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Buying Optimization

Fashion retail merchandisers face a tough problem before every season. They need to decide their buy portfolio across the new season’s SKUs that have been released by the brands. This is not an easy decision – one needs to estimate the eventual demand for the new SKUs and this decision has huge ramifications on company profitability. Underestimation of demand leads to potential lost sales and an overestimation of demand leads to eventual markdowns/clearance of stock which hurts the bottom line. In addition, buyers also face complications such as Minimum Order Quantity, Vendor buyback on certain products, the need for offering additional discounting above purchases of certain amounts and myriads of other such factors which complicate their decision-making abilities. In the age of social media, influencer marketing and the explosion of fast-fashion retail, it is important to get the assortment and the order quantity right.

Merchandisers need to answer many questions as a part of the buying process. Questions such as:

  • How much of each SKU should be bought?
  • In how many stores should these products be sold?
  • What shall we assume to be the average selling price?

This results in buyers of retail organizations having to run and maintain tedious calculations across multiple spreadsheets. Any small error in estimating “Sell-through Rate” or order quantity can snowball into costly clearance events.

Impact Analytics’ Buy$mart software empowers the “Buy Process” with a data-driven approach while considering several practical constraints faced by buyers. The software product provides an intuitive interface which enables buyers to easily build and execute their buy portfolios.

Some of the key features of the solution include:

  • Grading – Of previous seasons’ products considering the depth of the product and inventory availability at each of the stores
  • Price Elasticity – Enabling planners to tweak with the planned discount percentage constantly keeping management in the loop
  • SKU Mapping – Mapping previous season’s SKUs with upcoming seasons based on AI/ML.
  • Buying Optimization – Given the cost and other related information, Impact Analytics’ customized algorithms solve the problem of “How much to buy?”, “How many stores should the product be sold at?” The algorithms optimize the Revenue/Margin of the entire portfolio of products. Additional customized constraints can also be added to the optimizer.

‘Buy$mart’ eases the buying process and is equipped with approval mechanisms at appropriate stages. The product can be integrated into any retail organization’s merchandising work-flow with ease. In our work with big-box retailers, Buy$mart has on average, stopped losses of $7 Mn per year.