Machine Learning Approaches for Improving Demand Forecasting Accuracy in Retail Supply Chains

Omar Sharafeldin Mohamed Abdelfatah

No 4z9be_v1, SocArXiv from Center for Open Science

Abstract: Accurate demand forecasting remains one of the most critical yet persistently challenging functions in retail supply chain management. Traditional statistical forecasting methods such as ARIMA and exponential smoothing have long served as industry standards; however, their limited capacity to capture nonlinear demand patterns, seasonal volatility, and external market signals has prompted growing interest in machine learning (ML) alternatives. This study investigates the comparative effectiveness of multiple ML approaches including Random Forest, Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) neural networks, and hybrid ensemble models against traditional baseline methods in the context of retail supply chain demand forecasting. Employing a quantitative research design, the study utilizes a panel dataset comprising 36 months of point-of-sale (POS) transaction records, promotional calendars, macroeconomic indicators, and weather data from 14 retail organizations operating across grocery, fashion, and consumer electronics segments. Forecasting accuracy is evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Forecast Bias metrics across multiple product categories and forecasting horizons (1-week, 4-week, and 12-week ahead). Results demonstrate that ensemble ML models particularly hybrid LSTM-XGBoost architectures achieve statistically significant improvements in forecasting accuracy over traditional methods, with MAPE reductions averaging 28.6% at the 4-week horizon. Feature importance analysis identifies promotional activity, competitor pricing signals, and lagged POS data as the most influential demand drivers. The study further reveals that ML forecasting benefits are heterogeneous across product categories, with highest gains 2 observed in high-velocity, promotion-sensitive SKUs and smallest gains in slow-moving, low-volatility items. A practical implementation framework is proposed, offering retail supply chain practitioners a structured pathway from data readiness assessment through model deployment and ongoing performance monitoring.

Date: 2026-04-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:4z9be_v1

DOI: 10.31219/osf.io/4z9be_v1

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