PREDICTIVE MACHINE LEARNING FRAMEWORK FOR POTENTIAL DELAY

Abstract

Delays relating to order deliveries in elastic manufacturing can significantly disrupt production timelines, affect supply chain efficiency, and diminish customer satisfaction. This research presents a machine learning-based framework to address these challenges by predicting manufacturing delays, explaining their underlying causes, and estimating delay durations to improve operational reliability. The study employs a three-layer architecture: a binary classification layer to predict whether an order delivery delay will occur, an explainability layer using Local Interpretable Model-Agnostic Explanations (LIME) to provide insights into each prediction, and a regression layer to estimate the duration of the delay and the expected delayed delivery date. The dataset, spanning more than three years (from 2021-01-08 to 2024-06-24), includes 37,411 order records and 75,723 delivery records, reflecting the complexities of elastic manufacturing order fulfillment. Extensive data preparation was conducted to standardize formats, handle missing values, and normalize features. Classification models such as Logistic Regression, XGBoost, and Neural Networks were utilized for classification tasks, while Linear Regression and XGBRegressor were employed for regression tasks. The findings demonstrate that the proposed framework not only accurately predicts delays but also offers actionable insights into their drivers, enabling proactive decision-making. By integrating predictive power with explainability, this study contributes to the advancement of intelligent supply chain management systems.