The Internet Of Things (IoT) is being introduced at a time when digitization is changing all sectors. Manufacturing has the chance to accelerate its progress and eventually realize the goal of an automated real-time, two-way information system and control loop that will significantly boost productivity and quality. The journey begins with obtaining equipment and process insights from sensors distributed throughout the environment, not only in important locations. The ultimate goal for manufactures to be able to automatically sends commands back to them in real-time.
A Platform for Data Management in Industrial IoT
Leading businesses across the globe are now using data management and analytics platform for storing, managing, and most significantly, driving insights from all their manufacturing data. Data from numerous sources may be readily ingested onto a single, unified, secure platform, integrating and associating IoT sensors data feeds with database servers, transaction data, customer data, external data, and much more.
Use Case for Industrial IoT
Predictive Maintenance
The use of real-time data to forecast and mitigate malfunctions can cut downtime by half. When you use IoT to gather and analyze data, your company may discover warning indications of future problems, anticipate when equipment requires maintenance, and service that equipment before problems arise.
Optimization of Operations
Manufacturers may use IoT to obtain a full perspective of what is going on at every stage of the manufacturing process and make real-time modifications to ensure a continuous flow of manufactured goods and eliminate errors. This allows them to see how the entire process is going and fix bottlenecks in real-time. It also minimizes the likelihood of human mistakes.
Optimization of the Supply Chain
Manufacturers may save between 20% and 50% on costs through real-time inventory monitoring and optimization techniques.
Advantages
Real-time predictive maintenance: By analyzing and interpreting time-series sensor data from manufacturing processes, the firm can now anticipate symptoms of mechanical wear and deterioration far before they are evident to factory employees, allowing them to intervene before a breakdown occurs.
Towards zero downtime: By integrating sensor data streams from factory floors with structured data from internal and external systems, corporate engineers can now detect and solve problems before they become evident to the operator and disrupt production.
Cost savings: Reduced downtime saves millions of dollars in lost production. Furthermore, when maintenance is required, machine learning skills assist the firm in rapidly matching the proper technician or engineer with an issue, allowing them to grow service globally without spending millions on new resources.
