Dynamic Cash Flow Analysis

99% of Cash Flow Models break in infrastructure. Mine saved $20M+. Here’s how I built it. And NO, it’s not rocket science. It’s just smarter plumbing. I don’t trust static cash flow forecasts. They’re too slow. Too rigid. Too disconnected from the field. If you’re using Excel to forecast a $200M project? You’re flying blind. Here’s how I build predictive cash flow models that think ahead. And how one telecom CFO cut burn rate by 21% with just one move. ✅ Step 1: Break the project into cash flow modules Every job has 4 cash flow zones: Milestone-based receipts Supplier payment triggers Payroll cycles High-risk cash gaps (weather, delays, rework) I map these as independent but interacting nodes. That’s where traditional models fail - they treat cash like a flat waterfall. Our model is a web. Not a waterfall. ✅ Step 2: Plug in real-time signals The AI doesn’t just run on assumptions. It pulls fresh signals. Things like: - > Daily progress % vs plan -> Change order approvals -> Payment release status -> Invoice cycle velocity -> Weather + supplier lag risk Example? A tower rollout in Texas had 18% of supplier payments stuck due to invoice mismatches. We surfaced it. Fixed the logic. Cleared $4M in trapped flow. Tools: dbt + Snowflake + Power BI + Prophet ✅ Step 3: Build scenario trees Infra cash isn’t linear. You need to model delays, FX spikes, labor shortages. We build out scenario branches using: Historical variance patterns External risk overlays (weather, FX, geopolitical) Contractual penalty triggers One construction firm used our delay-risk tree to renegotiate 2 milestone payment terms. They avoided $2.1M in interest costs. ✅ Step 4: Embed alerts + confidence bands Forecasts without alerting are just fancy spreadsheets. We overlay: - Alert thresholds (e.g. 10% burn deviation) - Confidence bands around projected flows - Approval status heatmaps This makes your forecast actionable. Not just "pretty." ✅ Step 5: Connect it to your dashboard layer What’s the point of a forecast no one uses? We embed it into the CFO dashboard. Usually built in Power BI or Looker. Finance sees the flow. Ops sees what’s stalling it. Executives see the cash risk curve. One client even layered it into project team dashboards to course-correct faster. What this means for you: If your forecast is a static spreadsheet with 2 tabs? That’s not forecasting. That’s wishful thinking. Cash in infra isn’t just delayed. It’s fragile. You need models that see ahead, flex with risk, and feed the field. AI makes that possible. Want to see what a predictive model would look like for your project? DM me “Forecast” and I’ll walk you through a real build step-by-step. Or repost if you know a CFO who’s stuck reviewing cash 45 days too late.

Uncollectable doesn't mean forgotten. In many cash flows, the focus is on sales and collections. But what about the sales we're not sure if we'll collect? This is where the uncollectable reserve comes in. Ignoring uncollectable A/R, or bad debts, means overestimating cash collections. This may raise serious questions about the integrity of the financial model. But showing a high level of bad debts may mean that the company is selling to deadbeat customers or high-maintenance companies that have to be constantly chased down. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗵𝗮𝗻𝗱𝗹𝗲 𝗿𝗲𝘀𝗲𝗿𝘃𝗲 𝗳𝗼𝗿 𝗯𝗮𝗱 𝗱𝗲𝗯𝘁𝘀 𝗶𝗻 𝗮 𝗰𝗮𝘀𝗵 𝗳𝗹𝗼𝘄 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁: (1) Include a single line for uncollectable reserve. This is the amount from each month's sales that will be carved out. Use historical rates or reasonable assumptions to calculate the percent of sales that won't be collected. Note that this line includes the amount 3.0%. What you don't see is that this is a dynamic label. The number updates whenever the value on the assumptions page is changed and is concatenated with the words "uncollectable reserve". (2) Roll the balance with the cumulative uncollectable Reserve. Because 3% of A/R is considered uncollectable, the amount from month 1 is added to the amount from month 2, and it continues to accumulate. That 3% doesn’t go away. It accumulates monthly as sales we recognize but never expect to convert into cash...until we do, or write it them off. (3) Manage the assumptions dynamically with check boxes. You Excel nerds will love this part. I insert a check box in the line that says "collect reserve?" These balances sit here untouched, until the modeler decides otherwise. The Treasurer or financial analyst can trigger the collection or write them off. If the check box is selected, the outstanding bad debts are collected in the line "receipts on uncollectable". In April 2025, this amounts to $230,000. 𝗪𝗵𝗮𝘁'𝘀 𝗴𝗿𝗲𝗮𝘁 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵? (A) It keeps the business grounded in reality. The AR roll forward is more honest and reflects what's actually going to happen, not just that 100% of sales comes in. (B) It provides a clear way to stress test recovery assumptions or show upside scenarios. (C) It reinforces a key FP&A mindset. Just because it's in the P&L, doesn't mean it hits the bank accounts. Business modeling isn't just about what's missing from the Excel formulas. It’s about what’s quietly not coming in.

Pick your predictor: here is a field guide to choosing the right statistical method for cash flow forecasting. Why is this hard? 📊 1. No One-Size-Fits-All Model There is no universal model that fits every forecasting scenario; influencing factors include data availability, business complexity, volatility, forecast horizons, etc. 🔄 2. Stationarity and Data Behavior Many statistical models assume that the underlying data is stationary—meaning its statistical properties (mean, variance) don’t change over time. However, cash flow data often exhibits trends, seasonality, or structural breaks (e.g., due to acquisitions or market shifts), making it non-stationary. This mismatch can lead to inaccurate forecasts unless the data is transformed appropriately. 🧠 3. Model Complexity vs. Usability More sophisticated models (e.g., stochastic simulations, machine learning) may offer better accuracy but are harder to explain, maintain, and govern. Simpler models may be easier to use but less responsive to complex dynamics. This trade-off between sophistication and usability is a persistent challenge. 🧩 4. Forecasting Accuracy Degrades Over Time Forecast accuracy tends to decline the further out you project. This is due to increasing uncertainty and the compounding effect of small errors. Additionally, model misspecification—such as omitting key variables—can significantly reduce reliability. 🌐 5. External Factors and Structural Shifts External shocks (e.g., pandemics, regulatory changes, macroeconomic shifts) can render historical patterns obsolete. Cash forecasting often underperforms compared to revenue forecasting because it must account for both internal operations and external liquidity risks. 🧭 6. Strategic Implications Cash forecasting isn’t just a technical exercise—it underpins liquidity management, investment decisions, and risk mitigation. As such, the choice of model carries strategic weight, making it essential to balance precision with interpretability and alignment with business goals. More here: https://lnkd.in/erjDcNEY