What is manufacturing analytics

Benefits of manufacturing analytics Provides contextual awareness in real time. Give decision makers a competitive advantage by digitizing business, optimizing costs, improving quality, accelerating innovation, and redefining the customer experience. Manufacturing analytics is helping manufacturing companies increase the productivity and profitability of their operations by working through a large amount of data. By using machine learning models and data visualization tools, manufacturers can gain insight into their data, optimize their processes, and maximize performance.

The manufacturing analytics process

The goal is to turn the data that is collected into insight that can then be turned into actions that positively affect the business. The process begins with identifying business use cases. Most manufacturers have similar goals that they are trying to achieve, including improving product quality and reliability, increasing their revenue, and creating an efficient factory. After first identifying the business use cases, the next step in is to gather the data. There is data from vendors, processes, teams, sales, and many other types of data. In addition, it is necessary to confront this data, join it, merge it, clean it, filter it if necessary, and prepare it for analysis. Once that is done, you can begin to automate processes to look for signs such as defects, warranty claims, downtime or performance in the data. After you have done an initial exploration, you can decide on standard ways you want to see things. Real-time monitoring applications and dashboards can be created that can be reused with new types of data. Beyond basic dashboards, advanced analysis applications can be used to create models for further analysis based on predictions. Models can be used to check or predict production volumes, equipment breakdowns, and product quality. Once a good predictive model has been achieved, alerts can be sent, for example through the company’s mobile devices.

Goals of manufacturing analytics

The objective of manufacturing analytics is to move from data collection and visualization to being able to take advantage of that data in real time to detect problems with processes and equipment, reducing costs and maximizing efficiency throughout the supply chain, with less overhead and lower risks. Manufacturing analytics make that knowledge available to everyone, from the company management to the plant worker. They can also help improve the quality of a company’s final product. They do this through various processes, such as data-driven product optimization, managing defect density levels, and analyzing customer feedback and purchasing trends. Data-driven product optimization can rely on IoT sensors and machine learning models to optimize production based on many factors. By analyzing product usage in detail, manufacturers can reduce or increase components that lead to higher usage rates. Manufacturers must keep the defect density ratio low and with data collected from digital factories, manufacturers can now more specifically understand the process states that lead to higher defect density. Customer analytics help you understand customer shopping habits and lifestyle preferences. By knowing information on future purchasing behaviors, manufacturers can more accurately produce and deliver what customers really want. Manufacturing analytics can also increase production throughput. One of the main ways they do this is by detecting anomalies. These can alert factory supervisors to defects in their products early in production so they can resolve problems quickly. Anomaly detection uses a combination of IoT sensors, historical data, and machine learning algorithms to detect unusual data that could be an indication of a developing problem. Manufacturing analytics can also reduce the risks and costs associated with equipment downtime or breakdowns. This is achieved by identifying bottlenecks or unprofitable production lines and anticipating breakdowns and decreasing machine downtime to reduce costs with predictive maintenance of critical assets.]]>