Maintaining efficiency and accuracy in quality control is often a challenge for manufacturers. Automated inspection tools have emerged as an optimal solution for their speed and precision, but as this technology grows, businesses should take care to avoid over-applying it. As helpful as automation can be, human oversight is still necessary.
The Automation Advantage
Automated quality control processes offer several noteworthy benefits. Improved lead times are among the most prominent, as machine vision can perform inspections with almost 100% efficiency, meaning end-of-line quality checks produce no bottlenecks.
Artificial intelligence (AI) and robotics also have the advantage of repeatability. Even experienced professionals can wane in their accuracy over time from tiredness, distraction or simple human error. By contrast, automation applies the exact same scrutiny in every pass, so accuracies remain consistent across the board.
Such speed and precision translate directly into financial savings. Some manufacturers have seen labor costs drop by 80% as a result of streamlining quality control workflows. Less time lost to inspection and fewer errors result in greater relative operating margins, eventually compensating for the initial technology investment.
Why Humans Are Still Necessary in Quality Control
Despite the clear benefits of robotics and AI, these innovations are not perfect replacements for human expertise. The key advantage humans have over automation is adaptability.
As consistent as automated solutions are, this consistency only applies when all other factors are repeatable. Differences in lighting and object orientation may produce false positives or negatives by altering non-monitored conditions in machine vision data. Humans, however, are naturally adaptable and can easily account for these discrepancies.
Data reliability and the threat of technical malfunctions also pose an issue. Businesses lose an average of $12.9 million annually to poor-quality data, as decisions based on inaccurate or misleading information are unreliable. Inefficient training data, inconsistent real-world conditions and cyberattacks can all impact data quality, leading to errors that may go unnoticed. Similarly, AI hallucinations or robotic glitches can produce sudden, unexpected mistakes.
How to Balance Automation and Human Oversight
Given the uneven benefits and downsides, factories should emphasize human-robot collaboration instead of using one over the other. Doing so effectively is possible through these steps.
Identify Which Tasks Suit Which Approach
The first measure in balancing automation and human expertise is to recognize where each is the ideal option. Employees and AI have complementary benefits, so optimal workflows distribute tasks according to which approach best suits the work in question.
Mechanical processes are typically better for high-volume production, as they’re far faster and more consistent than manual alternatives. Human quality control inspectors, by contrast, are better at adjusting to varying conditions and performing inspections where less hard data is available.
Manufacturers can account for these differences in multiple ways. Automating quality checks for high-volume, low-value parts while letting employees manage more delicate but less-produced items is one option. Alternatively, companies can use AI for initial inspections while double-checking flagged products with human experts.
Place Humans Before and After Automated Workflows
Humans and automated equipment will likely end up collaborating on the same workline. In these cases, one of the best ways forward is to place employees before and after automatic processes to bring needed flexibility into largely robotic workflows.
Workers can ensure products on an assembly line are all in the same orientation before they reach machine vision systems. That way, they provide the consistency necessary for automated inspections to achieve their full accuracy potential. Similarly, human experts can verify automatic results to catch any missed errors.
This mixed approach minimizes the risk of defects while balancing the costs of both labor and technology. That’s important because quality-related expenses should account for 10% to 15% of revenue but they can reach as much as 40% at some organizations. These hybrid methods make it easier to land in that optimal range.
Adapt Over Time
Finally, manufacturers must measure relevant key performance indicators to judge the efficacy of their human-robot collaboration over time. Striking the ideal balance between human oversight and automation is challenging. Consequently, businesses won’t likely get it right on the first try.
Monitoring quality control workflows makes it easier to see how and where operations can improve. Experimenting with different manual and automated workflows can reveal which option produces the best results. With these insights, manufacturers can ensure consistent improvements in the long term.
Humans and Robots Must Collaborate in Quality Control
The perfect quality control pipeline is fast, reliable and adaptable. Factories can only consistently achieve all three when they combine the advantages of experienced employees and AI. Learning where to use each is the first step in optimizing these vital workflows.
