How AI and predictive analytics are improving supply chain management

Artificial intelligence (AI) has a lot to offer supply chain managers, but its use isn’t all that widespread yet. Only 12 percent of supply chain professionals surveyed in the latest MHI Annual Industry Report are currently using AI in their operations, with 60 percent expecting to use it in the next five years. On the other hand, 28 percent of respondents are using predictive analytics. Still, there is a large gap between those using these tools and those who are not. So, what exactly are those who are not yet using these tools missing out on?

AI makes supply chain management smarter

There are huge benefits to be reaped from using AI in the supply chain, but that is only if it is based on solid fundamentals that take into account the diverse and dynamic nature of today’s modern supply chains, according to Christian Wibbe, Member of the Management Board at Miebach Consulting.

“AI broadly, and more specifically machine learning, has great potential in proactive management of the supply chain, and we’re testing a number of approaches,” said Per Ädelroth, Chief Supply Chain Officer at Axis Communications.

Companies can benefit from using AI-based supply chain management solutions to better manage their supply chain through reductions in carrying costs and overhead time spent analyzing and expediting, improved decision making, faster production speeds, real-time data and through better customer satisfaction due to increased on-time deliveries.

One possible obstacle for AI solutions in supply chain management, though, is access to data, and for many companies access to a “good” data stream isn’t that simple.

In fact, Wibbe pointed out that in order to get the optimal value in supply chain risk management, an AI solution comes with some prerequisites — one of which is access to real-time data inside and outside your company (and permission to use it). It is the collection and interpretation of the right data along the supply network that is the key enabler of effective supply chain risk management.

Be proactive with data and predictive analytics

The ability to be proactive and know what is coming could help companies avoid supply chain disasters. That is why predictive analytics has so much value to offer supply chain managers.

“Data analytics and predictive features are being used to improve supply chain management by helping businesses understand trending, and more importantly, how to burrow through mass amounts of data to find the trends that may be difficult to see/find without analytics and prediction,” said Jim Tuttle, Senior Solutions Architect at Aptean.

It also can help supply chain managers pull together and analyze customer/order demand, future/projected demand, sales/shipments history, inbound goods and consumer trends. “These solutions can pull all of this data together into simple views, surface areas that need attention (i.e., demand is going to exceed supply in week X or month Y), and point out when consumer demand is shifting (up or down), so that focus can be given to items/product lines that are going to have issues before they arise,” added Ken Weygand, Solutions Architect at Aptean.

For manufacturers like Axis Communications, data and data analytics are a critical component of the company’s “know sooner, act faster” motto.

“We’re constantly looking for greater visibility both downstream into our suppliers and upstream to our partners and distributors to see what stock levels they hold. We also use this data to model scenarios around potential disruptions and increased in demand which help identify and address potential bottlenecks in the supply critical components,” explained Ulrika Magnusson, Global Supply Chain Director at Axis Communications.

Particularly in periods of disruption these tools are even more critical. They allow for proactivity, helping companies focus their attention on certain areas, prepare as early as possible for potential supply chain risks and mitigate future failures.