Industrial companies like manufacturers, energy utilities, and food processors want to utilize their equipment data to drive faster and better-informed decisions, but much of this data cannot easily be collected, processed, or monitored.
Extracting data from thousands of sensors and equipment across different locations is time-consuming and expensive because sensor data is often stored locally in specialised servers that lack a common data format, and retrieving the data and placing it in a format useful for cross-site analysis requires significant developer resources and expertise.
Once developers have a data collection pipeline to aggregate data across different pieces of equipment, they still have to attach context, such as the equipment type, facility location, and relationship to other equipment. Customers then have to write custom applications to calculate and compare performance metrics across multiple facilities to drive operational insights.
To address this major challenge, Amazon Web Services (AWS) last Thursday unveiled the general availability of AWS IoT SiteWise, a managed service that collects data from the plant floor, structures and labels the data, and generates real-time key performance indicators (KPIs) and metrics to help industrial customers make better, data-driven decisions.
Customers can use SiteWise to monitor operations across facilities, quickly compute industrial performance metrics, create applications that analyse industrial equipment data to prevent costly equipment issues, and reduce gaps in production. This allows customers to collect data consistently across devices, identify issues with remote monitoring more quickly, and improve multi-site processes with centralised data.
SiteWise makes it easier to collect data from the plant floor, structure and label the data, and generate real-time metrics. Customers begin by modelling their industrial equipment, processes, and facilities by adding context (e.g. equipment type and facility location) to the collected data, and defining common industrial performance metrics (e.g. overall equipment effectiveness and uptime) on top of the data using SiteWise’s built-in library of mathematical functions.
Once a customer’s environment is modelled and their data ingested into AWS, the service automatically computes the metrics at the interval defined by the customer (e.g. report uptime every hour). All uploaded data and computed metrics are sent to a fully managed time series database, which is uniquely designed to store and retrieve time-stamped data with low latency, making it significantly easier for customers to analyse equipment performance over time. From within the SiteWise console, customers can also create custom web applications (without any coding) to visualise key metrics across end-user devices in near real-time. These portable web applications can help customers monitor equipment performance on any web-enabled desktop, tablet, or phone to spot anomalies, helping them reduce waste, make faster decisions, and optimize their plant performance.
“Industrial customers tell us that getting their data into the cloud and using it to understand their operational performance is the biggest opportunity they see when evaluating IoT solutions,” said Dirk Didascalou, vice president of IoT, AWS. “With SiteWise, industrial customers can now use the power of AWS to collect, organise, and monitor their industrial equipment data at scale. SiteWise will help industrial customers move beyond data collection and enable them to visualise and monitor all their equipment, so they can focus on their main job of optimising their operations.”
In addition to using software running on an edge device, SiteWise provides interfaces for collecting data from modern industrial applications through MQ Telemetry Transport (MQTT) messages or its Application Programming Interface (APIs). SiteWise is available today in the US East (N. Virginia), US West (Oregon), Europe (Frankfurt), and Europe (Ireland) AWS regions, with additional regions coming soon.
Early adopters onboard
Already, several industrial customers have started using AWS’s new managed IoT service.
German-based Volkswagen Group is developing the Volkswagen Industrial Cloud to further improve the efficiency of its manufacturing and logistics processes.
“Machine data generally has no context when extracted from a machine. To make the data useful, it requires the addition of context through enrichment with other data, labelling, filtering and transforming that data before analysing”, said Dr. Roy Sauer, Director Enterprise & Platform Architecture, Volkswagen Group. “With SiteWise we are able to easily ingest manufacturing shop floor data into the cloud, model and organise those different machine assets within our plants, and then visualise operational data from our cylinder production line in a web application."
Bayer Crop Science uses SiteWise in working towards its goal of providing food for over nine billion people by 2050.
“We are constantly striving to optimise yield not only in the crop fields but also in our production plants”, said Peri Subrahmanya, IoT product lead, Bayer Crop Science. “Visibility of operational metrics across our crop processing sites is critical in helping us identify production bottlenecks and then take corrective actions to increase productivity. Using SiteWise across nine corn production plants in North America, we collect data from the plant floor, and then measure and analyse Overall Equipment Effectiveness (OEE) of our machinery in near real-time to identify production inefficiencies. With SiteWise we are now able to onboard a crop site in less than a few hours instead of a few weeks, which is critical in allowing us to scale the use of SiteWise to other crop sites like soy, in a cost-effective manner."
Bayer Crop Science is a division of Bayer AG that provides products and services to enable sustainable agriculture for farmers.
Likewise, Pentair, a global provider of water filtration systems to breweries, fish farms, and other industrial and commercial customers, is now using SiteWise.
"To optimise filter maintenance windows and maintain production uptime for our Beer Membrane Filtration system, we are building machine learning models to predict the next filter cleaning cycle." says Rama Budampati, senior director, Smart Products & IoT, Pentair.
He added: "To support this predictive maintenance application, we created our own industrial asset management system, however we needed a more flexible data ingestion and data modelling capability that allowed us to quickly adjust data models for our different systems, and test new operational metrics in near real time and over historical data. With SiteWise, we are able to run a digital twin of several of our beer membrane filters, creating virtual representations of the different elements of our assets, which expanded our ability to model the machine behaviour much closer to reality.
