Tuesday, 28 January 2014

Predicting Profits: The Power of the Profit Equation

Are you ready for this? This is probably one of the most important and insightful business guides I’ve ever written – a guide that will show you how to predict your business profits with uncanny accuracy using the powerful profit equation.

Economic modeling is an area of expertise for me, and one of the major things I do as a business data analyst. Analysts try to develop algorithms (systems of equations) that explain or predict the behavior of markets and profits. 

Don’t get spooked – analysts simplify findings in a way that is useful to the end-user, so read on with confidence. No technical mumbo-jumbo here, just stuff you can use directly and apply to your business.

OK, let’s get started.

The Profit Equation

Your business is a mathematical system that processes input (the factors of production) and generates output (sales and profits). In effect, your business can be modeled using a series of business equations.

[Related Article: Modelling Important Business Decisions with Game Theory]

For this article specifically, we will be looking at the Profit Equation – a model that explains business profits, and why they fluctuate. Please see the equation below.

Again, don’t panic! There is nothing technical about the profit equation above, and I will explain everything. Don’t forget that this is the equation that determines how much profit your business actually makes, so you might want to get familiar with it.

The first thing you notice is that the equation has two parts: the gain section and the loss section.

The loss section is time-dependent, while the gain section may be time-dependent or product-dependent (you lose money hourly while running a business, but you can either make money hourly or per head of goods sold).

How Profits Behave (and How to Control Them)

Profits are not random – far from it. Profits fluctuate, but usually because of changes in the gain section:

·         Changes in demand (and resulting fluctuation of daily sales volume)

·         Changes in consumer buying behavior (and resulting fluctuation in the actual distribution of goods bought per time – the sales ratio)

The loss section is mostly fixed – and far more controllable. You can increase profits by:

·         Reducing work hours per day

·         Increasing actual work days (consider working on weekends and maybe some holidays)

·         Lowering wages

·         Minimizing equipment and space rentals (consider purchasing your own equipment and getting your own facility)

·         Moving to an area with cheap utility costs (cheap water, power, gas, and transportation)

Now that you understand how profits behave and how you can control your profit, let’s take a closer look at the profit equation to show you how you can always accurately predict your business profit.

First off, WHY is it Important to be Able to Predict Business Profits?

Sometimes, you need to have an idea of how profitable a business will be BEFORE you get started with it. 

Imagine using your pension or life savings to invest in a business idea that fails to get off the ground? It can be a devastating experience – so it’s always better to know where you’re going to land before you actually jump.

For those of you already in business, it can’t hurt to see WHY you’re making profit in a particular range – and what you can do to improve your business performance.

OK, now for the analysis.

I’m going to use a fictitious company called ABELHAMMER SUPPLIES as a case study to show you how the profit equation can be used to accurately predict your business profits.

Tip: By the end of the article, you would have fully understood how to use the profit equation to accurately predict your business profits. I invite you to apply what you learn here to your own business and see if it tallies – promise me you’ll send your feedback to let me know how it goes!

Case Study: Predicting Business Profits

Let’s start by building a company profile for ABELHAMMER SUPPLIES:


PRODUCT DETAILS: Cables, Power Tools, and Plumbing Fittings.

SALES RATIO (POPULARITY OR DEMAND): Cables (20%), Power Tools (30%), Plumbing Fittings (50%) – total 100%

PRODUCT PRICE: Cables – per pack ($3.50), Power Tools – per set ($1,500), Plumbing Fittings – per box ($250)










We’re now ready to start. Launch your calculator app or grab a handy one.

1. Gain Section – ABELHAMMER

Recall the gain section is given as follows:

That was simple enough.  Now for the loss section.

2. Loss Section – ABELHAMMER 

This is the tally of operational costs per day and is obtained as follows:

3. Monthly Profit – ABELHAMMER

We’re now ready to predict monthly profit for ABELHAMMER supplies:

Business must be very good for ABELHAMMER!

We have successfully calculated monthly profits for this case study. I invite you to use the equation and check out your profits! Meanwhile, enjoy the following bonus topics.

Bonus #1: Effect of Increasing Consumer Confidence on Profits

When consumer confidence in your products rises, your profits will rise as well. You should invest in improving the quality of your products, workplace, or facility to raise consumer confidence. See the illustration below:

Bonus #2: Effect of Lowering Work Hours per Day on Profits

This might not sound right to you, but actually LOWERING your daily work hours improves your profits dramatically. See the illustration below:

Wow – that was some good stuff, wasn’t it? Please try this for your business and let me know how it goes! Post your comments if you have any questions, and I’ll get right back to you!
Thanks for visiting the Bistro today – always come back!

Monday, 27 January 2014

Product Costing for Small Businesses

Congratulations on finally deciding to start your own small business. If you’re going to be doing some small-scale manufacturing as part of your business venture, then this article is a must-read for you.

Today we’re looking at product costing for small businesses – how to arrive at a selling price for your home-made or small-scale goods.

It takes a lot of courage and vision to start up your own business – and one thing I know every entrepreneur loves more than actually kick-starting a business idea is actually making it profitable. 

[Related Article: 4 Best Kept Secrets of Product Marketing]

You want your goods to sell and you want your business to be self-sustaining. It’s achievable, but there are a few ground rules – product costing is one of them because getting it right the first time can mean the difference between success and failure.

How Do You Cost a Product?

As described in the figure above, your selling price is simply the sum of your product cost per unit (how much it cost you to create a single unit of your product) and a convenient markup (a small percentage added for profit).

The key to arriving at an ideal selling price that will guarantee maximum profit at the market is twofold:

  1. Correctly figuring out your Product Cost Per Unit (PCPU) – this is critical.

2. Selecting the Proper Scaled Markup (PSM), and not just any arbitrary markup (very important!)

Let’s take a look at these two important factors in turn.

Step 1: Correctly Evaluating Your PCPU

As illustrated in the figure above, your PCPU (product cost per unit) is the sum of three other costs:

·         Material Cost Per Unit (cost of materials for a single unit of your product)

·         Labor Cost Per Unit (cost of labor – wages paid per single item produced)

·         Machine Cost Per Unit (total cost of machines, power, etc., used to produce a single unit of your product)

Nice. We now know how to arrive at our PCPU. But like all things in life, it’s a bit more complicated than just adding these three components up. 

The reason we can’t just add them up is that two of the three components (labor cost per unit and machine cost per unit) are functions of time (depend on time) and must be evaluated separately.

Moving along, we combine these two time-dependent costs into a single cost called the plant cost. It can be calculated with the following simple formula:

Once we have this plant cost per unit, we simply add it to the material cost per unit to obtain our PCPU. Now for an example, so you can see it all in practice.

Example: PCPU of Freshly Packed Fruit Product

For this example, let’s imagine we’re opening a small business that supplies freshly-packed fruits to local stores and schools.

Our aim is to calculate the PCPU for this business. Our first move is to draw up a profile of the business as follows:

Staff (n) = 2

Wages = $10 per hour

Fruits packed = 1 Apple (60 cents) + 1 Banana (80 cents) + 1 Cucumber (75 cents) = $2.15

Cost of Packing Bag = $ 0.2 per unit

Rental Cost for Packing Machine = $5 per hour

Time to pack 1 bag = 2 minutes = 2/60 hours = 0.033 hours

Operational Costs per hour = $0 (negligible infrastructure costs)

What is the PCPU?

PCPU = Material Cost per Unit + Plant Cost per Unit

PCPU = (Cost of Fruits + Cost of Bag) + Packing Time x (Wage x Staff + Rent Per Hour + Operational Cost Per Hour)

PCPU = ($2.15 + $0.2) + 0.033 x ($10 x 2 + $5 + $0)

PCPU = $2.35 + 0.033 x $25 = $2.35 + $0.825 = $3.175

PCPU ≈ $3.18

The product cost per unit for this product is $3.18. That’s how you do it! 

I guess that was a bit of a workout – but it was totally worth it. 

I’m sure by now you’re thinking “Wow, I didn’t go through ALL THAT to cost my product!” Well, as you can see, doing it right takes a few more steps than would normally come to mind.

Tip: Please make a note of these steps in your journal, so you can do your own calculations and come up with the actual PCPU for your product.

We’re not done! We still have to figure out the Proper Scaled Markup for our product before we can arrive at the selling price for the product.

Step 2: Select the Approved Markup (PSM)

A lot of people actually just guess what markup to use for their product. This can be very dangerous, and can actually harm sales of your product.

This is because the market works like a magnet and attracts demand on the basis of price. The lower the price, the higher the demand (for a reasonable level of quality).

If you select the wrong markup, your product will be overpriced and attract very little demand (poor sales). If you under-price your product, you will be operating a loss on principle. You have to get it right the first time – and you need a little formula called the PSM (Proper Scaled Markup):

The Quality Factor is a percentage that expresses how people perceive the quality of your entry-level product relative to more established competitors. 

For example, if you are producing home-made soap, and you intend to sell in the same market with factory-manufactured brands, a quality factor of 40% - 50% is reasonable. 

For our freshly-packed fruit example, a quality factor of 80% - 95% is reasonable, since we are dealing with a natural product that requires no refinement or processing and very little packaging.

Let us assume Q = 90% = 0.9 for this example.

Let’s also assume that the price of the directly-competing product in the market is $5 (P0 = $5.00). Recall that our PCPU = $3.18.

From the equation for PSM, our approved markup is (0.9 x $5) - $3.18 = $1.32

This means you should add no more than $1.32 to your PCPU to arrive at an optimized selling price for your product.

Finally, we can find the optimal selling price for our product as:

Selling Price = PCPU + PSM = $3.18 + $1.32 = $4.50

So, while it cost you $3.18 to make your product, and the competition sells at $5.00, you should sell at $4.50 to generate the highest sales for your product!

I hope you’ve learned a lot today. If you have questions, please post a comment below! Thanks for reading – always come back for more great business and technology content!

Sunday, 26 January 2014

A Simple and Free Forecasting Model for Your Small Business

If you run a small business, then you know how important it is to have an idea of what your monthly profits or sales may be, so you can plan ahead and better schedule your growth.

Thankfully, this is possible with tools such as forecasting. A business forecast studies the behavior of important business indicators like sales and expenses over long periods of time to arrive at a predictive model for future performance in those areas.

[Related Article: Modelling Important Business Decisions with Game Theory]

The validity of forecast models is guaranteed by the mathematical nature of business processes; your business can be modeled as a mathematical system receiving inputs (factors of production) and producing output (sales and profits).

Accuracy of forecast models is guaranteed by the fact that business performance fluctuates between reasonably fixed high and low points. 

These fluctuations may be seasonal (such as sales highs during holidays or profit lows in the first quarter of a new year), cyclic (consisting of alternating highs and lows), or reactive (consisting of nearly-random small changes). 

In any case, these fluctuations can be reasonably predicted over the long term using certain mathematical systems called forecast models. Software companies have successfully packaged these models into forecasting software that accurately predict key business performance indicators.

The problem is that many of these models are very complicated, and may not be suitable for relatively simple business processes – like those of small businesses. As a result of the complex nature of many of these programs, they are also very expensive. 

This post focuses on a simple and free forecast model for small businesses

Introducing the Mean Adjustment Prediction Model (MAPM) for sequential business data: 

With this model you can predict new monthly values of data given a substantial set of historical data by performing a simple calculation on a spreadsheet (e.g. MS Excel)

Read on to discover the actual equation for the model, as well as the code (formula) for EXCEL, so you can use this at home or in the office.

This forecast model is very simple and only requires that you obtain the mean (average) of the values of data you are interested in, and add an adjustment factor.

It is remarkable to note that this model does not sacrifice accuracy on account of its simplicity. For a detailed background of this model (open only if you are mathematically inclined), see Mean Adjustment Prediction.

For a simpler analysis, consider the following:

The MAPM is especially recommended for slightly-varying non-constant data, such as small business sales and profits.

Notice the high accuracy of the model (ranging between 87% and 120% for the example above). In the experiment shown above, MAPM was used to guess random sales figures ranging between $7,000 and $9,000 with very impressive results. Accuracy values in excess of 100% indicate an optimistic prediction (greater than the actual result).

Tip: I recommend you use MAPM in a spreadsheet to quickly calculate a prediction for applications such as planning and budgeting.

To use MAPM with Excel, you can use the following formula:

=AVERAGE(B3:G3) + 0.5*(G3 - B3)*(1 - 1/N)

B3 = first value in sequence
G3 = last value in sequence
N = number of values in sequence

I understand it’s a little technical, but I assure you it works great every time – and of course, it’s free. So by all means get started with MAPM in Excel and let me know if you need any assistance! Thanks for reading!