Business Forecasting Software: A short history and explanation

This has led to an interesting challenge in the world of business forecasting software. Business forecasting software, otherwise familiar as sales forecasting software or demand forecasting software, is used by businesses to know how much demand they should expect in the upcoming months, quarters, or years. Budgeting and forecasting software are known by many names and can sometimes even be confused with weather forecasting. Like weather forecasting, financial forecasting software predicts the bad, the good, and the neutral so that a company can be well prepared for what is to come.

Predicting the future of a company with 100% accuracy is impossible but over the past few decades that budget forecasting software, and any computer software, has existed, the accuracy has of forecasting software systems has constantly been improved. To understand how inventory forecasting software systems, have advanced, you first need to understand how they work.

In order for a traditional sales forecasting software system to provide any level of accuracy, a product should usually have some sales history. Sales history for forecasting software should include the product name, the location that the product was sold at, the amount of the product that was sold, and the date that the product was sold on. For further reporting and decision-making purposes, business forecasting software can also typically accept additional information including price, cost, case number, color, and any other detail of piece of information about the item so that the forecasting and reporting of a product can be refined and distributed easily in one place. It is also possible to use financial forecasting software to create a demand prediction without sales history, however we will talk about this method later on.

Once a product has at least two years of sales history, including the required factors we listed above, a demand forecasting software system can then take that sales history and identify certain patterns using several algorithms. These algorithms check for specific details about the data such as seasonality, trends, moving averages, and other important factors used in data prediction.

Algorithms that look at seasonality would look for periods of time when sales were high or low throughout the year. For example, if your company sells ice cream, you will likely see a sharp increase in sales during hot months with a steady decline and large drop-off during cold months. This can get tricky when you have multiple locations across the globe. For example, you may have a few ice cream shops in the USA and a few ice cream shops in Australia, you would see your US products increase sales in June – August and your Australian products increase in December – February.

An inventory forecasting software algorithm that focuses on trend projection would look at your most recent sales and see what direction your company is heading. For example, you may have had 100,000 sales in January of 2018, but then your sales steadily decreased until January of 2019 when you had only 50,000 sales. Your sales then may have continued to decrease steadily and now it is December 2019. If you are looking for a forecast for January 2020, the system will detect this trend and likely predict around 25,000 sales. This is a simplified example but relays the general way demand forecasting software predicts sales.

Above, we used an example of 2 algorithms. Generally, the more algorithms a system has, the more accurate that system will be because more trends are considered. A system like Avercast for example, uses over 200 forecasting algorithms to predict the upcoming demand. In their case, the system would put your sales history up against 200 different algorithms designed to detect unique and general trends. The system would then decide which algorithm it feels will present the most accurate picture of each products’ demand and makes the assignation accordingly. With multiple products and factors to consider, it is highly unlikely that every product will receive the same algorithm used to create the sales forecast.

Many budget forecasting software systems also provide you with the option to select a custom algorithm for each product. For example, perhaps you have some inside information of why a product is performing how it does, such as a particular promotion you run in March. The system would naturally think your product is seasonal towards March. However, you know that this year, that promotion is going to take place in October. You could fix this forecast in 2 potential ways.

The first way you can change a forecast using financial forecasting software is to manually select a particular algorithm, different from the one that the system originally chose, and see how it compares to the original based on your inputs. This way you may find an algorithm that is closer to the type of forecast you were expecting. You can also alter the forecast by making custom adjustments to each product. For example, the forecast may look great, except for on month. You can go in and adjust that month alone so that you have a more accurate representation of what to expect. This is more tedious but provides more flexibility with top products.

As you can see, demand forecasting software can be extremely beneficial when planning for upcoming demand. Most systems, however, offer more than just forecasting. Many forecasting software systems also include the ability to actually make plans within the system through the creation of purchase orders, identification of prioritized items through ABC analysis, optimization of inventory by moving overstocked product into places where that product is understocked, and much more!

We have just scratched the surface of what a capable sales forecasting software system can do. For more information, check out www.avercast.com and schedule a quick call or extensive demo to discover the full potential business forecasting software has.