Do you ever struggle to make important business decisions? That’s normal, as running a business naturally comes with some risk, and sometimes you have to make tough choices. Fortunately, you can reduce uncertainty by forecasting. A sales forecast helps management to make informed, strategic decisions on important matters such as budgeting, ordering inventory, employee scheduling, and production, just to name a few. Knowledge is power, and analyzing data really helps you to proceed confidently in areas where the future is unknown. In particular, quantitative methods can be quite effective when enough data is available.
What Is Quantitative Forecasting?
Quantitative forecasting is when you use past experiences, previously established data, and numerical facts to make predictions that help you to make more informed decisions. To put it simply, it’s when you use concrete data to form conclusions. On the other side of the spectrum is qualitative forecasting, which is when you use soft data, such as opinions, judgment, and instinct, to form your approach. Both methods have their pros and cons, and using both can help you to form a well-rounded point of view. Quantitative forecasting can be broken down into a variety of different methods.
Trend analysis is the most straightforward quantitative forecasting method. It’s when you examine the same type of data over a long period of time to get an accurate feel for its behaviour. Trend analysis is used frequently to predict the rise and fall of stocks, but it can be used in a business setting too. For example, say you notice that your office supply expenses rose significantly in one month and then decreased the next month. Now you can see that there’s an overlap, so you didn’t actually need to reorder supplies that second month. This gives you an accurate time frame to work with so you don’t end up ordering more supplies that you need at any given time.
Seasonality analysis is similar to trend analysis, except you’re examining the behaviour of your business based on time. For instance, you could look at your monthly sales figures to determine which months have high sales and which months have low sales. With that information, you can make preparation decisions such as how much product to manufacture or how much inventory to keep on hand. Additionally, you could plan to focus more heavily on sales during the off months.
A moving average allows you to get a better picture of data that’s unaffected by abnormal changes. To find your moving average, you take the averages from multiple, similar time frames so you can get a more well-rounded view. For instance, imagine you want to get an idea of your sales figures for the past three months. Instead of just analyzing the movement from January through March, you can find the average from January through March, and the average from February through April, and the average from March through May, and so on. You would then find the average of all those figures so your overall three-month sales average isn’t affected by month-specific trends.
Exponential smoothing is similar to the moving average, in that your goal is to find a more accurate average. However, the approach is different. Using the exponential smoothing method, you put more weight on the most recent figures and ignore old, outdated data. For example, the fluctuations of your sales figures from two years ago may not be relevant anymore, especially if your company or the market has changed significantly.
Regression analysis is when you compare your data with one or more specific variables. For example, if you see that your sales have dropped, you could compare that data with variables such as the local economy, population, or average income. This method helps you to determine the reasons why your data is what it is. It helps you to understand what kind of shifts may occur in the future if those variables continue down the same path or if they change. Say your sales rise when your local economy is thriving. It would be smart to ramp up production during those periods of prosperity.
The Pros and Cons of Quantitative Forecasting
Quantitative forecasting can be incredibly useful, but it can also be expensive and time-consuming. In short, it’s best saved for when you know the results are worth the effort. For example, if you’re simply placing an order for pens and pencils for your small office, you may end up spending more money performing quantitative forecasting than you would simply buying supplies based on your judgment (qualitative forecasting).
Quantitative forecasting is also ideal when you really need accuracy. Say you’re planning on launching a new product. Deciding how many products to manufacture for the launch is a pretty big deal, as you don’t want to end up with a surplus of unsold products, and you don’t want to pay for unnecessary labour and materials. In that case, having accurate figures based on prior launches would be quite useful, so quantitative forecasting may be worth pursuing.
Of course, quantitative forecasting is also only effective if you have plenty of data to work with. If your company has only been around for a few months, you probably haven’t accrued enough data for quantitative methods to be worthwhile. Data analysis can be a huge money saver, but it can also be a huge waste of time and resources. Take each situation as it comes, and don’t feel like every single data analysis situation requires a quantitative approach.