Chief Executive Officer,
Tri-Point Oil & Gas Production Systems
[0:00] Jeff Martini:
Well, thank you very much. I appreciate you all coming out. And what we’re going to talk about here is something a little bit more of a technical kind of a conversation about asset-based lending. And the goal here is to sort of walk through the mechanics of how these things work and talk a little bit about what the challenges are associated with these kinds of facilities. So a little about me, me. So I am CEO of Tripoint, Tripoint is an oil and gas company, a full-service company based out of Houston. My background, I’ve got about 20 years in the industry, in about 15 of those as a standalone CFO rolls across about five or six different private equity-backed and publicly held companies. There we go. So a few of the topics will cover. So we’ll talk a little bit about the company that I work for, just to give a little bit of background as far as the lens that will be looking at the ABL through and how it behaves. We’ll talk about some of the challenges and we’ll just sort of walk through top to bottom the order to cash cycle and how that order cash cycle looks like both from just a normal cash basis versus laboring in a NABL in top on top of all that. So Tripoint, as I said, is an oilfield services company. We have about 500 employees. And it’s spread out across the oil patch in the US from the Rockies to West Texas, East Texas and Oklahoma and then we’ve got some presence up in Pennsylvania, Ohio, hub that direction as well. What we do is we manufacture separation equipment. So you see some of our products on the right-hand side there. What when, when an exploration production company produces oil or gas, there are always three phases that come up out of the well. So there’s oil, there’s water, and there’s gas. So the separation equipment that we manufacture helps the producer separate those three phases. That’s the name Tripoint the three phases. So the water and the oil generally are stored onsite on the production pad in tanks.
[2:16] Jeff Martini:
So we manufacture the tanks that hold that. And then the gas is generally separated out through these types of separators. And that’s compressed and sent on a data pipeline for ultimate industrial or residential use. To walk through one of our facilities looks a lot like this picture on the left. So we see one of our welders here is actually welding one of the end caps if you will, on a vessel. So to oversimplify a bit – We start with flat steel and we end up with circles and put caps on the ends and that’s what a separator looks like more or less. So, Tripoint is funded, among other ways, but through an asset-based lending facility. So what is a borrowing base, so a borrowing basis is really what helps to inform the ABL. So really a borrowing base is nothing more than just the collateral of the business. And it’s the maximum amount that the business can borrow. And it’s composed of current assets. Typically you’ll find AR and inventory; inventory, obviously being split between raw material and finished goods. And it’s also paired with a revolver. So revolver, of course, is, think of it as a credit card. It’s something that you borrow and pay down on a continuous basis. So sort of an analogy, just to make sure that we’re all level set here before we start diving in a little deeper on the complexities of what it would look like if we had a borrowing base associated with our own personal lives and what our credit card might look like. So think of it in terms of maybe a little silly example here. How much food is in your refrigerator? How much gas is your gas tank, reset that on a weekly basis, that’s your credit limit is on your credit card. And then the other cycle here is how much you have borrowed on your credit card, how much you’ve spent on Amazon versus how much you’ve paid down. So that’s the availability on your credit card. And then it’s how much cash you have in the bank, add the cash to your availability on your credit card. And that’s for the sake of this conversation. That’s what we’ll call liquidity. So the summation of cash plus the amount of amount you have available to borrow.
[4:31] Jeff Martini:
So there are quite a few challenges associated with it. And then really, the rest of the presentation here sort of dives into some of those challenges. So the frequency of reporting is one of those – more frequent is more challenging. It requires a pretty robust ability to forecast your receivables and your inventory as we’ll see. And then, really, it’s what informs the liquidity of the business is really the Underlying performance of that business. So having a great understanding of the rhythm of the forecasting of the commercial situations that are associated with that business, that’s really the most important thing to understand to be able to manage your borrowing base. So as far as the frequency is concerned, again, the more frequent the more challenging. So what we’ll talk about may be the most challenging a weekly reporting cycle, which some businesses are in quarterly is easier, but no less important to be able to project these things. So on a weekly basis, I need to be able to predict, as I said, the A/R and the inventory. So your ability to the amount you can borrow changes every period and your liquidity is changing continuously. So it can really feel like you’re riding a bucking bronco as you move through this liquidity, tidal wave or whatever. What have you on the receivable side, it’s obviously important to be able to project what your invoicing is and what your receivables are, again, on a weekly basis that can get pretty tough, especially in a capital equipment business, like what I described Tripoint is, it’s pretty lumpy. So it’s not $5 ticket sizes, we’re talking 500,000 to a million dollar plus invoice size. So that gets pretty irregular as far as the patterns are concerned. And then there are these things called ineligible, so inevitably the bank is going to find ways to reduce the borrowing base that’s in their best interest.
It’s in the company’s best interest to inflate the borrowing base as much as possible. So there’s this tension built-in. But in the rules of the borrowing capacity or the borrowing facility, rather, there are several different categories of things that can be pulled out. So anything over 90 past the due date is sort of market. That’s generally what’s excludable from the borrowing base. So being able to predict not only know what this week’s over 90 is, but over 13-week cash flow, knowing what your aging is going to look like on an invoice by invoice basis, and even a customer by customer basis when you get into things like cross aging, and then also dilution. So these topics are a little bit beyond the scope of what we’ll talk about here, but they’re worth mentioning because they can impact liquidity pretty critically as well. On the inventory side, again, in capital goods, you got raw material and finished goods. So the raw material is going to change based on the timing of your receipts versus the shipments or the allocation to work in process. And then the finished goods are really changing based on the shipments of the customers and fate versus the time that you’re completing those finished goods. So there’s constant churn and all of these categories of assets. Now, I’d be remiss if I didn’t point out that on top of all this their covenants associated with the borrowing facility. Typically they’re going to be minimum availability covenants where you can’t borrow more than a certain amount of your borrowing base, whether that’s a 95% or 90%, or some dollar amount perhaps. Or there might be other covenants associated with historical liquid or historical profitability or things along those lines. But in any case, if you bump into any of those, then that results in default and the bank is going to get a whole lot more interested in your business then you’d probably appreciate.
[8:53] Jeff Martini:
Now as far as tools to try and predict all this stuff, they’re imperfect, right? So, because it starts with people, you need really bright people that have this broad understanding of the business. And they said it’s really the business that’s helping to inform the borrowing base and form the liquidity of the business. There are other tools that you need to rely on to be able to have these inputs into the model, your playing old favorite Excel templates that everybody knows and loves – or not, Collection software, so that that’s going to help inform the future patterns of receivables. The financial forecasting tools are going to inform your revenue and your invoicing. And then ERP data informs what your future inventories are going to look like. And then finally, our favorite sponsor here HighRadius. I wouldn’t let the opportunity pass without a small commercial for their artificial intelligence software that helps inform that as well. So what I’d like to do now is sort of walk through the actual mechanics. Like I said, the order to cash process, looking at both from a cash standpoint as well as from an ABL standpoint. So again to level set us all here, I think it’s important to just walk across here very briefly of what the order to cash process looks like for the sake of this presentation. The first step will place the order for the inventory, will receive that inventory, will pay for it, will assign it to a work order and pass it through the factory, will add labor as it passes through the manufacturing process, will close the work order out, will ship it to the customer. And then we’ll -God Willing, will actually collect the cash from the customer at the end there. So there are 4 pieces of this little spreadsheet that we’ll walk down. The first one is cash. So there’s going to be a cash rhythm to all of this there’ll be a working capital rhythm which is the next section. We’ll see the impact of the borrowing base and then finally the impact of liquidity as we walk across. And again liquidity is the cash on hand plus your availability under your borrowing base. So the first step is placing the order for the inventory.
So nothing happens at that point as far as liquidity is concerned, other than you have issued a piece of paper to a vendor saying I’d like for you to ship me some steel as a for instance, or, or valves or whatever the product might be. So there’s no cash outlay and there’s no liquidity impact, but something pretty cool happens as soon as you receive the inventory. When you receive the inventory – in this case, I have assumed that we will receive in $60 worth of raw materials. The $60 worth of raw materials came in, I still owe on that, but I haven’t had any cash impact, right? So the far left-hand side, there’s no cash impact. We see $60 of raw materials come in, we see this advanced rate section that’s now showing up on this on the spreadsheet as well. So the advanced rate is different for each category of asset. Okay. So the way the bank is going to look at this is that in a worst-case scenario if they had to liquidate the assets on your behalf, what do they expect that they would get for that? So in a sort of a worst-case, we shut the doors at the end of the day, and toss the keys to the bank. This is how they’re thinking about it. So they think that your raw material in this for instance, what’s this kind of market as far as these advanced rates are concerned, 25% of the value on raw materials. So multiply 25 by the 60. And we find that we have $15,000 or $15 hereof borrowing base capacity. Again, zero of cash plus 15, a borrowing base. So we have $15 of liquidity so we can borrow $15,000,015 against the $60 of the inventory. So again, just by receiving inventory, some good things happen to our liquidity. The next step here is we’re going to pay for that inventory. Okay, so $60 of cash leaves the business because we just paid for the inventory, we still have the $60 of raw material. So nothing’s actually happened to the borrowing base. So the same $15 that was there before, it’s still there. But I have $60 of cash that’s left the business. So 60 plus the negative 60 plus the 15, with negative $45 of liquidity associated with this transaction so far.
[13:34] Jeff Martini:
Now, step four, we’re going to sign inventory to work order. So something bad is going to happen here because the bank doesn’t believe that your work in the process is worth anything. Because it’s partially completed. It’s in the middle of your factory. It’s not saleable. There is no market for work in the process. So what their position is likely going to be sort of a market is that it’s not worth anything and they’re not going to advance anything on a working process. So just by Virtual booking a journal entry in your system, your liquidity has gone from negative 45 to negative 60. Get negative 60% of cash now you’ve got no advance ability against your inventory. So one more step- We’re going to add labor to the work order make things a little bit worse now. So it’s sitting in the middle of the factory with paid employees $20 to add labor to this, they’re, they’re welding, they’re assembling, they’re turning wrenches, whatever they might be doing. So we have negative liquidity of $80 at that point.
[14:34] Jeff Martini:
Now, next up, we’re gonna close the work order. So now we’re gonna get some restart clawing some of that liquidity back. So we still have $80 of cash that’s left the business but we’ve booked another journal entry, we’ve moved and progressed the process from working process to finish goods. Finished Goods advanced rate is a little bit richer, it’s 50%. So 50% times the 80, you’re $40 of borrowing base capacity, that offsets your cash impact of $80. So you now have negative liquidity of just 40 at that point.
[15:11] Jeff Martini:
Want to build a customer now – So now we’re nearing the end of the transaction. So the advanced rate on AR is much richer, it’s 85%, which is again, roughly market 85% times the hundred dollars that we’ve invoiced the customer because we are endeavoring to make $20 on this transaction is total liquidity of $5. So we’re money ahead, even though we haven’t collected from the customer. So I talked a little bit about the sort of the rocky road that can be this liquidity ride and the importance of being able to forecast and keep on top of your assets. So the next step we’re going to assume that something pretty bad happens that the invoice ages out past 90 days. Okay, so on day 89, you think you’re doing pretty well, you run your aging on the next day and say, something bad’s happened here, I’ve got an old receivable, the bank hands you back what’s your borrowing basis and you discover that that’s not advanceable any longer. So what you thought was thought you’re a pretty good situation with $5 of positive liquidity. The next day you find out you’ve got $80 of negative liquidity. So this is that bucking-bronco – it’s one of those impacts that can be tough to predict, tough to forecast. And it’s important to keep these things in mind and to keep a close eye on your aging as one for instance.
[16:46] Jeff Martini:
But don’t worry, things get better on step eight here we actually do collect from the customer. we’ve cleared out all the working capital, cleared out all the current assets at that point, the non-cash current assets at that point and all we’re left with is the cash impact, we get $100 in from the customer. And we have $20 of cumulative liquidity at that point, which is the gross margin on having earned, having supplied the customer with that product.
[17:16] Jeff Martini:
So, what’s interesting here or what makes it difficult is that obviously there are dozens, hundreds, thousands of transactions that are following this pattern that is moving through the financials, moving through the bank account, moving through the borrowing base. And the snapshot can catch any transaction on here at any point in time. So that’s sort of the challenge here of keeping an eye on the big numbers, keeping an eye on the rhythms of the business. And, and looking for outliers, really because as Sashi was talking about yesterday, this to be able to map the business and use AI and then sort of this is like the holy grail to be able to Sort of map your business and predict what’s going to happen. The AI software that’s out there right now does a great job of predicting receivables. I think it’s moving towards a sort of predicting of payables. But there are lots of other transactions here that lend themselves towards some AI and towards some predictive modeling, that Excel just doesn’t do very well. So there’s lots of room here for help from software, that can make this job easier. So that really sort of rounds out the prepared items here and, wanted to hear if the group has any questions or comments or thoughts. Yeah. (Audience Inaudible)
[18:55] Jeff Martini:
So we’re using it on the A/R side. So we’re using it to predict the timing of collections, which is which, which is sort of that step seven-a, that was really the OMG moment after I got caught out on Step seven-a a couple of times, but I really need some help and need some better tools in place to be able to predict that and understand what my future aging is going to look like, based on what customers have done.
[19:40] Jeff Martini:
That’s right. So they’re doing the net. They’re doing the math, basically. So we’re providing the data, and then they spit back what the answer is. But that’s always in the rearview mirror. Right. So what they’ll tell Is what just happened. So, you know, especially in a weekly reporting environment, that doesn’t help me for next week or two weeks from now, especially if I’m living and I mean, there’s no secret that oilfield services sort of a stressed industry or stressed sector right now. So living in a thinly thin liquidity environment, knowing what that weekly rhythm looks like 13 weeks from now, is make or break. It’s absolutely foundational to how at least Tripoint is run in this narrow, narrow liquidity environment. So, the importance of using A/R predicting A/R as a specific example. I mean it really informs both sides of the equation and informs what our revolver is going to look like because obviously there’s cash coming in from the customers. But it also informs what that last column, that A/R is going to look like. So it helps to inform both with the borrowing bases and what the borrowed balances. So that was an important thing to understand and a good target-rich area to start using AI and that was sort of a natural fit. Didn’t mean this to be a HighRadius commercial but that was the natural fit and that’s what made sense for us to go attack first. (Audience Inaudible)
[21:33] Jeff Martini:
We got pretty good at it. So it is; Yes yeah. So the dumps that we’re providing are all ERP based. So that’s the historical stuff or the right stuff really the real-time data. So that’s all coming out of the RP system. And but as far as the forecasting. So to the answer, your question is it’s probably on the order of on a weekly basis, four to six hours. Since we’re in weekly reporting right now, it is. Umm, there’s analytical stuff required, right? So it’s after actually so. So it’s a shared responsibility. So the mechanical, so we’re providing data feeds to the bank, the bank sends back what the analogies are. And then as we have self-reporting, okay, so we take that bucket of eligibles, we roll forward our own AR, we roll forward our own inventory. So that self-reporting is really what takes the time, and then making sure that makes sense. It hangs together and it’s right. So that’s what takes more time than anything.
[23:07] Jeff Martini:
So it can often be the only available. So the next best is nothing at all, in some cases, and I think that’s where, where you’d find a lot of businesses that have a murky liquidity outlook. Again, I’m burdened with this, this oilfield service space that I’m living in personally. That term debt is certainly preferable. It’s easier to manage. It’s more predictable, but it comes at a higher cost. In some cases, depending on the situation, and it might not be as available. So this is pretty concrete. If you got the assets, you’ve got the ability to borrow, term loans require some level of confidence as far as the ability to forecast earnings and the ability to repay. So this is less dependent on the health of the business if you will, or the predictability of the business certainly.
[24:16] Jeff Martini:
Right. So that’s that’ll be contractual. So it’s based on specific appraisals. So, these are pretty close to what I experienced at Tripoint. So this is based on an appraisal of our specific inventory in our specific industry. So that can obviously change based on those, as well as industry outlooks. And it’s all colored by this net orderly liquidation value. That’s really what those percentages represent. And then the bank will typically haircut that song because then they have some expenses to incur to liquidate. So it’s a NOLV minus something minus 15 or something like that is what you typically see. So 85% of the NOLV is what’s advanceable.
So, I’m having a hard time trying to figure out how to, like say, a rate that will compare against the weighted average.
[25:36] Jeff Martini:
Got it? So interest rate I see it Yeah, so I didn’t put the interest rate up here. Right. So it’s going to be live or plus. Again, it depends on a lot of things like life or plus, six to eight, it’s probably on a mid-range, sort of credit risk. Or you can probably do better than with a more stable distribution business or something like that, where it’s got less of a capital goods kind of a rhythm to it. (Audience Inaudible)
[26:14] Jeff Martini:
Not necessarily it – short terms relative to, right. So it plugs a gap, I think is a good way to say it. But there are distribution companies out there that sort of use this as their base form of borrowing. And again, their cost of capital is going to be something less than what I just said. So they’re more competitive rates, I think out there, again, depending on the business in particular. Right, so again, just make sure that we’re clear. So the advanced rates are really the quantum of the borrowing capacity, not so much the cost of capital.
[27:05] Jeff Martini:
So the rest of them were going to point out that the time cost of this is more than the compliance more than the reporting. It’s predictive – so that we spend two to three times that much time on a weekly basis on predicting what the borrowing base is going to look like. That’s what takes more time. Right? So it’s the analytics. So that’s where, where I think that there’s some incremental value and some more AI and things like that to help us out. Well, thank you very much. Appreciate it.
[0:00] Jeff Martini: Well, thank you very much. I appreciate you all coming out. And what we’re going to talk about here is something a little bit more of a technical kind of a conversation about asset-based lending. And the goal here is to sort of walk through the mechanics of how these things work and talk a little bit about what the challenges are associated with these kinds of facilities. So a little about me, me. So I am CEO of Tripoint, Tripoint is an oil and gas company, a full-service company based out of Houston. My background, I’ve got about 20 years in the industry, in about 15 of those as a standalone CFO rolls across about five or six different private equity-backed and publicly held companies. There we go. So a few of the topics will cover. So we’ll talk a little bit about the company that I work for, just to give a little bit of background as far as the lens that will be looking at the ABL through and how it behaves. We’ll talk about some of the challenges and we’ll just sort of walk through top to bottom the order to cash cycle…
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