Data-Driven Simulation Model for Quality-Induced Rework Cost Estimation and Control Using Absorbing Markov Chains

Abstract

This paper aims to develop a novel, data-driven simulation model to quantitatively assist decision support systems in quality-induced rework cost estimation and control for construction product fabrication. At the core of the model is a specialized absorbing Markov chain, which stochastically models the construction product fabrication process while considering quality-induced rework uncertainty. The model parameters are dynamically updated using real-time quality management and cost management information to achieve more accurate and reliable simulation outputs. Furthermore, two types of decision-support metrics are developed to support rework cost management processes, namely (1) rework cost estimation during the project planning phase, and (2) rework cost control during the project execution phase. An illustrative example is provided to demonstrate the functionalities of the model and the implementation of the decision-support metrics. Finally, the proposed approach is integrated into the previously developed simulation-based analytics framework and implemented by an industrial pipe fabrication company in Edmonton, Canada. The presented case study demonstrates the applicability and feasibility of the proposed approach to industrial pipe welding processes.