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

Jun 06, 2022
  • IT
  • operations
  • discrete manufacturing
  • artificial intelligence & RPA

AI plays a key role in many Industry 4.0 projects. To get the most out of  AI investment, you need to align its deployment with underlying business strategy. Avoid a disconnected mess of ‘smart projects’ and ensure everyone is on the same page. Read on.

For a successful transition into Industry 4.0, companies need to start with a clear business strategy. This means prioritising either operational excellence, human centricity sustainability, or business model innovation. “Your strategy will determine which technologies you need to focus on: smart machines, sensors, IoTs, cloud computing, data and analytics, robotics, AR/VR, AI and machine learning,” says Wouter Labeeuw, manager for data science and engineering at delaware. “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.”

To get the most value out of tech, you need to align the way you’re deploying the technology with your strategy
Wouter Labeeuw, Manager, Data Science and Engineering

Picking your AI strategy

A tangible and profitable technology

This is certainly true for AI - 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 on production waste and retours, or they can use AI to predict the downtime of machines. Companies that want to prioritise ‘human centricity’ may leverage AI to create a safer and more attractive working environment for their employees.

Playing a role in business model innovations

AI can also play a role in business model innovations. Smart products, for instance, collect a lot of data on how customers are using a product after they bought it. With AI, organisations can offer additional services to these customers, like predictive maintenance and other usage recommendations. The same goes for product personalisation: AI can make configuration suggestions based on the consumers behaviour. 

Finally, AI can also help organisations in achieving certain sustainability goals, like optimising energy consumption via smart algorithms. 

Reorient towards interpretation

It is also important to consider is who will be using the technology. After you’ve successfully collected insights, you need to make sure they find their way back to the operator and the shop floor. To do this, you have to carefully consider which systems and interfaces to 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 cannot rely on simple rules of thumb. Instead, they have to be able to evaluate minor details, and weigh them 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. This brings us back to the importance of strategy. 

Implementing Industry 4.0 technologies such as Artificial Intelligence on the shop floor will have a major impact on the hiring and training process, alongside the entire organisational structure.  At times, 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 all stakeholders within the company.

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