How to integrate IoT, big data and analytics into Industry 4.0

Ten years ago, Industry 4.0 was just a theory. Now it comes to life with real-life examples and best practices for projects.

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Photo: wilaporn1973 / Shutterstock

Industry (or Manufacturing) 4.0 began as a German government initiative in 2011. It refers to a fourth industrial revolution characterized by smart factories that use robots, autonomous processes, and
The Internet of things

Big Data, Analytics, Artificial Intelligence, IT Convergence and Operational Technology. The goal is to create efficient, agile and intelligent manufacturing.

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There was no mandatory Industry 4.0 methodology for manufacturers to follow, so early users tried different methods to see which worked best. Now, 10 years

Later, we come to an inflection point where best manufacturing practices 4.0 emerge, and big data, Internet of Things, artificial intelligence and automation play important roles.

“We focus on the capabilities that [Manufacturing 4.0] “Technology can deliver to our customers. From this perspective, there are really four technology capabilities that are defined time and time again across research and implementation experience,” said Steven Laber, director of Deloitte’s Smart Plant.

According to Laber, as companies focus their efforts on Industry 4.0 in:

  • Factory asset intelligence and performance management.
  • Factory synchronization and dynamic scheduling.
  • Quality sensing and detection.
  • Engineering collaboration and the digital twin.

All of these initiatives include big data, automation, artificial intelligence, and the Internet of Things. These technologies must also be integrated with existing corporate systems.

Complex integrations, and the need for strong security on high-end networks and devices are likely two of the reasons 80% of respondents to a 2020 Deloitte-MAPI survey of 1,000 manufacturing leaders cited by Lapierre said they employ at least one of these industries The four. initiatives, however less than 40% were able to fully activate their deployment.

“They are struggling to scale up,” LaPierre said. This expansion includes scaling up big data capture and analysis, real-time data capture for IoT and implementation of critical intelligence and machine automation. In every business situation where IoT, analytics, artificial intelligence, and big data are deployed, the designs for business processes and integration are different.

From Laaper’s and Deloitte’s experience, the companies most successful in deploying big data, artificial intelligence, IoT, and analytics technologies at scale in Industry 4.0 initiatives are those focused on tackling a specific business problem. This way, they don’t set their sights too wide. “Then they determine how that technology will fit into their existing technology suites and how they can scale from experimental to full deployment,” Laber said.

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There is also work to be done on the part of the people.

“There has to be interaction with the stakeholders that will be affected by the deployment, from the factory floor to the management office,” Lapier said. “This way, you are proactively engaging the people who will be affected by the deployment process.”

Once the technology is implemented, resources are deployed to ensure that changes to newly created business processes continue and that any newly generated data is accurate, useful, and (most importantly) used.

Laber explained how one company changed its manufacturing using these methods. “We’ve partnered with our client, a high-profile aerospace manufacturer with an 80-year-old plant,” Laber said. “They were suffering from inefficient workers and assets, excessive inventory and inadequate resolving constraints. They were also using manual tools to manage production and needed help designing and implementing a significant plant upgrade.”

To modernize manufacturing, the company implemented a proprietary solution for plant synchronization and dynamic scheduling to improve human planning and constraints. Use the RFID (Radio Frequency Identification) solution for inventory tracking and technology integration across the company’s solution providers. Deloitte’s role was to provide deployment and change management support to the plant teams.

After implementing the project, the company found that:

  • Increased productivity by 12%, by optimizing asset utilization.
  • Reduce Work In Progress (WIP) by 15%, by effectively managing restrictions.
  • Save $11.6 million in labor costs by improving the efficiency of direct work and support.

What has worked for this industrial Internet of Things application?

The company has chosen a very specific manufacturing area to focus on; I implemented only the IoT, AI, analytics, and automation technologies I needed; Involved staff and management stakeholders in the project up front; Define and achieve results goals.

Most successful [Industry 4.0] Lapierre said shifts, regardless of the technologies used, are transforming the capabilities of their employees in line with the introduction of new technology. Start with a strategy and a clear definition of the value you seek to create. Involve experts who have the ability and experience to design a solution that includes multiple technology vendors and manage the change needed on the factory floor. Then, experiment and iterate to determine the value before scaling.”

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