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A Review of Forecasting Studies for the Restaurant Industry: Focusing on results, contributions and limitations

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Purpose: The purpose of this study is to explore restaurant forecasting studies through a comprehensive review of existing studies, discuss the contributions and limitations of those studies and uncover possible directions for future research. Design/methodology/approach: Key restaurant forecasting studies were collected by searching keywords in Google Scholar and selected based on their citation times. A final pool of 29 key studies in the restaurant field was analyzed and discussed. Findings: According to this study, most forecasting studies have focused on model building and its accuracy. These studies used combined and hybrid models more frequently than single qualitative or quantitative models and focused on sales volume. By calculating citation counts and average citation times per year, this study ranked articles published to show their impact on the body of knowledge in this field. In addition, by reviewing the affiliations of the first authors of these studies, this paper found that the United States has been publishing the earliest and most papers related to forecasting in the restaurant industry. Research limitations/implications: This study used the citation count to measure the impact of a study. While this serves the purpose of this study, citation counts may not perfectly represent the value and impact of a study. The findings of this study provide a guide post for future studies. Originality/value: This study provides an updated, comprehensive literature review of the overall development, contributions and limitations of forecasting studies in the restaurant field. The researchers can see the subjects and types of research that have already been conducted and which topics remain unexamined.

Abstract

Purpose: The purpose of this study is to explore restaurant forecasting studies through a comprehensive review of existing studies, discuss the contributions and limitations of those studies and uncover possible directions for future research. Design/methodology/approach: Key restaurant forecasting studies were collected by searching keywords in Google Scholar and selected based on their citation times. A final pool of 29 key studies in the restaurant field was analyzed and discussed. Findings: According to this study, most forecasting studies have focused on model building and its accuracy. These studies used combined and hybrid models more frequently than single qualitative or quantitative models and focused on sales volume. By calculating citation counts and average citation times per year, this study ranked articles published to show their impact on the body of knowledge in this field. In addition, by reviewing the affiliations of the first authors of these studies, this paper found that the United States has been publishing the earliest and most papers related to forecasting in the restaurant industry. Research limitations/implications: This study used the citation count to measure the impact of a study. While this serves the purpose of this study, citation counts may not perfectly represent the value and impact of a study. The findings of this study provide a guide post for future studies. Originality/value: This study provides an updated, comprehensive literature review of the overall development, contributions and limitations of forecasting studies in the restaurant field. The researchers can see the subjects and types of research that have already been conducted and which topics remain unexamined.

참고문헌 (43)

  1. Boomija, G., "Restaurant visitor time series forecasting using autoregressive integrated moving average", Journal of Computational and Theoretical Nanoscience, 15(5), pp.1590~1593 (2018)
  2. Chambers, J. C., "How to Choose the Right Forecasting Technique", Harvard Business Review, ~ (1971)
  3. Choi, J. G., "Developing restaurant industry business cycle model and analyzing industry turning point", Journal of Global Business and Technology, 3(1), pp.40~48 (2007)
  4. Cranage, D. A., "A comparison of time series and econometric models for forecasting restaurant sales", International Journal of Hospitality Management, 11(2), pp.129~142 (1992)
  5. Davis, R., "A survey of recent developments in queue wait time forecasting methods", Proceedings of the International Conference on Foundations of Computer Science (FCS), pp.84~ (2016), The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing
  6. Forst, F. G., "Forecasting restaurant sales using multiple regression and Box-Jenkins analysis", Journal of Applied Business Research, 8(2), pp.15~ (1992)
  7. Ganesan, V. A., "Forecasting food sales in a multiplex using dynamic artificial neural networks", Science and Information Conference, pp.69~80 (2019), Springer, Cham
  8. Ghalehkhondabi, I., "A review of demand forecasting models and methodological developments in tourism and passenger transportation industry", Journal of Tourism Futures, 5(1), pp.75~93 (2019)
  9. Green, Y. N., "Approaches, techniques, and information technology systems in the restaurants and foodservice industry : A qualitative study in sales forecasting", International Journal of Hospitality & Tourism Administration, 9(2), pp.164~191 (2008)
  10. Hidayatullah, M. C., "Demand forecasting analysis using time series methods at Ayam Lodho Pak Yusuf restaurant", Proceedings of the International Conference on Ethics of Business, Economics, and Social Science (ICEBESS), pp.375~381 (2016), Faculty of Economics, Yogyakarta State University
  11. Hu, C., "Forecasting in short-term planning and management for a casino buffet restaurant", Journal of Travel & Tourism Marketing, 16(2-3), pp.79~98 (2004)
  12. Jiao, E. X., "Tourism forecasting: A review of methodological developments over the last decade", Tourism Economics, 25(3), pp.469~492 (2019)
  13. Kim, C. S., "Review of reviews: A systematic analysis of review papers in the hospitality and tourism literature", International Journal of Hospitality Management, 70, pp.49~58 (2018)
  14. Kim, S. Y., "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models", Economic Modelling, 36, pp.354~362 (2014)
  15. Kimes, S. E., "Restaurant revenue management: Applying yield management to the restaurant industry", Cornell Hotel and Restaurant Administration Quarterly, 39(3), pp.32~39 (1998)
  16. Lee, K., "Exploring the impacts of key economic indicators and economic recessions in the restaurant industry", Journal of Hospitality Marketing &Management, 21(3), pp.330~343 (2012)
  17. Li, G., "Introduction to the special issue : Tourism forecasting-New trends and issues", Tourism Economics, 25(3), pp.305~308 (2019)
  18. Li, X., "Review of tourism forecasting research with internet data", Tourism Management, 83, pp.104245~ (2021)
  19. Liu, H., "Hot topics and emerging trends in tourism forecasting research: A scientometric review", Tourism Economics, 25(3), pp.448~468 (2019)
  20. Liu, L. M., "Data mining on time series: An illustration using fast-food restaurant franchise data", Computational Statistics & Data Analysis, 37(4), pp.455~476 (2001)
  21. Love, C. L., "Forecasting Accurate Customer Counts in the Quick Service Restaurant Industry", Doctor, pp.2021
  22. Ma, X., "Predicting future visitors of restaurants using big data", 2018International Conference on Machine Learning and Cybernetics (ICMLC), 1, pp.269~274 (2018), IEEE
  23. Meneghini, M., "Quantitative demand forecasting adjustment based on qualitative factors: Case study at a fast food restaurant", Sistemas & Gestão, 13(1), pp.68~80 (2018)
  24. Messersmith, A. M., "Forecasting in Foodservice" (1991), John Wiley & Sons, Inc
  25. Miller, J. L., "Forecasting menu-item demand in foodservice operations", Journal of the American Dietetic Association, 88(4), pp.443~449 (1988)
  26. Miller, J. L., "Food production forecasting with simple time series models", Hospitality Research Journal, 14(3), pp.9~21 (1990)
  27. Miller, J. J., "Foodservice forecasting using simple mathematical models", Hospitality Research Journal, 15(1), pp.43~58 (1991)
  28. Otudi, I., "The impact of forecasting methods on demand projection in fast food restaurant", PalArch's Journal of Archaeology of Egypt, 2021, pp.18(15), 334~343
  29. Padgett, C. S., "Building Forecasting Models for Restaurant Owners and Managers : A Case Study", American Journal of Entrepreneurship, 9(2), pp.84~109 (2016)
  30. Puspita, F. M., "Application of Material Requirement Planning with ARIMA Forecasting and Fixed Order Quantity Method in Optimizing the Inventory Policy of Raw Materials of Sederhana Restaurant in Palembang", 5th Sriwijaya Economics, Accounting, and Business Conference (SEABC 2019), pp.71~76 (2020), Atlantis Press
  31. Posch, K., "A Bayesian approach for predicting food and beverage sales in staffcanteens and restaurants", International Journal of Forecasting, 38(1), pp.321~338 (2021)
  32. Song, H., "A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting", Annals of Tourism Research, 75, pp.338~362 (2019)
  33. Reynolds, D., "Econometric modeling of the US restaurant industry", International Journal of Hospitality Management, 34, pp.317~323 (2013)
  34. Ryu, K., "The evaluation of forecasting methods at an institutional foodservice dining facility", The Journal of Hospitality Financial Management, 11(1), pp.27~45 (2003)
  35. Takeyasu, K., "A Hybrid Method to Improve Forecasting Accuracy In the Case of Japanese Food Restaurant", Journal of Computations & Modelling, 6(1), pp.27~53 (2016)
  36. Tanizaki, T., "Demand forecasting in restaurants using machine learning and statistical analysis", Procedia CIRP, 79, pp.679~683 (2019)
  37. Tanizaki, T., "Restaurants store management based on demand forecasting", Procedia CIRP, 88, pp.580~583 (2020)
  38. Versluis, H. F., "Daily sales forecasting in foodservice:developing a model for application in an expert system", Doctor, pp.2016
  39. Witt, S. F., "Forecasting tourism demand:A review of empirical research", International Journal of forecasting, 11(3), pp.447~475 (1995)
  40. Xinliang, L., "University restaurant sales forecast based on BP neural network-In Shanghai Jiao Tong University case", pp.338~347 (2017), International conference on swarm intelligence
  41. Youn, H., "Predict US restaurant firm failures:The artificial neural network model versus logistic regression model", Tourism and Hospitality Research, 10(3), pp.171~187 (2010)
  42. "Can User-Posted Photos Serve as a Leading Indicator of Restaurant Survival?" (2018)[URL]
  43. Zháo, M. A., "Forecasting Daily Visitors and Menu Demands in an Indonesian Chain Restaurant using Support Vector Regression Machine", 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), pp.1~6 (2021), IEEE

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