Business Intelligence: PMMI Contextualizes the Place for Artificial Intelligence
The promises of AI are sparking lively debate and reshaping perspectives in 2024.
In the United States, a significant proportion of domestic machinery production still lags global adoption of cutting-edge technologies such as AI-driven manufacturing and robotics. Analysts expect key investments—including those backed by the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence—to spur demand for machinery across manufacturing sectors.
For packaging machine OEMs, in particular, AI is expected to have a net benefit when it comes to improving machine design and functionality, improving productivity and enhancing support and services.
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A recent whitepaper, “The AI Advantage in Equipment: Boosting Performance and Bridging Skills Gaps,” published by The Association for Packaging and Processing Technologies (PMMI), aligns the definition of artificial intelligence and its subsets (machine learning, deep learning, generative AI) with the National Artificial Intelligence Initiative and White House Executive Order on AI, as follows: “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.”
In a complementary PMMI podcast, “AI in Packaging: Driving Innovation and Overcoming Barriers,” George Blunt, a consulting analyst with Interact Analysis and an author of the whitepaper, commented on the findings and named five areas where AI is demonstrably affecting packaging: AI co-pilots (based on large language models), machine vision (the use of cameras and sensors), predictive maintenance (using generative AI to scale up advanced machine monitoring), digital twins (virtual representations that run simulations of the machines and the whole plant) and connected worker platforms (digital management systems for controlling metrics on parts, processes and staff performance).
The good news for those concerned about AI skills development, according to Blunt, is that packaging employees don’t need to be bogged down with the details of AI technology. Specialized knowledge comes into play when a packaging company wants to develop its own AI tool, such as a generative AI chatbot, without involving another company.
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Beneficial Aspects for Manufacturers That Choose to Implement AI
The PMMI whitepaper outlines the ongoing benefits and challenges associated with the use of AI tools and technologies. Benefits include:
- Better machine performance.
- Efficiency and productivity. AI frees up employees’ time by carrying out routine tasks such as data entry and coding.
- Filling skills gaps and mitigating labor issues.
Barriers associated with AI Deployments
The PMMI report also discusses challenges and barriers to successful deployment of AI solutions, such as:
- Concerns around cybersecurity.
- Inconsistencies with the quality of data and collection methods.
- Resistance to change, particularly among older workers.
- Fears about job replacement.
- Problems associated with data hallucination.
Download the whitepaper, “The AI Advantage in Equipment: Boosting Performance and Bridging Skills Gaps,” at PMMI’s website.
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Expand Your List of AI Resources
The following list depicts just a sampling of AI topics and real-world capabilities presented at PACK EXPO International 2024.
The Future of Labeling: AI-Powered Machine Diagnostics
This session, presented by Travis Younger, VP & general manager of P.E. Labellers North America, based in Cincinnati, Ohio, described a new AI-powered solution, TelescoPE, which offers augmented labeler performance metrics, customizable KPIs and proactive identification of inefficiencies, delivering business-specific recommendations on enhancing productivity tailored to all levels within the organization, from operators to engineers and management alike.
Effectively Deploy Generative AI in Packaging and Processing
Presented by Deepak Padgaonkar, an electrical engineer and a founding member and EVP Technology at V3iT Consulting, Inc., this session explored various AI-driven methodologies that enhance design, efficiency, customization and sustainability. Attendees learned to delineate among various AI strategies and tools for implementing them, including how Generative Adversarial Networks (GANs) facilitate design prototyping and synthetic data generation; how AI-driven 3D printing enables rapid prototyping and custom packaging solutions; why reinforcement learning optimizes processing parameters and enhances robotic automation, whereas augmented and virtual reality technologies offer immersive virtual prototyping; and employee training. Additionally, he discussed how data-driven decision-making processes optimize supply chains and analyze consumer behavior, thereby creating more appealing packaging designs.
AI-Enabled Vision: Inspection & Robotic Guidance Application
Traditional rules-based machine vision excels at inspecting highly repeatable products. But Harley Green’s presentation argued that with the help of AI, machine vision can “inspect in greater detail and tackle more complex problems, such as products with a high variance of naturally occurring organic variability.” The vice president of Strategic Business at Oxipital AI discussed how AI-enabled machine vision incorporated into the production process in the food industry makes it possible to grade cuts of meat to help manufacturers appropriately price their products or to check the quality of received produce to ensure manufacturers aren’t overpaying for low-quality products.
Packaging in 2025 and Beyond: How AI Can Accelerate Process and Insight to Your Packaging Program
Michael Schwabe, director of Market Intelligence, Surgere unpacked opportunities for the use and success of AI within packaging operations for warehouse, inventory and transportation applications. The session focused on the role of AI in business applications, including where to start with AI and what the impact of introducing this advanced technology within your company operations could mean.
Industrial AI: Practical Examples for CPG Manufacturing
Chris Barnes, a leader in Data & AI Consulting at Rockwell Automation, presented practical manufacturing applications to demonstrate how AI can address key business challenges. Use cases were culled from CPG manufacturers using AI to gain competitive differentiation.
AI & GenAI Application in Industrial and Packing Solutions
Juergen Weichenberger, VP, Artificial Intelligence & Strategy at Schneider Electric, presented a solution for PLC Code Generation, which allows operators to create code for their PLC without being an expert coder.
About the Author
Rehana Begg
Editor-in-Chief, Machine Design
As Machine Design’s content lead, Rehana Begg is tasked with elevating the voice of the design and multi-disciplinary engineer in the face of digital transformation and engineering innovation. Begg has more than 24 years of editorial experience and has spent the past decade in the trenches of industrial manufacturing, focusing on new technologies, manufacturing innovation and business. Her B2B career has taken her from corporate boardrooms to plant floors and underground mining stopes, covering everything from automation & IIoT, robotics, mechanical design and additive manufacturing to plant operations, maintenance, reliability and continuous improvement. Begg holds an MBA, a Master of Journalism degree, and a BA (Hons.) in Political Science. She is committed to lifelong learning and feeds her passion for innovation in publishing, transparent science and clear communication by attending relevant conferences and seminars/workshops.
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