Life cycle prediction: a comparison of methods for a lighting products retailer

Abstract

Product life cycle (PLC) prediction is one of the most challenging yet critically important aspects of supply chain management. Lost sales and excess inventory costs arise when there is a mismatch between demand and supply, especially at the beginning of a product’s life cycle when a new product is launched. Our proposed framework contributes to the application of decision-support systems in the prediction of PLCs of new products. In this study, we fit piecewise-linear curves, nth order polynomial curves, and Bass diffusion curves for PLC prediction and compare their effectiveness using real data from a retailer specializing in lighting products. We estimate the PLCs of 2 615 lighting products using these models and select the best-fit curve to predict their PLCs. We also develop an algorithm to address challenges posed by imbalanced datasets and apply it in neural networks for predictive modeling to determine a product’s PLC stage, demand class, and stocking decisions. The findings show that fourth-order polynomial curves can accurately predict the PLCs of 63% of the products. Bass diffusion curves emerge as the second-best performer. Our approach can be generalized to other products in other industries, and it can effectively guide end-of-life inventory decisions.