Solving Manufacturing Problems with AI

men working on three computer monitors on a plant floor room

Dan Riley | January 9, 2024

Industry 4.0 has become one of the most talked about topics in our industry and their potential to revolutionize the manufacturing landscape with smarter, self-optimized facilities. To define it, it is the integration of cutting-edge technologies such as artificial intelligence (AI), the Internet of Things (IoT), Big Data analytics and advanced robotics. While there is no doubt these technologies have the potential to transform industry, there’s valid concerns about unrealistic expectations and the challenges it brings. Long-term success only comes with the balance of the good, bad, and ugly of Industry 4.0.

Creating a road map

Before diving into the world of AI or Big Data, you must first figure out your plant's readiness. Using the Plant Maturity Model, a framework to guide implementing such solutions, you may find gaps, areas for improvement, and a roadmap to your end goal. It’s a systematic approach to slowly take you through digital transformation without skipping steps that may have consequences.

Evaluating AI’s suitability

AI has proven itself in the manufacturing world, but it may it is not a solution to every problem. Start with evaluating your need and capabilities in these 6 areas: 

  1. Data availability AI requires large amounts of quality data. Having this at your plant is a good start.
  2. Complexity If you have a complex, intricate problem, AI excels in these areas and may be a suitable option.
  3. Repetitive tasks AI can efficiently automate repetitive tasks such as inspections, quality control or data analysis. If your problem involves time-consuming, repetitive tasks that can be automated, AI might be beneficial.
  4. Predictive analytics Using historical data, AI can predict equipment failure, optimize processes, or forecast demand.
  5. Adaptability AI models are able to adapt to its environment, making it a perfect candidate to ever-changing conditions.
  6. Scalability AI’s versatility in size allows it to handle large amounts of data or be used in multiple lines or facilities.

Once you’ve determined AI is a good fit for you and your plant, there’s also a few steps to take for the best success:

  • Define clear objectives: For any investment, beginning with clearly defined objectives is crucial. Identify your problems and set SMART goals to help you choose the right AI technology.
  • Data quality and availability: As stated before, AI relies on large amounts of high-quality, representative data. Having proper data management practices should be in place before implementing AI.
  • Integration with existing systems: Look at your current technology such as hardware, software, and communication protocols. Seamlessly integrating with these systems can minimize disruptions.
  • Skills and expertise: When implementing AI, you’ll want a team with expertise in data science, AI, manufacturing processes, and OT and IT infrastructure. Look for any gaps you need filled through hiring, training, or partnerships.
  • Partners: When partnering with others to integrate an AI solution, look for complementary skills, expertise in the above areas, and a proven record of success.
  • Change management: Be prepared for significant changes in the way manufacturing processes operate. Communicate the benefits of AI and provide trainings as part of the implementation phase. This can ensure a smooth adoption and minimize resistance.

This article was originally published on Automation World.