Industry Insights

What is Industry 4.0?

31 May

The marriage of advanced manufacturing techniques with information technology, data, and analytics is driving another industrial revolution—one that invites manufacturing leaders to combine information technology and operations technology to create value in new and different ways.

What is Industry 4.0?

Industry 4.0 is a set of technological changes to create a coherent framework to be introduced in the manufacturing process. Of course, the backbone of Industry 4.0 relates to how products are made, the phenomenon will most likely affect every part of our world and has implications for all types of business. A simplistic definition of Industry 4.0 is the “application of the IoT, cloud computing, cyber-physical systems (CPS), and cognitive computing into the manufacturing and service environment. Automation and connectivity within the manufacturing world is not new.

It is classified as Industry 4.0 because it follows the third industrial revolution of the age of computers and takes that a step further and refers to self-running computers fueled by data and machine learning. As factories become smarter, learning from an influx of data from all its systems, they will become more productive and less wasteful. “Industrie 4.0” was initially coined in 2013 by the German government and is part of its High Tech strategy with the intention to maintain and avoid losing industrial advantages against other countries.

The main use cases of Industry 4.0

1.

Interoperability and artificial intelligence

The maturity of cyber-physical systems allows humans, the product itself and smart-factory machines to connect and communicate with each other and derive insights in real time. Not only is there human-machine interaction, but with decentralized cyber-physical systems, machines can make decisions on their own. A great example of human to machine interaction comes from the automotive industry. Highly specialized workers wear bracelets which can track their movement and alert them when a move in a wrong direction happens or during assembly when a torque applied is enough. This not only enhances security purposes but also avoids repeated wrong movements that could lead to work injuries and may worsen over time.

2.

Predictive Analytics

McKinsey study corroborates the promise: “A big data/advanced analytics approach can result in a 20 to 25 percent increase in production volume and up to a 45 percent reduction in downtime.” Downtime is expensive and lowers your OEE KPI. Moving from a reactive to a proactive approach will be key for strongly competing.

3.

Machine Learning

Advances in machine learning have led to the increasing adoption of lean manufacturing and Six Sigma practices. Machine learning techniques employ an emerging class of algorithms that actually learn from the data presented to them and automatically construct the best possible model for each dataset. As such, it empowers analysts who have little expertise in statistics and modelling to solve complex problems otherwise beyond their reach. These developments have directly resulted in product quality improvements and reduced waste or product rework. Applying data analysis on a multitude of productions parameters helps to understand the best setup of machineries for a specific order or avoiding machinery settings that actually may produce a bad quality and lead to waste.

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