--- name: spend-analytics-engine-9322bd178f91 description: Procurement spend analysis skill with classification, consolidation, and savings identification allowed-tools: - Read - Write - Glob - Grep - Bash metadata: specialization: supply-chain domain: business category: analytics priority: high graph: domains: [domain:supply-chain] specializations: [specialization:supply-chain-optimization] skillAreas: [skill-area:procurement-management, skill-area:financial-planning, skill-area:data-analytics] workflows: [workflow:vendor-onboarding, workflow:vendor-evaluation] roles: [role:supply-chain-analyst, role:procurement-manager, role:financial-analyst] --- # Spend Analytics Engine ## Overview The Spend Analytics Engine provides comprehensive procurement spend analysis capabilities. It cleanses and classifies spend data, identifies consolidation opportunities, detects maverick spending, and quantifies savings opportunities to drive procurement value. ## Capabilities - **Spend Data Cleansing and Normalization**: Data quality improvement - **UNSPSC/Commodity Classification**: Standard category assignment - **Supplier Consolidation Analysis**: Fragmentation identification - **Price Variance Identification**: Unit price analysis across transactions - **Maverick Spend Detection**: Off-contract purchasing identification - **Contract Compliance Analysis**: Spend vs. contract terms - **Savings Opportunity Quantification**: Addressable spend and savings potential - **Spend Trend Visualization**: Historical pattern analysis ## Input Schema ```yaml spend_analysis_request: spend_data: transactions: array - supplier: string description: string amount: float quantity: float date: date business_unit: string cost_center: string period: start_date: date end_date: date reference_data: supplier_master: array category_taxonomy: object contracts: array analysis_scope: analysis_types: array # classification, consolidation, compliance focus_categories: array thresholds: object ``` ## Output Schema ```yaml spend_analysis_output: spend_summary: total_spend: float supplier_count: integer transaction_count: integer by_category: object by_supplier: object by_business_unit: object classification_results: classified_spend: float unclassified_spend: float category_distribution: object consolidation_opportunities: fragmented_categories: array supplier_rationalization: array estimated_savings: float price_variance_analysis: variance_by_item: array outliers: array benchmark_comparisons: object maverick_spend: off_contract_spend: float percentage: float top_violations: array contract_compliance: compliant_spend: float non_compliant_spend: float compliance_issues: array savings_opportunities: total_addressable_spend: float estimated_savings: float initiatives: array - initiative: string category: string addressable_spend: float savings_potential: float confidence: string visualizations: object ``` ## Usage ### Comprehensive Spend Analysis ``` Input: 12 months AP transaction data Process: Cleanse, classify, analyze patterns Output: Complete spend analysis with savings opportunities ``` ### Supplier Consolidation Analysis ``` Input: Classified spend by category Process: Identify fragmentation, model consolidation Output: Consolidation recommendations with savings ``` ### Contract Compliance Review ``` Input: Spend data, contract terms Process: Match spend to contracts, identify leakage Output: Compliance report with violation details ``` ## Integration Points - **Spend Analytics Platforms**: Coupa, SAP Ariba, Jaggaer - **ERP Systems**: AP data extraction - **Classification Services**: Automated categorization - **Tools/Libraries**: Spend analytics, classification algorithms ## Process Dependencies - Spend Analysis and Savings Identification - Category Management - Strategic Sourcing Initiative ## Best Practices 1. Establish regular data refresh cadence 2. Maintain category taxonomy consistency 3. Validate classification accuracy periodically 4. Focus on actionable savings opportunities 5. Track savings realization against projections 6. Communicate insights to stakeholders regularly --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Marketing, Operations, Sales Roles: Business Development, Marketing Analyst, Supply Chain Manager Specialties: API, Database Tools: Erp Systems, Spend Analytics Platforms Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 9c1a2010de93e73d35e50fe0eade438f2d270dad00770ca2970835c7d1131470