Supplier Scorecard Engine
SkillDev toolsAutomated supplier performance scorecard generation with KPI tracking and trend analysis
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Then ask your AI: use the Supplier Scorecard Engine skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/business/supply-chain/skills/supplier-scorecard-engine/SKILL.md and read by ahel’s review.
Overview
The Supplier Scorecard Engine automates the generation and maintenance of supplier performance scorecards. It aggregates performance data across multiple KPI categories, calculates weighted scores, tracks trends, and generates actionable insights for supplier management.
Capabilities
- OTIF Calculation: On-Time In-Full delivery performance
- Quality Metrics Aggregation: PPM, defect rate, inspection results
- Cost Performance Tracking: Price variance, savings achievement
- Responsiveness Scoring: Issue resolution, communication metrics
- Sustainability/ESG Metrics: Environmental and social performance
- Weighted Composite Scoring: Configurable weighting by category
- Trend and Benchmark Analysis: Performance trending and peer comparison
- Action Plan Tracking: Improvement initiative monitoring
Input Schema
scorecard_request:
supplier_id: string
evaluation_period:
start_date: date
end_date: date
performance_data:
delivery:
orders_received: integer
on_time: integer
in_full: integer
quality:
units_received: integer
defects: integer
returns: integer
cost:
contracted_spend: float
actual_spend: float
savings_target: float
responsiveness:
issues_raised: integer
issues_resolved: integer
avg_resolution_time: float
sustainability:
certifications: array
esg_score: float
weighting_profile: object
benchmark_data: object
Output Schema
scorecard_output:
supplier_id: string
period: object
category_scores:
delivery:
otif_percent: float
score: float
trend: string
quality:
ppm: float
score: float
trend: string
cost:
variance_percent: float
score: float
trend: string
responsiveness:
resolution_rate: float
score: float
trend: string
sustainability:
score: float
trend: string
composite_score: float
rating: string # A, B, C, D, F
benchmark_comparison: object
action_items: array
trend_analysis: object
Usage
Monthly Scorecard Generation
Input: Previous month's delivery, quality, cost data
Process: Calculate KPIs, apply weights, generate score
Output: Comprehensive supplier scorecard with rating
Trend Analysis
Input: 12 months of scorecard history
Process: Analyze performance trajectory by category
Output: Trend report with improvement/decline identification
Benchmark Comparison
Input: Supplier scorecard, peer group data
Process: Compare against category averages and best-in-class
Output: Relative performance positioning
Integration Points
- ERP Systems: Purchase orders, receipts, quality data
- Quality Systems: Inspection results, NCRs
- BI Platforms: Scorecard visualization and distribution
- Tools/Libraries: Scorecard templates, analytics frameworks
Process Dependencies
- Supplier Performance Scorecard
- Quarterly Business Review (QBR) Facilitation
- Supplier Development Program
Best Practices
- Define clear KPI definitions and measurement methods
- Establish data collection automation where possible
- Calibrate weights based on category importance
- Share scorecards with suppliers transparently
- Link scorecard results to business allocation
- Review weighting profiles annually
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
supplier-scorecard-engine- Source
- github.com/a5c-ai/babysitter
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