Development of a Residual Biomass Supply Chain Simulation Model Using AnyLogistix: A Methodical Approach

Bernardine Chidozie (), Ana Ramos, José Vasconcelos and Luis Pinto Ferreira
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Bernardine Chidozie: Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Departmento de Economia, Gestao, Engenharia Industrial e Turismo (DEGEIT), University of Aveiro, 3810-193 Aveiro, Portugal
Ana Ramos: Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Departmento de Economia, Gestao, Engenharia Industrial e Turismo (DEGEIT), University of Aveiro, 3810-193 Aveiro, Portugal
José Vasconcelos: Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Departmento de Economia, Gestao, Engenharia Industrial e Turismo (DEGEIT), University of Aveiro, 3810-193 Aveiro, Portugal
Luis Pinto Ferreira: School of Engineering, Polytechnic of Porto (ISEP), Rua Dr. António Bernardino de Almeida, Associate Laboratory for Energy, Transports and Aerospace (LAETA-INEGI), 4200-465 Porto, Portugal

Logistics, 2024, vol. 8, issue 4, 1-18

Abstract: Background: In the pursuit of sustainable energy sources, residual biomass has emerged as a promising renewable resource. However, efficiently managing residual biomass poses significant challenges, particularly in optimizing supply chain operations. Advanced modeling approaches are necessary to address these complexities. This study aims to develop a comprehensive methodological framework for creating simulation models tailored to agroforestry residual biomass supply chains. Methods: The study employs a hybrid simulation approach, integrating geographic information system mapping with a case study analysis. The simulation was conducted over a 365-day period, using the anyLogistix software (version 2.15.3.202209061204) to model various supply chain dynamics. The framework also accounts for financial, operational, customer satisfaction, and environmental metrics. Results: The simulation results showed a total expenditure of EUR 5,219,411.3, with transportation being the primary cost driver, involving 5678 trips and a peak capacity of 67.16 m 3 . CO 2 emissions were measured at 487.7 kg/m 3 . The model performed as expected, highlighting the need for sustainable logistics strategies to reduce costs, lower losses, and improve productivity. Conclusions: This study presents one of the first detailed methodological frameworks for simulating agroforestry residual biomass supply chains. It provides valuable managerial insights into the financial, operational, and environmental aspects of supply chain management. The findings may stakeholders make informed decisions to enhance the sustainability of biomass utilization in energy production.

Keywords: anyLogistix; key performance indicators (KPIs); simulation; supply chain; methodology; residual biomass (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:8:y:2024:i:4:p:107-:d:1501947

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