1. SPM 11.0.1 / 11.1 : Key Enhancements Overview

2. SPM 11.0.1 Key Forecasting Enhancements
Variable number of history slices by method
Number of history slices can be set by forecast method for all statistical methods (except Crostons and Same as Last Year) to improve forecast accuracy. For example, Winters (seasonal) can use three years while exponential smoothing can use one year.
Bestfit recommended forecast method uses variable # of history slices. Forecast parameter ‘Number of history slices’ supercedes stream config history slices.
Causal Forecast for multiple streams
Multiple streams can be set with forecast method = Causal in stream config detail
Example: Separate causal forecast for maintenance and unplanned removals with different failure rates and BOMs
Ability to filter by forecast stream on causal forecast detail, prod rollout and causal value pages
Product Rollout /Causal Values views (with OTF for a part)
Ability to add/modify/delete causal values and product rollout amounts
Easy access to causal values and product rollout pages from causal forecast detail page.
To and from links between causal forecast detail and stocking policy page
Multi-threading for Causal Forecasting
Improves processing time for causal forecasting (also available in )

3. 11.0.1 Next Gen Causal Forecasting
Causal forecast for multiple streams
Forecast stream added to Prod_rollout, Causal_value, Failure_rate, Part_causal_type , Failure_rate_global and cf_forecast_detail tables
Ability to generate/view multiple causal forecasts for the same part/location

4. 11.0.1 Next Gen Causal Forecasting
Visibility to Product Rollout and Causal Value pages
Ability to add/modify/delete causal values and product rollout amounts

5. Links from Causal Forecast Detail
Easy to review the Product Roll out and Causal Values for a pair

6. SPM 11.1 Key Forecasting Enhancements
Variable number of history slices by method
Number of history slices can be set by forecast method for all statistical methods (except Crostons and Same as Last Year) to improve forecast accuracy. For example, Winters (seasonal) can use three years while exponential smoothing can use one year.
Bestfit recommended forecast method uses variable # of history slices. Forecast parameter ‘Number of history slices’ supercedes stream config history slices.
Causal Forecast for multiple streams
Multiple streams can be set with forecast method = Causal in stream config detail
Example: Separate causal forecast for maintenance and unplanned removals with different failure rates and BOMs
Ability to filter by forecast stream on causal forecast detail, prod rollout and causal value pages
Product Rollout /Causal Values views (with OTF for a part)
Ability to add/modify/delete causal values and product rollout amounts
Easy access to causal values and product rollout pages from causal forecast detail page.
To and from links between causal forecast detail and stocking policy page
Multi-threading for Causal Forecasting
Improves processing time for causal forecasting (also available in )