Tuesday, Sep 03 2019

How Machine Learning and AI Are Improving Forecasting

Written by

How Machine Learning and AI Are Improving Forecasting

Forecasting is a critical part of business operations. For example, being able to estimate future customer demand and sales numbers can help companies anticipate revenue. Similarly, technology departments have to predict ongoing storage needs, computing requirements, and other areas that fall in their purview while planning departments have to prepare for the number of products that may be needed to meet demand.

The ability to forecast accurately is often deemed critical. Without reasonable numbers, operational decisions may not align with what actually occurs over the next weeks, months, or years.

Often, companies rely heavily on people to make forecasts. However, as machine learning and artificial intelligence (AI) become more accessible, we are on the precipice of a forecasting transformation; one that has already started to take root.

If you are wondering how machine learning and AI are improving forecasting, here’s what you need to know.

The Typical Forecasting Workflow

For companies that have yet to embrace machine learning and AI for forecasting, their workflows look very similar. First, an analyst extracts aggregated data using an available software tool. Next, that information is dumped in an Excel spreadsheet.

At that point, the analyst has to handle a very manual process, setting various calculations or macros to create a forecast. Then, the results are sent to decision-makers. Finally, the process starts again, creating a forecasting cycle.


The Benefits of Integrating AI and Machine Learning

The above approach is often less than ideal. It can be tedious and cumbersome, requiring a lot of time on the part of the analyst. Additionally, accuracy can be an issue. A single typo by an analyst can produce a highly inaccurate result, and it may or may not be noticed before the forecast is delivered.

With automation, many of the steps can be handled by the system. This is a significant time-saver, allowing the analyst to focus their energy elsewhere.

Additionally, it eliminates many errors. The system is designed to pull, organize, and analyze data in a particular way using predefined calculations, so you typically get a more accurate result.

AI can also be designed to integrate data from third-party sources, allowing the system to examine factors beyond internal data. It can add data sources that can provide additional insights, such as examining information that could anticipate customer behavior based on specific actions.

Finally, AI and machine learning empower companies to explore what-if scenarios. They could define a possible action, set of circumstances, or potential influences and ask the system to forecast the likely result.

While transitioning to a forecasting model that embraces AI and machine learning can be challenging – both from a technical and cultural standpoint – the potential gains make it worth exploring. The ability to increase accuracy, consider more data sources, and evaluate a variety of scenarios could be powerful for decision-makers, enabling them to take the company in a better direction.


Contact The Armada Group to Discuss Forecasting Technology Questions

If you’d like to find out more, the staff at The Armada Group can help. Contact usto discuss your forecasting technology questions with a member of our experienced team today and see how our AI and machine learning expertise can benefit you.