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<p>This study presents a comprehensive framework for a sustainable inventory model, addressing the critical need for environmentally conscious inventory management. The model incorporates key elements such as fuzzy learning parameters for demand estimation, preservation technology to mitigate constant deterioration rates, waste management strategies, carbon emission reductions via green technology investments, and time-varying holding costs. By optimizing inventory dynamics over an infinite time horizon with negligible lead times, the model ensures seamless transitions between stock depletion and backlogged shortages. The study highlights the synergistic benefits of integrating preservation technologies to extend product shelf life, reduce spoilage, and lower overall waste generation. Furthermore, it demonstrates the importance of learning and adaptability in fuzzy environments to enhance operational efficiency and decision-making under uncertain conditions. Investment in green technologies significantly reduces carbon footprints, supporting businesses in meeting sustainability goals and regulatory requirements. Key managerial insights emphasize that businesses adopting this model can achieve higher profitability, enhanced resource efficiency, and alignment with environmental sustainability objectives. The findings underscore the importance of integrating advanced technological and environmental considerations into inventory management practices, offering actionable strategies for businesses to remain competitive in a rapidly evolving and eco-conscious market landscape.</p>

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Benefits of Learning and Preservation on Sustainable Inventory Model with Fuzzy Learning Approach and Partial Backordering

  • 期刊论文

  • 发表日期：

    2025年8月21日

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  • Ummeferva Zaidi,
  • S. R. Singh,
  • Rishab Chauhan,
  • Surendra Vikram Singh Padiyar,
  • Aastha Chauhan

摘要

This study presents a comprehensive framework for a sustainable inventory model, addressing the critical need for environmentally conscious inventory management. The model incorporates key elements such as fuzzy learning parameters for demand estimation, preservation technology to mitigate constant deterioration rates, waste management strategies, carbon emission reductions via green technology investments, and time-varying holding costs. By optimizing inventory dynamics over an infinite time horizon with negligible lead times, the model ensures seamless transitions between stock depletion and backlogged shortages. The study highlights the synergistic benefits of integrating preservation technologies to extend product shelf life, reduce spoilage, and lower overall waste generation. Furthermore, it demonstrates the importance of learning and adaptability in fuzzy environments to enhance operational efficiency and decision-making under uncertain conditions. Investment in green technologies significantly reduces carbon footprints, supporting businesses in meeting sustainability goals and regulatory requirements. Key managerial insights emphasize that businesses adopting this model can achieve higher profitability, enhanced resource efficiency, and alignment with environmental sustainability objectives. The findings underscore the importance of integrating advanced technological and environmental considerations into inventory management practices, offering actionable strategies for businesses to remain competitive in a rapidly evolving and eco-conscious market landscape.