Latest / Cash Flow Conversations with Fexingo: Working Capital, Receivables, and Small Business Finance / How a Bakery Used Rolling Receivables Forecasts to Fix Cash Flow
Transcript
- Lucas: So there's this bakery on the north side of Chicago, Morning Glory Baking, and they were profitable every single month last year. And yet, at the end of June, the owner, Maria, couldn't make payroll without dipping into her personal savings. That paradox—profit on paper, no cash in the bank—is exactly what we're digging into today. Luna: Yeah, that's such a familiar story. How does a profitable business end up that tight on cash? Lucas: Well, the short version is that Maria sold about seventy percent of her bread and pastries wholesale to cafés and small grocery stores. And those clients paid on net thirty terms. But her flour supplier wanted payment in ten days, and her butter bill was due on delivery. So she was paying her biggest costs two to three weeks before her customers paid her. And that gap was eating her alive. Luna: So it's a timing mismatch. But she'd been doing this for years, right? Why did it suddenly get so bad? Lucas: That's the interesting part. It wasn't sudden. It was creeping. Her sales grew about forty percent over the past two years, and as she added more wholesale clients, her accounts receivable balance grew faster than her sales. Because some of those new clients were slower payers. She just didn't have a clear picture of that until the cash crunch hit. Luna: Right, growth can mask a lot of problems. So what did she do about it? Lucas: She did what a lot of small business owners do first—she went to her bank and got a line of credit. And that helped in the short term. But her bookkeeper, a woman named Priya, convinced her that the line of credit was a band-aid, not a fix. So they sat down and built a rolling receivables forecast. Luna: Okay, and that's different from just looking at your accounts receivable aging report, isn't it? Lucas: Exactly. The aging report tells you what's past due, but it doesn't tell you what's going to happen next month. Maria and Priya built a spreadsheet that projected cash collections for the next twelve weeks, based on each customer's invoice history and their typical payment behavior. Luna: So they were predicting which invoices would actually be paid on time. How did they figure that out? Lucas: That's the key. They went through a year of data and assigned each customer a probability of paying within, say, thirty days, within sixty days, or later than that. For example, one of her biggest café clients, a local chain with four locations, had a history of paying about ten days late on average. So Maria scheduled that cash to arrive in week six of her forecast, not week four. Luna: And that gave her a much more realistic picture of when the money would actually hit her account. Lucas: Right. And the effect was immediate. In the first month, she saw a projected cash shortfall in week seven that she'd never caught before. Because the aging report just said 'this invoice is thirty days old,' but her forecast said 'this client typically pays late, so you're going to be short in the middle of August.' Luna: So she could actually do something about it in advance. What did she do? Lucas: She had a couple of options. She could call the client and ask for early payment, or she could delay a planned equipment purchase. But the bigger win was that the forecast helped her renegotiate terms with her slowest payers. She went to her top five customers and said, 'If you pay net fifteen instead of net thirty, I'll give you a two percent discount.' And three of them took her up on it. Luna: A two percent discount for faster payment—that sounds like a great trade, if it works. Lucas: For her, it did. She calculated that her cost of capital was around eight percent annually, so paying a two percent one-time fee to get cash thirty days sooner was a bargain. Over the next six months, those three customers alone cut her average days sales outstanding from fifty-two days to thirty-seven. Luna: Whoa, that's a huge improvement. Fifty-two to thirty-seven is like a fifteen-day swing. Lucas: Yeah, and it directly translated into cash. By the end of the third month, her receivables balance had dropped by about eighty thousand dollars, and her line of credit was completely paid off. She told me she felt like she'd found an extra salary. Luna: That's a great result. But I'm curious—what did the forecast actually look like? Like, was it a super complicated spreadsheet? Lucas: Not at all. It was actually pretty simple. Priya set up a twelve-week rolling sheet with columns for each customer, each open invoice, the invoice date, the amount, and then a row for each week showing the probability of payment. They updated it every Friday with new invoices and payment statuses. It took maybe thirty minutes a week. Luna: And you mentioned they used payment probabilities. Did they actually use percentages in the spreadsheet, or was it more of a judgment call? Lucas: So they started with rough buckets—like high, medium, low probability—and then as they got more data, they refined it to actual percentages. For instance, one café had a ninety-five percent chance of paying within thirty days, so they scheduled ninety-five percent of that invoice in week four, and the remaining five percent in week six. It's a bit of an art, but the point is it forces you to think about when cash actually arrives, not when it's technically due. Luna: So the forecast is basically a cash flow projection that accounts for the reality of customer behavior. What about the downside? I mean, what if a customer pays even later than you predicted? Lucas: That's the thing—you build in a buffer. Maria kept a minimum cash reserve of about two weeks of operating expenses. And if her forecast showed a dip below that buffer, she'd hold off on non-essential spending or follow up on a specific invoice. The forecast didn't make cash magically appear, but it gave her time to react. Luna: That kind of proactive cash management is something a lot of small businesses could use. So, what was the actual impact on her operations beyond the line of credit being paid off? Lucas: Well, she hired a second delivery driver, which was a big deal because she'd been putting that off for months. And she launched a small retail line of cookies at two farmers markets, which brought in a new revenue stream that didn't have the same receivable issues. But more than that, she told me she sleeps better at night. Luna: That's the real benefit, right? Not just the numbers—the peace of mind. Lucas: Absolutely, and I think that's the thing that doesn't show up on a balance sheet. She went from dreading the end of the month to feeling like she had a handle on her finances. Luna: So, if someone listening owns a small business and deals with similar wholesale terms, what's the first step they should take? Just build a spreadsheet like that? Lucas: I'd say start by pulling your last year of invoice payment data and just look at the patterns. Which customers pay on time? Which ones consistently pay late? Just that awareness alone can change how you forecast. Then, even a simple twelve-week projection based on those patterns is a massive improvement over guessing. Luna: And if you're a smaller operation, you might not have a bookkeeper like Priya. Could you do this on your own with just a spreadsheet template? Lucas: Definitely. There are plenty of templates online, but honestly, the spreadsheet doesn't need to be fancy. What matters is the discipline of updating it weekly and being honest about the probabilities. Luna: Yeah, and I love that you said 'honest about probabilities,' because I think a lot of us are optimists when it comes to our customers. We assume they'll pay on time just because we'd like them to. Lucas: Right, and that's exactly what gets businesses into trouble. So, using historical data to set those probabilities is what makes the forecast realistic. Luna: So, before we wrap up, I want to say something about today's episode. If this conversation has been useful to you—maybe it's made you think about your own receivables differently—that's exactly why we do this. And the way we keep these conversations free and ad-free is through listener support. So if you've gotten something out of today, you can buy us a coffee at buy me a coffee dot com slash fexingo. It's a small way to keep the show going. Lucas: Yeah, and we really appreciate anyone who's ever done that. It genuinely helps us keep doing this. Luna: All right, back to cash flow. So we've talked about the forecast and the discount incentive. But what about the clients who didn't take the discount? Did Maria have to do anything different with them? Lucas: Good question. She did. For her two slowest payers, she implemented a late fee of one and a half percent per month after thirty days. That was a new policy for her, and she had to brace for pushback. But interestingly, only one client complained, and she ended up keeping that account by offering a slightly longer payment term in exchange for a smaller delivery schedule. The other client just started paying on time. Luna: So it's not just about forecasting—it's also about changing the payment culture with your customers. Lucas: Exactly. And that's the bigger lesson. The forecast gave her the visibility to see where the pain points were, and then she used that information to take action—whether that was offering discounts, enforcing late fees, or even renegotiating terms. It's a comprehensive approach. Luna: Nice. So, what's the takeaway for a listener who runs a small business? Aside from building a forecast, any other quick win? Lucas: I'd say look at your top ten customers and see what percentage of your receivables they account for. If a couple of them are consistently late, that's a concentration risk. And maybe consider asking for a deposit or partial payment upfront, especially for larger orders. Luna: That's a good tip. So, the bottom line: a rolling receivables forecast can turn a cash flow crisis into a manageable, even comfortable, situation. And it doesn't require a finance degree. Lucas: Right, it's just about being proactive and realistic. And in Maria's case, it took her from barely making payroll to actually growing the business again. That's the whole game.