||Implementing a Data-Driven Simulation Method for Quantifying Pipe Welding Operator Quality Performance
||Wenying Ji and Simaan Abourizk
||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.
|Year of publication:
||Industrial Construction, Pipe Fabrication, Quality Management, Fraction Nonconforming, Markov Chain Monte Carlo (MCMC)
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,