Implications of Financial Modeling Metrics on Capex and Project Decision Making

[00:00:00] H.J. James Choo, PhD: Okay, so next, we actually have a very interesting topic. As you guys already have seen, we work with many universities and coll we collaborate with many universities to actually have certificate program or research programs. One of the opportunities that we actually had this summer is actually have Alex join us.

[00:00:22] H.J. James Choo, PhD: He joined us as an intern, summer intern, but he was so good he decided to stay on and help us figure some of the questions out. And as you guys all know, projects are just an not just, but a means to an end, right? The ends could be bringing new products and services to the market or continuing to deliver the same products and services to market at a greater capacity or upgrading it during the during the delivery, or maybe removing the products and services if it no longer meets the market needs, however, these actually require investment.

[00:01:00] H.J. James Choo, PhD: And as we actually worked with many companies around the globe in different sectors, what we realized each company has a different financial metric that they focus on. Not only do they actually drive what projects goes forward and doesn’t go forward, but also drives the behavior of people during the projects.

[00:01:19] H.J. James Choo, PhD: And so what we wanted to do is spend some time understanding what are all these metrics, and more importantly, what are some of the good good intentioned decisions that may actually have detrimental effect on the outcome of construction projects. Alex has joined us, as we said. Alex is a junior student at the North Northeastern University.

[00:01:43] H.J. James Choo, PhD: It’s the Moore McKim School of Business. And he is currently serving as a financial analyst, so we’re gonna actually ask him to join us. and share what he’s actually learned and what he’s found out. In addition, we’re going to ask Chet Carlson, who is our lead production system analyst who’s actually been working closely with Alex to assist in the actual presentation.

[00:02:08] H.J. James Choo, PhD: Yeah, Alex? Thank you,

[00:02:10] Alex Bone: Dr. Chu. Hello, everyone. Today, I’ll be discussing the implications of financial modeling metrics on capital expenditures and project decision making. In this presentation, I’ll be going over three key areas. First, I’ll be talking about the metrics prioritized by each industry, and then I’ll be talking about the optimization tradeoffs between these metrics.

[00:02:28] Alex Bone: And to wrap it up, I’ll be talking about the inputs, process, and outputs of optimizing these financial metrics. Thanks. Through careful analysis of the top company’s 10Ks, filings across different sectors, I wa I was able to identify these key metrics that drive decision making in each industry. And we’ll examine how these metrics can create important tradeoffs in project planning and execution.

[00:02:57] Alex Bone: So here we can see the energy. We have three sectors. We have the energy sector data center and digital infrastructure, and then infrastructure by itself. We can see an interesting pattern between all of these financial metrics and what they prioritize as our energy focuses on our free and operating cash flow with emphasis on EBITDA margins and debt EBITDA.

[00:03:17] Alex Bone: And you can just think of these as financial metrics for EBITDA and debt EBITDA, financial metrics that just help to manage the substantial amount of capital expenditures in a project. For digital infrastructure and infrastructure, you can see the similarities between an emphasis on ROIC EBITDA margins, debt EBITDA, and also development yield as well.

[00:03:41] Alex Bone: And the key difference between these two is one focuses on free cash flow, so the cash flows of the whole project infrastructure focuses more on the operation side. So how is able, so how in the operation sector those cash inflows and outflows. Before diving deep, deeper into the relationships between these metrics let’s understand what each metric measures.

[00:04:07] Alex Bone: So as you can see here ROCE measures how efficiently a project utilizes it’s cap total capital to generate profits. While ROE indicates the project’s profitability from equity investors showing how efficiently a company is able to use the shareholder’s capital to generate revenue. Additionally, in ROIC, we can see that it evaluates the project’s ability to generate returns relative to the capital invested on the project.

[00:04:33] Alex Bone: This provides additional insight into operational efficiency. While getting into our cash flows, free cash flow, like I said, Cash available after accounting for capital expenditures and also working capital needs. This just shows the project’s ability to generate excess cash for debt repayments, distribution, or also reinvest in.

[00:04:53] Alex Bone: Moving on into operating cash flow, this shows the cash generated from a project’s core operations, reflecting its ability to sustain itself and fund ongoing activities. And the final metric we have here is development yield. Development yield is typically used in real estate projects, but can be used in infrastructure projects as well.

[00:05:13] Alex Bone: And this measures the potential return of a developed project by comparing the expected value by its total development costs. And to help your understanding, we can split all these metrics into certain groups. We have our return metrics, which focus on capital efficiency, while we have our cash flow metrics, which focuses on project profitability.

[00:05:31] Alex Bone: And then we have our development yield, which shows overall project success.

[00:05:38] Alex Bone: So looking at ROCE’s components, you can look at the input breakdown for ROCE. And as we can see, calculating ROCE is calculated by EBIT over capital employed. EBIT in this case would be earnings before interest and tax. And EBIT also has inputs of its own, through revenue minus COGS, or cost of goods sold, minus their operating expenses as well.

[00:06:01] Alex Bone: And through those, we have our direct inputs of asset revenue we have our operating expenses of labor, some insurance, permits, and licenses as well. Looking at capital employed we can see capital employed is calculated through total assets minus your current liabilities. Our total assets are fixed up into two key components.

[00:06:22] Alex Bone: We have our fixed and our non fixed, or in this case, our current and our non current assets. And in our fixed assets, we have our property plant and equipment, and we also have our work in process, and also construction and progress, which is important to keep in mind for later when I talk about our optimization efforts.

[00:06:41] Alex Bone: And in current assets, we have our stock inventory or our cash, for example. And our current liabilities can look something like our accounts payable or our material supplier payments. So now that we’re breaking down ROCE, let’s look into one more key metric, which is development yield. Development yield can be calculated through our annual NOI or our expected annual NOI.

[00:07:03] Alex Bone: NOI would just be our net operating our net operating income. This is similar to ROCE where it has our revenue and our operating expenses as well. Looking into our total development costs, this would be all of the costs to create or, for our capital expenditures for our project. So this would be looking like our hard costs for our capital expenditures, our materials, our labor our equipment, and many other inputs as well.

[00:07:33] Alex Bone: We have our soft costs like insurance, our financing and then we have our contingency for project risks and market risk as well. So the question is, are these metrics related? And to answer this question, it’s yes, but it’s a little bit more complicated than we may think. Showing this impact of optimization between financial metrics, this shows the number of inputs that are shared between each financial metric.

[00:08:00] Alex Bone: So let’s take an example. We have ROCE. It shares four similar inputs to ROIC, and about two to development yield as we saw earlier. And the dotted arrow is depending on what We optimize. This can change the direction of if we’re increasing the other inputs or if it just stays the same or possibly decreases as well.

[00:08:24] Alex Bone: And however, just sharing inputs doesn’t tell the whole story. The relationship between metrics depends on which inputs are shared, which and how these inputs affect each other metric, and also whether we’re in the construction or operational phase of the project as well. So let me explain the relationship between ROIC and free cash flow using the diagrams to show why optimizing one can affect the other.

[00:08:57] Alex Bone: So this diagram shows us how ROIC and free cash flow are connected through our shared inputs. As we know, ROIC covers notepad, so net operating profit after taxes, similarly with unlevered free cash flow as well. And that’s where you could see the similarities between each metric. So looking at the highlighted section in the matrix, going back here, with free cash flow and ROIC, when optimizing ROIC, you can improve either the numerator or the denominator between ROIC.

[00:09:29] Alex Bone: So if we increase, you can improve it by increasing the NOMPAT or increasing our invested capital. And this is why we see the variable relationship between free cash flow and ROC. We see the arrows are red, so it depends on other factors as well. While we’re looking at the black arrows, there’s more of a direct relationship between these metrics.

[00:09:52] Alex Bone: And you can think about it like this. If you improve ROIC through operational efficiency, let’s say, free cash flow might increase because you are generating more NOPAT, or net operating profit after taxes. So this would increase ROIC and unlevered free cash flow as well. But if you improve ROIC through new investments, like free cash flow, you might decrease free cash flow because you’re using cash for these new investments.

[00:10:22] Alex Bone: You can look at it as if we have a project and we need new equipment. Using that cash to further our investment to hopefully making and generating more revenue can hurt free cash flow. But what if we look at it the other way? If we’re looking at unlevered free cash flow, the relationship with RSE becomes a little more clear.

[00:10:42] Alex Bone: To maximize free cash flow, you typically want to minimize your investments. You want to hold on to that cash. And this is why we see a black downward arrow or in this case red arrows to indicate there’s more to the story. And this explains why the optimization relationship isn’t symmetrical. The effect depends on which metric you’re focusing on and how you are focusing on it.

[00:11:11] Alex Bone: And here is another example just to give you some potential negatives of optimizing each metric. Like I said, if we were focusing our early and we had a higher capital expenditure or initial investment, this would reduce cash flow because we’re putting that investment in, let’s say, are. Sorry if we look at looking to are we for another example, if we were to have more leverage, this would raise our debt EBITDA as well in operating cash flow.

[00:11:40] Alex Bone: This could have a negative effect on our EBITDA margins. Although I didn’t really get into our EBITDA margins or our debt EBITDA just for time’s sake, we can think of it as, like, how much you make depending on your COGS, or your cost of goods sold. So this would be in our operational focus. So moving on, how can optimal production systems enhance these metrics?

[00:12:04] Alex Bone: Looking at our flow diagram, we can see how increasing fill rate Creates a cascading effect on our operational improvements in this operational improvement can have the cascading effect on all of our other financial metrics. When we optimize production systems by increasing fill rate, we can see improvements in one, our operational efficiency, so our reduced back orders and whip, shorter cycle times, and also increased throughput.

[00:12:32] Alex Bone: And this creates financial benefits, reducing labor costs, Reduce capital expenditures needed through asset utilization and also greater revenue through improved throughput. So how can these metrics be calculated for production system optimization efforts? So let’s take ROIC as an example. Our initial input collection would look something like this.

[00:12:55] Alex Bone: We would get our revenue, our direct and indirect costs, and then we would get our capital employed, production and process designs, and then our production policies, risks, and other variabilities. And the calculation process would look something like this as well. We would run the model to calculate our variable production, which is our output of the model, and then we would calculate our EBIT and then factor in our capital employed to calculate our ROCE.

[00:13:20] Alex Bone: We also have ROAC, which is just the average of capital employed, because I know in some companies they like to use the averages instead of just focusing on ROCE.

[00:13:32] Alex Bone: And if you look at the graph, we have our ROCE before and our ROCE after. As you can see, we have the initial dip, so this is the operational phase of, operational phase, and once our operations and our profits become in, we can see the huge increase in ROCE.

[00:13:53] Alex Bone: Let me give you another example with Delvella and Yield. Our inputs are similar with our assets, our direct and indirect costs, But we can see our modeling output would be production costs. So looking at the calculation process for this, if we were to run the model to calculate variable production costs, we would then calculate our estimated annual net operating income from the project, and then we would calculate our total development costs.

[00:14:18] Alex Bone: You can see in the optimized version of development yield, it has a slight increase. And just to keep in mind, all of these tables are just example data and not super accurate. So this is just for show. So the key takeaways from our analysis are financial metrics are interconnected, but each serves a distinct purpose in measuring our investment.

[00:14:42] Alex Bone: When we’re looking at ROCE and ROIC, or even development yield, each provides unique insights into performance. And also production systems optimization is a powerful tool that can enhance all of these metrics together through operational efficiency through improvements, better resource utilization, reduced costs, and improved throughput, which most importantly, PSO provides a structured methodology that allows us to, one, systematically improve operations, measure the impact of the financial metrics, and also make beta decisions about investments and operations.

[00:15:18] Alex Bone: That is all, thank you. If you have any questions, I know I have a little bit of time to answer some.

[00:15:28] Audience Member: In the work you’re doing to get these metrics, have you noticed that the different sectors prioritize some of the enhancements, like the improvements? Are they driven by the KPIs after, or are they just whole cart going for just improvements overall and just hoping for everything to get better?

[00:15:43] Alex Bone: Yeah, I would say looking at it from a perspective of how their projects are.

[00:15:49] Alex Bone: Their output of the project, how well their project is doing if their initial investment is, if you can lower it through like the construction phase of the project, that would be great. You can see that through the pieces as well, like reducing our labor costs would lower our initial investment, like looking at our I see, for example, our invested capital would be our total like our initial investment because.

[00:16:11] Alex Bone: All of these initial investments would fall under construction and progress, which is one of the key which is actually an asset. So looking at ROIC, for example, if you’re able to decrease that then we would have an overall, once operations start our Metrics improve as well. So I think it depends the approach, what they want to improve on, and you have to look at the business model as well.

[00:16:37] Alex Bone: Each sector has its own challenges. And they also have their own focuses as well. So I think that’s important to keep in mind. When we’re looking at some of these financial metrics and what they want to improve on.

[00:16:53] Todd R. Zabelle: We have a customer that is a hyperscale data center player. And they use what we call developers to build and then lease these data centers. And we have another customer that serves the first customer. And they do have different metrics themselves. So one’s all in on development yield because they’re a private equity funded operation where the hyperscaler is concerned about revenue by selling whatever technology services they do to their customers.

[00:17:24] Todd R. Zabelle: So I think another of this might be is what happens when you have two different Performance metrics that are colliding between the two different organizations. And then you probably cascade that down the value chain to maybe OEMs and contractors that don’t use any of this. They’re more, more EBITDA driven.

[00:17:44] Gary Fischer, PE: Very good observation. I think it’s pretty common. I know within Chevron we had multiple financial metrics. We always, if it started out now, we’re always a little confused. Is it return on capital employed? Discount of productivity index? Return on investment? And those things conflict, can conflict with each other.

[00:18:03] Gary Fischer, PE: Net present value? Oh my goodness. You can get on a rabbit hole with net present value. That’s what happens. Production system modeling can help you dial in on whatever it is you want to optimize. I think that’s one line, right?

[00:18:16] Alex Bone: Exactly.

[00:18:17] Gary Fischer, PE: All right.

[00:18:17] H.J. James Choo, PhD: Can I add one thing? So right before this we actually talked about commercializing The new technologies, right?

[00:18:24] H.J. James Choo, PhD: And then we actually talked about how do we actually go through the death valley and then how to get raise funding and so forth Those guys are actually determining whether they should make investments or not based on those metrics that we actually purchased when So what are some of the, so this might be a loaded question and as I said before, what are some of the decisions that a project can make that may look make the financial metrics look great, but are at the end of the day detrimental to the project based on your PSO experience?

[00:18:57] Chet Carlson: Because work in process or construction in process is typically accounted for as an asset On one end, increasing construction progress or work in process might look good on an asset perspective for project financials. But what we understand from modeling and optimizing production systems is that the increase in work in process has negative impacts on the project.

[00:19:23] Chet Carlson: It extends cycle times, which ends up extending duration and increasing cost. Just because it looks good on a financial statement doesn’t necessarily mean it looks good or it’s going to be good for the project.

[00:19:40] Gary Fischer, PE: Okay. Thank you, gentlemen.

[00:19:42] Alex Bone: Thank you.