Step 1. Forecast potential sales on a daily basis for each location
The forecast is based on a machine learning algorithm incorporating multiple factors, such as:
- Historical item-level sales by location
- Calendar characteristics, like the month, season, day of week, etc.
- Daily traffic drivers, like the weather
- Recent trends
Step 2. Determine the optimal stocking levels per item on a daily basis
The optimal amount is based on multiple factors, including:
- Sales forecast and sales uncertainty
- Cost to produce or purchase the item
- Revenue per item
- Potential substitutes
- Last minute discounts
Step 3. Make the answer easy to use
Measure runs behind the scenes to enable your staff, by:
- Connecting directly to existing point-of-sale systems to update data
- Refreshing the optimal levels daily using the latest data
- Bringing the answer to store staff through iOS app to drive orders or production
- Standardizing operations approaches across multiple locations
- Simplifying training of new staff
Step 4. Track the improvement in your fresh program
Key information to monitor impact should be transparent, such as:
- Extra contribution margin being generated each day
- KPIs like sales and out-of-stock estimates over time
Measure is designed and developed by Better Optima, a leader in operations and optimization AI solutions.