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.

1. Accenture. (2017). Cross-border ecommerce. Accenture. Retrieved from https://goo.gl/vNX9dR
2. Ali Cross Border E-Commerce Research Center. (2016). Future of global trade: Connecting the world through e-commerce. Ali Cross Border E-Commerce Research Center. Retrieved from https://goo.gl/44sSQK
3. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (4th ed.). Hoboken, NJ: John Wiley & Sons.
4. Chopra, S., & Meindl, P. (2007). Supply chain management: Strategy, planning & operation. In C. Boersch & R. Elschen (Eds.), Das summa summarum des management (pp. 265–275). Boston, MA: Springer Gabler.
5. Chung, H.-K. (2004). Simulation models for inventory policies in retails stores – a case of Taiwans general welfare service (Unpublished document). Department of Information Management, National Defense Management College, Taoyuan County, Taiwan.
6. Heel, B. V., Lukic, V., & Leeuwis, E. (2014). Cross-border e-commerce makes the world flatter. BCG Perspectives. Retrieved from https://goo.gl/2LqhCy
7. Levi, D. S., Kaminsky, P., & Levi, E. S. (2003). Designing and managing the supply chain: Concepts, strategies, and case studies. Boston, MA: McGraw-Hill.
8. Lin, Y.-C. (2015). The optimal order fulfillment model for cross border e-commerce firms under different service level (Unpublished master thesis). Department of Business Administration, National Taiwan University, Taipei, Taiwan.
9. Polcharoensuk, T., & Yousapornpaiboon, K. (2017). Factors affecting intention to repurchase for e-commerce in Thailand. Journal of Administrative and Business Studies, 3(4), 204-211. doi:https://doi.org/10.20474/jabs-3.4.5
10. Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory management and production planning and scheduling (Vol. 3). New York, NY: Wiley.
11. Stevenson, W. J. (2014). Operations management. New York, NY: McGraw-Hill/Irwin.
12. Wu, S.-C. (2006). Searching the optimal (s, s) inventory policy of a cross-docking system (Unpublished thesis). Department of Business Administration, National Dong Hwa University, Hualien, Taiwan.
13. Yang, H.-C. (2011). The optimal order fulfillment model for global electronic commerce firms (Unpublished master thesis). Department of Business Administration, National Taiwan University, Taipei, Taiwan.

Huang, L. Wu, Y., & Cheng, C. (2018). retailer’s optimal inventory policies for cross-border e- commerce. International Journal of Business and Administrative Studies, 4(1), 37-44. doi: https://doi.org/10.20469/ijbas.4.10005-1