Amazon Web Services (AWS) has introduced a managed service that collects data from the plant floor, structures and labels the data, and generates real-time key performance indicators and metrics to help industrial users make data-driven decisions.
 
AWS IoT SiteWise can 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 users to collect data consistently across devices, identify issues with remote monitoring more quickly, and improve multi-site processes with centralised data.
 
Industrial companies such as manufacturers, energy utilities and food processors want to use their equipment data to drive faster and better-informed decisions, but much of these 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 are often stored locally in special servers that lack a common data format, and retrieving the data and placing them 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. Users then have to write custom applications to calculate and compare performance metrics across multiple facilities to drive operational insights.
 
SiteWise helps overcome these challenges by making it easier to collect data from the plant floor, structure and label the data, and generate real-time metrics. In SiteWise, users begin by modelling their industrial equipment, processes and facilities by adding context such as equipment type and facility location to the collected data, and defining common industrial performance metrics such as overall equipment effectiveness and uptime on top of the data using SiteWise’s built-in library of mathematical functions.
 
Once the environment is modelled and data ingested into AWS, the service automatically computes the metrics at the interval defined by the user, such as report uptime every hour. All uploaded data and computed metrics are sent to a fully managed time series database, which is designed to store and retrieve time-stamped data with low latency, making it easier for users to analyse equipment performance over time.
 
From within the SiteWise console, users 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 monitor equipment performance on any web-enabled desktop, tablet or phone to spot anomalies, helping reduce waste, make faster decisions and optimise plant performance.
 
“Industrial customers tell us that getting their data into the cloud and using them to understand their operational performance is the biggest opportunity they see when evaluating IoT,” said Dirk Didascalou, vice president of IoT at 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 industrial applications through MQ Telemetry Transport (MQTT) messages or its APIs.
 
SiteWise is available in North Virginia, Oregon, Frankfurt and Ireland AWS regions, with additional regions coming soon.
 
Car maker Volkswagen is developing an industrial cloud to improve the efficiency of its manufacturing and logistics processes.
 
“Machine data generally have no context when extracted from a machine,” said Roy Sauer, director at Volkswagen. “To make the data useful requires the addition of context through enrichment with other data, labelling, filtering and transforming those data before analysing. 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.”