🤖 AI Summary
Retail demand data are often plagued by strong seasonality, irregular spikes, and noise, which undermine the accuracy of traditional forecasting methods and hinder effective supply chain decision-making. To address this challenge, this work proposes an end-to-end three-stage framework: it begins with exploratory data analysis, followed by a systematic evaluation of deep time series models—specifically N-BEATS and N-HiTS—to identify the best-performing predictor. The superior forecast from N-BEATS is then integrated into an integer linear programming (ILP) model that generates feasible delivery plans minimizing total distribution time under constraints on budget, capacity, and service level. By combining high-accuracy deep learning forecasts with interpretable constrained optimization, the approach successfully translates four-week demand predictions for 1,918 units into cost-optimal, executable logistics plans, substantially enhancing operational efficiency.
📝 Abstract
Although demand forecasting is a critical component of supply chain planning, actual retail data can exhibit irreconcilable seasonality, irregular spikes, and noise, rendering precise projections nearly unattainable. This paper proposes a three-step analytical framework that combines forecasting and operational analytics. The first stage consists of exploratory data analysis, where delivery-tracked data from 180,519 transactions are partitioned, and long-term trends, seasonality, and delivery-related attributes are examined. Secondly, the forecasting performance of a statistical time series decomposition model N-BEATS MSTL and a recent deep learning architecture N-HiTS were compared. N-BEATS and N-HiTS were both statistically, and hence were N-BEATS's and N-HiTS's statistically selected. Most recent time series deep learning models, N-HiTS, N-BEATS. N-HiTS and N-BEATS N-HiTS and N-HiTS outperformed the statistical benchmark to a large extent. N-BEATS was selected to be the most optimized model, as the one with the lowest forecasting error, in the 3rd and final stage forecasting values of the next 4 weeks of 1918 units, and provided those as a model with a set of deterministically integer linear program outcomes that are aimed to minimize the total delivery time with a set of bound budget, capacity, and service constraints. The solution allocation provided a feasible and cost-optimal shipping plan. Overall, the study provides a compelling example of the practical impact of precise forecasting and simple, highly interpretable model optimization in logistics.