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Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

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  • João Reis

    (Industrial Engineering and Management, Faculty of Engineering, Lusófona University, 1749-024 Lisbon, Portugal)

Abstract

Background : Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods : This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results : This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions : While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes.

Suggested Citation

  • João Reis, 2023. "Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python," Logistics, MDPI, vol. 7(4), pages 1-19, December.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:4:p:91-:d:1292513
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    References listed on IDEAS

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    1. Inesh Hettiarachchi, 2025. "Exploring Generative AI Agents: Architecture, Applications, and Challenges," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 8(1), pages 105-127.

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