What is digital manufacturing?

Digital manufacturing is a term that is used in abundance nowadays. But what is digital manufacturing, and how are companies harnessing it to their advantage?

In a nutshell, digital manufacturing is when a company leverages digital technologies to benefit their manufacturing operations. With digital manufacturing, manufacturers can create a factory that is a connected, networked and fully integrated environment, enabling them to use real-time data analytics to optimize the entire manufacturing process and realize productivity gains of 10 to more than 1,000%. Those extra zeros aren’t typos.

Digital manufacturing enables manufacturers to eliminate bottlenecks, reduce inventory, improve quality, shorten time to market, pivot quickly to meet customer needs, and expand the number of products made. Digital factory investments have led to an average increase of 10 percent in production output, 11% in factory capacity utilization, and 12% in labor productivity, according to Deloitte.

Adopting smart factory initiatives can help U.S. manufacturers triple the labor productivity rate through 2030, compared to the sluggish rates of 2007-2018, Deloitte said.

Problems and people first

Before adopting any digital manufacturing technologies, manufacturers should determine the business problem, such as more efficient production or faster time to market, that they’re trying to solve. Then, set measurable, incremental goals for ROI. Some early adopters bought digital factory technology with only a vague idea of what they wanted to do, then floundered.

People are the next priority. A strong, visionary leader who understands digital manufacturing will keep digital factory programs moving forward. Above that leader, the board also needs to be engaged and educated on the benefits. Below that leader, listening to frontline workers about their work tasks, bottlenecks and expectations will make a difference between technology that improves the bottom line or sits abandoned in a corner.

Digital manufacturing use cases

According to Deloitte and others, use cases for digital manufacturing initiatives include:

  • Predictive maintenance to avoid costly breakdowns.

  • Quality sensing and detecting to monitor and test equipment and products in real time with visual analytics.

  • Predictive efficiency to avoid costly bottlenecks.

  • Temperature monitoring to make needed adjustments in cutting tools for hotter temperatures.

  • Optimizing work stations to benefit the entire production line.

  • Smart conveyance to automate moving material, ensure continuous material flow, and eliminate backups.

  • Engineering collaboration and digital twin for fast prototyping, virtual production cell configuration and digital product modeling.

  • Real-time asset tracking.

  • The ability to adjust production to meet changing customer needs and hot orders.

Siemens digital factory is a strong use case example

Siemens stands out as a high achiever. In 2010, the company began digitizing its own manufacturing plant in Amberg, Germany. Over the next decade, the factory evolved from 25% digital/automated to 75%. Productivity improved by an astonishing 1,400%.

Before digitization, the plant could manufacture five products. In a 12-month period, the factory was in a position to make 1,300 different products, with the ability to make 9,000 in total, said Alastair Orchard, vice president of digital enterprise for Siemens Digital Industries Software. Time from order to delivery: 24 hours.

Instead of focusing only on optimizing production at individual work cells, the Siemens digital factory, and others, takes the long, broad view to optimize the entire process or even multiple processes.

How that plays out: Imagine one factory machine can only drill holes. A smart factory robot can drill holes but also can solder, perform some assembly processes, pick and place electrical components and test components. When work begins for the day, the digital twin determines the most efficient way to operate the entire factory and manufacture each product based on all the tasks at hand. Identical products may be produced using different combinations of work cells according to the entire day’s workload, Orchard said.

“Any product can go on any journey through the factory interacting with any combination of machines and people,” Orchard said. “The product can interact with one machine, 10 machines, 15 machines. We have introduced a completely new paradigm.”

Defects dropped, but speed dropped too

Defects dropped from 150 per million to nine, Orchard said. Initial quality improvement came at the expense of speed because the factory X-rayed every product to look for missing components and soldering defects.

“That was a critical part of the process but it slowed everything down,” Orchard said. “We were trying to make something every single second. The X-rays were eating up a considerable amount of time. It was a massive bottleneck.”

But Siemens was unwilling to sacrifice quality. Instead, the company decided to get smart about which parts to X-ray.

Siemens used data analytics to look at the 30m data sets the factory had from parts X-rayed in the past. The company also looked at about 200 factors such as the supplier, machines used, specific operators involved, temperature, humidity, maintenance information about machines, and more.

“We were able to train a machine learning algorithm and predict based on all the values collected whether or not there would be quality issues,” Orchard said. “We don’t X-ray anything now unless the machine learning algorithm says there more than a small chance this product may fail. We have removed the bottleneck. We are able to run the factory at full speed and X-ray only the few items deemed to potentially have problems. We were able to balance quality and productivity.”

Digital twins play an important role in digital factories

Finally, digital twins also play a role for optimizing production. For companies such as Siemens that make and use digital factory platforms, a digital twin can easily show customers what the technology can do for them.

One Siemens employee described the digital twin as giving him “the factory in his pocket.”

Siemens also is pushing digital manufacturing initiatives back down the line at its suppliers. When the factory continually had problems with the contacts on its smart buggies, the company encouraged its supplier to use the Siemens’ smart connectivity platform to collect data and sell a maintenance service to the factory. When the contacts start to fail even slightly, the system triggers an action to send the buggy to a cleaning station, as opposed to halting an entire production line.

When seeking bids for machines for this digital factory, Siemens insists its suppliers bid via a digital twin. “Each supplier comes back to us with a working digital model of a machine. We can see it doing its job. We can predict the torque, the power usage, the maintenance intervals – all of this in the virtual world. We choose the best machine, not on paper but in this very detailed predictions.”

Siemens gets the digital twin of a new machine about a month before getting the actual machine and virtually commissions the digital twin to test production. When the real machine arrives, the factory needs only 40 minutes to begin full production.

“We can plug it in and it’s good to go,” he said.

Laggards worry about digital manufacturing risks and challenges

Despite the clear advantages of digital factory technology, 49% of U.S. manufacturers remain stuck in pen and paper mode, according to Deloitte. If they use data analytics, the information may be months old.

There are reasons for this reluctance. Executives worry about making mistakes that stop production lines; they also worry about security breaches. Those legitimate fears helps explain why 19% of Deloitte’s respondents aren’t even thinking about digital factory transformation and 30% are thinking about the idea but not planning to move forward.

Early adopters have cleared the way

But there is good news for those yet to embark on digital factory transformation:

  • Early adopters have already identified common stress points and failures. (Remember GE’s fail fast?) Now, late-comers can learn from those lessons.

  • The cost of digital platforms is dropping. Digital factory software is possible now even for smaller shops.

  • Digital factory technology is more easily integrated across vendors. Vendors understand that manufacturers can’t rip and replace everything in their shops so they’re making their platforms work well with existing technology.

Carefully evaluate and address the security risks

But the risks, especially cyber risks of digital factories, remain compelling. Before proceeding, manufacturers should carefully evaluate the challenges and risks. A fully integrated security platform can take advantage of real time data analytics to fight cyber threat actors. A smart approach to assessing risk, according to Deloitte:

  • Evaluate all new digital technologies based on human safety.

  • When connecting the factory and its assets to the broader communications network, implement a nimble, evolving risk mitigation plan.

  • Avoid single point failures by segmenting the production line and having more than one network channel.

  • Create standard policies for risk and disaster recovery across all vendors providing connected equipment.

  • Consider a layered security approach to strengthen resilience.

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Originally published: https://www.themanufacturer.com

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