Abstract
We present forecasting models based on extreme gradient boosting to predict demand using real-world data of a German intermediary company in the media sector. The data set comprised the daily demand of 196,767 products from three years (mid-2017 to mid-2020) and meta information for each product including product type affiliation. Models were trained separately for each product type either to predict the demand on group or product level. For the latter, training was in rolling format based on the last 12 weeks to then predict the product’s short-term demand one-week ahead. Performance, evaluated via the coefficient of determination, is especially precise for specific product types. Engineered features consisting of seasonal information, statistical indices, and general performing indices obtained via fuzzy c-means clustering over time improved the prediction. Especially, predictions for the upcoming week on product level are challenging but of high value for future business decisions regarding inventory planning and purchase orders.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Non-linear effects in the demand behavior are particularly challenging and can be explained by competition among suppliers, the bullwhip effect, and mismatch between supply and demand [7].
References
Guo, Z.X., Wong, W.K., Li, M.: A multivariate intelligent decision-making model for retail sales forecasting. Decis. Supp. Syst. 55(1), 247–255 (2013)
You, Z., Si, Y.-W., Zhang, D., et al.: A decision-making framework for precision marketing. Expert Syst. Appl. 42(7), 3357–3367 (2015)
Blackburn, R., Lurz, K., Priese, B., et al.: A predictive analytics approach for demand forecasting in the process industry. Int. Trans. Oper. Res. 22(3), 407–428 (2015)
Hofmann, E., Rutschmann, E.: Big data analytics and demand forecasting in supply chains: a conceptual analysis. IJLM 29(2), 739–766 (2018)
Nguyen, T., Zhou, L., Spiegler, V., et al.: Big data analytics in supply chain management: a state-of-the-art literature review. Comput. Oper. Res. 98, 254–264 (2018)
Fildes, R., Ma, S., Kolassa, S.: Retail forecasting: research and practice. Int. J. Forecast. (2019, in press)
Seyedan, M., Mafakheri, F.: Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. J. Big Data 7(1), 1–22 (2020)
Wang, G., Gunasekaran, A., Ngai, E.W., et al.: Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int. J. Prod. Econ. 176, 98–110 (2016)
Makridakis, S., Spiliotis, E., Assimakopoulos, V.: Statistical and machine learning forecasting methods: concerns and ways forward. PLoS One 13(3), e0194889 (2018)
Huber, J., Stuckenschmidt, H.: Daily retail demand forecasting using machine learning with emphasis on calendric special days. Int. J. Forecast. 36(4), 1420–1438 (2020)
Punia, S., Nikolopoulos, K., Singh, S.P., et al.: Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail. Int. J. Prod. Res. 58(16), 4964–4979 (2020)
Vairagade, N., Logofatu, D., Leon, F., et al.: Demand forecasting using random forest and artificial neural network for supply chain management. In: Nguyen, N.T., Chbeir, R., Exposito, E., et al. (eds.) Computational Collective Intelligence, pp. 328–339. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28377-3_27
Boylan, J.E., Chen, H., Mohammadipour, M., et al.: Formation of seasonal groups and application of seasonal indices. J. Oper. Res. Soc. 65(2), 227–241 (2014)
Chen, I.-F., Lu, C.-J.: Sales forecasting by combining clustering and machine-learning techniques for computer retailing. Neural Comput. Appl. 28(9), 2633–2647 (2016). https://doi.org/10.1007/s00521-016-2215-x
Thomassey, S., Fiordaliso, A.: A hybrid sales forecasting system based on clustering and decision trees. Decis. Supp. Syst. 42(1), 408–421 (2006)
Chen, T., Guestrin, C.: XGBoost. In: Krishnapuram, B., Shah, M., Smola, A., et al. (eds.) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM, New York (2016)
Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Nwadiugwu, M.C.: Gene-based clustering algorithms: comparison between denclue, fuzzy-c, and BIRCH. Bioinform. Biol. Insights 14, 1–6 (2020)
Tavenard, R., Faouzi, J., Vandewiele, G., et al.: Tslearn, a machine learning toolkit for time series data. J. Mach. Learn. Res. 21(118), 1–6 (2020)
https://xgboost.readthedocs.io/en/latest/parameter.html. Accessed 10 Mar 2021
Kilimci, Z.H., Akyuz, A.O., Uysal, M., et al.: An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Complexity 2019, 1–15 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lingelbach, K., Lingelbach, Y., Otte, S., Bui, M., Künzell, T., Peissner, M. (2021). Demand Forecasting Using Ensemble Learning for Effective Scheduling of Logistic Orders. In: Ahram, T.Z., Karwowski, W., Kalra, J. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-80624-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-80624-8_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80623-1
Online ISBN: 978-3-030-80624-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)