Modelling and Sustainability Index Assessment of Biocomposites Supply Chain Networks Using Bayesian Belief Networks: A Case Study on Hemp-based Pellet Production

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Abstract
Planning sustainable supply chains (SCs) for emerging biocomposite materials is critical for balancing environmental, economic, and social dimensions in strategic decision-making, towards industrial success. In biocomposite SCs, early-stage pre-processing activities, such as biomass collection and particle size reduction, play a pivotal role in determining downstream sustainability outcomes. However, evaluating the performance of different SC scenarios under changing conditions remains difficult due to the non-linear interdependencies and uncertainty across SC components. This study presents a knowledge-driven Bayesian Belief Network (BBN) framework to support sustainability assessment of a complex hemp-based biocomposite SC case study under uncertainty. A novel metric, the Supply Chain Sustainability Index (SCSI), is introduced to quantify the overall probability of achieving the target sustainability performance level and assess the vulnerability of each underlying indicator. The BNN model integrates both expert insights and empirical relationships through regression-informed Conditional Probability Tables (CPTs) and causal graphs, across 15 proposed criteria of measurement spanning economic (e.g., Net Present Value (NPV), Conditional Value at Risk (CVaR), costs), technical (e.g., product quality, Technology Readiness Level (TRL)), social (e.g., job creation), and environmental (e.g., carcinogenic and ecotoxic impacts) factors. The scenario-based simulation and entropy-based sensitivity analyses are also conducted to identify the most influential factors among the criteria trade-offs. The results showed that the economic and technical factors, in the present case study, have the greatest influence on overall sustainability, while the social and environmental indicators revealed comparatively moderate effects.

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