Inventory Allocation for Offline and Online Stocks for Fashion Industry

S. Sarifah Radiah Shariff, Shahrul Irda Said


Fashion Industry is a fast moving industry that is always related to the demands and trends. In this industry, the fashion retailer’s supply chain covers the whole process flow of a product from the supplier or the vendor to the retailer as well as sales to the consumers. Nowadays, most retailers in fashion industry have both online and offline selling platforms. Hence, the right stock allocation for both online and offline is very important. When the allocation is not properly managed, product might not be able to be shipped even though it is physically available in the warehouse. The same scenario might happen in offline store where there  are not enough stocks to cater to the walk in customer especially during promotion and special launches. The failure in managing the inventory properly will result in the loss of sales and lead to customers’ loss of interest. In this study, the right allocation for both online and offline stocks is proposed through simulation technique which is based on the average probability of the current stocks demand for both online and offline ratio. The simulation results are then used to calculate the total inventory cost. It is shown that the total costs are comparable for current and proposed approach but the stock out possibility is minimized in the proposed allocation.


Fashion Industry, online and offline business, inventory management, simulation for ratio

Full Text:



Borgonovo, E. & Peccati, L. (2007). Global sensitivity analysis in inventory management. Int. J. Production Economics, pp. 302–313

Caro, F. & Gallien, J. (2010). Inventory Management of a Fast-Fashion Retail Network. Journal of Operations Research, vol. 58, pp. 257-273.

Doherty & Chadwick (2010). Internet retailing: the past, the present & the future. International Journal of Retail & Distribution Management, Vol. 38 Issue: 11/12, pp.943-965.

Johnson, M. E., & Whang, S. (2002). Eâ€business and supply chain management: an overview and framework. Production and Operations management, 11(4), 413-423.

Jones, C., & Kim, S. (2010). Influences of retail brand trust, offâ€line patronage, clothing involvement and website quality on online apparel shopping intention. International Journal of Consumer Studies, 34(6), 627-637.

Liu, Q, Zhang, X , Liu, Y. & Lin, L. (2013). Spreadsheet Inventory Simulation & Optimization Models & Their Application in a National Pharmacy Chain. INFORMS Transactions on Education, vol. 14(1), pp.13-25.

Preuss, C. (2013). Retail Marketing & Sales Performance: A Definitive Guide to Optimizing. Springer Gabler.

Radhakrishnan P. & Jeyanthi, N. (2013). Application of Genetic Algorithm to Supply Chain Inventory Optimization. Journal of Contemporary Research in Management. pp. 418-422

Sezen, B., & Kitapci, H. (2007). Spreadsheet simulation for the supply chain inventory problem. Production Planning and Control, 18(1), 9-15.

Swani, K., Milne, G. R., Brown, B. P., Assaf, A. G., & Donthu, N. (2017). What messages to post? Evaluating the popularity of social media communications in business versus consumer markets. Industrial Marketing Management, 62, 77-87.

Yohanes K., Petri, H. & Takala, J. (2010). Strategic Inventory Allocation for Product Platform Strategy. Journal of Advances in Management Research. pp.233-249.



  • There are currently no refbacks.

Copyright (c) 2019 Advances in Transportation and Logistics Research

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Advances in Transportation and Logistics Research

ISSN: 2622-5778 (online)
Published by: Institut Transportasi dan Logistik Trisakti, Jakarta - Indonesia

ATLR by is licensed under a Creative Commons Attribution 4.0 International License.