Effectively Prediction and Performance Grinding Mechanism for Sewing Line Using Time Series Data

Abstract

Industry 4.0 revolution has opened up opportunities to factory plants to manufacture products faster with higher quality and low cost. With cutting-edge technologies such as the Internet of Things (IoT), cloud computing, machine learning and etc. Manufacturing Execution System (MES) is a result of the new revolution. MES is a critical component of the new revolution since providing an end-to-end digitalization. It executes, monitors, tracks, and reports operations on the factory floor in real-time. With the increase, there is a wide availability of operational data that was not available. These data can be used to gain more insight and new pattern which can be used to enhance factory performance. Efficiency prediction and Grading Mechanism tool aims to predict the most basic KPI which acts as a performance tool to help factory managers plan ahead and manage resources in an efficient method. It also aims to develop a grading hypothesis and develop an algorithm based on the hypothesis to give performance grading to a selected node in the factory organizational hierarchy in order to give the top tier managers and interested party's transparency of the node from the performance aspect..