Some of the significant changes in the manufacturing industry over the past few years are associated with big data. Big data is vital in achieving efficiency and productivity gain as well as unveiling new insights to drive more innovations.
With big data analytics, manufacturers, as well as data analytics companies, discover new information and recognize new patterns that enable them to improve processes, enhance supply chain efficiency, and identify variables that influence production.
Big data stores a massive amount of data and helps analysts to observe, detect, and examine the irregularities within a network. By doing so, big data analytics allows organizations to prevent cybercrimes. Another way to ensure network security is to use a VPN that efficiently encrypts your internet traffic and provides protection against all snooping eyes.
The security-related information available from big data lessens the time required to detect and address an issue, enabling cyber analysts to predict and avoid the possibilities of intrusion and invasion.
The CSO online report revealed that 84% of the businesses are using big data to block cyber-attacks, which are increasing at an unprecedented rate. Moreover, they also said that there was a slight decline in security breaches after introducing big data analytics in their operations.
AI and IoT sensors make it possible to analyze and receive massive quantities of data in real-time. According to a study conducted in 2016, 67% of the manufacturers are investing in big data analytics technology. Many also consider this data as the most valuable asset of the modern manufacturer.
The big data analytics tools can be used to detect cybersecurity threats like malware and ransomware attacks, malicious insider programs, compromised and vulnerable devices. It is where big data analytics tools look most beneficial in improving cybersecurity along with production efficiency.
Read the remaining post to get an insight into how manufacturing analytics improves production efficiency?
Interruption can be a high cost for many owners, as it reduces the overall productivity by 20%. By preserving the automated machines is essential for keeping the production process moving. But, it can be hard to notice machine breakdowns before they occur. The significant changes in temperature, timing, and vibration can be too subtle for human operators to see.
The AI-powered data analysis tools are exceptional at detecting abnormal states. Several wireless sensors rely on second-to-second information about instrument timing, power usage, temperature, and vibration to AI. When some severe changes or patterns occur that are undetectable to an individual, then AI efficiently recognizes these patterns and alerts on imminent failures.
The intelligent big data analytics tools allow the experts to establish a predictive model that can issue a warning as soon as it sees an entry point for some cybersecurity attack. Also, AI proved to be beneficial as three out of the five surveyed firms believed that the use of AI improves the accuracy and efficiency of cyber experts. Here AI and ML play a central role in developing such a mechanism. The analytics-based solutions allow you to gear up and predict all the possible events throughout the process.
This ability gives big data analytics companies time to reroute production, get the replacement parts, or otherwise prepare for maintenance.
Improving Machine Efficiency:
Running at ineffective and inefficient settings can result in a severe resource sink for automated machines. For example, inefficiencies in compressed air systems caused manufacturers to lose 43.2 billion yearly. Proper compressed air systems auditing and monitoring help the manufactures to keep the track records of efficiency and performance.
The sensors continuously monitor these systems and provide manufacturers with a data set that they can use to search for inefficiencies in the system. Does a 25% increase in the power to perform the same work as a 5% increase does? Does a machine is as efficient when it is working at an average temperature versus extreme temperatures? The data gathered by the sensors answers all questions like these.
Different variables, such as instrument power, are usually left entirely in the hands of workers. These employees do not have access to the data required to see how certain power levels or tool settings might be inefficient. With big data collection and analysis, the manufacturer can often look for relationships and patterns between variables like instrument power and production efficiency and later pass that information to the workers, thus reducing energy waste and saving resources.
Demand Forecasting and Supply Source Analytics: