So, we've talked about missing costs and missing line items.

What about the key assumptions, the other question I asked?

And you may have tried a bunch of different techniques but the idea

of how you service key assumptions is the same as we discussed in the last lecture.

The first thing you do [COUGH] is you can move the numbers in ranges.

What I mean by move in ranges, I mean think about what the highest and

lowest possible number for each assumption is.

And try putting in that highest and lowest number into the model.

So one thing that we had in the model, for

example, was the average number of desks per order.

So if we think that number could range from 2 to a 100, put 2 end and

put a 100 and see how much it affects your outcomes.

And in terms of figuring out how big those number changes could be,

how big the range of possible assumption numbers could be,

you want to think about what Jon and Joanne actually know.

So, we're already expecting it in the most and

therefore they're going to be the least uncertainty.

In this case I would imagine that would be around the production and

manufacturing of desk.

Because John and Joanne are carpenters, they know that market really well.

So maybe the cost per desk which I think they have is $120 could be between $115

a $125 but it's unlikely they were so bad at pricing from talking to suppliers that

it could be somewhere between $5 and $1000.

On the other hand, sales is something that we know from the case that Jon and

Joanne know much less about.

So, it's much more possible that the range for

things like the number of sales calls per day could be much larger both minimum and

maximum than what Jon and Joanne discussed.

And you get more information by looking at the quality of the sources.

What information do they have?

Where is the data for the assumptions coming from?

If you want to get fancy,

you can do the sensitivity analysis by moving all of the numbers simultaneously.

Or you can just move them individually.

What you see on the side of your screen, that staircase chart,

also called a tornado chart when done vertically, is a sensitivity analysis.

How much do the profits and the numbers change if we move those numbers up and

down in their ranges?

And this is just one example.

I'm not saying these are the exact numbers,

because again, we made up this case.

But you could generate a similar chart by moving those numbers in the ranges and

then seeing which one has the largest possible effect.

If you're familiar with more advanced techniques like Monte Carlo simulations,

which would let you run all the numbers and move them simultaneously.

This is a chance to do that, as well.

But again, if you don't know what that is or if you don't have experience with it,

you don't need to do a Monte Carlo simulation to get answers.

The simple sensitivity analysis where you move the numbers in each range,

use the highest and lowest figures and

see what effect it has on your outcome is a very powerful in and of itself.

So the next step in the business would be to match these key assumptions to

milestones.

So you've just surfaced assumptions that might be large.

Perhaps that's the direct sales price per desk, that might be a very big assumption.

So we now have the list of milestones that they're hoping to achieve.

And we have this assumption that we would now identify our key assumptions.

We're going to fill in this chart and this is something that you should take sometime

to do now, to think about when something to excel price per desk could be tested.

In this case we'd certainly contested the market study and

we could probably test it in the mock up of the sample desk for

troops in the simulation of sales and marketing.

So, is that where we want to test this, we have to think about that.

And you're going to figure out your key assumptions and

key milestones by thinking about which milestones test the most assumptions.

And how early or late this is in the process of starting our business.

So if our big concern is about sales calls and whether or

not how many sales calls per day can be made or how many desks per order...

You'll notice that if we look at the milestone chart,

we don't really test that until fairly later in the business.

So perhaps we need to think about a way of preselling our product earlier.

Maybe we could do this by launching a kickstarter campaign to see if people will

support this desk, maybe we go out, and try and

pre-sell to couple architect shops and see how many desks we get per order.

But there isn't milestone there to test then very late in the process so

this gives us the idea that maybe we're not testing key assumptions early enough.

So that Better Desk example, is a great way to start building your own model.

And again I've heard from many students who found this a very useful technique for

thinking through their business desk.

So even it's not as complex as the Better Desk model,

you'll still find this be useful.

So I strongly recommend building and discovering your own plan.

And it will take you a couple hours of your time, but

can save you a thousand of dollars and

how many of these numbers makes it big different in your long term business.

At the same time the problem with the discoverage of your model is at steady

state right?

And your business is hopefully growing overtime.

This is not a good way to model future sales for us,

it's a good way to look at a steady state model that can give you some insights at

your business is not necessarily a representation of all the cost and

issues that you will be dealing with.

So just remember the model it's just a model, it's a tool for you to use,

it's not a religious scripture.

So thinking about using this in that kind of way as one of the tools informing you

about your assumptions, the next steps in your business, future plans.

And spend some time looking through the Better Desk example to understand how

everything is connected and how it works together.