Forecasting software is a beneficial tool to all companies that implement it in their inventory planning process. Accuracy in forecasting software is also extremely important, which is why there are multiple methods that make up an accurate forecasting software.
While alone they sound great, it’s important to take all quantitative and qualitative forecasting methods into consideration as you forecast demand. For example, naïve forecasting is a common method, and when used with other methods it can help in inventory planning processes.
The two main categories of forecasting are quantitative and qualitative forecasting. Each of these categories are made up of multiple methods.
Quantitative forecasting uses measurable data. It uses historical data that is reliable and accurate, for example past sales, labor reports, and a company’s statistics. The use of this type of data eliminates risks of inaccuracy and bias in forecasts.
The quantitative forecasting method can be separated into the following 4 approaches: the naïve approach, trend projection, moving averages, and exponential smoothing.
The naïve approach considers what happened in the previous period and predicts the same thing will happen again.
Example: Last month you sold 250 computers, so you predict that this month you’ll sell 250 computers again.
Trend projection is one of the most classic kinds of business forecasting. It’s rather simple to understand, as it takes the previous trends of a time-series and predicts that the same trends are going to take place.
Example: Last year your sales were steady, but around the holidays they nearly doubled. Using trend projection, you prepare for the same spike in sales during the next holiday season.
Moving averages looks at the average of previous periods and assumes the next period will be in the range of that average.
Example: In the previous 4 sales periods you’ve averaged a net income of $4,500. In the upcoming sales period, you will have a net income around $4,500.
Exponential smoothing also uses averages. Rather than just getting an average and using it as the next forecast, it decreasingly weights exponents depending on outside factors, like season or age of a product. This is done to ‘smooth’ the averages and create a reliable forecast.
Example: As your product gets older, you use exponentially decreasing weights in your averages to determine price adjustments. Consumers often expect to pay less for older products.
Unlike Quantitative methods, Qualitative forecasting uses data that can’t be measured. It relies on opinions and expert advice and is useful for new companies that don’t have any or much historical data. Qualitative methods are useful, but it’s important to take the information into account in a nonjudgmental and unbiased manner.
The qualitative forecasting method can be separated into the following 4 approaches: executive opinion, Delphi method, sales force estimates, and consumer surveys.
This happens when a group of executives makes decisions regarding the future of a company.
Example: Coca-Cola executives use their industry-experience to make an important decision regarding the marketing campaign for the new year.
The Delphi method is used as a group of industry professionals give an opinion regarding the future of the company. This opinion is given to another group of experts, who interprets and discusses the opinion before presenting the ‘final’ opinion to company decision makers.
Example: A manager has all employees anonymously submit their predictions for sales over the next quarter. He then compiles them, looks them over, and submits a final forecast to his boss who will create the actual forecast.
This happens when individual sales employees use their experience and knowledge to make their own sales forecasts.
Example: Sales representatives are expecting to close three major deals this month, so the forecast is adjusted to represent their predictions.
Consumers give their opinions on products. These opinions are considered when forecasting.
Example: You hand out surveys to each customer that makes a purchase. Each survey has the same questions and they’re submitted anonymously.
There are multiple methods that go into creating a reliable demand planning software. While each of these approaches are useful, it’s important to take all of them into account when forecasting. That is, if the goal is to have an accurate forecast.
At Avercast, we use all the above quantitative and qualitative forecasting methods. Our software uses over 200 algorithms to produce the most accurate forecasts possible. Accurate forecasts are a strengthening benefit to any company’s inventory planning.
If you think your company could improve in its inventory planning, or if you’re interested in implementing an extremely effective forecasting software or demand planning software, contact the Avercast team today.
We have the software that’s going to make you stronger. You can never be too prepared in meeting consumer demand, and with Avercast you’ll feel more prepared than ever. Contact us today for a free demo or call!