Models are critical for any business. They improve
efficiency, they help in identifying opportunities for both process improvement
and
cost reduction, and that’s just a fraction of what they provide. However, they are not a magic bullet. Misused, they
can easily lead to bad decisions. In my experience,they
are most often misused when outputs are treated as gospel and not questioned
thoroughly enough. For example, Bayesian
models (while fairly predictive) struggle with outliers because they expect
regression towards the mean. An example
of this flaw can be seen in sports analysis.
Someone looking at Bayesian hockey models would look at someone like
Alex Ovechkin who has a career shooting percentage of 12.6% and say that they
expect him to regress to the league average of under 10%. This would mean predicting
he would score almost 200 fewer goals over his career (If that were the case he
would’ve landed at number 52 on the all-time goals list rather than number 13). Such a prediction doesn’t take into account
that Alex Ovechkin is a particularly skilled shooter and that he can sustain a
higher than average shooting percentage.
Most good hockey analysts are aware of the flaws in their Bayesian
models, but not all. In fact, this lead
to debates in the early days of hockey analytics on whether or not Alex
Ovechkin is good at hockey. Even casual fans know this debate is ridiculous.
Now that you understand a bit about why it’s important to
understand the flaws in your models, let’s look at an example that I’ve
encountered repeatedly in the business world.
Business Example
You work for a $10M custom
manufacturing company that uses a
costing model that normalizes your costs as a % of revenue based on assumptions
of the costs needed to operate a $12M company.
Your company is currently operating at about 5% EBITA ($500K).
A potential customer comes to you with a $5M opportunity and
provides you with a target price to win the business. They also provide you
with a schedule which confirms that they’ll place two orders per month (24
times per year). Of course you’re
thrilled at the opportunity and get started on designing the product, sourcing
materials, running labor calculations, etc… You get all the information
together and enter your top-line costs into your pricing model. Ultimately, you’re
disappointed because it shows that you can’t take on the business as the model
shows an EBITA of negative $100K and you turn down the opportunity.
The above model is wrong.
Had you known the flaws of your model you’d be able to make adjustments
to the bottom line and not turn down the opportunity. For the sake of simplicity, let’s assume top-line
costs were manually entered and are correct.
Let’s breakdown some of the issues with the above model
output and create a more correct income statement
1. The model normalizes costs using percentages for
operating a $12M company. This project
would make your company a $15M company, so right away the costing structure is
going to be different as your fixed costs are now spread out over a larger
amount of revenue (i.e. your fixed costs are a smaller % than they appear
above).
2. In terms of admin costs this is not a very
intensive project, yet the model is saying that this project is going to cost
you $50K in admin costs. That’s probably
close to 1/3 of your rolled up admin costs for the entire year. This business only requires 24 orders per
year and it costs your business roughly $150 to process an order, so the
reality is that your admin cost is much closer to $3.6K (big difference). There’s no need to spread that non-realistic
$50K cost into this project and price yourself out of the opportunity.
3. Selling cost is normalized at 6% here so we’re
looking at $300K in cost. $300K in cost
is basically saying 2 Sales Managers spending 100% of their time on this
project + travel expenses, etc… That’s pretty ridiculous and there’s no way you
should put that level of cost here.
Let’s look at a more realistic breakdown (leaning towards the high side
of cost to be safe) of the costs, which end up being closer to $100K.
a. Sales Manager 20% = $30,000
b. Sales Director 20% = $50,000
c. Travel Expenses = $10,000
d. CSR 10% = $7,500
e. Total = $97,500
4. Finally, you do an in-depth analysis of the
manufacturing costs including preventative maintenance and repair of the
equipment + the amount of time indirect labor will spend on this, etc… and you
come up with a fairly conservative estimate of $600K (which is actually what
would happen if you spread the costs over $15M instead of $12M).
Now that we’ve gone through the real costs for this project
let’s look at the new estimated income statement
As you can see our projected EBITA has gone up from -$100K
all the way up to roughly +$300K. That
is massive as it will increase your company’s revenue by 50% while increasing
your EBITA by 60%. Had you followed the
financial model blindly, that decision would’ve cost your business $300K in
EBITA.
Professionals will often lean on their models as it’s both
easier and safer, but the costs of not understanding their potential and how to
leverage models correctly can cost you significant money. So make sure that when you implement a model
in your business you are able to adequately train employees not only on how to
use it, but on how to understand the inputs and outputs and how the model might
be flawed.
