Murugan Govindasamy
Introduction
Artificial Intelligence (AI) is revolutionizing manufacturing, particularly in the realm of packaging and inspection processes. This thought leadership article explores the transformative impact of AI on manufacturing, the evolution of automation from Industry 1.0 to Industry 5.0, and how AI can be effectively integrated to enhance efficiency and quality in packaging lines. As AI continues to evolve, it is not merely about replacing traditional automation but about enhancing human intelligence and decision-making in complex inspection tasks.
AI and the Evolution of Manufacturing Automation
AI represents a leap forward from traditional automation in manufacturing. While early automation (Industry 1.0) was about mechanization and basic production efficiency, the advent of Industry 5.0 is characterized by the integration of intelligence—enabling machines to perform tasks that require human-like judgment and decision-making. This transition has given rise to sophisticated AI technologies such as machine learning and deep learning, which allow systems to learn from data, recognize patterns, and make informed decisions autonomously.
Applications of AI in Packaging and Inspection
AI has the potential to significantly improve the packaging and inspection processes in manufacturing. It can handle complex tasks that were previously challenging for traditional vision systems, such as character verification, label inspection, and package integrity checks. For instance, AI can detect subtle defects or variations that are difficult for humans to identify, thus reducing error rates and false rejects. This capability is especially crucial in packaging lines where precision is critical to product quality and safety.
For example, in the pharmaceutical industry, AI has been integrated into packaging lines to inspect tablets before they are sealed in bottles. Traditional vision systems struggled with capturing perfect images due to lighting and positioning issues. By adding AI to the vision system, a pharmaceutical company reduced the false reject rate to well below 1%. This not only improved product quality but also cut costs significantly.
The integration of AI into these systems extends beyond basic automation. It involves coupling AI with human feedback to refine processes and optimize decision-making. For instance, ergonomic studies are now being conducted using AI to calculate the most efficient picking methods, which improves both productivity and worker safety. This shows that AI is not just about replacing manual tasks; it’s about enhancing the overall human-machine collaboration.
Considerations for Implementing AI in Packaging Lines
Despite its benefits, implementing AI in existing packaging lines comes with challenges. A notable concern is the high failure rate of AI projects—Gartner’s study found that as high as 85% of AI projects fail. These failures are often due to a lack of thorough analysis to determine if AI is genuinely needed and will provide value. Organizations must conduct detailed assessments to ensure that AI integration will yield real benefits, such as improved accuracy and reduced manual errors, rather than simply adding to the complexity and cost.
Challenge:
Pharmaceutical Tablets and Capsules packaged in a pharma plant required visual inspection prior to being filled in bottles. 360-degree inspection of every single pill at a very high speed with allowed physical characteristic variations posed challenges. There were no conventional technologies available to achieve precision inspection without generating high false rejects.
Solution:
AI technology based Automated Vision Inspection was developed, tested and implemented intoexisting packaging equipment. The system worked extremely well and identified defects while not rejecting good products with allowed manufacturing variations that resemble closely with defects.
Results:
First AI based technology in the industry for this application has proved that it can mimic human intelligence in identifying and eliminating defects from good products, resulting in considerable yield improvement and also confirming no defects can pass by, assuring quality and patient safety.
By focusing on AI’s potential to enhance decision-making and align with existing systems like PLCs, the company achieved better quality control and reduced rework.
Effective implementation of AI also requires careful planning and resource allocation. This includes integrating AI with existing systems such as Programmable Logic Controllers (PLCs) and sensor data to yield actionable insights and optimization opportunities. By doing so, companies can not only improve operational efficiency but also enhance the quality of their products.
Advantages and Challenges of AI-Based Inspection
AI-based inspection offers several advantages over traditional methods. It reduces manual errors, decreases false rejects, and provides a higher level of precision in detecting issues. This is particularly valuable in industries like food and beverage, pharmaceuticals, and automotive, where product quality and safety are paramount. However, the cons include the costs associated with technology adoption and the need for new skills. Organizations must invest in training their workforce to operate and maintain AI systems effectively
Challenge:
A CPG company packaging multiple products had the challenge of controlling label inspection process in some of their primary packaging processes. Some of the lines were using a manual inspection process to address existing automated inspection technology limitations.
Solution:
An off the shelf, cost effective vision inspection system with AI tools was tested to enhance inspection. The initial trials proved that using AI software algorithms helped create recipes and eliminate inspection challenges.
Results:
Programming of this AI based inspection was extremely easier when compared to conventional complex AI vision systems. The implementation process was ongoing at the site and expected to complete. Successful usage of the system will prove the benefits of adopting AI even for simpler applications like this one.
The Future of AI in Manufacturing
Looking ahead, AI’s role in manufacturing will only expand. The focus will be on making AI more accessible and user-friendly, thus lowering the barriers to entry for smaller companies. Additionally, as AI technologies continue to mature, the challenges of cost, skills, and integration will be addressed, making AI a standard rather than a supplementary tool in manufacturing.
AI is not just about vision systems; it represents a broader shift in how manufacturing processes are managed. It’s about creating a synergy between human workers and machines—enabling smart factories where intelligent decision-making is augmented by data-driven insights. The key takeaway is that AI should be viewed as a tool to replace complex decision-making tasks rather than just simple automation.
Conclusion
The future of manufacturing lies in a harmonious integration of AI with human intelligence. AI technologies have the potential to transform the way packaging inspection is conducted, leading to greater accuracy, efficiency, and safety. However, the path to widespread adoption requires careful planning, investment in technology and skills, and a clear understanding of the challenges and opportunities that AI presents. By embracing AI, manufacturing companies can not only improve their processes but also drive innovation and deliver superior products to their customers.
About The Author:
Murugan Govindasamy
Chief AVI & Automation Technology Coach, FSO Institute
Former Senior Digital Technology Leader at Pfizer Pharmaceuticals where he led the global automated vision inspection (AVI) technology initiatives at Pfizer for 26 years interfacing successfully with 100+ internal & external manufacturing sites.
