Robotics and automation: transforming the food and beverage industry

By Glenn Johnson, Editor
Tuesday, 05 August, 2025

Food and beverage manufacturers are increasingly adopting more advanced robotics and automation technologies to enable more flexible and sustainable production.

The food and beverage manufacturing industry is in the midst of a technological revolution. Faced with rising consumer expectations, labour shortages, stringent safety regulations and sustainability demands, manufacturers are turning to robotics and automation to enhance productivity, ensure quality and futureproof their operations.¹ No longer confined to packaging lines or palletising stations, modern automation systems — now driven by artificial intelligence, machine vision and smart data analytics — are being integrated into every aspect of food and beverage manufacturing.

Smart robotics and automation in primary and secondary processing

Historically, the use of robotics in food manufacturing was limited to the packaging and palletising end of the process. Today that is changing rapidly.

Soft robotics and AI-driven machine vision systems have made it possible for robots to handle delicate, irregularly shaped and perishable food items with precision. In meat processing, for example, robots equipped with pressure-sensitive grippers and real-time imaging can now identify muscle groups, detect fat content and make precise cuts. These systems improve yield consistency, reduce waste and improve worker safety by removing humans from dangerous cutting operations.²

In dairy and beverage manufacturing, automated systems can now handle tasks such as inline blending, homogenisation and aseptic filling with near-zero human intervention. PLCs and advanced HMIs ensure accuracy in complex operations such as pH adjustment, enzyme dosing or temperature-controlled fermentation.³

Quality assurance and inspection with machine vision

Ensuring food safety and consistent product quality is an imperative in food and beverage manufacturing. Automation systems with advanced machine vision and AI-based inspection tools have become essential in maintaining these standards.

Modern vision systems can now go beyond traditional defect detection. Using multispectral and hyperspectral imaging, they can detect foreign objects, check the internal quality of products and assess freshness. In bakery operations, for instance, vision-guided systems can evaluate the browning and volume of baked goods and reject items outside acceptable ranges.⁴

It is now also possible to apply AI-powered anomaly detection to learn what normal product variation looks like and identify outliers. This capability reduces false rejects and helps operators quickly pinpoint process deviations, improving overall efficiency and reducing waste.

Cobots on the factory floor

One of the most significant shifts in manufacturing automation is the widespread deployment of collaborative robots. Because cobots are designed to work safely alongside human operators, they are ideal for high-mix, low-volume production environments common in many food and beverage facilities.

Cobots are now used in applications such as:

feeding ingredients into machines;
packaging and end-of-line palletising (where weight is not as much of a consideration);
labelling and inspection; and
repetitive handling of trays or crates.

Their flexibility and ease of programming mean they can be redeployed quickly to different tasks, making them especially valuable in facilities producing seasonal or short-run products. Cobots also help address labour shortages by taking over repetitive or ergonomically hazardous tasks, freeing up human workers for higher-value roles.

Automated clean-in-place and sanitation

In a regulated industry like food and beverage manufacturing, sanitation is just as important as production. Cleaning processes have traditionally been labour-intensive and time-consuming, but in more recent years automated CIP systems have enabled manufacturers to clean tanks, pipes and other process equipment with minimal manual involvement.

These systems use programmable recipes to control cleaning cycles, water temperature, chemical dosing and rinse times. Sensors validate cleaning effectiveness in real time by measuring flow rates, turbidity, conductivity and microbial levels.

Now robots are also making inroads into open-plant cleaning. Some manufacturers are deploying autonomous robots that can clean floors, conveyors and external equipment surfaces using a combination of water jets, brushes and UV light.⁵ This automation reduces downtime, improves hygiene, and minimises chemical and water use.

Autonomous materials handling and intralogistics

The movement of materials within any factory is today a key area of focus for automation. Traditionally reliant on forklifts and conveyors, many food manufacturers are now turning to autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) for intralogistics.

Using onboard sensors, AI-driven navigation and Wi-Fi or 5G networks, AMR systems can dynamically adjust to avoid obstacles, schedule their own routes, and be integrated with warehouse management and ERP systems.

In cold storage environments or hazardous zones, AMRs and AGVs significantly reduce the risk of injury to human workers while maintaining a high throughput. As food and beverage plants grow in scale and complexity, automating intralogistics ensures safer, leaner and more traceable operations.

Digital twins and cyber-physical systems

One of the most forward-looking trends in food and beverage manufacturing is the adoption of digital twins — virtual replicas of physical production systems. These digital models use real-time data to mirror production environments, enabling simulation, optimisation and scenario testing, as well as recipe development.

In an automated bakery line, for example, a digital twin can simulate dough flow, oven temperature gradients and cutting speeds, allowing operators to predict how recipe changes will affect product outcomes without interrupting production.

Researchers in Australia have shown that digital twins can also be used to ensure texture-modified foods such as purees and soups are safe for vulnerable Australians who are at risk of dysphagia — applying advanced AI techniques to predict and optimise production outcomes, such as product quality, during live production.⁶ A digital twin of the physical production machine draws insights to improve productivity and determine timelines for making the best-quality product to meet demand.

Combined with robotics, digital twins allow seamless commissioning of new lines, real-time process optimisation and faster response to changing market demands. They also support traceability and compliance by linking product attributes with process parameters at every stage of production.

Sustainability through automation

Automation is fast becoming a critical enabler of sustainability in food and beverage manufacturing. Robotics and control systems help reduce energy and water use, minimise raw material waste and improve yield through better process consistency.

Examples of using automation to improve sustainability include:

automation of HVAC and refrigeration systems to minimise unnecessary energy consumption;
precision dosing systems that reduce overfilling and product giveaway;
automated recipe management that ensures consistent batching, minimising rejects and waste;
energy-efficient motors and smart drives that reduce consumption on conveyors and mixers; and
real-time data analytics that help identify inefficiencies in resource use.

As ESG (environmental, social and governance) pressures increase, food manufacturers are needing to further leverage automation to meet both regulatory requirements and consumer expectations for responsible production.

Edge computing and data-driven control

All this automation and robotics has the effect of requiring — and producing — far more data than was previously needed to operate a food and beverage processing plant. Much of these vast volumes of data is also time-sensitive. The result is that it is necessary to implement edge computing, where data is processed at or near the source rather than in a remote cloud service.

Edge-based controllers can make real-time decisions for robotics systems — adjusting speeds, triggering quality checks or rerouting product flow based on immediate sensor feedback. Edge devices equipped with AI can also be used to detect micro-failures or trends invisible to human operators, improving line efficiency and product consistency.

Workforce upskilling and human–machine collaboration

All this increasing automation will have a large effect on the food manufacturing workforce, which has traditionally been quite labour-intensive. As automation reshapes the factory floor, the role of human workers is evolving so that future food manufacturing plants will require operators who can work with automation systems, interpret data and maintain robotic equipment.

Food manufacturers therefore need to invest in training and upskilling programs, often in partnership with vocational institutions, to build a workforce capable of collaborating with automation. Human–machine interfaces are also becoming more intuitive, using touchscreen displays, gesture recognition and even augmented reality to simplify interactions with complex systems.

Rather than replacing humans, these advances foster collaborative ecosystems where humans, robots and automation each focus on what they do best — creativity and problem-solving for humans; speed and precision for machines.

Conclusion

The convergence of robotics, AI, machine vision and edge computing is propelling food and beverage manufacturing into a new era. From the handling of raw ingredients to final packaging and sanitation, automation is improving productivity, safety and quality in unprecedented ways.

While challenges remain — such as integration complexity, upfront capital costs and workforce transition — the long-term benefits are clear. Manufacturers that embrace these technologies are positioning themselves for greater resilience, agility and competitiveness in a demanding global market.

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3. Tetra Pak Australia & New Zealand 2024, Inline blending takes the guesswork out of plant-based beverages, <<https://www.tetrapak.com/en-anz/insights/cases-articles/inline-blending-takes-the-guesswork-out-of-plant-based-beverages>>

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6. Swinburne University of Technology 2025, Creating safer, more accessible processed foods with innovative AI-powered digital twin system, <<https://www.swinburne.edu.au/news/2025/01/creating-safer-more-accessible-processed-foods-with-innovative-ai-powered-digital-twin-system>>}