Scenario Analysis the best cure for uncertainty
15 July | by Carl Seidman and Lance Rubin
Introduction to the co-author
Carl Seidman, founder of Seidman Global LLC and Seidman Financial, is a trusted business advisor specializing in financial planning & analysis (FP&A), business strategy, and finance transformation. He coaches and advises FP&A professionals at Fortune 500 corporations and middle-market companies, helping establish greater uniformity in practices and process.
At the same time, he brings finance professionals greater control over their careers by helping them build their competencies while eliminating time-wasting activities and mistakes.
His strategic finance training curriculums and FP&A development programs are among the most in demand in North America and are frequently delivered by the leading financial seminar companies on the continent.
Carl occasionally serves as a CFO advisor to a select number of lower mid-market and entrepreneurial businesses throughout the United States and Europe. These companies often contend with strategic finance issues around growth or restructuring.
Carl is a CPA and has earned other professional credentials including CIRA, CFF, CFE, and AM (Accredited Member in business valuation). He has a master’s degree in accounting and bachelor’s degree in finance and economics. He lives in Chicago with his wife and twin sons.
Why did Carl select the topic and why is he passionate about it?
I accept that I can never know with confidence what the future will bring. That’s a reality in business and life.
But just because reality is inherently uncertain doesn’t mean I can’t plan for it and can’t take calculated risks.
To me, that’s what scenario planning is all about – describing a future that doesn’t yet exist based upon what we believe will be true.
If I were to plan for my personal future, I’d have to make a lot of guesses. Many will be accurate and many will be wild guesses.
For instance, I can reasonably estimate what my average compensation over the next 3 years will be with a high degree of confidence. However, I can’t estimate with a high degree of confidence what my children’s post-secondary education costs will be because there are so many variables and long planning horizon. It could be zero or it could be hundreds of thousands of dollars. How can I effectively position for it all?
Planning for such a wide range of economic possibilities is quite difficult, thus the savings and student debt crises many people find themselves in.
If I were to plan for my business future, I’d still need to make a lot of guesses. For example, I can reasonably estimate my headcount as I know my current headcount and my plans for future hiring and cost-of-living adjustments.
However, it may be more uncertain how my revenues will materialize. While I know certain clients will continue to work with me at the current run-rate, others may choose to conclude their relationship.
Still more, I may bring on new clients that I’ve never worked with before.
Rather than marry myself to one set of assumptions and find myself scrambling when those assumptions change, scenario planning allows me to contemplate the future even before that future happens. Indeed, it’s guess-work. But it’s important to contemplate what could happen well before it does, allowing me to plan well in advance. I can also plan for greater flexibility and implement greater risk management to weather volatility.
Topic and context in no more than 3 sentences
Scenario analysis and related planning is all about uncertainty and the possibilities that can arise and the need to have agility in decision making.
Rather than come up with one set of assumptions (which we know are likely to be wrong and will change), scenario management encourages us to come up with many different assumptions, run them in isolation or together, and track the results against each other and of course reality.
As our assumptions change over time, we can easily pivot from one scenario to another which we believe will be more reflective of reality.
If you had to teach this topic in a class to school kids what key tips would you give them to focus on?
While I don’t teach school-age kids, I do facilitate live training and development programs for up to 3,000 entry-level, middle-management and senior-level strategic finance and FP&A professionals each year.
When I speak about scenario planning with them, I share with them the following:
A common approach used by financial modelers is to use three scenarios – often a lower case, mid case, and upper case.
The rationale for this is to capture pessimistic and optimistic floor and ceilings and know that future results are unlikely to exceed either threshold.
The mid case usually represents a ‘best’ or ‘most-likely’ case. For example, several years ago I was helping a major cultural institution in a major US city manage its liquidity. I began by illustrating three cases as highlighted above. In other words, if the institute realized stronger performance, it would track toward the ‘worst’ case; however, if it realized weaker performance, it would track toward the ‘pessimistic’ case. Realistically, the institute would fall somewhere in the middle.
While these are good starting points, we shouldn’t just proceed with three cases as the rule-of-thumb – we should contemplate as many or few cases as useful for our decision-making. Having too many is equally not useful due to information overload.
I’ve advised companies using a binary, two-scenario forecast and I’ve advised companies using several dozen scenarios. I’d caution any financial modeler not to subscribe to the rule-of-3 under all circumstances.
Like sensitivity analysis, scenario planning should not be done in isolation without consideration of risk and probability, knowing that all forecasts are wrong and some are useful.
If we were to contemplate four scenarios for liquidity – default, pessimistic, conservative, optimistic – I’d want to understand: a) the probability of each scenario, and b) the assumptions and risks that are inherent.
I can’t simply splash four lines on a graph to demonstrate the range of possibilities. Instead, I may assign probabilities of 10%, 30%, 50%, and 10% respectively, which skews our scenarios and gives higher weighting to the conservative scenario.
Knowing these scenario probabilities allows us to plan with a heightened degree of confidence in our forecast and, ultimately, our decisions.
Like a sensitivity analysis, we should contemplate how we can more effectively manage risk across the scenario by focusing on the key (and not all) individual drivers.
In this case, I may believe that loss of a large customer could put us into a liquidity crisis; however, the likelihood of that loss is extremely low (thus the 10% weighting applied to the default scenario).
On the flip side, perhaps we are in the final stages of executing a letter of agreement with a large customer but believe there’s low probability of us winning the contract. We translate that into 10% weighting on the optimistic scenario.
Every scenario should be justified and have a strong basis for the assumptions being made.
What practical steps can people take now to learn more?
Scenarios exist in virtually all aspect of our lives. They are the “what-ifs” inherent in everything.
If you plan to go on a vacation and some of your plans go awry, what will you do if your flight gets cancelled?
What will you do if you get sick?
Because you know these possibilities exist, you buy travel insurance and you get immunized/health insurance.
While the likelihood of a cancellation or illness may be small, if they are realized, they could bring major disappointment to your life. Thus, there’s big business in insurance and often this can be the safer option than leaving it to a model or chance to make a decision. Little in a model is ever guaranteed.
When it comes to scenario planning in business, think about possibilities, contingencies, and effects that most people have not thought about.
Apply probabilities and quantify the likelihood of them materializing into something worthy of action. Learn to communicate with non-financial people in a way they can easily understand what they should be considering and the implications of their decisions.
When it comes to tools, as a financial modeler, there are almost unlimited resources available to mechanically execute scenario planning.
The classic examples are Excel and Google Sheets, both of which are easy to learn and manage. In fact, I’ve run and managed scenario planning for $500 million companies using Excel.
Indeed, these basic platforms become difficult to manage when dealing with more complicated businesses. One might have to consider other platforms outside Excel or Google sheets when the data being used is too large or nuanced.
In summary, scenario planning is both a mechanical exercise (selecting a tool and managing the process on that platform) and an intellectual one.
When a client retains me, they rarely hire me to build a model or analysis – they hire me to advise them on how to make better decisions. Building scenarios shouldn’t be difficult but it can be complicated and full of risk.
Indeed, models can be great mechanisms for insights. But while anyone can become a good financial analyst and modeler, the greatest values of a financial analyst are being able to effective create and manage scenarios, support those scenarios with well-vetted assumptions, and interpret the findings.
That takes awareness, diligence, and experience.
Where are good places (links) to find out more on the topic?
This is a video I did about 4-5 years ago for the American Management Association for a course I’ve delivered on an off for them for about 10 years:
Here is a webinar I did with one of my partners on FP&A (with a little bit of discussion on sensitivities) about 4 years ago:
I am always publishing new content on my Linked In profile and websites:
o www.linkedin.com/in/carlseidman
How important is this skill in the context of learning Financial Modeling?
Financial models are almost always wrong. I sometimes joke, that if your models are always right, you’re either lucky or you’re cheating.
Because of this inherent error, we must run scenarios to see what our results would look like under different circumstances and when our assumptions change.
Perhaps I’m biased because I’m writing this post, but I believe scenario planning and related decision-making is the essence of what financial modeling is all about.
How does all this disruption, AI and automation talk impact this topic
Scenario planning is nothing new and most organizations companies do some degree of scenario planning.
However, disruption in scenario planning is everywhere.
As it relates to AI and automation, these tools continue to offer tremendous opportunities, and risks, to scenario planning and related decision-making.
Machine-driven scenario planning has been successfully implemented with companies we interact with daily.
Whether it be the airlines, ridesharing, or ecommerce, machines are able to evaluate a range of scenarios and recommend decisions related thereto.
For example, when dealing with a flight cancellation, it is not a room full of humans running scenarios and simulations who auto-rebook the flight for another day.
Instead, it is a software platform licensed by the airlines that facilitates the rebooking and contemplates the scenario with an abundance of data points.
In ridesharing, algorithms for pricing and route adjust based upon supply and demand, time of day, distance of destination from pick-up, weather, and saturation of the network among other factors.
In ecommerce, packaging arrangements, delivery cost, and delivery time are estimated based upon the order size, complexity, location of the receiver, orders placed by other customers, and a wide-array of logistics considerations.
The list could go on and on and it will continue to evolve very quickly. When machines can complete low-risk, yet high-complexity, scenarios we should be enthusiastic to utilize their capabilities.
As I mentioned in my post on sensitivities, we need to be cautious, however, in that machine-learning is far off from being able to emulate human decision-making.
Examples I share above are logistical nightmares for humans to execute, both in terms of time commitment and likelihood of error. We are well beyond the days of having a room full of airline customer service representatives hand-crafting travel itineraries. Even though the stakes may be high to us as travelers, they are low risk business decisions for a machine to make.
In a high-risk business decision, requiring elements of human insight, it may be disastrous to outsource scenario planning and related decisions to machines.
As I write this article, I am on a flight from Boston to Chicago. With talk about autonomous vehicles like cars and trucks, it wouldn’t seem outrageous to envision a time with self-piloted planes.
In the event of a storm, a plane would be faced with scenarios – maintain current altitude, change altitude, maintain current speed, change speed, maintain current course, change course, among other decisions.
We’ve seen recent accidents with the Boeing 737 MAX and tragedies like Air France Flight 447, which crashed in Brazil in 2009 due to pilot misunderstanding the computer-driven recommendations.
There must be a balance between high-risk scenario-related decisions managed by human and low-risk scenario-related decisions managed by machines.
In the not-so-distant-future, we will undoubtedly see more scenarios and simulations facilitated by AI that has historically been done by humans, but we must have the prudence to know what the power of outsourcing does not outweigh its risks.
If you want to find out more and follow the rest of the article series be sure to download the Financial Modelling App
If you want to find more information on financial modelling and content visit the Model Citizn website.15 July | by Carl Seidman and Lance Rubin
Introduction to the co-author
Carl Seidman, founder of Seidman Global LLC and Seidman Financial, is a trusted business advisor specializing in financial planning & analysis (FP&A), business strategy, and finance transformation. He coaches and advises FP&A professionals at Fortune 500 corporations and middle-market companies, helping establish greater uniformity in practices and process.
At the same time, he brings finance professionals greater control over their careers by helping them build their competencies while eliminating time-wasting activities and mistakes.
His strategic finance training curriculums and FP&A development programs are among the most in demand in North America and are frequently delivered by the leading financial seminar companies on the continent.
Carl occasionally serves as a CFO advisor to a select number of lower mid-market and entrepreneurial businesses throughout the United States and Europe. These companies often contend with strategic finance issues around growth or restructuring.
Carl is a CPA and has earned other professional credentials including CIRA, CFF, CFE, and AM (Accredited Member in business valuation). He has a master’s degree in accounting and bachelor’s degree in finance and economics. He lives in Chicago with his wife and twin sons.
Why did Carl select the topic and why is he passionate about it?
I accept that I can never know with confidence what the future will bring. That’s a reality in business and life.
But just because reality is inherently uncertain doesn’t mean I can’t plan for it and can’t take calculated risks.
To me, that’s what scenario planning is all about – describing a future that doesn’t yet exist based upon what we believe will be true.
If I were to plan for my personal future, I’d have to make a lot of guesses. Many will be accurate and many will be wild guesses.
For instance, I can reasonably estimate what my average compensation over the next 3 years will be with a high degree of confidence. However, I can’t estimate with a high degree of confidence what my children’s post-secondary education costs will be because there are so many variables and long planning horizon. It could be zero or it could be hundreds of thousands of dollars. How can I effectively position for it all?
Planning for such a wide range of economic possibilities is quite difficult, thus the savings and student debt crises many people find themselves in.
If I were to plan for my business future, I’d still need to make a lot of guesses. For example, I can reasonably estimate my headcount as I know my current headcount and my plans for future hiring and cost-of-living adjustments.
However, it may be more uncertain how my revenues will materialize. While I know certain clients will continue to work with me at the current run-rate, others may choose to conclude their relationship.
Still more, I may bring on new clients that I’ve never worked with before.
Rather than marry myself to one set of assumptions and find myself scrambling when those assumptions change, scenario planning allows me to contemplate the future even before that future happens. Indeed, it’s guess-work. But it’s important to contemplate what could happen well before it does, allowing me to plan well in advance. I can also plan for greater flexibility and implement greater risk management to weather volatility.
Topic and context in no more than 3 sentences
Scenario analysis and related planning is all about uncertainty and the possibilities that can arise and the need to have agility in decision making.
Rather than come up with one set of assumptions (which we know are likely to be wrong and will change), scenario management encourages us to come up with many different assumptions, run them in isolation or together, and track the results against each other and of course reality.
As our assumptions change over time, we can easily pivot from one scenario to another which we believe will be more reflective of reality.
If you had to teach this topic in a class to school kids what key tips would you give them to focus on?
While I don’t teach school-age kids, I do facilitate live training and development programs for up to 3,000 entry-level, middle-management and senior-level strategic finance and FP&A professionals each year.
When I speak about scenario planning with them, I share with them the following:
A common approach used by financial modelers is to use three scenarios – often a lower case, mid case, and upper case.
The rationale for this is to capture pessimistic and optimistic floor and ceilings and know that future results are unlikely to exceed either threshold.
The mid case usually represents a ‘best’ or ‘most-likely’ case. For example, several years ago I was helping a major cultural institution in a major US city manage its liquidity. I began by illustrating three cases as highlighted above. In other words, if the institute realized stronger performance, it would track toward the ‘worst’ case; however, if it realized weaker performance, it would track toward the ‘pessimistic’ case. Realistically, the institute would fall somewhere in the middle.
While these are good starting points, we shouldn’t just proceed with three cases as the rule-of-thumb – we should contemplate as many or few cases as useful for our decision-making. Having too many is equally not useful due to information overload.
I’ve advised companies using a binary, two-scenario forecast and I’ve advised companies using several dozen scenarios. I’d caution any financial modeler not to subscribe to the rule-of-3 under all circumstances.
Like sensitivity analysis, scenario planning should not be done in isolation without consideration of risk and probability, knowing that all forecasts are wrong and some are useful.
If we were to contemplate four scenarios for liquidity – default, pessimistic, conservative, optimistic – I’d want to understand: a) the probability of each scenario, and b) the assumptions and risks that are inherent.
I can’t simply splash four lines on a graph to demonstrate the range of possibilities. Instead, I may assign probabilities of 10%, 30%, 50%, and 10% respectively, which skews our scenarios and gives higher weighting to the conservative scenario.
Knowing these scenario probabilities allows us to plan with a heightened degree of confidence in our forecast and, ultimately, our decisions.
Like a sensitivity analysis, we should contemplate how we can more effectively manage risk across the scenario by focusing on the key (and not all) individual drivers.
In this case, I may believe that loss of a large customer could put us into a liquidity crisis; however, the likelihood of that loss is extremely low (thus the 10% weighting applied to the default scenario).
On the flip side, perhaps we are in the final stages of executing a letter of agreement with a large customer but believe there’s low probability of us winning the contract. We translate that into 10% weighting on the optimistic scenario.
Every scenario should be justified and have a strong basis for the assumptions being made.
What practical steps can people take now to learn more?
Scenarios exist in virtually all aspect of our lives. They are the “what-ifs” inherent in everything.
If you plan to go on a vacation and some of your plans go awry, what will you do if your flight gets cancelled?
What will you do if you get sick?
Because you know these possibilities exist, you buy travel insurance and you get immunized/health insurance.
While the likelihood of a cancellation or illness may be small, if they are realized, they could bring major disappointment to your life. Thus, there’s big business in insurance and often this can be the safer option than leaving it to a model or chance to make a decision. Little in a model is ever guaranteed.
When it comes to scenario planning in business, think about possibilities, contingencies, and effects that most people have not thought about.
Apply probabilities and quantify the likelihood of them materializing into something worthy of action. Learn to communicate with non-financial people in a way they can easily understand what they should be considering and the implications of their decisions.
When it comes to tools, as a financial modeler, there are almost unlimited resources available to mechanically execute scenario planning.
The classic examples are Excel and Google Sheets, both of which are easy to learn and manage. In fact, I’ve run and managed scenario planning for $500 million companies using Excel.
Indeed, these basic platforms become difficult to manage when dealing with more complicated businesses. One might have to consider other platforms outside Excel or Google sheets when the data being used is too large or nuanced.
In summary, scenario planning is both a mechanical exercise (selecting a tool and managing the process on that platform) and an intellectual one.
When a client retains me, they rarely hire me to build a model or analysis – they hire me to advise them on how to make better decisions. Building scenarios shouldn’t be difficult but it can be complicated and full of risk.
Indeed, models can be great mechanisms for insights. But while anyone can become a good financial analyst and modeler, the greatest values of a financial analyst are being able to effective create and manage scenarios, support those scenarios with well-vetted assumptions, and interpret the findings.
That takes awareness, diligence, and experience.
Where are good places (links) to find out more on the topic?
This is a video I did about 4-5 years ago for the American Management Association for a course I’ve delivered on an off for them for about 10 years:
Here is a webinar I did with one of my partners on FP&A (with a little bit of discussion on sensitivities) about 4 years ago:
I am always publishing new content on my Linked In profile and websites:
o www.linkedin.com/in/carlseidman
How important is this skill in the context of learning Financial Modeling?
Financial models are almost always wrong. I sometimes joke, that if your models are always right, you’re either lucky or you’re cheating.
Because of this inherent error, we must run scenarios to see what our results would look like under different circumstances and when our assumptions change.
Perhaps I’m biased because I’m writing this post, but I believe scenario planning and related decision-making is the essence of what financial modeling is all about.
How does all this disruption, AI and automation talk impact this topic
Scenario planning is nothing new and most organizations companies do some degree of scenario planning.
However, disruption in scenario planning is everywhere.
As it relates to AI and automation, these tools continue to offer tremendous opportunities, and risks, to scenario planning and related decision-making.
Machine-driven scenario planning has been successfully implemented with companies we interact with daily.
Whether it be the airlines, ridesharing, or ecommerce, machines are able to evaluate a range of scenarios and recommend decisions related thereto.
For example, when dealing with a flight cancellation, it is not a room full of humans running scenarios and simulations who auto-rebook the flight for another day.
Instead, it is a software platform licensed by the airlines that facilitates the rebooking and contemplates the scenario with an abundance of data points.
In ridesharing, algorithms for pricing and route adjust based upon supply and demand, time of day, distance of destination from pick-up, weather, and saturation of the network among other factors.
In ecommerce, packaging arrangements, delivery cost, and delivery time are estimated based upon the order size, complexity, location of the receiver, orders placed by other customers, and a wide-array of logistics considerations.
The list could go on and on and it will continue to evolve very quickly. When machines can complete low-risk, yet high-complexity, scenarios we should be enthusiastic to utilize their capabilities.
As I mentioned in my post on sensitivities, we need to be cautious, however, in that machine-learning is far off from being able to emulate human decision-making.
Examples I share above are logistical nightmares for humans to execute, both in terms of time commitment and likelihood of error. We are well beyond the days of having a room full of airline customer service representatives hand-crafting travel itineraries. Even though the stakes may be high to us as travelers, they are low risk business decisions for a machine to make.
In a high-risk business decision, requiring elements of human insight, it may be disastrous to outsource scenario planning and related decisions to machines.
As I write this article, I am on a flight from Boston to Chicago. With talk about autonomous vehicles like cars and trucks, it wouldn’t seem outrageous to envision a time with self-piloted planes.
In the event of a storm, a plane would be faced with scenarios – maintain current altitude, change altitude, maintain current speed, change speed, maintain current course, change course, among other decisions.
We’ve seen recent accidents with the Boeing 737 MAX and tragedies like Air France Flight 447, which crashed in Brazil in 2009 due to pilot misunderstanding the computer-driven recommendations.
There must be a balance between high-risk scenario-related decisions managed by human and low-risk scenario-related decisions managed by machines.
In the not-so-distant-future, we will undoubtedly see more scenarios and simulations facilitated by AI that has historically been done by humans, but we must have the prudence to know what the power of outsourcing does not outweigh its risks.
If you want to find out more and follow the rest of the article series be sure to download the Financial Modelling App
If you want to find more information on financial modelling and content visit the Model Citizn website.