How These Two CPGs Boosted OEE and Speed With Digital Twins

In the beverage CPG's case, this meant engineers could recreate the yo-yo behavior inside a safe simulation, make coordinated HMI and controls adjustments, test new logic branches, and observe downstream mechanical interactions, all without touching actual production. The result was a materially faster and more confident fix than the plant could ever have orchestrated on a live packaging line.A CPG plant applied a virtual batch-testing digital twin to speed new product introductions. The model cut time-to-market by up to 32 weeks, delivered more than $24 million in first-year profit gains, and avoided 4–5 weeks of capacity loss per NPI.

Extending that same logic upstream tp process work, Negron explained that another CPG factory wrestling with the relentless pace of New Product Introductions (NPI). The manufacturer’s product portfolio was both broad and constantly evolving, creating an ongoing need to introduce new recipes, ingredients, and batch processes. Traditionally, each NPI required significant physical batch testing at pilot scale. Scheduling those batch trials was slow. Running them was expensive. And every hour of physical testing consumed limited capacity the plant could otherwise devote to commercial production.

Negrón's team built a virtual batch-testing environment that allowed engineers to test formulation and processing scenarios long before the first physical prototype run. 

“When a new product comes to that prototyping phase, there's a lot of testing that needs to be done on a recipe level, at a physical production level, et cetera,” he said. “With a digital twin, you can now provide the opportunity to run most of those tests digitally before hitting a physical space.” 

For companies constrained by premium line availability, this can be transformative.

Many digital twin projects fail because the models are too abstract, or worse, not grounded in physics.  

With “...process model integration… your digital twin should not try to replace best-in-class simulation tools. It should try to incorporate them or integrate them,” Negrón said. 

His team gathered mechanistic and data-driven models of past batch runs and built them into a testable digital representation of the production cell. They constructed an MVP around the plant’s most challenging unit operation, validated the approach, and scaled it across the site and network.

The results were striking: up to 32 weeks shorter time-to-market for an NPI; more than $24 million in incremental profit from faster commercialization; four to five weeks of capacity protected per new product; and a faster yield ramp-up that meaningfully reduced cycle time.

Together, these two cases make a broader argument about where digital twins fit within packaging and CPG operations. Historically, digital twins were seen as advanced simulation tools or commissioning aids for new equipment. Negrón still considers those functions foundational, but he now frames twins as integrated operational tools that live across the lifecycle of a process or packaging asset, from design and commissioning through operator training and into ongoing optimization. As he put it, digital twins “shouldn’t be just a fancy picture or a 3D visualization. These should be things that are bringing you profit, that are paying themselves back.”

For packaging- and processing-focused CPGs, that lifecycle emphasis matters, because the operational heartbeat of a plant — its packaging lines — rarely stay static for long. Controls changes, product variations, shift-to-shift process adjustments, and equipment upgrades all alter how a line actually behaves. A digital twin, if maintained as those changes occur, becomes a continuously relevant tool for everything from validating new HMI screens to testing accumulation strategies to experimenting with changeover sequences without jeopardizing uptime. As Negrón noted, fully realized twins even “start doing predictions into the future.”

For brand owners, the value proposition is direct. Faster troubleshooting means less unplanned downtime. Higher OEE means better asset utilization and more predictable throughput to meet retailer demand. Shorter commissioning windows mean capital projects pay back faster. And accelerating NPIs translates into earlier distribution, faster learning cycles with retailers, and more profitable product launches. Negrón stressed that his team’s goal is for a digital twin to “pay itself back in roughly six to nine months,” and the case studies showed how that becomes feasible when twins are embedded into everyday engineering decisions rather than treated as advanced side projects.

Packaging lines, in particular, stand to gain the most immediate benefits. Because they are often the most complex, highly automated, and sensitive to subtle interactions between discrete equipment, they generate the kinds of intertwined mechanical and controls behaviors that are difficult to diagnose in real time. Digital twins give CPG engineers something they rarely have: a safe, pressure-free system in which to explore ideas, test changes, reproduce faults, validate OEM logic, and train operators without shutting down production. The beverage manufacturer’s experience showed how a problem that defied half a year of real-world troubleshooting could be solved in days once engineers had a virtual model that behaved like the real machine.

More broadly, digital twins are rapidly becoming a competitive differentiator for CPGs, especially in packaging. They allow teams to shift from reactive troubleshooting to proactive experimentation, from constrained physical testing to abundant virtual testing, and from theoretical process changes to validated ones.