How Manufacturing Companies Venture into IIoT by Retrofitting Legacy Equipment

In this article, we will focus on IIoT solutions and discover how businesses can adopt a data-driven approach to manufacturing without considerable upfront investments.

Manufacturing companies invest in custom and 3rd-party technology systems to increase production output, measure overall equipment efficiency (OEE), prevent machinery downtime, and control plant assets remotely.

By 2025, the economic impact of the Fourth Industrial Revolution (4IR) could reach $3.7 trillion. Researchers list 3D printing, Artificial Intelligence, robotics, and the Industrial Internet of Things (IIoT) among the transformative technologies that boost the manufacturing industry.

In this article, we will focus on IIoT solutions and discover how businesses can adopt a data-driven approach to manufacturing without considerable upfront investments.

Less Than 30% of Manufacturers Use IIoT Solutions Extensively. What is Holding the Industrial Internet of Things Back?

A common way to implement IoT solutions in industrial settings is to enhance manufacturing equipment with data acquisition, analysis, and visualization tools. These include sensors, IoT gateways, human-machine interfaces (HMIs), and cloud-based analytics tools that transform raw equipment health and performance data into actionable insights.

On a global scale, 85% of factories’ inventory and machines are not yet connected to the Internet.

There are several factors hindering the digital reinvention of the industrial sector:

  • Legacy equipment, which usually has a lifespan of 30-60 years, neither supports data-driven tools nor provides a wide range of connectivity options.
  • Much of the machinery used by manufacturing companies today has not yet reached its depreciation limits. The purchase of new equipment with built-in IIoT capabilities is therefore economically unreasonable.
  • Many enterprises lack the skills and expertise to develop and operate IIoT solutions.
  • Among manufacturers, there’s a general distrust towards technologies outside the traditional Supervisory Control and Data Acquisition (SCADA) toolbox.
  • 70% of IoT projects get stuck in pilot purgatory. When it comes to the Industrial Internet of Things initiatives, only 15% of business executives can implement smart factory solutions at scale. Replacing costly equipment to validate an IIoT concept and establish a business use case is impractical.

Could retrofitting legacy equipment take manufacturing companies one step closer to the digital future?

Retrofitting Helps Turn Legacy Equipment into a Wealth of Data

Retrofitting is the process of adding sensors, connectivity, and additional hardware and software components to existing equipment.

Retrofitting is the process of adding sensors, connectivity, and additional hardware and software components to existing equipment.

Cloud-driven sensor data analysis helps enterprises eliminate abusive equipment usage, predict machinery failure, and reduce waste among other things.

The manufacturers of molded plastic components, for example, can install water temperature and motion sensors on aluminum or steel molds. The sensing devices automatically detect misaligned molds and monitor coolant temperature and flow rate. The data is intercepted by IoT gateways and securely transmitted to the cloud. Based on this information, injection molding machine operators can reduce plastic waste and flashing and prevent mold and tie bar damage.

To capture, process, and act on equipment performance data, retrofitted IIoT solutions rely on several functional components:

  • Sensors. Custom or ready-made battery-powered sensing devices measure key equipment performance metrics and operating conditions. Equipment sensors send the data to IoT gateway devices using energy-efficient wireless connectivity technologies: Bluetooth/BLE, Zigbee, Z-Wave, etc. The most common types of industrial sensors include voltage, temperature, vibration, pressure, humidity, and sound level sensors.
  • IoT gateways. Gateway devices capture the data broadcast by industrial sensors and relay it to on-premise or cloud servers via cellular or Wi-Fi networking technologies. In cases when data latency (i.e., the time between when the gateway device sends a request to the cloud and receives a response) becomes critical, sensor data processing can be partially shifted from the server side to the intelligent gateway device. The method also helps industrial companies reduce the amount of traffic traversing the network and prevent data tampering.
  • Data storage and analytics solutions. To set up a data analytics infrastructure, manufacturing companies can opt for an IIoT platform like PTC ThingWorx or create a custom solution powered by AWS, Microsoft Azure, or Google Cloud. End-to-end IIoT platforms offer pre-configured data storage, analytics, and visualization modules that require little coding. On the other hand, the custom approach allows companies to build cloud applications that are tailored to their needs and scale along with their business.
  • Data visualization. IIoT data visualization tools may range from mobile applications to dynamic dashboards and real-time equipment interfaces. In addition to displaying equipment health and performance data, these tools may visualize the information retrieved from external applications. The latter include manufacturing execution systems (MESs), quality management software (QMS), and enterprise resource planning (ERP) solutions.
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Three Options to Give Dumb Machines an IIoT Overhaul

  • Original equipment manufacturer (OEM) upgrade. The easiest way to add data acquisition, processing, and visualization capabilities to legacy equipment is to make use of an IIoT kit developed by an original equipment manufacturer, as long as the vendor still produces and supports similar equipment. Among the OEMs that help industrial companies innovate on the cheap are Bosch, SKF, and Festo.
  • Third-party retrofit kits. If the original manufacturer has no incentive to upgrade legacy equipment, an enterprise can partner with a technology company that offers the necessary hardware components alongside a software as a service (SaaS) application for sensor data management. Some examples of retrofit IIoT solutions include the Bosch Cross Domain Development Kit (XDK), digital retrofit kits developed by HARTING, and the data analytics platform for injection molding plants created by Krammer Technology.
  • Custom IIoT solutions. Some enterprises contract technology companies that offer industrial automation services to design custom IIoT solutions for their production lines. The digital overhaul usually starts with Business Analysis. This approach helps manufacturers identify critical equipment that may significantly affect their production output if it fails or causes unanticipated downtime. Further, it is necessary to determine what type of data could boost a factory’s operational efficiency and find the optimum way to collect it. In the next phase, an IIoT vendor designs custom sensing devices and analog-to-digital converters (ADCs) and creates low-level software (typically, bare-metal firmware), which allows the sensors to send data to an IoT gateway. To detect patterns in sensor data, developers train and deploy Machine Learning algorithms in the cloud. A manufacturing company may also integrate the newly built system with the existing enterprise software on the API level to achieve transparency on the factory floor and optimize maintenance operations.

Remember This

Due to significant up-front investments, rip and replace factory overhauls are rare for the manufacturing industry.

Retrofitting strikes a perfect balance between costly equipment replacements and smart functionality.

Following the pandemic, IoT-enabled technologies that allow companies to reduce equipment maintenance costs and operate assets remotely are set for long-term growth. Studies indicate that the global industrial IoT gateway market will top $1.39 billion by 2021, while industrial sensors could become a $1.34 billion industry in six years.

Retrofit kits, wireless sensors, and cloud services that require little customization make it easier to dive into the Industrial Internet of Things. When planning an IIoT project, however, manufacturing companies should start with the business objectives and identify the equipment critical to the factory operations.