Integration framework of simulation-based data analytics and Internet of Things for lean manufacturing decision-making

• Auteur principal: Abd Rahman, Mohd Soufhwee
• Format: Thèse
• Langue: anglais; anglais
• Publié: 2022
• Sujets: T Technology (General); TS Manufactures
• Accès en ligne: http://eprints.utem.edu.my/id/eprint/26099/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=121371

Lean Manufacturing (LM) is a deep-rooted mechanism which has been extensively utilised in diverse business sectors globally so as to constantly enhance operations. Evolving technologies have taken manufacturing systems to a new level because of the blend of digital and physical systems in the Industry 4.0 progression. A literature review indicates that the significance of Industry 4.0 in LM has been extremely underlined in recent times. Notably, the swift development of main Industry 4.0 technologies has triggered enormous data in the production sector. This is because production activities are deploying Industry 4.0 technologies for catering to demands of consumers for all-round products. Besides this expansion, there are rising apprehensions about making intricate decisions appropriately. Data is not being utilised diligently by means of digital systems, and hence the resulting variables utilised in the simulation model are imprecise. The Internet of Things (IoT) is seen as a technology to back LM; however, there is not enough deployment of this technology. IoT is not being utilised widely to gather, store, and link data into the simulation as a rooted system in the decision-making procedure. According to a semi-structured interview of 15 firms, 87.7% intensely concur that IoT should be enabled as a platform to bolster LM decision-making. As decision-making is supplemented by a huge volume of data and simulation to carry out the analysis, it is tough to build a framework which integrates simulation, data, and IoT technology. Hence, the study intends to recommend a conceptual structure called iLMDM, which assimilates LM and simulation-based data analytics by way of IoT to enable decision-making in process enhancements. This study is carried out by deploying the systematic literature review approach by means of a mixed-methods technique. Thus, the iLMDM structure is an extremely effectual way of backing management decision procedures about process enhancement and custom-made Industry 4.0 execution. This study's key contributions are related to the 4WRD policy objectives to bolster the shift to Industry 4.0. With regards to the economy, the policy targeted RM392 billion to propel the national economy, whereas productivity rose by 30% and skill workers rose by 35%. Hence, the outcomes of the iLMDM-based study are focused on productivity, economic, and worker utilisation. The management can now ascertain other resource configurations which have to be examined and optimised on the basis of the performance which has been brought into line.