Cash flow forecasting from Tally: a practical guide for Indian SMBs

Most Indian SMBs do not have a cash flow forecast. They have a feel. The owner knows roughly what the bank balance will look like next Monday because they have done this for fifteen years.

That feel breaks at three points: when the business grows past a certain size, when a key customer changes payment terms, or when the owner is on a flight and someone else has to decide whether to release a payment.

A simple forecast, pulled directly from Tally, fixes all three.

What a useful forecast actually contains

For an SMB doing ₹10 to 100 Cr a year, you do not need a treasury-grade model. You need three buckets, weekly:

1. Expected inflows from customer invoices that are about to come due.
2. Expected outflows: supplier payments due, salaries, GST, EMIs.
3. Discretionary buffer: what is left to spend on capex, advances, dividends.

Forecast it for the next 13 weeks. That is the sweet spot: long enough to see the big payments coming, short enough that you actually trust the numbers.

Pulling the inflow side from Tally

The data you need lives in three Tally tables (or their equivalents in TDL/ODBC):

• Bills Outstanding: open invoices with due dates.
• Ledger for each customer, to compute their typical lag (do they pay on the due date, or 18 days after?).
• Sales register for recently booked invoices not yet due.

The model:

expected_inflow(week) =
  sum over open_invoices where (due_date + party_typical_lag) falls in that week

Party typical lag is the median of (payment_date − due_date) over the last 12 months. It is usually 8 to 25 days for Indian B2B. If you don’t model that lag, your forecast is fiction.

Pulling the outflow side

Outflows are easier and harder. Easier because supplier bills have hard due dates. Harder because most SMBs run a “we will pay when sales picks up” policy that doesn’t show up in vouchers.

What to pull:

• Open purchase invoices with due dates.
• Standing entries: salaries (last 3 months average), rent, EMI schedules.
• Statutory: GST payable as of the last day of each month is due by the 20th of the next.
• TDS payable: due by the 7th of next month.

We usually also add a manual line for known one-offs (a property advance, a machinery payment). The forecast should let you type these in, not just read from Tally.

The three places this usually breaks

After running this on dozens of Tally companies, the same three failures show up:

1. Old open bills nobody noticed

A bill from 14 months ago is sitting in “open” status because someone forgot to mark a payment against it. The forecast inflows look great because the bill is “due tomorrow”. It isn’t. It is dead.

Fix: any bill open for more than 180 days past due gets excluded from the model and surfaced as a separate “needs review” list.

2. Party master with no credit terms

If the credit period field is empty, you cannot compute the due date. About 30% of party masters in real Tally companies have this blank.

Fix: assume 30 days when blank, but flag those parties separately so the team fills them in.

3. Cash sales not tagged

Counter sales recorded as receipts against a “Sundry Cash” ledger don’t have a sales register row tied to them, so the forecast misses them as recurring inflows.

Fix: pull cash sales separately from the daybook and bucket them as recurring weekly inflows.

Sample 13-week output

A typical owner cash flow report looks like this:

The cumulative bank column is what the owner actually cares about. Red cell = trigger.

Doing this with AnalytAI

The model above is not exotic. It is the kind of view we build for most distribution / manufacturing clients in their first week. The Tally connector pulls bills outstanding and party ledgers; a scheduled report emails the 13-week forecast every Monday morning at 7am IST.

If you want to see this on your own Tally data, book a demo. Bring one company file. We will show you next Monday’s number before the call ends.

Related reads:

• The owner’s morning dashboard
• GST input credit leakage in Tally
• Connect Tally to live dashboards