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Holistic Practice Management – Forecasting Part 2


Welcome back to the second part of this Holistic Practice Management Forecasting series.

In Part I, I focused on various inputs to establish your own forecasting model that fits within your marketplace.

As I stated before forecasting is tough to establish!

You are taking various inputs from the past, present, and future to establish a model. You are also looking at other significant inputs that contribute to your model. I presented the various inputs in Part I and gave you a seven step list so you can create your own robust model, but I only addressed five of the seven points on the list!

In Part II, I will complete the list by addressing part six and seven.

These parts are more quantitative/derivative based and I will give you tangible formulas for you to use in your practice on a daily basis. My goal is to help you maximize the yield/profits the product you buy for the optical at the same reducing the liability that you undertake.

DO NOT forget your optical is basically a retail establishment that is a living organism. If you do not properly care for this part of your business than it can consume some/majority of the profits you make for the practice.

Let us first address step six “make the forecast” for your practice.

As I have shown you in Graph 1 using the various inputs you acquired from Part I of the series, you have made your forecast model. The baseline I gave you helped push all the numbers within your model to give you a better forecasting inventory narrative.

Graph 1

The model was put through 1000 iterations and based upon seasonal buying patterns, standard deviations of 10% for each individual input, and a normal forecasting distribution (10% per input).

As you are making your forecast I address one input that sometimes just gets forgotten when forecasting called moving average.

What a moving average does is it uses a number of historical data points to generate a yearly forecast.

Mathematically it is represented like this…

Moving Average= Sum of demand from previous demand (1-12 months)


n=number of periods/months in the moving average

For example, you have an established six month cycle and you want to forecast the moving average of the next six months.

You take the numbers from January to June and come up with a sum of the demand 100. You take that 100 and divide it by 6 (number of periods you want in your moving average) and you get 16.67 for the month of July.

You then take February –July and do the same as before and so on until you reach December. This moving average forecast will help you budget and make better decisions regarding frame purchases, lenses, and contact.

You can also use weighted moving average if you have detectable trends/pattern present in your data.

It places more emphasis on more recent values present within your forecasting model.

Mathematically it is represented like this…

Weighted Moving Average= Sum((Weight for period n)+(demand in period n))

                                               Sum of All Weights

n=number of periods/months in the moving average

Let’s say in January you sell 10, February 12, and March 13. You want to find out April’s weighted moving average so you first find the sum of the weights by assigning more weight to the recent data.

March would be 3, February would be 2 and January would be 1.

The sum of these weights are 6 and that gets your denominator in this equation.

Your numerator in this equation would be to take March weight (which is 3) and multiply that by 13, February weight (which is 2) and multiply that by 12, January weight (which is 1) and multiply by 10. Add those three answers and you get 87 and then you divide by the sum of the weights (6) and the weighted moving average is 12.16 (or just 12).

This gives you are forecast for April of 12.

Remember though this formula places more emphasis on recent values so use it strategically to properly represent your forecasting model based on your marketplace patterns for your practice. .

Using these two types of moving averages is great for the current year, but what if you want a longer range cycle?

That is when seasonal trend projections come into play.

These seasonal projections are based on your yearly numerical outputs of the category (frames, lenses, contact lenses, etc.) or of the entire optical practice.

You can use just regular trend projections but it does not take into account the seasonality within your optical. It would be a fruitless endeavor and not set up the model properly.

As you see the example in Graph 2, this is the demand over the previous three years in the optical.

Screen Shot 2016-06-22 at 8.33.28 AM
Graph 2

To establish this spreadsheet there are some needed steps to properly represent seasonal trend projections.

Step one find the average historical demand for each season.

You do this by summing the demand for that month in each year and dividing by the numbers of years of data available.

Step two you compute the average demand over all the months by dividing the total average annual demand by the number of seasons.

Step three compute a seasonal index for each season by dividing that month’s historical average demand (from step one) by the average demand over all months (step two).

Step four estimate next year’s total annual demand.

Step five divide this estimate of total annual demand by the number of seasons, then multiply it by the seasonal index for each month (Render, 2014). This graph and explanation will give you the seasonal forecast for the demand you are expecting so you strategically maximize the dollars you spend year over year.

Now we have “made the forecast” (step six) from the various inputs we can move on to step seven from Part I of how to start a forecast which is “validate and implement the results”.

This last step takes the results you got from surveys, previous selling data, practice capacity, push/pull system, and various formulas.

You holistically look over all those inputs so when you are making inventory decisions you strategically maximize the dollars spent. Always be making audits to this model because it is a living organism.

This never is an easy task but now you have the needed information to make more complete business decisions based on all the information you are able to garner from this forecasting model you have established.

As a friend of mine stated in his book “Life is about constant change.”

The challenge is how we choose to accept and manage changes, or deny and flee from them. The latter of the two choices appears to be the path of least resistance or to contain the fewest consequences. However, that depends upon the choices we make for our personal standards. One individual will choose to accept and manage these changes; while another hides and ignores the issues. The first choice requires discipline and courage, and though not the path of least resistance, often pays the greatest dividends” (Casel, 2003)

Accept this challenge of forecasting. Embrace the process and this will pay big dividends for yourself, the staff, and the patients that will be shopping within your optical!


Casel, R. M. (2003). Eat More Cookies. Reno, NV: EMC2.

Render, J. H. (2014). Principles of Operations Management. New Jersey: Pearson.

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About Sean Carranco MBA

Sean Carranco MBA
Sean Carranco is the owner of Carranco Lunettes which is a consulting firm that helps independent practices in the marketplace. A versatile professional with over 15 plus years of executive operational, financial, marketing and retail experience, creating company guidelines and protocols to preside over your practice environment. He has a B.S. from The University of Texas and an Executive M.B.A from The University of Texas at El Paso.

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