Design of A Digitalization System for Machine Scheduling and Allocation in Flexible Job Shop Heavy Equipment Manufacturing Industry
DOI:
https://doi.org/10.37385/jaets.v7i1.5089Keywords:
Digitalization, Heavy Equipment Industry, Scheduling, On-time Production, EDDAbstract
This study aims to develop a digitalized scheduling system based on the Flexible Job Shop (FJS) model to optimize production efficiency in the heavy equipment manufacturing industry. The heavy equipment manufacturing industry faces significant challenges in achieving production efficiency due to its high-mix, low-volume (HMLV) nature and the complexity of production processes. The research follows a structured approach, beginning with Focus Group Discussions (FGDs) to gather stakeholder requirements. These requirements are translated into a House of Quality (HoQ) matrix to prioritize features for the dashboard. A literature review identifies optimal scheduling methods, with a focus on FJS and heuristic scheduling rules. The dashboard is developed using JavaScript, PHP, Node.js, and PostgreSQL, and deployed on Amazon Web Services (AWS). The system undergoes black-box testing to ensure functionality and reliability before implementation. The study identifies the Earliest Due Date (EDD) method as the most effective scheduling approach, with an average delay of 3.2 days, utilization of 29%, and completion time of 14.33 days. The implementation of the digitalized scheduling system increased on-time production from 70.56% to 92.8% and improved production achievement from 92.78% to 97.4%. The dashboard application successfully integrates real-time data, adaptive scheduling, and operational features, such as a start-stop system and machine load recommendations. The findings highlight the importance of digital transformation in manufacturing, particularly in optimizing resource allocation, reducing delays, and improving production efficiency. This research contributes to the field of digitalized scheduling and real-time production management by providing a practical, data-driven solution tailored to the HMLV characteristics of heavy equipment manufacturing.
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