Rapid ROI: Replacing Manual Inspection with UnitX AI Inspection

Intro

Today’s manufacturers face extreme competition, and must deliver faster, cheaper, and higher quality products to maintain and grow market share. To do so, manufacturers are increasingly adopting automated inspection processes that are more effective and efficient than manual inspection.

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Manual Inspection

Some manufacturers still rely on manual inspection processes– in fact, in 2022, US manufacturers employed over 350,000 inspectors. Manufacturers might choose manual inspection for the following reasons:

Complexity– Human inspectors can handle a high degree of variability in products whereas rules-based vision systems may require significant programing
Low volume– Human inspectors can be more adaptable for early prototypes and new products just ramping production
Lower upfront costs– Human inspectors require less upfront investment than automated solutions, although their costs add up and exceed those of automated solutions over time

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However, manual inspection is often more laborious, erroneous, and costly than manufacturers think.

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Automated Inspection

Other manufacturers are automating inspection with machine vision systems, including both rule-based and AI-based.

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Rule-based systems require explicit manual programming of rules and are effective for well-defined and consistent inspection tasks. AI-based systems, on the other hand, learn from training data to develop algorithms that identify subtle and complex defects. These systems are well-suited for complex inspection tasks where variability is high.

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Automated inspection, especially AI-based, offers many benefits over manual inspection:

	Manual Inspection	Rule-Based Vision	AI Inspection
Accuracy	Accuracy limited by fatigue, attention, subjectivity, and environmental constraints, leading to quality escapes and scraps	Highly accurate for well-defined and consistent inspection tasks	Highly accurate for variable and complex defects; able to compare against tight tolerances
Repeatability	Inconsistent decisions over time and between inspectors, leading to low repeatability and inconsistent quality	Centrally developed quality standards enforced across all inspection systems with high repeatability
Speed	Slow detection, limited by biological constraints	Fast once configured, but slow to set up and adapt to new requirements	Fast to set up, adapt, and make decisions on the edge and pass those decisions to PLCs. Inspects more parts faster
Insightfulness	No records of what inspectors saw; tribal knowledge leaves if employees leave; slow root cause analysis	Centrally stored inspection results, able to be analyzed for root cause analysis, continuous improvement, and insights to optimize OEE over time
Scalability	Requires effort to hire, train, and retrain	Easily scaled up to meet production demands. Runs 24/7 with no fatigue

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Manufacturers who automate inspection with AI-based technologies ultimately improve their product quality, efficiency, and scalability.

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Making the Transition

Manufacturers who want to transition from manual to automated inspection need to first get executive buy-in and justify the investment. A critical part of this business case is the calculated ROI of the AI inspection technology, which is defined as:

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ROI = (Net Benefit / Cost of Investment)

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Net benefit = Cost savings, Productivity gains

Cost of Investment = Cost of technology, cost of integration, cost of operations

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An AI inspection’s net benefit can be calculated by considering three factors: labor, OEE, and defects.

Labor– Since AI inspection replaces manual inspectors, its net benefit includes the recouped cost of manual inspector wages
OEE– Manual inspection causes yield loss by overkilling parts, failing to support efficient root cause analysis, and ultimately reducing production throughput. AI inspection’s net benefit includes its yield improvements over manual inspection
Defects– Manual inspection is inaccurate and leads to quality escapes. AI inspection’s net benefit includes the reduction in costs of escapes, including customer complaints, recalls, and supplier rating downgrades

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Let’s go through a real UnitX customer example to show exactly how the ROI of UnitX’s automated inspection is calculated.

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In this example, UnitX is helping an automotive supplier inspect aluminum parts for structural defects like blisters. Jointly with the customer, we determined that we provide the company a net benefit of $1,354,000/year.

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Labor

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OEE

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Defects

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Getting Started

UnitX helps manufacturers automate inspection with AI for better quality and yield. If you’re interested in learning more, our team will walk you through UnitX technology, how it works, and how it differs from other solutions. We’ll then run a proof of concept on your own parts and defects to demonstrate our solution’s efficacy and accuracy. Finally, we help you build your own tailored business case using this calculator so you understand UnitX’s ROI for your specific applications. Reach out to learn more here.

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