Muhammad Shafee, Nur Ain Qistina (2025) An integrated support system with internet of things for lean manufacturing. Doctoral thesis, Universiti Teknikal Malaysia Melaka.
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Abstract
Lean Manufacturing (LM) has been widely recognised as a systematic approach to improving operational efficiency and eliminating waste across industries. However, in the fast-evolving context of the Fourth Industrial Revolution (IR4.0), traditional LM approaches face significant limitations due to their lack of real-time responsiveness, data-driven adaptability, and intelligent decision-making capability. Current research remains largely conceptual, with many studies relying on simulated or theoretical models rather than live industrial data, which limits their reliability and industrial relevance. Moreover, there is still no comprehensive framework that effectively integrates LM, Decision Support Systems (DSS), and Internet of Things (IoT) technologies to support continuous improvement. Existing applications of data analytics in LM are often restricted to monitoring functions instead of enabling real-time optimisation, while the connection between LM tools such as Line Balancing, Single-Minute Exchange of Die (SMED), and Kanban with IoT-based systems remains insufficiently developed. To address these theoretical and empirical gaps, this study developed iDSS-ProLean, an integrated, sensor-driven DSS designed to align LM principles with IoT-based real-time data analytics for adaptive manufacturing environments. Implemented on a semiconductor backend line comprising seven sequential stages, the system utilised ESP32 microcontrollers and multiple sensors (HC-SR04, SW-420, DHT11, BMP280) connected to a Firebase database and Android interface for live monitoring. Results from sixty trial runs demonstrated significant performance improvements: Line Balancing Efficiency value increased from 0.82 to 1.00, SMED ratios improved from inefficient changeovers, greater than 1.0, to optimised setups in less than 1.0, and Kanban inventory levels were stabilised within the target range of 500 ± 5 units based on the best kanban card available on the production line. Statistical validation confirmed reliable data transmission (p > 0.05) and a significant correlation between the parameters and each LM Tools indicators. Expert evaluation further showed high user acceptance, confirming iDSS ProLean as a reliable, economical, and scalable framework for smart manufacturing aligned with IR4.0 principles. The significance of this study lies in establishing the first empirically validated framework that unifies LM, DSS, and IoT within a single, scalable, and adaptive model tailored to semiconductor manufacturing. This study extends the boundaries of LM by embedding sensor-driven intelligence and real-time analytics into traditional continuous improvement systems. Methodologically, it demonstrates a novel approach for integrating sensor-based data acquisition with DSS modelling, thereby offering a replicable and reliable validation process for industrial-scale experimentation. Practically, iDSS-ProLean provides an economical, accessible, and scalable digital solution capable of transforming static LM practices into adaptive, self-optimising systems aligned with IR4.0 principles. By addressing long-standing theoretical and empirical deficiencies, this research contributes to the advancement of smart manufacturing and positions iDSS-ProLean as a viable model for sustainable, data-driven industrial excellence.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | Internet of Things, Decision Ssupport systems, Lean manufacturing, Data Aanalytics, Sensors architecture |
| Subjects: | T Technology T Technology > TS Manufactures |
| Divisions: | Library > Tesis > FTKIP |
| Depositing User: | Norhairol Khalid |
| Date Deposited: | 17 Mar 2026 07:13 |
| Last Modified: | 17 Mar 2026 07:13 |
| URI: | http://eprints.utem.edu.my/id/eprint/29648 |
| Statistic Details: | View Download Statistic |
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