Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques
This study addresses critical challenges in supply chain management, particularly focusing on enhancing forecast accuracy and optimizing inventory management. Traditional methods often fall short in accuracy, leading to inventory imbalances and inefficiencies. To overcome these limitations, the stud...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10769082/ |
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| author | Lixing Bo Jie Xu |
| author_facet | Lixing Bo Jie Xu |
| author_sort | Lixing Bo |
| collection | DOAJ |
| description | This study addresses critical challenges in supply chain management, particularly focusing on enhancing forecast accuracy and optimizing inventory management. Traditional methods often fall short in accuracy, leading to inventory imbalances and inefficiencies. To overcome these limitations, the study employs a combination of Transformer models for demand forecasting and Particle Swarm Optimization (PSO) for inventory parameter optimization. The methodology involves a comprehensive approach: data collection includes historical sales data and inventory levels, which are preprocessed through cleaning, normalization, and feature extraction. Transformer models are used for predicting demand, leveraging their ability to capture complex patterns in time-series data. PSO is applied to optimize inventory parameters, addressing multi-objective optimization problems in the supply chain. Results from the study indicate significant improvements. The Transformer model achieved a reduction in Mean Absolute Error (MAE) from 15.8 to 8.2 and Root Mean Squared Error (RMSE) from 22.3 to 11.5, demonstrating enhanced forecasting accuracy. The application of PSO led to a 12% reduction in overall operational costs and a 25% improvement in order fulfillment times. Additionally, inventory holding costs decreased by 18%, and transportation costs were reduced by 10%. Integrating Transformer models with PSO presents a robust solution for modern supply chains, offering substantial improvements in efficiency and cost-effectiveness. The study recommends adopting these advanced methodologies for better forecasting and inventory management, and suggests further research into additional machine learning techniques and real-time data integration to enhance supply chain performance. |
| format | Article |
| id | doaj-art-5bf0042e914644cfad6e00c06c4692b2 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5bf0042e914644cfad6e00c06c4692b22025-08-20T01:59:09ZengIEEEIEEE Access2169-35362024-01-011218345118346510.1109/ACCESS.2024.350716110769082Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence TechniquesLixing Bo0https://orcid.org/0009-0000-8239-0644Jie Xu1https://orcid.org/0009-0001-6611-1550Business School, Chongqing City Vocational College, Chongqing, ChinaSchool of Finance and Tourism, Chongqing Vocational Institute of Engineering, Chongqing, ChinaThis study addresses critical challenges in supply chain management, particularly focusing on enhancing forecast accuracy and optimizing inventory management. Traditional methods often fall short in accuracy, leading to inventory imbalances and inefficiencies. To overcome these limitations, the study employs a combination of Transformer models for demand forecasting and Particle Swarm Optimization (PSO) for inventory parameter optimization. The methodology involves a comprehensive approach: data collection includes historical sales data and inventory levels, which are preprocessed through cleaning, normalization, and feature extraction. Transformer models are used for predicting demand, leveraging their ability to capture complex patterns in time-series data. PSO is applied to optimize inventory parameters, addressing multi-objective optimization problems in the supply chain. Results from the study indicate significant improvements. The Transformer model achieved a reduction in Mean Absolute Error (MAE) from 15.8 to 8.2 and Root Mean Squared Error (RMSE) from 22.3 to 11.5, demonstrating enhanced forecasting accuracy. The application of PSO led to a 12% reduction in overall operational costs and a 25% improvement in order fulfillment times. Additionally, inventory holding costs decreased by 18%, and transportation costs were reduced by 10%. Integrating Transformer models with PSO presents a robust solution for modern supply chains, offering substantial improvements in efficiency and cost-effectiveness. The study recommends adopting these advanced methodologies for better forecasting and inventory management, and suggests further research into additional machine learning techniques and real-time data integration to enhance supply chain performance.https://ieeexplore.ieee.org/document/10769082/Predictive analyticstransformer modelsparticle swarm optimization (PSO)supply chain optimizationinventory management |
| spellingShingle | Lixing Bo Jie Xu Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques IEEE Access Predictive analytics transformer models particle swarm optimization (PSO) supply chain optimization inventory management |
| title | Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques |
| title_full | Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques |
| title_fullStr | Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques |
| title_full_unstemmed | Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques |
| title_short | Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques |
| title_sort | enhancing supply chain efficiency resilience using predictive analytics and computational intelligence techniques |
| topic | Predictive analytics transformer models particle swarm optimization (PSO) supply chain optimization inventory management |
| url | https://ieeexplore.ieee.org/document/10769082/ |
| work_keys_str_mv | AT lixingbo enhancingsupplychainefficiencyresilienceusingpredictiveanalyticsandcomputationalintelligencetechniques AT jiexu enhancingsupplychainefficiencyresilienceusingpredictiveanalyticsandcomputationalintelligencetechniques |