What is Industry 4.0?

Industry 4.0 technologies are technologies that act as a bridge between the physical and digital world—which serve as a foundation for smart and autonomous systems. Although businesses and supply chains are already using some of these technologies, their full potential is realized when they’re used together. Here are seven of the most important Industry 4.0 technologies:
 

1. Cloud computing. Cloud computing is the backbone of Industry 4.0 since the data that drives most Industry 4.0 technologies resides in the cloud.
2. Internet of Things. The Internet of Things (IoT) is a concept that aims to extend the benefits of the regular internet—including constant connectivity, remote control ability, and data sharing—to goods in the physical world. Physical things such as devices, machines, robots, and products have embedded sensors to provide real-time insights into their condition, performance, or location. Get the IoT Signals: Manufacturing Spotlight report.
3. Digital twins. A digital twin is the digital representation of a physical object, such as a simulation of a machine, process, factory, system, or supply chain. It sometimes includes 3D renderings of physical assets. A core component of Industry 4.0, a digital twin allows businesses to increase productivity, improve workflows, and design new products.
4. Big data and analytics. Big data analytics systems can handle the sheer volume of data generated from monitoring every function of a manufacturing operation. Using machine learning and AI technologies, data is quickly processed in real time to improve decision-making and automation over an entire manufacturing operation.
5. Edge computing. Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data. This is especially beneficial since it reduces latency time, which is the time from when data is produced to when a response is required.
6. AI and machine learning. AI and machine learning are essential to the Industry 4.0 transformation and the move to smart factories and manufacturing. AI takes the data generated from smart factories to optimize machinery, improve workflows, and streamline logistics to drive efficiencies.
7. Cybersecurity. There’s increased connectivity between physical and digital environments in Industry 4.0, meaning there’s also an increase in potential threats from malicious attacks and malware. However, technologies like machine learning make it possible to automate threat detection, protection, and response while minimizing breaches and production delays across networks.
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There are many benefits of Industry 4.0 technologies, including:
 

• Productivity. Productivity improves because AI and autonomous systems augment factory workers to increase production efficiency and quality and reduce downtime.
• Automation. Automation is often the solution to maximizing production efficiency. Technologies like AI and machine learning make automation possible.
• Asset utilization. Greater flexibility across the entire manufacturing operation from Industry 4.0 technologies means improved asset utilization and the potential for increased revenue.
• Optimized processes. All Industry 4.0 technologies are deployed for one purpose—to optimize manufacturing processes for greater efficiency and improved customer service.
• Operation continuity. Ensuring business and operational continuity is critical since downtime is especially costly in manufacturing and production.
• Improved quality. Agile factories and smart manufacturing processes improve overall throughput and quality while reducing costs.
• Streamlined logistics. With visibility into the entire supply chain because of AI and data analytics, manufacturers can optimize production and delivery.

Making the transition to Industry 4.0 comes with its own set of challenges. For instance, integrating existing assets into the digital transformation process could prove difficult and time consuming. Another hurdle to overcome is potential skills gaps among new staff in crucial areas like data science, AI, and cybersecurity combined with the loss of retiring staff.

With digital transformation, cybersecurity vulnerabilities are always a concern given the larger attack surface from the convergence of so many technologies. Data security will continue to be a key concern to protect safe and efficient commerce. Finally, the sheer volume of data generated from digital transformation comes with the need for more storage and processing power. The data path alone is generally several layers deep, so enabling those layers to communicate openly can be expensive and complex.

As explained earlier, transitioning to Industry 4.0 has many benefits. Let’s look at a few real-world examples of Industry 4.0 technologies at work.
 

Expanding data insights

Dow broke down data silos and accelerated digital manufacturing with Microsoft solutions—seeing improvements in equipment uptime, production efficiency, and employee collaboration. Read the Dow story.
 

Enhancing performance

Novo Nordisk used mixed reality to optimize shop-floor processes—allowing technicians to carry out tasks in a more efficient, optimized, and collaborative way. Read the Novo Nordisk story.

Achieving faster insights

Stanley Black & Decker achieved faster insights by implementing IoT sensors on multiple machines to see a broad range of performance metrics.

These real-world examples point to the potential of the factory of the future. When imagining the factory of the future, it’s not difficult to predict increased automation and perhaps more use of robotics in production. Factories are already becoming more agile through intelligent manufacturing solutions and smart factory strategies. For instance, a Lighthouse factory (as defined by the World Economic Forum’s Global Lighthouse Network) has implemented advanced manufacturing and AI-driven technology, at scale, and achieved operational and financial gains.

Learn more about the factory of the future.