A signed contract isn't cash until the invoice is paid. This dashboard tracks the billing-to-collections pipeline for a rental yard — total billed, what's actually collected, and how much is aging in receivables — so slow-paying accounts surface before they age into write-offs.
What this report answers
The collections page answers the owner's and office manager's cash question: of everything we billed, how much have we collected, and what's stuck? Collection Rate is amount collected over invoice amount — a SUM-over-SUM ratio, the truest read on cash conversion. Open AR is the total outstanding receivable. AR Over 60 Days isolates the 61–90 and 90+ buckets — the dollars most likely never to be collected. The aging bar (Current / 1–30 / 31–60 / 61–90 / 90+) shows the shape of the receivable, and the billed-vs-collected bar by branch shows who's converting.
The metrics that matter
Collection Rate is the headline — a yard collecting the large majority of what it bills is healthy; a rate dragging below that signals disputes, slow payers, or write-offs worth chasing. AR Over 60 Days is the early-warning line, because the longer an invoice ages the less likely it pays. Open AR by customer type is the diagnostic: Municipal and Government accounts notoriously pay slowly, and seeing slow-paying segments by name tells the office which accounts to call before month-end.
Why the data is trapped
Invoices and aging buckets originate in the rental ERP — Point of Rental, InTempo, Alert EasyPro — but cash application and the true AR ledger live in QuickBooks. Collection rate and aging by branch and customer type require reconciling billed dollars from the ERP with collected dollars from accounting, which is exactly the join that doesn't happen automatically. So most yards watch a single AR total instead of seeing which branches and segments are actually slow.
How to read it
Start with Collection Rate and Open AR for the company-level read, then go to the aging bar — anything in 61–90 and 90+ is the work-now list. The billed-vs-collected bar by branch flags which locations have a collections problem versus a billing problem. Drop to the customer-type breakdown to find the slow segments; if Municipal/Gov dominates the over-60 bucket, that's a known pattern and a candidate for tighter terms or deposits.
The sample uses fully synthetic, anonymized data — no real invoices, customers, or balances.
Metrics it tracks
| Metric | What it means |
|---|---|
| Total Billed ($) | SUM(invoice_amount) across all invoices in the period. |
| Collection Rate % | DIVIDE(SUM(amount_collected), SUM(invoice_amount)) — share of billed dollars actually collected. |
| Open AR ($) | SUM(open_balance) where open_balance = invoice_amount - amount_collected — total outstanding receivable. |
| AR Over 60 Days ($) | SUM(ar_over_60) — outstanding balance in the '61-90' and '90+' buckets (column is open_balance when aging_bucket is one of those two else 0), a plain SUM. |
| Invoice Count | SUM(invoice_count) — plain count of invoice rows (invoice_count is integer 1 on every row). |
Used by: Owner / office manager watching cash, open invoices and slow-paying accounts
Frequently asked questions
What is a good collection rate for a rental yard?
Collection rate is amount collected divided by amount billed. A healthy yard collects the large majority of what it bills; a rate dragging well below that points to disputes, slow-paying accounts, or write-offs. It's a better cash-health measure than total billed because it reflects what actually lands in the bank.
Why does AR over 60 days matter most?
The older an invoice gets, the less likely it ever pays. Isolating the 61–90 and 90+ buckets focuses the office on the dollars most at risk, so those accounts get worked before they age into a write-off. Watching only a single AR total hides which receivables are actually in danger.
Which customers tend to pay slowly at a rental yard?
Municipal and government accounts are the classic slow payers because of procurement and approval cycles, and some large contractors stretch terms. Open AR broken out by customer type surfaces the slow segments by name so the office can tighten terms, require deposits, or simply call earlier.
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