Unfortunately, there isn't a crystal ball that will show you the future of your business (we're working on it), but there is a way to predict the demand for a product or service.
Demand forecasting is a way of estimating future demand for a product over a certain time-period. In just the same way that the future isn't set in stone, neither is demand. If done well, a demand forecast will not only allow a company insight into what the next few months or years will look like, but it can also be integral in making decisions that will create the best possible outcome.
While there is an inherent unpredictability in owning a company, there are ways to mitigate risk and increase the company's probability of success. One of the ways a company can increase the likelihood of a good outcome is to use demand forecasting to eliminate foreseeable mistakes.
Demand forecasting can prevent:
Don't let the name fool you. Demand forecasting isn't just helpful for future preparation.
Companies have goals, and if forecasting shows that a company is not on track to reach that objective, it may be time for a tactical change.
For example, let’s assume that Company A has a goal of selling a certain amount of product per month. Company A met 70% of their sales goal in January, 90% in February, and 110% in March. At first glance, it looks as if the sales will continue to increase, and the company will far exceed their goal. However, this is not the case.
After demand forecasting, the company sees that they are not on track to meet their goal. The February and March sale patterns were an anomaly, and the company must adjust accordingly if they plan to meet their annual goal.
Demand forecasting can help a company know how much inventory to order, what types of marketing to invest in, and how their team or product is performing - but it can also be highly misleading if done incorrectly.
Qualitative demand forecasting is made from deductions based on judgment, opinions, and expertise, while quantitative demand forecasting is based mainly on data.
An example of qualitative demand forecasting is opinion polling. By gathering opinions from experts, a company can get a fuller perspective on future demand. Like many other forms of qualitative research, this method is open to weaknesses such as bias and the inherent risk of human error.
An example of quantitative demand forecasting is the trend progression method, which uses past data to predict future data. Past trends can give important insight into the relationship between factors and demand. However, this method alone can be overly simplistic should one not have many years of data to draw on, so it's crucial to have proper analysis. Life isn't as predictable as identifying a trend.
Both types of demand forecasting have weaknesses. Qualitative demand forecasting is subject to human error, while certain quantitative data types can give predictable outcomes in a largely unpredictable world.
To expect a forecast to be accurate, a company must consider factors which may not even exist yet. There could be thousands of possible scenarios. The best way to account for this inherent unpredictability is to be ready for every situation.
If you asked a person to predict what their day would look like tomorrow, they'd find it much easier to answer than if they were asked to predict their day in five years' time. The same applies to demand forecasting. The longer-term a prediction is, the more unexpected situations may arise.
Here's when to use short vs. long-term forecasting.
Causal is a Monte Carlo simulation tool which can help companies build custom demand forecasts, in just a matter of minutes. You simply lay down the input assumptions relevant to your business, model how these assumptions change over time, and then you can easily visualise your model outputs in tables and graphs.
Causal doesn't just mitigate the inaccuracy of unpredictability but embraces it to give customers a full range of possibilities. The interactive dashboard allows team members to see how specific situations would impact demand in real-time using clean graphs, readable formulas, and quick scenario planning.
Causal's is proof that you don't have to be psychic to see into the future.