IDEAS home Printed from https://ideas.repec.org/a/apa/ijbaas/2018p37-44.html
   My bibliography  Save this article

Retailer’s Optimal Inventory Policies for Cross-Border E-Commerce

Author

Listed:
  • Ling Huang

    (Tamkang University, New Taipei, Taiwan)

  • Ya-LingWu

    (Tamkang University, New Taipei, Taiwan)

  • Chi-Bin Cheng

    (Tamkang University, New Taipei, Taiwan)

Abstract

This study considers a supply chain formed by multiple suppliers and a cross-border retailer facing a non- stationary demand process. We build a multi-period inventory model with (s, S)-type inventory policy and (s, Q)-type inventory policy. Using this model, demands can be forecasted on the basis of two demand processes, i.e., ARIMA and average demand process. Performances of the two inventory policies, (s, S)-type and (s, Q)-type, are assessed and compared in terms of average delivery time, stock-out frequency, and cost of selling. Through the analysis of 6489 purchase orders of an online shop in Taiwan, covering a period from January 2012 to July 2017, the results present a near-optimal (s, S)-type inventory policy for a cross-border distribution network with multiple suppliers. The model is a synthesis of two components: (i) the inventory policy analysis at a retailer, and (ii) order demand forecasting. We use action research to analyze the performances of inventory models in a cross-border retailer. The results indicate that the semiannual average method using (s, S)-type inventory policy best suits the case company for demand forecasting, as it can decrease the order delivery time from 7.08 days to 0.63 days, and decrease the stock-out frequency from 100.00% to 9.49%. The key contribution of the findings is the seamless integration of the two components to analyze order history data for cross-border supply chains between retailer and suppliers. We anticipate that the research findings may enhance our understanding of inventory control and provide insights into cross-border retailers’ future inventory policies decision.

Suggested Citation

  • Ling Huang & Ya-LingWu & Chi-Bin Cheng, 2018. "Retailer’s Optimal Inventory Policies for Cross-Border E-Commerce," International Journal of Business and Administrative Studies, Professor Dr. Bahaudin G. Mujtaba, vol. 4(1), pages 37-44.
  • Handle: RePEc:apa:ijbaas:2018:p:37-44
    DOI: 10.20469/ijbas.4.10005-1
    as

    Download full text from publisher

    File URL: https://kkgpublications.com/business-v4-i1-article-5/
    Download Restriction: no

    File URL: https://kkgpublications.com/wp-content/uploads/2018/12/ijbas.4.10005-1.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.20469/ijbas.4.10005-1?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. Kelle, Peter & Milne, Alistair, 1999. "The effect of (s, S) ordering policy on the supply chain," International Journal of Production Economics, Elsevier, vol. 59(1-3), pages 113-122, March.
    2. Theeraphat Polcharoensuk & Khanchitpol Yousapornpaiboon, 2017. "Factors affecting intention to repurchase for e-commerce in Thailand," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 3(4), pages 204-211.
    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. Yuan Liang, 2020. "The Impact of Trade Facilitation on Cross-Border E-Commerce Exports of China Based on the Gravity Model," International Journal of Business and Economic Affairs (IJBEA), Sana N. Maswadeh, vol. 5(4), pages 138-155.

    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. Meixell, Mary J., 2005. "The impact of setup costs, commonality, and capacity on schedule stability: An exploratory study," International Journal of Production Economics, Elsevier, vol. 95(1), pages 95-107, January.
    2. Chandra, Charu & Grabis, Janis, 2005. "Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand," European Journal of Operational Research, Elsevier, vol. 166(2), pages 337-350, October.
    3. Wang, Xun & Disney, Stephen M. & Ponte, Borja, 2023. "On the stationary stochastic response of an order-constrained inventory system," European Journal of Operational Research, Elsevier, vol. 304(2), pages 543-557.
    4. Miragliotta, Giovanni, 2006. "Layers and mechanisms: A new taxonomy for the Bullwhip Effect," International Journal of Production Economics, Elsevier, vol. 104(2), pages 365-381, December.
    5. Luong, Huynh Trung, 2007. "Measure of bullwhip effect in supply chains with autoregressive demand process," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1086-1097, August.
    6. Yan, Xi Steven & Robb, David J. & Silver, Edward A., 2009. "Inventory performance under pack size constraints and spatially-correlated demand," International Journal of Production Economics, Elsevier, vol. 117(2), pages 330-337, February.
    7. Kelle, Peter & Akbulut, Asli, 2005. "The role of ERP tools in supply chain information sharing, cooperation, and cost optimization," International Journal of Production Economics, Elsevier, vol. 93(1), pages 41-52, January.
    8. Giannoccaro, Ilaria & Pontrandolfo, Pierpaolo, 2002. "Inventory management in supply chains: a reinforcement learning approach," International Journal of Production Economics, Elsevier, vol. 78(2), pages 153-161, July.
    9. Wang, Shu-Jen & Liu, Shih-Fei & Wang, Wei-Ling, 2008. "The simulated impact of RFID-enabled supply chain on pull-based inventory replenishment in TFT-LCD industry," International Journal of Production Economics, Elsevier, vol. 112(2), pages 570-586, April.
    10. Nyoman Pujawan, I, 2004. "The effect of lot sizing rules on order variability," European Journal of Operational Research, Elsevier, vol. 159(3), pages 617-635, December.
    11. Wang, Xun & Disney, Stephen M., 2016. "The bullwhip effect: Progress, trends and directions," European Journal of Operational Research, Elsevier, vol. 250(3), pages 691-701.
    12. Jing Wu & Dan Zhang & Yang Yang & Gongshu Wang & Lijie Su, 2022. "Multi-Stage Multi-Product Production and Inventory Planning for Cold Rolling under Random Yield," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
    13. So, Kut C. & Zheng, Xiaona, 2003. "Impact of supplier's lead time and forecast demand updating on retailer's order quantity variability in a two-level supply chain," International Journal of Production Economics, Elsevier, vol. 86(2), pages 169-179, November.
    14. Kwak, Jin Kyung & Gavirneni, Srinagesh, 2011. "Retailer policy, uncertainty reduction, and supply chain performance," International Journal of Production Economics, Elsevier, vol. 132(2), pages 271-278, August.
    15. Li, Xiaoming & Sridharan, V., 2008. "Characterizing order processes of using (R,nQ) inventory policies in supply chains," Omega, Elsevier, vol. 36(6), pages 1096-1104, December.
    16. Noblesse, Ann M. & Boute, Robert N. & Lambrecht, Marc R. & Van Houdt, Benny, 2014. "Characterizing order processes of continuous review (s,S) and (r,nQ) policies," European Journal of Operational Research, Elsevier, vol. 236(2), pages 534-547.
    17. Ryu, Kwangyeol & Moon, Ilkyeong & Oh, Seungjin & Jung, Mooyoung, 2013. "A fractal echelon approach for inventory management in supply chain networks," International Journal of Production Economics, Elsevier, vol. 143(2), pages 316-326.
    18. Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
    19. Thomas J. Sargent & John Stachurski, 2024. "Dynamic Programming: Finite States," Papers 2401.10473, arXiv.org.
    20. Junhai Ma & Xiaogang Ma, 2017. "Measure of the bullwhip effect considering the market competition between two retailers," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 313-326, 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:apa:ijbaas:2018:p:37-44. 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: Professor Dr. Bahaudin G. Mujtaba (email available below). General contact details of provider: https://kkgpublications.com/business/ .

    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.