Contributed by Center of Excellence, NextGen Invent Corporation
Introduction
It’s 2033 and PTIS and NGI have a long-standing strategic partnership and are the recognized “GoTo” source for developing and implementing strategic and tactical packaging value chain use cases for Generative AI. We’ve infused millions of dollars in new revenue throughout the packaging sector by deploying ongoing Generative AI applications for sustainability and innovation. Covering every aspect of the value chain, our efforts include pioneering undergraduate and graduate Packaging AI majors across Universities worldwide.
“(A decade ago,) approximately 75% of our rubbish generated by packaging, a few simple steps – buying loose fruit and veg, choosing products with recyclable packaging, and avoiding individually wrapped portions – can have a big impact.” Sheherazade Goldsmith, British Environmentalist and Columnist.
In the contemporary retail landscape, ubiquitous packaging underscores the imperative for judicious material use to minimize waste. Artificial Intelligence (AI) tools revolutionized waste management through smart bins, featuring integrated cameras and AI technology for autonomous waste categorization, fostering eco-conscious recycling. Aligned with the challenge of reducing 75% of packaging-generated waste, AI in waste sorting exhibits a notable 30% increase in recycling efficiency.
Packaging AI Use Cases
1. Sourcing
• Material Selection: Material Selection in packaging AI involves predicting long-term chemical composition. This aids in creating eco-friendly materials, ensuring they can be naturally disposed of over time.
• Material Composition Prediction: Even more, AI plays a crucial role in optimizing packaging processes by accurately predicting the size and shape of packages required for items with varying sizes and weights. Unlike manual methods, where determining the precise composition of packaging materials for factors like strength, corrosion resistance, and water durability is challenging, AI simplifies this task. Machine Learning models, specifically Regressors such as Linear Regressor, RandomForest Regressor, and Decision Tree Regressor, can be employed to identify the optimal combination of materials. One notable example is Amazon’s utilization of an AI tool called PackOpt, which resulted in a substantial 60,000-ton annual reduction in cardboard usage.
2. Manufacture
• Designing Packages: Artificial Intelligence techniques, such as Generative Adversarial Networks (GANs), excel at assimilating information from existing designs and generating novel design concepts in the form of artistic images. Leveraging AI-generated ideas can significantly enhance the visual appeal of packaging, catering to increased consumer demand as discussed in this previous blog.
3. Distribution
• Distribution of Packages: AI models, including those addressing complex problems like the Traveling Salesman Problem, can approximate near-optimal paths, demonstrating their efficacy in optimizing travel routes and minimizing associated costs, a task that is challenging and time-consuming when done manually.
4. Use
• Reducing Packaging Errors and Faults: Utilizing Convolutional Neural Networks (CNN) within AI enables the automated detection of packaging defects such as cracks, dents, tears, or incorrect labeling. After training on images of defective packages, CNN predicts a similar label for a new test image with a certain accuracy which is generally more than 95%. This avoids manual inspection of the packaging process and thus helps automate the process of finding the faulty packets.
• Forecasting Items Demand: The application of Recursive Neural Networks (RNN) in processing time series data allows for self-training to predict future values at different intervals so businesses can optimize inventory management, mitigate the risk of excess product accumulation and minimize wastage in manufacturing. This AI method proves invaluable in forecasting the demand for diverse products in the short term and in upcoming months.
5. End of Life
• Recycle Reuse and Disposal of Packaging Waste: Various methods can be employed to effectively sort packaging waste, prioritizing recycling or reuse -– while calculating true Life Cycle Assessment impacts on the environment. A systematic approach involves conveying waste on a moving belt, where AI-based robotic devices utilize CNN to analyze and categorize the waste types (e.g., metal, plastic, glass, paper, biological waste).
How to Get Started with AI?
Organizations evolve through distinct AI strategy tiers, progressively embracing heightened sophistication and maturity in AI adoption. This trajectory involves advancing through stages, each characterized by increased intricacy, reflecting the evolving landscape of artificial intelligence integration.
• Exploratory Stage: In the initial phase, organizations explore AI’s benefits through rigorous testing and proof-of-concept studies. Prioritizing internal expertise is key, focusing on AI’s complexity and its cross-disciplinary applications. The goal is to establish a solid foundation for future AI advances. Concurrently, research and experimentation with AI tools, like TensorFlow and PyTorch, tailored for the packaging industry, are undertaken.
• Collaboration Stage: Forge collaborations with specialists in packaging and AI fields to address sustainability and innovation in packaging. Ensure a holistic strategy by engaging packaging experts, machine learning engineers, and data scientists.
• Experimental Stage: In this phase, organizations shift from AI exploration to implementation. To showcase value and collect data, they deploy AI in targeted use cases or industries. Emphasis is placed on crafting initial business cases and conducting comprehensive trials to identify practical AI applications. Primary objectives include validating AI practicality and leveraging its potential for tangible business outcomes.
• Operational Stage: With AI integrated into business operations, the focus shifts to realizing measurable benefits and enhancing operational efficiency. AI technologies are deployed in industrial settings for quantifiable gains which leads to scaling AI programs, driving substantial advancements across diverse business domains.
• Transformative Stage: In this advanced stage, businesses leverage AI as a disruptive force to reshape strategy and business models. Integrating AI into core operations sparks innovation, generates new revenue streams, and paves the way for transformative discoveries. The focus now centers on strategic AI use to achieve a sustainable competitive advantage.
Conclusion
The growing awareness of environmental preservation has fueled research into enhancing packaging materials through Artificial Intelligence applications. AI plays a crucial role in the real-time detection of packaging faults and the creation of appealing designs. It facilitates the selection of eco-friendly materials and accurately forecasts demand, preventing overproduction that can lead to excess stock and wasted packaging materials. To boost sustainable packaging practices, governments are incentivizing companies to utilize eco-friendly materials and implement recycling initiatives. Given that only a few companies prioritize sustainability in packaging, specialized training programs for stakeholders within production companies can further drive progress toward sustainable goals. This comprehensive approach seeks to harmonize technological advancements with environmental stewardship in the realm of packaging.