In electronics production, Original Equipment Manufacturers (OEMs) are vital for the quality and dependability of electronic parts. One of the key aspects of maintaining high standards is the effective communication of defect data back to engineering teams, particularly those involved in Printed Circuit Board (PCB) design and manufacturing. This article delves into the process steps, conditions for success, and key insights into how OEMs can close the loop and foster a two-way communication channel with engineering teams.
Defect data feedback is essential for continuous improvement in the manufacturing process. By analyzing defect data, engineering teams can identify patterns, root causes, and areas for improvement. This feedback loop is crucial for several reasons:
Enhancing Product Quality: Regular feedback helps find and fix design problems, resulting in better products. Following quality control closely can control costs, cut down on waste, and prevent the harm to reputation and legal issues that come with faulty products.
Reducing Costs: Early detection and correction of defects can significantly reduce the costs associated with rework, scrap, and warranty claims. Quality control programs can impact cost and on-time delivery significantly. Without proper quality control, factories will produce higher scrap levels and incur increased labor costs from extra handling and rework.
Accelerating Time-to-Market: Efficient feedback mechanisms can streamline the design and manufacturing process, reducing the time required to bring new products to market. Quality defect metrics are key for companies to measure how well they’re doing in quality. Good metrics let businesses see how often and how badly products fail, helping them decide where to focus on getting better and how to use their resources wisely.
The use of deep learning in defect detection has been gaining traction. This technology can classify product defects into categories, and its application in ultrasonic testing, filtering, machine vision, and other technologies used for defect detection has shown promising results with accuracy rates being seen as high as 99.4%. It should be noted there is a range of success in this emerging field (from as low as 88% accuracy). This is an example of how advanced technologies are being leveraged to enhance the effectiveness of defect data feedback in manufacturing.
Inspection and Testing: Defects are identified through rigorous inspection and testing processes during and after manufacturing. This can involve the use of digital calipers, automated inspection systems, and other statistical process control (SPC) tools. For instance, in tool monitoring, OEMs first check a clean wafer for any initial defects. Then, they put it through a certain machine and check it again. Any new defects found were caused by that machine.
Data Logging: Every product issue is recorded carefully, noting what the problem is, where it is, and how serious it is. This information might come from customer feedback, quality checks, or records of the manufacturing process.
Root Cause Analysis: Engineering teams analyze the defect data to determine the root causes of the issues. This involves using tools such as histograms, control charts, and Pareto charts to reveal trends for analysis.
Trend Analysis: Looking at patterns in product issues helps find ongoing problems. This lets quality leaders make changes to get better.
Reporting: Detailed reports are generated and shared with the relevant engineering teams. These reports can provide visibility into defects discovered across products and teams.
Meetings and Reviews: Regular meetings and reviews are conducted to discuss the defect data and potential solutions. These discussions can help in risk analysis and prioritization of defects.
Design Modifications: Based on the feedback, design modifications are made to address the identified defects. This involves making the development team aware of the issue identified, which needs to be analyzed and fixed.
Process Improvements: Manufacturing processes are adjusted to prevent the recurrence of defects. This can involve ensuring strict adherence to standardized processes.
Testing of Modifications: The modified designs and processes are tested to ensure that the defects have been effectively addressed. New designs and methods are tested to make sure problems are fixed. This testing can be done digitally, which helps find issues sooner in the production line.
Continuous Monitoring: Ongoing monitoring is conducted to ensure that the corrective actions are effective. There’s always a check to see if the fixes are working. This includes tracking the quality of parts to review how things are made and making sure that the instructions match the goal of making good parts with less waste.
Product Lifecycle Management (PLM) Systems: PLM systems facilitate the management of product data and processes, ensuring that defect data is integrated into the overall product lifecycle. These systems are part of a growing shift towards fully digital, agile organizations in the industrial sector.
Data Analytics Tools: Advanced analytics tools help in the analysis of defect data, enabling root cause and trend analysis. These tools are part of the digitally enabled factory of today, where manufacturers can choose from hundreds of potential solutions and tech applications to improve their ways of working.
Collaboration Platforms: Tools like Slack, Microsoft Teams, and specialized engineering collaboration platforms facilitate seamless communication between OEMs and engineering teams. These platforms are part of the modern technological landscape that connects product design, marketing, and production processes. Since 2020, there has been a more than doubling of the spend on these platforms, demonstrating their usefulness and adoption in mainstream business (figure 1).
An interesting point to note is the rise of Smart Manufacturing, which infuses innovations like big data, IIoT devices, connected worker platforms, augmented and virtual reality (AR/VR), and robotics into the end-to-end production cycle. This highlights the importance of leveraging advanced technological tools and platforms in the manufacturing process.
Figure 1: Spend on Collaboration Platforms (Slack, MS Teams, Zoom) 2020-2024
For the defect data feedback process to be effective, certain conditions need to be in place:
Easy Communication: It is important for manufacturers and engineers to talk clearly and directly with each other. They should use effective tools, encourage everyone to speak openly, listen well, and use pictures and other help to share information about making products.
Set Roles and Duties: Making sure everyone knows their job and what they’re responsible for helps keep track of product issues and handle them well. These covers dealing with suppliers and customers, and how different parts of the company and workers talk to each other and work together.
Regular Training and Development: Continuous training and development programs help in keeping the teams updated with the latest tools and techniques for defect analysis and communication. This can lead to very real benefits, such as fewer workplace accidents, better production efficiency, higher product quality, and greater customer as well as employee satisfaction.
Cultural Alignment: Fostering a culture of continuous improvement and open communication is crucial for the success of the feedback loop. This involves setting clear objectives, using real-time, two-way communication, considering the employee’s perspective, and using push notifications sparingly.
Despite their key role, many manufacturing employees are disengaged. According to a recent study published by Gallup, just 25% of manufacturing employees are engaged, 8 percentage points lower than the national average for U.S. employees. This highlights the importance of effective communication in the manufacturing industry.
Implementing a robust defect data feedback loop offers several benefits:
Better Product Quality: Getting constant feedback helps find and fix problems, leading to better products. This effort for top quality requires a planned method that encourages continuous improvement and new ideas.
Savings: Catching and fixing issues early cuts down on extra work, wasted materials, and customer refunds. Feedback points out what’s not working well, helping companies make their operations smoother, cut out unnecessary steps, and save money. This means they can do more with less, setting the stage for growth and new developments.
Happier Customers: High-quality products make customers happy and keep them coming back. Feedback gives important information about how products and services are doing, and what employees and customers think. Companies can use this feedback to focus on what needs to get better.
Quicker to Market: Good feedback systems make designing and making products faster, so new products can be sold sooner. Feedback turns personal views into hard facts, letting companies make smart choices based on real information, not just guesses.
Edge Over Competitors: Businesses that handle feedback about product issues well can stand out by always offering top-notch products. Using feedback well gives companies lots of advantages, making them more efficient and ready to respond to changes.
Feedback loops are essential for turning customer opinions into useful actions. This shows why strong feedback is so important in making things.
We have underscored the pivotal role of OEMs in maintaining the quality and reliability of electronic components, particularly in the realm of PCB design and manufacturing. The effective communication of defect data back to engineering teams is a key aspect of this process.
The importance of defect data feedback, with its potential to enhance product quality, reduce costs, and accelerate time-to-market, is key. The role of advanced technologies, such as deep learning, in enhancing the effectiveness of defect data feedback, is growing in importance.
The process of bringing defect data back to engineering teams involves critical steps, including data collection, analysis, communication, implementation of corrective actions, and verification and validation. The use of advanced technological tools and platforms, such as MES, PLM systems, data analytics tools, and collaboration platforms, is crucial for effective communication of defect data.
Conditions for effective communication have been outlined, including clear communication channels, defined roles and responsibilities, regular training and development, and cultural alignment. We concluded by highlighting the benefits of a robust feedback loop, including improved product quality, cost savings, enhanced customer satisfaction, faster time-to-market, and competitive advantage.
The importance of closing the loop in the electronics manufacturing industry, fostering a two-way communication channel between OEMs and engineering teams, and leveraging advanced technologies to ensure the quality and reliability of electronic components, should be clear. This process, when effectively managed, can lead to significant benefits for organizations, transforming them into lean, responsive machines capable of delivering high-quality products consistently.
Simon is a supply chain executive with over 20 years of operational experience. He has worked in Europe and Asia Pacific, and is currently based in Australia. His experiences range from factory line leadership, supply chain systems and technology, commercial “last mile” supply chain and logistics, transformation and strategy for supply chains, and building capabilities in organisations. He is currently a supply chain director for a global manufacturing facility. Simon has written supply chain articles across the continuum of his experiences, and has a passion for how talent is developed, how strategy is turned into action, and how resilience is built into supply chains across the world.
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