A leading footwear company achieves a revenue uplift of $15M using AI based replenishment solution

About

The client, a leading footwear company in North America, owns and operates several footwear and accessory brands, and has retail outlets across US and Canada.

Challenges

  • With traditional demand planning software, the client was able to achieve a 3-month inventory accuracy of 45%, which increased obsolescence.
  • The obsolescence increased the other variable costs (OVC) of the client and revenue dropped by 4.6%.
  • The conventional approach used for replenishment planning hindered the client’s inventory control across product categories and 1000+ retail outlets.

Solution

Tenzai team conceptualized and developed an AI powered replenishment solution based on the principles of Purpose-driven AI Framework. The new solution predicts store level replenishments for each SKU across 1000+stores.

To create the solution, internal data like shipping, inventory, POS, product data, along with promotions, events and store information were considered.

A forecasting model, using an ensemble of multiple forecasting techniques was built to predict demand at store and SKU levels.

The forecasting model was then overlaid with constraints like store size, minimum order quantity, inventory thresholds, product lead time etc.

The replenishment solution was deployed across outlets through dashboards that were configured with email and mobile notifications.

Why not use data to gain insights, guide efficiency efforts, and make predictions? Contact us.

Business Impact

The AI powered replenishment solution provided a single and detailed view of inventory across all outlets.
Business users and store managers had access to real time insights in the form of dashboards and alerts to make timely decisions.
The solution helped the client to identify revenue loss due to stockout and lost opportunities.

  • Overall inventory costs were reduced by 12%.
  • It helped in reducing 65% of time spent by category managers and demand planners.
  • Store Inventory levels were reduced by up to 20%, and out-of-stocks were reduced by up to 26%.
  • The solution contributed to a revenue lift of $15 Mn.