Taraneh Sowlati

Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials.

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.

Natural fiber-based biocomposite are increasingly favored in green engineering for their sustainable properties. Designing their Supply Chains (SCs) requires strategic decisions around pre-processing equipment while accounting for environmental, economic, social, and technical criteria. This article aims to introduce a comprehensive sustainability-based multicriteria decision-making framework for a hemp-based biocomposite SC design, using an Analytical Network Process (ANP) model incorporating both inner and outer dependencies among decision criteria and alternatives. Also, a nonlinear optimization-based sensitivity analysis model is proposed to assess the robustness of the decisions. A combination of full/half screen hammer mill and square/round baler, as pre-processing equipment options, are evaluated along with input data uncertainty. Evaluating four preprocessing scenarios, results show the round baler with half-screen hammer mill as the most sustainable option, while the full-screen and square baler setup performs best economically and environmentally. The trade-off analysis highlighted key drivers: demand-sensitive pricing, carcinogenic impact, job creation, and product quality.