Interactive demo

The finance dashboard an acquisition actually tests.

A multi-branch distributor buys another branch, and the new entity arrives with its own aging, its own vendor habits, and its own close. Company totals absorb the damage and look fine. This demo is the control tower a controller would want instead: working capital, receivables and payables aging, three-way match exceptions, and close status, per entity, with the acquired branch converging to company norms in plain sight. Built on the KPI definitions a controller would recognize: DSO and DPO on average balances, point-in-time aging, and a close that ends at sign-off, not at a date.

Synthetic dataPython + numpy generatorStatic Next.js, no backend

Prefer the written version? Read the close and AR package write-up on the blog.

Entity:

DSO (days sales outstanding): 44 days
DPO (days payable outstanding): 32 days
Cash conversion cycle: 67 days
Gross margin: 25.0%
AR past due: 21.7%
Three-way match auto-clear: 89.8%

Synthetic data. Seeded generator, no real company.

Company totals for the latest month. They look fine, and that is the problem: totals average away the one entity that needs attention. Filter to the acquired branch to see what the roll-up hides.

Receivables aging, month by month

Open AR balance split by how far past due it is. Aging is computed from line-level open items at each month end, not from a running total, so a point-in-time as-of view stays correct.

Current1-30 days past due31-60 days past due61-90 days past due90+ days past due

Watch the stack composition, not just the height. A growing balance made of current invoices is a growing business; a flat balance rotating into the 61-90 and 90+ bands is a future write-off.

The cash conversion cycle, decomposed

How long a dollar is tied up in operations before it comes back as cash: days in inventory, plus days waiting on customers, less the days vendors finance us. The waterfall shows which lever moved when the number moves.

All three components use average balances over the period, not the spot balance, so one big invoice at month end cannot distort the ratio. That choice is the difference between a KPI a controller will sign off on and a chart that argues with the GL.

DSO trend, with the acquisition window shaded

Days sales outstanding per month: average receivables over revenue, times days in the period. The shaded band marks the first three months of the acquired branch on the platform.

The company-wide bump inside the band is the acquisition arriving with its own aging. Without the entity filter above, that bump is all a CFO would see; with it, the cause and the recovery are one click away.

Three-way match: auto-clear rate and what the exceptions are

Every AP invoice line is matched against its purchase order and goods receipt. Clean matches auto-clear and are logged; the rest route to a worklist with a reason code. The value of the control is that people only touch the risky remainder.

Auto-clear rate by month

Exceptions by reason code, latest month

Price and quantity variances route to the buyer, missing receipts to the branch dock, duplicates to the AP supervisor with payment held. Reason codes are what turn an exception pile into a work queue with owners.

Month-end close: checklist and tie-outs

Close status for the latest month across all entities. A close is done when every task is signed off and every reconciliation ties out, not when the calendar says so.

Checklist by workstream

Cash6/6
AP8/9
AR7/8
GL and JE10/11
Accruals7/7
Intercompany4/5
Reporting5/6

Reconciliation tie-outs

Bank to GL cash$0Ties out
AP subledger to GL control$184Ties out
AR subledger to GL control$92Ties out
Intercompany$12,840Break

The intercompany break is left visible on purpose. A close dashboard that is always green is not reporting; the point is that a break shows its unexplained difference and links to the items behind it, with preparer and reviewer identities on the sign-off. Subledger-to-control tie-outs are the evidence that the GL these KPIs stand on can be trusted.

How this was built

A seeded numpy script generates line-level open AR and AP items for each branch and month, then computes every aggregate from those items: aging buckets from days past due, DSO and DPO from average balances over the period, match outcomes from a tolerance-based three-way match. The acquired branch is engineered to onboard with weak receivables discipline and off-PO spend, then converge as standardization lands, because that trajectory is what a finance-integration dashboard exists to make visible. The result is exported to a static JSON that this page reads at build time and renders with the hand-rolled, dependency-free SVG charts this site uses everywhere, so the charts are in the HTML before any script runs. In a real deployment the same shapes come from the ERP and subledger extracts, computed upstream on a schedule, not in the browser or in the BI tool. Everything here is synthetic and models no real company, vendor, or customer.

View the raw data that drives this page.

Is your close a checklist or a control?

If month end is a spreadsheet and an email thread, or an acquisition just handed you a second set of books, this is exactly the kind of problem I like to take end to end.

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The finance dashboard an acquisition actually tests.

Synthetic dataPython + numpy generatorStatic Next.js, no backend

How this was built