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

Author

Listed:
  • 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. Prajogo, Daniel & Olhager, Jan, 2012. "Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration," International Journal of Production Economics, Elsevier, vol. 135(1), pages 514-522.
    2. Felipe Caro & Jérémie Gallien & Miguel Díaz & Javier García & José Manuel Corredoira & Marcos Montes & José Antonio Ramos & Juan Correa, 2010. "Zara Uses Operations Research to Reengineer Its Global Distribution Process," Interfaces, INFORMS, vol. 40(1), pages 71-84, February.
    3. Melissa R. Bowers & Jeffrey D. Camm & Goutam Chakraborty, 2018. "The Evolution of Analytics and Implications for Industry and Academic Programs," Interfaces, INFORMS, vol. 48(6), pages 487-499, November.
    4. Christofi, Michael & Vrontis, Demetris & Thrassou, Alkis & Shams, S.M. Riad, 2019. "Triggering technological innovation through cross-border mergers and acquisitions: A micro-foundational perspective," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 148-166.
    5. Ana Esteso & David Peidro & Josefa Mula & Manuel Díaz-Madroñero, 2023. "Reinforcement learning applied to production planning and control," International Journal of Production Research, Taylor & Francis Journals, vol. 61(16), pages 5772-5789, August.
    6. Rafia Mumtaz & Arslan Amin & Muhammad Ajmal Khan & Muhammad Daud Abdullah Asif & Zahid Anwar & Muhammad Jawad Bashir, 2023. "Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques," Energies, MDPI, vol. 16(16), pages 1-30, August.
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