Efficient utilization of industrial equipment is critical to the success of many businesses, and its failure can significantly impact productivity and profits. Optimization and maintenance of equipment thus impact smooth business operations, their scale, and success. Per an Upkeep survey, the global maintenance, repair, and operations market’s estimated value was $616.01 billion in 2020, and maintenance costs are estimated to range between 15% and 40% of total production costs.
In recent years, there has been a growing adoption of Industry 4.0 use cases like predictive analytics to improve the maintenance and performance of industrial equipment. Predictive analysis uses historical data, statistical algorithms, and machine learning techniques to predict future outcomes. By analyzing historical data on equipment performance, maintenance, and usage, predictive analytics can identify patterns and trends that indicate when equipment is likely to fail or require maintenance. This allows businesses to proactively schedule maintenance and repairs, reducing downtime and avoiding costly emergency repairs.
Industrial transformation at scale
There are several technologies that can be deployed at a manufacturing plant and can deliver solutions for IoT and data management and analysis, but these technologies only create a point solution that are difficult to scale beyond a Proof of Concept (PoC) and technology demonstrators. The real challenge is scaling these PoCs, creating pervasive solutions, and deploying them across multiple plants to generate usable data to improve business efficiency and increase scale.
Leverage right technologies for deployment at scale
By leveraging the platform services of a hyperscaler platform, companies can effectively build predictive maintenance solutions that can be deployed across plants to benchmark their performance among themselves and against industry standards to identify opportunities for improvement. Using a platform such as AWS can help companies optimize their operations, reduce costs, and improve overall performance. The advantages include:
- Scalability: AWS platform services can easily handle large amounts of data from multiple sources, making comparing and analyzing equipment performance data across multiple plants or locations easier.
- Cost-effectiveness: With AWS platform services, companies can reduce the cost of storing and processing large amounts of data related to equipment performance and maintenance.
- Real-time data analysis: AWS facilitates sourcing data from PLC and plant systems in real time and analyzing data from multiple sources, allowing companies to identify issues and take corrective action quickly.
- Improved accuracy: By analyzing large amounts of data from multiple sources, the solution can provide a more accurate picture of equipment OEE matrices, allowing companies to identify areas for improvement.
- Collaboration: AWS platform services enable multiple users to access and analyze data from different locations, making it easier for teams to collaborate on benchmarking activities.
Furthermore, as the scale grows across different plants, basing it on a platform service makes the process automated. The manufacturer need not worry about the scaling, maintenance, and uptime of any particular machinery.
Accelerating digitization of industrial equipment
Cyient has partnered with AWS to work together on industrial data fabric as a solution. The industrial data fabric solution is a collection of platform services from AWS and partner technologies, including HighByte and Element. It allows data to be sourced from shop floor machines and enterprise systems (OT and IT systems) and be contextualized using enterprise hierarchy to provide a unified view aligned to the enterprise domain model.
There are multiple platform services from AWS that help in data management, insights, dashboards, and digital twin. AWS Sitewise allows organizations to model assets they want to manage and monitor, define the hierarchy in which they are arranged, and maintain time-series information of the various sensors and data feeds corresponding to the asset. This platform combines a graph database, a relational database, and a time-series database into one service managed and scaled for you by AWS. Here are some use cases that help in implementing a robust predictive maintenance platform solution for accurate equipment management:
- AWS Sitewise collection of streaming data from OT systems into the enterprise asset hierarchy.
- AWS Twinmaker enables 3D visualizations and twins to be created and wired with PLC data and alarms sourced into the time series or sitewise.
- Quicksight and Managed Grafana enable creating operational dashboards that connect to the ingested or analyzed data.
- Amazon Lookout for Equipment enables an anomaly detection solution based on a trained model from historical data and its inference on real-time data.
- Amazon Sagemaker provides multiple AI/ML algorithms to create models, do hyperparameter tuning, and create inferencing APIs to act on real-time data feeds.
These platform services also allow users to build a solution that follows a no code/low code/minimal code paradigm. It is not about building an application or interface but consuming and integrating the services to achieve business objectives. Thus, time-to-market to roll out a use case and the ability to scale more use cases with the same platform stack is the value derived based on our other scalar technology.
The industrial data fabric solution is thus a proven stack of technologies and platform services that can be used to build the foundation for data collection from OT and IT systems and develop multiple use cases on the platform. These use cases can be scaled and become a powerful tool for benchmarking industrial equipment performance. By providing scalability, cost-effectiveness, real-time data analysis, improved accuracy, and collaboration capabilities, hyperscaler technology can help companies identify opportunities for improvement and optimize their operations.
About the Author
Jitendra Thethi is Head of Cloud Platform Solutions at Cyient. He has 27 years of experience delivering technology-led innovation for customers across multiple industries. In his current role, he is responsible for building digital platform solutions leveraging cloud, data, and AI.