Manufacturers Embrace Digital Twins for Predictive Maintenance

Food and consumer goods companies leverage cloud-based monitoring and AI to reduce unplanned downtime

Apr. 7, 2026 at 9:12pm

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A highly detailed, glowing 3D illustration of various industrial sensors and hardware components connected to a central digital monitoring platform, with neon lights tracing the flow of data and analytics across the cybernetic system.Predictive maintenance powered by digital twins and cloud analytics helps manufacturers proactively address equipment issues before they disrupt production.Ames Today

Food and consumer goods manufacturers are increasingly turning to digital twin and cloud-based monitoring platforms to enhance their predictive maintenance programs. Companies like Amcor and Mars are using these advanced analytics tools to identify equipment anomalies, coordinate maintenance activities, and reduce unplanned downtime across their production facilities.

Why it matters

As manufacturers face pressure to do more with less, predictive maintenance powered by digital twins and cloud platforms has emerged as a key strategy to boost efficiency, cut costs, and stay competitive. By gaining real-time visibility into equipment performance and automating anomaly detection, companies can proactively address issues before they lead to costly production disruptions.

The details

Rockwell Automation CEO Blake Moret noted at the 2025 Automation Fair that manufacturers are increasingly turning to automation, AI, and machine learning to enhance efficiency. In the food and consumer goods sectors, companies are specifically investing in digital twin and cloud monitoring solutions to scale their predictive maintenance capabilities. For example, Amcor Flexibles used AVEVA's MES platform, CONNECT data services, and Advanced Analytics tool to detect anomalies across 200 blow and injection molding assets at multiple plants. The system was able to identify issues like a dryer constantly dropping temperature, allowing the team to address the problem before it led to unplanned downtime. Amcor saw a 2% reduction in unscheduled downtime across its facilities in the early stages of the program. Mars also piloted a predictive maintenance initiative using Datadog's digital twin solution running on Microsoft Azure IoT Edge. The system enabled Mars to model normal and abnormal equipment conditions, providing operators with alerts to quickly troubleshoot issues. After 18 months, Mars found that early involvement of the signal team was crucial to successfully deploying the digital twin platform at scale.

  • Rockwell Automation CEO Blake Moret discussed the rise of automation and AI in manufacturing at the 2025 Automation Fair.
  • Amcor Flexibles first tested its predictive maintenance program using AVEVA's solutions at its Ames, Iowa facility.
  • Mars piloted its Datadog-powered predictive maintenance program across multiple plants over an 18-month period.

The players

Blake Moret

CEO of Rockwell Automation, who highlighted the growing use of automation and AI in manufacturing during his keynote at the 2025 Automation Fair.

Carlos Paredes

Controls engineering manager at Amcor Flexibles, who discussed the company's rollout of an anomaly detection initiative across its blow and injection molding assets.

Luiz Fraga

Senior lead AIOPS and data production architecture at Mars, Inc., who presented on the company's pilot of a predictive maintenance program using Datadog's digital twin solution.

Jim Toman

MES functional consultant at Grantek, a system integrator that helps clients standardize asset data architecture for predictive maintenance.

David Ariens

Founder at the IT/OT Insider, who discussed the data requirements and business case for predictive maintenance initiatives.

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What they’re saying

“Starting with a focused pilot limited to solving known issues on one or two high-impact assets allows teams to build a repeatable playbook with confidence before scaling.”

— Jim Toman, MES functional consultant

“The data requirements are comparatively straightforward with predictive maintenance: vibration, temperature, current draw — often from sensors already in place. The physics are well modeled, the failure modes documented, and the business case is easy to articulate. That combination makes it fertile ground for quick wins.”

— David Ariens, Founder

“When developing this process, we didn't have the right notifications. We didn't really know how we were going to interact with the operations.”

— Carlos Paredes, Controls engineering manager

“The monitoring solution is able to understand patterns, so when an anomaly appears, operators receive alerts to understand if a device is not working appropriately or is not able to connect as expected.”

— Luiz Fraga, Senior lead AIOPS and data production architecture

“It's become apparent that companies cannot simply rely on the system outputs. Double-checking work and overall analysis of data outputs is a step that is often skipped but is vital to operational efficacies and one lesson that many organizations are learning.”

— Michael DeMaria, Director, product management

What’s next

As Amcor and Mars continue to scale their predictive maintenance programs, the companies will likely focus on further integrating the digital twin and cloud monitoring solutions into their broader maintenance and operations workflows. This could include automating more of the anomaly detection and response processes, as well as expanding the use cases beyond just equipment monitoring to areas like energy management and inventory optimization.

The takeaway

The rise of digital twins and cloud-based predictive maintenance platforms is enabling food and consumer goods manufacturers to boost efficiency, reduce unplanned downtime, and stay competitive in an era of increasing pressure to do more with less. However, successful deployment requires not just the right technology, but also close collaboration between IT, operations, and maintenance teams to ensure the solutions are properly integrated and adopted across the organization.