They can seem unapproachable at first, but IIoT, digitalization, cloud computing and virtualization can combine to make many processes more flexible and many jobs easier. The good news is anyone can implement them by following a few basic steps, according to Luigi De Bernardini, CEO of Autoware S.r.l., a system integrator in Vicenza, Italy, and a certified member of the Control System Integrators Association (CSIA).

“Process application designs and operations in the industrial manufacturing sector are significantly impacted by the convergence of IIoT, digitalization, cloud computing and virtualization,” says De Bernardini. “The impact of each single technology is amplified by the combination of the others, enabling otherwise impossible scenarios.”

More specifically, De Bernardini explains:

• IIoT devices enable real-time monitoring and data collection from various machinery and processes on the factory floor, which would otherwise be too expensive or impossible, and creates a data availability otherwise difficult to imagine.
• Digitalization leverage the data and helps streamline operations by integrating various systems and automating routine tasks. This integration improves communication and coordination across different departments, enhancing overall operational efficiency.
• Cloud computing offers scalable resources and storage, allowing manufacturers to handle large volumes of data without investing in extensive on-premises infrastructures. This flexibility is crucial for managing variable workloads and large datasets generated by IIoT devices. Moreover, it enable better collaboration between globally dispersed teams or plants.
• Virtualization reduces hardware total cost of ownership (TCO) and increases resource utilization, assuring simultaneously business continuity.

“The combination of all four technologies enables the creation of solutions that dramatically transform manufacturing operations,” says De Bernardini.

Bring on the benefits

De Bernardini reports the primary advantage of combining IIoT, digitalization, cloud computing and virtualization is improved operational efficiency. “With IIoT, manufacturers gain real-time insights into their operations, enabling them to monitor and adjust processes instantaneously,” he explains. “This real-time monitoring helps reduce downtime, and optimizes production. Digitalization further enhances efficiency by automating routine tasks, thereby reducing the need for manual intervention and minimizing human error.”

Cost reduction is another major advantage. IIoT enables predictive maintenance, which allows manufacturers to anticipate equipment failures and perform maintenance only when necessary. This predictive approach reduces maintenance costs, and prevents unexpected downtimes that can be costly. Cloud computing supports this by providing scalable resources, meaning manufacturers can scale up or down based on demand, paying only for the resources they use and avoiding large upfront investments in infrastructure.

“Flexibility and agility are also greatly enhanced,” adds De Bernardini. “Virtualization allows multiple applications and processes to run on a single physical server, optimizing resource usage and making it easier to scale operations as needed. Cloud platforms enable remote access to data and applications, facilitating better collaboration among teams, regardless of their geographic locations. This accessibility ensures that decision-making is more informed and timely.”

In addition, IIoT, digitalization, cloud computing and virtualization improve quality control and compliance, according to De Bernardini. “Advanced analytics and machine learning (ML), integral components of digitalization and IIoT, provide continuous monitoring and analysis of production processes,” he explains. “This constant vigilance helps quickly identify and address defects, ensuring higher product quality. Moreover, digital systems streamline compliance by automatically collecting and analyzing data required for regulatory reporting, ensuring adherence to industry standards and reducing the risk of non-compliance.”

Get your subscription to Control's tri-weekly newsletter.

Mix up your best combination

Based on his experience, De Bernardini recommends that users follow a systematic evaluation approach to deploy IIoT and digitalization. An initial assessment and defining goals is key for any project success, along with a strong sponsorship and involvement of the highest company levels. Here’s how users can proceed:

1. Assess current state and needs. Start by conducting a thorough assessment of current processes and systems. Identify key pain points, inefficiencies, and areas that could benefit from improved data collection, real-time monitoring, and automation. Understand the specific needs of your organization, whether it's reducing downtime, improving quality control, or enhancing supply chain visibility.
2. Define objectives and goals. Clearly define what you aim to achieve with IIoT and digitalization. These goals could range from enhancing operational efficiency and reducing costs to improving product quality and achieving regulatory compliance. Having well-defined objectives will guide the selection and implementation of the right technologies.
3. Evaluate technological fit. Consider the compatibility of IIoT and digitalization technologies with your existing infrastructure. Evaluate how well these technologies integrate with your current systems and processes. Technologies like MQTT and AMQP, for instance, can simplify integration by providing standardized communication protocols.
4. Pilot and prototype. Implement pilot projects or prototypes to test the feasibility and impact of IIoT and digitalization on specific processes. This allows you to gather data and insights on the effectiveness of these technologies before scaling them across the organization. It also helps in identifying potential challenges and refining the implementation strategy.
5. Analyze data and feedback. Use the data collected from pilot projects to analyze performance improvements and operational impacts. Gather feedback from stakeholders, including operators and managers, to understand the practical benefits and any issues encountered during the pilot phase.
6. Scale and customize. Based on the insights gained from the pilots, customize and scale the deployment of IIoT and digitalization technologies across the organization. Ensure that the solutions are tailored to address the specific needs identified during the assessment phase and are scalable to meet future demands.
7. Continuous improvement. Finally, adopt a continuous improvement approach. Regularly monitor the performance of IIoT and digitalization implementations, and be prepared to make adjustments as needed. Keep abreast of technological advances, and be open to integrating new solutions that could further enhance your processes.

Messaging and the cloud lend a hand  

Because IIoT is rooted in networking, De Barnardini adds that lightweight messaging protocols like MQTT and AMQP can greatly simplify connectivity.

“These protocols decouple devices from applications, and make it far easier to move data from the field to higher-level systems. But the most significant step toward true simplification is the convergence around information models such as OPC UA, and increasingly the combination of OPC UA over MQTT,” explains De Bernardini. “Protocols solve how data travels; semantic models solve what the data means, which is where most of the integration cost has historically been. We also see the Unified Namespace (UNS) concept gaining traction as a practical, vendor-neutral way to organize data across the plant. So I would say standards are simplifying the landscape, but the value comes less from any single protocol and more from pairing a transport standard with a shared information model.

Likewise, Amazon Web Services (AWS), Microsoft Azure and other cloud-computing services are fulfilling similarly assistive roles.

“AWS, Azure and others are increasingly making their presence felt on the plant floor, particularly in applications that integrate edge and cloud functions,” adds De Bernardini. “These platforms are becoming essential for building modern manufacturing operations due to their ability to offer scalable, reliable and efficient solutions for data processing and analytics. Based on my experience they still need to be complemented by industrial platforms to fulfill specific, shop-floor requirements.”

De Barnardini reports a typical pattern Autoware observes is hyperscaler cloud services handling analytics, storage, and AI in the cloud, while an industrial edge platform takes care of deterministic connectivity, protocol translation, and local processing on the floor. The two are complementary, so rather than competing, the cloud brings scale, while the industrial platform brings the real-time reliability and domain context that manufacturing demands.

“For example, we implemented a global MES solution for one of the Tier 1 beer manufacturers, and rolled it out at 27 plants worldwide in about 24 months,” adds De Bernardini. “This solution relied on virtualization, private cloud and IIoT, and provided mobile access to data and information. Without a deep usage of these technologies, it wouldn’t have been possible to successfully roll out such an initiative on that scale and timeline, with consistent processes and real-time visibility across every site.”

Four more best practices

Based on his and Autoware’s experience with projects like its recent global MES rollout, De Bernardini reports that four main lessons can be learned:

Success depends far more on governance than technology. Strong program and change-management governance is critical. Without commitment from the highest levels, clear objectives, and a structured way to manage scope, risks and people through the change, even excellent solutions stall.

A rollout isn’t a series of duplications of the pilot solution. It’s is a project in its own right that has to be managed appropriately. The pilot proves the concept, but scaling it across many sites introduces complexity—different processes, systems, cultures and constraints at each plant—that has to be governed deliberately, not assumed away.

Global and local approaches have to be managed simultaneously. You need a global template and common data models to guarantee consistency of the solution across the organization, while at the same time accommodating local needs, so each site actually gains value in its own operations. Getting that balance right—global consistency with local relevance—is what separates a rollout that delivers value everywhere from one that’s adopted in some places and bypassed in others.

Treat the program as a continuous-improvement journey rather than a one-time project. The technology will keep evolving, and the organizations that benefit most are the ones that build the capability to keep evolving with it.