Digital Twins in the Pet Food Industry: From Simulation to Predictive Control

What Digital Twins Are and How They Are Used Today

A digital twin is a dynamic, virtual representation of a physical object, process, or entire an production system. Unlike static simulation, it continuously utilizes real data from sensors and control systems, enabling it to accurately reflect the current state of the process and predict its future behavior.

According to IBM, digital twins are currently used in manufacturing to improve operational efficiency, optimize processes, reduce failures, accelerate product development, and enable predictive maintenance. In industrial environments, their application ranges from individual production lines to entire plants, integrating operating variables, energy consumption, quality, and equipment performance, as well as supporting plant planning, virtual testing of new products, layout optimization, and control of complex processes, among other uses.

From Simulation to Predictive Decision-Making

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Technological Revolution in the Pet Food Sector: Automation and IA are Changing the Industry

María Candelaria Carbajo

The advancement of digital twins is closely linked to the convergence of process simulation, industrial sensors, artificial intelligence, and cloud computing. This integration enables manufacturers to move from a reactive model—based on manual sampling and subsequent adjustments—to a predictive and preventive approach.

According to an article by StartUs Insights, the market for digital twins applied to manufacturing could reach USD 714 billion by 2032, driven by the need to optimize complex processes and reduce operational inefficiencies. The same report indicates that more than 81% of global companies are already actively exploring the industrial metaverse, and that 62% increased their investment in these technologies over the past year.

These figures reflect a structural shift: simulation is no longer limited to the design stage but is becoming a central tool for day-to-day plant management.

The study, Digital Twin applications in the food industry: a review, identifies four main approaches to applying digital twins in the food industry, defined by their role within the production system. First, forecasting digital twins are used to anticipate the future behavior of processes or equipment based on the analysis of historical data and current conditions, enabling the prediction of deviations, inefficiencies, or failures before they occur. Second, reactive simulation models allow real-time process monitoring and autonomous responses to deviations, adjusting operating variables and recommending corrective or preventive actions. A third approach is virtual commissioning, which utilizes digital twins to test, validate, and optimize new technologies, equipment, or plant configurations in a virtual environment before physical implementation. Finally, synchronization-based simulation keeps the digital twin aligned in real time, or near real time, with the physical system, creating a highly accurate representation of the process that is especially valuable for scenario analysis, operational optimization, and improved decision-making in complex systems.

How Do Digital Twins Contribute to the Pet Food Industry?

Focusing specifically on the pet food industry, raw material variability is one of the main factors affecting final product quality. Ingredients, such as cereals, protein meals, fats, and animal by-products naturally fluctuate in moisture, protein content, fat levels, and particle size distribution.

According to a technical analysis published by Haskell, these variations directly affect critical operations such as extrusion and drying, influencing attributes such as texture, density, nutritional stability, and product shelf life. Traditional control methods often detect these deviations only after the product has already been produced, leading to reprocessing, waste, and efficiency losses. Digital twins, by contrast, anticipate these effects before they impact the final product.

In pet food production, a digital twin is built from models that represent the thermal, mechanical, and dynamic behavior of each unit operation (mixing, conditioning, extrusion, drying, and cooling). These models are powered in real time with data from sensors installed in the plant, such as ingredient moisture measurements, extruder barrel temperature, screw speed, pressure, airflow, and dryer parameters. This information synchronizes the virtual model with the real process, creating a living representation of the plant in operation.

In closed-loop control systems, besides observing the process, digital twins predict how variations in raw materials will affect the final product and automatically adjust operating parameters to compensate—often even before the ingredient enters the extruder.

Benefits of Implementation

Implementing digital twins delivers tangible benefits at multiple levels. First, it significantly improves product consistency by reducing batch-to-batch variability, a key factor for consumer trust and brand reputation.

By preventing out-of-spec production, raw materials and energy waste are reduced. This approach also optimizes energy consumption and increases throughput without compromising quality, directly impacting operating costs.

Another strategic benefit is to hasten product development. Formulations can be tested virtually, evaluating their performance in the process before conducting physical trials, thereby reducing time, risk, and costs associated with industrial testing.

Added to this is the ability to integrate predictive maintenance, using digital twins to detect deviations in equipment performance and anticipate failures, avoiding unplanned downtime.

Digital Twins: Key Technology for Building Truly Connected Plants

The incorporation of digital twins marks a turning point in how pet food production plants are managed. It is no longer just about automation, but about deeply understanding the process, anticipating deviations, and making decisions based on real, comparable data.

In a context where efficiency, sustainability, and quality are increasingly decisive, digital twins are consolidating their role as a strategic tool for manufacturers seeking to scale, differentiate, and build truly connected and resilient plants.

By Candelaria Carbajo – All Pet Food
Source: All Pet Food Magazine

References
Gallagher, Nick (Updated October 17, 2025) What is a Digital Twin? IBM
Prasser, David R. (July 21, 2025). Future of Manufacturing: 13 Trends Driving 2026-2035 Growth. StarUs Insights
Abdurrahman, Emadaldin Elfatih M. & Ferrari, Giovanna. (April 3, 2025). Digital Twin applications in the food industry: a review. Frontiers
Haskell. (December 19, 2025). A Process Engineering Perspective on Digital Twins in Pet Food Manufacturing.