AI in Food Industry: Transforming Food with AI and Robotics

The continued focus and rise of malware attacks, such as ransomware, on the manufacturing industry is due to several unique factors that make the manufacturing industry a lucrative target. Attackers recognize that any disruption to manufacturing operations can have a ripple effect, impacting multiple industries. Furthermore, manufacturing companies typically have a very low tolerance for downtime due to just-in-time contracts, high-capacity utilization, and the inability to make up for lost production time.

AI systems can collect and analyze data on production processes, consumer preferences, and equipment performance. This data-driven approach helps businesses make informed decisions, optimize operations, and innovate in product development. With less human error and lower labor expenses, this combination assures quick and reliable sorting. With AI technology, food manufacturers can uphold quality standards, cut waste, and improve the effectiveness of their supply chains, ultimately giving customers access to fresher and safer goods. Furthermore, AI-driven analytics offer insightful data that supports process optimization and ongoing development.

These catastrophic disruptions underscore the urgent need for innovative solutions to supplant outdated, legacy supply chain tools. With the sector in the U.S. expanding rapidly, an estimated 3.8 million new employees will need to be recruited by 2033 to meet demand. If the talent shortage isn’t addressed, nearly half of these positions could go unfilled, creating significant productivity and growth concerns for the sector. Food and beverage production requires advanced quality assurance, particularly in the fast-moving consumer goods (FMCG) sector, due to its “high-speed” nature. Equipment breakdowns and faulty products can hinder that; however, integrating AI can boost efficiency, cost-effectiveness and product quality and safety. Likewise, Rolls-Royce, in collaboration with IFS, uses AI in aerospace manufacturing through the Blue Data Thread strategy.

Predicting Food and Beverage Manufacturing Trends for 2024

These systems typically do not receive regular updates or support from vendors, leaving them exposed to known vulnerabilities. Moreover, their incompatibility with modern cybersecurity tools further exacerbates the risk, as it becomes challenging to implement effective protective measures. Phishing is a well-known type of social engineering, where attackers send fraudulent messages designed to trick individuals into revealing sensitive credentials or clicking on a malicious link.

The growing size and complexity of systems are increasing the difficulty of those activities. Software as a service (SaaS), risk-based validation, computer-software assurance (CSA), and industry 4.0 initiatives all are influencing changes in validation requirements and solutions. Transparency in digital activities refers to an application’s ability to display its components.

Artificial Intelligence: Still Needs Fine Tuning To Succeed In Manufacturing

Training programs created through AI allows it to be tailored to individual employee needs, considering skill levels, job roles, and performance data. This ensures each worker receives relevant training to improve knowledge retention and skill development. AI also facilitates virtual simulations and real-time feedback that lets employees practice complex tasks in a controlled environment to enhance learning outcomes and safety. Moreover, AI-powered tools predict skills gaps by analyzing workforce data that enables proactive training interventions to keep the workforce aligned with industry demands. AI accelerates the training process, making it more accessible and effective, which aids manufacturers to ensure their workforce remains competitive and adaptable.

Thus, DTs are now the main drivers for AI/ML industrialization because of their ability to create virtual replicas of physical processes, equipment, and systems. Those capabilities allow for real-time monitoring, predictive maintenance, optimization, and simulation — all of which are crucial for enhancing efficiency, reducing downtime, and improving overall productivity and quality in industrial settings. In conclusion, AI and machine learning technologies represent a paradigm shift in industrial automation, offering manufacturers unprecedented opportunities to enhance efficiency, quality, and flexibility.

In response, the GenAI tool produces between one and 100 design solutions that accurately fit into those parameters. AI can be used to create frontline worker documentation — i.e., a consolidated list of all machines and standard operating procedures on how to handle issues, Iversen said. A worker can audibly ask or type into a GenAI tool a question about what to do if a machine isn’t operating at the correct output, and the tool gives a reason why, he said.

Further, AI-driven production planning optimizes resources and streamlines scheduling by predicting demand and adjusting production schedules to reduce lead times and improve operational efficiency. AI enables predictive maintenance in manufacturing by predicting equipment failures before they occur. AI systems use machine learning algorithms to analyze sensor data and historical records to detect patterns and provide real-time insights into machinery conditions. It saves costs by focusing maintenance on equipment that needs attention and extends equipment lifespan through timely interventions. AI-powered predictive maintenance enhances workplace safety by reducing the risk of accidents caused by malfunctions and improves operational efficiency by ensuring machinery operates at peak performance. It has applications across various industries, including automotive and energy, where equipment reliability is critical.

VR simulations, on the other hand, enable manufacturers to design, prototype, and optimize products in virtual environments, minimizing costs, accelerating time-to-market, and fostering innovation. By leveraging AR/VR, manufacturers can transcend the limitations of traditional manufacturing methods, unlocking new possibilities in design, collaboration, and customer engagement. Advanced analytics and artificial intelligence can further aid in quantifying the impact of cybersecurity measures. These technologies enable real-time monitoring and analysis of cybersecurity efforts, providing insights into threat trends, the effectiveness of security protocols, and areas requiring improvement. This data-driven approach enhances visibility into the ROI of cybersecurity investments, helping to build a stronger business case for adequate funding. Crowdsourcing talent through platforms like Kaggle allows manufacturers to solve specific AI challenges and gain insights from a global pool of data scientists and machine learning experts.

Products can be tailored to the needs of the regional cluster, while local supply chains support resilience and reduce logistical complexity, upholding standards of quality. The ultimate goal is to reach a model of “rapid agile” on-demand manufacturing, he says, which would drastically shorten the supply chain through highly digitised and interconnected production facilities. Under this model, the consumer purchases the garment, and then local micro-factories use advanced robotics and automation to make the garment in 18 days or less, according to Westland. “This is a complete disruption, because at the moment manufacturers make things and then later try to sell them. And if you don’t make anything before you’ve sold it, there’s no dead stock,” he explains.

One significant hurdle in modern manufacturing is interoperability among different software systems. AI can solve this by enabling seamless communication between disparate platforms and teams. Through machine learning algorithms, AI can facilitate the exchange of information across systems, creating a cohesive and integrated operational environment.

What has captured the attention of manufacturers, designers and engineers is that it is an advanced system that can understand complex questions and provide very accurate answers almost immediately. Vendor management must also extend beyond onboarding and encompass continuous monitoring and assessment to manage risks effectively. Ongoing risk assessments should be conducted at all levels, including company-wide and with regards to specific products/services, artificial intelligence in manufacturing industry to identify and evaluate potential cybersecurity threats. Manufacturers can utilize security ratings and automated questionnaires to continuously monitor vendors’ cybersecurity postures. These tools provide real-time insights into vendors’ security status and help quickly identify emerging risks. In conclusion, integrating AI in manufacturing transforms the industry, turning futuristic concepts into present-day realities.

This process involves selecting relevant variables, modifying them to highlight important patterns, or creating new features that provide valuable insights. Effective feature engineering can significantly boost the predictive power of AI models, making them more accurate and reliable. They can expect increased productivity, substantial cost reductions, and enhanced innovation. Conversely, those who fail to address these challenges may stay caught up in an increasingly competitive market, facing missed opportunities, inefficiencies, and operational obstructions. “AI has become a base technology that industrial manufacturers apply at every stage of the value chain,” says Ramachandran. From initial product design to sales and distribution, organizations see opportunities to apply AI tools to filter and analyze their growing volumes of data to enhance results — and at speed.

They helped PepsiCo’s Frito-Lay gain 4,000 hours of manufacturing capacity annually through its predictive maintenance systems that decreased unplanned downtime and costs at four Frito-Lay plants. However, traditional machine learning (ML) models, such as machine vision and graph-based natural language processing, are beginning to scale, he said. It’s no secret that manufacturers were among the laggards in adopting digital technologies, a deficit laid bare during the COVID-19 pandemic when many companies struggled to adapt to remote work, changing customer demands and supply chain disruptions.

A focused approach on business outcomes first, followed by a robust data quality and governance process, are critical to drive business value at scale. For instance, respondents in the EY/Microsoft survey note hurdles such as how data must be collected and cleansed to be easily connected to AI solutions in production. Too often, siloed functions and unintegrated platforms don’t forge the needed links to make AI effective.

• The global AI in manufacturing market is projected to grow from USD 3.8 billion in 2023 to USD 156.1 billion by 2033, with a compound annual growth rate (CAGR) of 45% from 2024 to 2033.
• Legacy infrastructure and systems of manufacturing facilities are not compatible with advanced artificial intelligence technology thereby driving the demand for novel hardware solutions.
• The ability to use AI to optimize processes, improve product designs, and enhance customer experiences gives these companies a competitive edge in the marketplace.

Market.us reports that the global market for AI in manufacturing is poised for rapid growth, with its value expected to surge from $3.8 billion in 2023 to $156.1 billion by 2033, reflecting a compound annual growth rate (CAGR) of 45%. AI is playing an increasingly vital role in improving manufacturing efficiency, precision and decision-making. The use of third-party vendors can introduce significant cybersecurity vulnerabilities into manufacturing operations. The interconnected nature of modern supply chains means that a single compromised vendor can have far-reaching impacts, potentially affecting multiple entities within the broader network. As manufacturers increasingly rely on third-party vendors for various components, services, and technologies, it becomes imperative to implement robust vendor management processes to mitigate these risks. As demand for AI-based services increases, improvements in communication technology make it possible for many AI solutions to be “manufactured” in one location and operated and maintained elsewhere.

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Among the upstream industries, the one most affected by the manufacturing industry is the construction industry; among the downstream industries, the one most affected by the manufacturing industry is the mining industry. In comparison, the manufacturing industry has more influence on the downstream industries of these two industries. At the same time, the correlation effect of the manufacturing industry on the above industries is negative and significant in all industries, indicating that the impact of AI on the labor market is mainly dominated by the substitution effect. For the creation effect formed by the expansion of production scale, upgrading of industrial structure and improvement of consumption structure has not yet been formed. From the point of view of the absolute size of the coefficient, the manufacturing industry has the smallest degree of influence on the financial industry, followed by accommodation and catering.

For example, 88 percent of industrial manufacturing respondents are confident their technology can improve efficiency and reduce costs; only 75 percent of all sectors measured say the same. Similarly, 84 percent of the industrial manufacturing executives surveyed are confident in the potential of their organization’s technology to help it build trust with third parties, against an average of 74 percent across all sectors surveyed. Siemens, IBM, Intel Corporation, NVIDIA Corporation, and General Electric are the top players in the artificial intelligence in manufacturing market. These artificial intelligence in manufacturing companies with advance robotics and AI technology trends with a comprehensive product portfolio and solid geographic footprint. The increasing demand for Artificial Intelligence (AI) in the Manufacturing industry of the US region is propelled by the imperative to effectively manage the growing volume and intricacy of data.

Overcoming data and talent barriers is important for fully utilizing AI’s transformative potential. Manufacturers who invest in high-quality data practices, upskill their workforce, and collaborate with academic institutions and external experts can achieve unmatched efficiency, innovation, and competitiveness. Embracing AI technology enables manufacturers to drive productivity and operational excellence, paving the way for a new era in manufacturing. The outstanding digital transformation groundwork in the sector to date means that industrial manufacturers are significantly more confident than peers in other sectors in the capacity of their current technology to deliver on strategic priorities.

AI-powered user interfaces provide intuitive, guided experiences that enable workers with limited technical knowledge to effectively use advanced tools. You can foun additiona information about ai customer service and artificial intelligence and NLP. By correcting mistakes and providing explanations, AI helps speed ChatGPT up the learning process for graduates and less experienced employees. This empowers them to take on more complex roles while also fostering a culture of continuous improvement and learning throughout the organization.

Predictive maintenance “is going to be a huge AI use case,” Iversen said, and it’s been rolled out by a handful of manufacturers. “If you have a robust [manufacturing execution system] or data analytics solution, you can already pretty effectively understand when a machine will have downtime, the root cause for why it’s occurring and get some insight into how to fix the problem,” he said. The first manufacturing use case for GenAI software was in computer-aided design (CAD) software, according to Iversen, and now, 70% of manufacturers are using the technology for discrete processes. And, earlier this year, Tesla announced plans to install a $500 million Dojo supercomputer at its New York gigafactory, which will be used to train AI systems that support autonomous driving. The millions of terabytes of data the Dojo supercomputer processes from the automaker’s electric vehicles will help improve the safety and engineering of Tesla’s autonomous driving features, the company said. The pandemic “really exposed the lack of [digital] investments they’ve made over time,” said Sachin Lulla, consulting industrial products sector leader at EY Americas.

Additionally, generative AI enables manufacturers to explore numerous design possibilities, leading to innovative product designs that are tailored to customer preferences. Collaborative robots, or “cobots,” can work alongside humans to perform repetitive tasks, such as assembly or packaging, reducing labor costs and increasing productivity. One of the most significant applications of AI in manufacturing is predictive maintenance. Traditional maintenance schedules often rely on time-based intervals, leading to unnecessary downtime or catastrophic failures. One of the primary challenges posed by legacy systems is the presence of unpatched security flaws. These flaws are well-documented and frequently exploited by cybercriminals, making legacy systems targets for attacks.

Moreover, AI-driven analytics enable manufacturers to unlock new levels of customization and personalization, catering to the diverse needs and preferences of consumers. AI algorithms analyze historical sales data, market trends, and external factors to produce accurate demand forecasts. This allows manufacturers to anticipate shifts in customer preferences and adjust production plans. The improved accuracy minimizes risks of overproduction or stockouts that lead to efficient inventory management and cost reductions. AI also optimizes production scheduling by integrating real-time data on demand fluctuations, resource availability, and production constraints.

Some roundtable participants reported that they are also creating new positions for specialists trained to use AI-based tools, although the demand for such skilled positions currently outstrips the number of available technicians and engineers. According to the participants, they are experimenting with GenAI most frequently in office operations, e.g., generating marketing copy, manufacturing timelines, and product tagging. They noted that the technology is especially useful in completing tedious, high-volume tasks such as composing product descriptions.

For example, KUKA Robotics is a German firm whose industrial robots integrate various AI functions such as machine learning, vision systems, and sophisticated control algorithms. First, AI models are trained on specific data sets, which might not represent accurately the diversity of process and equipment states. Many AI algorithms, such as deep-learning neural networks (NNs), can be complex and opaque, making it difficult for us to understand or document their decision-making processes (the “black-box” concern).

Moreover, the reputational damage resulting from such breaches can erode customer trust and market position, further exacerbating the financial impact. In factories, smart sensors, the internet of things and AI enable predictive maintenance — used by 68% of survey respondents — to save ChatGPT App costs and extend the lifespan of important assets. Then there are  digital twins, which are virtual replicas of a product, process or piece of equipment to use in simulations. In the survey, 62% say they have adopted digital twins — for example, in making supply chains more resilient.

While skilled inspectors can identify many issues, human error and fatigue can lead to inconsistencies in inspection results. AI/ML is transforming methods in most every element of biopharmaceutical commercialization, from drug discovery and development to postmarketing surveillance. For instance, the World Health Organization (WHO) recently published guidance about AI/ML design, development, and clinical implementation (10). However, such technologies are opening new opportunities for increasing manufacturing-process efficiency and quality and for emulating human capabilities in monitoring and control (11).

The AI industry has a foothold in various business functions, from cloud computing for datasets to streamlining company decision-making. The success of products like the Apple Watch and Fitbits is set to boost the global wearable AI market value. As AI continues to spread throughout the manufacturing sector, its impact will only intensify. This growing trend will set the stage for smarter, more efficient and more sustainable manufacturing practices. In another benefit, AI’s capability to instantly process large volumes of data enables it to anticipate bottlenecks or inefficiencies before they arise, facilitating proactive adjustments and better decision-making.