How SAS data analytics improves forecasting

SAS data analytics

When I ask a company representative working in any industry what they want SAS data analytics to do for them, they tend to tell you the same thing—the ability to predict consumer demand.

It’s not hard to understand why.

Whether it is growing competition in the retail industry or the need for value-based healthcare, the ability to anticipate industry trends for the next few years is essential. Among many other benefits, it supports more efficient resource allocation.

This, however, begs another question. Most organisations already use forecast models to predict industry trends—what can SAS data analytics platforms, like SAS Viya, do that their current models aren’t already doing?

The difference lies in machine learning. I have already discussed machine learning in the past but its influence on data analytics platforms is impressive. One reason is the results. This technology allows SAS data analytics models to operate more efficiently and deliver better results to analysts.

In this post, I explain how SAS data analytics can take forecasting to another level. I also discuss the problem with current forecasting models and how SAS platforms resolve this issue.

The problem with current forecasting models

Traditional forecasting models, like Excel and Enterprise Resource Planning (ERP) systems, are the norm when it comes to forecasting models.

These systems work by analysing a large volume of data to create a picture of where the industry is heading in the next few years. It’s simple, effective, and has worked for several years.

So what’s the problem?

Well, the fatal flaw in traditional forecasting models is in how they function. Most traditional forecasting models assume that markets in the future operate the same way they do in the present—with little to no change. You can see where the problem is because no industry operates in a vacuum.

As the pandemic demonstrated, industries can expand, contract, or completely transform based on unforeseen developments.

One problem could be the limited use of data sources. Conventional forecasting models fail to use data sources beyond the organisation’s internal data sources. This is where SAS analytics provides a huge advantage to users.

What SAS data analytics does right with forecasting

SAS data analytics perform a deeper, more comprehensive level of analysis than traditional forecasting models to deliver accurate results.

The main difference between SAS models and conventional models is machine learning. Machine learning algorithms can incorporate additional internal and external sources of information to make more accurate, data-driven predictions.

Thanks to machine learning, SAS data models deliver more accurate readings than their predecessors.

Unlike most forecasting models, machine learning can evolve and become more sophisticated, as you feed them more data too. If you have a business-oriented mind, that means a higher ROI for your SAS data analytics model.

Machine learning engines work with both structured and unstructured data. What does this mean, though? Only that you can draw on sales reports, social media signals, weather forecasts, and marketing poll data. I’m talking about a variety of data sources at your disposal, here. You will have an easier time making connections between the most unlikely sources of data.

In the long run, you will also enjoy more accurate data analysis, which means an easier time predicting what the future will look like. Most organisations already use SAS data analytics as a failsafe against uncertainty, when introducing new products to the market, for example.

SAS data analytics platforms can expand what data analysts do. With previous forecasting models, you could perform ‘’demand planning”—the process of estimating demand at different points in the supply chain. With SAS data analytics, however, you can perform demand sensing.

Demand sensing is an evolution of demand planning. It is a new concept in planning, which captures real-time fluctuations in the industry. Think of it as an evolution of demand planning, given its focus on real-time shifts in data.

Preparing for the future with SAS platforms

SAS data analytics can optimise planning for any organisation. Of course, that is not to negate the other benefits of SAS analytics. These data analytics models can speed up the data analysis process considerably.

The ability to predict industry trends with greater accuracy, however, is crucial. This is because it gives organisations the means to act smarter and more efficiently than before. It means lower operating costs and the more innovative use of resources, which is what every organisation wants to do, no matter what industry they operate in.

Want to know more about SAS analytics? Explore our blog and other resources for more insights.

Cameron Lawson