Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
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 f...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/770 |
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| author | Hajar Fatorachian Kulwant Pawar |
| author_facet | Hajar Fatorachian Kulwant Pawar |
| author_sort | Hajar Fatorachian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-181e152aed574978bd0e9390480f068a |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-181e152aed574978bd0e9390480f068a2025-01-24T13:20:45ZengMDPI AGApplied Sciences2076-34172025-01-0115277010.3390/app15020770Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management ModelsHajar Fatorachian0Kulwant Pawar1Leeds Business School, Leeds Beckett University, Leeds LS1 3HB, UKNottingham University Business School, University of Nottingham, Nottingham NG8 1BB, UKThe 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.https://www.mdpi.com/2076-3417/15/2/770cold chain logisticsArtificial Intelligence (AI)demand forecastingsustainable waste managementInternet of Things (IoT) integration |
| spellingShingle | Hajar Fatorachian Kulwant Pawar Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models Applied Sciences cold chain logistics Artificial Intelligence (AI) demand forecasting sustainable waste management Internet of Things (IoT) integration |
| title | Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models |
| title_full | Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models |
| title_fullStr | Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models |
| title_full_unstemmed | Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models |
| title_short | Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models |
| title_sort | sustainable cold chain management an evaluation of predictive waste management models |
| topic | cold chain logistics Artificial Intelligence (AI) demand forecasting sustainable waste management Internet of Things (IoT) integration |
| url | https://www.mdpi.com/2076-3417/15/2/770 |
| work_keys_str_mv | AT hajarfatorachian sustainablecoldchainmanagementanevaluationofpredictivewastemanagementmodels AT kulwantpawar sustainablecoldchainmanagementanevaluationofpredictivewastemanagementmodels |