# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 6 Conclusions  
This study underscores the transformative role of Artificial Intelligence (AI) and predictive modeling in promoting sustainability and enhancing operational efficiency within cold chain logistics. Focusing on Company A as a case study, the research demonstrated that ARIMA and Multiple Linear Regression (MLR) models can effectively optimize demand forecasting and inventory control, directly contributing to waste reduction and aligning inventory more closely with consumer demand. Despite limitations stemming from the lack of real-time IoT data, these models exhibited moderate success in forecasting seasonal and demand-driven inventory needs, suggesting a promising foundation for AI's practical application in cold chain logistics. The findings reveal that AI-driven predictive models offer substantial advantages in reducing overproduction and spoilage risks, which are particularly pertinent for temperature-sensitive industries like food and pharmaceuticals. By aligning inventory levels more precisely with sales cycles, cold chain operators can simultaneously reduce waste and support environmental sustainability, achieving integration of profitability and responsibility. The research further highlights the significant role of IoT integration, as real-time environmental data, such as temperature and humidity metrics, could refine predictive accuracy, protect product quality, and prevent spoilage. Theoretically, this study contributes to the Resource-Based View (RBV) and Supply Chain Resilience Theory, illustrating that AI and IoT can serve as strategic resources that provide a competitive edge and foster resilience. According to RBV, resources that are valuable, rare, and inimitable--such as advanced AI analytics and IoT systems--create sustainable advantages by enhancing waste management and operational efficiency. Supply Chain Resilience Theory is also supported, as the adaptability of predictive models enables cold chains to respond dynamically to demand fluctuations, thereby strengthening sustainability and resilience against disruptions in perishable goods logistics. Future research should prioritize the integration of IoT sensors, including temperature and CO\({}_{2}\) monitors, to capture real-time demand patterns and facilitate precise, responsive inventory management. Expanding data sources to encompass customer demographics, historical waste records, and logistics metrics could also enhance model robustness, providing a more comprehensive understanding of waste drivers and supporting sustainable practices. In conclusion, as the logistics industry increasingly shifts towards data-driven practices, the integration of AI and IoT will be pivotal in shaping sustainable cold chain management. This pathway provides cold chain operators with effective tools to reduce waste, enhance resilience, and contribute to broader environmental goals. Expandingthis approach to additional sectors, such as pharmaceuticals, will further validate AI's versatility and its potential to drive sustainable logistics practices across industries. Future research could explore the application of these predictive models in various sectors that require temperature-sensitive logistics, beyond food and pharmaceuticals, to further validate AI's effectiveness. Additionally, examining the impact of integrating blockchain technology with AI and IoT in cold chain management could yield insights into transparency and traceability improvements. Research should also investigate adaptive machine learning models that can evolve with changing environmental data, ensuring sustained accuracy in dynamic logistics environments. Methodology: H.F.; Validation: K.P. and H.F.; Formal Analysis: K.P. and H.F.; Investigation: K.P. and H.F.; Writing--Original Draft Preparation: [PERSON]; Writing--Review and Editing: [PERSON] and H.F.; Visualization: [PERSON]; Supervision: [PERSON]; Project Administration: H.F. All authors have read and agreed to the published version of the manuscript.