Imagine coming across an ad for some lucrative discounts of your favorite brand of jeans. You are excited about upgrading your wardrobe without burning a hole in your pocket, only to figure out that specific product is unavailable when you visit the store. The store manager tells you that it would be available in a couple of days, yet your second visit yields the same result. Not only do stock outs lead to disappointed customers, but they also result in lost sales from potential customers. The flip side of the story is companies incurring heavy losses on the stocks that don’t get sold. The stock outs and overstocking results from inefficient inventory management and the retailers across the board find it a challenging task to get it right.

The client is a billion-dollar fast fashion jewelry chain that operates approximately 2000 stores selling about 3000 SKUs each. The inventory management process from inventory forecasting, pricing, cost of storing inventory, demand forecasting to inventory status, etc. was becoming extraordinarily unwieldy and heuristic.

Some of the major pain points of the clients were:

  1. The mismatch between the sales and the inventory (stock) at hand was alarmingly high – which reflected a mismatch between the demand in the market and the forecast.
  2. There were large stocks of low selling items across the outlets – the sales velocity, that is the pace at which the ‘items sell’ was not taken into account while deciding how much of the stock is required.
  3. The labor cost to support inventory ordering and the cost incurred due to overstocking was affecting the bottom line.The client was looking for a smart, efficient yet simple solution for all the inventory management woes.

Solving inventory management comes down to getting the 5R’s right – the desired product should be available at the ‘right time’ in the ‘right place,’ and the customers should be able to get the product in the ‘right quantity’ at the ‘right price’ and the ‘right quality.’

An application that can read and analyze past data, understand the demand for various products and can forecast the right amount was something that would quickly solve the problem. Impact Analytics’s Automated Inventory Replenisher (AIr) was the perfect solution for the client. It enabled the decision makers to

Accurately forecast demand at a granular (SKU) level for each store/outlet using advanced statistical and AI-based models
Fine tune their decisions and forecast according to the business requirement and needs

AIr with its intuitive and easy interface helped the decision makers visualize, validate and adjust the demand forecasts. It also lets the user download customizable reports as well as provide multiple options to visualize the results as per the context and situation.

Impact Analytics’ AIr application empowered the client with the following business-critical and value-generating outcomes:

Accurate sales forecasting: Driven by advanced statistical models, accurate forecasts reduced weeks of inventory by 50%, from 30 weeks of revenue to 15. The stocking of fast selling items was also optimized.

Reduced stockouts: The granular level approach helped eliminate the menace of stock outs – stockouts were reduced by an impressive 40%.

A-B-C Classification of products: Classifying the products based on their sales velocity which becomes a critical business input while estimating the forecast.

The outcomes of AIr application deployment can be delineated as follows:

  • Reduction in weeks of inventory – $16 million savings
  • Elimination of stock outs across stores – $10 million in savings
  • Savings based on labor optimization – $2 million.
  • Net Savings – $28 million savings annually

Impact Analytics provided a holistic solution to the client through the deployment of AIr. It solved multiple issues plaguing the inventory management process and helped them save millions, thereby positively impacting their balance sheet. The client saved $28 million by implementing our Automated Inventory Replenisher solution in the last financial year (2017).