Open Access
| Issue |
E3S Web Conf.
Volume 694, 2026
Third International Conference on Green Energy, Environmental Engineering and Sustainable Technologies 2025 (ICGEST 2025)
|
|
|---|---|---|
| Article Number | 04016 | |
| Number of page(s) | 8 | |
| Section | Infrastructure, Transports and Sustainability | |
| DOI | https://doi.org/10.1051/e3sconf/202669404016 | |
| Published online | 16 February 2026 | |
- M. Qi, H.-Y. Mak, Z.J.M. Shen, Data-driven retail operations: Enhancing real-time monitoring and reporting, Nav. Res. Logist. 67, 595-607 (2020). https://doi.org/10.1002/nav.21880 [Google Scholar]
- R.P. Rooderkerk, N. DeHoratius, A. Musalem, Integrated information systems and operational efficiency in retail supply chains, Prod. Oper. Manag. 31(10), 3727-3748 (2022).https://doi.org/10.1111/poms.13820 [Google Scholar]
- A. Lutfi, M. Alrawad, A. Alsyouf, M.A. Almaiah, A.L. Al-Khasawneh, A.F. Alshira’h, M.H. Alshirah, M. Saad, N. Ibrahim, The strategic impact of artificial intelligence on business innovation and firm performance: A resource-based view approach in the retail sector, J. Retail. Consum. Serv. 70, 103129 (2023). https://doi.org/10.1016/j.jretconser.2022.103129 [Google Scholar]
- A. Seetharaman, I. Niranjan, V. Tandon, A.S. Saravanan, Impact of big data on the retailing industry: Evidence from supply chain integration, Corp. Ownersh. Control 14(4), 506-515 (2016).https://doi.org/10.22495/cocv14i4c3art11 [Google Scholar]
- T. Seothan, The Impact ofAutomated Systems on Inventory Management and Fulfillment Speed, Int. Res. J. Modernization Eng. Technol. Sci. 6/02, 1-15 (2025). https://doi.org/10.56726/IRJMETS49577 [Google Scholar]
- W. Villegas-Ch, Optimization of Inventory Management through Computer Vision Platforms in Warehouses, Comput. Ind. Eng. 182, 107-123 (2024). https://doi.org/10.1016/j.cie.2024.112011 [Google Scholar]
- A. Pritchard, Teaching Data-Driven Decision Making for Inventory Management and Decision Systems Under Uncertainty, Decis. Support Syst. 171, 103-118 (2025). https://doi.org/10.1111/dsji.12328 [Google Scholar]
- H. Ihsanil Huda, H. Kusumo, E. Endaryati, Optimization of Supply Chain Processes in the Retail Sector: A Data-Driven Simulation Approach for Inventory Management, J. Manag. Inf. Syst. 3(1), 47-60 (2024).https://doi.org/10.51903/jmi.v3i1.47 [Google Scholar]
- D. Pencheva, Fast-Moving Consumer Goods Retail: Business Intelligence Approach in Retail Information Systems, Egypt. Comput. Sci. J. 45(3), 47-59 (2021). https://doi.org/10.21608/ecsj.2021.55847 [Google Scholar]
- R. Modak, Generative AI for Automated Business Report Generation and Analysis, World J. Adv. Eng. Technol. Sci. 15(2), 0610 (2025). https://doi.Org/10.30574/wjaets.2025.15.2.0610 [Google Scholar]
- M.U. Majigi, Big data transfer service architecture for cloud data centers, J. Cloud Comput. 14, 96 (2025).https://doi.org/10.1007/s10791-025-09682-3 [Google Scholar]
- T.U. Daim, C. Harris, Site selection for a data centre-a multi-criteria decision-analysis framework, Int. J. Inf. Syst. Model. Simul. 4(3), 32-52 (2013). https://doi.org/10.1080/19397038.2012.719554 [Google Scholar]
- L. Ye, Forecasting Seasonal Demand for Retail: A Fourier Time-Varying Grey Model, Int. J. Forecast., vol. 40, pp. 235-247, 2024, https://doi.org/10.1016/j.ijforecast.2023.02.003 [Google Scholar]
- S. Jandhyala, Automated retail inventory management through enterprise integration, Int. J. Sci. Technol. 16(1), 15-29 (2025). [Google Scholar]
- M. Al Fikri, F.A. Putra, Y. Suryanto, K. Ramli, Risk assessment using NIST SP 800-30 revision 1 and ISO 27005 combination technique in profit-based organization: Case study of ZZZ information system application in ABC agency, Procedia Comput. Sci. 161, 1206-1215 (2019).https://doi.org/10.1016/j.procs.2019.11.234 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.