Subscribe and listen to AW’s podcast!
Subscribe and listen to the Automation World Gets Your Questions Answered podcast!
Listen Here

Approaching the Changes in Cloud Evolution

When it comes to connecting IT and operations technology, the wide range of requirements from operations, marketing, fi nance, and maintenance mean that preplanning is a requirement. Here’s how to approach it.

Charlie Norz

Industrial manufacturers have recognized the many benefits of plant floor digitalization. Leaders in both IT and OT (operations technology) must work together to decide on the degree of information that needs to be collected as well as what type of analytics are required. Due to the wide range of requirements from other departments, such as operations, marketing, finance, and maintenance, pre-planning is the key for any IIoT (Industrial Internet of Things) application.

New consumers of industrial data want immediate opportunities to improve efficiency, profitability or prevent downtime in ways that were not possible before. Cloud services and scalable storage are certainly drivers of this trend. Many controllers, for example, support the mainstream protocols commonly deployed in factories, but also add new messaging schemes like MQTT and REST APIs to support these new applications.

Cloud challenges
There are challenges with these approaches, for sure. For example, cloud providers have their own set of APIs and services unique to their solution. Customers who are looking to adopt a cloud platform need to consider how portable their data is and how easily they can tie into existing systems within their organization. A controller that allows for open development, especially those that are Linux-based, will generally support more platforms than a closed system.

Cloud platforms commonly use MQTT for secure IIoT communications, leaving the integrator to determine how the JSON payload is structured; this adds flexibility but can also create interoperability challenges. Specifications like SparkPlug B help by defining the namespace to address this challenge. But while Sparkplug B is supported, relatively few cloud platforms have formally adopted it yet.

Cloud platforms are constantly evolving. For example, if you are using AWS Greengrass on a PLC and AWS releases a new machine learning service, a customer can easily adopt this new feature. A new driver or update used to mean installing and licensing new software on multiple servers, hoping there are no conflicts. Fortunately, this labor-intensive task might soon be a thing of the past.

First steps
Data collection, storage and retrieval is the first step in digital transformation. Many cloud agents can orchestrate and manage your assets in a centralized way, allowing changes to be scheduled and rolled back as necessary. It is also possible to remotely deploy code to a group of edge devices for non-real-time tasks. This is a fundamental change in the way controllers can be programmed and managed, potentially making a significant impact on our industry.

Central to the IIoT approach is edge computing— collecting and processing data where it is generated. There is a lot happening in the edge of network space. This is an enabling technology that greatly simplifies software development and testing, which is more in line with mainstream software development.

Adopting an open and easy approach to industrial controls and supporting IIoT is accomplished by designing controllers that support established protocols and programming specifications, while at the same time extending support for new solutions or hybrid control schemes. This approach gives controls engineers flexibility to deploy the software applications that best meet their design goals.

Fill out the form below to request more information about Approaching the Changes in Cloud Evolution
Discover New Content
Access Automation World's free educational content library!
Unlock Learning Here
Discover New Content
Test Your Machine Learning Smarts
Take Automation World's machine learning quiz to prove your knowledge!
Take Quiz
Test Your Machine Learning Smarts