Waste Generation Patterns and Mitigation Strategies in Cold Chains

This study explores waste generation patterns in cold chain logistics, emphasizing the interrelationships between product categories, promotional activities, and inventory inefficiencies. Using real-world data from Company A, a comprehensive methodological approach, including time-series analysis and Ordinary Least Squares (OLS) regression, was employed to identify critical drivers of waste. The findings demonstrate that promotional activities significantly increase waste levels, notably through overproduction and misaligned demand forecasting. Furthermore, clear seasonal patterns emerged, pinpointing specific periods of peak waste linked to promotions and festive demand spikes. The analysis also highlighted warehouse inefficiencies as key contributors to waste, suggesting targeted logistical optimizations as essential. The study’s novelty lies in its application of the Technology-Organization-Environment (TOE) framework to structure insights into AI-driven waste reduction strategies specifically tailored for cold chain operations. Unlike existing research, this study integrates AI-powered predictive analytics, sustainable packaging solutions, and waste categorization models, offering an empirically validated, actionable framework for supply chain managers. These results contribute significantly to existing literature by moving beyond generic operational improvements, directly addressing how technological, organizational, and regulatory factors collectively influence waste mitigation. The practical implications highlight the necessity for organizations to adopt a holistic, technology-enabled, and sustainability-oriented approach, ensuring long-term economic and environmental benefits.