It's Your Move: Creating Valuable Decision Options When You Don't Know What to Do

The followings is the first chapter excerpt from my newly published tutorial.

Business opportunities of moderate to even light complexity often expose decision makers to hundreds, if not tens of thousands, of coordinated decision options that should be considered thoughtfully before making resource commitments. That complexity is just overwhelming! Unfortunately, the typical response is either analysis paralysis or "shooting from the hip," both of which expose decision makers to unnecessary loss of value and risk. This tutorial teaches decision makers how to tame option complexity to develop creative, valuable decision strategies that range from "mild to wild" with three simple thinking tools.

It's Your Move from Robert Brown

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Read more here.

RFP Competitive Price Forecasting Engine

Developing a competitive price in response to an RFP is difficult and fraught with uncertainty about competitor pricing decisions. "Priced to Win" approaches often lead to declining margins. Our approach and tool set allow you to develop a most likely price neutral position that helps you focus more attention on providing "intangible" benefits that differentiate your offering in a way that is more valuable to your potential client.

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RFP Competitive Price Forecasting Engine from Robert Brown

Business Case Analysis with R

The following is the first chapter excerpt from my newly published book.

Business Case Analysis with R

A Simulation Tutorial to Support Complex Business Decisions

1.2 Why use R for Business Case Analysis?

Even if you are new to R, you most likely have noticed that R is used almost exclusively for statistical analysis, as it's described at The R Project for Statistical Computing. Most people who use R do not frequently employ it for the type of inquiry which business case analysts use spreadsheets to select projects to implement, make capital allocation decisions, or justify strategic pursuits. The statistical analysis from R might inform those decisions, but most business case analysts don't employ R for those types of activities.

Obviously, as the title of this document suggests, I am recommending a different approach from the status quo. I'm not just suggesting that R might be a useful replacement for spreadsheets; rather, I'm suggesting that better alternatives to spreadsheets be found for doing business case analysis. I think R is a great candidate. Before I explain why, let me explain why I don't like spreadsheets.

Think about how a spreadsheet communicates information. It essentially uses three layers of presentation:

1. Tabulation
2. Formulation
3. Logic

When we open a spreadsheet, usually the first thing we see are tables and tables of numbers. The tables may have explanatory column and row headers. The cells may have descriptive comments inserted to provide some deeper explanation. Failure to provide these explanatory clues represents more a failing of the spreadsheet developer's communication abilities than a failing of the spreadsheet environment, but even with the best of explanations, the emergent pattern implied by the values in the cells can be difficult to discern. Fortunately, spreadsheet developers can supply graphs of the results, but even those can be misleading chart junk.

To understand how the numbers arise, we might ask about the formulas. By clicking in a cell we can see the formulas used, but unfortunately the situation here is even worse than the prior level of presentation of tables of featureless numbers. Here, we don't see formulas written in a form that reveals underlying meaning; rather, we see formulas constructed by pointing to other cell locations on the sheet. Spreadsheet formulation is inherently tied to the structural presentation of the spreadsheet. This is like saying the meaning of our lives should be dependent on the placement of furniture in our houses.

While the goal of good analysis should not be more complex models, a deeper inquiry into a subject usually does create a need for some level of complexity that exceeds the simplistic. But as a spreadsheet grows in complexity, it becomes increasingly difficult to extend the size of tables (both by length of indices that structure them and the number of indicies used to configure the dimensionality) as a direct function of its current configuration. Furthermore, if we need to add new tables, choosing where to place them and how to configure them also depends almost entirely on the placement and configuration of previously constructed tables. So, as the complexity of a spreadsheet does increase, it naturally leads to less flexibility in the way the model can be represented. It becomes crystalized by the development of its own real estate.

The cell referencing formulation method also increases the likelihood of error propagation because formulas are generally written in a quasi-fractal manner that requires the formula to be written across every element in at least one index of a table's organizing structure. Usually, the first instance of a required formula is written within one element in the table; then, it is copied to all the appropriate adjacent cells. If the first formula is incorrect, all the copies will be, too. If the formula is sufficiently long and complex, reading it to properly debug it becomes very difficult. Really, the formula doesn't have to be that complicated or the model that complex for this kind of failure to occur, as the recent London Whale VaR model and Reinhart-Rogoff Study On Debt debacles demonstrated.[1]

All of this builds to the most important failure of spreadsheets -- the failure to clearly communicate the underlying meaning and logic of the analytic model. The first layer visually presents the numbers, but the patterns in them are difficult to discern unless good graphical representations are employed. The second layer, which is only visible unless requested, uses an arcane formulation language that seems inherently irrational compared to the goal of good analysis. The final layer--the logic, the meaning, the essence of the model--is left almost entirely to the inference capability of any user, other than the developer, who happens to need to use the model. The most important layer is the most ambiguous, the least obvious. I think the order should be the exact opposite.

When I bring up these complaints, the first response I usually get is: "ROB! Can't we just eat our dinner without you complaining about spreadsheets again?" But when the population of my dinner company tends to look more like fellow analysts, I get, "So what? Spreadsheets are cheap and ubiquitous. Everyone has one, and just about anyone can figure out how to put numbers in them. I can give my analysis to anyone, and anyone can open it up and read it."

Then I'm logically--no, morally--compelled to point out that carbon monoxide is cheap and ubiquitous, that everyone has secrets, that just about everyone knows how to contribute to the sewage system, that just about everyone can read your diary and add something to it. Free, ubiquitous, and easy to use are all great characteristics of some things in their proper context, but they aren't characteristics that are necessarily universally beneficial.

More seriously, though, I know that what most people have in mind with the common response I receive is the low cost of entry to the use of spreadsheets and the relative ease of use for creating reports (which I think spreadsheets are excellent for, by the way). Considering the shortcomings and failure of spreadsheets based on the persistent errors I've seen in client spreadsheets and the humiliating ones I've created, I think the price of cheap is too high. The answer to the first part of their objection--spreadsheets are cheap--is that R is free. Freer, in fact, than spreadsheets. In some sense, it's even easier to use since the formulation layer can be written directly in a simple text file without intermediate development environments. Of course, R is not ubiquitous, but it is freely available on the internet.

Unlike spreadsheets, R is programming language with the built in capacity to operate over arrays as if they were whole objects, a feature that demolishes any justification for cell-referencing syntax of spreadsheets. Consider the following example.

Suppose we want to model a simple parabola over the interval (-10, 10). In R, we might start by defining an index we call x.axis as an integer series.

x.axis <– -10:10

which looks like this,

> [1] -10  -9  -8  -7  -6  -5  -4  -3  -2  -1 0  1  2  3  4  5  6  7  8  9  10

when we call x.axis.

To define a simple parabola, we then write a formula that we might define as

parabola <– x.axis^2

which produces, as you might now expect, a series that looks like this:

>[1] 100  81  64  49  36  25  16  9  4  1  0  1  4  9  16  25  36  49  64  81 100.

Producing this result in R required exactly two formulas. A typical spreadsheet that replicates this same example requires manually typing in 21 numbers and then 21 formulas, each pointing to the particular value in the series we represented with x.axis. The spreadsheet version produces 42 opportunities for error. Even if we use a formula to create the spreadsheet analog of the x.axis values, the number of opportunities for failure remains the same.

Extending the range of parabola requires little more than changing the parameters in the x.axis definition. No additional formulas need be written, which is not the case if we needed to extend the same calculation in our spreadsheet. There, more formulas need to be written, and the number of potential opportunities for error continues to increase.

The number of formula errors that are possible in R is directly related to the total number of formula parameters required to correctly write each formula. In a spreadsheet, the number of formula errors is a function of both the number of formula parameters and the number of cell locations needed to represent the full response range of results. Can we make errors in R-based analysis? Of course, but the potential for those errors is exponentially smaller.

As we've already seen, too, R operates according to a linear flow that guides the development of logic. Also, variables can be named in a way that makes sense to the context of the problem[2] so that the program formulation and business logic are more closely merged, reducing the burden of inference about the meaning of formulas for auditors and other users. In Chapter 2, I'll present a style guide that will help you maintain clarity in the definition of variables, function, and files.

However, while R answers the concerns of direct cost and the propagation of formula errors, its procedural language structure presents a higher barrier to improper use because it requires a more rational, structured logic than is required by spreadsheets, requiring a rigor that people usually learn from programming and software design. The best aspect of R is that it communicates the formulation and logic layer of an analysis in a more straightforward manner as the procedural instructions for performing calculations. It preserves the flow of thought that is necessary to move from starting assumptions to conclusions. The numerical layer is presented only when requested, but logic and formulation are more visibly available. As we move forward through this tutorial, I'll explain more how these features present themselves for effective business case analysis.

1.3 What You Will Learn

This document is a tutorial for learning how to use the statistical programming language R to develop a business case simulation and analysis. I assume you possess at least the skill level of a novice R user.

The tutorial will consider the case in which a chemical manufacturing company considers constructing a new chemical reactor and production facility to bring a new compound to market. There are several uncertainties and risks involved, including the possibility that a competitor brings a similar product online. The company must determine the value of making the decision to move forward and where they might prioritize their attention to make a more informed and robust decision.

The purpose of the book is not to teach you R in a broad manner. There are plenty of resources that do that well now. Rather, it will attempt to show you how to

• Set up a business case abstraction for clear communication of the analysis
• Model the inherent uncertainties and resultant risks in the problem with Monte Carlo simulation
• Communicate the results graphically
• Draw appropriate insights from the results

So, while you will not necessarily become a power user of R, you will gain some insights into how to use this powerful language to escape the foolish consistency of spreadsheet dependency. There is a better way.

1.4 What You Will Need

To follow this tutorial, you will need to download and install the latest version of R for your particular OS. R can be obtained here. Since I wrote this tutorial with the near beginner in mind, you will only need the base install of R and no additional packages.

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Notes
1: You will find other examples of spreadsheet errors at Raymond Panko's website. Panko researches the cause and prevalence of spreadsheet errors.

2: Spreadsheets allow the use of named references, but the naming convention can become unwieldy if sections in an array need different names.

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Read more here: Or, if you prefer Amazon or Scribd.

Never Tell Me The Odds?

In a previous post, I discussed the meaning of expected value (EV) and how it's useful for comparing the values of choices we could make when the outcomes we face with each choice vary across a range of probabilities. The discussion closed by comparing the choice to play two different games, each with different payoffs and likelihoods. Game 1 returns an EV of $5, even though it could never actually produce that outcome; and Game 2 returns an EV of $4, also being incapable of producing that outcome.

But let's say that you hate it when C-3PO tells you the odds, so you commit to Game 2 because you like the upside potential of $15, and you think the potential loss of $5 is tolerable. After all, Han Solo always beat the odds, right? Well, before you so commit, let me encourage you to look into my crystal ball to show you what the future holds…not just in one future, but many.

Figure 1: Han Solo shakes his finger.
C-3PO: “Sir, the possibility of successfully navigating an asteroid field is approximately 3,720 to 1.”
Han Solo: “Never tell me the odds.”

I set up an Analytica model with the following characteristics. A sequence index (Play Sequence) steps from 1 to 1,000. Over this index I will toss two "coins," one with Probability of Win of 50% (Game 1), and the other (Game 2) with Probability of Win of 45% according to the way I set up the game in my last post. Each Game tosses the coin independently across the Play Sequence, recording the outcome on each step. A Game Reward ($10 for Game 1; $15 for Game 2) is allocated to a win, and a Game Penalty ($0 for Game 1; -$5 for Game 2) is allocated to a loss. Then, I cumulate the net Game Earnings across the Play Sequence to show on what value the cumulative earnings might converge for many repeated games choices. Not only do I play the games over 1000 sequence steps, I also play the sequences across 1000 universes of parallel iterations. From this point forward, I will refer to an "iteration" as the pattern that occurs in one of these parallel universes across the 1000 games it plays in sequence.

Figure 2: Analytica Influence Diagram. After each game step, wins are assigned a reward; losses, a penalty. Net returns are accrued.

What do we observe? In one iteration, we see you start off accruing net earnings in excess of the higher valued Game 1 across the first 400 or so games; however, your luck turns. You end up regretting not taking Game 1.

Figure 3: A streak of early wins can deceive your long term anticipations.

In another iteration, you marginally regret not taking Game 1. You can easily imagine that the outcome could be slightly reversed from this, finding yourself happy that Game 2 just beat out Game 1. Would that outcome prove anything about your skill as a gambler?

Figure 4: You might come close to beating the odds. It appears conceivable that you can. Maybe your luck will turn in the next game you play.

But of course, your luck might turn out much worse. In another iteration, you really regret your bravado.

Figure 5: Yikes!

"Of course," you say, "because the probabilities tell me that I might expect some unfortunate outcomes as well as some beneficial ones. But what overlap might there be over all the iterations at play? Is there some universe in which the odds ever really are in my favor?"

Here's what we see. For Game 1, the accrued earnings range from ~$4,500 to ~$5,500 by the 1000th step.

Figure 6: the accrued earnings range from ~$4,500 to ~$5,500 by the 1000th step for Game 1.

For Game 2, the accrued earnings range from ~$3,000 to ~$5,100. Clearly, some overlap potential exists out there.

Figure 7: the accrued earnings range from ~$3,000 to ~$5,100 for Game 2.

In fact, in the early stages of the game sequences, the potential for overlap appears to be significant, and there seems to be a set of futures where the overlap persists. You might just make that annoying protocol droid wish he had silenced his electronic voice emulator.

But take a second look. That second-from-the-top band for Game 2 converges on the second-from-the-bottom band in Game 1. These are the upper and lower 5th percentile bands of the outcome, respectively.

Figure 8: The final distributions of the two games shows that there are some universes in which your luck holds up...a very small amount of up.

When we count how likely it is that Game 2 ends in winning conditions at various intervals points along the way, the perceived benefit in the higher potential reward of Game 2 decays rapidly. Before you even start, the chance that you could be in a better position by step 1000 for taking Game 2 is around half a percent.

Figure 9: The long-term perspective of maintaining a winning position in decays rapidly for Game 2.

In the model, note that the average earnings by step 1,000 for Game 1 is $5,000 (i.e., 1000*EV₁) and that the earnings for Game 2 is $4,000 (i.e., 1000*EV₂). It's as if the imputed EV of each game inexorably accumulated over time…a very long time and over many universes.

So it is in the fantasy of Hollywood that the mere mention of long odds ensures the protagonist's success. Unfortunately, life doesn't always conform to that fantasy. Over a long time and many repeated occasions to play risky games, especially those that afford little opportunity to adjust our position or mitigate our exposure, EV tells us that our potential for regret increases for having chosen the lesser valued of the two games. Depending on the relative size of the EVs between the two choices, that potential for regret can occur rapidly as the inherent outcome signal implied by the EV begins to overwhelm the potential short-term lucky outcomes in the random noise of the game.

So how can you know when you will be lucky? You can't. The odds based on short-term observations of good luck will not long be in your favor. Your fate will likely regress to the mean.

(This post was also simultaneously published at the Lumina Blog.)

Will You Be Mine?

Business is not war. In fact, I'm getting tired of the trope that relies on this analogy. I think it's destructive and counterproductive - just like war.

I can understand the attractiveness of the metaphor as business sometimes looks like its getting all Lord of the Flies. Companies come and go, apparently succumbing to the forces of competition. Profits are made and lost. People's jobs, like lives on a battlefield, are on the line. Kill or be killed. It all frequently feels like a zero sum game.

And war usually is a zero sum game. One side wins and the other side loses. Well, I'm not even sure that is entirely accurate. War involves losses on both sides, the value of which may actually exceed the estimated value of going to war. In the cases of so called Pyrrhic Victories, the victor simply cannot afford to keep engaging in excursions of conquest. Martial conquest does not guarantee profit.

But as similar as business can seem to war, business is not quite the same. Sure, competition is ever present. Contracts are violated. Deals fail to close or are lost to another offeror. There's espionage and subterfuge. But whereas war usually involves two fronts—red versus blue, Joe versus Charlie, Allied versus Axis—business involves at least three fronts: you, the competition, and your customers.

In war, the primary focus rests on the competition, and the goal is to eliminate them, either by all out destruction or by dousing their will to contend. In war, one side usually surrenders to the other. But this is really not the case in business. The primary goal is not to eliminate the competition (it may be counterproductive to do so), but to win the attention of your customers. The real goal of business is to make a transaction in which at least two sides mutually benefit more than if no transaction occurred. The competition is present, and possibly corrosive, but it's not the primary concern.

The competition itself may evolve and satisfy needs and preferences that your own offerings don't satisfy. And so, in this way, business works out to be something more like a complex ecosystem of niche partitioned agents who are seeking to sustain their ability to generate ongoing profitable transactions. And yet it's not so much White Fang as much as it is…well…Pride and Prejudice. (Do I lose my man card for saying that?)

That's right. I think the best metaphor for business is romance in which we as suitors vie for the attention of our beloved—the customer. Again, the competition is there, but it's not our primary concern. We have to learn to deal with it and respond to it; however, if our attention on the competition dominates our activities versus our attention on our customers, current and potential, we may wind up winning a fight but losing our reason for existence, like two boys fighting it out in the school yard over a girl who walks away in disgust.

There is so much more to long term success than revenue generation. But success doesn't happen because we have no competition. Success happens because we provide something that satisfies a need better than the alternatives, even if one of those alternatives is nothing more than what our customers are already doing. What those real needs and preferences are is often hard to identify, but we don't find those out by going to war. We discover them the way lovers learn to fulfill each other's needs. While war is dehumanizing and often provides the psychological barriers that permit actions against others we would normally never consider, romance is about fulfillment.

Let me close with this quote from Marc Hedlund's Blog, in which Marc discloses his thoughts on closing his company, Wasabi:

You can't blame your competitors or your board or the lack of or excess of investment. Focus on what really matters: making users happy with your product as quickly as you can, and helping them as much as you can after that. If you do those better than anyone else out there you'll win.

Tighten up the Ship or build an Airplane? - How to decide?

Tighten up the Ship or build an Airplane? - How to decide? by Robert D. Brown, III and Gerald A. Bush by Robert Brown

Fooling Ourselves

For some peculiar reason, this NPR article brought back memories of when I was a math and physics teacher.  One of the several perennial questions my students used to ask me was, "When will we ever use this, Mr. B.?" Of course, there was always, "Will this be on the test?"

One of my standard responses to the first question (the second usually received a scowl) was that mathematics (insofar as it is actually useful) provides a great tool for determining if you're being cheated. Learning to use it effectively increases our ability to avoid becoming someone else's stooge.

But the NPR article serves to remind us that often the greatest threat to being cheated comes from within. We all need to learn the "algebra" that helps us overcome our own internal scam.

Incite!Sales: Sales Portfolio and Forecasting System

Sales forecasts are notoriously biased, which leads to misallocation of resources and financial surprises. Our sales portfolio & forecasting system removes bias from forecasts to give you a more accurate view of your sales reality so that you can make more informed decisions about opportunities to pursue. http://incitesales.incitedecisiontech.com

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Sales Portfolio & Forecasting System from Robert Brown

A Brief Explanation of Expected Value

When helping people analyze the risks they face in complex decisions, I frequently receive requests for an explanation of expected value, as expected value is a measure commonly used to compare the value of alternate risky options. I’ve found that by now most people understand the concept of net present value (NPV) rather well, but they still struggle with the concept of expected value (EV)*. Interestingly enough, and fortunately so, the two concepts share some relationship to each other that makes an explanation a little simpler.

NPV is the means by which we consistently compare cash flows shaped differently in time, assuming that money has a greater meaning to us when we get it or spend it sooner rather than later. For example, NPV would help us understand the relative value of a net cash stream that experienced a small draw down in early periods but paid it back in five years versus a net cash stream that makes a larger draw down in early periods but pays it back in three years.

EV is similar. By it we consistently compare future outcome values that face different probabilities of occurring.

When we do NPV calculations, we don’t anticipate that the final value in our bank account necessarily will equal the NPV calculated. The calculation simply provides a way to make a rational comparison among alternate time-distributed cash streams.

Likewise, when we do EV calculations, we don’t anticipate that the realized value necessarily will equal the EV. In fact, in some cases it would be impossible for that outcome to be the case. EV just simply provides a way to make a rational comparison among alternate probability-distributed outcomes.

Here’s a simple example. Suppose I offer you two gambles to play in order to win some money. (Not really, of course, because the State of Georgia reserves the right to engage in games of chance but prohibits me from doing so.)

In the first game, there are even odds (probability=50%) that you will win either $10 on the outcome of a head or $0 on a tail.

In the second game, which is a little more complicated, I use a biased coin for which the odds are slightly less than even, say, 9:11 (probability=45%), of your winning. If you win, you gain $15; lose, you pay me $5. Which is the better game to play? Believe it or not, the answer depends on how you frame the problem, most notably from your perspective of risk tolerance and how many games you get to play. If you can’t afford to pay $5 if you lose the second game on the first toss, you’re better off to go with the first game because you will lose nothing at least and gain $10 at best. However, if you can afford the possible loss of $5 and you can play the game repeatedly over numerous times, expected value tells us how to compare the two options.

We calculate EV in the following way: EV = prob(H)*(V|H) + prob(T)*(V|T).

For the first game, EV1 = 0.5*($10) + 0.5*(0) = $5.

For the second game, EV2 = 0.45*($15) – 0.55*($5) = $4.

So, since you prefer $5 over $4 (you do, don’t you?), you should play the first game, even though the potential maximum award is alluringly $5 more in game two than one.

But here's the point about the outcomes. At no time in the course of playing either game will you have $5 or $4 in your pocket. Those numbers are simply theoretical values that we use to make a probability-adjusted consistent comparison between two risky options.

In a follow up post, I will describe what your potential winnings could look like if you choose to play either game over many iterations across many parallel universes.

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*To be honest, I think part of the persistent problem in understanding is contributed by the term "expected" itself. Colloquially, when people use and hear this term, they think "anticipated." In discussions about risk and uncertainty, the technical meaning really refers to a probability weighted average or mean value. Unfortunately, I don't expect that you should wait for us technical types to accommodate common usage. [back]

Getting the Lead Out - of Poor Thinking

New research finds Pb is the hidden villain behind violent crime, lower IQs, and even the ADHD epidemic. And fixing the problem is a lot cheaper than doing nothing.
-"America's Real Criminal Element: Lead"

I'm inclined to believe the recent news (critique, rebuttal) about lead poisoning being the primary culprit in the cause of the crime wave from the 1960s through the 1990s will prove out.

But I'm a spectator here. Notwithstanding the commitment to my current convictions, I do think more study should be pursued to find disconfirming evidence to determine just how strong this hypothesis really is. See, I do not want to believe something is true because I've been convinced it's true. I want to believe because all other alternative explanations have been excluded either by logic or evidence. I hope we're all properly skeptical in this way because the value of removing hypotheses that don't explain the data well might, as is indicated in "America's Real Criminal Element: Lead," prove immensely valuable.

However, I think there is something else for us all to learn here, not just about the pernicious effect of lead in the environment, but also about the toxic effects of bias in our thinking, planning, and policy making and the long term effects of them.

As you read the three linked articles above, ask yourself:

• How often do we insist that we know, know beyond a shadow of a doubt, the cause of some puzzling event because of our ideology, superstitions, or by confusing causation with correlation? 
• Similarly, how often do we consider just how ignorant we really are, completely unaware of hidden variables at work beneath the façade of preconceived notions? 
• How often do we take credit or assign credit to our heroes for desired outcomes on the heels of decisive action when good luck is probably just as good an explanation? 
• Or conversely, how often do we blame ourselves or others for undesirable outcomes when bad luck is also probably just as good an explanation?

...we all have a deep stake in affirming the power of deliberate human action.

There is power in knowing, but there is also power in being aware of just how little we know. And that might prove to be more valuable.

Got Correlation?

And now for your daily correlation versus causation alert.

Countries whose people drink more milk win more Nobel prizes, according to research published Tuesday in Practical Neurology, a serious British journal. It builds on research last year in the New England Journal of Medicine that found a nearly identical link between chocolate consumption and Nobel success.

This is good to know, because I drank chocolate milk every day for lunch from first grade through high school. Of course, it was government milk, so the benefits may not translate to me.

So, What Is Your Algorithm?

I thought this story about Schwan’s was interesting for this reason: a 3-4% improvement on revenues of ~$3 billion (2010 Annual report) over less than one year didn’t feel that significant to me.  In fact, if you look at it this way, Schwan’s improved sales by 3.5% * $3billion/3million purchasers = $35/purchaser.  That’s two additional entrees, or 5 additional pizzas, per customer over 1 year!

The 3-4% improvement over 1 year doesn’t mean, either, that Schwan’s will maintain annual revenue growth of 3-4%, especially if their customer base doesn’t grow.  In fact, according to their 2011 Annual Report, revenues remained at the ~$3 billion level as 2010. So what I see here, in the limited amount of information in this story, is that Schwan’s system lifted sales to make their fleet incrementally more efficient.

Average inflation for 2011 was reported to be 3.2%. (See "Table of Inflation Rates by Month and Year (1999-2012)")  I would be interested to know if Schwan’s own expenses grew at the inflation rate.  If so, Schwan’s merely kept up with inflation through 2011.

Depending on the cost of the system, the system may have made sense from a stand alone ROI perspective.  Call me skeptical, though, but this doesn’t feel like a sustainable game changing improvement at Schwan’s. I hope the story is different for Schwan's one year down the road from the original publication date of this article.

All that aside, I’m not sure this is a good example of avoiding the kinds of decision/thinking failures that Daniel Kahneman talked about because it seems to me the Schwan’s fleet is simply getting a little bit better information about how to implement an existing strategy, as opposed to Schwan’s avoiding the kinds of biases that make people pick the wrong strategy. Maybe that’s the real story here – Schwan’s let the siren song of advanced technology convince them to continue following a margin sensitive strategy to 1 (!) more decimal place.  They are solving the wrong problem with increasing precision. In fact, this story about Schwan’s isn’t really consistent with the Moneyball story of Billy Beane and the Oakland A’s.  In that example, Billy Beane and Paul Podesta challenged long held beliefs about the value of players’ capabilities, tested their own hypotheses, and bought the resources they needed to win at bargain rates.  They weren’t just squeezing more runs out of superstar players.  They found value where everyone else who esteemed themselves as experts said that it couldn’t be found.  The effect was actually, pardon the pun, game changing for the A’s.  They were no longer building a baseball team or playing baseball the way everyone else said that it had to be managed and played.  They became a uniquely good team versus being a team that tried only to improve within notions of conventional wisdom with declining marginal returns for the effort.  Did they use statistical analysis to do their job? Yes, but I think they really only needed the statistical analysis to indicate the presence of an inefficiency in the baseball marketplace, and then to find the resources they needed. They formulated and pursued a strategy around this idea of exploiting information inefficiencies.

What does this mean for us —those of us who desire to be better at making more valuable and creative decisions or helping others in that endeavor?  First, in the age of Big Data, I think we need to be careful about showcasing Big Data applications as examples of how decision analysis methodologies work.  The story about Schwan’s in this article is an application of data analytics and information technology that gleaned narrow improvements from statistical information, not necessarily good creative decision making.  We need to be careful about the distinction in what some people are calling applications of decision science and what we do with decision analysis and management.  I don’t doubt that the Opera solution is doing advanced analytics. I just doubt that Schwan’s engaged in good decision making. :/

What I see that valuable decision analysis provides is a mindset, a meta-system, to avoid the kind of system 1 (biased intuition) and system 2 failures (intellectual laziness) that Kahneman describes. A thorough application of decision analysis should consider multiple alternate strategies to achieve something more than incremental improvements.  With decision analysis we should seek disconfirming evidence and logic for biased assumptions.  Decision analysis of the kind I think we want to do avoids cognitive inefficiencies that arise from cognitive and motivational biases, information that has been aggregated at too gross of a level, and creative laziness. The questions we ought to help the consumers of our thinking answer are not just whether they need better information systems, but whether, for example, a better information system is the best application of resources to achieve game changing returns.  I think this kind of thinking leads to qualitatively different kinds of question.  I’m not saying that advanced data analysis can’t be powerful. We know that it can be.  But it might not always be the best solution.