# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
###### Abstract  
The integration of advanced predictive models is pivotal for optimizing demand forecasting and inventory management in cold chain logistics. This study evaluates the application of machine learning techniques--ARIMA (Auto-Regressive Integrated Moving Average) and Multiple Linear Regression (MLR)--to forecast demand trends and analyze key drivers in a mid-sized cold chain operation. Trained on a multi-year sales dataset, the ARIMA model excelled in capturing seasonal patterns, while the MLR model effectively incorporated multivariable factors such as temperature, product type, and promotional activity. Both models demonstrated strong predictive accuracy, with low Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), offering reliable and computationally efficient solutions for mid-sized operations. The findings underscore the novelty of combining ARIMA's time-series capabilities with MLR's multivariable analysis to address complex demand drivers. By aligning with Resource-Based View (RBV) and Supply Chain Resilience Theory, this research advances the understanding of AI-driven predictive models as strategic tools for enhancing operational efficiency, reducing waste, and promoting sustainability in cold chain logistics. This work sets the stage for future innovations in AI-driven supply chain optimization. cold chain logistics; Artificial Intelligence (AI); demand forecasting; sustainable waste management; Internet of Things (IoT) integration 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE)  
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Multiple Linear Regression (MLR) for multivariable influence assessment, are proving instrumental in cold chain logistics by forecasting demand, optimizing stock levels, and reducing spoilage risk [4]. Industry 4.0 technologies, including AI and IoT, have the potential to address cold chain inefficiencies by enhancing operational and waste management processes, particularly in high-stakes environments like food logistics. Despite their benefits, cold chain logistics generate approximately 2.5 billion metric tons of CO\({}_{2}\) emissions annually, exacerbating environmental concerns [2]. Furthermore, waste generated from overproduction and spoilage, much of it preventable, leads to significant financial losses and intensifies the environmental footprint of cold chains. For instance, it is estimated that temperature fluctuations alone, often due to gaps in monitoring and response, can result in substantial losses in both the food and pharmaceutical sectors [5]. Predictive analytics, when paired with real-time IoT data, has the potential to address these issues by dynamically aligning inventory with demand and mitigating waste. This study explores the application of AI models to enhance sustainability in cold chains by reducing waste and emissions. Employing predictive models in conjunction with IoT data, this research aims to bridge the gap between real-time monitoring and proactive waste management, providing a scalable solution for the industry. The use of AI for demand forecasting and real-time inventory adjustments represents a proactive approach to addressing waste, benefiting not only logistics operations but also aligning with global sustainability goals [6]. While the literature has extensively explored the benefits of IoT and AI independently in cold chains, few studies address their combined application for predictive waste management. IoT-based solutions are well-documented in enabling environmental monitoring; however, there remains a limited exploration of how IoT data can be fully integrated with AI-driven predictive models to address waste from an end-to-end perspective [7]. Specifically, gaps exist in the literature on the use of ARIMA and MLR models to optimize waste reduction by forecasting demand and enhancing inventory control [8]. [PERSON] et al.