IDEAS home Printed from https://ideas.repec.org/a/gam/jlogis/v7y2023i4p91-d1292513.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2305-6290/7/4/91/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2305-6290/7/4/91/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Panagiotis Trivellas & Georgios Malindretos & Panagiotis Reklitis, 2020. "Implications of Green Logistics Management on Sustainable Business and Supply Chain Performance: Evidence from a Survey in the Greek Agri-Food Sector," Sustainability, MDPI, vol. 12(24), pages 1-29, December.
    2. Lee, Sangjoon & Sheu, Jiuh-Biing & Yoo, Seung Ho, 2024. "Multi-period quality and pricing decision in production-focused and market-focused supply chains: How to manage a long-term contractual relationship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    3. Priom Mahmud & Sanjoy Kumar Paul & Abdullahil Azeem & Priyabrata Chowdhury, 2021. "Evaluating Supply Chain Collaboration Barriers in Small- and Medium-Sized Enterprises," Sustainability, MDPI, vol. 13(13), pages 1-28, July.
    4. Sung Tae Kim & Hong-Hee Lee & Taewon Hwang, 2020. "Logistics integration in the supply chain: a resource dependence theory perspective," International Journal of Quality Innovation, Springer, vol. 6(1), pages 1-14, December.
    5. Prajogo, Daniel & Toy, Jordan & Bhattacharya, Ananya & Oke, Adegoke & Cheng, T.C.E., 2018. "The relationships between information management, process management and operational performance: Internal and external contexts," International Journal of Production Economics, Elsevier, vol. 199(C), pages 95-103.
    6. Kembro, Joakim & Näslund, Dag & Olhager, Jan, 2017. "Information sharing across multiple supply chain tiers: A Delphi study on antecedents," International Journal of Production Economics, Elsevier, vol. 193(C), pages 77-86.
    7. Dominik Oehlschläger & Andreas H. Glas & Michael Eßig, 2024. "How Digital Twins Impact Responsiveness: A Dynamic Fit Approach to Information Processing for High-Involvement Product Demand Management," Schmalenbach Journal of Business Research, Springer, vol. 76(4), pages 661-706, December.
    8. Lu Han & Hanping Hou & Z. M. Bi & Jianliang Yang & Xiaoxiao Zheng, 2024. "Functional Requirements and Supply Chain Digitalization in Industry 4.0," Information Systems Frontiers, Springer, vol. 26(6), pages 2273-2285, December.
    9. Jacobs, Mark A. & Yu, Wantao & Chavez, Roberto, 2016. "The effect of internal communication and employee satisfaction on supply chain integration," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 60-70.
    10. Ra’ed Masa’deh & Ismail Muheisen & Bader Obeidat & Ashraf Bany Mohammad, 2022. "The Impact of Supply Chain Integration on Operational Performance: An Empirical Study," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    11. Li, Kunpeng & Liu, Tengbo & Ram Kumar, P.N. & Han, Xuefang, 2024. "A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    12. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    13. Del Vecchio, Pasquale & Secundo, Giustina & Garzoni, Antonello, 2023. "Phygital technologies and environments for breakthrough innovation in customers' and citizens' journey. A critical literature review and future agenda," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    14. Delios, Andrew & Li, Jiatao & Schotter, Andreas P.J. & Vrontis, Demetris, 2024. "Challenging the orthodoxy in international business research: Directions for “new” research areas," Journal of World Business, Elsevier, vol. 59(4).
    15. Muhammad Irfan & Mingzheng Wang & Naeem Akhtar, 2019. "Impact of IT capabilities on supply chain capabilities and organizational agility: a dynamic capability view," Operations Management Research, Springer, vol. 12(3), pages 113-128, December.
    16. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    17. Chamdan Purnama & Ludi Wishnu Wardana & Yusriyah Rahmah & Dinda Fatmah & Mirhamida Rahmah, 2020. "The Impact of External Integration and Internal Integration to Product Innovation and Competitive Advantage on Small and Medium Enterprises (SMEs)," International Journal of Innovation and Economic Development, Inovatus Services Ltd., vol. 6(4), pages 82-95, October.
    18. Agata Mesjasz-Lech, 2014. "The Use Of It Systems Supporting The Realization Of Business Processes In Enterprises And Supply Chains In Poland," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 10(2), pages 94-103, December.
    19. Mahavarpour, Nasrin & Marvi, Reza & Foroudi, Pantea, 2023. "A Brief History of Service Innovation: The evolution of past, present, and future of service innovation," Journal of Business Research, Elsevier, vol. 160(C).
    20. Naif Al Azmi & Ghaleb Sweis & Rateb Sweis & Farouq Sammour, 2022. "Exploring Implementation of Blockchain for the Supply Chain Resilience and Sustainability of the Construction Industry in Saudi Arabia," Sustainability, MDPI, vol. 14(11), pages 1-17, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlogis:v:7:y:2023:i:4:p:91-:d:1292513. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.