NEW TECH FOODS

From energy costs to consumer demand for sustainability, few sectors operate under as much pressure and scrutiny as food and beverage manufacturing. As the industry continues its push toward automation and real-time decision-making, one challenge persists: the quality of the data used to run operations.

Digital twins offer a compelling path forward. These are living, evolving models that replicate the physical world in precise digital form. By integrating data from sensors, production systems and AI models, digital twins allow manufacturers to simulate, test and optimise physical processes without interrupting them. For an industry where precision, safety and efficiency are paramount, that opens up new possibilities.

And perhaps more importantly, it offers a way to bridge the persistent gap between what’s happening on the ground and the digital systems designed to increase efficiency and productivity, observes Alex de Vigan, CEO and founder of French technology company Nfinite.

What is a digital twin?

At its core, a digital twin is a high-fidelity replica of a physical product, system or process. But what makes it transformative is its ability to mirror reality in motion. Unlike static CAD files or isolated datasets, a digital twin is fed by real-time inputs (temperature, flow rates, pressure, viscosity, energy use) making it dynamic and interactive.

In the context of F&B, this means manufacturers can simulate how a new recipe will perform under production conditions, identify where water usage can be reduced in a clean-in-place cycle or forecast how packaging changes will affect the entire logistics chain.

By essence, they are operational tools. It is this blend of physics-informed modeling, live data and spatial context that makes digital twins so powerful.

Why it matters for the food and beverage industry

In F&B manufacturing, precision is not optional. Whether mixing dairy emulsions or calibrating thermal treatment lines, there is little margin for error. Digital twins allow for an unprecedented level of simulation and scenario testing before any physical change is made.

• Process optimisation: By modeling entire production lines, manufacturers can run virtual stress tests on bottlenecks, trial different throughput levels or simulate cleaning cycles, all before implementation. This minimises downtime and avoids costly trial-and-error approaches.

• Resource efficiency: Digital twins can expose hidden inefficiencies in water, energy and raw material use. By correlating sensor data with real-time process flows, systems can suggest optimised configurations for resource savings, crucial in a sector under mounting environmental regulations.

• Predictive maintenance: By tracking the digital twin of a machine alongside its real-world counterpart, maintenance can shift from reactive to predictive. Anomalies become easier to detect, service schedules more accurate and asset lifespan extended.

• Quality and compliance: In highly regulated segments, digital twins offer traceability. Any deviation from predefined standards (temperature, humidity, ingredient ratios) can be flagged immediately. This strengthens food safety protocols and streamlines audits.

Unlike industries that deal in discrete components or uniform materials, food and beverage manufacturing faces a unique blend of variables: biological inputs, perishable goods, tightly integrated supply chains and ever-shifting consumer trends.

This complexity is precisely why digital twins are such a strong fit. Rather than treating each part of the system (processing, packaging, distribution) as isolated nodes, digital twins offer a unified view of how one decision affects the whole.

For example, a formulation change in a beverage product might alter viscosity, which in turn affects flow rates, cleaning cycles, and fill accuracy. A digital twin can model all of this in advance, avoiding costly surprises during roll-out.

Overcoming the barriers to scale

Despite the clear benefits, deploying digital twins at scale is not without challenges. The technology sits at the intersection of engineering, data science and operations, requiring a shared language across teams that may not naturally collaborate.

Common barriers include:

• Data integrity: Many legacy systems were not built with real-time data flow in mind. Harmonising datasets across operations, engineering and IT teams remains a heavy lift.

• Model calibration: Twins must be continuously updated to stay accurate. That means constant validation against real-world measurements and processes.

• Sensor coverage: Without a robust network of reliable sensors, the twin’s insight is limited. Gaps in measurement lead to gaps in prediction.

• Change management: Perhaps most significantly, digital twins shift how decisions are made. This requires trust. Teams must evolve from intuition-led choices to model-informed strategies.

These hurdles are not insurmountable, but they do require long-term commitment and a clear governance structure to fully embrace Physical AI.

A practical roadmap for manufacturers

For food and beverage companies exploring the potential of digital twins, a phased, pragmatic approach is best. Here are four principles that can guide implementation:

1. Start with a high-impact use case: Choose a process or asset where the return on better visibility is clear, such as a thermal processing line or packaging station.

2. Build strong data foundations: Ensure sensor networks are reliable, and data governance is in place.

3. Combine physics-based and AI models: Hybrid models are often more robust than pure machine learning. They provide interpretable results grounded in domain knowledge.

4. Measure what matters: Set technical KPIs from the start. These could include reduction in energy use, predictive accuracy or mean time between failures. Let the metrics speak.

The path forward

Digital twins are not a silver bullet, but they are a compelling strategic lever. For food and beverage manufacturers navigating a world of growing complexity, evolving regulation and shrinking margins, they offer a new way to work: more precise, more agile, and more data-driven.

For F&B Leaders, this is an opportunity to rethink what visibility, planning, and control can look like when your systems anticipate the future.