The Most Beneficial Applications of AI in Manufacturing

Artificial intelligence (AI) is the hottest topic in industrial manufacturing and for good reason: AI can address some of the industry’s top priorities. Automating repetitive tasks can reduce production costs, leverage scarce skilled labor, and cut general and administrative expenses. Demand forecasting, quality management, predictive maintenance and other AI-powered tools can enhance manufacturing productivity, tighten the supply chain and improve delivery reliability. On the front end, AI can boost sales by offering customers what they want, at price points that work, quickly. All this cascades down to a bottom line of greater profitability.

 

While AI solutions for manufacturers have been maturing quickly, they remain in their infancy. Considering where we’re at in these early stages of AI deployment, how should an industrial manufacturer position itself to exploit AI at scale as AI technologies go mainstream and proliferate?

 

Two examples of embedded AI technologies available to manufacturers today are intelligent product recommendations and AI-assisted visual inspection.

 

With intelligent product recommendation, a large language model (LLM) extracts customer requirements from unstructured data sources such as notes, emails or tender documents. It hands the distilled requirements off to a machine learning model. That model then makes product recommendations and estimates prices with little or no support from experienced salespeople. The system can cut time to configured quote by 60-95%.

 

Maschinenfabrik Reinhausen GmbH, as an example, is poised to use intelligent product recommendation to help its sales team determine the optimal configurations of complex industrial equipment based on each customer’s needs.

 

AI-assisted visual inspection moves the manual, repetitive, error-prone task of visual inspection into the digital realm, increasing productivity by up to 50% and boosting defect-detection rates by up to 90% over human inspection. Smart Press Shop has piloted use of this technology to inspect the precision of glue bead placement in its process of adhering automotive body panels, and now they’re looking to extend the pilot into diverse inspection applications. 

 

To start applying AI to business processes, manufacturers should get serious about transitioning to the cloud if they haven’t already. AI thrives in the cloud, where it can access scalable, flexible, easily upgradable computing resources and troves of data from diverse sources that can’t easily be assembled from siloed on-premises systems. 

 

Manufacturers should also recognize that AI integration will be an ongoing process with three foreseeable phases. The first involves AI automating and optimizing business-process execution across lead-to-cash, design-to-operate, record-to-report and source-to-pay processes. While AI still has a long way to go in these areas, the process has already begun.

 

The second phase will harness AI to reassess and rationalize how manufacturers go about their work. Rather than optimizing existing business processes by automating discrete steps, AI will help manufacturers holistically transform business processes. Central to this is AI’s ability to harness unstructured and semi-structured organizational knowledge, enabling optimized planning and far greater agility in ever-changing environments. 

 

The third phase will extend AI optimization towards autonomous interactions with vendors, customers and suppliers. Here, AI agents can request quotes from vendors, create offers for customers, negotiate prices and delivery dates, manage production and address supply chain hiccups.