Decision Making in a Competitive Business Environment

The electric power generation industry has been in a highly regulated business environment for most of its existence. Over the last several years, however, much of the industry has been moving to a different, more market-driven business environment and companies have had to make a hard adjustment in their basic thinking and their approach to risk. For better or worse much of the world’s electricity generating industry now finds itself in an increasingly competitive business environment and decision-making must evolve from a risk-adverse to a risk-management style.
For most of the 100+ years of its existence our industry has been in a highly regulated business environment. That is because of the very high cost of developing the infrastructure required to generate, transmit and distribute electricity. Electricity is also a commodity that cannot be easily stored so that when you turn on a light switch some generating unit somewhere must increase its output.
So to ensure that electricity would be available to all at a reasonable price and that the investors in electric companies would get a reasonable return on their investments each investor owned company was granted a monopoly in its service area but with a high degree of regulation and oversight. Each company had a compact with its regulating agency such that if the company spent money prudently (building and maintaining a plant, transmission lines, distribution transformers. etc.) the regulating agency would allow that cost plus a set profit to be collected by the company from its customers and distributed to its investors. The basic equation governing decision making was therefore:

Cost (prudent) + Profit (mandated) = Price

Under this compact there is little incentive for any company to take any risks. As an example, consider two mutually exclusive options that a company could invest in. Option “A” will guarantee a savings of twice the cost while option “B” will yield ten times the savings but only have a 50/50 chance of success. The proper decision for a regulated company would always be to chose Option A since they could not keep any of the additional “upside” of option B (beyond the allowable rate of return) if it were successful and may have to completely “write-off” the cost if option B were unsuccessful (a 50/50 possibility).
Hence, the appropriate decision making mindset in a regulated industry is to avoid risk! And historically this behavior has often been observed throughout the management chain in every regulated company.
Consider now what changes take place when competition is introduced to the generation industry. The governing equation now changes to the following:

Price (market) – Cost (total) = Profit

Now the company’s profit is the dependent variable (not price as in the regulated model) and that profit is dependent upon 1) the price set by the market (with little control by the company) and 2) each company’s total cost, including successful and unsuccessful investments and their total (not just regulated) returns.
Since no one is evaluating the prudency of how the company spends its money (except its investors), the company should now always choose the 10-1 option B (even with only a 50/50 chance of success) over the 2-1 “sure thing” of option A since the company now gets to keep all of the upside when it is successful, making the mathematical odds of option B a 5-1 return (50% change of a 10-1 = 5-1), much better that option A (2-1 return). Eventually, companies consistently making these managed risk decisions will put competing companies that make avoided risk decisions out of business. The better all employees are at evaluating their decision options are in terms of their Reward-to-Risk Ratios the better the results will be.

In addition companies that create a mindset of identifying, quantifying and managing risk will continually examine their failures (as well as successes) to try to learn why it failed (or succeeded) and apply that knowledge to the next group of decisions. Risk-adverse companies have a motivation to “bury their mistakes as deep as possible” and are often slower at improving.

Reward/Risk Decision – Example 1

This is a simple example of how to evaluate options while playing a video poker game from the mindsets of 1) risk avoidance and 2) risk management.
You are playing a “jacks or better” video poker game and you have been dealt an ace-high straight which pays $20 for a $5 investment. Four of the cards in the straight are hearts (the 10, jack, king and ace) while the queen is a spade.
You now have the choice of 1) keeping all five cards you were dealt and guaranteeing a $20 payoff (a 4-1 return) or 2) discarding the queen of spades and hope to be dealt the queen of hearts, making a royal flush with a payoff of $2000!

What do you do?

Risk = $5.00
Option 1 – $20 @ 100% probability = $20.00
Option 2 – $2000 @ 1/47 probability = $42.55

Option 1 – $20/$5 = 4
Option 2 – $42.55/$5 = 8.51 (Actually the ratio would be slightly higher since other winning cards could be drawn; i.e. a different heart for a flush or a different queen for a straight or a non-heart jack, king or ace for a pair that will pay off.

We can readily see that high risk – high reward Option 2 is over a 2-1 better option than Option 1 (8.51 / 4) = 2.12. This is true even though Option 2 will only pay off one time out of every 47 times this deal occurs whereas Option pays off every time! But Option 2 makes so much money when it does hit it is well worth the risk.

A decision-maker in a regulated market will always choose Option 1 since he can’t keep but a small percentage of the huge upside Option 2 offers when it does hit. However, in a market business environment his company can keep all of Option 2’s upside when it hits and the total payoff more than makes up for the many times it fails.

Reward/Risk Decision – Example 2

If I change the example so that you have been dealt a king-high straight which still pays $20 for a $5 investment? Four of the cards in the straight are hearts (the 9, 10, jack and king) while the queen is a spade.
Again you have the choice of 1) keeping all five cards you were dealt and guaranteeing a $20 payoff (a 4-1 return) or 2) discarding the queen of spades and hope to be dealt the queen of hearts, making a straight flush but now with a payoff of only $250 instead of the $2000 payoff for a royal flush.

Now what do you do?

Risk = $5.00
Option 1 – $20 @ 100% probability = $20.00
Option 2 – $250 @ 1/47 probability = $5.32

Option 1 – $20/$5 = 4
Option 2 – $5.32/$5 = 1.06 (Actually the ratio would be slightly higher since other winning cards could be drawn; i.e. a different heart for a flush or a different queen for a straight or a non-heart jack, king or ace for a pair that will pay off.

We can readily see that high risk – high reward Option 2 is a much worse option than Option 1. This is because the reward is much lower while the risk is the same.

These simple examples demonstrate that the best decisions can only be determined by considering the rewards as well as the risk and the Reward/Risk ratio is a good way to evaluate investment options.


What strategy should you employ to maximize your chance of winning at the horse track? It depends on how you will be graded! If you are being graded on your winning percentage (how often you cash a winning ticket) you should bet the favorite to come in third place. That way if the favorite (probably the best horse) comes in 1st, 2nd or 3rd you will win! However, this very risk-adverse strategy is almost guaranteed to lose money over the long term. But that’s not the way I said you will be graded!!! So you shouldn’t care since you will have a high winning percentage.

If, however, you were told that you would be graded on how much money you have at the end of the evening you should adopt a much different strategy. You would start by trying to forecast the “true odds” of each horse winning (using either forecasts from the track handicappers or your own method) using all available data.
However, now you will need to factor in the payoff for each horse and not simply choose the horse with the best chance of winning. Just before the start of the race you can check the “tote board” that has information about the payoff for each horse based on which horses other people at the track have bet on. This is called pari-mutual wagering which takes the total amount bet on each horse, extracts a set percentage, usually ~ 18%, for distribution to the winning horses plus state taxes and track expenses and profits, etc. and then divides the remainder by the number of tickets sold on that horse. Dividing each horse’s payoff by the odds of its winning will give you the Reward to Risk Ratio. The horse that has the highest Reward/Risk Ratio is the one you should bet on!

Horse Track Reward/Risk Example Risk-Management Winning Strategy

Below are the horses in the first race and the odds that they will win the race (as set by the track handicapper) and the payoff rates (as set by the betting public)
Horse Odds Payoff Reward/Risk Ratio
Secretariat 2-1 1.5/1 0.75/1
Whirlaway 4-1 6/1 1.5/1
Citation 5-1 10-1 2/1
Gallant Fox 7-1 10-1 1.43/1
Alysheba 8-1 4-1 0.5/1
Seattle Slew 10-1 17-1 1.7/1
Northern Dancer 12-1 20-1 1.67/1
Swaps 15-1 25-1 1.67/1
War Admiral 17-1 30-1 1.76/1
Aristides 20-1 35-1 1.75/1

Which horse would you bet on?

From the table we can see that “Citation”, while only the third most likely horse to win at odds of 5-1, will have a payoff of 10-1 when it does win. Therefore if this exact scenario was repeated 10 times with you betting $10 on Citation each time and Citation did win twice (once every 5 races as you predicted) then you would collect $200 (2 wins X $10 per bet X 10/1 payoff) and you had bet a total of $100 (10 races at $10 per race).
Even though Citation only won 2 out of the 10 races and the favorite, Secretariat, won half (5) times, betting on Secretariat would have only paid $75 (5 wins X $10 X 1.5 payoff) for your total bet of $100. So your “Secretariat” .500 “batting average”, while it would guarantee that you would get into the Hall of Fame if this were baseball, would have a far inferior financial return compared to “Citation’s” .200 “batting average” (which would have ensured that you would never even make it to the major leagues at all) but a much larger profit.

As a company’s management moves away from risk-adverse decision making toward risk-management decision-making you should expect certain changes to occur.
1) The staff should begin to identify all technically viable decision options, even ones that may only promise to solve less than 100% of the problem (remember the 80/20 rule that states that 80% of the problem can be solved for 20% of the resources required to solve 100% of the problem – so don’t always request the “perfect” solution – use incremental B/C analysis to ensure the best use of company resources across the fleet.
2) The uses of historical performance data to forecast future performance with and without the proposed option will become even more important in estimating each option’s Reward/Risk Ratio.
3) All staff must get used to using Reward/Risk Ratios when making decisions and compare results to expectations instead of ignoring your mistakes since in a market business environment you should be willing to examine your decision making processes and seek to get better at identifying, quantifying and evaluating all viable decision options using Reward/Risk analyses.

In concluding this case study I am reminded of a story I told several years ago at a workshop I held for the United Nations Development Program in China. It seemed that two men were hiking in the woods when they came to a clearing and saw a large bear attacking them. One man turned to run away while the second man took off his hiking shoes and changed into his track shoes. The first man said “You can’t outrun that bear” to which the second man replied “I don’t have to, I just have to outrun you”! So as that big bear of competition starts attacking your company remember, you don’t have to be the fastest to change, you just can’t be the slowest!!!

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