The results are in: Artificial intelligence (AI) has significant value for many business sectors. But what are the most effective ways to apply AI to your supply chain management company?

Executives of many leading companies are looking for ways to integrate AI into their operations. A recent McKinsey & Co. study estimates that 40% of the potential value associated with data analytics today comes from techniques called “deep learning.” Deep learning refers to AI that can monitor a system and make adjustments rather than simply repeate a single task. Industry use of deep learning techniques remains relatively low as companies determine how best to use AI to meet business needs.

Using AI this way can improve forecasting accuracy by 10-20%, allowing companies to reduce inventory costs by as much as 5% and generate 2-3% increased revenue. Click To Tweet

Supply chain management is one of the business sectors that can get the most value out of deep learning techniques. Brick-and-mortar retailers tend to see a 1-2% increase in sales revenue when they use AI to personalize promotions based on customer data analytics. Supply chain management organizations can expect to see greater benefit from AI that forecasts demand by analyzing the underlying market factors. Using AI this way can improve forecasting accuracy by 10-20%, allowing companies to reduce inventory costs by as much as 5% and generate 2-3% increased revenue.

In addition to reducing inventory costs in supply chain management organizations, deep learning can create trillions of dollars in value in marketing and sales revenue and save costs through predictive maintenance. The best way to determine how AI can serve your needs is to look at how your organization uses traditional analytics techniques: AI can often provide higher performance in conducting analytics and introduce additional layers of analysis. The technology is always improving, so the potential value for companies adopting deep learning techniques is expected to continue to rise.

There are, however, obstacles facing companies looking for ways to use AI techniques. It is essential to plan for the security and privacy issues of data analytics. Deep learning works best with large-scale datasets, which not all organizations can assemble or access. The level of expertise necessary to implement and service AI technology comes with added expenses, but determining the right way to apply AI to your organization makes these costs and challenges a rewarding investment.

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