Structured database design for IDSS-ProLean a decision support system for lean semiconductor manufacturing

Muhammad Shafee, Nur Ain Qistina and Mohamad, Effendi and Abd Rahman, Mohd Soufhwee and Azlan, Nor Asmaa Alyaa Nor and Shukor, Mohd Hamdi Abd and Nurdiansyah, Rudi (2024) Structured database design for IDSS-ProLean a decision support system for lean semiconductor manufacturing. In: 2024 International Conference on TVET Excellence & Development (ICTeD), 16 December 2024 through 17 December 2024, Melaka, Malaysia.

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Abstract

This work describes the thorough planning and execution of a structured database for the intelligent decision support system (DSS) called iDSS-ProLean, which was created to maximise semiconductor manufacturing lines. SMED (Single-Minute Exchange of Dies), Kanban, and Line Balancing are just a few of the critical lean manufacturing technologies that are supported by the carefully designed database. An IoT interface layer is also included for real-time data collection from the factory floor. Critical operations like etching, cutting, and assembly are handled by the system, which has database tables made specifically for efficient data management and resource allocation. The database structure ensures smooth communication across several manufacturing stages by including essential components including machinery, labour, and production data. By streamlining data flow and providing a robust platform for decision-making, this work demonstrates how the structured database enhances iDSS-ProLean’s ability to manage production resources, improve efficiency, and meet demand targets more effectively in semiconductor manufacturing environments.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Database structure, Decision support system (DSS), Internet of things, Lean manufacturing, Semiconductor industry
Divisions:	Faculty Of Industrial And Manufacturing Technology And Engineering
Depositing User:	Wizana Abd Jalil
Date Deposited:	17 Dec 2025 07:30
Last Modified:	17 Dec 2025 07:30
URI:	http://eprints.utem.edu.my/id/eprint/29340
Statistic Details:	View Download Statistic

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