IDEAS home Printed from https://ideas.repec.org/p/hal/wpaper/hal-00235717.html
   My bibliography  Save this paper

Aide À La Planification Avec Incertitude, Imprécision Et Incomplétude Sur La Demande

Author

Listed:
  • François Galasso

    (LAAS-MOGISA - LAAS - Laboratoire d'analyse et d'architecture des systèmes - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INSA Toulouse - Institut National des Sciences Appliquées - Toulouse - INSA - Institut National des Sciences Appliquées - UT - Université de Toulouse - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse)

  • Caroline Thierry

    (IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage - IRIT - Institut de recherche en informatique de Toulouse - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse - TMBI - Toulouse Mind & Brain Institut - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse, UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse)

Abstract

La prise en compte de la demande client pour la planification tactique dans les chaînes logistiques est un enjeu crucial pour leur bon fonctionnement. L'environnement concurrentiel actuel rend difficile l'engagement des clients sur le moyen terme. Dès lors, il est nécessaire de développer des méthodes et outils pour s'adapter au mieux à une demande fluctuante. Dans ce contexte, cet article s'attache à appliquer à la gestion de la demande une terminologie issue de la théorie de la décision sous incertitude. Les liens entre cette terminologie et une approche industrielle basée sur la notion de risques sont présentés. Ensuite, un outil de simulation orienté sur la relation clientfournisseur est présenté. Cet outil a pour objectif d'évaluer, grâce à un ensemble de critères d'aide à la décision dont l'application est rendue possible par la démarche étymologique initiale, de guider un décideur sur ses choix en matière de planification. Cette orientation est basée à la fois sur une évaluation des gains possibles suite à l'application d'une stratégie de planification donnée mais aussi au degré d'optimisme associé au sens du critère d'Hurwicz.

Suggested Citation

  • François Galasso & Caroline Thierry, 2008. "Aide À La Planification Avec Incertitude, Imprécision Et Incomplétude Sur La Demande," Working Papers hal-00235717, HAL.
  • Handle: RePEc:hal:wpaper:hal-00235717
    Note: View the original document on HAL open archive server: https://hal.science/hal-00235717
    as

    Download full text from publisher

    File URL: https://hal.science/hal-00235717/document
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tang, Christopher S., 2006. "Perspectives in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 103(2), pages 451-488, October.
    2. Bartezzaghi, Emilio & Verganti, Roberto, 1995. "Managing demand uncertainty through order overplanning," International Journal of Production Economics, Elsevier, vol. 40(2-3), pages 107-120, 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. François Galasso & Caroline Thierry, 2008. "Cooperation Support In A Dyadic Supply Chain," Working Papers hal-00235808, HAL.
    2. Jie Wu & Zhixin Chen & Xiang Ji, 2020. "Sustainable trade promotion decisions under demand disruption in manufacturer-retailer supply chains," Annals of Operations Research, Springer, vol. 290(1), pages 115-143, July.
    3. Pasura Aungkulanon & Walailak Atthirawong & Pongchanun Luangpaiboon & Wirachchaya Chanpuypetch, 2024. "Navigating Supply Chain Resilience: A Hybrid Approach to Agri-Food Supplier Selection," Mathematics, MDPI, vol. 12(10), pages 1-41, May.
    4. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    5. Zhao, Na, 2019. "Managing interactive collaborative mega project supply chains under infectious risks," International Journal of Production Economics, Elsevier, vol. 218(C), pages 275-286.
    6. Boulaksil, Youssef, 2016. "Safety stock placement in supply chains with demand forecast updates," Operations Research Perspectives, Elsevier, vol. 3(C), pages 27-31.
    7. Hassan Arabshahi & Hamed Fazlollahtabar, 2018. "Classifying Innovative Activities Using Decision Tree and Gini Index," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 1-14, June.
    8. Li, Yongjian & Zhen, Xueping & Qi, Xiangtong & Cai, Gangshu (George), 2016. "Penalty and financial assistance in a supply chain with supply disruption," Omega, Elsevier, vol. 61(C), pages 167-181.
    9. Seyyed Mohammad Seyyed Alizadeh Ganji & Mohammad Hayati, 2016. "Identifying and Assessing the Risks in the Supply Chain," Modern Applied Science, Canadian Center of Science and Education, vol. 10(6), pages 1-74, June.
    10. Laurent Lim, Lâm & Alpan, Gülgün & Penz, Bernard, 2014. "Reconciling sales and operations management with distant suppliers in the automotive industry: A simulation approach," International Journal of Production Economics, Elsevier, vol. 151(C), pages 20-36.
    11. Qazi, Abroon & Dickson, Alex & Quigley, John & Gaudenzi, Barbara, 2018. "Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks," International Journal of Production Economics, Elsevier, vol. 196(C), pages 24-42.
    12. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    13. Mazur, Christoph & Hoegerle, Yannick & Brucoli, Maria & van Dam, Koen & Guo, Miao & Markides, Christos N. & Shah, Nilay, 2019. "A holistic resilience framework development for rural power systems in emerging economies," Applied Energy, Elsevier, vol. 235(C), pages 219-232.
    14. Talebian, Masoud & Boland, Natashia & Savelsbergh, Martin, 2014. "Pricing to accelerate demand learning in dynamic assortment planning for perishable products," European Journal of Operational Research, Elsevier, vol. 237(2), pages 555-565.
    15. Fan, Yingjie & Schwartz, Frank & Voß, Stefan, 2017. "Flexible supply chain planning based on variable transportation modes," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 654-666.
    16. Yang, Taho & Wen, Yuan-Feng & Wang, Fang-Fang, 2011. "Evaluation of robustness of supply chain information-sharing strategies using a hybrid Taguchi and multiple criteria decision-making method," International Journal of Production Economics, Elsevier, vol. 134(2), pages 458-466, December.
    17. Xiaohong Liu & Liguo Zhou & Yen-Chun Jim Wu, 2015. "Supply Chain Finance in China: Business Innovation and Theory Development," Sustainability, MDPI, vol. 7(11), pages 1-21, November.
    18. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    19. Sutter Daniel & Ewing Bradley T., 2016. "State of Knowledge of Economic Value of Current and Improved Hurricane Forecasts," Journal of Business Valuation and Economic Loss Analysis, De Gruyter, vol. 11(1), pages 45-64, June.
    20. Bilsel, R. Ufuk & Ravindran, A., 2011. "A multiobjective chance constrained programming model for supplier selection under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1284-1300, September.

    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:hal:wpaper:hal-00235717. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.