Enhanced Inventory Demand Forecasting with Machine Learning

DOI

10.2991/978-94-6463-276-7_45How to use a DOI?

Keywords

demand estimation; gradient boosting decision trees; demand analytics

Abstract

Modeling inventory demand is critical for businesses to manage resources and ensure customer satisfaction. Traditional economic models, rooted in utility functions and structural approaches, often face challenges due to stringent assumptions and inability to adapt to real-world data. This research harnesses machine learning, specifically the LightGBM algorithm, to enhance demand prediction. Unlike traditional models tied to Gaussian distribution, LightGBM adapts to actual data distributions, capturing complex, non-linear relationships. The results highlight sales channels and product types as pivotal demand drivers. This study blends traditional econometric techniques with modern machine learning, offering a roadmap for future demand forecasting research.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haoyuan Ren
PY  - 2023
DA  - 2023/10/27
TI  - Enhanced Inventory Demand Forecasting with Machine Learning
BT  - Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)
PB  - Atlantis Press
SP  - 420
EP  - 428
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-276-7_45
DO  - 10.2991/978-94-6463-276-7_45
ID  - Ren2023
ER  -