IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i2p336-d1322667.html
   My bibliography  Save this article

An Icon-Based Methodology for the Design of a Prototype of a Multi-Process, Multi-Product, Aggregated Production Planning Software

Author

Listed:
  • Erick Miranda-Meza

    (Department of Industrial Engineering, Universidad de Santiago de Chile, Santiago 8370458, Chile)

  • Iván Derpich

    (Department of Industrial Engineering, Universidad de Santiago de Chile, Santiago 8370458, Chile)

  • Juan M. Sepúlveda

    (Department of Industrial Engineering, Universidad de Santiago de Chile, Santiago 8370458, Chile)

Abstract

This paper proposes an icon-based methodology for the design of prototype aggregated production planning software that addresses the complexity of multi-process and multi-product production. Aggregate planning is a critical task in production management, which involves coordinating the production of multiple products in different processes to meet demand efficiently. The approach focuses on the use of visual icons to represent key elements of the production process, such as products, processes, resources, and constraints. These icons allow an intuitive representation of information and facilitate communication between production team members. In addition, this paper presents a conceptual structure that defines the relationships between the icons and how they are used to model and simulate aggregate production planning. The prototype software based on a conceptual foundation allows planners to easily create and adjust production plans in a visual environment. This method improves the ability to make informed and rapid decisions in response to changes in demand or production capacity. The prototype is based on icons and programmed in Excel spreadsheets to facilitate the planner’s planning. At the end of the document, the application of a case study is shown.

Suggested Citation

  • Erick Miranda-Meza & Iván Derpich & Juan M. Sepúlveda, 2024. "An Icon-Based Methodology for the Design of a Prototype of a Multi-Process, Multi-Product, Aggregated Production Planning Software," Mathematics, MDPI, vol. 12(2), pages 1-25, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:336-:d:1322667
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/2/336/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/2/336/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gerald Brown & Joseph Keegan & Brian Vigus & Kevin Wood, 2001. "The Kellogg Company Optimizes Production, Inventory, and Distribution," Interfaces, INFORMS, vol. 31(6), pages 1-15, December.
    2. Miltenburg, John, 2008. "Setting manufacturing strategy for a factory-within-a-factory," International Journal of Production Economics, Elsevier, vol. 113(1), pages 307-323, May.
    3. Nam, Sang-jin & Logendran, Rasaratnam, 1992. "Aggregate production planning -- A survey of models and methodologies," European Journal of Operational Research, Elsevier, vol. 61(3), pages 255-272, September.
    4. Jonsson, Patrik & Kjellsdotter Ivert, Linea, 2015. "Improving performance with sophisticated master production scheduling," International Journal of Production Economics, Elsevier, vol. 168(C), pages 118-130.
    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. Stüve, David & van der Meer, Robert & Lütke Entrup, Matthias & Agha, Mouhamad Shaker Ali, 2020. "Supply chain planning in the food industry," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 317-353, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    2. Habibi, M.K. Khakim & Battaïa, Olga & Cung, Van-Dat & Dolgui, Alexandre, 2017. "Collection-disassembly problem in reverse supply chain," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 334-344.
    3. Ornella Tarola, 2010. "Public Utilities: Privatization without Regulation," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 4(1), pages 062-078, March.
    4. Hrabec, Dušan & Hvattum, Lars Magnus & Hoff, Arild, 2022. "The value of integrated planning for production, inventory, and routing decisions: A systematic review and meta-analysis," International Journal of Production Economics, Elsevier, vol. 248(C).
    5. George Dikos & Stavroula Spyropoulou, 2013. "Supply Chain Optimization and Planning in Heracles General Cement Company," Interfaces, INFORMS, vol. 43(4), pages 297-312, August.
    6. Li, Gang & Jiang, Hongxun & He, Tian, 2015. "A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem," Omega, Elsevier, vol. 50(C), pages 1-17.
    7. Chen, Liang-Tu, 2014. "Optimal dynamic policies for integrated production and marketing planning in business-to-business marketplaces," International Journal of Production Economics, Elsevier, vol. 153(C), pages 46-53.
    8. Estefania Tobon-Valencia & Samir Lamouri & Robert Pellerin & Alexandre Moeuf, 2022. "Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0," Sustainability, MDPI, vol. 14(19), pages 1-28, October.
    9. Javier Faulin & Pablo Sarobe & Jorge Simal, 2005. "The DSS LOGDIS Optimizes Delivery Routes for FRILAC’s Frozen Products," Interfaces, INFORMS, vol. 35(3), pages 202-214, June.
    10. Yossiri Adulyasak & Jean-François Cordeau & Raf Jans, 2014. "Optimization-Based Adaptive Large Neighborhood Search for the Production Routing Problem," Transportation Science, INFORMS, vol. 48(1), pages 20-45, February.
    11. Seokcheol Chang & Jaewoo Chung, 2013. "Optimization Models for Production Planning in LG Display," Interfaces, INFORMS, vol. 43(6), pages 518-529, December.
    12. Sourabh Devidas Kulkarni & Priyanka Verma, 2023. "A fuzzy-QFD approach to manufacturing strategy formulation," OPSEARCH, Springer;Operational Research Society of India, vol. 60(3), pages 1407-1432, September.
    13. Caner TaskIn, Z. & Tamer Ünal, A., 2009. "Tactical level planning in float glass manufacturing with co-production, random yields and substitutable products," European Journal of Operational Research, Elsevier, vol. 199(1), pages 252-261, November.
    14. Ilkyeong Moon & Yoon Jea Jeong & Subrata Saha, 2016. "Fuzzy Bi-Objective Production-Distribution Planning Problem under the Carbon Emission Constraint," Sustainability, MDPI, vol. 8(8), pages 1-17, August.
    15. Habibi, Muhammad Khakim & Hammami, Ramzi & Battaia, Olga & Dolgui, Alexandre, 2024. "Simultaneous Pickup-and-Delivery Production-Routing Problem in closed-loop supply chain with remanufacturing and disassembly consideration," International Journal of Production Economics, Elsevier, vol. 273(C).
    16. Zakaria Chekoubi & Wajdi Trabelsi & Nathalie Sauer & Ilias Majdouline, 2022. "The Integrated Production-Inventory-Routing Problem with Reverse Logistics and Remanufacturing: A Two-Phase Decomposition Heuristic," Sustainability, MDPI, vol. 14(20), pages 1-30, October.
    17. Manousakis, Eleftherios G. & Kasapidis, Grigoris A. & Kiranoudis, Chris T. & Zachariadis, Emmanouil E., 2022. "An infeasible space exploring matheuristic for the Production Routing Problem," European Journal of Operational Research, Elsevier, vol. 298(2), pages 478-495.
    18. Donya Rahmani & Arash Zandi & Sara Behdad & Arezou Entezaminia, 2021. "A light robust model for aggregate production planning with consideration of environmental impacts of machines," Operational Research, Springer, vol. 21(1), pages 273-297, March.
    19. Singer, Marcos & Donoso, Patricio, 2008. "Empirical validation of an activity-based optimization system," International Journal of Production Economics, Elsevier, vol. 113(1), pages 335-345, May.
    20. Olhager, Jan & Rudberg, Martin & Wikner, Joakim, 2001. "Long-term capacity management: Linking the perspectives from manufacturing strategy and sales and operations planning," International Journal of Production Economics, Elsevier, vol. 69(2), pages 215-225, January.

    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:jmathe:v:12:y:2024:i:2:p:336-:d:1322667. 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.