A Whirlwind Decade

On May 20th, 2013, an EF5 tornado hit Moore, Oklahoma, marking the fourth major tornado in 15 years to carve a swath of devastation through the suburban town near Oklahoma City. I was a 21-year-old rising college senior, beginning a research internship developing lithium-ion battery materials and trying to decide what direction to pursue in my career post-graduation. I had recently lived a few months in Joplin, volunteering with the recovery efforts from the deadliest tornado of this century, so I was deeply affected when I heard the news. I spent the next 3 nights sleeplessly reading about tornadoes, their causes and behaviors, their physics of intensification, and why they might take similar paths. On the third night, I had what can only be described as a lightbulb moment, where within minutes my basic governing theory coalesced and I could envision the warm-air pockets rising and rapidly cooling into the rotating low-pressure core. I followed this idea deep into the rabbit hole but was surprised to find very little literature linking topography with tornadogenesis, an unnerving but exciting prospect for a young and passionate STEM student.

Rather than keep my potential discovery close to the vest, I immediately ran the theory by anyone and everyone I knew. Russel and George, scientifically-minded connections I had met in Joplin, corroborated that many Midwesterners believe that tornadoes tend to take certain paths preferentially. I asked several Rice professors across multiple departments – anyone with a background in fluid mechanics, transport phenomena, or atmospheric science – for their input, which refined my then-raw understanding of wind flows over complex terrain. I received perhaps the most impactful advice from my research advisor, Dr. Lisa Biswal, who told me that a truly original discovery is worth pursuing because a scientist may not have another one for their entire career. During a few pivotal meetings, she gave me a roadmap for testing my theories, seeking outside support, and undergoing the arduous but rewarding process of drafting a scientific paper. This advice really meant a lot coming from a young professor who had built a successful lab based upon an open-minded approach, keeping several projects active simultaneously in the hopes of that big, defining breakthrough.

The tornado project would go through several iterations over the next few years. First, I developed a steady-state model of a tornado using Matlab, a 3-dimensional numerical simulation that allowed me to conceptualize the forces present at various points in and around a tornado. Two years later, I enlisted the help of brilliant Vanderbilt undergrad Lily Williams to help me transfer this model into Python and begin to add perturbation conditions. When I left Vanderbilt in 2016, I studied land-surface models intensively and eventually coded one to reflect the steady-state conditions of the pre-storm environment. I attended multiple conferences and presented the work to academics and NOAA scientists, who were broadly impressed by my determination but wanted to see more (read: years of data on predictive efficacy). Seeing that it would be very difficult to accomplish my goals as an independent scientist, I pivoted toward the private sector, testing the entrepreneurial waters at Springfield Startup Weekend and presenting my work to investors. I had planned to launch a web platform that can generate real-time surface heatmaps, a goal that I was pretty close to accomplishing during the first couple months of the COVID lockdown.

When various pressures brought me to Texas in June 2020, the project was abruptly tabled. Over 2 years passed before I had the time to pick up where I left off, and sadly this did not go well. It took me about 200 hours of work to rebuild my Python environment and get the old code running on my new laptop – I was that far behind on package updates and had lost a step when it came to writing code. Many of my former connections had either sold their atmospheric models, moved on to different research pursuits, or retired. I had lost the fiery passion to solve the mystery of why tornadoes take the paths that they do, in part because of my separation from the tragedy of tornadoes and in part because I saw that the lifesaving value of my work would be ultimately limited. Despite this cynical ending, I am proud that I took a major leap and developed the land surface model for tornado simulation – though somewhat sad to lose what had become a major part of my identity. Perhaps the tornado project will take on life again in the future, but for now, I am trying to enjoy the fair weather while finding my professional next steps.

An Engineered Economy

While studying for the FE Exam, I recently dusted off a subject that I learned a decade ago as a college senior: engineering economics. It didn’t fully make sense at the time, a compendium of formulas that manipulate capital and operating costs with annualized cash flow estimates, interest rates, inflation adjustments, depreciation from planned obsolescence, and more. I understand the rationale behind teaching this to engineering students, as these analysis tools would be useful when engineers are elevated to project management roles that deal directly with financial resources. However, in my experience, the business-savvy decision-makers often do not understand these financial metrics, instead embracing a less mathematical, all-or-nothing mode of thinking. The disconnect between engineering economics and business practice was striking to me, in a way explaining some of the current volatility experienced within the economy.

To establish a basis, I’ll start by summarizing the “engineering school edition” of project economics, which mirrors financial tools used by other fields. We learn accounting principles, including how to create a balance sheet and apply terms like book value, gross margin, ROI ratio, leverage, liquidity and solvency. We learn several ways to calculate asset depreciation (most notably MACRS) to justify equipment purchases considering tax implications. We learn how to weigh benefits and costs in many different scenarios, quantifying societal benefits (in dollars) for municipal projects and projecting cash flows for industrial investments. Sensitivity analysis – including risk assessment, which is a key aspect of engineering design – adds rigor to these cost-benefit evaluations. As a preliminary design step, we learn to use discount factors to assess the profitability of a project against a nominal interest rate from investing in a bond or long-term index fund, for example, adjusting for inflation and manipulating the bases of capital and operating costs to fit the conventional yearly terms. Many of the discount factor calculations over a wide range of cash flows and interest compounding strategies are tabulated in the document below; incidentally, it is an excerpt of the reference handbook for the FE Exam, and I find it a comprehensive yet succinct resource for these value-fudging formulas.

engineering-economics-fe-1Download

In industry, at least in my experience with small-to-medium-sized companies, all of these formulas go out the window. I’ve sat in many meetings with owners and investors, featuring rolling leather chairs and unbuttoned blazers and ego-stroking small talk and, if we’re lucky, perhaps a few PowerPoint slides. These guys invariably want to skip straight to the bottom line: what is my money doing for me? Investors are not impressed by uniform theoretical cash flows or future worth regressions; rather, they want to know the minimum amount of money they can put in for an acceptable return. As the engineer overseeing technical aspects of the project, I would be responsible for coming up with an itemized list of expected capital and labor costs (treated as upfront expenditures, never annualized) and an implementation timeline. Exact figures were generally expected, necessitating detailed equipment quotes instead of cost index estimates or other shorthands. The finance team, led by investors or owners, would generate their own revenue projections based on idealized profit margin and market share estimates. The resulting negotiation between finance and engineering was usually one-sided, as they would try to make line-by-line cuts, especially to labor, in the hopes of saving as much money as possible while keeping their glistening business projections intact.

One might argue that this is the normal give-and-take of business, but it often led to what I would consider ill-informed and dangerous decision-making. The financial pressure to make cuts to critical safety infrastructure and ignore expensive code requirements was strong and ever-present. When implementing a design for a hand sanitizer blending process, I had to fight hard to get critical safety features like automated fill/level control for the stainless steel blend tanks and a fire alarm-sprinkler system for the building. I was forced to sacrifice other requisite items like FDA-compliant ancillary equipment for material handling, EPA-compliant pollutant testing equipment, and IFC-compliant safety ventilation. The owners had made the calculation that saving several hundred thousand dollars up front was worth the possibility of a fine, as any regulatory enforcement would take months or years to take effect and the amount of penalty is generally limited by law (e.g. USDOT can assess a maximum fine of $250,000 for a shipping violation, TCEQ can assess no more than $25,000/day, etc.). They would sooner stop production or disband the company in the event of a regulatory crackdown, as long as they have made it to the bank first – an attitude that I encountered with a more extreme twist in 2020 when numerous lawsuits and unpaid wage claims never caught up with the people behind a fly-by-night manufacturing operation in rural west Texas.

When investors see their peers garner massive returns in startup companies with often-nefarious business practices, they want massive returns for themselves. One investment group declined to invest in a Felix Tech project because they “will not invest in anything that doesn’t guarantee at least a 500% return within 5 years.” We didn’t even qualify for a meeting with another group, which required that investment candidates already have an operating revenue of at least $2 million at a margin of at least 30% (i.e. an established, highly profitable business…and these guys had the audacity to call themselves ‘angel’ investors). When this expectation of high returns is coupled with an aversion to risk, the pressure to make unrealistic promises is immense. Wishful thinking prevails, making the interest rate formulas for comparing returns with the bond market seem droll and irrelevant. But there is a sobering reality that every project is at risk of losing its funding to another profitable venture, an extraordinarily difficult challenge in the increasingly financialized climate of the last five years where massive gains in the stock market, cryptocurrency, and real estate assets were commonplace. When a financier decides to pull $500,000 out of a $2 million build to invest in cryptocurrency instead, it has a debilitating effect on the project and sends shockwaves that impact everyone connected to the business.

The broader point here is that when I watch the recent bank runs at Silicon Valley Bank, First Republic Bank, and others, the overall behavior feels familiar. Greedy investments were made into tech companies that were pressured into promising the moon. The banks entrusted with safeguarding these investments also got in on the action, seeking to maximize returns for their own shareholders. When the first dominoes fell, the parties with the riskiest positions had to race to pull their money from these banks. Meanwhile, the banks have a backstop from the federal government, who has committed to reimbursing account holders up to $250,000 or likely more through the FDIC. The American people will pay for this two-fold, as taxpayer money is used to subsidize financial impropriety and as the larger economy suffers from the speculative instability of major banks collapsing. Maybe the business attitude will undergo a much-needed adjustment as time goes on – especially as the explosive growth of financialized assets cools and investors must consider investing again in longer-term projects that generate solid, lasting returns. Or maybe the financial system needs to borrow from engineering logic, appropriately assessing its risks and recalibrating toward a realistic rate of stable growth for the future.