Analysis of production cycle-time distribution with a big-data approach
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Analysis of production cycle-time distribution with a big-data approach Xu Tan1,2 · Lining Xing1,2
· Zhaoquan Cai3 · Gaige Wang4
Received: 4 August 2019 / Accepted: 29 January 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In production planning, one of the most crucial issues involves cycle time forecasting and distribution. Particularly, the parameter aids in realizing high delivery reliability. In the production planning process that involves computer component manufacturing, an estimation of the tasks’ cycle time offers an important basis for dispatching control, material purchase, and due date assignment. In this study, a big-data approach was proposed and examined to determine how it could be used to predict cycle time distribution. Also, the research context involved computer components manufacturing systems. Indeed, the motivation was to determine how the proposed mechanism could improve delivery reliability in manufacturing systems.Regarding the implementation and design of the CT forecasting system, with the proposed DP-RBFN framework being a model to be implemented in computer components manufacturing, components of the system constituted three major parts. The first part, being the basic platform, played the role of Hadoop series software installation. This installation had its role lie in enabling the parallel computing of big data. Another part of the framework design and implementation involved data preprocessing. In this case, the role of the data preprocessing procedure lay in the extraction, transformation, and loading of data to CTF. The third part that followed the basic platform design and data preprocessing procedure involved CT forecasting. Results demonstrated that the proposed model performs superiorly than the contrast or other comparative methods on both the computer components manufacturing system dataset and benchmark datasets. From the findings, the proposed framework (DP-RBFN) exhibited superior performance compared to previous performance outcomes that had been reported relative to the use of the RBFN algorithm. These findings held for both MAD and SD—relative to the selected datasets. Keywords DP-RBFN · RBFN · Cycle time (CT) · Computer components manufacturing · CT forecasting · Parallel training
Introduction To improve manufacturing processes and overcome the currently rigid planning, key elements that have been proposed involve real-time data analytics methods. In particular,
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Lining Xing [email protected]
1
School of Transportation and Logistics, Central South University of Forestry and Technology, Changsha 410004, People’s Republic of China
2
School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, People’s Republic of China
3
Department of Information Science and Technology, Huizhou University, Huizhou 516007, People’s Republic of China
4
Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, People’s Republic of China
these methods fulfill the
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