Development Of IoT - Enabled Data Analytics Enhance Decision Support System For Lean Manufacturing Process Improvement

• Main Authors: Mohamad, Effendi, Abd Rahman, Mohd Soufhwee, Abdul Rahman, Azrul Azwan
• Format: Article
• Language: English
• Published: SAGE Publications Ltd 2021
• Online Access: http://eprints.utem.edu.my/id/eprint/25539/2/1063293X20987911_N.PDF; http://eprints.utem.edu.my/id/eprint/25539/; https://journals.sagepub.com/doi/full/10.1177/1063293X20987911

For over three decades, production firms have extensively espoused lean manufacturing (LM) approach for constantly enhancing their operations. Of late, due to the fusion of physical and digital systems within the Industry 4.0 evolution, production systems can upgrade by applying both notions and lift operational excellence to a new high. This is primarily the reason why digital business transformation has gained significance. Moreover, Industry 4.0 that is led by data assures huge strides in output. The sheer volume of pertinent data from the production systems employing servers, sensors, and cloud computing have made the data exchange procedure more gigantic and intricate. However, conventional systems do not extensively support LM in the context of Industry 4.0. Moreover, the previous studies by researchers in the same field, shown that there was no standard platform to manage the new technologies in LM. This study presents a discussion on the interrelated framework about the way Industry 4.0 has transformed production into an industry focusing on connective mechanisms and platforms which utilize data analytics from the real world. The theoretical framework proposed in this paper integrates LM, data analytics, and Internet of Things (IoT) to enhance decision support systems in process improvement. Data analytics in simulation is employed through Internet of Things to improve bottleneck problems by maintaining the principle of LM. The main information flow route within LM decision support system is demonstrated in detail to show how the decision-making process is done. The decision support mechanism has undergone up-gradation and the suggested framework has shown that the assimilated components could function together to augment the output