Data drives everything today, and the data analytics space has become very strong. There is an increased use of embedded analytics, and customers want to have analytics happening on the cloud, as well as on the device.
In the manufacturing sector, data plays an important role to develop the right product for the customers. To do this, machines have to be fed in with the appropriate data. For which the machines must have the ability to analyze the data that has been fed in. So, we now have data plus analytics. It is a well-known fact that any incorrect data fed into the machine will be incorrectly analyzed, resulting in a faulty product.
Ask any CIO, he opines that the rise of intelligent machines, along with artificial intelligence (AI) has led to this situation. Machines are more sophisticated today and can analyze the data that is fed in. And, here's where the role of predictive analytics also comes into the fray.
Predictive Analytics enables transparency, benchmarking, and intelligence. It provides manufacturing operations with the ability to extract valuable insight from complex and diverse data. In all, it enables Industry 4.0 technology to the respective manufacturing sector.
Predictive Analytics allows predictions outcomes using historical data, combined with statistical modeling, data mining techniques, and machine learning (ML). Companies also employ predictive analytics to find patterns in this data to identify risks and opportunities.
In some cases, data analytics can be integrated with simulation. This helps to take the insight from the data, and build better and smarter products using simulation. Here is where predictive analytics plays a key role. With this, you are driving more efficiency into your products and business. This is happening across industry verticals, such as automotive, industrial automation, insurance, healthcare, retail, banking, etc. which use predictive techniques, to make several important decisions. Some companies have also gone ahead in having a digital twin.
Sudden breakdown of machinery and equipment is a perennial headache for the manufacturers. Companies incur huge losses from downtime due to sudden breakdowns, that are hard to notice at times. However, it is critical for manufacturers to avoid breakdowns and ensure the smooth running of all automated machines. If neglected, it leads to massive production downtime.
How does Predictive Analytics help manufacturers in such situations? It helps to easily access the historical performance of machines, correlates insights, and gets accurate forecasts on when a machine is likely to suffer a breakdown. With this valuable information, manufacturers can plan their maintenance cycles and in such a way that all anticipated breakdown scenarios are addressed optimally. The timely alerts help conduct pre-emptive checks to curb breakdowns.
Today, with digitalization is transforming the industry, CIOs must think ahead to the future of the industry. They must focus on enhancing flexibility and increasing efficiency. They also need to optimize holistically, increase the quality, and make their product or solution future-proof and data-driven.
Predictive analytical techniques and methods need to be carefully selected and thoroughly studied. No ‘one technique’ can do it for all! Aligning the supply chain is equally important to make good products. We need to have new approaches. There has to be real-time process monitoring that needs predictive analytics and has an immediate implementation of the learnings.
People often confuse predictive analytics with machine learning, even though the two are very different disciplines. The types of predictive models that are in use include decision trees, regression, and neural networks.
In conclusion, predictive analytics makes use of statistics and modeling techniques to determine the future performance of any product, process, or solution. Predictive models can also help make weather forecasts, translate voice-to-text messages, take customer service decisions, develop investment portfolios, and even video games.