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Supplier Performance Scorecard

Scored on delivery. Scored on accuracy. Scored on everything that matters.

A weighted composite performance rating for every supplier - computed from your live delivery, invoice, credit and recovery data. No surveys. No manual spreadsheets. Just the numbers, scored automatically.

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4
Scoring dimensions
0–100
Composite rating
Nightly
Auto-refresh
Performance Scorecard
Live
FS Foodservice Ltd
284 orders · £142,847 spend · all-time
Good
Composite score
84.2 / 100
Delivery Reliability
30%
80
7.6% of deliveries had variances
Credit Quality
25%
80
3.1% credit ratio (acceptable)
Invoice Accuracy
25%
94.7
match PO amounts exactly
Credit Recovery
20%
68
of expected credits received
Total spend
£142,847
Order count
284
Avg credit days
14.3d
The data-driven supplier review

Performance, quantified.

Four dimensions that capture how a supplier actually behaves - not how they pitched in the meeting. Computed from goods receipts, invoices, credit notes and recovery data already flowing through the platform.

4
Scoring dimensions
Delivery reliability, credit quality, invoice accuracy, credit recovery
0–100
Composite score
Weighted aggregate with 5 rating bands · capped at 100
04:00
Nightly refresh
Recomputed daily across all orgs · also on every detail view
Side-by-side
Comparison
Compare multiple suppliers with org-wide average benchmark
The four dimensions

Four numbers. One supplier truth.

Each dimension scored independently with five distinct bands - Excellent, Good, Acceptable, Poor, Critical. The composite is a weighted sum of all four. Hardcoded weights of 30/25/25/20 keep the scoring consistent across orgs and over time.

Delivery Reliability
Dimension 01
30%
Weight

What percentage of deliveries had variances against the purchase order. Source: completed GRNs with variances ÷ total completed GRNs × 100. Detail surfaced as "7.6% of deliveries had variances".

5 scoring bands · variance rate
<5%Excellent 5–15%Good 15–30%Acceptable 30–50%Poor >50%Critical
A supplier who consistently shorts deliveries or sends wrong items costs you time, money and operational disruption. This dimension catches it from the first GRN.
Credit Quality
Dimension 02
25%
Weight

What percentage of total spend comes back as credit notes. Source: credit note value ÷ invoice value × 100 per supplier account. Detail surfaced as "3.1% credit ratio (acceptable)".

5 scoring bands · credit ratio
<2%Excellent 2–5%Good 5–8%Acceptable 8–12%Poor >12%Critical
High credit ratios signal systematic issues - wrong items, damaged goods, pricing errors. A supplier at 12%+ is costing you significant margin.
Invoice Accuracy
Dimension 03
25%
Weight

What percentage of invoices match the original PO amount exactly. Source: invoices where ordered == invoiced ÷ total matchable invoices. Direct percentage scoring - 94.7% match rate becomes a 94.7 score.

Direct percentage · match rate
≥90%Excellent ≥75%Good ≥55%Acceptable ≥35%Poor <35%Critical
Invoice mismatches create reconciliation work, delay payments and erode trust. A supplier who consistently invoices different amounts from the PO is costing finance team hours.
Credit Recovery
Dimension 04
20%
Weight

What percentage of expected credits have actually been received. Source: received credit value ÷ expected credit value × 100, capped at 100. If no credits expected, defaults to 100 (no penalty).

Direct percentage · recovery rate
≥90%Excellent ≥75%Good ≥55%Acceptable ≥35%Poor <35%Critical
When a delivery comes short and you flag a credit, does the supplier actually send it? Low recovery means money left on the table - this catches suppliers who agree to credits but never follow through.

Plus an additional informational metric - avg days credit outstanding - surfaced alongside the score but not included in the composite weighting. Useful for catching suppliers who honour credits but take 60+ days to do it.

The composite

Four dimensions. One number.

Weighted sum of all four dimension scores. Capped at 100, rounded to one decimal place. The same hardcoded weights apply to every supplier in every org - consistent, deterministic, comparable.

Composite formula
Delivery×30%
+
Credit×25%
+
Invoice×25%
+
Recovery×20%
=
Score 0–100
90–100
Excellent
Top-tier supplier - reward with volume
75–89
Good
Reliable - minor improvement areas
55–74
Acceptable
Adequate - monitor and address weak dimensions
35–54
Poor
Underperforming - review the relationship
0–34
Critical
Serious issues - consider alternatives
Side-by-side comparison

Three suppliers. One spreadsheet of truth.

Pass multiple supplier IDs to the comparison endpoint and get every dimension lined up next to every other dimension - plus the org average across the queried suppliers as a benchmark line. The "is this supplier better than my alternatives" question, answered in one call.

  • Multi-supplier in one call · supplier_account_ids array · returns one response per supplier plus the org average
  • Org average benchmark · computed across the queried suppliers · the only benchmark we publish (everything is org-internal)
  • Date range filtering · same date_from / date_to params apply · "did this supplier improve since we raised it with them?"
  • Weakest dimension surfaced · "Brakes weakest on credit recovery (42%)" - the line that drives action
Comparison · 3 suppliers · this quarter
Org avg 72.4
Supplier
Score
Dimension breakdown
FS Foodservice
284 orders · £142k
84Good
Deliv
80
Credit
80
Invoice
95
Recover
68
Brakes Foodservice
198 orders · £89k
71Acceptable
Deliv
80
Credit
60
Invoice
88
Recover
42
Fresh Direct Ltd
87 orders · £33k
62Acceptable
Deliv
60
Credit
60
Invoice
75
Recover
55
Insight
FS Foodservice leads on invoice accuracy (94.7%) and credit quality. Brakes is weakest on credit recovery (42%) - agreed credits, never honoured. All three sit above org average on delivery.
Cached scores · supplier list

Every supplier. Scored on the list page.

Scores are cached on the SupplierAccount record so the supplier directory shows them on every row - no per-row API call, no N+1 query, sortable by score column. Refreshed on every detail-view request and again nightly across all orgs at 04:00 UTC.

  • performance_score · 0–100 composite, persisted on the supplier row
  • performance_rating · excellent / good / acceptable / poor / critical
  • performance_scored_at · timestamp of last computation, surfaced as "2h ago"
  • Two refresh paths · on-demand (any /performance call) and nightly Modal cron
  • Single MAX() group-by populates score across the whole list - no N+1 problem
Suppliers · sorted by score ↓
Score
Supplier
Score
Rating
Last scored
FS Foodservice
284 orders · £142k
84
Good
2h ago
Premier Foods Ltd
156 orders · £67k
79
Good
2h ago
Brakes Foodservice
198 orders · £89k
71
Acceptable
2h ago
Fresh Direct Ltd
87 orders · £33k
62
Acceptable
3h ago
Linen & Co
42 orders · £18k
58
Acceptable
5h ago
ABC Catering Supplies
19 orders · £4k
31
Critical
5h ago
Before vs after

From "how do you feel about FS Foodservice?" to "FS Foodservice scored 84.2 this quarter".

The difference between supplier reviews built on relationship and supplier reviews built on data. The data was always there - in your invoices, your goods receipts, your credit notes. It just wasn't aggregated into anything you could act on.

×
Without OmniPATH Scorecard
The relationship review
  • Supplier performance assessed by gut feel and anecdote
  • No delivery accuracy data - issues reported ad hoc, never aggregated
  • Credit notes tracked in spreadsheets - if at all
  • Invoice match rate unknown - reconciliation pain felt but not measured
  • Credit recovery not tracked - money left on the table invisibly
  • Supplier reviews based on relationship, not data
  • No benchmark - "is this supplier better or worse than average?"
✓
With OmniPATH Scorecard
The data-driven review
  • 4-dimension weighted score from live GRN, invoice and credit data
  • Delivery reliability quantified - every GRN variance counted
  • Credit ratio and frequency measured per supplier with assessment bands
  • Invoice accuracy tracked - PO vs invoice match rate surfaced instantly
  • Credit recovery rate shows who honours credits and who doesn't
  • Score visible on the supplier list - sort, filter, compare at a glance
  • Org average benchmark - "are they above or below the fleet?"
  • Nightly auto-refresh - always current, never stale
LEDGE × Scorecard

Ask LEDGE about your worst suppliers.

Every scoring dimension is a LEDGE-accessible endpoint. Ask in plain English, get the answer in seconds. Sort, compare, drill into the why - without writing a single SQL query.

"What's FS Foodservice's performance score?"
"Compare delivery reliability between our top 3 suppliers."
"Which suppliers have a credit recovery rate below 50%?"
"Show me suppliers rated 'poor' or 'critical'."
"Has Fresh Direct's invoice accuracy improved this quarter vs last?"
"What's the org average score across all active suppliers?"
"Which supplier has the highest delivery variance rate?"
Frequently asked

The practical details.

Is the scoring AI-powered?
Honest answer - no, it's a deterministic weighted formula, not machine learning. Each dimension is computed from a clear source (GRN variances, credit ratio, invoice match rate, credit recovery rate), scored against a fixed band table, then weighted and summed. The advantage of this approach is consistency and explainability - you can always trace why a supplier scored what they scored. The disadvantage is no predictive element. We've intentionally chosen explainability over a black-box model for something this consequential.
Can I see how a supplier's score has changed over time?
Only the current score is stored - there's no historical snapshot table that lets you plot the score over time today. You can approximate trend by calling the endpoint with different date_from / date_to ranges (e.g. last quarter vs this quarter) to see how the score has moved between two periods. Persistent score history is a feature we'd build if there's demand - it's not in today.
Can I configure the dimension weights?
Not currently - the weights are hardcoded at 30/25/25/20 and apply across every org. We made that choice deliberately so scores are comparable: a supplier scoring 84 in your org means the same thing as a supplier scoring 84 in another org running the same platform. Per-org configurable weights would break that comparability and create an internal politics problem ("why is delivery weighted higher in Ops's view than in Finance's view?"). If you have a strong case for a different weighting we'd love to hear it.
Can I add more dimensions - payment speed, communication, compliance?
Four dimensions today, all derived from data the platform already captures (deliveries, invoices, credits). Adding payment speed, communication responsiveness, or compliance metrics would mean either capturing new data the platform doesn't see (response times, paperwork completion) or adding judgement calls that make the score less objective. We've taken the position that the score should only reflect what's empirically measurable from the procurement data flow - and let other dimensions live in your supplier review process around the score.
Will the platform automatically suspend a supplier whose score drops below a threshold?
No - the score is informational only. It surfaces in the supplier list, in comparisons and via LEDGE - but it doesn't trigger automated actions like suspending the supplier, blocking new POs, or changing payment terms. Suspending a supplier is the kind of decision that needs a human in the loop with the wider context (existing contracts, alternative suppliers, operational dependencies). The score helps you make that decision faster - it doesn't make it for you.
Will I get a notification when a supplier's score drops?
No score-change notifications today. The Insights Agent does generate alerts for individual triggers (duplicate invoices, after-hours POs, VAT variance, early-payment opportunities) and can flag specific incidents that would also affect a supplier score - but there's no "FS Foodservice dropped below 75" notification. Score-drop alerts would be a reasonable addition; flag if it'd matter to you.
Are there industry benchmarks - how does my supplier compare to other organisations'?
No external benchmark data. Every comparison the platform makes is internal to your organisation - the org average that appears in comparison views is computed across the suppliers you queried, not against a wider dataset of other companies' suppliers. Cross-tenant benchmarking would raise meaningful privacy concerns and we've taken the position that scores should compare your suppliers to your other suppliers, not to abstract industry averages.
Can I export a scorecard as a PDF report?
JSON via the API today - no formatted PDF report card. The data is fully accessible (single supplier, comparison, with date filtering) but rendering it as a printable document isn't built. CSV / XLSX export is supported on the wider supplier analytics endpoints if you want to bring the data into another tool to format. PDF report-card generation is a reasonable addition if there's demand.
What happens when a new supplier has no GRN or credit history yet?
Dimensions with no underlying data default to a score of 100 - the supplier isn't penalised for the absence of evidence. So a brand-new supplier with one delivery and no credits will show with a high or maximum score initially; the score reflects reality as more activity accumulates. The detail strings make this visible - for example "No deliveries recorded" instead of "0% variance" - so you can tell when a high score is real performance versus an empty data set.
How does multi-currency work?
Spend, credit and invoice amounts are converted using the currency_conversion_rate stored on each purchase at the time it was posted. The score doesn't move when FX rates move - it reflects what the invoice was worth on the day it was raised, which matches standard accounting practice and prevents historical scores from drifting as rates change.
Ready when you are

See your suppliers scored.

Bring three of your real suppliers - your favourite, your problem child, the one you're not sure about - and we'll run the scorecard live on the demo. Four dimensions, one number, the answer your supplier review meeting needs.

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30-min demo · Bring three suppliers