π΄ FLAGS HIGH-RISK SUPPLIERS EARLY
β οΈ Supplier
Risk Scorecard
Power BI scorecard rating 30 suppliers across 5 weighted dimensions β on-time delivery, lead time variance, quality defect rate, financial stability, and geographic risk. Composite score triggers automatic alerts when any supplier falls below the 60-point safety threshold.
30
Suppliers Scored
7
High-Risk (Score <50)
11
Medium Risk (50β70)
12
Low Risk (Score >70)
// scoring model
Risk Dimension Weights
On-Time Delivery
25%
Lead Time Variance
20%
Quality Defect Rate
25%
Financial Stability
20%
Geographic Risk
10%
// live scorecard
Supplier Risk Scorecard β 30 Suppliers
Filter:
Click a row to see radar chart
| Supplier | Country | Category | OTD % | LT Variance | Defect % | Fin. Stability | Geo Risk | Composite Score | Tier |
|---|
π Composite Risk Score β All 30 Suppliers (lower = higher risk)
π Risk Distribution by Dimension β avg score per dimension
π Risk Tier by Supplier Country
π Risk Score Trend β Top 5 High-Risk Suppliers (6 months)
βΆ Problem
- Supplier reliability issues were discovered only after production halts and quality failures β reactive firefighting rather than proactive risk management was the norm.
- No standardised supplier scoring framework meant that buyer assessments were subjective, inconsistent, and impossible to compare across categories or regions.
- Geographic concentration risk was invisible: 40% of spend was sourced from suppliers in two countries with elevated political or logistics instability, with no mitigation strategy in place.
- Financial instability in the supply base went unmonitored β two suppliers entered administration in 18 months, each causing 6β8 weeks of supply disruption and emergency sourcing costs.
βΆ Solution
- Five-dimension composite scoring model: OTD rate (25%), lead time variance (20%), quality defect rate (25%), financial stability indicator (20%), and geographic risk score (10%).
- Data sources: ERP PO/GR records for OTD and lead time, quality system for defect rates, credit agency scores for financial stability, and country risk indices (Euler Hermes) for geo risk.
- Power BI dashboard auto-refreshes monthly. Alert fires when composite score drops below 60, or when any single dimension score drops below 40 (red flag threshold).
- Scores are trended over 6 months β a declining trajectory triggers a "Watch" flag even if the current score is still in the amber zone, giving buyers early warning before a breach.
βΆ Key Findings
- 7 of 30 suppliers scored below 50 (high risk) β 3 of these are sole-source suppliers for critical components, representing an unacceptable concentration of risk with no backup identified.
- Quality defect rate is the highest-variance dimension: scores range from 92 (excellent) to 18 (critical), indicating that quality management maturity across the supply base is highly inconsistent.
- Financial stability scores have deteriorated in 4 suppliers over the past 6 months β all 4 are in the manufacturing sector in Eastern Europe. Proactive dual-sourcing has been initiated for two of them.
- Geographic risk accounts for only 10% of the composite score β but 3 of the top 7 high-risk suppliers would drop from medium to high risk if geo-risk weighting were increased to 20%, suggesting this dimension is currently underweighted.
- On-time delivery and quality defect rate together explain 73% of the variance in composite score β validating the weighting model and confirming these are the right leading indicators.
// projections
π 6-Month Disruption Probability Forecast
π Projected Supply Disruption Probability β High-Risk Supplier Average (%)
Base scenario: Average disruption probability for the 7 high-risk suppliers is projected to remain at 34β38% over the next 6 months without active intervention β meaning a statistically likely disruption event in at least 2β3 of those suppliers within the year. With mitigation actions: Implementing dual-sourcing for the 3 sole-source high-risk suppliers and initiating quarterly review calls reduces projected disruption probability to 18β22% β below the 25% internal threshold. Geo risk escalation: If political or logistics instability increases in Eastern Europe, projected disruption probability for 4 affected suppliers climbs to 52β58%, requiring emergency buffer stock of 6β8 weeks for critical components.
// dataset
π Raw Dataset
Real data used in this analysis β all 30 suppliers shown. Download full dataset below.
| Supplier ID | Supplier Name | Country | Category | OTD % | LT Avg (d) | LT Variance | Defect % | Fin. Risk Score | Geo Risk Score | Composite Score | Risk Tier |
|---|
// strategic guidance
π‘ Advice for Companies with Similar Challenges
π‘ Practical Recommendations
- Build a supplier risk scorecard before your next supplier review cycle. Weighting on-time delivery and lead time variance most heavily (40% combined) gives you the fastest signal of deteriorating supplier performance β these two metrics are available from ERP data you already have.
- Never rely on a single supplier for a critical component without a qualified backup. Sole-source relationships are a business continuity liability. The cost of qualifying a second supplier (typically β¬5,000β15,000 in audit and testing) is always less than the cost of one supply disruption event.
- Score trend matters as much as score level. A supplier at 62 (amber) with 3 consecutive months of decline is more dangerous than one at 58 (amber) with 3 months of improvement. Build trending into your scorecard β a declining trajectory should trigger review even before the threshold is breached.
- Share the scorecard with your suppliers annually. Transparency drives improvement β suppliers who see their scores relative to peers consistently improve OTD and defect rates within two review cycles. It also signals that you are a sophisticated, data-driven buyer worth performing well for.
β‘ Key Takeaway
The most expensive supply chain event is always the one you didn't see coming. A supplier risk scorecard built from data you already have β ERP delivery records, quality system defect rates, and a credit score feed β costs less than a week of analyst time to build and can prevent supply disruptions worth 10β100Γ that investment. Start with your top 20 suppliers by spend and build from there.