AO_PAI_ML_PriceMarginIntelligence
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Build information
Analytical Object | AO_PAI_ML_PriceMarginIntelligence |
Query Object | QO_PAI_ML_PriceMarginIntelligence |
Data Source | PAI Machine Learning |
Cab File | PriceMarginIntelligence.cab |
Query Object Primary Table | PAI_PRC_MARGIN_INTEL_DETAIL PAI_PRC_MARGIN_INTEL PAI_MARGIN_FEATIMPORTANCE |
Query Object Tables | PAI_PRC_MARGIN_INTEL_DETAIL PAI_PRC_MARGIN_INTEL PAI_MARGIN_FEATIMPORTANCE IPCS_PART_MASTER IPCS_LOC_MASTER IPCS_PRICING_MARKET IPCS_PRC_STREAM_MONTHLY_FINANCE IPCS_CURRENCY IPCS_CURRNT_CURRNCY_EXCH_RATES IPCS_SKU IPCS_PRICE_STRATEGY_CODE IPCSCUST_SKU IPCSCUST_PART_MASTER IPCSCUST_LOC_MASTER IPCS_PART_TYPE IPCS_PART_FAMILY IPCS_PRICE_LINE IPCS_PRICE_CLASS IPCS_DEMAND_HISTORY IPCS_PRICING_STREAM |
Parameters | Name | Description | Default |
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PO_PAI_ML_PricingStreamId | Pricing Stream ID | 1 |
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Predecessor Jobs | These AutoPilot processes need to run before building the cube: • Price Margin Intelligence |
Build Type | The following build types are supported: • Full |
Build Frequency | Build the cube when new SKUs are fed into the system. The Price Margin Intelligence dashboard must be built before this dashboard can be run. • Weekly • Monthly |
| • Cluster • Location • Outlier • Part Family • Part Number • Part Type • Price Class • Price Line • Pricing Market |
| Measure | Mathematical Function |
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Cluster Count | Count of Clusters created across the dataset | SKU Count | Count of SKUs in selected clusters | Average Margin | Margin across the selected clusters | Margin Std Dev | Standard Deviation used to calculate Recommended Margin | Rec Margin | Recommended Margin across cluster without considering outliers across selected clusters. Calculated as [(rec price - current cost) / rec price] | Rec Margin Min | Minimum Recommended Margin across cluster considering standard deviation. Subtract Standard Deviation from Recommended Margin to get value. | Rec Margin Max | Maximum Recommended Margin across cluster considering standard deviation. Add Standard Deviation to Recommended Margin to get the value. | Current Margin | Current Margin across selected clusters Calculated as [(price - cost) / price] | Outlier Count | Outlier count across selected clusters | MSE | Mean Squared Error calculated by the ML Algorithm | Cost | Cost of SKU | Price | Price of SKU | Rec Price | Recommended Price of SKU considering cost and Recommended Margin | Sample Count | Total number of SKUs per cluster | Sales Volume | Sales volume for the sku aggregated across selected clusters | Profit Opportunity | Profit Opportunity across selected cluster | Rec Revenue | Recommended Revenue considering Recommended Price across selected clusters | Current Revenue | Current Revenue across selected clusters | Revenue Opportunity | Revenue Opportunity across selected clusters |
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