Implementing a Production Performance Management Platform for Better Production Data Management, Improved Surveillance and Forecasting for Mature Fields – A Case Study in Sharjah, UAE

2020 ◽  
Author(s):  
Mahmoud Moustafa ◽  
Armando Guillen ◽  
Abdulla Jany ◽  
Siddharth Jain ◽  
Hemant Kumar ◽  
...  
Author(s):  
Jing Huang ◽  
Qing Chang ◽  
Yu Qian ◽  
Jorge Arinez ◽  
Guoxian Xiao

Abstract The advancement in Web-/Internet-based technologies and applications in manufacturing sector have increased utilization of cyber workspace to enable more efficient and effective ways of doing manufacturing from distributed locations. This work introduces a novel continuous improvement framework to improve the performance of production lines through multi-plant comparison and learning among identical or similar production lines in different locations by leveraging the information stored on factory cloud. In this work, production data from multiple identical production lines are collected and analyzed to learn the “best” feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index and the available data. A real case study based on multiple similar automotive plants is provided to demonstrate the method and the increase of throughput is predicted.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yeong Wei Ng ◽  
Joshua Chan Ren Jie ◽  
Shahrul Kamaruddin

Shop floor performance management is a method to ensure the effective utilization of people, processes, and equipment. Changes in the shop floor might have a positive or negative effect on production performance. Therefore, optimal shop floor operation is required to enhance shop floor performance and to ensure the long-term efficiency of the production process. This work presents a case study of a semiconductor industry. The punching department is modeled to investigate the effect of changes in the shop floor on production performance through discrete event simulation. The effects on the throughput rate, machine utilization, and labor utilization are studied by adjusting the volume of parts, number of operators, and flow pattern of parts in a series of models. Simulation results are tested and analyzed by using analysis of variance (ANOVA). The best model under changes in the shop floor is identified during the exploration of alternative scenarios.


2014 ◽  
Vol 1073-1076 ◽  
pp. 2036-2041
Author(s):  
Ping Ping Yu ◽  
Jian Ping Chen ◽  
Miao Yu ◽  
Zhao Wu ◽  
Dong Yue Chen

In the era of big data, new information technologies introduced into the study of mining exploration to realize the wisdom prospecting has important significance. Based on 3S technology, 3D modeling and visualization technology, database technology and virtual reality technology, this paper studied the 3D integrated digital mine construction of big data era and presented a new concept of 3D visualization and data management integration modeling of digital mine. A case study of eastern Gejiu Sn-Cu deposit in Yunnan province of China achieved the integrated modeling of ground and underground, and also the multi-information integration and analysis of geology, geography, 2D and 3D. An integrated management platform was built in the application to integrate a variety of mine data organically, which provided support for mine production management, the deep prospecting practice and the comprehensive study and application of geological big data of mine.


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