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


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


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Industry (or Manufacturing) 4.0 started as a German government initiative in 2011. It refers to a Fourth Industrial Revolution characterized by smart factories using robotics, autonomous operations, the
Internet of Things

, big data, analytics, artificial intelligence, and a convergence of IT and OT. The goal is to create efficient, agile and intelligent manufacturing.

SEE: Hiring Kit: IoT developer (TechRepublic Premium)

There wasn’t a prescriptive Industry 4.0 methodology for manufacturers to follow, so early adopters tried various approaches to see which worked best. Now, 10 years

later, we have reached an inflection point where Manufacturing 4.0 best practices are emerging, and big data, IoT, AI and automation are all playing critical roles.

“We focus on the capabilities that [Manufacturing 4.0] technology can deliver for our clients,” said Stephen Laaper, principal and smart factory leader at Deloitte. “From this perspective, there are really four technology capabilities that are repeatedly identified across our research and implementation experience.”

According to Laaper, where companies are focusing their Industry 4.0 efforts are 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 involve big data, automation, AI and IoT. These technologies must also be integrated with existing corporate systems.

Complex integrations, and the need for robust security on edge networks and appliances, are likely two of the reasons 80% of respondents in a 2020 Deloitte-MAPI survey of 1,000 manufacturing leaders that Laaper cited said they were employing at least one of these four manufacturing initiatives, yet less than 40% had managed to fully operationalize their deployment.

‘”They’re struggling to scale,” Laaper said. This scaling involves the expansion of big data capture and analysis, the real-time data capture of IoT and the implementation of critical intelligence and machine automation. In every business case where IoT, analytics, AI and big data are deployed, the integration and business process designs are different. 

From Laaper’s and Deloitte’s experience, the companies that are most successful in deploying big data, AI, IoT and analytics technologies at scale in Industry 4.0 initiatives are those that focus on addressing a specific business problem. In this way, they don’t set their sights too broadly. “They then determine how that technology will fit into their existing technology stacks and how they can scale from pilot to full deployment,” Laaper said.

SEE: Tech projects for IT leaders: How to build a home lab, automate your home, install Node-RED and more (free PDF) (TechRepublic)

There is also work to be done on the people side.

“There must be engagement with stakeholders who will be affected by the deployment, from the factory floor to the management office,” Laaper said. “In this way, you proactively engage people who will be affected by the deployment.”

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

Laaper explained how one company transformed its manufacturing by using these approaches. “We partnered with our client, a high-profile manufacturer for the Aerospace industry with an 80-year-old factory,” Laaper said. “They were experiencing poor worker and asset efficiency, excessive inventory and inadequate constraint resolution. They were also using manual tools to manage production and needed help to architect and implement significant factory modernization.”

To modernize its manufacturing, the company implemented a proprietary factory synchronization and dynamic scheduling solution to optimize human and constraint planning. The solution employed RFID (radio frequency identification) to track inventory and integrate technology across the company’s solution providers. Deloitte’s role was to provide deployment and change management support for factory-floor teams. 

After the project was implemented, the company found that it:

  • Increased throughput 12%, by improving asset utilization.
  • Reduced work in process (WIP) by 15%, by effectively managing constraints.
  • Saved $11.6M in labor costs by optimizing direct- and support-labor efficiency.

What worked in this Industrial IoT implementation?

The company chose a very specific area of manufacturing to focus on; it only implemented the IoT, AI, analytics and automation technologies it needed; it engaged employee and management stakeholders in the project upfront; and it defined targets for results and achieved them.

“The most successful [Industry 4.0] transformations, regardless of the technologies deployed, transform their employee capabilities in alignment with the introduction of new technology,” Laaper said. “Start with strategy and a clear definition for the value you’re seeking to create. Engage experts with the capability and experience to architect a solution that encompasses multiple technology vendors and the change management needed on your factory floor. Then, pilot and iterate to establish value before scaling.”

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