Inventory Optimization

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!!!!