Key AI Capabilities for Midsized Manufacturers

Like most industrial manufacturing companies — and most businesses, for that matter — your organization has likely been doing its due diligence on artificial intelligence, sifting through the growing array of intelligent technologies to identify the best fit and value for the use cases you have in mind.

For some manufacturers, that search is leading to a surprisingly obvious source. Instead of opting for stand-alone AI point solutions that may or may not scale well or readily integrate with the digital platforms their organization uses, they’re finding what they’re looking for inside those platforms themselves.

For manufacturers to get the most out of AI, those intelligent capabilities must reside as close as possible to their data and processes. This close proximity tends to yield the highest quality insights, where the most relevant actions can be determined with the greatest certainty and reliability. An enterprise resource planning (ERP) system has that proximity. And when that ERP system resides in the cloud as a software-as-a-service (SaaS) offering, the AI capabilities embedded within it are easier for a business to access, consume and scale, making it a logical entry point for a midsized manufacturer’s AI journey.

In a recent survey of 2,100 small and mid-sized businesses, Oxford Economics found that ERP systems are highly prevalent among industrial manufacturers: 70% have them. And because an ERP system typically touches so many aspects of a business, scaling the AI (and machine learning) capabilities within that system to various parts of the business can be a more straightforward, cost-effective and time-efficient exercise. What’s more, as new AI features and functionalities become available within a SaaS ERP system, they can be quickly implemented across departments and facilities.

These are especially important considerations for midsized manufacturers that need to run leaner and be more nimble to compete with larger and perhaps better-resourced companies.

As powerful and versatile as AI is proving itself to be, only about 18% of industrial manufacturers are currently using it, according to Oxford Economics. However, a large share say they plan to start using AI in the next 12 months. And the innovation pipeline appears more than ready to meet this growing appetite for AI by producing new use cases with regularity.

Following are four uses cases that look particularly promising for midsized manufacturers:

Finish incomplete sales orders. Instead of sales orders being delayed by missing or inaccurate information, companies can use AI to find and provide recommendations about a missing part number or a misspelled ship-to party, for example. Once the agent signs off on those recommendations, they can prompt the system to update the sales order and send it to fulfillment.
Keep customers informed about order fulfillment issues. A simple query to a generative AI copilot embedded in a cloud ERP system could help uncover order delays, their cause and potential remedies. For example, a sales operations manager could ask the copilot to identify any sales orders that are overdue for shipping. Once those are identified, the sales operations manager could then task the copilot to identify possible causes, such as a supply chain issue that caused a material shortage on the shop floor. Further queries could yield a recommendation for an alternative supplier and an estimated timeline for fulfilling the order if that back-up supplier is used. The sales operations manager then can accept that suggestion, instruct the system to place the order with the alternative supplier and inform the customer of the revised delivery date.
Servicing equipment in the field. Intelligent capabilities can analyze visuals of a piece of equipment or part, along with operating data from that equipment or part, then provide maintenance or repair documentation and suggestions to the service worker in the field. This can be really helpful, not only to inexperienced service workers, but to seasoned service personnel, too.
Sales process support. As complex and highly configurable as many manufacturers’ products are, AI can support their salespeople by developing recommendations tailored to a specific customer for the most suitable products and configurations based on historical sales and customer data, as well as other information extracted from email or other digital conversations. Guided by prompts from a generative AI-driven recommendation engine during an interaction with a customer, a salesperson can ask the questions required to arrive at the specific product and product configuration that’s right for the customer. If the product is a pump, for example, does the intended application involve transferring a fluid? Is it acidic or a dairy product? What’s the required flow rate and the environment in which it will operate? The recommendation engine quickly processes responses, relates them to the data it has on hand, then offers the salesperson product and configuration suggestions, with associated confidence levels to meet the customer’s needs, along with a price estimate, production lead time and more. These capabilities can even enable a company to deliver highly specific recommendations to customers via a self-service interaction, without requiring an agent.

The list of potential use cases for AI across an industrial manufacturing value chain grows longer seemingly by the day. It’s helping companies with demand forecasting and planning, which in turn informs inventory management. It’s a valuable quality control tool, with its ability to analyze images and data from product lines and finished goods to identify defects and irregularities in real time. It enables organizations to enact predictive maintenance programs to cut downtime and costs. It can help an organization optimize its production processes and better manage its energy consumption.

In many cases, capabilities like these could already be at hand and ready to unlock new value for your business as part of your ever-evolving ERP system in the cloud.

David Koenig and Nico Dahl focus on cloud-based ERP systems and adoption with industrial manufacturers at SAP.