Optimizing Grocery Quality and Supply Chain Efficiency Using AI-Driven Predictive Logistics

References

[1] Adedeji, A. E. (2020). Fresh Food Products Inventory Control Management: the challenges in avoiding perishability at the retailers' shelf. In.

[2] Aderibigbe, A. O., Ani, E. C., Ohenhen, P. E., Ohalete, N. C., & Daraojimba, D. O. (2023). Enhancing energy efficiency with ai: a review of machine learning models in electricity demand forecasting. Engineering Science & Technology Journal, 4(6), 341-356.

[3] Aleruchi, T. C. (2019). Strategies to Minimize Perishable Food Loss in the Retail Grocery Business. Walden University,

[4] Aljohani, A. (2023). Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 15(20), 15088.

[5] Avittathur, B., & Ghosh, D. (2020). Excellence in supply chain management: Routledge.

[6] Bharadiya, J. P. (2023). Machine learning and AI in business intelligence: Trends and opportunities. International Journal of Computer (IJC), 48(1), 123-134.

[7] Birkel, H., Kopyto, M., & Lutz, C. (2020). Challenges of applying predictive analytics in transport logistics. Paper presented at the Proceedings of the 2020 on Computers and People Research Conference.

[8] Blazquez, D., & Domenech, J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, 130, 99-113.

[9] Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of retailing, 93(1), 79-95.

[10] Cichosz, M., Wallenburg, C. M., & Knemeyer, A. M. (2020). Digital transformation at logistics service providers: barriers, success factors and leading practices. The International Journal of Logistics Management, 31(2), 209-238.

[11] da Costa, T. P., Gillespie, J., Cama-Moncunill, X., Ward, S., Condell, J., Ramanathan, R., & Murphy, F. (2022). A systematic review of real-time monitoring technologies and its potential application to reduce food loss and waste: Key elements of food supply chains and IoT technologies. Sustainability, 15(1), 614.

[12] Denuwara, N., Maijala, J., & Hakovirta, M. (2021). The impact of unmanned stores' business models on sustainability. SN business & economics, 1(10), 143.

[13] Dev, N. K., Shankar, R., & Qaiser, F. H. (2020). Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance. Resources, Conservation and Recycling, 153, 104583.

[14] Ellahi, R. M., Wood, L. C., & Bekhit, A. E.-D. A. (2023). Blockchain-based frameworks for food traceability: a systematic review. Foods, 12(16), 3026.

[15] Hosseini, S., Tajik, N., Ivanov, D., Sarder, M., Barker, K., & Al Khaled, A. (2019). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213, 124-137.

[16] Islam, J. (2023). Leveraging Advanced Analytics for Backorder Prediction and Optimization of Business Operations in the Supply Chain.

[17] Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management, 29(2), 513-538.

[18] Kaur, K., Priyanka, Kaur, G., Singh, B., Sehgal, S., & Trehan, S. (2023). Artificial Intelligence (AI) as a Transitional Tool for Sustainable Food Systems. In Sustainable Food Systems (Volume II) SFS: Novel Sustainable Green Technologies, Circular Strategies, Food Safety & Diversity (pp. 305-328): Springer.

[19] Kirci, M., Isaksson, O., & Seifert, R. (2022). Managing perishability in the fruit and vegetable supply chains. Sustainability, 14(9), 5378.

[20] Mahroof, K., Omar, A., & Kucukaltan, B. (2022). Sustainable food supply chains: overcoming key challenges through digital technologies. International Journal of Productivity and Performance Management, 71(3), 981-1003.

[21] Marr, B. (2019). Artificial intelligence in practice: how 50 successful companies used AI and machine learning to solve problems: John Wiley & Sons.

[22] Neboh, N., & Mbhele, T. (2020). Supply chain resilience and design in retail supermarkets. Journal of Contemporary Management, 17(2), 51-73.

[23] Nimmagadda, V. S. P. (2021). Artificial Intelligence for Real-Time Logistics and Transportation Optimization in Retail Supply Chains: Techniques, Models, and Applications. Journal of Machine Learning for Healthcare Decision Support, 1(1), 88-126.

[24] Nimmagadda, V. S. P. (2023). Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies. Journal of Machine Learning in Pharmaceutical Research, 3(1), 87-120.

[25] Pegorin, E. F. (2023). Data-driven Insights for Grocery Retailers: Developing a Serverless Tool for Business Analysis.

[26] Pettersson, I., & Skäremo, J. (2023). Quality Control: Detect Visual Defects on Products Using Image Processing and Deep Learning. In.

[27] Pounds, K., Bao, H., Luo, Y., De, J., Schneider, K., Correll, M., & Tong, Z. (2022). Real-time and rapid food quality monitoring using smart sensory films with image analysis and machine learning. ACS Food Science & Technology, 2(7), 1123-1134.

[28] Rane, N., Choudhary, S., & Rane, J. (2023). Artificial Intelligence (AI) and Internet of Things (IoT)-based sensors for monitoring and controlling in architecture, engineering, and construction: applications, challenges, and opportunities. Available at SSRN 4642197.

[29] Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7(1), 53.

[30] Shin, Y., Van Thai, V., Grewal, D., & Kim, Y. (2017). Do corporate sustainable management activities improve customer satisfaction, word of mouth intention and repurchase intention? Empirical evidence from the shipping industry. The International Journal of Logistics Management, 28(2), 555-570.

[31] Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, 100026.

[32] Umair, M., & Dilanchiev, A. (2022). Economic recovery by developing business starategies: mediating role of financing and organizational culture in small and medium businesses. Proceedings book, 683.

[33] Verboeket, V., & Krikke, H. (2019). Additive manufacturing: A game changer in supply chain design. Logistics, 3(2), 13.

[34] Xie, Y., Zhao, Y., Chen, Y., & Allen, C. (2022). Green construction supply chain management: Integrating governmental intervention and public–private partnerships through ecological modernisation. Journal of cleaner production, 331, 129986.

[35] Zhang, J., Wedel, M., & Bloem, M. W. (2022). Mitigating food waste in the retail supply chain: Marketing solutions. Journal of Sustainable Marketing, 3(2), 87-97.

@article{1704902,
      author = {Areeba Farooq, Anate Benoit Nicaise Abbey, Ekene Cynthia Onukwulu},
      title = {Optimizing Grocery Quality and Supply Chain Efficiency Using AI-Driven Predictive Logistics},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {1},
      pages = {661-668},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704902.pdf},
      abstract = {The grocery supply chain is a complex network that demands precision in managing product quality, minimizing waste, and ensuring efficient logistics. This paper explores the transformative potential of AI-driven predictive logistics in optimizing grocery supply chains, focusing on its ability to enhance forecasting accuracy, reduce waste, and ensure product freshness. It examines the role of predictive analytics in addressing supply chain challenges and highlights AI?s contributions to improving quality monitoring and real-time tracking. The paper also identifies barriers to implementation, such as data availability, integration complexities, and cost, while presenting actionable strategies for stakeholders to overcome these challenges. By integrating advanced technologies and fostering collaboration, grocery supply chains can achieve unprecedented efficiency, sustainability, and customer satisfaction.},
      keywords = {Predictive logistics, Grocery supply chain, Artificial intelligence, Product quality optimization, Real-time tracking, Supply chain efficiency},
      month = {July},
  }