Industry 4.0 and AI: how your business strategy impacts your AI strategy

May 11, 2022
  • IT
  • discrete manufacturing
  • artificial intelligence & RPA

Artificial intelligence or AI plays a key role in many Industry 4.0 projects. But to get the most out of your AI investment, you need to align its deployment with your underlying business strategy. In this way, you’ll not only avoid creating a disconnected mess of ‘smart projects’, but ensure that everyone in the company is on the same page.

To be successful with Industry 4.0, companies need to start from a clear business strategy. This means prioritizing either operational excellence, human centricity sustainability, or business model innovation. “Your strategy will determine which technologies you need to focus on: smart machines, sensors, IoT, cloud computing and analytics, robotics, extended/augmented reality, AI and machine learning, …” says Wouter Labeeuw, manager data science and engineering at delaware. “But it also works the other way around: to get the most value out of technology, you need to align the way you’re deploying it with your strategy.”

Pick your AI strategy

This is certainly true for AI, which is one of the most tangible and profitable technologies used in Industry 4.0 today. For example, businesses who want to focus on improving operational excellence, can choose to perform automated visual quality control with AI technology, reducing production waste and retours, or they can use AI to predict the downtime of machines. Companies that want to prioritize ‘human centricity’ may leverage AI to create a safer and more attractive working environment for their employees. 

AI can also play a role in business model innovations. Smart products, for example, collect a lot of data on how customers are using a product after they bought it. Thanks to AI, organizations can offer additional services to these customers, like predictive maintenance and other usage recommendations. The same goes for product personalization: AI can make configuration suggestions based on the consumers behavior. Finally, AI can also help organizations in achieving certain sustainability goals, like optimizing energy consumption via smart algorithms. 

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Re-orient towards interpretation

Another point to consider is who will be using the technology. Wouter: “After you’ve successfully collected insights, you need to make sure they find their way back to the operator and the shopfloor. To do this, you have to carefully consider which systems and interfaces you’ll use, and what training to provide to employees.”

In quality control, for example, improved automatic identification of potential defects will shift the focus for employees to accurate interpretation. “The exact definition of ‘good quality’ often differs between quality engineers. So when they are required to make that final interpretation, they can’t rely on simple rules of thumb. Instead, they have to be able to evaluate minor details, and weigh those against their in-depth knowledge of the entire process. Often, this requires a major re-orientation for operators.”

Endorsed by all

At the same time, however, it’s important that operators trust the model, even when it goes against their own intuitions. Which brings us back to the importance of strategy. “Implementing Industry 4.0 technologies like AI on the shop floor will have a major impact on hiring, training, organizational structure, etc,” Wouter concludes. “Sometimes, the IT department has no knowledge of the long-term business strategy, resulting in disconnected initiatives. To ensure that your projects generate value, the strategy needs to be endorsed by everyone in the company.”