Industry 4.0, sometimes also called the fourth industrial revolution, is a trend of data exchange and automation in changing manufacturing and manufacturing technologies.
This data exchange and automation is meant to make factories and plants smarter. Some of these technologies include cloud computing, cognitive computing, artificial intelligence, machine learning, and the Internet of Things.
Companies and facilities that subscribe to the industry 4.0 philosophy use embedded software, robotics, and advanced sensors to not only collect data but also analyze it so it helps with decision-making and improvements in the manufacturing process.
At the heart of industry 4.0 is data that ties everything together. Because data is such an important part of this wave of the industrial revolution, we need to examine its important impact and how data is changing the manufacturing landscape.
Helping Monitor and Improve Product Quality
The different technologies that are part of industry 4.0 have created opportunities for manufacturers to use the massive amounts of data they collect to monitor and improve the quality of their products. Manufacturers no longer have to do manual inspections or test samples because each product can be tested automatically using sensors and related technologies. Any deviations in quality can be noted before the product leaves the conveyor belt.
The data collected from the products that are not up to the required quality standards can be used to assess where a manufacturer can make changes to reduce the number of defective products and quality deviations.
Manufacturers can also use this data to convince their customers. The incredible amount of data collected can be distilled down to show how various products are developed, manufactured, and tested. Seeing this data is much more convincing than looking at marketing hype that does not talk about how the quality of the product was achieved and continues to be maintained.
Reducing Downtime and Costs
Things move very fast in the manufacturing world, and downtime can be very costly for a business. Technology has made predictive maintenance possible by providing data on which machines need maintenance and which are likely to fail soon. Keeping an eye on all manufacturing processes like this is an integral part of digital manufacturing which is concerned with increasing efficiency output and controlling the entire manufacturing process.
Apart from ensuring continuous production, predictive maintenance also helps reduce the cost of spare parts. Repairing machines before they break down is usually cheaper than replacing broken parts. In many cases, one failure in the manufacturing process can cause a cascade of failures that can be very expensive to fix.
Data can be used to predict which parts will fail so the manufacturer only has to replace or repair the part more likely to fail rather than deal with other parts that fail in the cascade.
Increasing Output and Efficiency
Every manufacturer wants to optimize their output such that they are producing an optimal number per unit time. This output is often detailed in their key performance metrics that are also used to predict productivity, revenue, and profits. Data-driven manufacturing is an integral part of ensuring responsive and efficient production systems.
By having the right data, manufacturers can compare their performance against their targets to know if they are reaching their goals. This data is often subdivided into the operating information of individual machines, and this granular data is then used in the analysis.
If an individual or multiple machines are not performing optimally, the manufacturer can use data to check why this is so and what is going on. They can then carry out maintenance or correct the anomaly to keep things running smoothly.
In the retail space, it is quite common for businesses to use data and data analytics for demand forecasting so that they know what to order. This use of data and data analytics is also becoming more common in manufacturing.
Manufacturers can use data to predict load forecasts for different manufacturing plants. These load forecasts can be compared against what individual factories make to predict demand for different products. The manufacturer can predict which products need to be produced in vast numbers and which manufacturing plants should handle which products.
Doing so is also a part of balancing plant loading because by predicting which factories will have a huge load, a manufacturer can pass on some of that load to other factories. This means all factories work together optimally, production is increased, and demand is met.
As industry 4.0 matures, we can expect to see more technologies being used in changing manufacturing. We can also expect data to continue laying a big role in making factories and manufacturing plants smarter.