In this post I would like to show you an example of using *Farseer* for planning a business case.

Let’s say you want to start a burger shop and you want to use *Farseer* to plan your financials and simulate how changing certain variables impacts your bottom line.

We’ll start with the number of burgers sold. This will be our business driver and the only thing that produces revenue. For the sake of simplicity, we’ll going to sell just one type of burger. The number of burgers sold would usually depend on several things, but to keep things simple in the beginning, we’ll just use the price of a single burger and include seasonality and growth effect. The relationship between price and number of burgers sold will be modeled by a simple demand curve.

We don’t have the real data, so we’ll just try to assume some reasonable numbers. For example, if the price of the burger was $0, we could sell 50,000 burgers a month. This will serve as a hypothetical imaginary upper limit of what our burger place could produce if it worked in full capacity. The other extreme is $10 per burger and the assumption is that we could sell 1000 burgers a month at this price. You can see the other numbers in the chart below, where the x-axis represents the burger price and the y-axis shows us the quantity of burgers sold.

OK, so let’s enter this data into the *Farseer’s* **Flow Manager**.

The first thing we need to do is to model the hierarchy. We’ll use one root cluster that will contain our entire business case. Let’s call it *Burger & Sons*. Inside, we’ll split everything into two sub-clusters: *Business* – which will contain all day-to-day business revenues and expenses, and *Investment* – in which we’ll enter our capital expenditures. We’ll split the business cluster into *Sales*, *Production*, *Marketing* and *HR*.

**Modelling the demand curve**

To model the demand curve, we’ll use the built-in **flow transform function**. The transform function is basically a table that transforms one number into another – similar to our demand curve. Here’s the table we need to enter

The transform function will automatically interpolate the number of burgers sold for in-between prices.

For burger price, we’ll create an **independent variable** so we can use it later in the **Simulator**. The initial value (representing the variable price which we want to simulate) will be $5.

**Modeling the production cost of a burger**

Due to the seasonality effect, we’ll model our burger production cost so it varies depending on the time of the year. Our basic production cost will be around $2 in August and $2.50 in January. We’ll use the built-in **flow regression** feature to calculate the production cost in the months between. This is the simplest way of modeling the cost, but we could have easily split the cost into different flows and model each ingredient separately.

**HR and marketing**

Again, we’ll just use a simple way to model the personnel and marketing costs: the number of employees will depend on the number of burgers sold and marketing costs will boost sales. We’ll assume an average gross salary of $2000 per month. The base number of employees will be 3. As the number of sold burgers becomes bigger, we’ll need more employees. This can be also modeled with the aforementioned built-in transform function using the following table:

Our Marketing model will work in a similar way. We’ll just assume that $1000 spent in marketing will boost our sales by 5%. Every additional dollar spent will have a smaller impact on overall sales.

**Investment and break even**

Let’s say that our initial investment was $100 000. By using the cumulative view on the consolidated cluster, we can see that the break-even point happens in September of 2018.

**Price simulation**

Now that we have some initial data set up, we’ll use the Simulator to find out what price point maximizes our total profit. Changing the price in the Simulator will impact the number of burgers sold, but it will also have an indirect effect on the number of employees we’ll need. Before starting the simulation, we need to define our own set of KPIs. We’ll keep things simple and use simplified P&L and few other interesting KPIs.

- Revenues
- Cost of goods sold
- Operational expenses (HR)
- Profit
- Profit margin
- Number of burgers sold

Here’s how this looks like in *Farseer*:

After testing out a few price points, we concluded that an optimal price for our burger is $6. Compared to the initial price of $5, our total revenue is now 20% lower. However, both the cost of goods sold and our operational expenses (HR) are significantly lower, so we get a $67k (+60%) net profit boost in year 1. Keep in mind that these results depend greatly on the demand curve. To figure out the exact demand curve, you’ll need to do the some in-depth market research.

## 1 Comment

## Sara

Excellent example and explanation, very useful. Thanks!