An Explainable Artificial Intelligence Algorithm for Optimal Decision Making from a Business Analytics Perspective

A feedforward neural network (FFNN) can effectively identify key factors that influence production system performance, which supports better decisions and cost reduction. This study utilizes an FFNN to compare the cost performance of two inventory approaches: fixed-time and fixed-size lots. The proposed algorithm has three artificial neurons representing three possible outcomes: fixed-size lots perform better, fixed-time lots perform better, and no significant difference in cost. The trained network’s parameters assess the importance of input to predict these outcomes. Moreover, an explainable artificial intelligence algorithm, called Shapley additive explanations (SHAP), is employed to explain FFNN outcomes. Moreover, two inventory conditions are included: lost sales and backordering. The analysis reveals that the key drivers of a lost-sales policy are modular availability and the degree of demand variability. Under the backordering strategy, demand variability, inventory holding cost, and stand-alone availability exert the strongest influence. The outcomes showed that fixed-size lots support a just-in-time approach when demand flexibility is low while there is high modular availability. However, when demand becomes more volatile and modular availability declines, results show that fixed-time lots provide a more cost-efficient alternative. These findings offer practical guidance for production control decisions, especially in high-volume discrete manufacturing settings such as automotive stamping operations.