How to Implement Artificial Intelligence for Demand Forecasting

AI demand forecasting can improve efficiency, accuracy, and detail in the forecasting process, giving businesses a major advantage. Conventional forecasting methods are often time-consuming and rely on large amounts of historical data.

With AI demand forecasting, your team can draw off of real-time data, disparate data sources, and even unstructured data. What are the benefits of this technology, and how can you apply it?

Benefits of AI Demand Forecasting

Businesses can experience many benefits from adopting AI demand forecasting. However, a few particular benefits are common across all industries and applications.

Automated, Real-Time Forecasting

The clearest advantage of AI demand forecasting over conventional forecasting methods is efficiency. AI and machine learning can fully automate the data analysis process at the heart of effective demand forecasting. Algorithms can rapidly analyze data much faster than a human. This speed allows AI to draw off of real-time data, as well.

Automating demand forecasting allows you to concentrate on the results rather than the process. This is especially helpful if working with a small team, limited resources, or a tight timeline. AI can take over the demand forecasting process so you can get quick results without sacrificing accuracy or insights.

More Accurate, Cost-Effective Forecasting

Studies by McKinsey & Company have found AI forecasting can result in up to 50% fewer errors, and up to 65% fewer lost sales and unavailable products. The higher level of accuracy demonstrated in AI demand forecasting can directly translate into cost savings and profit gains.

AI algorithms excel at pattern recognition, making them ideal for data analysis. As a result, they are not only faster than a human but often more accurate. This is especially true in situations where historical data is limited.

Using AI can ensure your team has the most accurate demand forecasting possible. This allows you to get the supply or materials you need while also making sure you don’t miss any potential growth opportunities.

No Need for Large or Labeled Datasets

Conventional demand forecasting methods rely on large, organized datasets. Collecting and maintaining large stockpiles of forecasting data can be expensive and time-consuming. It may even be prohibitive to effective forecasting in situations where there isn’t much data available.

AI demand forecasting can extract insights from small datasets and unlabeled or unstructured data. Of course, data cleansing and larger datasets can be helpful. However, they are not critical to successful forecasting with AI and machine learning.

How to Implement AI Demand Forecasting

How can businesses apply AI demand forecasting? This technology has many practical uses, ranging from product development to supply chain management.

Seasonal Demand

Predicting seasonal demand is one of the most common applications for AI demand forecasting. This could be for products, raw materials, or even hiring. In fact, the top benefits of demand forecasting include better stocking and production as well as improved supply chain management. This is particularly important today, given ongoing supply chain disruptions.

Your team can use AI demand forecasting to stay ahead of shortages and delays as demand for certain products fluctuates throughout the year. AI can draw off data from previous years and real-time data about developing market trends. For example, AI could factor in data from sources like social media or recent sales figures.

New Product Development

Expanding your product range can be a creative, exciting process. However, it can be challenging to get started when you aren’t sure what products would be most helpful or desirable to your customers. AI demand forecasting can be a big help in this initial research-focused product development phase.

AI can extract insights and patterns from many disparate data sources, which is particularly helpful when analyzing customer interests. Even if you just use your sales data to identify potential growth niches, AI can often spot trends your team might miss. For instance, there might be a particular product type you don’t sell much of but always sells out.

Even if you already have some new product concepts, AI demand forecasting can help predict which ones will be the most successful. This data-based viewpoint can aid in deciding which new products to invest in.

Project Budget Estimation

AI demand forecasting can help businesses create accurate budgets more easily. Supply chain disruptions can make it extremely difficult for companies to anticipate a project’s cost, such as a marketing campaign or construction project. In fact, project financing is the number one cause of delays in the construction industry. Inaccurate or volatile budgeting can be a serious issue in any industry, though.

Applying AI in the budgeting process can improve efficiency and accuracy. Since AI demand forecasting doesn’t rely on structured data, you can include real-time data sources. Using many disparate data sources allows the algorithm to draw on the most information possible.

Doing this manually would make the process too time-consuming, but AI can manage the large data volume easily. As a result, you can get the most accurate predictions for your project costs, reduce the likelihood of overspending, and help investors make more informed decisions.

Innovating Demand Forecasting With AI

AI can transform the demand forecasting process, equipping your team with rapid insights drawn from various data types. The high efficiency of AI demand forecasting allows you to include disparate data sources as well as unlabeled or unstructured data. Plus, AI algorithms’ pattern recognition skills let them deliver more accurate data analysis compared to conventional methods.

You can apply AI demand forecasting in countless ways, such as budgeting, product development, and supply chain management. Utilizing AI in your demand forecasting process will ensure you make informed decisions using all available data.

Article by Zachary Amos. Zac is the Features Editor at ReHack, where he covers data science, cybersecurity, and machine learning. Follow him on Twitter or LinkedIn for more of his work.