Like a 15th century expression, “Children should be seen but not heard,” artificial intelligence (AI) is typically an embedded software technology that operates quietly behind the scenes keeping a production or packaging system running smoothly—and is silent unless something goes out of control and is not easily corrected. Then, the system sounds an alarm, possibly shutting down the process until the problem—which AI has already identified—is remedied by humans.

Some packaging and control system vendors have begun to tout the use of AI in their systems—and with good reason. AI technologies are finally becoming ready for primetime, provided they have sufficient inputs from sensors and/or accumulated data originating from packaging machines, robotics, vision inspection systems, motors and other software applications such as MES, inventory, logistics and process controls. Without this data, AI is blind.

I thought it might be interesting to “ask” an AI system (ChatGPT) what it considers to be the benefits that AI can bring to food packaging systems. While AI probably won’t put writers and editors out of a job yet, ChatGPT’s responses were as summed: “AI can help food packaging systems operate more efficiently, effectively and sustainably, leading to improved quality, reduced waste and increased profitability.” For the complete response, see the box, “AI chatbot reveals AI benefits in packaging system operations.”

Building on AI

For suppliers of packaging equipment, the road to AI has had many signposts along the way. For example, in 2018 Harpak-ULMA established a vision and plan to transform itself into an innovative, agile supplier of smart and connected packaging platforms, says Alexander Ouellet, innovation manager. Five years into this multiyear, multi-phase digital transformation plan, the company is now demonstrating how and why digital transformation delivers customer value in packaging operations by dint of the commercial acceptance exhibited by prospects and customers.

“This strategy,” says Ouellet, “rooted in delivering the smart, connected machines that establish a foundation for digital transformation (data), builds progressively to integrate IoT, augmented reality (AR), digital twins and predictive maintenance capabilities. The company plans to commercially release embedded AR and digital twin capabilities in 2023 following multiple-year betas with fortune 500 customers.”

AI analysis enabled Harpak-ULMA to improve the effectiveness of its augmented reality work instructions dramatically—helping untrained technicians to perform a complex task in 75% less time and with better accuracy than an experienced technician.Photo courtesy of Harpak-ULMA

 

The application of machine-learning and AI technologies based on large volumes of collected data are key to the final phase of a planned predictive maintenance offering across its packaging platforms, adds Ouellet. While this is the major customer value proposition being targeted, the company has taken a number of smaller steps to leverage AI for evaluating, analyzing and identifying various incremental improvements around existing processes or offerings.

Other AI applications are being investigated by Harpak-ULMA. For instance, the ability of the platform to alert/recommend rebuilding a tray sealer tool based on cycle counts and other runtime variables (note that this is not true of condition-based monitoring). Another aspect of this kind of AI is being investigated for inventory management of spare parts. AI is also being tested for analyzing service requests and support tickets to identify patterns that can help establish a more proactive customer response approach. Finally, the company is actively working to incorporate an AI tool that targets machine troubleshooting and production management for a packaging platform.

One of the interesting business models that successful predictive maintenance enables through extensive data warehousing is an industry benchmarking service, says Ouellet. While still in the conceptual phase at this time, massive amounts of captured, anonymized data can be mined to generate an array of best-in-class operational performance metrics that could help producers better understand the efficacy of their packaging operations as compared to their peers.

AI-based supply chain and analytics software help extend packaging and control capabilities. For example, Peak Analytics software can leverage AI and machine learning algorithms to provide better outcomes in quality control and predictive maintenance in the food packaging industry, says John Dwinell, president of Peak Technologies. For quality control, Siena Analytics software can develop AI models that analyze images of packaging materials during the production, packaging and distribution process to detect any defects or irregularities in the labeling and seals, as well as any physical defects in the packaging itself. This can help to reduce waste, improve product quality and ensure compliance with regulatory requirements.

Without monitoring/control equipment and sensors, AI wouldn’t have the data it needs to keep machines running at their peak. Red Lion Controls offers a wide range of products, including sensors, I/O modules, controllers, HMI’s and IIoT gateways that can be used for data acquisition and edge computation of field device data. This data can be integrated with AI-powered applications to enhance food packaging processes, says Barry Turner, technical business development manager.

Siemens Digital Industries has developed IIoT solutions covering a range of critical challenges in manufacturing in general and packaging in particular, says Alexander Epple, head of MindSphere industry solutions with Siemens. For example, production losses due to downtime of equipment typically costs the food industry from $20K-30K per lost hour. Two AI-based solutions can predict equipment failure.

First, Siemens’ Senseye is built from the ground-up using sophisticated AI and predicts the future of machines. More specifically, Senseye PdM integrates with any asset, system or data source, using data manufacturers already collect or with newly installed sensors as part of a complete package. Second, Edge Analytics provides AI capabilities to automatically detect anomalies in vibration spectra of rotating machines and alarms operators early about issues. In addition, it provides the tools needed to identify root causes of problems and define targeted mitigating actions.

In addition to these software solutions, GE Digital provides Proficy Operations Analytics and Proficy CSense, which are software solutions aiming to optimize food and beverage production using AI technologies, says Cobus van Heerden, senior product manager, ML and analytics.

Do Food Processors Care About AI?

I own a couple of AI-based graphics applications, for example, one that sharpens photos due to camera shake or are a little out of focus. Did I buy Topaz Labs’ Sharpen AI specifically because it runs on AI? No! All that matters to me is it gets the job done, saves time and is affordable. I sense that many food and beverage processors feel the same way about the AI-based products they buy and install.

“In our experience, production managers and engineers do not seek ‘AI’ for the purpose of using the technology but hope that it can solve specific production challenges for them where more traditional approaches fall short or fail altogether,” says Siemens’ Epple. “Hence, we create solutions around AI-technologies for well-defined common production challenges and design and package them so that customers can use them without in-depth knowledge about machine learning methods.”

Working standalone or as a Photoshop (or in this case as an Affinity Photo) plugin, Topaz Labs’ Sharpen AI has a built-in AI engine that can sharpen images blurred by camera shake or a slightly out-of-focus setting while also reducing image noise. The system includes built-in AI models so users do not have to fiddle with adjustments. Many industrial graphics imaging systems used in inspection systems are now employing AI before making decisions to pass or fail products.Photo and screen dump courtesy of Wayne Labs

 

“The Association for Packaging and Processing Technologies (PMMI) is reported to claim that 59% of food and beverage companies are currently using or planning to use AI technologies in their operations,” says Harpak-ULMA’s Ouellet. “The exact applications of AI in these circumstances are unclear. Certainly, the market drumbeat emphasizing the importance and potential of digital transformation is closely tied to producers’ evolving interest and expectations.

“However, we believe widespread AI adoption in packaging predictive maintenance is still in an emergent phase,” adds Ouellet. “Vendors face pragmatic challenges associated with both the costs and feasibility of large-scale data aggregation, heterogenous automation environments, connectivity and AI modeling/development efforts.”

In general, manufacturers’ main goals are to deliver high-quality products at the lowest cost, improve operational performance (or overall equipment effectiveness (OEE)), ensure product safety and traceability from ingredients to pallet, and exceed sustainability goals and measures, says GE Digital’s van Heerden. That said, any solutions that help them achieve these goals are what food and beverage manufacturers are looking for, and the majority are seeing AI as a way to level up their operations. In the case of AI being a solution, AI can support corporate operations goals and energy OEE optimization, producing the right amount of product with quality.

Quality control, predictive maintenance and supply chain management are all areas experiencing growing demand, says Red Lion’s Turner. “A significant number of food and beverage processors believe that AI is ready for mainstream adoption and will soon become an anticipated component of packaging systems, alongside other advanced tools.”

“CPG manufacturers hear about AI in different domains and they are curious,” says Kevin Hoorne, Siemens industry manager CP&R for digital manufacturing. “They want to know more; they want to understand [AI] better and they want to learn how it could help them in the challenges they are facing. We don’t feel like they are actively looking. Instead, they want to hear from us where the value lies. This is where we work closely together with our customers by bringing our solutions to meeting their needs.”

As long as AI can contribute to address issues which may cause unnecessary loss of time and money, such as predicting in advance when equipment maintenance should be done, our customers will embrace it, says Gian Paolo De Salvo, Siemens Digital industry manager for manufacturing operations management in process industry.

“As AI technology continues to advance and demonstrate its value, it is likely that it will become more widely accepted and expected as just another tool integrated into the overall system,” says Preet Murthy, Peak Analytics’ product marketing. “Companies that succeed the most with the adoption of AI are the ones that recognize that it is a mindset shift and build up the capabilities to the support the change. Packaging systems planning and operations will always require some human involvement, but increasingly successful adopters have realized that AI and machine learning can handle the bulk of the standardized processes—especially highly repeatable low cognition tasks,” says Murthy.

Where to Begin with AI

“It depends on the goals and objectives of the manufacturer along with where they are in their digital transformation journey,” says GE’s van Heerden. “With our clients we co-develop what we call outcomes maps to determine what are the specific measures and metrics our clients are aiming to deliver. Then, based on the outcomes map we outline the priorities with regard to their digital transformation journey and the timing of deployment and implementation. At the end of the day, our priorities are the clients’ priorities, and we put their needs first. We won’t implement or recommend solutions unless the client is ready and has laid sufficient groundwork in their digital transformation to even be able to leverage the full benefits that AI software can offer.”

AI software such as GE Digital’s Proficy CSense can support corporate operations goals including energy and OEE optimization, helping manufacturers make products with the highest quality and least waste.Graphic courtesy of GE Digital

 

“Any application of AI technologies at Harpak-ULMA will be tied directly to our solutions and market positioning, which is focused on optimizing packaging processes,” says Ouellet. “Simply put, clients are looking for solutions to problems—not evaluating whether or not to apply AI in their operations. AI is simply one more technology tool—and its application or prioritization will be based on its effectiveness, reliability and affordability in that specific context.”

As AI becomes increasingly reliable, affordable and “componentized,” the breadth of applications will increase—for example, targeting operational performance issues regarding packaging efficiency/productivity, waste reduction, quality control and perhaps even helping to optimize supply chain management related to spare parts and consumables, optimizing plant utilities usage, etc., adds Ouellet.

“We prioritize our AI applications based on our clients’ needs and priorities,” says Peak Technologies’ Dwinell. “We work closely with our clients to understand their pain points and identify areas where AI can provide the most significant impact. Our approach is to start with the areas where the greatest impact can be made in terms of efficiency, waste reduction, quality control and supply chain optimization. We also consider the potential ROI and feasibility of implementation when deciding where to implement AI first. Ultimately, our goal is to provide solutions that align with our clients’ priorities and deliver tangible benefits to their business.”

What is the Future of AI in Packaging Applications?

The future of AI in packaging is about OEE optimization; that is, making quality products to order, on-time and without waste, says van Heerden. When executing the manufacturing process, it is critical to package products as efficiently as possible while avoiding unplanned downtime. Therefore, it’s necessary to fine-tune machine settings to produce and package the right amount of product at the right quality while optimizing energy consumption. But overall optimization doesn’t stop there. Planning and scheduling will also become more critical, given the continual volatility in supply chains. Thus, AI will become increasingly vital in the future as it empowers these allied applications.

“We expect that AI technologies will increasingly become core components of software solutions for the manufacturing process,” says Siemens’ Epple. Initially, the focus is on providing insights through anomaly detection, forecasting or classification. In the future, we expect an increase of prescriptive applications, e.g., the roadmap for our solution Quality Prediction Hub that includes capabilities for the automatic calculation for optimal settings for production machines. This further increases the value that can be realized by adopting this solution.”

As data—and lots of it—are key to a successful AI implementation, Red Lion Controls builds products and solutions that can simplify the process of extracting sensor and process data from food and beverage applications, and channel it to the desired location, irrespective of whether it is AWS, Google, Azure, or a platform that uses generic MQTT.Illustration courtesy of Red Lion Controls

 

Red Lion Controls builds products and solutions that can simplify the process of extracting sensor and process data

AI can add tremendous value to a smart manufacturing solution for packaging, but there should always be an assessment if other approaches—physical simulation models or discrete-event simulation values—can tackle the problem at hand more effectively, says Epple. “Hence, we do not use machine learning technologies brute force, but always make a conscious decision about what tool will bring the best solution for our customers.”

Resources

“Artificial intelligence simplifies industrial control for operations staff who are not data analytics experts,” Engineering R&D, FE, 25 February 2021

“Artificial intelligence/machine learning reduces mistakes and waste, FE, 9 May 2022

“AI, IIOT help take the guesswork out of maintenance planning,” Engineering R&D, FE, 23 February 2018

“AI helps control greenhouse automation systems,” Engineering R&D, FE, 3 June 2022

“AI/ML-based real-time sensing techniques turn art of winemaking into exact science,” Engineering R&D, FE, 12 July 2022

“Getting Consistent Results from Fermentation,” FE, 10 September 2021

“Finding new AI-based methods to boost productivity requires digital data,” FE, 13 February 2020