In early 2024, ETQ surveyed 750 senior-level quality professionals across the U.S., U.K. and Germany who were asked about their views on quality in the workplace. The goal was to collect real-world feedback on the trends, issues and challenges that affect quality professionals in those markets. Responses provided valuable insights into the current state of quality management. They came from several industries, including heavy industry, electronics, medical devices, pharmaceuticals, food and beverage, chemicals, logistics, automotive and aerospace.
False Sense of Quality Success in Organizations
Despite other survey responses suggesting otherwise, half of respondents think their organization is doing a good job at managing quality, leveraging digital solutions, providing employee training and treating quality as a strategic initiative. But if organizations really are doing such a great job managing quality, why are there still so many product recalls?
Strategic Role of Frontline Workers in Quality Decisions
Increasingly, responsibility for quality management is shared between those in the office and those on the factory floor. This is because more frontline workers have access to connected electronic devices that facilitate real-time updates and ongoing communication. An impressive 85% of survey respondents said either all or most of their plant floor workers have access to electronic devices, tools and technologies that help them participate in quality management decision-making.
Imminent Use of AI in Quality Processes
When it comes to using AI in quality processes, it’s not a question of if but when. A staggering 99% of survey respondents said they are either currently using AI or plan to use AI for key quality applications. 47% plan to use AI in the next two years.
There are many ways to use AI in quality management, and there are many benefits. From the opportunities of AI-powered connected worker technology to the challenges of AI integration, the survey responses dived deep into the topic of artificial intelligence in quality management.
Role of Connected Workers and AI in Quality Management
While frontline factory workers have always played a crucial role in implementing quality initiatives, they are rapidly becoming an integral part of the quality strategy as well. Frontline workers are more connected than ever, both to one another and to the hardware and software systems on the factory floor. This connectivity helps workers anticipate quality issues before they happen and provides opportunities to enhance efficiency across all stages of production.
Identifying and Predicting Quality Issues
Networks of IoT sensors, smart devices and wearable tech enable real-time monitoring of production processes, equipment performance and even environmental conditions. For example, a sensor on a machine could detect temperature fluctuations that indicate potential overheating and send a notification to a worker’s tablet in an instant. Over time, machine learning algorithms will pick up on quality trends, enabling the system to predict when maintenance is needed before any signs of wear and tear occur.
AI-Augmented Search and Task Optimization
In addition to predicting and preventing breakdowns, AI also plays a crucial role in optimizing tasks and processes. Modern systems with AI-augmented search capabilities improve upon traditional keyword searches by understanding the searcher’s intent and prioritizing results based on relevancy. Faster, more accurate searches save time and reduce the likelihood of errors.
AI can also optimize workflows by identifying redundancies and inefficiencies and suggesting process improvements, such as:
- Removing or combining steps of a workflow
- Reordering the sequence of tasks
- Adjusting resource allocation
Current and Future AI Usage in Manufacturing
Artificial intelligence is no longer a far-off, futuristic concept. It’s here, and most manufacturers are either already using it in some capacity or plan to use it soon.
99% of survey respondents are currently using AI or plan to use it eventually. 47% said they’re planning on using it within the next two years.
For many manufacturers, the past few years have been about discovering and validating the real-world use cases for AI technology in quality management. ETQ predicts the market is moving past the “shiny object” phase and into a phase of increased integration of AI in quality workflows.
Benefits of AI in Quality Processes
Manual monitoring and inspections are time-consuming and prone to inaccuracy, but AI enables real-time monitoring and automated defect detection and correction. This proactive approach means staying ahead of potential issues to ensure products consistently meet quality standards. Ultimately, AI-driven quality processes lead to fewer product recalls, improved customer satisfaction and measurable cost savings.
Benefits of AI in Manufacturing Quality
Improved product quality and consistency are just the tip of the iceberg when it comes to the benefits of AI in manufacturing. Modern manufacturing environments use AI to anticipate maintenance needs and enhance data processing and decision-making.
Real-Time Monitoring and Inspections
AI-driven systems enable real-time monitoring of production processes, ensuring immediate detection and correction of potential defects. Automated visual inspections use machine learning to identify product imperfections at high speeds, improving accuracy and reducing error rates. This approach enhances quality consistency, minimizes waste and reduces the likelihood of recalls by addressing issues as they arise.
Predictive Maintenance
AI enables manufacturers to prevent equipment failures by analyzing machine data and identifying early signs of wear. AI-driven predictive maintenance programs allow for timely interventions before a breakdown occurs, reducing the likelihood of production schedule disruptions and product quality issues. Predictive maintenance also extends equipment lifespan and cuts down on costs related to unplanned downtime and emergency repairs.
Data Processing and Decision Making
AI algorithms make it easier to process the vast amounts of data generated in manufacturing and translate raw information into actionable insights. Using that data, AI-powered systems can identify inefficiencies and optimize resources better than any human worker. AI can also automate repetitive tasks, reducing human error and increasing worker productivity.
By using AI in manufacturing quality efforts, companies can make data-driven decisions that enhance productivity, reduce waste and ultimately improve profitability.
Challenges of Integrating AI in Quality Operations
Despite the various benefits of using AI in quality management, organizations have valid concerns about integrating AI into their quality operations. Although it will eventually be common in the industry, the technology still feels relatively new, and businesses face a few key challenges when integrating it into their existing processes:
Initial and Ongoing Costs
The initial costs of implementing AI systems are high, and there are also costs associated with ongoing maintenance and training employees to use the new system, including transferring data to a new system. Any downtime during the transition represents an additional cost. In short, some companies are hesitant to invest and may be skeptical about the potential ROI.
Worker Resistance
In every industry, there is a certain amount of caution and fear around the idea of artificial intelligence replacing human workers, and manufacturing is no exception. Some workers hesitate to embrace AI systems and tools out of fear that the technology will eventually render them obsolete and leave them jobless.
Security and Compliance
AI technology presents some unique challenges when it comes to security and compliance. Because AI systems often rely on a significant amount of data, they can become targets for cyberattacks aimed at accessing sensitive information. And because AI technology is still relatively new, it’s vulnerable to novel threats. The regulatory landscape for AI is also new, and governance and regulations are subject to change frequently.
Strategic Approach to AI Integration
Addressing the above challenges and achieving successful AI integration requires a well-planned strategy with a structured approach.
Foundation for Operationalizing AI
A firm foundation is essential for safe and effective AI integration — without it, there’s a risk of misaligned goals and failure to achieve the desired improvements. To successfully operationalize AI, organizations need robust data infrastructure, clear strategic objectives and well-defined processes. By taking a systematic approach, manufacturers can move past the “shiny object” phase of AI technology adoption and focus on practical implementation.
Human Role in AI-Enhanced Workflows
Despite the fear of AI displacing human workers, it will more realistically play a supporting role. Successful AI integration means recognizing that AI works best by enhancing, rather than replacing, workers’ productivity and decision-making. Frontline workers’ judgment and creativity are still a huge asset on the factory floor, and AI can’t replicate those things. Additionally, human oversight ensures AI decisions are interpreted appropriately and automated recommendations are applied correctly.
Logical and Pragmatic Integration
AI integration should begin logically and should not be rushed. It’s always good to test AI applications on a small scale before a full rollout. Feedback from pilot projects ensures AI tools and applications can deliver measurable, predictable results. A phased approach reduces risk and allows quality professionals to refine implementation strategies based on real-world performance.
Embracing the Future of Quality Management
The market is seeing obvious opportunities for real-time monitoring, predictive maintenance and AI-powered data processing.
However, there are some valid hesitations and challenges to overcome. Some workers are reluctant to embrace AI for fear it could replace them, and AI technology is new enough that security and governance can be unpredictable. A strategic approach to AI integration is paramount.
Looking ahead, AI will play an increasingly important role in enhancing quality and driving efficiency for manufacturers worldwide. For more actionable survey insights, download the full report to learn how an enterprise quality management system can help manufacturers examine their quality processes to ensure proper frontline training and effective quality processes, connect workflows, enhance training and build a quality-driven culture.
