An opportunity for small manufacturers

by Janice Allen
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Darcy is the CEO of Buttonmaker Inc., using software and networks to rebuild American manufacturing for the 21st century.

As CEO of a small manufacturing company, I spent years competing with larger, better capitalized manufacturers on the other side of the world. Right now, we’re at a sudden turning point where new AI technologies are likely to make being small and nimble a significant advantage, giving savvy smaller manufacturers opportunities to get ahead of bigger competitors.

In recent decades, manufacturing benefits have been achieved by embracing three key concepts in lean manufacturing methodology:

• Single-Minute Exchange of Die (SMED), aimed at reducing the costs associated with moving from one process to another.

• Kaizenthat focuses on continuously identifying ways to improve processes.

• Root cause analysis, analyzing the causal chain to find out where problems arise and address them at source, rather than just treating symptoms.

All three of these concepts can benefit significantly from the adoption of new AI technologies that will be accessible even to small manufacturers. The initial cost of the expertise required to deploy automation and digital manufacturing technologies is about to drop, enabling digital SMED through robotics, automated kaizen, and analytics-driven root cause analysis.

Robots like SMED

One of the key opportunities to improve production efficiency lies in the use of general-purpose robots rather than purpose-built machines for assembly automation. General purpose industrial robots with flexible parts supply systems can realistically be used to create assembly lines that, rather than a rebuild on the factory floor, can switch directly via software from one type of item assembly to an entirely different part or process.

Until now, most small and medium manufacturing environments have faced robotics integration costs that are often a factor three to five times more expensive than the robots themselves. Now, however, anyone with a ChatGPT+ subscription has access to the ability to generate the code that powers these robots. As technology advances, using GitHub Copilot, Google’s Bard, or the regularly evolving stream of interfaces available from OpenAI and Microsoft is likely to quickly improve the effectiveness of this approach.

Analytics for Kaizen

In addition, AI-controlled robots can practice the core principles of kaizen, learn from their own experiences and adaptation to their environment, which can further reduce integration and programming costs. Rather than requiring extensive manual programming for each specific task, these robots can use AI algorithms to analyze their own performance data, identify patterns and optimize their actions accordingly.

As a result, these self-learning robots can become more efficient and effective over time, reducing the need for costly manual adjustments and programming updates. This should also enable manufacturers to be more flexible in their manufacturing processes, as AI-powered robots will be able to more easily adapt to new tasks or changes in the manufacturing environment.

Analysis for root cause analysis

The application of AI-managed analytics can also be used to identify where problems or opportunities for process improvement exist, and more efficiently address maintenance and repair needs. Software that continuously monitors all data in the business, from sales data and production processes to repair information and customer support, should be able to automate improvements in every part of the business and trace customer satisfaction data and reliability issues all the way back to the improvements needed at the point of manufacture to solve a problem at source.

First steps

There are several steps companies can take now to get to grips with what’s to come and what’s possible.

1. Identify a curious champion.

Find someone in your organization who understands your processes and equipment and is curious about the possibilities of both robotics and AI. Encourage them to experiment with what’s possible through the publicly available interfaces offered by OpenAI, Google, Microsoft, and other providers, and to participate in the discussions happening online on places like Reddit and Twitter, where other researchers talk about what they are doing and what kind of results they are getting. These discussions often follow the latest developments for just a few hours, and people are eager to share.

2. Experiment and play.

The cost to access most new AI models currently ranges from free to very cheap. Sign up and experiment with the available tools from all the major providers, including Google, OpenAI, Microsoft, Amazon, Facebook and anyone who touches interesting technology in this field. If you already have a license for a tool that integrates with some models, such as Zapier or GitHub, use the integrations to see what you can do. Test them for your purposes. Ask them questions that would otherwise be time-consuming for you to determine the answers to, and check the quality of their answers. Space is advancing so fast right now that what’s possible next week is likely to be significantly different than what’s possible this week, so spending some time diving regularly is likely to yield new and surprising results.

3. Check your results and be careful with your data.

Current AI models have a known problem with what is colloquially known as “hallucinations“: If they don’t know the answer, they might make it up. It’s critical that you test the validity of whatever is generated by any of the models and don’t just take it for granted.

In addition, there are concerns about the models using user ownership data to link back to the models, and possibly then to anyone in the world. Make sure you and your staff think hard about not entering any of your data that is extremely sensitive or proprietary.

4. Feed your excitement.

Manufacturing has historically been slow to adopt new technologies and has not been a top priority for investors when considering opportunities for significant ROI. But what we’re seeing now, and what we’re likely to see in the years to come, has the potential to transform manufacturing on a scale not seen since the start of the industrial revolution. So don’t be afraid to embrace your excitement about these new opportunities!


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