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This paper introduces a three-stage framework for supply chain analytics, combining exploratory data analysis, time series forecasting using N-BEATS and N-HiTS deep learning models, and integer linear programming (ILP) for optimization. The study found that N-BEATS outperformed N-HiTS in forecasting accuracy for the given retail dataset of 180,519 transactions. The forecasted values from N-BEATS were then used as input for an ILP model to minimize total delivery time under budget, capacity, and service constraints, resulting in a cost-optimal shipping plan.
Deep learning forecasting models like N-BEATS can be effectively integrated with integer linear programming to create optimized and cost-effective supply chain solutions, even with noisy and irregular retail data.
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.