A Decision Support System for rapid ramp-up of industry 4.0 enabled production systems

2020 ◽  
Vol 116 ◽  
pp. 103190 ◽  
Author(s):  
Stefanos Doltsinis ◽  
Pedro Ferreira ◽  
Mohammed M. Mabkhot ◽  
Niels Lohse
2021 ◽  
pp. 1063293X2098791
Author(s):  
Mohd Soufhwee Bin Abd Rahman ◽  
Effendi Mohamad ◽  
Azrul Azwan Bin Abdul Rahman

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.


Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 348-353
Author(s):  
Rishi Kumar ◽  
Christopher Rogall ◽  
Sebastian Thiede ◽  
Christoph Herrmann ◽  
Kuldip Singh Sangwan

2019 ◽  
Vol 32 ◽  
pp. 100444 ◽  
Author(s):  
Katia Regina Evaristo de Jesus ◽  
Sérgio Alves Torquato ◽  
Pedro Gerber Machado ◽  
Catiana Regina Brumatti Zorzo ◽  
Bruno Oliveira Cardoso ◽  
...  

Author(s):  
Denny Trias Utomo ◽  
Pratikto ◽  
Purnomo Budi Santoso ◽  
Sugiono

Industry 4.0 is an integration between automation and manufacturing industry which requires the use of information technology to implement it. In the operational management framework, there is a supply chain management function that is strongly influenced by quality suppliers. Because choosing a quality new supplier is not an easy thing, we need a reliable application tool and utilizing web-based artificial intelligence technology for the selection of suppliers in the manufacturing industry. The selection of new suppliers is a complex problem because it involves multiple criteria, therefore it is necessary to make a decision support system that is able to complete supplier selection properly. Although the selection of suppliers in the manufacturing industry is not new, in the era of Industry 4.0, a decision support system that is used to choose an absolute supplier based online that must be accessible via the web or mobile application. Therefore the writer's idea in this paper is very relevant to be implemented in the manufacturing industry in all fields in the industrial era 4.0.


1997 ◽  
Vol 8 (4) ◽  
pp. 195-207 ◽  
Author(s):  
Santhanam Harit ◽  
G. Don Taylor ◽  
C. Ray Asfahl

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