Quality is often viewed as a direct driver of customer satisfaction and operational efficiency in manufacturing. Yet some organizations fail to recognize the hidden costs of quality issues, significantly impacting the bottom line.
How Autonomous AI Tackles Quality Control Costs
Ed Tiongson VP, Product Management | Palladyne AI
Quality is one of the most critical metrics in business, yet it is often the most undervalued. While many executives understand that poor quality has a cost, the true cost of quality is more significant than most companies think.
The cost of poor quality (COPQ) can account for 15-20%1 of total sales revenue for manufacturing companies and can damage their bottom line. As manufacturing becomes more competitive, businesses are under increasing pressure to reduce costs. While these cost-cutting measures might help improve profitability, they can hurt maintaining high-quality standards.
Measuring a company’s quality costs can also be challenging. Quality is often viewed as a direct driver of customer satisfaction and operational efficiency in manufacturing. Yet some organizations fail to recognize the hidden costs of quality issues, significantly impacting the bottom line.
The drive to increase quality to the ultimate level of perfection requires Quality Control (QC) managers to support an integrated approach to measuring and improving quality.
Rising Challenges
Manufacturers face rising costs in quality inspection — all while trying to sustain high-quality standards and meet customer demands. Some visible costs of poor quality are apparent, such as repairing, reworking, or addressing customers’ concerns. Then, there are hidden failures such as loss of sales, diminished reputations, waste, lost production time, and operation bottlenecks that can significantly impact overall business performance and directly affect the bottom line.
A myriad of costly inspection challenges that manufacturers may face:
- Labor-intensive and time-consuming processes: Complex components often require hundreds of inspection points for quality checks, which a worker on the manufacturing floor must manually and visually inspect and track.
- High precision requirements: Potential multiple variations of product models and varying Stock Keeping Units (SKUs) are often produced and tested daily, with inspections at every stage requiring precision and traceability. Since each SKU might have its own checkpoints and instruction set, it requires custom tools and highly skilled technicians, making operations time-consuming and difficult to automate.
- Scaling quality inspections: Increasing production volume and model variations can make thorough manual inspections harder and time-consuming, and scaling production can become problematic in the long term.
- Consistency: Human inspectors face fatigue and performance variability, affecting quality. Numerous stoppages across the entire production process might occur daily, contributing to higher downtime costs and more training required. Any delay or error could significantly slow down daily production yields.
These activities are difficult to automate effectively with traditional robotics techniques, necessitating extensive human quality checks. The tasks can be exhausting for workers, and issues quickly escalate in the event of errors or delays.
Automation Answer
Leveraging advanced technologies that streamline monitoring and real-time reporting is a smart way to balance cost and quality. Manufacturing QC teams can lean into automating inspection processes to identify inefficiencies and areas for improvement without sacrificing quality standards.
Implementing Palladyne IQ robotic AI software helps automate inspection and streamline production across many industries. Palladyne IQ can help implement more agile manufacturing with robots through AI software that enables robotic systems to adapt and adjust to different product models autonomously with minimal retraining, fostering the flexibility to meet changing manufacturing requirements.
At the core of the Palladyne AI software platform lies a groundbreaking approach called “closed-loop autonomy.” It uses artificial intelligence (AI) and machine learning (ML) technologies to empower industrial robots and collaborative robots (cobots) to observe, learn, reason, and act with unprecedented agility—much like humans—to adapt to changing situations and business needs.
Enabling robots to perceive variations or changes in the real-world environment and teaching them to adapt dynamically means tasks like inspections that have historically been complex or costly to automate can now be autonomously addressed through robotics.
Palladyne IQ software on a cobot, for example, can autonomously perform inspections at various points through a manufacturer’s assembly line. These AI-powered cobots can improve the quality of inspections, detecting defects to give assembly lines more flexibility and agility.
Palladyne IQ software can perform several inspection tasks on an assembly line:
- Identify products autonomously
- Conduct inspections specific to each model
- Capture images of assembly and compare them with spec photos to enable traceability
- Execute detailed testing sequences that are specific to each model
Turning Point
Palladyne IQ allows manufacturers to lower full-time employee costs related to final quality control (FQC) testing. With this system, inspections can be conducted around the clock, moving away from reliance on multiple manual testers to a fully automated quality inspection process. This autonomous solution not only frees up human resources but also enhances accuracy by minimizing human error. Moreover, it reduces downtime by ensuring greater consistency and fewer delays.
Palladyne IQ software enables robots to quickly scan and analyze products using advanced object detection and edge-based AI/ML technology. With easy low-code/no-code retraining, robots can learn new tasks with minimal downtime and rapidly adapt to changing SKUs. This flexibility allows for increased inspection volumes to meet evolving demands.
In addition, the Palladyne IQ platform can help manufacturers with key cost benefits:
- Time savings and improved ROI by increasing yields and reducing human error
- Accurate and consistent product quality, timely and efficient production
- Ability to better manage labor shortage and scheduling issues
The hidden costs of quality in manufacturing are complex, and addressing these costs is essential for manufacturers aiming to remain competitive and profitable. Manufacturers of all sizes should consider implementing automation at various stages of assembly to help reduce these hidden expenses.
By adopting proactive quality management strategies and utilizing automation-enhancing solutions, such as Palladyne IQ robotic AI software, manufacturers can quickly and efficiently identify and resolve quality control issues. This approach ensures that their products meet the highest standards and can help companies secure a long-term competitive advantage in the manufacturing sector.
1 American Society for Quality (ASQ) 2023. Measuring the cost of quality. ISSE.
The content & opinions in this article are the author’s and do not necessarily represent the views of RoboticsTomorrow
Palladyne AI Corp.
Welcome to Palladyne AI, where we unleash the power of robotics with our revolutionary AI software platform for the physical world. In a world where robots are progressing toward human-like adaptability, seamlessly navigating dynamic environments and conquering complex tasks with unparalleled efficiency, Palladyne AI stands as the beacon of innovation toward that future reality. Through our cutting-edge artificial intelligence (AI) software platform, we are redefining the boundaries of robotics. Our goal is simple: Help companies with autonomy in their robotics operations by addressing key challenges of traditional robotic deployments.
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