IDEAS home Printed from https://ideas.repec.org/a/rnd/arjsds/v4y2013i5p236-241.html
   My bibliography  Save this article

The Multi-Site Order Fulfillment-Planning Model: A Global Corporation Case Study

Author

Listed:
  • Yin-Yann Chen
  • Hsiao-Yao Fan

Abstract

A multi-site order fulfillment-planning model for the thin film transistor–liquid crystal display (TFT-LCD) panel industry is proposed. The order allocation problem is solved using a mathematical programming model considering practical characteristics, including product structures, customer preferences, alternative bill-of-material, and production constraints. A practical global corporation case in Taiwan will be employed to testify the feasibility of the proposed order fulfillment-planning model. Besides, the adaptability and comparison of different planning approaches in an environment of various market demands are discussed. Through the analysis of experiments, the proposed mathematical programming model is found to be better than the current popular method.

Suggested Citation

  • Yin-Yann Chen & Hsiao-Yao Fan, 2013. "The Multi-Site Order Fulfillment-Planning Model: A Global Corporation Case Study," Journal of Social and Development Sciences, AMH International, vol. 4(5), pages 236-241.
    • Handle: RePEc:rnd:arjsds:v:4:y:2013:i:5:p:236-241
    DOI: 10.22610/jsds.v4i5.757
    as

    Download full text from publisher

    File URL: https://ojs.amhinternational.com/index.php/jsds/article/view/757/757
    Download Restriction: no
    File URL: https://ojs.amhinternational.com/index.php/jsds/article/view/757
    Download Restriction: no
    File URL: https://libkey.io/10.22610/jsds.v4i5.757?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Timpe, Christian H. & Kallrath, Josef, 2000. "Optimal planning in large multi-site production networks," European Journal of Operational Research, Elsevier, vol. 126(2), pages 422-435, October.
    2. Tsai, Kune-muh & Wang, Shan-chi, 2009. "Multi-site available-to-promise modeling for assemble-to-order manufacturing: An illustration on TFT-LCD manufacturing," International Journal of Production Economics, Elsevier, vol. 117(1), pages 174-184, January.
    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. Becker, Tristan & Lier, Stefan & Werners, Brigitte, 2019. "Value of modular production concepts in future chemical industry production networks," European Journal of Operational Research, Elsevier, vol. 276(3), pages 957-970.
    2. Seitz, Alexander & Grunow, Martin & Akkerman, Renzo, 2020. "Data driven supply allocation to individual customers considering forecast bias," International Journal of Production Economics, Elsevier, vol. 227(C).
    3. Xiao, Yongbo & Chen, Jian & Lee, Chung-Yee, 2010. "Optimal decisions for assemble-to-order systems with uncertain assembly capacity," International Journal of Production Economics, Elsevier, vol. 123(1), pages 155-165, January.
    4. Steffen Rebennack & Josef Kallrath, 2015. "Continuous Piecewise Linear Delta-Approximations for Bivariate and Multivariate Functions," Journal of Optimization Theory and Applications, Springer, vol. 167(1), pages 102-117, October.
    5. Tsai, Kune-muh & Wang, Shan-chi, 2009. "Multi-site available-to-promise modeling for assemble-to-order manufacturing: An illustration on TFT-LCD manufacturing," International Journal of Production Economics, Elsevier, vol. 117(1), pages 174-184, January.
    6. Zhang, Xiaochen & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2022. "Subgroup analysis for high-dimensional functional regression," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    7. Azaron, A. & Brown, K.N. & Tarim, S.A. & Modarres, M., 2008. "A multi-objective stochastic programming approach for supply chain design considering risk," International Journal of Production Economics, Elsevier, vol. 116(1), pages 129-138, November.
    8. Shiva Moslemi & Mohammad Hossein Zavvar Sabegh & Abolfazl Mirzazadeh & Yucel Ozturkoglu & Eric Maass, 2017. "A multi-objective model for multi-production and multi-echelon closed-loop pharmaceutical supply chain considering quality concepts: NSGAII approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1717-1733, November.
    9. Andjelka Kelic & Zachary A. Collier & Christopher Brown & Walter E. Beyeler & Alexander V. Outkin & Vanessa N. Vargas & Mark A. Ehlen & Christopher Judson & Ali Zaidi & Billy Leung & Igor Linkov, 2013. "Decision framework for evaluating the macroeconomic risks and policy impacts of cyber attacks," Environment Systems and Decisions, Springer, vol. 33(4), pages 544-560, December.
    10. Han, Guanghua & Dong, Ming & Liu, Shaoxuan, 2014. "Yield and allocation management in a continuous make-to-stock system with demand upgrade substitution," International Journal of Production Economics, Elsevier, vol. 156(C), pages 124-131.
    11. Sun, X.T. & Chung, S.H. & Chan, Felix T.S. & Wang, Zheng, 2018. "The impact of liner shipping unreliability on the production–distribution scheduling of a decentralized manufacturing system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 242-269.
    12. Ben Ali, M. & D’Amours, S. & Gaudreault, J. & Carle, M-A., 2018. "Configuration and evaluation of an integrated demand management process using a space-filling design and Kriging metamodeling," Operations Research Perspectives, Elsevier, vol. 5(C), pages 45-58.
    13. Dwi Iryaning Handayani & Ilyas Masudin & Ahmad Rusdiansyah & Judi Suharsono, 2021. "Production-Distribution Model Considering Traceability and Carbon Emission: A Case Study of the Indonesian Canned Fish Food Industry," Logistics, MDPI, vol. 5(3), pages 1-21, September.
    14. J. Behnamian & S. M. T. Fatemi Ghomi, 2016. "A survey of multi-factory scheduling," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 231-249, February.
    15. Cheng Guo & Merve Bodur & Dionne M. Aleman & David R. Urbach, 2021. "Logic-Based Benders Decomposition and Binary Decision Diagram Based Approaches for Stochastic Distributed Operating Room Scheduling," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1551-1569, October.
    16. Jans, R.F. & Degraeve, Z., 2005. "Modeling Industrial Lot Sizing Problems: A Review," ERIM Report Series Research in Management ERS-2005-049-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    17. Vahid Nooraie, S. & Parast, Mahour Mellat, 2016. "Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 8-21.
    18. Sun, X.T. & Chung, S.H. & Chan, Felix T.S., 2015. "Integrated scheduling of a multi-product multi-factory manufacturing system with maritime transport limits," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 110-127.
    19. Mula, Josefa & Peidro, David & Díaz-Madroñero, Manuel & Vicens, Eduardo, 2010. "Mathematical programming models for supply chain production and transport planning," European Journal of Operational Research, Elsevier, vol. 204(3), pages 377-390, August.
    20. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.

    More about this item

    Statistics

    Access and download statistics

    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:rnd:arjsds:v:4:y:2013:i:5:p:236-241. 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: Muhammad Tayyab (email available below). General contact details of provider: https://ojs.amhinternational.com/index.php/jsds .

    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.