Multi-Domain Inventory Supply Optimization: A Comparative Analysis Using Seasonal-Trend Decomposition (STL) and Predictive Analytics

DOI

10.2991/978-94-6239-693-7_116How to use a DOI?

Keywords

Demand forecasting; time series modeling; STL decomposition; Holt-Winters exponential smoothing; retail operations; inventory management; seasonal demand; fashion e-commerce; Amazon e-commerce; grocery retail; MAPE metric; supply chain optimization

Abstract

Demand forecasting plays a critical role in the operations of any retail because it enables the control of the inventory and facilitates a supply chain and maximises revenues. A close examination of time series forecasting methods adopted in three retailing industries i.e. fashion e-commerce, Amazon e-commerce and grocery retail have been incorporated in the paper. A model proposal, which is a hybrid of Seasonal-Trend decomposition (STL) and LOESS and Holt-Winters exponential smoothing would be an effective method of determining complex temporal fluctuations in the demand data. Real life data of three large retailers which possess different demand characteristics was used to test the methodology. We have found that the mean absolute percent error (MAPE) of our strategy is 12.75% in fashion retail, 10.40% in Amazon e-commerce, and 13.2% in grocery retail. This model particularly finds application in products in high volume and also has the ability to have automated demand forecasting systems, which can be operated under numerous retail environments. The article contributes to the body of existing literature on the topic of retail analytics by demonstrating how traditional time series tools can be applied and adapted to fit the current e-commerce and omnichannel retail processes.

Copyright

© 2026 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  - Karan Dalania
AU  - Shashank Saxena
AU  - B. Sowmiya
PY  - 2026
DA  - 2026/06/16
TI  - Multi-Domain Inventory Supply Optimization: A Comparative Analysis Using Seasonal-Trend Decomposition (STL) and Predictive Analytics
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 1214
EP  - 1229
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_116
DO  - 10.2991/978-94-6239-693-7_116
ID  - Dalania2026
ER  -