Predicting the Lead time of auto parts orders in the supply chain using machine learning

This research aims to investigate the effective factors in predicting lead time (LT) and create a predictive model of LT to improve sustainability and resilience for Kanban orders in the lean supply chain (LSC). The study follows the data mining (DM) method, and the dataset includes 103023 observations from the Kanban system, which were extracted in compliance with the requirements of the dataset quality indicators in the period 1402/6 to 1402/11. First, indicators affecting the LT of orders were extracted. Process mining was used to identify influential variables in high-variance processes to improve performance and accuracy. A stepwise analysis approach was used to select features for the model fitting stage. Also, tuning the parameters of non-parametric approaches was used. The predictive model uses Multiple Linear Regression, Multiple with curvature, Lasso, Elastic Net, Boosted Decision Tree, Bootstrap Random Forest, K-Nearest Neighbor, and Boosted MultiLayer Perceptron. The performance of the fitted regression models has been confirmed using R^2, RASE, and validation of the results and model. The results showed that the logistical features are effective in LT, and the Boosted Multi-Layer Perceptron is the best for predicting orders' LT with an accuracy of 96% and an error of 5.84. Using the model's predictive capability for new data in the Kanban system, the results obtained within four months have been used. The improvements from using DM capabilities in the Kanban system all express the significant impact of combining lean and machine learning (ML) tools to empower and resilient Lean Supply Chain Management (LSCM).