DfR, IoT and PLM: How to get more powerful products? https://www.automation.com/files/pluginfiles/item_99573/field_376/dfr_reg.jpg
By Chuck Cimalore, VP of Strategy, Arena Solutions
Design for Reliability (DfR) identifies key design parameters and potential failure rates inherent in a system designing and developing procedures to ensure that a product meets its reliability requirements fulfills the duration of its life. Digital transformation affects the reliability of products. Trends in product personalization and connected devices are making DfR increasingly important in today's marketplace. According to LNS Research most manufacturers lack the reliability technology strategy and sponsorship of top management to have successful reliability programs.
Successful reliability engineering requires the ability to predict which parts of a product may fail, such as the performance, safety, and economic impact of the failure. It requires transparency for current and past issues to improve predictions, respond to errors, and continually improve. Successful DfR must be supported by effective product management strategies.
With the advent of the Internet of Things (IoT) and centralized data management platforms such as Product Lifecycle Management (PLM) technology, companies can create a closed, data-driven solution to improve predictability, reliability, and product performance.
DfR's Top Challenges
According to a survey by LNS Research the four key challenges to DfR's success are the lack of top management sponsorship, a late involvement in launching new products ( "New Product Introduction") and isolated processes and technology and poor predictability. Research also shows that manufacturers manage DfR data mainly with spreadsheets and electronic documents.
- 90% of surveyed companies manage requirements in spreadsheets and electronic documents.
- 83% Manage FMEAs (Failure Modes and Effects Analysis) in Spreadsheets and Electronic Documents
- 70% Manage New Product Testing Data in Spreadsheets and Electronic Documents
How many product design and development processes are This separate, document-centric approach requires a significant obstacle to the analysis to produce accurate historical reliability statistics and lessons learned. It also creates silos between DfR teams and processes, reducing cross-functional engagement and creating interdisciplinary process silos, ultimately leading to late DfR engagement.
PLM technology and the IoT are used here. Together, they can help master the main challenges of DfR by automating processes, improving visibility / access to critical data, and early involvement of DfR in the NPI process.
Early Integration of DfR with PLM
PLM technology provides a centralized place to manage all product information in a controlled environment, and provides the platform for inserting reliability and IoT data. Reliability teams tend to engage in the product development cycle at a later stage after the bill of materials is completed. By using PLM as a data platform, reliability engineers in all teams can gain better insight into product information and get involved earlier in the development process / NPI phase. PLM can be used to link information from multiple sources to improve DfR, such as IoT data, incidents and problems from testing labs. This information can be returned via PLM to engineering and other key departments, including the c-level.
In this real scenario, for example, a medical device company designer designed the same sensor part with a high failure rate. Having no central field-level tracking and management system, the engineering had no insight into this problem and the impact the sensor had on the product. Did the error trigger a service call? What did the sensor component cost the company? By giving engineers access to data on how products with IoT integrated PLM are developing in the marketplace, their product specifications may include data that goes beyond design design, such as: Reliability, quality and marketing.
IoT is a double-edged sword
IoT data can often deliver data points in real time, eliminating much of the traditional ambiguity in product performance and usage. Product planning, design, and quality departments can use IoT to learn about the product's behavior in order to improve the features most commonly used by customers. Product quality issues, field errors, software errors, and / or customer feedback data fed into PLM enable manufacturers to track and configure product design requirements based on usage patterns and to redesign parts or systems to improve quality. IoT devices use the technology to report data to acquisition and analysis systems. H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H? Problems and tolerance violations of IoT devices can also initiate support and quality processes in the PLM system.
The IoT can be a double-edged sword for security reasons. It creates product complexity that is inherently a reliability challenge, but also provides much better insights into equipment usage. These findings enable predictive maintenance of existing products, improving availability. Capturing the actual usage and other conditions leading to failure will also provide data that can significantly improve next-generation products. Properly used, the IoT can result in significant increases in product reliability and availability.
For example, a global manufacturer of medium, heavy and heavy duty service vehicles began to monitor fleets as a service remotely. Through this work, the company found "canaries" or early failures that indicate a broad problem with the fleet, much like canaries in a coal mine. The truck manufacturer identifies a pattern in the sensor data by reviewing the events and data leading to component failure, and then automatically checks to see if the same pattern exists.
This era promises product feedback in real time, is a big change for engineering and there are many great opportunities. In the past, engineers have created product designs from a set of requirements and handed the finished model to manufacturing, which controls its production processes with its own systems and tools. Field service and maintenance personnel usually have another record related to the product and housed in a separate system. In some disciplines, information is likely to be shared, apart from a common record.
An open PLM platform can be integrated with IoT applications to collect data, triggering automated workflows and alerts to address issues. PLM becomes a platform for pushing and pulling information that helps identify problems early in the design process.
Closed-Loop Data-Controlled DfR
As mentioned earlier, today's spreadsheet and electronic document approach is the source of many downstream DfR challenges, such as: According to a recent report from LNS Research, What is your design for reliability (DfR) data plan? LNS recommends that DfR use a closed, data-driven approach that establishes a formal, change-managed process associated with PLM, the NPI system. This method provides connectivity to capture risk and reliability experiences across multiple business and operating systems in a centralized model. It is important to organize these experiences and associated metrics against data objects that are relevant and reusable for the DfR, including parts, configurations, and serial numbers. This closed-loop, data-driven approach ensures better accuracy and transparency in previous product issues, and compares the predicted with the experienced reliability of the current product. This approach also allows product development teams to more fully engage DfR and incorporate it into their development decisions. It allows DfR teams to participate earlier in the lifecycle, as well as providing a closed connection between virtual / physical testing, DfR, and product development.
The DfR and the end result
The DfR can contribute to the end result in many ways. Improvements in warranty costs and return and recall rates, customer satisfaction and safety may result from a successful DfR program. It is clear that the market has changed and with it the importance of reliability. The trends in IoT / connected devices and the personalization of products require manufacturers to use new processes and technologies to accurately capture, analyze, and solve problems. Enterprises should use their PLM platform, the system used to develop their product, as a source to connect other systems / data, such as DfR and IoT, to provide a holistic view of the product and support data-driven processes.Did you enjoy this article?
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