Stock-Out Problem Reduction of Shoe Retail Store: A Case Study of XYZ Company
Abstract
The objective of this research is to present a framework to reduce the shoes stock out problem stemmed from unavailable of appropriate shoes’ size that match customers’ requirement. Besides, the aforementioned problem has long been found significant and well recognized in retail store management. The research model is analytical research using sample sizes of 14,967 customers and data analyzing technique of that retrieving from the retail’s point of sales (POS) system. Then the data cleaning process is conducted separately for each interested factor. After cleaning data, the lower and upper bounds of appropriate stock quantities corresponding to different shoe sizes are provided. The obtained results for each objective are explained as follows: 1) Result according to the first objective: Using sales record during year 2561-2563, the relationship between sales and shoe sizes can be determined by using K-mean clustering technique. The cleaned data can be clustered into three clusters as (1) the most popular shoe sizes group (9 and 9.5), (2) the medium popular shoe sizes group (10-10.5 and 7.5-8.5), (3) the lowest popular shoe sizes group (4.5-7 and 11-15) and 2) Result according to the second objective: To define the appropriate stock quantity, the 95% confidence interval can be used by its lower limit represented min (reorder point) and the upper limit denoted max (target value) in min-max inventory policy.
References
ณัฏภัทรศญา เศรษฐโชติสมบัติ. (2560). การใช้เทคโนโลยีสารสนเทศและนวัตกรรมในโลจิสติกส์ โซ่อุปทาน และโซ่คุณค่า. วารสารวิทยาลัยโลจิสติกส์และซัพพลายเชน, 3(1),4-15.
อนุช นามภิญโญ. (2562). อิทธิพลของการจัดการลักษณะการกระจายสินค้าและประสิทธิภาพการบริการขนส่งสินค้าของธุรกิจค้าปลีกขนาดใหญ่ที่สร้างความพึงพอใจให้ลูกค้าในเขตปริมณฑล. วารสารวิทยาลัยโลจิสติกส์และซัพพลายเชน, 5(1), 82-98.
Bensoussan, A., Çakanyildirim, M., Li, M., & Sethi, S.P. (2016). Managing inventory with cash register information: Sales recorded but not demands. Production and Operations Management, 25(1), 9–21.
Bijvank, M., & Vis, I.F.A. (2011). Lost-sales inventory theory: A review. European Journal of Operational Research, 215(1), 1–13.
Calli, M.K., & Weverbergh, M. (2009). Forecasting newspaper demand with censored regression. Journal of the Operational Research Society, 60(7), 944–951.
Chen, L. (2010). Bounds and heuristics for optimal Bayesian inventory control with unobserved lost sales. Operations Research, 58(2), 396–413.
Chuang, H.H.-C., Oliva, R., & Perdikaki, O. (2016). Traffic-based labor planning in retail stores. Production and Operations Management, 25(1), 96–113.
DeHoratius, N., & Raman, A. (2008). Inventory record inaccuracy: An empirical analysis. Management Science, 54(4), 627–641.
Ehrenthal, J. C. F., Honhon, D., & van Woensel, T. (2014). Demand seasonality in retail inventory management. European Journal of Operational Research, 238(2), 527–539.
Gruen, T. W., Corsten, D. S., & Bharadwaj, S. (2002). Retail out-of-stocks: A worldwide examination of extent, causes and consumer responses. Washington, DC: Grocery Manufacturers of America.
Lee, S., & Allenby, G. M. (2014). Modeling indivisible demand. Marketing Science, 33(3),
–460.
Mersereau, A. J. (2015). Demand estimation from censored observations with inventory record inaccuracy. Manufacturing & Service Operations Management, 17(3), 335–349.
Nachtmann, H., Waller, M. A., & Rieske, D.W. (2010). The impact of point-of-sale data inaccuracy and inventory record data errors. Journal of Business Logistics, 31(1), 149–158.
Reiner, G., Teller, C., & Kotzab, H. (2013). Analyzing the efficient execution of in-store logistics processes in grocery retailing - the case of dairy products. Production and Operations Management, 22(4), 924–939 .
Rekik, Y., Sahin, E., & Dallery, Y. (2008). Analysis of the impact of the RFID technology on reducing product misplacement errors at retail stores. International Journal of Production Economics, 112(1), 264–278 .
Ramanathan, U., & Muyldermans, L. (2010). Identifying demand factors for promotional planning and forecasting: A case of a soft drink company in the UK. International Journal of Production Economics, 128(2), 538–545.
Sachs, A.-L., & Minner, S. (2014). The data-driven newsvendor with censored de- mand observations. International Journal of Production Economics, 149, 28–36 .
Shangdong Mou, David J. Robb, Nicole DeHoratius (2017). Retail store operations: Literature review and research directions. European Journal of Operational Research. 265(2), 1 March 2018, 399-422.
Wang, F., Fang, X., Chen, X., & Li, X. (2016). Impact of inventory inaccuracies on products with inventory-dependent demand. International Journal of Production Economics, 177, 118–130.
