Ah yes, there's planning and then there's operations. Rarely do they coincide. What to do? This is one place where risk management comes in.
In an earlier post, I referred to a very large problem here in the Northwest caused by assuming past sales are a good predictor of future sales. Utility planning's failure to address sales risk led to literally billions in wasted money, money that could have been used to provide goods and service people wanted. This was quite a spectacular event. One result of that event was the creation of a new regional planning agency chartered by an act of Congress. Another result was a change in forecasting methods that contributed to this spectacular failure.
How does a small business with little money for sophisticated resources and guidance navigate these waters? One answer is to seek guidance from organizations like the Small Business Administration, USDA, and local and regional organizations established to provide technical assistance to such businesses.
There are some fairly simple methods that small businesses and start ups can use to try and at least narrow the amount of uncertainty they face. One approach is to develop multiple budgets. As was mentioned in an earlier post, it's often the case that a business oscillates between a conservative budget and a dream budget. This can be 'modeled' using what's called a triangular risk distribution.
Here's the approach,
1. Make a judgment call about what you think your most shaky variable is in your budget (there are likely more than one).
2. For that variable, determine what you think the most conservative value is, what you think a typical - or average value is, and what you think a very optimistic value is.
3. Build three budgets using the three values for what you consider the most risky element in your budget.
This is a very simple approach to do on your desktop in EXCEL. You can easily expand from one to more variables, with not too much more complication using EXCEL.
Now, one very subjective element of this approach is your personal attitude towards risk. What this means is different people have different reactions to risk. This is what underlies portfolio recommendations by age with a younger person advised to hold a riskier portfolio than someone closer to retirement.
This doesn't need to get fancy. When you've developed several different budgets, then step back and consider how you respond to risk. If you're more conservative, you'll prefer the budget somewhere between the most conservative you could think of and what you considered your average or expected budget. If you're more of a gambler, you'll prefer a budget somewhere between the expected budget and your aggressive one.
While this can be made very complex, and there are models that use large amounts of data to include risk in making decisions, that's not what we're talking about here. We don't want to go there. Though, we also don't want to just develop one budget and assume life will in fact mirror that budget. It will not.
Thursday, March 11, 2010
Monday, March 8, 2010
What About When Costs and Revenues Are Interdependent?
In the prior post, it was implicitly assumed that costs and revenues are independent. This is how you can develop costs and then determine needed revenues. What about when costs and revenues are interdependent?
It's not uncommon for costs and revenues to be interdependent. While discussions like those in the prior two posts are easier with an assumption of interdependence, this is not often the case. Some of the links mentioned in the prior post did touch on this issue. For example, one link raised issues like insufficient inventory or sales support staff when sales exceed expectations.
If your business has a fixed monthly payment for a space, such as a monthly lease, then this cost is clearly known. However, what if you rent space as the need arises? What if you're able to have all your costs as flexible - variable - as this? Here then, costs and revenues are interdependent. Thankfully, if you have very little lead time between incurring a cost and meeting a demand for your product or service, there's little problem with this type of interdependence.
However, when this lead time lengthens, for example, with advertising costs, staffing needs, space rental, it becomes more difficult to simplify the budgeting problem by working with costs. In these situations, costs and revenues are linked.
One other implication of this kind of linkage is that to simplify it as much as possible by keeping costs as variable as possible with the shortest lead times possible before needing to commit to a cost.
It's not uncommon for costs and revenues to be interdependent. While discussions like those in the prior two posts are easier with an assumption of interdependence, this is not often the case. Some of the links mentioned in the prior post did touch on this issue. For example, one link raised issues like insufficient inventory or sales support staff when sales exceed expectations.
If your business has a fixed monthly payment for a space, such as a monthly lease, then this cost is clearly known. However, what if you rent space as the need arises? What if you're able to have all your costs as flexible - variable - as this? Here then, costs and revenues are interdependent. Thankfully, if you have very little lead time between incurring a cost and meeting a demand for your product or service, there's little problem with this type of interdependence.
However, when this lead time lengthens, for example, with advertising costs, staffing needs, space rental, it becomes more difficult to simplify the budgeting problem by working with costs. In these situations, costs and revenues are linked.
One other implication of this kind of linkage is that to simplify it as much as possible by keeping costs as variable as possible with the shortest lead times possible before needing to commit to a cost.
Forecasting (Budgeting) When You Have Little Solid Data
In an earlier post, I raised the challenge of forecasting revenues for budgeting noting that it's likely the most challenging step in budgeting for a new business. One link that addresses this issue confirms that perspective. In this link, the authors talk about ways to narrow the range of uncertainty - risk - around what you think will happen by focusing on reactions to your pre-launch marketing survey.
Another link contains an interesting give and take with various people on this issue. One writer aptly notes that it's more art than science when you've really not got any good data. A post by rogercbryan on 1-4-08 contains a good deal of wisdom. Among his sage advice are the following:
- "Start with expenses, not revenues. When you're in the startup stage, it's much easier to forecast expenses than revenues"
He then includes some rules of thumb for cost forecasting that seem on point. Note: Much the same advice is contained in this link.
On revenue forecasting, he proposes that you "Forecast revenues using both a conservative case and an aggressive case. If you're like most entrepreneurs, you'll constantly fluctuate between conservative reality and an aggressive dream state..."
What's good in his advice is the idea of making multiple revenue forecasts. Very large multi-billion dollar businesses have made fatal errors by not forecasting multiple revenue scenarios. These multiple scenarios reflect different sales possibilities. Here in the Pacific Northwest, this very error was committed in the past with electric utility panning that ultimately led to billions of wasted dollars and mothballed and incomplete nuclear plants.
One approach I like is to work your cost numbers hard and gin up several different cost scenarios. Then, for each cost scenario, generate a revenue forecast - in terms of customers, product sold, prices, etc., that supports that cost scenario. Then, you step back and examine the various product sales projections, examine their likelihood, or what would need to happen for it to be true. At the beginning, this is again more art than science.
This is a way of being systematic and structured in your analysis, even in the presence of a great deal of uncertainty about both costs and revenues. Through this process, you'll document your assumptions. At this point, even though you have a great deal of uncertainty - risk - in your analysis, at least you've proceeded with a systematic approach. Now, subject your analysis to rigorous review by some others not involved with your dream. Use them to challenge your assumptions and analysis. Use them to identify what is strong and what is weak in your analysis.
Another link contains an interesting give and take with various people on this issue. One writer aptly notes that it's more art than science when you've really not got any good data. A post by rogercbryan on 1-4-08 contains a good deal of wisdom. Among his sage advice are the following:
- "Start with expenses, not revenues. When you're in the startup stage, it's much easier to forecast expenses than revenues"
He then includes some rules of thumb for cost forecasting that seem on point. Note: Much the same advice is contained in this link.
On revenue forecasting, he proposes that you "Forecast revenues using both a conservative case and an aggressive case. If you're like most entrepreneurs, you'll constantly fluctuate between conservative reality and an aggressive dream state..."
What's good in his advice is the idea of making multiple revenue forecasts. Very large multi-billion dollar businesses have made fatal errors by not forecasting multiple revenue scenarios. These multiple scenarios reflect different sales possibilities. Here in the Pacific Northwest, this very error was committed in the past with electric utility panning that ultimately led to billions of wasted dollars and mothballed and incomplete nuclear plants.
One approach I like is to work your cost numbers hard and gin up several different cost scenarios. Then, for each cost scenario, generate a revenue forecast - in terms of customers, product sold, prices, etc., that supports that cost scenario. Then, you step back and examine the various product sales projections, examine their likelihood, or what would need to happen for it to be true. At the beginning, this is again more art than science.
This is a way of being systematic and structured in your analysis, even in the presence of a great deal of uncertainty about both costs and revenues. Through this process, you'll document your assumptions. At this point, even though you have a great deal of uncertainty - risk - in your analysis, at least you've proceeded with a systematic approach. Now, subject your analysis to rigorous review by some others not involved with your dream. Use them to challenge your assumptions and analysis. Use them to identify what is strong and what is weak in your analysis.
Starting a new Business - A Flight into the Unknown
Where I live - Joseph, Oregon - there's a group of people who want to start a new art school. They're starting a new non-profit group, working to bring in people from other places to teach, want to buy two older buildings, and want to do all of this at the same time with virtually no money. This is a good example of how to start a new business with an immense amount of risk.
Are all these ideas worthy? Sure. Why not? Yet, that's not the question. One good question is what are the priorities? What do you really want to do? What do you really want to accomplish? What is your primary purpose?
Start-ups need cash reserves. Start-ups don't have a track record which significantly complicates budgeting. Under such conditions, budgeting is a shot in the dark. Because of the great many unknowns a start-up faces, there's a need to build in a lot of ability to flex with circumstances you really have very little idea about.
Not only are costs challenging to scope, they are probably easier to scope than revenues. This is especially true when there is little hard data that can be used to firm up the revenue forecasts. Now, people will say "oh, that's not really that much of a problem. There's other places that hold art classes and we can just use their experience." There's a host of reasons why that view is dangerous.
Extrapolating from the experience of other's - which is what that argument amounts to - is problematic. There's a good deal of statistical discussion on this issue - extrapolating beyond your data. By it's very nature, extrapolation is a journey into the unknown. It might be the best you've got. Here's the thing though, you need to look at the broader circumstances of the experience of others and compare those circumstances to your own to come to some sense of how relevant their experience is to your new enterprise.
I'm not saying it's a wrong path to travel down. Rather, that you need to be cautious as you travel down that path. I've seen people who are really just grasping for confirmation grab onto whatever data they can get their hands on as justification for what they want to be true. Whether it's actually true or not is another story, and people who question such approaches are sometimes treated as inconvenient pessimists.
Are all these ideas worthy? Sure. Why not? Yet, that's not the question. One good question is what are the priorities? What do you really want to do? What do you really want to accomplish? What is your primary purpose?
Start-ups need cash reserves. Start-ups don't have a track record which significantly complicates budgeting. Under such conditions, budgeting is a shot in the dark. Because of the great many unknowns a start-up faces, there's a need to build in a lot of ability to flex with circumstances you really have very little idea about.
Not only are costs challenging to scope, they are probably easier to scope than revenues. This is especially true when there is little hard data that can be used to firm up the revenue forecasts. Now, people will say "oh, that's not really that much of a problem. There's other places that hold art classes and we can just use their experience." There's a host of reasons why that view is dangerous.
Extrapolating from the experience of other's - which is what that argument amounts to - is problematic. There's a good deal of statistical discussion on this issue - extrapolating beyond your data. By it's very nature, extrapolation is a journey into the unknown. It might be the best you've got. Here's the thing though, you need to look at the broader circumstances of the experience of others and compare those circumstances to your own to come to some sense of how relevant their experience is to your new enterprise.
I'm not saying it's a wrong path to travel down. Rather, that you need to be cautious as you travel down that path. I've seen people who are really just grasping for confirmation grab onto whatever data they can get their hands on as justification for what they want to be true. Whether it's actually true or not is another story, and people who question such approaches are sometimes treated as inconvenient pessimists.
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