Outcomes at PrimeCorp

As a result of the risk-adjusted forecasts, both the baseline and with leadership expectation of the impact of the proposed strategic management system, PrimeCorp had satisfactorily completed its cost-benefit analysis and projected payback. Curtis (PrimeCorp’s CEO) soon thereafter approved the project as proposed, and our work was underway. Working closely with Jim (PrimeCorp’s head of strategic planning), we undertook to transform the way in which PrimeCorp managed its strategy. The transformation took about two years.

During the first year, we used the discipline of balanced scorecard strategy maps and measuresto capture and refine a vision of strategy for the PrimeCorp enterprise, as well as its several divisions. The process revealed cultural tensions; resistance to change (even though no one on the management team really liked the existing strategic management process), and tension between corporate-level governance and a tradition of division-level autonomy. Both in one-on-one discussions and leadership meetings, the appropriate role of the corporation was portrayed variously as simply that of a holding company and a bank providing capital, to a necessary facilitator of innovation, synergy, and simple scale economies between division accustomed to operating with limited oversight. While neither extreme was realized, there was a perceptible shift towards more robust corporate governance, with integrated strategic planning and financial forecasting at its core.

Our comprehensive program of strategy communication brought the message of strategy and personal participation in execution to hundreds of middle-level managers across the organization; a population to whom the content of corporate and divisional strategy had previously been opaque.

While our effort to justify the initial investment introduced the dimension of risk into financial forecasting, the idea of risk did not become part of the everyday vocabulary of forecasting. Our mandate was to transform strategic planning; budgeting and forecasting was outside of our scope. Exposure to the idea of risk management was simply not enough motivation to re-design PrimeCorp’s tradition financial forecasting.

Over the following years, the improvement in PrimeCorp’s financial performance well exceeded even the most liberal of the scenarios used to justify the effort in the first place.It wasn’t our work that created the improvement; it was the tenacious effort of hundreds of executives, senior and middle managers, front-line supervisors and employees; armed with a clear vision of PrimeCorp’s strategy that made it happen.

The Offer to You

I have developed a spreadsheet-based model of the risk-adjusted forecast approach to share with you, in the hope that you too will make a better business case for enhanced strategic management. As promised in the first post, the first five people to send a note to me describing your situation at info@tenacioustortoise.com will receive a copy of the tool, and an hour of guidance in its use by telephone at no charge. I am looking forward to hearing from you.

Needless to say, this series of posts has been much lengthier and detailed than most posts here. Was this useful? Too much? I need your feedback. Please offer your questions and comments below.

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Putting the Model to Work

The first revelation of PrimeCorp’s risk-adjusted forecast was that it was lower than the original forecast.

To illustrate how this happened, imagine forecasting your personal income for next year. Let’s say that your salary is X dollars per year, so that is your original forecast. But with a risk-adjusted view of your future earnings, you might also acknowledge a 15% chance of getting a bonus equal to 10% of your salary, and you might also acknowledge a 20% chance of losing your job and not finding another one for the rest of the year. So 15% of the time your expected income would be X / 2 (this reflects an assumption that you have an equal chance of losing your job on any day of the year, in which case your expected income would be the same as losing your job halfway through the year). 60% of the time your income would simply be X, and 25% of the time your income would be 1.1 * X.

By combining these possible outcomes with their odds of taking place, we get:

( .15 * 1.1 * X) + ( .65 * X) + (.20 * .5 * X) = .915 * X

In other words, the risk-adjusted forecast for your income is 91.5% of your salary.  

In capturing the characteristics of ‘below plan’ and above plan years at PrimeCorp, we learned that given an equal chance of each, there was more downside potential in the ‘below plan’ years than upside potential in the ‘above plan’ years. As a result, their risk-adjusted forecast was below the original forecast. This was a bit of a revelation to PrimeCorp’s leadership team.   

Changing the Odds

To then forecast the prospective impact of a strategy management program, we asked PrimeCorp’s leaders to adjust the odds of having a ‘below’ ‘above’, and ‘as planned’ year given successful execution of their strategy. The responses ranged from no or very modest impact from the skeptics, to a cautiously larger impact from those who supported the idea of implementing our proposed strategic management process.

After some discussion, their sense of the impact was consolidated into three scenarios:

• ‘Low’ impact, with a small avoidance of the ‘below plan’ outcome, the same odds of an ‘as planned’ outcome, and no improvement in the likelihood of the ‘above plan’ outcome, with respective odds of 20%, 55% and 25%;
• ‘Moderate’ impact, shifting the curve, with respective odds of 20%, 49%, and 31%;
• ‘Improved’ impact, with respective odds of 15%, 55%, and 30%.

Running the model with PrimeCorp’s own expectations yielded definitive results. Even under the most conservative scenario, the investment in the strategic management system would be paid back in under three years. And the forecast was based entirely on inputs from PrimeCorp.

Next: Outcomes at PrimeCorp, and the case summary.

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The Different Approach

We already knew that a key element of PrimeCorp’s existing strategic planning process was its financial forecasts. The annual planning book (hundreds of pages, highly confidential, and not shared beyond the executive team), contained page after page of spreadsheets describing past and expected future performance of each of PrimeCorp’s several divisions, as well as an enterprise-wide roll-up of the numbers. The executive team, which consisted of the heads of each division (as well as such corporate functions as HR Finance, and IT) annually created their individual division forecasts as a function of past performance, and their own expectations of the next five years of future results. This process was time-consuming and filled with understandable tension – between division leaders’ desire to soft-peddle the numbers, and CEO and board pressure to raise revenue and manage costs to achieve year-over-year improvement in profitability.

What was evident in the forecast numbers was their inability to capture the uncertainty in establishing them. This was our opening to capture the value of our improving PrimeCorp’s strategic planning process. From Part I, you’ll recall that I challenged Curtis and Jim to pay us $25,000 for a couple of weeks of effort to make the case for our proposal. I said, “Instead of just looking at your forecasts as a set of fixed numbers, we’ll consider the likelihood of the forecasts playing out. Our firm won’t introduce any numbers into the calculation – we’ll just use your and your team’s own expectations.” Curtis accepted the approach, and on a handshake, we got started that day.

Choosing the Levers

Like any business, PrimeCorp’s profitability is the result of inputs. Looking at the forecasts, it became clear that for each division, profit was the result of revenue, cost of goods sold, operating expense, and the cost of invested capital. To improve profit, a division had to accomplish some combination of increasing revenue, managing the cost of inputs and transforming them to outputs (the cost of goods sold, or COGS), manage its overhead costs (such as corporate overhead and IT), or reduce the cost of invested capital (primarily in each division’s necessary and sizable inventory). Of course, each of these four inputs were themselves the result of a multitude of contributing factors. But to manage complexity, we chose only these four inputs for each division as the appropriate level of detail for the exercise.

Capturing Uncertainty

Having both forecast and actual results for each division in several prior years, it was easy to judge the quality of PrimeCorp’s forecasting process. Revenue and capital cost were easier to forecast than COGS and operating expense, and variation in all resulted in years with significant over- and under-performance to the forecast, as well as years near forecast. There was no apparent bias in the forecasts; performance had been significantly above or below plan about the same number of times over the past ten years.

To quantify the uncertainty in the forecast for the next fiscal year, we imagined repeatedly ‘rolling the dice’ on the outcomes of the upcoming year. Sometimes the outcomes would be below the forecast, sometimes above. Ranking all of these hypothetical outcomes from poorest to best, we could then put these outcomes in three buckets; the bucket for the poorest 25% of them would be called ‘below plan,’ the middle 50% of them would be in a bucket called ‘as planned,’ and bucket for the best 25% would be called ‘above plan.’ Note that this was not an exercise in predicting the likelihood of any of these outcomes – by fixing the probabilities for each bucket, we could then ask a different pair of questions, “how bad would a ‘below plan’ year look, and how good would an ‘above plan’ year look? We assigned the original forecast values of revenue, COGS, operating and capital costs to the ‘as planned’ scenario. The first key input to the model were answers to the question, “what would the four inputs look like in the ‘below’ and ‘above’ plan year scenarios?”

To capture these inputs, my hope was to interview each division head and capture their own expectations for next year. But Curtis was less comfortable with the subjectivity of that approach, and instead we turned to past history. By comparing the forecast vs. actual outcomes for the four inputs in each of the last ten years of history, we could come up with reasonable estimates of the inputs parameters for the ‘above’ and ‘below’ scenarios (for those with statistics backgrounds: we actually determined the standard deviation for each of the four inputs, and used the forecast plus or minus one standard deviation for the parameters). Historically, above plan years happened when revenue increased and costs, especially operating costs, were held in check. Below plan years were more likely to be the result of higher COGS, not significantly reduced revenue. Although the likelihood of a below and above plan year were equal in the model, we learned that above plan years were only modestly above plan, while below plan years could be significantly below plan.

Rolling Up to a Revelation

Pulling all the input parameters together made it possible to create a risk-adjusted forecast for the upcoming year (and years beyond). The first revelation was that the risk-adjusted forecast was lower than the original forecast.

Next: Putting the Model to Work

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A few years ago, I was faced with a unprecedented challenge by a client, to attach a dollar value to the benefit of a proposed consulting engagement. The details of the organization are not important to the concepts in the case, but suffice to say I was hungry for the opportunity to consult to this large organization.

A Bit More Background

The organization in question was (and still is) a large, for-profit business with a national presence across the U.S. (let’s call it PrimeCorp), and a decades-long track record as either the largest or second largest player among the few occupying a unique industry niche. Two of my colleagues had been engaged in attempting to sell PrimeCorp on the idea of revising and enhancing its strategic planning process, a process that would take at least two years of design, facilitation, and coaching on our part. Our firm was up to the task we were proposing, but my two colleagues hadn’t yet made the case.

Competing with a few other consulting firms, we had successfully qualified ourselves as PrimeCorp’s preferred consultant. Jim, PrimeCorp’s head of strategic planning, was familiar with our firm and our approach, and had invested much of his energy in paving the way for us. But Jim couldn’t award us the work – the decision lay exclusively with Curtis, the firm’s forceful and charismatic CEO. And he wasn’t buying, yet. The question wasn’t whether we would do the work – it was whether PrimeCorp would make the investment at all.

The Fly in the Ointment

Curtis was secure in his position as CEO, having occupied the job for many years prior to our arrival. He had close ties to members of PrimeCorp’s board, and the loyal support of his executive team. But his firm was suffering from declining profitability, relatively flat sales, and an emerging threat of technology-based competition from outside the industry. Curtis was frustrated with PrimeCorp’s lack of strategic clarity, and poor track record of executing on past strategic intent. With margins becoming thinner, PrimeCorp’s overall management style was a continuation of its long history of risk-aversion.

Determined to better manage rising costs, Curtis had recently imposed a rigorous capital planning discipline on his executive team. All requested capital investments over a low threshold would require sponsors to submit a comprehensive cost-benefit analysis, including projected payback periods. The CBAs would go to PrimeCorp’s board of directors for approval. No CBA, no investment. Since Curtis himself would by necessity be the sponsor of the proposed revamp of strategic planning, he wasn’t about to make an exception for himself from his own policy. So  Jim’s challenge to us was to help him and Curtis make the case for the investment. My colleagues tried to present anecdotal evidence of the benefits of improved strategic planning in other organizations, but Curtis and Jim weren’t convinced. To them, our anecdotes were just marketing hype.

A Different Approach

I had joined the PrimeCorp team quite late in the process, when it appeared that we wouldn’t be winning the business. My task was to scour our firm’s scant data on past client performance, and develop a more compelling set of data describing the benefits they had received. But there simply wasn’t enough data, and what little there was couldn’t be assembled into anything convincing. After some head-scratching, I came up with a different approach. But I needed to pitch it directly to Jim and Curtis. With little to lose, we scheduled the meeting.

Our Challenge to PrimeCorp

Jim and Curtis really wanted to do the work, but needed proof. In what could have been our final presentation at PrimeCorp, I apologized to Curtis for our failure to make the case. “Let’s approach this differently. Instead of using other companies’ data, we’ll only use PrimeCorp’s. Give us two weeks, complete access to both your existing strategic planning documents and your executive team, and $25,000 (for our time), and we’ll make the case.If we’re successful, we’ll credit you the $25k against our overall proposal. If we’re not, we’ll part company.” So PrimeCorp’s downside risk was only $25,000. Curtis was intrigued. “Tell me more,” he said.

Next: The Different Approach

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We have all grown up in an era in which computing technology has had an increasing influence on the way we think about pretty much everything. In my first year of high school, slide rules were required for those taking physics classes. By the time I took the physics class (in my senior year), we were able to instead share the one calculator the school had bought for each classroom in the physics department. I suspect that some of today’s technologies will someday be as outdated as slide rules are today. If you don’t even know what a slide rule is, don’t worry (but you can read more about it here).

One of the ways in which we’ve been influenced is our tendency to represent uncertainty as single numbers. If you’re filling out a form in which you’re asked to estimate your household income for next year, you likely wouldn’t be able to enter anything but a single number. But a single number simply can’t capture any of the uncertainty of the estimate. To try to help most people understand even the most basic concepts of statistics is an extreme challenge. One cell in a spreadsheet only holds one number.

If you don't know what this is, don't worry.

The concepts of “uncertainty” and “risk” are so poorly understood in organizational settings that they may even be used interchangably. As a result, conversations about risk are difficult, because people understand the concept of risk in different ways. As I hope to cover in the coming weeks and months, one of our biggest challenges today is to introduce the concepts of risk and uncertainty into the strategic planning process. But thanks to Professor Savage, we can enhance our understanding with some plain language examples.

Uncertainty is simply the state of not being able to know an outcome. We don’t know for certain about the outcome of a coin flip, a dice roll, or tomorrow’s stock market performance. By contrast, risk is the probability of a loss or injury.

Suppose I offer you either $100 or nothing based only on the outcome of an uncertain event, a coin flip. Heads, you win, tails you get nothing. But I also offer you a choice; to skip the coin toss in exchange for a guaranteed $30. What would you do?

Of course, the average result of the coin flip is $50, but we’re only going to flip the coin once. So this average outcome (the expected value for those with a statistics background) is a bit of a red herring here. If you’re poor and looking for your next meal, you’d probably take the sure $30. But you might otherwise be willing to accept the risk of getting nothing in exchange for the possibility of $100.

Now imagine the same offer, but instead, the offer is $1 million riding on the coin flip, with an alternative of a sure $300,000. I suspect that you would take the sure $300,000. Unless you’re as wealthy as Bill Gates.

Uncertainty is unavoidable – it is a feature of the universe we live in. No one knows how the coin will flip. Risk is in the eye of the beholder, and a reflection of the impact of the loss or injury to an individual or group. Giving up a sure $300,000 in exchange for an uncertain outcome means something different to you or me than to Bill Gates. Our attitudes towards risk (utility, if you’ve taken the economics class) is different.

Every assumption and decision that goes into the strategic planning process has a degree of uncertainty, and (typically) a level of risk that is poorly understood. By improving our understanding of risk and our attitude toward risk, we can improve the quality and outcome of our strategic decisions. More to come.

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A classic example of the Flaw of Averages involves the Statistician who drowned crossing a river that was on average 3 ft. deep.

Desperately casting around for a topic to write about today, I was grateful to see a link to an interview in the San Jose Mercury News with Stanford professor Sam L. Savage about his book, The Flaw of Averages (great title!). I’ve not read the book yet, but the review has certainly piqued my interest:

How does General Motors, Sam L. Savage wonders, explain the pathetic performance of its crystal ball? When Americans started driving hybrids, GM was still pushing Hummers. Executives at the giant carmaker — fully aware of union contracts, presumably prepared for rising gasoline prices and economic uncertainty — drove straight into the ditch of bankruptcy.

“Probability management” is often mismanaged by business leaders, says Savage, a consulting professor of management science and engineering at Stanford University and a fellow at the Judge Business School at the University of Cambridge. Savage, who has performed probability studies for Royal Dutch Shell, set out to right statistical wrongs in his book “The Flaw of Averages.”

The Information Age has transformed statistics into a vital field of study, yet Savage says many habits and practices have been slow to change from the “steam era statistics” of the Industrial Age.

Written for a business audience, “The Flaw of Averages” leavens the math with levity, even the occasional cartoon.

Well alright then. Working with business executives and their measures for so many years, I continue to be amazed at how easily decisions are made on the basis of numbers with little consideration for the risks and consequences of those decisions. I’ve been meaning to write at some length about the need for the discipline of risk management in change programs, but before doing so, we all need to take a deep breath and consider the magnitude of our collective innumeracy.

The topic has been covered before. I just pulled Innumeracy: Mathematical Illiteracy and its Consequencesby John Allen Paulos from my bookshelf, and thumbing through it, I remember how much I appreciated the book, but that is wasn’t the easiest read. From the back cover description:

Why do even well-educated people understand so little about mathematics? And what are the costs of our innumeracy? John Allen Paulos, in his celebrated bestseller first published in 1988, argues that our inability to deal rationally with very large numbers and the probabilities associated with them results in misinformed governmental policies, confused personal decisions, and an increased susceptibility to pseudoscience of all kinds. Innumeracy lets us know what we’re missing, and how we can do something about it.

Sprinkling his discussion of numbers and probabilities with quirky stories and anecdotes, Paulos ranges freely over many aspects of modern life, from contested elections to sports stats, from stock scams and newspaper psychics to diet and medical claims, sex discrimination, insurance, lotteries, and drug testing. Readers of Innumeracy will be rewarded with scores of astonishing facts, a fistful of powerful ideas, and, most important, a clearer, more quantitative way of looking at their world.

SinceI found that Innumeracy was not especially accessible, I haven’t yet found occasion to use examples from it. Perhaps The Flaw of Averages will be better. It looks promising. From the interview with Savage:

Q. What are the most common ways people foolishly apply the law of averages? Is it the faith placed in “average returns” on retirement portfolio?

A. Plenty of people have been caught off base by the Flaw of Averages in investing, but here is an example that is closer to home. Imagine that both you and your wife are right on time for appointments, on average.

When you go somewhere together, however, you will be late, on average. Why? If we model being early or late for each of you by flipping a coin (heads is early, tails is late), then the only way you will not be late as a couple, is if neither of you is late. This is like flipping two heads in a row, or one chance in four. Now expand this to a big industrial project with thousands of tasks, and you can imagine the implications.

We don’t have to imagine the implications – we live with them every day. More to come (soon, I hope), on the topics of innumeracy and strategic risk management.

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Newborn gazelle hunkering down for safety, Serengeti, Tanzania (© Erika Bloom)

Reading blogs, scanning headlines, and staying in touch with old friends, it seems to me that right now there is a lot of hunkering down going on. Hunkering down, like dodging bullets and any port in the storm are vivid metaphors for the actions of people when there is danger about. During a global recession, individuals naturally think about protecting themselves and their families from the risk of unemployment, investment failure, and other threatening stuff.

Organizational behavior is a ‘soft’ science that begins with the premise that organizations exhibit collective behaviors. This too is natural. Fish and birds move in unison. Bees, ants, and other insects live in highly-ordered societies that act in concert. Wolves hunt in packs. Evolutionary biologists explain these behaviors as adaptations not just for the survival of the group, but the survival of the species.

Human organizations behave this way as well, but there is no apparent species being protected by organizational behavior, just the organization itself. Some organizations are risk-takers, while others are more prudent about risk. Right now, risk-taking seems at an all-time low.

As we slowly emerge from the depths of economic decline, we’ll likely see tipping points inside organizations as those advocating seizing new opportunities hold sway over those who want to continue hunkering down. National and global economies will reach tipping points as well, when optimism outweighs fear.

Economic downturns perform a function akin to forest fires – clearing the dense underbrush to enable vigorous new growth. Some companies, and perhaps even some industries may not survive the flames. But those that will survive have the best opportunity to thrive by acting before the tipping points. This isn’t just speculation, it is a pattern that has been repeated following previous downturns.

Many of my clients over the past several years have sought to foster more risk-taking as part of larger cultural changes, and have even gone so far as to codify this objective in their strategy maps. But is has been far easier for leaders to express a desire for more risk-taking than to actually take more risk for the organization.

I expect that in a few years, we’ll be reading and writing the case studies of those firms who even now are focusing on seizing new opportunity. They’ll have to act early, and act quickly. Is your organization going to be one of them? Does your organization have the capacity and the will to change?

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