Decision-Support for the Factory Floor

Advanced technology supports human operators in manufacturing

Despite decades of technology advances, humans remain a critical component to many manufacturing processes.

For many small and medium sized enterprises the cost of automating production for short-run or customized products is uneconomical. However, from a quality assurance perspective, many are interested in technological advancements than can aid human operators in subjective decision-making, for consistent and reliable product quality.

Adding automated decision-support to manual manufacturing, production and assembly processes help speed assembly and inspection rates, improve quality from end to-end, and provide qualitative product evaluation and operational data from all key points to ensure processes are repeatable and traceable.

AI and Decision-Support Apps

Vodkow, a dairy distillery, and DICA, an electronics manufacturer, use AI-based decision-support tools to support their operators manufacturing products not well-suited to fully automated quality inspection processes.

Vodkow uses a suite of AI-based decision-support during their manual in-process manufacturing and final inspection steps. By eliminating subjective decision-making and human error, the distillery avoids production downtime, reduces waste, and ensures consistent brand appearance for its products.

“As a premium brand, there’s an important human element to our processes — from the distilling to packaging — but technology helps ensure high-quality products consistently go out the door,” said David Geros, Chief Operating Officer, Dairy Distillery. “Packaging errors translate into downtime, slower production, and higher costs. AI helps remove ambiguity and stress for our employees. As a QC tool, decision-support helps increase our confidence in both our manual and automated processes.”

Whereas, DICA uses AI and machine learning to help their operators detect errors commonly missed by automated optical inspection (AOI), such as component orientation, solder defects, through-hole issues, and labelling. The manufacturer also uses product tracking and reporting apps to gather data around its manual processes to help speed resolution when an in-field issue is detected.

AI supports human operators and manual processes in several ways:

  • It ensures consistent, reliable and traceable human decision-making for incoming, in-process, and outgoing inspection and assembly steps.
  • It trains new employees on assembly, key brand elements and product packaging variations to reduce errors, waste, and production delays.
  • It helps users gain data from manual manufacturing processes for real-time end-to-end operations insight.
  • It will close the gap on “data black holes” in manual manufacturing for analysis, tracking & reporting, and continuous improvement initiatives.

With AI decision-support tools, training and deployment can be simplified so any manufacturer can leverage the skills and expertise of their best inspectors across multiple production runs, facilities, or even with newly hired operators. As a result, manufacturers can start using more advanced technologies to help ensure higher quality, lower costs, and ultimately increased profitability.

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