The LSTM technique for demand forecasting of e-procurement in the hospitality industry in the UAE

Elezabeth Mathew, Sherief Abdulla

Abstract


The hospitality industry is growing at a faster pace across the world which has resulted in the accumulation of a huge amount of data in terms of employee details, property details, purchase details, vendor details, and so on. The industry is yet to fully benefit from these big data by applying ML and AI. The data has not been fully investigated for decision-making or revenue/budget forecasting. In this research data is collected from a chain hotel for advanced predictive analytics. Descriptive and diagnostic analytics is done to an extent across the hotel industry, whereas predictive and prescriptive analysis is done rarely. Demand forecasting for spend and quantity is done using the LSTM technique in e-procurement within the hospitality industry in the UAE. Five years of historical data from a chain hotel in the UAE is used for deep learning in this study. The results confirm the ability of LSTM model to predict e-procurement spend and order forecast for six months. LSTM time series analysis is considered the most suitable technique for demand forecasting to optimize e-procurement.

Keywords


Deep learning, Demand forecasting, E-procurement, Hospitality, Long- and short-term memory, Machine learning

References


Bogetić, S., Đorđević, D., Ćoćkalo, D. & Bešić, C. (2017). The Analysis of Quality Aspects in the Development of Competitiveness of Domestic Hotel Enterprises. International Journal “Advanced Quality”, vol. 44 (2), p. 1.

Mariani, M., Baggio, R., Fuchs, M. & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, vol. 30 (12), pp. 3514-3554.

Edghiem, F. & Mouzughi, Y. (2018). Knowledge-advanced innovative behavior: a hospitality service perspective. International Journal of Contemporary Hospitality Management, vol. 30 (1), pp. 197-216.

Gomezelj, D. (2016). A systematic review of research on innovation in hospitality and tourism. International Journal of Contemporary Hospitality Management, vol. 28 (3), pp. 516-558.

Brandon-Jones, A. & Kauppi, K. (2018). Examining the antecedents of the technology acceptance model within e-procurement. International Journal of Operations & Production Management, vol. 38 (1), pp. 22-42.

Lamba, K. & Singh, S. (2017). Big data in operations and supply chain management: current trends and future perspectives. Production Planning & Control, vol. 28 (11-12), pp. 877-890.

Massa, S. & Testa, S. (2009). A knowledge management approach to organizational competitive advantage: Evidence from the food sector. European Management Journal, vol. 27 (2), pp. 129-141.

Wang, G., Gunasekaran, A., Ngai, E. & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, vol. 176, pp. 98-110.

Greasley, A. (n.d.). Simulating business processes for descriptive, predictive, and prescriptive analytics. Frankfurt: Walter de Gruyter GmbH & Co.

“Artificial Intelligence Trends: Decision Augmentation”. (2020). Available at: https://www.gartner.com/en/documents/3979508/artificial-intelligence-trends-decision-augmentation

Król, K. & Zdonek, D. (2020). Analytics Maturity Models: An Overview. Information, vol. 11 (3), p. 142.

Delen, D. & Demirkan, H. (2013). Data, information, and analytics as services. Decision Support Systems, vol. 55 (1), pp. 359-363.

Zhang, J., Yang, X. & Appelbaum, D. (2015). Toward Effective Big Data Analysis in Continuous Auditing. Accounting Horizons, vol. 29 (2), pp. 469-476.

Buhalis, D. & Leung, R. (2018). Smart hospitality—Interconnectivity and interoperability towards an ecosystem. International Journal of Hospitality Management, vol. 71, pp. 41-50.

Bendoly, E. (2012). Real-time feedback and booking behavior in the hospitality industry: Moderating the balance between imperfect judgment and imperfect prescription. Journal of Operations Management, vol. 31 (1-2), pp. 62-71.

Ampazis, N. (2015). Forecasting Demand in Supply Chain Using Machine Learning Algorithms. International Journal of Artificial Life Research, vol. 5 (1), pp. 56-73.

Song, H., Qiu, R. & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, vol. 75, pp. 338-362.

Wen, H., Li, S., Li, W., Li, J. & Yin, C. (2017). Comparison of four machine learning techniques for the prediction of prostate cancer survivability. Institute of Information Science and Engineering, (3).

Wen, H., Li, S., Li, W., Li, J. & Yin, C. (2017). Comparison of four machine learning techniques for the prediction of prostate cancer survivability. Institute of Information Science and Engineering, (3).

Wu, C., Patil, P. & Gunaseelan, S. (2018). Algorithms for Multiple Regression on Black Friday Sales Data. IEEE explore, vol. 14 (1), pp. 17-21.

Alonzo, L., Chioson, F., Co, H., Bugtai, N. & Baldovino, R. (2018). A Machine Learning Approach for Coconut Sugar Quality Assessment and Prediction. Engineering Research and Development for Technology (ERDT) of the Department of Science and Technology (DOST).

Goudarzi, F. (2019). Travel Time Prediction: Comparison of Machine Learning Algorithms in a Case Study. 20th International Conference on High-Performance Computing and Communications, vol. 4 (20), p. 1.

Raschka, S., Julian, D. & Hearty, J. (2016). Python deeper insights into machine learning. 2nd edn. Birmingham B3 2PB, UK.: Packt Publishing Ltd.

Subramanian, G. (2015). Python Data Science Cookbook. 1st edn. Birmingham B3 2PB, UK.: Packt Publishing.

Pahwa, K. & Agarwal, N. (2019). Stock Market Analysis using Supervised Machine Learning. International Conference on Machine Learning, Big Data, Cloud and Parallel Computing

Bandara, K., Bergmeir, C. & Hewamalage, H. (2020). LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series With Multiple Seasonal Patterns. IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.

Lihong, D. & Qian, X. (2020). Short-term electricity price forecast based on long short-term memory neural network. Journal of Physics: Conference Series, vol. 1453, p. 012103.

Siami-Namini, S. & Namin, A. (2018). "Forecasting Economic and Financial Time Series: ARIMA vs. LSTM". arXiv.org [online]. Available at: https://arxiv.org/abs/1803.06386

Yeom, H., Kim, J. & Chung, C. (2020). LSTM Improves Accuracy of Reaching Trajectory Prediction From Magnetoencephalography Signals. IEEE Access, vol. 8, pp. 20146-20150.

Hua, Y., Zhao, Z., Li, R., Chen, X., Liu, Z. & Zhang, H. (2019). Deep Learning with Long Short-Term Memory for Time Series Prediction. IEEE Communications Magazine, vol. 57 (6), pp. 114-119.

Mathew, E. (2019). Big Data Analytics in E-procurement of a Chain Hotel. Advances in Internet, Data, and Web Technologies [online]. Vol. 29, pp. 295-308. Available at: https://link.springer.com/chapter/




DOI: http://doi.org/10.11591/ijai.v9.i4.pp%25p
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