Article Sidebar

Download
PDF
Statistic
Read Counter : 10 Download : 0

Main Article Content

Abstract

Purpose: This conceptual and simulation-based study aims to examine the potential digital transformation of the logistics system at the Kasemen SPPG Warehouse. It specifically investigates the ability of RFID integration to reduce inaccuracies in food inventory data, evaluates the responsiveness of an IoT-based network architecture using the MQTT protocol for environmental early-warning transmission, and measures automation-driven cycle-time efficiency in inbound and outbound logistics processes.


Research Method: This study applies a Research and Development (R&D) approach, focusing on conceptual framework design and performance evaluation using secondary data. The analysis includes Inventory Record Inaccuracy (IRI) measurement using RMSE, evaluation of notification responsiveness via MQTT transmission delay, and time-motion analysis of inbound and outbound cycles, supported by an automated FEFO algorithm.


Results and Discussion: The simulation results provide preliminary conceptual support for the proposed operational hypotheses. IRI decreased by 88.86%, from 6.91 to 0.77 RMSE units. Notification responsiveness improved by 99.98%, from 21,600 seconds in the manual system to 2.2 seconds using MQTT. Automation also reduced cycle time by 92.59% in inbound activities and 93.33% in outbound activities.


Implications: The findings offer a practical blueprint for shifting warehouse operations from reactive to preventive, reducing the risk of food waste, and supporting digitally standardized logistics SOPs.


Originality: This study presents an integrated IoT-based Smart Warehouse model that combines RFID, MQTT, and automated FEFO to support food logistics decision-making.

Keywords

smart warehouse internet of things inventory record inaccuracy notification responsiveness cycle time

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Ayu Fadhilah, G., Ariesta, G., Rufa’i, A., Rusdiansyah, D. ., & Hidayat, S. (2026). Smart Warehouse Ecosystem: An IoT-Based Framework for Real-Time Environmental Monitoring in a Nutrition Fulfillment Service Unit (SPPG) Warehouse. Advances in Business & Industrial Marketing Research, 4(2), 182–196. https://doi.org/10.60079/abim.v4i2.856
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Ali, S. S., Khan, S., Fatma, N., Ozel, C., & Hussain, A. (2024). Utilisation of drones in achieving various applications in smart warehouse management. Benchmarking: An International Journal, 31(3), 920–954. https://doi.org/10.1108/BIJ-01-2023-0039
  2. Fernando, Y., Suhaini, A., Tseng, M.-L., Abideen, A. Z., & Shaharudin, M. S. (2024). A smart warehouse framework, architecture and system aspects under industry 4.0: A bibliometric networks visualisation and analysis. International Journal of Logistics Research and Applications, 27(12), 2688–2711. https://doi.org/10.1080/13675567.2023.2215179
  3. Glock, C. H., Syntetos, A. A., & Rekik, Y. (2025). On the measurement of inventory record inaccuracies. The International Review of Retail, Distribution and Consumer Research, 35(1), 1–24. https://doi.org/10.1080/09593969.2025.2608729
  4. Khan, M. G., Huda, N. U., & Zaman, U. K. (2022). Smart warehouse management system: Architecture, real-time implementation and prototype design. Machines, 10(2), Artikel 150. https://doi.org/10.3390/machines10020150
  5. Sahara, C. R., & Aamer, A. M. (2022). Real-time data integration of an internet-of-things-based smart warehouse: A case study. International Journal of Pervasive Computing and Communications, 18(5), 622–644. https://doi.org/10.1108/IJPCC-08-2020-0113
  6. Selvaraj, A. S., & Anusha, S. (2021). RFID enabled smart data analysis in a smart warehouse monitoring system using IoT. Journal of Physics: Conference Series, 1717(1), Artikel 012022. https://doi.org/10.1088/1742-6596/1717/1/012022
  7. Singh, S., & Singh, R. (2024). Analysis of smart warehouse in the context of India's National Logistics Policy and digital-push: An ISM-MICMAC technique. International Journal of Productivity and Performance Management, 74(2), 683–707. https://doi.org/10.1108/IJPPM-10-2023-0533
  8. Tang, Y.-M., Ho, G. T. S., Lau, Y.-Y., & Tsui, S.-Y. (2022). Integrated smart warehouse and manufacturing management with demand forecasting in small-scale cyclical industries. Machines, 10(6), Artikel 472. https://doi.org/10.3390/machines10060472
  9. Tiwari, S. (2023). Smart warehouse: A bibliometric analysis and future research direction. Sustainable Manufacturing and Service Economics, 2, Artikel 100014. https://doi.org/10.1016/j.smse.2023.100014
  10. Van Geest, M., Tekinerdogan, B., & Catal, C. (2021). Design of a reference architecture for developing smart warehouses in industry 4.0. Computers in Industry, 124, Article 103343. https://doi.org/10.1016/j.compind.2020.103343
  11. Van Geest, M., Tekinerdogan, B., & Catal, C. (2022). Smart warehouses: Rationale, challenges and solution directions. Applied Sciences, 12(1), Artikel 219. https://doi.org/10.3390/app120100219
  12. Zhen, L., & Li, H. (2022). A literature review of smart warehouse operations management. Frontiers of Engineering Management, 9(1), 31–55. https://doi.org/10.1007/s42524-021-0178-9