AWS IoT SiteWise is on Track to Transform Manufacturing. Here’s How.

Volkswagen, Bayer, and Pentair are experienced adopters of AWS IoT SiteWise, an industrial IoT platform that helps enterprises collect, organize, monitor, and visualize industrial data at scale. In July 2020, Amazon Web Services made SiteWise publicly available, meaning more manufacturing companies can now give their business processes a digital overhaul.

The spread of SiteWise can spur the growth of the global Industrial IoT market, which is predicted to hit $110.6 billion by 2025, growing at a CAGR of 7.4% between 2020 and 2025. Its key drivers include tech advancements in semiconductor and electronic devices, greater use of cloud computing platforms, and standardization of IPv6.

What is Holding Manufacturers Back from IoT?

Much of the machinery deployed at factories today comes from the pre-IoT era: the average age of equipment is around 20 years old, and about 85 percent of the inventory is not connected to the Internet or each other.

When companies venture into upgrading old unconnected machines, they can find themselves stuck in pilot mode. According to the McKinsey Global Industry 4.0 Expert Survey, organizations are piloting eight different IT solutions on average, with 70% of the pilots failing to deliver on the expected value.

In the meantime, the adoption of IoT has its roadblocks and challenges. In 2019, Microsoft interviewed 3,000 decision-makers at enterprises across the US, UK, Germany, France, China, and Japan and discovered the main barriers to IoT adoption:

• Complexity and technical challenges (the main reasons why 38% of companies don’t implement IoT)
• Lack of budget and staff resources (29%)
• Lack of knowledge (29%)
• Difficulty finding the right solution (28%)
• Security concerns (19%)

What about the Benefits and Drivers?

Enterprises start investing in IoT to uncover inefficiencies in production operations, increase product quality, reduce costs, boost operational and staff productivity, and automate tasks. Emerging technologies such as Artificial Intelligence and its subsets contribute to making data-driven decisions faster and with higher accuracy.

According to the Microsoft survey, the top 5 factors driving IoT adoption in the enterprise are:

• Operations optimization (56%)
• Improvement of employee productivity (47%)
• Safety and security (44%)
• Effective supply chain management (40%)
• Quality assurance (40%)

Although several studies predicted a 10% decline in the industrial market in 2020 due to COVID-19, the pandemic has accelerated automation adoption. Primetals Technologies, for instance, developed a robot that performs potentially dangerous tasks in the metal industry in place of humans. The meatpacking giant Tyson invested in safety equipment like thermal scanners and workplace partitions to ensure social distancing on the factory floor.

A Look from the Inside: Implementing AWS IoT SiteWise

When implementing solutions within the Industrial Internet of Things, companies enhance their equipment with data acquisition, analysis, and visualization solutions. The process of retrofitting legacy equipment includes adding sensors and IoT gateways, creating human-machine interfaces, and introducing cloud-based analytics tools.

AWS IoT SiteWise is an IoT service from Amazon Web Services for manufacturing companies, which automates collecting, storing, monitoring, organizing, analyzing, and visualizing equipment data. The solution helps companies reinvent manufacturing, logistics, and other processes.

Manufacturing giants such as Volkswagen Group use AWS IoT SiteWise to collect shop floor data into the cloud, model and organize different machine assets within their plants, and visualize operational data in a web application. Another AWS customer, Genie (a manufacturer of lifting and material processing products and services), needed a paint process monitoring solution that would help them detect inconsistent and improper pretreatment parameters and improve paint quality in near real-time.

The work of SiteWise starts with extracting sensor data from equipment across all facilities — from manufacturing lines to assembly robots. The industrial data is being sent for storage and analysis to the AWS Cloud. Before it appears in the cloud, the data already obtains a standard format for exchange and context: the information includes equipment type, facility location, and relationships with other equipment.

Once the data gets ingested into AWS, the service automatically computes the performance metrics, which the manufacturer specified earlier — like uptime and overall equipment effectiveness. Both operational data and performance metrics are available in web applications across multiple devices — on desktop, tablet, or smartphones. Technicians use this information to monitor and analyze performance.

The adoption and further use of AWS IoT SiteWise comprise the following aspects:

Data Collection

The service provides IoT gateway software, which connects to the on-premises equipment, captures sensor data, and then uploads it to the AWS Cloud. Equipment sensors send the data wirelessly using Bluetooth/BLE, Zigbee, or Z-Wave.

The SiteWise solution ingests the information over OPC Unified Architecture (OPC-UA), a standard for the secure and reliable data exchange in industrial environments. The OPC-UA specifications enable standardized communication between multiple facilities such as laboratory equipment, systems, devices, and datasets, making them compatible with each other for data exchange.

When using SiteWise, manufacturers can also collect data from industrial applications through MQTT messages or APIs.

Data Management

AWS IoT SiteWise allows companies to create context and hierarchies for assets, processes, and facilities. With asset modeling, it’s possible to structure and label equipment by types and locations and define some of machinery as controlled assets. Manufacturers need to specify performance metrics for equipment and processes. As data is swallowed into the cloud, AWS IoT SiteWise can measure metrics and detect production gaps or quality issues.

Data Visualization

The AWS IoT SiteWise console allows you to create a web application without coding to display industrial operation data and calculated metrics. Using these apps, technicians can make data-driven decisions.

Use of Artificial Intelligence

Artificial Intelligence techniques bring more capabilities to data ingestion and analysis. Manufacturers can feed the algorithms with datasets collected from industrial equipment and train Machine Learning models, which help predict asset failure or downtime. At the same time, AI-assisted software can interpret data faster than humans.

Here’s How SiteWise Transforms Manufacturing

1. Better understanding of business objectives and ways to achieve them

When implementing AWS IoT SiteWise, manufacturers need to specify business goals they want to achieve by generating sensor data from industrial equipment. The objectives may vary from an increase in the number of units produced per day to possible ways to improve efficiency on the plant floor. Simultaneously, companies can automate some parts of the process, thus eliminating human involvement.

2. Unleashing data and bringing insights

Before the introduction of IIoT solutions, manufacturers missed out on tons of valuable data from unconnected equipment, isolated systems, and historical datasets. This information was hard to collect, structure, compare, and decipher. The value it would have brought inevitably slipped out of reach. On top of that, separate metrics were needed to evaluate equipment availability, product quality, and resource productivity.

With SiteWise and AWS migration, companies get access to never-before generated data. It’s possible to regularly collect sensor information, link it together, and display it to stakeholders with different levels of access. Also, as it’s unnecessary to store lots of unprocessed data in the cloud, manufacturers can decide what data they need to store locally on the gateway and what information should be sent to the cloud.

For example, Volkswagen teamed up with AWS to generate real-time data from 122 manufacturing plants. Multiple Amazon services, including SiteWise, helped them manage assembly equipment, track parts, and track vehicles.

3. Making decisions based on data, not guesses

Within asset modeling, manufacturers need to determine metrics that evaluate industrial equipment, facilities, and processes. This requires companies to define how to measure effectiveness — track the number of produced units and their quality, monitor equipment wear and tear, and evaluate team productivity. This data helps predict asset failure or downtime, reduce gaps in production and extra expenses and, as a result, improve processes in the factory environment.

One of Softeq’s clients, Krammer Technology, aimed to collect and process data from water temperature and mold movement sensors installed on injection molding machines. The implementation of the IoT system allowed human operators to get notifications about abnormal equipment behavior before it fails.

4. Facilitating digital transformation on the factory floor

As 85 percent of factory equipment is unconnected, venturing into SiteWise may require manufacturers to retrofit old machines — i.e., install sensors, add connectivity gateways, engineer hardware and software components to make it possible to collect, process, and analyze equipment data. Depending on the factory environment, smart factory equipment may incorporate temperature, pressure, humidity, vibration, and voltage sensors.

Additionally, you may need to contact the equipment manufacturer to figure out which parameters to calculate using the existing equipment and which parts need retrofitting.

5. Making data available to all stakeholders and presenting it in a meaningful way

AWS IoT SiteWise allows users to create web applications that display industrial data within a factory environment. IIoT data visualization tools may range from mobile apps to real-time equipment interfaces. These tools can have different access and display data levels, which plant operators or non-technical specialists can use to detect and respond to problems. A tech vendor’s task is to ensure the dashboards have a consistent and easily navigated information hierarchy, give a high-quality digital experience, and apply best practices of user interface design.

By Tatsiana Tsiukhai (Copywriter at Softeq)