Simulation-Based Analytics for Fabrication Quality-Associated Decision Support

TL;DR

This paper develops a Bayesian-based, simulation-driven decision support system to improve fabrication quality management by integrating real-time data, analytical modeling, and reliable metrics, enhancing decision-making and reducing practitioner workload.

Contribution

It introduces a novel, analytically-based approach combining Bayesian statistics and simulation for construction fabrication quality management, with real-time data integration and decision-support metrics.

Findings

Enhanced decision-support with real-time data integration

Improved modeling accuracy for fabrication quality

Reliable metrics for quality and cost analysis

Abstract

Automated, data-driven quality management systems, which facilitate the transformation of data into useable information, are desired to enhance decision-making processes. Integration of accurate, reliable, and straightforward approaches that measure uncertainty of inspection processes are instrumental for the successful implementation of automated, data-driven quality management systems. This research has addressed these needs by exploring and adapting Bayesian statistics-based approaches for fraction nonconforming posterior distribution derivation purposes. Using these accurate and reliable inputs, this research further develops novel, analytically-based approaches to improve the practical function of traditional construction fabrication quality management systems. Multiple descriptive and predictive analytical functionalities are developed to support and augment quality-associated decision-making processes. Multi-relational databases (e.g., quality management system, engineering design system, and cost management system) from an industrial company in Edmonton, Canada, are investigated and mapped to implement the novel system proposed. This research has contributed to academic literature and practice by: (1) advancing decision-support systems for construction management by developing a dynamic simulation environment that uses real-time data to enhance simulation predictability; (2) developing integrated analytical methods for improved modeling in fabrication quality-associated decision making; and (3) creating reliable and interpretable decision-support metrics for quality performance measurement, complexity analysis, and rework cost management to reduce the data interpretation load of practitioners and to uncover valuable knowledge and information from available data sources.