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Taking one step at a time to smarten up

20 August 2023

Suzanne Gill asked what the most important elements for a successful smart food factory might be… and why.

Engineers in food manufacturing plants today have multiple challenges. They are under pressure to improve the speed, reliability and efficiency of their processes while also ensuring consistency and quality along with a need for increased productivity and reduced costs. More recent challenges include the high cost of raw materials and energy alongside the need to reduce food waste and a greater focus being placed on efficient and sustainable food production. All of these factors mean that factories need to become more flexible, scalable and smarter.

According to Steve Ward, Director, Application Engineering EMEA at Emerson, a smart food factory utilises emerging digital technologies, including intelligent sensors, computing power, connectivity, artificial intelligence (AI) and advanced data analytics to improve traditional manufacturing processes. “In terms of the most important elements of a ‘smart food factory,’ firstly, there needs to be a digital ecosystem linking equipment and systems, enabling data to be continuously collected and shared so that processes can learn and adapt,” he said. “It is essential to have a seamless flow of operational technology (OT) data, related to all aspects of the production process and utilities, delivered from the factory floor up into the information technology (IT) environment to support decision-making.”

According to Steve, real-time monitoring is key. Utilising edge computing, software and AI to perform analytics provides valuable insights to operations teams to help them optimise their processes and also creates the opportunity for learning, self-diagnosing issues and continuous improvements. “Machine performance and reliability, for example, are a key part of improving productivity. By comparing real-time data against historical data or data from other machines, AI can help to self-diagnose machine issues and support predictive maintenance strategies that eliminate unexpected downtime,” he said.

Another important element is the ability to take plant floor data and move it into the cloud to provide greater accessibility to insights by those who need it. Open communications, Internet connectivity and cloud-based solutions enable secure access via a web browser. 

“The journey to become a smart factory often starts by identifying a clear use case and the appropriate IT and OT personnel to implement suitable solutions,” continued Steve. “From there, organisations can take pragmatic steps to prove value and return-on-investment. This serves as the foundation on which to ensure the right skillsets are collaborating, principals and standards are being set and justification for adopting a solution or technology is realised. The next step is to scale up.” 

Explaining in more detail the first steps towards a smarter food factory, Steve said: “Food manufacturers must mine data to obtain valuable insights that help teams optimise their processes. Although some new sensors may be required, data streams already often exist that can be used to detect quality or machine performance issues, and process drift or potential machine failures and the root causes of these. Likewise, a range of sensors are available to collect data to determine and reduce energy consumption and other resources, such as raw materials, water, steam and compressed air. Smart manufacturing systems and industrial software are designed to turn this data into actionable insights.”

A dose of reality
“The utopic view of a smart factory is one where data flows seamlessly from the beginning to the end of the process; and captures everyone and everything in it – from consumer experience to supplier management, ingredient mixing to intuitive packaging,” said Keith Thornhill, Head of Food & Beverage UK & Ireland at Siemens Digital Industries. “It is agile, efficient and transparent, and links all assets together to improve predictability. Those are the key elements that for me make a smart food factory. 

“However, I’m always wary of overhyping, and I think it’s important to distinguish between reality today and aspiration. A lot of organisations are still learning how to incorporate technology into their processes, rather than joining it all up, and that’s okay. You need smart processes and repeatability before you get to smart factories.”

Keith advises that engineers take the time to understand their processes and what needs to be achieved. “Interrogate everything, be open to making changes and set out a clear roadmap that aligns with where the problems are and where the opportunities lie. It’s also important to define what success looks like. Not only does this remove some risk by ensuring that you get out what you put in, but it also gives you something to aim for.” 

Keith also recommends breaking digitalisation down into focus areas to make it more manageable. ‘Siemens worked with a company who decided to just focus on a 20-minute mixing process. Because it had always been this way and because it worked, no-one had ever thought to question the process. We used computational fluid dynamics and simulation technology to test some assumptions and discovered that what had been taking 20 minutes could be done in five, without impacting product quality. The result was a faster, more efficient, and therefore less wasteful, process. Big results can come from small changes,” he said. “As a final thought, remember that people and culture are at the heart of making digitalisation work, so making sure everyone is on board is a vital step. You can’t do it without a shared understanding of what you want to achieve and why.”

Reducing process errors
According to Ed Goffin, Senior Manager, Marketing at Pleora, one of the most important reasons to digitalise is to reduce human error in processes, including production, inspection and packaging. He said: “It is easy to assume that many of these steps in food manufacturing are already automated, but in reality human decisions often still play a large role. This is especially true for those specialising in short-run or custom production, where it can be too expensive to deploy full automation.” 

Pleora is working with customers that want to ensure consistent, reliable and traceable human decisions so they can reduce downtime, waste, production delays and gather data for continuous improvement around these human decisions. “For example, we are working with a distillery on labelling and packaging inspection, where they estimate an error on a bottle takes five minutes to correct. They are a higher value, lower volume manufacturer and produce a few thousand bottles per day. If they find an operator is making a mistake, or an automated process is out of alignment after producing hundreds of bottles, that five minutes adds up quite quickly,” explained Ed.          
 
“We often recommend a scalable approach of digitalisation towards automation, especially for those that are just starting their journey towards smarter processes. If you start with an error-prone manual process, these steps could include using vision and AI-based tools for inspection and traceability to help ensure operators always make consistent decisions while also gathering data around their decisions.”

Automating decision support can give a quick return-on-investment as it is possible to target an area of the business where errors cost time and money. Just as important, by digitising this process it is possible to start to gather insight that provides a roadmap for the next step towards automation. “There is often a next step in the process, such as a production checklist, that can also be digitised to save time and money,” continued Ed. “As engineers look ahead to wider scale automation, they can also gather crucial data that can help streamline that deployment. For example, digitising a visual inspection task will also gather and store product images and data that will be valuable around designing inline inspection applications.”  

Barriers?
I also asked our smart food factory spokespeople what their believe the barriers to the adoption of smart technologies are in the food factory and how they can be overcome. Their answers follow:

Steve Ward: A lack of capital investment finance can limit the adoption of smart technologies, but many solutions provide the opportunity to start small, evaluate the results and then scale up – be that by adding extra measurement points or implementing solutions across multiple lines or plants. Cybersecurity is another concern and is a critical element of a smart food factory. Increased connectivity places greater focus on cybersecurity, and when integrating OT and IT and moving plant floor data into the cloud, this must be carefully considered. 

Keith Thornhill: Cultural acceptance and a willingness to change are among the biggest barriers for smart factory technology adoption. To overcome these challenges, education and demonstration is needed – clear, real-life use cases that show the benefits of digitalisation. 
It’s also about the cultural desire for change and having a plan for success, because without people backing a clear vision it’s doomed to fail. Breaking upgrades into small stepping stones can make it feel more manageable – incremental change is better than nothing at all. Meanwhile, flexible financing solutions, such as performance-related contracts, can also help alleviate some of the hesitancy with upfront investment.

Ed Goffin: One of the biggest barriers is cultural resistance, as companies try to deploy digital transformation strategies. It’s human nature that we will resist change. Manufacturers often underestimate the importance of training and communications for their human operators. We have worked with one manufacturer who has done a great job considering the ‘human factor’ in its path towards becoming a smart facility. It has been able to highlight to its operators how automation is reducing stressful decision-making. This is where a more scalable approach towards automation can be beneficial. If operators can see how they will benefit, they will be less resistant to further change.  


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