Effectively managing product quality is critical to your brand’s success. Quality must be a focus from the first idea, through the design process, and all the way to the delivery of the final product.
Oracle Fusion Cloud Quality Management is closed-loop quality management software (QMS) in the cloud that helps you identify, analyze, correct, and predict quality issues. Quality Management is an integrated feature of Oracle Fusion Cloud Product Lifecycle Management (PLM), embedded in the Oracle Fusion Cloud Supply Chain & Manufacturing suite, making it part of every process and accessible to every team.
These configurable dashboards provide access to all quality performance data in one place, giving you a real-time view of product quality across your portfolio, including new products you’re prototyping. Here you can see all your defective items and how many times they’ve caused a quality issue.
Having access to this type of quality information on an intuitive screen means teams can collaborate and iterate on designs to meet customer expectations. It also allows you to be proactive when designing new products because you can factor in existing data.
Teams—including those that are global or remote—can use Quality Management to record, visualize, monitor, and collaborate on potential quality issues from any device. With built-in machine learning and adaptive intelligence capabilities, you can discover insights and run root cause analysis to solve quality problems fast or before they happen.
Like many products, robotic arms are complex and integrate hardware with connected software, making it difficult to manage quality. Now that products can be delivered as a service, you need a way to predict quality problems before they happen.
Here it looks like Oracle Fusion Cloud Internet of Things Intelligent Applications has identified a potential incident that needs your attention. This robotic arm could pose a quality risk.
By drilling into IoT Intelligent Applications data, you can verify an anomaly was detected on an attribute of this robotic arm. This dashboard allows quality teams to monitor products during production and in the field. Receiving automatic notifications about anomalies lets you address issues before they become problems.
A single source of product data means you can use out-of-the-box statistical process control to monitor production data using the specified torque range to predict potential failures.
When investigating the open incident further with Quality Management, you can see the IoT application automatically generated a problem report. There seems to be potential for a severe torque exception that needs to be escalated.
Having this information on a single system allows you to connect requirements and the design failure mode and effect analysis (DFMEA) for faster quality assessment. This means that quality can be measured and evaluated holistically across engineering design, manufacturing, and the entire supply chain.
As part of their analysis, your quality team can review the technical requirements and run predictive what-if scenarios to see if adjusting the current torque specifications or testing parameters will fix the problem.
Other risk mitigation techniques can be modeled within the system to further enhance the quality data, such as detailed risk analysis.
Your quality team can review the DFMEA to see if the risk was predicted and aligned with the operational requirements to determine if a possible resolution can be identified.
You can also determine potential risk by scoring and classifying parts in your bill of materials. Risk scoring helps teams keep an eye on items that may become issues.
Here we see risk scoring used early in the design cycle to select items to monitor for possible problems. By aggregating factors from quality, supplier, procurement, and other information, the risk score helps determine the probability of the item creating an issue.
These potential issues are prioritized by their score, and your quality teams can use the composite score to decide which issues to escalate and which require preventative action.
With the closed-loop process, root cause analysis is initiated in a new workflow, and the extended team can work together to mitigate and contain the potential issue across your manufacturing sites before the robotic arm causes issues in the field.
On this screen you see the problem report across various manufacturing sites and the inner relationships between different quality and product data to help you understand the scope of the problem. From here you’ll be able to determine the best resolution for the issue.
Having contextual root cause analysis, modeled in this case as an Ishikawa/fishbone diagram, helps tie the product to technical requirements and mitigate supply chain problems earlier.
Here the root cause was determined to be a failure on the motor, delivered by an external supplier that would not meet the torque specification.
For all quality issues related to any of your suppliers, you can initiate an audit and investigate.
In this case, the audit notifies the supplier of the potential issue with their motor. We can see that the supplier received a request to correct the overall quality of the motor and improve their production process.
Until the supplier resolves the issue, your engineering teams can initiate a change of supplier to one whose motor performs to specifications and has increased torque capabilities. This also prevents future assets from being manufactured with defective parts.
In taking this type of action throughout the design and manufacture of the robotic arm, you maintain the standard of quality across manufacturing locations.
After the supplier change and the resulting new motor, by monitoring the asset in the field with IoT connectivity, you can see that the robotic arm is performing to standards.
This closed-loop quality process allows you to record and predict issues and act before an issue impacts the customer in the field.
A graphical representation keeps every channel in the organization involved in the quality process earlier. This screen lets you drill down at any stage of the product lifecycle to better understand the relationships between interconnected data and the interdependence of quality issues. Here you can search for and see the impact of a quality event.
With all this quality information available in context, you can iterate on your initial designs to improve products and continuously satisfy customers. You can also use the same information to inform innovators of potential new ideas for future products.
Siloed quality applications no longer support customer demands. Quality Management is an integrated feature of Oracle Cloud PLM embedded in the Supply Chain & Manufacturing suite that creates a single digital thread to drive product data through the closed loop and accelerate end-to-end quality. The result is better customer loyalty, brand recognition, and ROI, and a stronger competitive edge.Learn more about Oracle Fusion Cloud Product Lifecycle Management