Analytics plays a very important role in all industries and business sectors. From medical to educational to even a small shopkeeper, collecting and analyzing data is something that can’t be ignored. Let us try and understand what analytics is or what analytics means? In general definition, Analytics is the process of examining and analyzing data or statistics for the discovery, interpretation and communication of meaningful patterns in data. These data patterns are applied towards effective decision making. The importance of analytics in manufacturing is as relevant as how customers are to buyers.
Manufacturing Analytics is the proper use by gathering data from operations and events in the industry, analyzing it with the help of advanced technologies such as cloud computing, digital twin, machine learning, AI, etc to ensure quality, increased performance and yield, reduced cost, and optimize the supply chain. Manufacturing analytics is a part of the smart manufacturing sector which comes under the huge umbrella; Industry 4.0. It relies upon predictive analysis, big data analytics, the IoT, machine learning, and edge computing to enable smarter, scalable factory solutions. In short, Manufacturing Analytics is the collection and analysis of data from an unlimited number of sources to help manufacturing companies increase the productivity and profitability of their operations.
Manufacturing analytics provides access to manufacturers on the real-time contextual awareness, and thus gives decision-makers a competitive edge by digitizing the business, optimizing costs, improving quality, accelerating innovations, and redefining customer experience. It is the conversion from data into insights into actions. That is, the analytics helps in identifying the business use cases (which include, supply chain, product quality, field service and support, creating an efficient factory), assembles the data which are collected from the factory floor, connected sensors and devices, the data for suppliers, sales, processes, equipment, etc. The assembled data is then put together, merged, cleansed, filtered, and prepared for analysis. The analyzed data helps to start the automation process and creates applications for real-time monitoring and dashboards, it also looks for signals that indicate defects, cycle time, production yield and other parameters and push-out alerts.
The rise in industrial automation in manufacturing, increased complexities in the supply chain, and growing demand for software systems that reduce time and cost. The current global manufacturing analytics market size is about $6 billion, which is predicted to reach $28.5 billion by 2026.
Manufacturing Analytics is a key factor in maintaining and conducting proper quality assurance at all stages of production. It helps in improving the quality of an end-product and increasing the production yield through anomaly detection. By adding the huge chunks of data collected from sensors, devices and AI visual inspection software within a powerful cloud-based manufacturing analytics platform, quality can be controlled at the micro-level. Data science and advanced manufacturing analytics make it possible for this process to be simulated. Choosing the right solution for manufacturing analytics and quality checks includes many factors. For example, the software provided by Lincode for quality inspection, LIVIS, is a cost-effective, AI-based, easily deployable solution that collects and analyzes data to inspect the product and check for defects in less than a second.