And how can this optimizing decision-making for your business.
How augmented machine learning is leading the way
One of the major developments in the future of forecasting is the use of augmented machine learning. It essentially involves combining and weighting a bunch of different models to make more accurate predictions. Some models are better at short-term stuff, while others are better for the long-term. By weighing them all together, you can get the best of both worlds.
This technique has been shown to be highly effective in predicting outcomes.
Specific models may be better suited to predicting certain time frames. For example, a model may be highly accurate at predicting sales one month ahead, while another model may be more accurate at predicting sales four months ahead. The key is to weigh these models in a smart way to get the best possible forecast.
The real game-changer, though, is using forecasting to optimize our goals. That means we're not just predicting the future anymore, we're actually using those predictions to make better decisions and maximize our resources.
Using better models and more data
We say, better models. But what does “better” really entail? Better models, such as deep learning models, can typically handle large amounts of data and identify complex patterns that traditional models cannot. These models use algorithms that are designed to automatically learn and improve from experience, making them ideal for data-rich environments.
Which brings us to data, and what happens when businesses collect more and more data. There is then a need for more advanced and tailored models to make sense of this data. The upside is that we’re able to process and analyze more data at a faster pace.
Moore's Law states that compute power doubles every two years. With such exponential growth, it means that we can expect to see even more advanced forecasting models in the future, leading to more accurate predictions.
The ability to make sense of this data and extract meaningful insights from it is a complex task, and that's where advanced and tailored models come into play. This is linked to the need for more complex and tailor-made models for different types of data. As businesses move towards more personalized forecasting, there is a growing need for specialized data scientists and machine learning experts to develop these models. (Indicio typically takes about 15 mins to build, develop AND test these models, which is a game changer for business looking to without expanding on their team of data scientists or quants.) Here's how.
Optimization for your goals - how to get there
The real game-changer, though, is using forecasting to optimize our goals. That means we're not just predicting the future anymore, we're actually using those predictions to make better decisions and maximize our resources.
Aside from predicting future sales, the future of forecasting is heading towards optimization. A quick example would be leveraging forecasting to optimize certain goals, such as maximizing EBITDA, given a product portfolio.
Another benefit of really getting down and gritty about optimizing is that it can help businesses to allocate resources effectively. By predicting future trends and outcomes, businesses can plan for the future, identify risks and opportunities (Doing a scenario analysis is a good way to test your sensitivities), allocate resources in a way that maximizes their return on investment.
The future of forecasting is constantly evolving, and businesses must be able to (or at least open to!) adjusting their strategies and models to meet changing demands and new technologies.
Ready to supercharge your forecasting? Book a quick demo and we'll show you how.
