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Paper: lc3-2017-011
| Paper title: | Implementing a Data-Driven Simulation Method for Quantifying Pipe Welding Operator Quality Performance |
| Authors: | Wenying Ji and Simaan Abourizk |
| Summary: | This paper proposes a framework for implementing a Markov Chain Monte Carlo (MCMC)-based posterior distribution determination approach to quantify pipe welding operator quality performance for industrial construction projects. The existing quality management data and engineering design data from a pipe fabrication company are processed and analysed to demonstrate the feasibility and applicability of the proposed approach. Through the use of a specialised Metropolis-Hastings algorithm, operator welding performance is quantified and uncertainty is incorporated. Practitioners can utilize outputs of the proposed method to infer operator welding quality performance of a particular weld type and identify operators with exceptional quality performance. Potential applications of the research findings are discussed from the perspectives of production planning, employee training, and strategic recruiting. |
| Type: | regular paper |
| Year of publication: | 2017 |
| Keywords: | Industrial Construction, Pipe Fabrication, Quality Management, Fraction Nonconforming, Markov Chain Monte Carlo (MCMC) |
| Series: | jc3:2017 |
| Download paper: | /pdfs/LC3_2017_paper_011.pdf |
| Citation: | Wenying Ji and Simaan Abourizk (2017). Implementing a Data-Driven Simulation Method for Quantifying Pipe Welding Operator Quality Performance. Lean and Computing in Construction Congress (LC3): Volume I Ð Proceedings of the Joint Conference on Computing in Construction (JC3), July 4-7, 2017, Heraklion, Greece, pp. 79-86, http://itc.scix.net/paper/lc3-2017-011 |
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