Production and inventory control with chaotic demands

This study explores an efficient approach for identifying chaotic phenomena in demands and develops a production lot-sizing method for chaotic demands. Owing to the butterfly effect of chaotic demands, precise prediction of long-term demands is difficult. The experiments conducted in this study reveal that the maximal Lyapunov exponent is very effective in classifying chaotic and non-chaotic demands. A computational procedure of the Lyapunov exponent for production systems has been developed and some real world chaotic demands have been identified using the proposed chaos-probing index. This study proposes a modified Wagner–Whitin method that uses a forward focused perspective to make production lot-sizing decision under chaos demands for a single echelon system. The proposed method has been empirically demonstrated to achieve lower total production costs than three commonly used lot-sizing models, namely: lot-for-lot method, periodic ordering quantity, and Silver–Meal discrete lot-size heuristic under a fixed production horizon, and the conventional Wagner–Whitin algorithm under chaotic demands. Sensitivity analysis is conducted to compare changes in total cost with variations in look-ahead period, initial demand, setup cost and holding costs.