3 Ways Manufacturing Companies are Using Artificial Intelligence

Automation is nothing new to the manufacturing industry. Everyone has seen footage of synchronized robots working on the assembly line of a cutting-edge factory. But as the UiPath AI Summit 2022 showed, manufacturers are looking beyond the factory floor to find a host of new automation opportunities.

Rehau automates to navigate a spike in orders

Rehau Group, a manufacturer of polymer solutions, is a case in point. Three years ago, the company experienced a major spike in sales orders. That’s something any business would like to see. But Rehau didn’t have the personnel it needed to quickly enter all those new orders into its enterprise resource planning (ERP) system. The company had two options: they could hire new employees to accommodate the larger workload, or they could begin to automate sales order processing and similar processes.

The leadership team quickly saw that automation and software robots were the way to a more efficient future. So, they hired a smaller group to manage the spike in orders in the short term and assembled a team to automate the sales order process on an ongoing basis.

For Rehau, emails and faxes were major sources of orders, so the team started with simpler scenarios and standardized purchase orders to develop a proof of concept (PoC). A select group of customers always submitted consistently formatted purchase orders, and Rehau used UiPath Document Understanding and machine learning to extract the needed information from those orders and enter the needed data into the ERP system.

The company was soon able to expand its scope and include non-standard purchase order formats in the process. Today, 25% of orders for its building solutions division are automated, as are 6% of orders for its furniture solutions division.

To date, Rehau has used automation to process 68,000 orders—saving an estimated 16 hours per day of manual work. They also plan to expand into other use cases such as shipment tracking freight invoicing, and finance and human resources (HR).

BSH builds a network of data science specialists

For global home appliance manufacturer BSH, the automation journey took a different course. The initiative started with the 2017 formation of a data science team within IT. That small team focused on creating PoC projects and promoting awareness of data science’s potential real-world applications. The team then started working with UiPath to create an intelligent automation group. And from the start, the driving vision was to combine robotic process automation (RPA) with artificial intelligence (AI) to develop smart robots.

In 2019, BSH consolidated all of its AI initiatives under a single umbrella to share experiences and train new data scientists. Today, the company has more than:

• 20 data science use cases up and running

• 400 data scientists across the enterprise developing new models

• 100 automations in production

One of the use cases the team created centers on quality management reporting. When customers have an issue with an appliance, a technician sends back a report to the corporate office. Before automation, the quality management team had to read through those reports and classify the data themselves. Now, an AI model collects the relevant data from those reports, and another translates the report from any of 30 different languages for consumption by the team. Other AI-driven use cases for internal chatbots and those capitalizing on UiPath Document Understanding are also in the works.

Drager simplifies intercompany invoice processing

Drager, a leading manufacturer of medical and safety technology, began their RPA journey with a pilot project in 2017. The company then went global with its RPA network and invited colleagues from around the world to its German headquarters to train on UiPath. Today, the company has more than 200 automations and is eager to explore the advantages of semantic automation.

The company’s first automation use case focused on intercompany invoices. Every Drager subsidiary submits invoices to headquarters. And every subsidiary’s invoice is slightly different. The company also uses two different ERP systems. For the system that subsidiaries use, Document Understanding powered by machine learning collects the invoices from inboxes and extracts the right information. Then a software robot enters the extracted information into the ERP system. AI assesses the validity of the information and forwards it to an employee for any needed review.

Ever-evolving role of AI in manufacturing

It's an exciting time for today’s manufacturers as they turn their attention to automating the processes that support a more efficient factory floor.

Want to dive deeper? Explore AI and automation’s evolving role in manufacturing. And all of the sessions from the UiPath AI Summit 2022 are currently available on demand.