CoV and ADI will help the system apply the proper forecasting algorithm to the level and nodes where the forecasts are generated.
The first step is to establish the different types of demand pattern categories to forecast.
To determine the characteristics of a forecast entities demand history, two coefficients are used:
- The first coefficient is the Average Demand Interval (ADI), it measures the regularity of a demand in time by computing the average interval between two demands. This is new.
- The second coefficient is the square of the Coefficient of Variation (CV²), it measures the variation in the demand quantities. In our case we will just use CV not CV²
To calculate ADI, we measure the average interval between two demands over the entire history. Per the following example:
The calculation for ADI is calculated as:
Per above, demand occurs on average every 1.83 periods.
Then we measure the coefficient of variation which is calculated via the following formula:
To compute the coefficient of variation, we will only consider the non-zero values of the demand history. In the example above, the average quantity equals to 10.57 while the standard deviation equals to 6.43.
Based on these 2 dimensions and the thresholds for these values set within system settings, we classify the demand profiles into 4 different categories. This will run with each new planning period as a prior bucket period passes. This analysis will be calculated in the forecast analytic page.
- ADI Threshold: 1.32 (this will be the default setting that will be overwritten)
- CoV Threshold: 0.70 (this will be the default setting that will be overwritten)
The above will fill in the thresholds to categorize the history that will be the basis for the forecast algorithm family selected:
- Smooth demand (ADI <= (ADI Threshold Value) and CoV <= (CoV Threshold Value)): The demand is very regular in time and in quantity. Apply exponential smoothing models - single, double, triple
- Intermittent demand (ADI > (ADI Threshold Value) and CoV <= (CoV Threshold Value)): The demand history shows very little variation in demand quantity but a high variation in the interval between two demands. Use Croston's (chosen best from Croston/Croston Standard/ Croston Syntetos Boylan) if demand does not exceed 25 units per period or Croston Syntetos Boylan model if greater than 25.
- Erratic demand (ADI <= (ADI Threshold Value) and CoV > (CoV Threshold Value)): The demand has regular occurrences in time with high quantity variations. Use Syntetos Boylan model only.
- Lumpy demand (ADI > (ADI Threshold Value) and CoV > (CoV Threshold Value)): The demand is characterized by a large variation in the quantity of demand and in the interval between two demands. This is quite impossible demand to reliable forecast, no matter what forecasting tools and methods are being used. Use bootstrapping.
In addition, if the forecasting history contains <= 2 buckets with values > 0, Simple Moving Average will be applied.