IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v116y2008i1p1-11.html
   My bibliography  Save this article

Determining inventory service support levels in multi-national companies

Author

Listed:
  • Don Taylor, G.
  • Love, Doug M.
  • Weaver, Miles W.
  • Stone, James

Abstract

Multi-national manufacturing companies are often faced with very difficult decisions regarding where and how to cost effectively manufacture products in a global setting. Clearly, they must utilize efficient and responsive manufacturing strategies to reach low cost solutions, but they must also consider the impact of manufacturing and transportation solutions upon their ability to support sales. One important sales consideration is determining how much work in process, in-transit stock, and finished goods to have on hand to support sales at a desired service level. This paper addresses this important consideration through a comprehensive scenario-based simulation approach, including sensitivity analysis on key study parameters. Results indicate that the inventory needs vary considerably for different manufacturing and delivery methods in ways that may not be obvious when using common evaluative tools.

Suggested Citation

  • Don Taylor, G. & Love, Doug M. & Weaver, Miles W. & Stone, James, 2008. "Determining inventory service support levels in multi-national companies," International Journal of Production Economics, Elsevier, vol. 116(1), pages 1-11, November.
  • Handle: RePEc:eee:proeco:v:116:y:2008:i:1:p:1-11
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925-5273(08)00215-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Karl T. Ulrich & Scott Pearson, 1998. "Assessing the Importance of Design Through Product Archaeology," Management Science, INFORMS, vol. 44(3), pages 352-369, March.
    2. Persson, Fredrik & Olhager, Jan, 2002. "Performance simulation of supply chain designs," International Journal of Production Economics, Elsevier, vol. 77(3), pages 231-245, June.
    3. Goetschalckx, Marc & Vidal, Carlos J. & Dogan, Koray, 2002. "Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms," European Journal of Operational Research, Elsevier, vol. 143(1), pages 1-18, November.
    4. Meixell, Mary J. & Gargeya, Vidyaranya B., 2005. "Global supply chain design: A literature review and critique," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(6), pages 531-550, November.
    5. Lowson, Robert, 2001. "Analysing the Effectiveness of European Retail Sourcing Strategies," European Management Journal, Elsevier, vol. 19(5), pages 543-551, October.
    6. Jammernegg, Werner & Reiner, Gerald, 2007. "Performance improvement of supply chain processes by coordinated inventory and capacity management," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 183-190, July.
    7. Ben Naylor, J. & Naim, Mohamed M & Berry, Danny, 1999. "Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain," International Journal of Production Economics, Elsevier, vol. 62(1-2), pages 107-118, May.
    8. Persona, Alessandro & Battini, Daria & Manzini, Riccardo & Pareschi, Arrigo, 2007. "Optimal safety stock levels of subassemblies and manufacturing components," International Journal of Production Economics, Elsevier, vol. 110(1-2), pages 147-159, October.
    9. Herer, Yale T. & Tzur, Michal & Yucesan, Enver, 2002. "Transshipments: An emerging inventory recourse to achieve supply chain leagility," International Journal of Production Economics, Elsevier, vol. 80(3), pages 201-212, December.
    10. Ghodsypour, S. H. & O'Brien, C., 2001. "The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint," International Journal of Production Economics, Elsevier, vol. 73(1), pages 15-27, August.
    11. Olhager, Jan, 2003. "Strategic positioning of the order penetration point," International Journal of Production Economics, Elsevier, vol. 85(3), pages 319-329, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wu, Wei-Ming, 2009. "An approach for measuring the optimal fleet capacity: Evidence from the container shipping lines in Taiwan," International Journal of Production Economics, Elsevier, vol. 122(1), pages 118-126, November.
    2. Ivanov, Dmitry & Sokolov, Boris, 2013. "Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty," European Journal of Operational Research, Elsevier, vol. 224(2), pages 313-323.

    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. Hallgren, Mattias & Olhager, Jan, 2006. "Quantification in manufacturing strategy: A methodology and illustration," International Journal of Production Economics, Elsevier, vol. 104(1), pages 113-124, November.
    2. Jeong, In-Jae, 2011. "A dynamic model for the optimization of decoupling point and production planning in a supply chain," International Journal of Production Economics, Elsevier, vol. 131(2), pages 561-567, June.
    3. Hasani, Aliakbar & Khosrojerdi, Amirhossein, 2016. "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 87(C), pages 20-52.
    4. Hoogstra-Klein, Marjanke A. & Meijboom, Kars, 2021. "A qualitative exploration of the wood product supply chain – investigating the possibilities and desirability of an increased demand orientation," Forest Policy and Economics, Elsevier, vol. 133(C).
    5. Naim, Mohamed M. & Gosling, Jonathan, 2011. "On leanness, agility and leagile supply chains," International Journal of Production Economics, Elsevier, vol. 131(1), pages 342-354, May.
    6. Feng, Cheng-Min & Wu, Pei-Ju, 2009. "A tax savings model for the emerging global manufacturing network," International Journal of Production Economics, Elsevier, vol. 122(2), pages 534-546, December.
    7. Guertler, Benjamin & Spinler, Stefan, 2015. "When does operational risk cause supply chain enterprises to tip? A simulation of intra-organizational dynamics," Omega, Elsevier, vol. 57(PA), pages 54-69.
    8. Iman Ghalehkhondabi & Dusan Sormaz & Gary Weckman, 2016. "Multiple customer order decoupling points within a hybrid MTS/MTO manufacturing supply chain with uncertain demands in two consecutive echelons," OPSEARCH, Springer;Operational Research Society of India, vol. 53(4), pages 976-997, December.
    9. Romano, Pietro, 2009. "How can fluid dynamics help supply chain management?," International Journal of Production Economics, Elsevier, vol. 118(2), pages 463-472, April.
    10. Farahani, Reza Zanjirani & Rezapour, Shabnam & Drezner, Tammy & Fallah, Samira, 2014. "Competitive supply chain network design: An overview of classifications, models, solution techniques and applications," Omega, Elsevier, vol. 45(C), pages 92-118.
    11. M. Melo & S. Nickel & F. Saldanha-da-Gama, 2014. "An efficient heuristic approach for a multi-period logistics network redesign problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 80-108, April.
    12. Cannas, Violetta G. & Gosling, Jonathan & Pero, Margherita & Rossi, Tommaso, 2020. "Determinants for order-fulfilment strategies in engineer-to-order companies: Insights from the machinery industry," International Journal of Production Economics, Elsevier, vol. 228(C).
    13. Van Engeland, Jens & Beliën, Jeroen & De Boeck, Liesje & De Jaeger, Simon, 2020. "Literature review: Strategic network optimization models in waste reverse supply chains," Omega, Elsevier, vol. 91(C).
    14. Sheu, Jiuh Biing & Kundu, Tanmoy, 2018. "Forecasting time-varying logistics distribution flows in the One Belt-One Road strategic context," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 117(C), pages 5-22.
    15. Demeter, Krisztina & Golini, Ruggero, 2014. "Inventory configurations and drivers: An international study of assembling industries," International Journal of Production Economics, Elsevier, vol. 157(C), pages 62-73.
    16. Häntsch, Marius & Huchzermeier, Arnd, 2016. "Transparency of risk for global and complex network decisions in the automotive industry," International Journal of Production Economics, Elsevier, vol. 175(C), pages 81-95.
    17. Hoppenheit, Steffi & Günthner, Willibald A., 2015. "Identifying Main Drivers on Inventory using Regression Analysis," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Operational Excellence in Logistics and Supply Chains: Optimization Methods, Data-driven Approaches and Security Insights. Proceedings of the Hamburg , volume 22, pages 141-169, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    18. Brandenburg, Marcus, 2017. "A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects," Omega, Elsevier, vol. 70(C), pages 58-76.
    19. Vernon N. Hsu & Kaijie Zhu, 2011. "Tax-Effective Supply Chain Decisions Under China's Export-Oriented Tax Policies," Manufacturing & Service Operations Management, INFORMS, vol. 13(2), pages 163-179, November.
    20. Agarwal, Ashish & Shankar, Ravi & Tiwari, M.K., 2006. "Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach," European Journal of Operational Research, Elsevier, vol. 173(1), pages 211-225, August.

    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:eee:proeco:v:116:y:2008:i:1:p:1-11. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.