Investing in Manufacturing Data Analytics is critical to any business’s growth. By incorporating these techniques into your production process, you can make better decisions about your product, reduce defects and improve warranty service. You can also increase production yield and throughput.
Predict failure and therefore cost
Performing a failure analysis using manufacturing data analytics can reveal the best times to perform maintenance and repair. This is useful in terms of cost savings. Performing maintenance during low-cost times can prevent delays in the production line and extend a machine’s life. Using machine data to perform predictive maintenance has been done before. However, the best time to perform maintenance depends on the specific machine and its current state of care. This is especially true in manufacturing settings, where production is interrupted by equipment failure. This can result in unhappy customers and lost revenue. In addition, the cost of unplanned downtime can be hundreds of thousands of dollars, not to mention the lost productivity.
A good failure analysis model will utilize historical and real-time features to improve prediction models. It will also use failure mitigation techniques to make the system safer and less prone to errors. An essential part of any failure analysis is determining the root cause of the failure. A sound system will detect failures before they happen and help a company avoid costly downtime or overtime.
Increase production yield and throughput
Using manufacturing data analytics, manufacturers can maximize production yield and throughput. This allows them to reduce energy use and increase the profitability of their final sales. Manufacturers have been under pressure to increase efficiencies. Rising raw material costs have been a significant factor. This has led many manufacturers to seek new ways to meet mandates and reduce overall costs. However, many attempts still need to achieve these goals. Data analytics for manufacturing provides accurate end-to-end visibility. Manufacturers can use data analytics for inventory management, quality control, and warehouse management. As a result, manufacturers can improve yield and throughput by reducing cycle time and re-work rates.
Manufacturing data analytics can also help companies improve their purchasing trends and customer feedback. These insights can be used to reduce defects and enhance the quality of the product. The analytics can also be used to create automatic business processes. This allows manufacturers to streamline complex production systems. Another way to improve yield and throughput is through predictive maintenance. Predictive maintenance examines equipment performance and can identify parts of the production process that fail frequently. This can reduce downtime and ensure the reliability of the equipment.
Minimize production defects and keep quality standards high
Keeping manufacturing quality standards high can significantly impact your company’s bottom line. If your customers find that you deliver quality products, they’ll be more likely to continue to do business with you. They’ll also be more likely to recommend your products to other customers. Quality defects can cost your company money and affect the customer experience. They can also hurt your brand reputation. And if you fail to address them, your business could experience significant setbacks, including product recalls and lawsuits. A manufacturing data analytics strategy will help you reduce the number of defective products and equipment. It will also help you increase your production yield. You can use IoT sensors, machine learning models, and other tools to analyze and improve your products and processes. Manufacturing data analytics can also help you reduce the risk of downtime. For example, you can use predictive maintenance to identify emerging issues before they cause equipment to fail. This will help you schedule maintenance tasks based on your measured failure rates. This will prevent random stop times, improve operator knowledge of equipment, and reduce the number of failure-prone parts.
Improve warranty service
Using manufacturing data analytics to improve warranty service can reduce costs, improve product performance, and increase customer satisfaction. This approach also enables companies to understand better and address reliability issues that may arise after a product enters the marketplace. Leading industrial companies are combining traditional tools with AI and ML to drive quality transformation. For example, they have used patented analytical models to identify emerging issues before they impact performance. They have also used these techniques to prioritize problems and isolate failure modes. The proliferation of data means that new solutions can be developed for old issues. For example, companies can improve product quality and efficiency by leveraging data from connected devices and automated warranty systems. They can also identify leaks and track quality-improvement progress. Using AI and ML for warranty processes requires a structured approach that integrates data from multiple sources. However, next-generation tools can help companies reduce warranty costs, improve customer satisfaction, and increase revenues. Companies should assess the current state of their warranty management systems and processes and determine how well they measure up to benchmarks. They should also review recent models and tools and look for opportunities to integrate AI/ML into their quality processes.