Project Controls in the AI EraForecasting cost and schedule outcomes · Lesson 12 of 22

Cash flow forecasting and the link to project finance

Article · 13 min · 8 min lecture

Video lecture

Cash flow forecasting and the link to project finance

9 chapters · about 8 min · full transcript

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Chapter 1 of 9

Profitable and still out of money

  • Cost is not cash
  • From cost forecast to cash forecast
  • Peak funding need and the link to project finance

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Chapters

Cost is not cash

A project can be within budget and still run out of money. Cost (the EVM view) tells you what work is worth; cash flow tells you when money leaves and arrives. Controls teams increasingly own or contribute to cash forecasts because they have the schedule, commitments and progress data that drive them.

From cost forecast to cash forecast

Convert time-phased cost into cash by applying payment terms and timing:

  1. Start from the time-phased ETC by control account.
  2. Apply payment terms. Subcontractor paid 30 or 60 days after valuation; materials paid on delivery or with deposits; staff paid monthly.
  3. Apply retention. Many construction contracts retain a percentage (commonly around 5–10%, contract-specific) released at completion or after a defects period.
  4. Add advance payments and their recovery. Advances received from a client are typically recovered by deduction from later payments.
  5. Model income (for contractors) from the client's payment terms, valuation cycle and any milestone payments.
  6. Net and accumulate to see peak funding need.

Worked example: a contractor's cash position

Illustrative. A fictional contractor in Jeddah has a 6-month contract worth $6M with monthly valuations, the client paying 45 days after each valuation, 10% retention, and a 10% advance recovered pro rata. Costs are paid, on average, 30 days after incurred. The result is typically a negative cash position in early months, sometimes several hundred thousand dollars, even though the job is profitable. Knowing the peak negative cash tells the finance team how much working capital or credit facility is needed and when.

Month           1      2      3      4      5      6
Cost incurred   800   1,100  1,200  1,100   900   500   (USD 000)
Cash out        –      800   1,100  1,200  1,100   900  (+500 in M7)
Valuation      900   1,200  1,300  1,200  1,000   400
Cash in (net)  600*    –     720    960   1,040   960   (* advance received M1)

(Numbers simplified: cash in reflects 45-day lag approximated to the following month or later, less retention and advance recovery.) The point is the method, not the specific figures.

Owner's perspective

For an owner or developer, the cash forecast drives funding drawdowns: equity injections, loan draws and the timing of financing costs. On project-financed assets (energy, infrastructure, PPPs), lenders require regular reporting of cost to complete and funding sufficiency, often verified by an independent engineer. If the forecast shows costs exceeding available funds, it triggers lender discussions long before money runs out.

Controls outputFinance use
EAC and contingency positionFunding sufficiency test (are sources ≥ uses?)
Forecast completion dateInterest during construction, start of revenue, debt repayment timing
Change and claims logPotential cost increases and contractual recoveries
Risk-adjusted ranges (P50/P80)Sizing contingency and standby facilities

The companion course Project Finance and Financial Modelling covers capital structure, cash-flow waterfalls and coverage ratios in depth.

S-curves for cash

Plot cumulative cash out, cash in and net cash. The gap between the cost S-curve and the cash-out S-curve shows payment lag; the minimum point of the net cash curve shows peak funding need.

Common mistakes

  • Assuming cash equals cost in the same month.
  • Ignoring retention, advances and their recovery.
  • Not updating the cash forecast when the schedule slips; delays shift both costs and income.
  • Forgetting taxes, duties and currency conversions in cash timing.
  • Treating the finance team's cash plan and the controls team's cost plan as unrelated.

Template: cash forecast

Month | Cost incurred | Payment lag | Cash out | Valuation/billing | Retention | Advance recovery | Cash in | Net | Cumulative net

AI note

AI tools can learn actual payment behaviour (which clients or subcontractors pay late) from accounts data and improve cash timing predictions. Review them against contract terms and relationships before relying on them.

Quick self-check

Ask finance and controls to compare their numbers for the next three months. Do the cash-out figures match the cost forecast shifted by payment terms? Do income assumptions match the client's actual payment behaviour? Is the peak funding need covered by available facilities with headroom? A short joint review each month, using one shared template, prevents the surprise of a profitable project that suddenly cannot pay its suppliers or has to draw on expensive short-term credit.

Hands-on: a cash forecast in Excel

Months in columns C:J; named inputs Ret (e.g. 10%), Adv_pct (e.g. 10%), Contract (e.g. 6,000).

Row 3  Cost incurred       (from time-phased ETC)
Row 4  Cash out            D4 =C3            (30-day lag: previous month's cost)
Row 5  Valuation           (from progress forecast)
Row 6  Retention           C6 =-C5*Ret
Row 7  Advance recovery    C7 =-C5*Adv_pct   (recovers the advance pro rata to valuation)
Row 8  Net certified       C8 =C5+C6+C7
Row 9  Cash in             D9 =C8  (about 45 days: shift one or two columns per contract terms); C9 adds the advance =Contract*Adv_pct
Row 10 Net cash            =C9-C4
Row 11 Cumulative net      C11 =C10 ; D11 =C11+D10
Peak funding need          =MIN(C11:J11)
Month of peak              =INDEX(C1:J1, MATCH(MIN(C11:J11), C11:J11, 0))

Release retention as a positive line in the month the contract allows (often after completion or a defects period).

Hands-on: the same model in Python

import pandas as pd

cost = pd.Series([800, 1100, 1200, 1100, 900, 500], index=range(1, 7))   # USD 000, illustrative
val = pd.Series([900, 1200, 1300, 1200, 1000, 400], index=range(1, 7))
ret, adv = 0.10, 0.10
months = range(1, 10)
cash_out = cost.reindex(months, fill_value=0).shift(1, fill_value=0)      # 30-day lag
net_cert = val * (1 - ret - adv)
cash_in = net_cert.reindex(months, fill_value=0).shift(2, fill_value=0)   # ~45 days → two-month lag here
cash_in[1] += 6000 * adv                                         # advance received in M1
df = pd.DataFrame({"cash_in": cash_in, "cash_out": cash_out})
df["net"] = df.cash_in - df.cash_out
df["cumulative"] = df.net.cumsum()
print(df, "\nPeak funding need:", df.cumulative.min(), "in month", df.cumulative.idxmin())

The lag convention (one or two months for 45 days) is a modelling choice; state it in the assumptions. Retention release is omitted here for brevity.

How to measure success

  • Controls and finance cash forecasts for the next three months agree within an agreed tolerance.
  • Peak funding need is known, dated and covered by facilities with headroom.
  • The cash forecast updates automatically when the schedule forecast moves.

Key takeaways

  • Cost shows value of work; cash shows timing. Profitable projects can still run out of cash.
  • Convert time-phased cost to cash using payment terms, retention and advance recovery.
  • Peak negative cumulative cash defines working-capital or funding need.
  • EAC, forecast dates and risk ranges feed funding sufficiency and lender reporting.

Check your understanding

Quick questions to lock in the lesson. They don’t count towards your certificate.

  1. Why can a profitable contract still create a cash shortfall?
  2. What does the lowest point of the cumulative net cash curve indicate?
  3. A 3-month schedule delay is forecast. What is the likely cash-flow effect for an owner financing the project with debt?

Put it into practice

Take a simple 6-month cost profile and convert it to a cash-out profile using a 30-day payment lag; plot cumulative cost vs cumulative cash.

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