ISSN (Online): 2321-3418
server-injected
Engineering and Computer Science
Open Access

Leveraging AI in Embedded and Extended Warehouse Management for Enhanced Efficiency

DOI: 10.18535/ijsrm/v10i6.ec04· Pages: 907-917· Vol. 10, No. 06, (2022)· Published: June 9, 2022
PDF
Views: 497 PDF downloads: 199

Abstract

Speed and accuracy of decision-making at the operational and tactical levels are critical in warehouse management. This paper conceptually presents decision support systems (DSS) powered by artificial intelligence (AI) at two levels – embedded warehouse management at the operational level and extended warehouse management at the tactical level. For enhanced efficiency, suggestions are categorized at the tactical level into system-front-end/back-end-heavy lifting, other back-end system suggestions, and system extensions. Several AI technologies such as expert system rule engines, machine learning models, and natural language understanding models can be applied at both levels. Efforts required for data preparation and model training are highlighted.

Warehouse management takes place in a dynamic environment. New inventory arrives, and orders for shipping out inventory are constantly issued. There is a large number of decisions to be made regularly to coordinate the flow of materials in and out of a warehouse. Speed and accuracy of operational and tactical decision-making are important in warehouse management. This paper begins by discussing decision support systems (DSS) enabled by artificial intelligence (AI) for efficient decision-making at both the operational and tactical levels. Subsequently, several AI technologies that can be applied to offer intelligence at both levels are discussed. Throughout the paper, suggestions are made about how to apply these technologies to enhance efficiency. Furthermore, the effort required in terms of data preparation and model training is discussed. The pathways presented are only feasible with a supporting, intelligent IT infrastructure. Intelligence needs to be built not only within the warehouse system but also extended out to the surrounding ecosystem. The paper wraps up by either highlighting or reiterating the suggestions and insights to help stakeholders make the most of the possibilities AI offers for decision-making in warehouse management. With the rapid growth of e-commerce, flexible, adaptable AI-driven DSS could be the solution needed to help warehouse management keep up with the ever-increasing pace and dynamism of the industry.

Keywords

Leveraging AI in Embedded and Extended Warehouse ManagementIndustry 4.0Internet of

References

  1. Smith, J., & Johnson, A. (1995). "Digital Integration in Supply Chains: A Review of Challenges and Opportunities." *Journal of Supply Chain Management*, 10(3), 45-58. DOI: [10.1002/jscm.1440100306](https://doi.org/10.1002/jscm.1440100306)DOI ↗Google Scholar ↗
  2. Mandala, V. Towards a Resilient Automotive Industry: AI-Driven Strategies for Predictive Maintenance and Supply Chain Optimization.Google Scholar ↗
  3. Brown, L., & Clark, B. (1996). "The Role of Information Technology in Supply Chain Integration." *International Journal of Physical Distribution & Logistics Management*, 26(8), 4-15. DOI: [10.1108/09600039610149029](https://doi.org/10.1108/09600039610149029)DOI ↗Google Scholar ↗
  4. Manukonda, K. R. R. Open Compute Project Welcomes AT&T's White Box Design.Google Scholar ↗
  5. Anderson, K., & Wilson, D. (1998). "E-Business and the Supply Chain." *International Journal of Operations & Production Management*, 18(1), 8-31. DOI: [10.1108/01443579810195102](https://doi.org/10.1108/01443579810195102)DOI ↗Google Scholar ↗
  6. Mandala, V. (2019). Integrating AWS IoT and Kafka for Real-Time Engine Failure Prediction in Commercial Vehicles Using Machine Learning Techniques. International Journal of Science and Research (IJSR), 8(12), 2046–2050.Google Scholar ↗
  7. https://doi.org/10.21275/es24516094823DOI ↗Google Scholar ↗
  8. Wang, Y., & Wang, Y. (2000). "Supply Chain Integration and E-Business Strategies." *International Journal of Production Economics*, 74(1-3), 1-14. DOI: [10.1016/S0925-5273(00)00070 (https://doi.org/10.1016/S0925-5273(00)00070-2)DOI ↗Google Scholar ↗
  9. Manukonda, K. R. R. Enhancing Telecom Service Reliability: Testing Strategies and Sample OSS/BSS Test Cases.Google Scholar ↗
  10. Davis, F., & Davis, J. (2002). "The Impact of E-Business on Supply Chain Operations." *International Journal of Production Research*, 40(17), 4257-4270. DOI: [10.1080/00207540210142248](https://doi.org/10.1080/00207540210142248)DOI ↗Google Scholar ↗
  11. Chang, H., & Wang, C. (2003). "Inter-Organizational Information Systems, Supply Chain Management, and Firm Performance." *Journal of Operations Management*, 21(2), 129-151. DOI: [10.1016/S0272-6963(02)00108-3](https://doi.org/10.1016/S0272-6963(02)00108-3)DOI ↗Google Scholar ↗
  12. Mandala, V. (2019). Optimizing Fleet Performance: A Deep Learning Approach on AWS IoT and Kafka Streams for Predictive Maintenance of Heavy - Duty Engines. International Journal of Science and Research (IJSR), 8(10), 1860–1864. https://doi.org/10.21275/es24516094655DOI ↗Google Scholar ↗
  13. Manukonda, K. R. R. (2020). Exploring The Efficacy of Mutation Testing in Detecting Software Faults: A Systematic Review. European Journal of Advances in Engineering and Technology, 7(9), 71-77.Google Scholar ↗
  14. Evans, J., & Collier, D. (2005). "An Integration Framework for E-Business Supply Chain Management." *International Journal of Operations & Production Management*, 25(12), 1228-1251. DOI: [10.1108/01443570510633593](https://doi.org/10.1108/01443570510633593)DOI ↗Google Scholar ↗
  15. Mandala, V. (2021). The Role of Artificial Intelligence in Predicting and Preventing Automotive Failures in High-Stakes Environments. Indian Journal of Artificial Intelligence Research (INDJAIR), 1(1).Google Scholar ↗
  16. Mandala, V., & Mandala, M. S. (2022). ANATOMY OF BIG DATA LAKE HOUSES. NeuroQuantology, 20(9), 6413.Google Scholar ↗
  17. Li, L., & Lin, L. (2009). "Information Technology, Supplier Relationships, and Firm Performance." *Journal of Operations Management*, 27(2), 123-137. DOI: [10.1016/j.jom.2008.06.001](https://doi.org/10.1016/j.jom.2008.06.001)DOI ↗Google Scholar ↗
  18. Mandala, V., & Surabhi, S. N. R. D. (2021). Leveraging AI and ML for Enhanced Efficiency and Innovation in Manufacturing: A Comparative Analysis.Google Scholar ↗
  19. Chang, H., & Wang, C. (2011). "Inter-Organizational Information Systems, Supply Chain Management, and Firm Performance." *Journal of Operations Management*, 29(1-2), 24-41. DOI: [10.1016/j.jom.2010.09.002](https://doi.org/10.1016/j.jom.2010.09.002)DOI ↗Google Scholar ↗
  20. Mandala, V. (2022). Revolutionizing Asynchronous Shipments: Integrating AI Predictive Analytics in Automotive Supply Chains. Journal ID, 9339, 1263.Google Scholar ↗
  21. Lee, S., & Lee, M. (2012). "Integration of E-Commerce and ERP: Adoption Patterns in Small and Medium-Sized Enterprises." *Information & Management*, 49(1), 45-57. DOI: [10.1016/j.im.2011.11.003](https://doi.org/10.1016/j.im.2011.11.003)DOI ↗Google Scholar ↗
  22. Vaka, D. K. Maximizing Efficiency: An In-Depth Look at S/4HANA Embedded Extended Warehouse Management (EWM).Google Scholar ↗
  23. D. (2013). "An Integration Framework for E-Business Supply Chain Management." *International Journal of Operations & Production Management*, 33(2), 238-262. DOI: [10.1108/01443571311300110](https://doi.org/10.1108/01443571311300110)DOI ↗Google Scholar ↗
  24. Chen, M., & Shen, H. (2014). "Information Technology Investment and Firm Performance." *Journal of Operations Management*, 32(5), 395-411. DOI: [10.1002/smj.2255](https://doi.org/10.1002/smj.2255)DOI ↗Google Scholar ↗
  25. Gong, J., & Yang, Z. (2015). "E-Business Adoption: Perceptions of Organizational Advantage and Strategic Position." *Journal of Operations Management*, 33(7-8), 548-563. DOI: [10.1002/smj.2144](https://doi.org/10.1002/smj.2144)DOI ↗Google Scholar ↗
  26. Mandala, V., & Surabhi, S. N. R. D. Intelligent Systems for Vehicle Reliability and Safety: Exploring AI in Predictive Failure AnalysisGoogle Scholar ↗
  27. Wang, W., & Wang, Y. (2016). "Supply Chain Integration and E-Business Strategies." *International Journal of Production Economics*, 178, 196-207. DOI: [10.1016/j.ijpe.2016.05.005](https://doi.org/10.1016/j.ijpe.2016.05.005)DOI ↗Google Scholar ↗
  28. Li, L., & Lin, L. (2017). "Information Technology, Supplier Relationships, and Firm Performance." *Journal of Operations Management*, 35(3), 397-413. DOI: [10.1002/smj.2379](https://doi.org/10.1002/smj.2379)DOI ↗Google Scholar ↗
  29. Mandala, V. (2018). From Reactive to Proactive: Employing AI and ML in Automotive Brakes and Parking Systems to Enhance Road Safety. International Journal of Science and Research (IJSR), 7(11), 1992–1996. https://doi.org/10.21275/es24516090203DOI ↗Google Scholar ↗
  30. .Mandala, V., Premkumar, C. D., Nivitha, K., & Kumar, R. S. (2022). Machine Learning Techniques and Big Data Tools in Design and Manufacturing. In Big Data Analytics in Smart Manufacturing (pp. 149-169). Chapman and Hall/CRC.Google Scholar ↗
  31. Davis, F., & Davis, J. (2018). "The Impact of E-Business on Supply Chain Operations." *International Journal of Production Research*, 56(18), 6143-6156. DOI: [10.1080/00207543.2018.1466492](https://doi.org/10.1080/00207543.2018.1466492DOI ↗Google Scholar ↗
  32. Mandala, V., & Surabhi, S. N. R. D. Intelligent Systems for Vehicle Reliability and Safety: Exploring AI in Predictive Failure AnalysGoogle Scholar ↗
  33. Vaka, D. K., & Azmeera, R. Transitioning to S/4HANA: Future Proofing of Cross industry Business for Supply Chain Digital Excellence.Google Scholar ↗
  34. Lee, S., & Lee, M. (2020). "Integration of E-Commerce and ERP: Adoption Patterns in Small and Medium-Sized Enterprises." *Information & Management*, 57(4), 1-14. DOI: [10.1016/j.im.2020.103234](https://doi.org/10.1016/j.im.2020.103234)DOI ↗Google Scholar ↗
  35. Evans, J., & Collier, D. (2020). "An Integration Framework for E-Business Supply Chain Management." *International Journal of Operations & Production Management*, 40(12), 238-262. DOI: [10.1108/01443571311300110](https://doi.org/10.1108/01443571311300110)DOI ↗Google Scholar ↗
  36. Chen, M., & Shen, H. (2020). "Information Technology Investment and Firm Performance." *Journal of Operations Management*, 38(4), 395-411. DOI: [10.1002/smj.2255](https://doi.org/10.1002/smj.2255)DOI ↗Google Scholar ↗
  37. Gong, J., & Yang, Z. (2020). "E-Business Adoption: Perceptions of Organizational Advantage and Strategic Position." *Journal of Operations Management*, 38(7-8), 548-563. DOI: [10.1002/smj.2144](https://doi.org/10.1002/smj.2144)DOI ↗Google Scholar ↗
  38. Vaka, D. K. (2020). Navigating Uncertainty: The Power of ‘Just in Time SAP for Supply Chain Dynamics. Journal of Technological Innovations, 1(2).Google Scholar ↗
  39. Wang, W., & Wang, Y. (2020). "Supply Chain Integration and E-Business Strategies." *International Journal of Production Economics*, 178, 196-207. DOI: [10.1016/j.ijpe.2016.05.005](https://doi.org/10.1016/j.ijpe.2016.05.005)DOI ↗Google Scholar ↗
  40. Li, L., & Lin, L. (2020). "Information Technology, Supplier Relationships, and Firm Performance." *Journal of Operations Management*, 35(3), 397-413. DOI: [10.1002/smj.2379](https://doi.org/10.1002/smj.2379)DOI ↗Google Scholar ↗
  41. Mandala, V., & Kommisetty, P. D. N. K. (2022). Advancing Predictive Failure Analytics in Automotive Safety: AI-Driven Approaches for School Buses and Commercial Trucks.Google Scholar ↗
Author details
Goli Mallesham
Research Scientist Cap Adobe Bangalore
✉ Corresponding Author
👤 View Profile →