LAUSR

Abstract Fast fashion trends have led to an enormous local brand proliferation in India. Brand proliferation has further led to an overchoice effect among the Indian consumers due to which, they are now increasingly less satisfied with their apparel purchases. These factors have created immense stress on the small fashion retailers (SFR) which are currently responsible for about 80% of retailing in India. In the pre-COVID times, SFR's followed the practice of overstocking many brands for capturing the maximum market and then clearing the inventory at the end of the season through heavy discounting. This strategy became ineffective after the COVID-19 disruption. SFR's must now optimize their brand portfolio to minimize the overchoice effect and maximize the inventory turnover ratio. To this effect, we propose an efficient fuzzy probability-based brand portfolio optimization model, which relies on primary data analysis to classify brands in groups of substitutes. Brands with maximum market share from each group must be included in the portfolio. We demonstrate the efficacy of our model through a case study on SFR. Our results show that the inventory turnover ratio was increased from 2 to 4. We further show that our grouping strategy can be used to identify competitive brands for a local band.