Data-driven decision support for inventory planning: A Lean, Agile, Resilient, and Green (LARG) perspective

Abstract

This study proposes a data-driven decision support framework for Maintenance, Repair, and Operation (MRO) inventory planning from a Lean, Agile, Resilient, and Green (LARG) perspective. The first stage of the proposed framework involves demand forecasting, which is implemented using a model that combines a genetic algorithm and an artificial neural network, initially focused on two of the most critical items used in railway track maintenance and using input indicators with a direct correlation with LARG. To allow the model to be replicated, the second stage of the framework begins, involving time series clustering with k-means and dynamic time-warping metrics, restarting the first phase, and replicating the model for items in the same cluster. Lastly, the proposed framework incorporates practical implications for each LARG paradigm precisely because of the connection between the first two stages of the framework and the LARG concept. The framework is applied to a case study using real data from a railway logistics operator. The research findings revealed superior demand forecasting performance compared to current company practices. The proposed framework is a crucial data-driven decision-support framework for enhancing MRO inventory planning within the LARG paradigm.