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Boosting productivity and sustainability while maintaining quality and safety is essential to feeding an increasing population with a variety of diets. A new era of digital revolution in manufacturing is primed to meet these challenges, with more flexible batch systems giving way to high-volume continuous process systems. The higher-speed and -capacity lines adopting continuous systems are expected to enable personalization, product variety, and further reduced lot sizes without compromising productivity, environmental sustainability, or fast time to market.
In addition, the U.S. Food and Drug Administration’s (FDA) process analytical technology (PAT) framework, originally designed for pharmaceutical processing, is now being proposed for the food processing quality by design (QbD) concept. The QbD model entails integrating production quality into the process design rather than the post-production quality assessment assessment (van den Berg et al. 2013). This will be a key concept to integrate into current digitalization momentum to monitor and control processes in real time, ensuring product quality and safety standards, improving operator safety, and reducing human errors.
Such a high level of control will be possible only with advanced automation leading to flexible, networked, self-organizing, and user-friendly systems. While traditional embedded systems—combinations of hardware and software—perform specific tasks, such as controlling motors, temperatures, and line speeds and their automation to maximize performance and efficiency, a cyber-physical system (CPS) enables connection of physical assets through computation, communication, and networking infrastructures.
CPS opens up new possibilities for food processing automation efforts: increased connectivity, joint functioning, interactive and smarter controls, and service-based models involving greater collaboration among humans and digitized assets. Witness the growing use of CPS already in smart agriculture, process control, distributed robotics, smart grid, disaster response, aerospace, and smart homes and cities (Jiang 2018). Coupled with current real-time communication infrastructures comprising the industrial internet of things (IIoT) infrastructures and automation, CPS will lead to the realization of smart factory models where communication and data sharing are enabled among machinery, supply chain, enterprise systems, and customers.
This will require a departure from traditional automation approaches to self-organizing CPS (i.e., performing tasks independently), which in turn will require strategies such as development of novel hierarchical frameworks to integrate enterprise and control systems, assessment of technology readiness levels, and investments in top-down digitalization. Implementing smart factory models will also require special attention to tactical incorporations of human aspects while addressing sustainability, data privacy, security, reliability, and data quality challenges associated with the digital transformation.
The food industry has relied on automation for the past several decades to improve productivity, in line with growth in the global automation market. The global food process automation market is expected to reach $29.4 billion by 2027, a 9.5% increase from 2020, according to Statista.
Current automation efforts in manufacturing center on factory and process automation, integration of artificial intelligence, robotics, drones, and adoption of industrial software at the process, factory, and enterprise levels. In food process automation, the leading segments are enterprise-level controls, palletizing and depalletizing, and beverage and distilleries based on component, application, and end uses, respectively, reports Meticulous Research. The beverage applications area is growing rapidly due to fast digitalization and implementation of enterprise resource planning and supervisory control and data acquisition solutions in the global beverage processing industry.
Manufacturing analytics through data sharing and computation is essential in automation settings. In a Rockwell Automation 2020 report, for example, Agropur dairy processing’s use of automation with real-time multifactory data collection translated into a 25% increase in overall equipment efficiency (OEE). OEE is a key productivity measure that can be increased with process equipment collecting real-time data through sensors in product quality, downtime, unproductive and cycle time, scraps, and predictive maintenance.
Kraft Heinz utilized analytics to increase production capacity by 10%, investing in upgraded control systems and plant automation, according to the Rockwell Automation report. A Siemens case study reported by Snack Food & Wholesale Bakery last fall found a 10% production improvement and a 20% savings in maintenance costs and energy at a snack manufacturing plant after old and degraded equipment was retrofitted with sensors, drives, and motors, and synchronized with existing systems.
One growing area of need is integrating increased customer interaction with enterprise and control systems as part of the CPS concept to provide uniform terminology for the CPSs. This enables consistency in information and operations model explanations of application functions, and in how information is used and exchanged.
For example, ISA-95 from the International Society of Automation provides a five-layer hierarchy interface between enterprise functions and control systems (Figure 1), while 5C architecture involves connection, conversion, cyber, cognition, and configuration levels. Both ISA-95 and 5C provide a basis for CPS and automation frameworks connecting operations to enterprise decision systems. But both hierarchies also focus on vertical integration, generally lacking horizontal integration for value chain and customer inputs and integration of value and product chains. To solve this problem, Taiwan National Central University researchers have proposed adding three additional layers involving 1) customer and value chain interaction during design, 2) production and after-sales, and 3) value chain with a traceability focus (Jiang 2018).
Advancements in robotics and data analytics are currently driving automation progress. In 2020, food and beverage industry robotics installation increased 3% and made up 12% of total industrial installations, according to the International Federation of Robotics. In addition, 76% of all robot installations were distributed in just five countries: China, Japan, the United States, South Korea, and Germany.
The implementation of robots in food processing is being propelled by the opportunity to reduce labor costs, increase productivity, and optimize product with common applications in palletizing, packaging, picking, and placing. Robotics requirements specific to the food industry include kinematics and dynamics, control, hygiene, productivity, worker safety, cost, and ease of operation and maintenance. Researchers also have identified an urgent need for seamless integration of CPS, IIoT, AI, and cybersecurity tools (Khan et al. 2018).
Within the food industry, foodservice is leading the way in employing robotics technologies. Researchers have found that recently deployed foodservice robots such as Julia, Pazzi, Cyberdog, Blendid, and Monty can provide multirecipe automation, but the robots cannot utilize intelligent (i.e., autonomous) automation. Previously developed Moley and BakeBot foodservice robots demonstrate the highest intelligence, such as understanding and learning recipes from humans (Mujtaba and Mahapatra 2019). None of these systems, however, has integrated fully autonomous structures such as learning and modifying recipes to fit the kitchen environment.
Although novel CPS aspect applications in food processing and supply chain automation remain unexplored, labor shortages will continue to drive a renewed interest in smart automation.
But even with increased automation, adding a human element at every level of the operation is important in making a smooth transition to CPS. Companies will need to consider everything from skill development strategies that help process operators keep up with increasing plant floor operations intelligence, to automation hierarchies that incorporate digital servitization models and integration into existing business ecosystems. The food industry’s low technology readiness levels and the cybersecurity challenges associated with full automation will likely delay complete utilization of CPS.
1. Preview how cyber-physical systems can enable a high level of control in food processing automation.
2. Assess how current automation efforts are improving food processing productivity.
3. Learn about the need for integrating increased customer interaction into current enterprise automation and control systems.
4. Understand advancements in robotics and data analytics.