Key Notes
- o9 AI Platform
- Integrated Business Planning, In memory codebase platform on graph model
- Supply Chain planning
- Enterprise Knowledge Graph
- Forecast demand, lead time, demand and supply uncertainty, right inventory levels
- Right size + right mix of inventory to be positioned across right locations
- Inventory Optimization terms
- Initial Forecast + Safety Stock - Weekly / Daily
- Recommended Safety Stock + Adjusted Levels
- Factors affecting Optimization
- Supply Chain Network
- Multiechelon optimization
- Recommendation with holistic view of network
- Inventory target across supply chain
- Dependent demand calculation
- Measure historical actuals and deviations
- Propagates forecast upstream / Forecast waterfall
- Lagged forecasts reconciled with actuals
- Lag correspond to lead times
- 3 week / 2 week lag
- ML to arrive at actual lead time
- Turn around time computation
- Lead time variability
Service Levels
- Classify portfolio
- Segmentation
- Service Levels per customer
- Margin Revenue / Volume
- Lead time variability
Product Features
CIO view
Planner view
Sensitivity Analysis
- 95% of baseline forecast
- 105% of the baseline forecast
- Scenario Analysis
Multi echelon optimization
- Cascade at the entire network level
- Check baseline / nostock scenarios
Feature Variables
- Understand the influencing factors
- Promotions
- Events
- Historical Data
- Weekend
- Temperature / Weather impact
- Consumer confidence
- Market Size / Market Share
Long Term / Short Term Forecasting
- 52-week horizon
- Short horizon (3 weeks)
- Holiday seasons
Forecasting 7 pointers
- Add a safety stock value. Total forecast = Forecast + Safestock value
- Lag time is identified, Forecast updated between 2-3 weeks, This is to get more accurate numbers as the lag time closes in. 3 months back value vs current forecast value
- Cluster into different products based on different lead time and then forecast
- Provide a dashboard for user to pick low range, medium, high range estimate as a graph
- Forecast should be from bottom up, DC to enterprise level. Each dc will forecast based on its location, weather, historical data
- The amount of money required to store items is also critical, Adjustments in stock need to be made to accomodate current safety stock quantity
- Feature variables for forecasting can be provided as configurable / Additional parameters Understand the influencing factors
- Promotions
- Events
- Historical Data
- Weekend
- Temperature / Weather impact
- Consumer confidence
- Market Size / Market Share
Keep Thinking!!!!
