–541.CrossRefGoogle Scholar
  30. Zeni, R.H. (2001) Improved forecast accuracy in revenue management by unconstraining demand estimates from censored data. PhD dissertation, ProQuest Information and Learning Company.Google Scholar
  31. Zickus, J.S. (1998) Forecasting for airline network revenue management: Revenue and competitive impacts. Master’s thesis, Massachusetts Institute of Technology.Google Scholar

Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Ltd 2010

Authors and Affiliations

  • Xiaodong Sun
    • 1
  • Dinesh K Gauri
    • 2
  • Scott Webster
    • 3
  1. 1.Department of Management ScienceAntai College of Economics & Management, Shanghai Jiao Tong UniversityChina
  2. 2.Syracuse UniversityNew YorkUSA
  3. 3.H.H. Franklin Center for Supply Chain Management, Whitman School of Management, Syracuse UniversityNew YorkUSA

Personalised recommendations