Application of a Bayesian belief network-based information processing model for quality assurance in demolition waste reverse logistics: insights from two case studies

Purpose This study aims to assess the applicability of the Bayesian belief network (BBN)-based conceptual information processing model for quality assurance (QA) within real-world reverse logistics supply chains (RLSCs) of demolition waste (DW) and to observe how it supports decision-making related to information processing for QA. Design/methodology/approach A multiple-case study strategy was adopted, focusing on two RLSCs in South Australia (SA). Focus group discussions were conducted within case studies to obtain expert-elicited probabilistic inferences for parametric learning in BBN-based modelling. A series of analyses was conducted using GeNIe software, including sensitivity analysis, root cause analysis and scenario analysis based on macro-, meso- and micro-level epistemic uncertainties. Findings The study confirmed that the BBN-based model is a useful decision-support tool for internal stakeholders in RLSCs. QA was most sensitive to micro-level workflow uncertainties and health and safety concerns, especially those related to the waste processor. Notably, RLSCs with small and medium-scale organisations were more vulnerable to epistemic uncertainties at all levels. Macro-level uncertainties emerged as root causes that propagate through the system and affect QA outcomes. Originality/value This is the first empirical application of a developed BBN-based information processing model specifically designed for QA in RLSCs of DW. This study contributes by demonstrating how this model can be operationalised as a decision-support tool, providing empirical insights into how epistemic uncertainties propagate through real-world supply chains.