Material Flow Analysis for Demand Forecasting and Lifetime-Based Inflow in Indonesia’s Plastic Bag Supply Chain

Erin Octaviani, Ilyas Masudin (), Amelia Khoidir and Dian Palupi Restuputri
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Erin Octaviani: Industrial Engineering, University of Muhammadiyah Malang, Malang 65151, Indonesia
Ilyas Masudin: Industrial Engineering, University of Muhammadiyah Malang, Malang 65151, Indonesia
Amelia Khoidir: Industrial Engineering, University of Muhammadiyah Malang, Malang 65151, Indonesia
Dian Palupi Restuputri: Industrial Engineering, University of Muhammadiyah Malang, Malang 65151, Indonesia

Logistics, 2025, vol. 9, issue 3, 1-27

Abstract: Background : this research presents an integrated approach to enhancing the sustainability of plastic bag supply chains in Indonesia by addressing critical issues related to ineffective post-consumer waste management and low recycling rates. The objective of this study is to develop a combined framework of material flow analysis (MFA) and sustainable supply chain planning to improve demand forecasting and inflow management across the plastic bag lifecycle. Method : the research adopts a quantitative method using the XGBoost algorithm for forecasting and is supported by a polymer-based MFA framework that maps material flows from production to end-of-life stages. Result : the findings indicate that while production processes achieve high efficiency with a yield of 89%, more than 60% of plastic bag waste remains unmanaged after use. Moreover, scenario analysis demonstrates that single interventions are insufficient to achieve circularity targets, whereas integrated strategies (e.g., reducing export volumes, enhancing waste collection, and improving recycling performance) are more effective in increasing recycling rates beyond 35%. Additionally, the study reveals that increasing domestic recycling capacity and minimizing dependency on exports can significantly reduce environmental leakage and strengthen local waste management systems. Conclusions : the study’s novelty lies in demonstrating how machine learning and material flow data can be synergized to inform circular supply chain decisions and regulatory planning.

Keywords: material flow analysis; plastic bags; XGBoost; waste management; CE (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: 2025
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