Category: Thinking/Analysis

Framing Disruptions

Simple approaches can focus the mind. When thinking through the means and mechanisms by which disruptions could affect my business, my community, or the economy, I often ask two simple questions rooted in economic fundamentals[1]:

  • Big or small? In other words, how impactful, whether positive or negative, would we expect this disruption or change to be on local infrastructure, needed supplies, or aggregate demand? 
  • Long or short? What is the likely duration, whether positive or negative, of this disruption or change on supplies or demand in the community, market, or industry?

As an example from my work in the forest industry, consider the impacts of natural disasters on timber markets. In August 2005, Hurricane Katrina, a Category 5 event, struck the Gulf States, brutally affecting homes, forests, and infrastructure. As a disruption, how would we provide context to its impact on timberland owners? Consider these two questions to organize our thinking:

  • Big or small? Big event locally; devastating to some forests and damaging to many. 
  • Long or short? Short-term increase in supplies; negative impact on timberland value.

Using Frameworks to Make Decisions

Simple frameworks add value to the extent that they prove useful. This analytic exercise supports decision analysis. Using experience, knowledge, and available data, it screens and ranks the relative importance of potential disruptions on lives, businesses, and markets. We can then probe assumptions and, while there may be no “right” answer, develop operable approaches to establish priorities.

With family or colleagues or neighbors, the resulting discussions can clarify how much control we have. Some disruptions, like a zombie apocalypse, get thrust upon us from the outside, while others, like new internet providers or car technologies, offer choices. And a big impact or opportunity for an individual, family, or single forest owner can be small for the overall market and community. 

With respect to applying this approach with executives and other decision-makers, I think in terms of “tactics” (operations) versus “strategies” (capital allocation). Disruptions that get handled on the ground via operations tend to qualify as shorter or smaller in this framework, while anything that requires a board meeting or act of Congress reflects longer-term disruptions affecting investments and strategic advantage. Given this approach, how do we develop strategies and assess exposures across multiple disruptions for these situations locally?

Time as a strategic idea remains malleable. Discussions about potential disruptions reinforce the arbitrary nature of the “short” and “long” term. The space and time required for impacts to realize themselves can extend beyond today, tomorrow, or next year. 


Regardless, we succeed by prioritizing in the name of preparation. My working assumption is that a person or firm that is ready for everything is ready for nothing. We avoid personal and organizational paralysis through taking positions on what matters, on where we have control, on eliminating the gnats and knots to go after the disruptions that threaten survival or offer outsized opportunities. 

In March 2020, I posted “Thinking Through Risk and Uncertainty: Contemplating the Coronavirus” and am returning to these themes as ideas and questions related to risk, disruption, and stability have become more frequent in my conversations. For those interested in a further discussion of strategic thinking (and how the coronavirus affects the forest industry), click here to read a five-page white paper.

[1] These questions are a variation on the frequency-severity framework used for insurance and risk management applications. In forestry, I have found it useful to think through duration – how long something lasts – rather than, or in addition to, the gap in time between occurrences. 

Data, Technology, and Judgment

We homo sapiens seem overwhelmed by information, technology, and choices. We sadly struggle to manage the daily deluge, cycling through frustration, indignation, anger, and melancholy. A simple scan of news headlines, op-eds, and online comments can fuel a sense of despair, even on the brightest, sunniest day during this era of peak human achievement and prosperity.

Now that I’ve cleared my throat on our societal condition with a sweeping generalization, I will transition to a few practical matters intended to ground our thinking and even the score.


Data, like a socket wrench, is useful if you have something to do with it. The value of data increases with a framework to apply or basic question to answer. How we collect and analyze data, and ultimately communicate results, profoundly affect understanding and insight. In The Effective Executive, the late Peter Drucker wrote about the problem of production data getting averaged out and “translated” for accountants:

“Operating people, however, usually need not the averages but the range and the extremes….”

This applies to investing, medicine, rocket science, and research. In forest economics, for example, there is no such unit as an “average timber market.” Timber markets are uniquely local, though the inputs for analyzing them, as with the ingredients for baking bread[1], are basic and known. Since having the necessary data and knowing how to apply it are two different things, we sometimes look to frameworks, models, and technologies.


Recalling what the dog said when dating the skunk, with technology “you gotta take the good with the bad.” Technology, like all things human-borne, can prove miraculous and curative or horrific and destructive. Consider technology a two-sided coin: nuclear bombs and nuclear energy; carbon emissions and carbon capture; radiation poisoning and radiation treatments. 

While technological applications support accessible education, they also facilitate misinformation conspiracies, and hate speech. In this dizzying relationship, we use technology in ways that create problems, and then we return to technology to mitigate and solve those problems. Ultimately, technology is an agnostic tool; its use and consequences depend on judgment.


Judgment, like trust and good relationships and bonsai, takes time to nurture and develop. Mistakes are okay. Bad judgment, however, is a virus that never leaves. Bad judgment leads to bad decisions and poor results. As Jim Rohn said years ago, 

“Failure is a few errors in judgment repeated every day.”

Across professions, from medicine to education to consulting, the assessment of competence, whether qualitative or quantitative, tests versions of, “Do you have the experience, knowledge, and judgment to improve the situation or help us make a better decision?” In other words, and in the end, do you know when or how to apply the information and technologies at hand?

[1] For reference: flour, yeast, water, and salt, in addition to and quoting a good forest economist friend, love. “Got to have love. Most important ingredient, Brooks.”

How to Conduct Analysis and Think for Yourself

Recently, I wrote a two-part series at Forisk on “Forest Products and the Economics of Timber Markets.” It focused on (1) the practical connection between the things we use and where they come from and (2) methods for organizing our thinking when making decisions. Having a clear understanding of how things work helps specify appropriate questions so we can focus mental resources on analyzing, prioritizing, and deciding.

Anything we can do to avoid trapping ourselves in outdated frameworks or conventional wisdom offers light for insight and new ideas. In my field of forestry, when talking to the same people at the same events, year after year, we can unintentionally find our thinking congeal around a cosmic group consensus. The colleagues and clients I work with want to avoid this, but we remain subject to this risk. What can all of us do to improve our chances for useful analysis and independent thought? 

Get Out of the Truck

First, get out of the truck.” Go visit mills and suppliers and clients. Read analysis from other industries and fields. Have a hobby that relaxes your mind and exposes you to other ideas. As travel guru Rick Steves says, “the more you see the more you see.” Talk to people in the field. Talk with researchers and equipment operators. Talk to bankers, lawyers, and accountants. Then, give yourself time and space to think about what you’ve seen and heard.

Harness Multiple Viewpoints

Second, harness multiple viewpoints. We don’t hire “yes men”; we rely on colleagues sharing different points of view and ways of looking at things. We have clients who feel the same. On multiple occasions, clients have assigned me a “devil’s advocate” role on behalf of Boards of Directors or investors, where my job is to challenge assumptions and come up with alternate scenarios.[1] Other people see things we miss, so avail yourself to that invaluable resource and opportunity to revisit your thinking. 

Deal in Specifics

Third, deal in specifics.[2] We rarely succeed when dealing in generalities. As an analyst, writer, and human, I find unsubstantiated, broad-based assertions to be damaging, distasteful, and unhelpful. While I value multiple viewpoints, I want people to have reasons and logic behind those views. Avoid the complacency of going along with a new idea, or abandoning your own, without understanding, at some level, the reasons and mechanism. 

As Seneca said, “Everything hangs on one’s thinking…”

[1] Ironically, just because someone asks you to disagree with their ideas or alternatives, it doesn’t mean they really want to hear them…

[2] This references number 8 from the post “Ten Observations of Human Behavior and Learning” which is “When we deal in specifics, we rarely fail.”

Ode to Weighted Averages

Several years ago, in the essay Average is the Enemy, I wrote about the “false shortcuts offered by averages” for making decisions. Averages give a sense for where the middle lies within a group. They offer a starting point for understanding a situation but, like stereotypes, are incomplete and can mislead. 

Mathematically, averages specify the arithmetic mean. Calculating an average represents one of many approaches for profiling data when conducting analysis. In fact, averages themselves come in different forms. When conducting forest industry research, my team at Forisk often uses rolling and weighted averages to address different issues and better leverage underlying data. 

Rolling Averages

A rolling, or “moving,” average provides a way to measure trends over time. This can be useful when studying the status of a situation from daily, weekly, or monthly data, such as housing starts, health trends, and the economics of different businesses. For example, in the forest industry, the COVID-19 pandemic initiated two years of extreme volatility with softwood lumber prices. Indexed monthly prices increased 71% in mid-2020 before resetting and spiking to an all-time high in mid-2021 and resetting and cycling steeply again in 2022 before, relatively speaking, stabilizing.

When evaluating product margins over time, we want to avoid over-exposure to outliers or random spot prices, such as when lumber exceeded $1,500 per thousand board feet (MBF) in mid-2021. A rolling average cuts a path through the cycle to “smooth out” reported prices while still including the most recent data. In this way, we might apply the last three, six or twelve-month average lumber price to fairly assess the break-even and potential profitability of a business or sector.

Overall, a rolling average provides a practical way to readily communicate insights from simple data series. The Economist calls them, “Among the unsung heroes of statistical methods…” I agree.

Weighted Averages

A weighted average accounts for the relative importance of certain aspects of the data. This differs from a simple average, which treats all observations in a data set equally. In this way, and depending on the question asked, a weighted average can improve our use of available data.

Consider another forest industry example. In 2022 in the U.S. South, the four-quarter rolling average price of pine sawtimber, the logs bought by sawmills to produce the softwood lumber used for homebuilding, was $27.79 per ton according to data from TimberMart-South. This number is a simple average of 11 state-level prices, from Texas to Virginia and down to Florida. However, when we weight those prices by log use (volume) by state, we get $28.59 per ton. 

The difference reflects how higher volume states with more sawmills and higher lumber production levels reported higher log prices and vice versa. For example, Georgia, with a 2022 average price of $33.43, consumed around 13 million tons of pine sawtimber in 2022, while Virginia reported a price of $21.17 while using around 3 million tons of sawtimber over the same period. 

We can also combine approaches to calculate a rolling weighted average. This will better reflect the state of the sawtimber market and the value of wood delivered regionally over time.


Systematic exploration of a situation benefits greatly from the proper, context-appropriate application of available tools and methods. The calculation of an average is, of itself, an agnostic act. However, its ability to clarify depends on the underlying data and question being asked. Rolling and weighted averages, and their combination, offer ways to improve our understanding of a given situation.

Clean Over Current

As leaders, parents, investors, and coaches, we often make decisions with imperfect and incomplete information. Therefore, we benefit by having an approach or philosophy for dealing with uncertainty. When screening and evaluating analysis, I start by confirming that what we have in hand is clean and accurate. Building a history of clean, error-free, detail-oriented work builds trust and puts you in a better position to influence decisions and lead the room.

Errors Inject Doubt

For strategic questions and market projections, I prefer clean data and analysis over rushed, subjective intel. Ideally, we have both, but if given the choice, I want things clean, with an “as of” date, over speculation on today’s unconfirmed events. It’s how we report things at Forisk, since we, like many market analysts, rely on government data and other sources that often lag actuals by weeks, months, or quarters, and this data often gets revised in future months. 

If the report I have has multiple errors, then I doubt everything it contains. If it’s clean but a little behind, we can still make decisions and assess performance. We can also evaluate the likelihood and implications of the most recent market intel when it comes in. Without a clean, verified understanding of historical events, we are poorly positioned to evaluate new theses or announcements. However, with a clean dataset and framework, we can develop intuition and scenarios on how changes affect the market and our clients.

Understand How Things Work on the Ground

When conducting forest industry analysis, teams I work with are mindful of the fact that “operations come first” to truly understanding how things function on the ground. If our analysis and understanding is inconsistent with what mill managers or procurement foresters see, then we have something to reconcile. In our role, we add value by connecting information across markets and over time, which prioritizes clean analysis to make our work credible for clients who need to make decisions. 

If we hear a piece of market intel that could change our thinking, we call local contacts working in the field and ask, “is this true? How could this affect you?” As with many things, news is often a rumor, and the impact is regularly overstated. 


There are situations and occupations where the most recent intel has more value, such as on the battlefield, in the operating room, or at the trading desk. However, for strategic questions and projections, and given the choice, I’ll take clean over current.