Problem

The retailer of crafts and DIY toys was carrying more than 150 product categories & over 7000 SKU’s in its store network. On account of most of the goods being imported in large quantities, the warehouse stock would have lasted a year.

The retailer wanted to freeze on the right product category mix for the store without spreading too thin. And, wanted to manage the product buying on a budget so that it respected working capital constraints.

Solution

The product categories were evaluated on multiple parameters

Sales: Sales analysis showed only 40 categories from 170 contributed to 80% of the business & 86% of RPM (rupee gross margin)

GMROI (Gross margin return on investment): The GMROI analysis showed the return each category fetched for the rupee invested in it over the period of a year. In other words, it showed the ROI performance of the category and not just sales contribution.

Sales v/s Margin Quadrant: Categories were plotted into Sales x Margin quadrant to see where they fell:

• High Sale High Margin
• High Sale Low Margin
• Low Sale High Margin
• Low Sale Low Margin

Impact of price points: Analysis was done with respect to price bands to see which price bands generated more sales than others.

Outcome

This analysis helped classify the product categories and recommend corresponding action plans that identified categories to be focused on, categories to be pruned if not seen being complementary to another category that mattered.

Another issue brought to the forefront was the very method of category grouping which needed to be customer centric, rather than product centric. Customers could have been grouped by gender, age groups, outdoors v/s indoors preference among other things.

Category sales and its ideal inventory holding were derived and mapped to (1) current stock on hand, its age (2) the minimum order quantity mandated by the supplier. A budgeted ‘buy plan’ was thus calculated for important categories for the next 6 months and as a method to be used on an ongoing basis.