Statistical Process Control
Advanced Process Monitoring Solutions for the Automotive Manufacturing Industry
Automotive manufacturers, including suppliers to the automotive industry, benefit from a multitude of STATISTICA products to achieve the most efficient processes in the business. Typical applications include monitoring processes, finding important controllable factors and anticipating issues before they occur. STATISTICA solutions available for these tasks include: STATISTICA Enterprise QC, STATISTICA Monitoring and Alerting Server (MAS), STATISTICA Enterprise Server, and STATISTICA Process Optimization and Root Cause Analysis.
One component that can aid in the Automotive Manufacturing Process is Predictive Quality Control, reducing scrap, rework and recalls, as shown in the following solution.
Areas of Application: Monitoring Processes with STATISTICA Enterprise QC and MAS
STATISTICA Enterprise QC monitors the various critical manufacturing processes that are taking place simultaneously at the facility during testing and assembly. Immediately knowing when a process gets off spec saves time and materials. STATISTICA Enterprise QC offers SPC solutions for automotive suppliers to monitor processes and part testing to ensure quality of parts and assemblies.
STATISTICA Monitoring and Alerting Server (MAS) provides automated monitoring and dashboard summaries for highly automated automotive manufacturing and assembly processes.
Collaborating with Suppliers using STATISTICA Enterprise Server QC
STATISTICA Enterprise Server QC enables automotive manufacturers to collaborate with suppliers through its web interface. This allows for the sharing of supplier data and collaborative review of results.
Anticipating Issues before they Occur with STATISTICA Process Optimization and Root Cause Analysis
STATISTICA Process Optimization and Root Cause Analysis is an exceptional tool for monitoring the manufacturing process at each step along the way, even anticipating quality control problems with unmatched sensitivity and effectiveness. By integrating cutting-edge predictive modeling and data mining techniques with the vast array of traditional quality tools including quality control charting, process capability analysis, experimental design procedures and Six Sigma methods, STATISTICA Process Optimization and Root Cause Analysis allows for complete process understanding, root cause analysis, and accurate predictions of quality outcomes during the manufacturing process.
STATISTICA Process Optimization and Root Cause Analysis allows you to take advantage of existing historical data and find patterns in the data that affect the final outcome. As most automated manufacturing processes involve a large number of steps to get to the end product and interactions between these effects often exist, a traditional experimental design would require far too many runs. Root Cause analysis uses your historical data to find factors and combinations of factors that affect the end product quality.
STATISTICA Process Optimization and Root Cause Analysis builds predictive models that reflect the relationship between manufacturing inputs and outcomes (e.g., conformance to specifications) of the manufacturing process. The models can then be used to simulate runs, finding optimal settings and improving overall quality of the process.
For an overview of the application of predictive modeling to manufacturing processes, read the article from Quality Digest, Finding Direction in Chaos, Data mining methods make sense out of millions of seemingly random data points
Warranty Cost Sharing
New Challenges Facing OEMs
- Auto manufacturers will begin submitting chargebacks to their original equipment manufacturers (OEMs) if quality thresholds are “breached”
- The implications for the OEMs are that (a) an earlywarning detection system is needed to identify quality problems much earlier in the process so that critical factors can be corrected before the issue impacts the overall quality scorecard and (b) improved techniques to identify root causes are needed in order to positively determine whether their part is the culprit
- Many companies use Excel and other manual analysis tools in an attempt to spot emerging complications, but simplistic approaches do not provide the advance notice that a supplier needs to rapidly identify and fixproblems
- The problem isn’t more data. The problem is to better leverage the data to detect patterns earlier in the process and then rapidly identify the root cause and fix the process
Improve Insights into Warranty Claims and Part Failures
In an attempt to reduce warranty costs per vehicle, top automakers are focusing on warranty cost analysis. One result is a warranty “chargeback” system that will become more punitive in 2012. Automotive companies including General Motors, Ford, Chrysler, and others are formalizing their warranty chargeback systems to reduce their expenses by passing the costs back to their suppliers.
Automotive suppliers can be prepared by improving product quality and increasing their ability to defend claims of part failures. A major fear of suppliers is that warranty costs will be charged without proper evidence and justification. With existing manual processes, OEMs agree that it will be difficult and time consuming to differentiate between actual part defects and systemrelated failures.
Manufacturers and suppliers must be empowered to recognize “real” part failures from the many other possible problems. They need to be able to review part failure rate data across customers, platforms, and other factors to discover the root cause of failure, but the information relevant to warranty claims is diverse and complex. A systematic approach to aggregate and organize the relevant data is needed, but the relevant data are cryptic, with both numeric and track-andtrace data from the manufacturing process and text data from the warranty claims themselves. Warranty coding of failures by mechanics is inconsistent and unstructured, making it difficult to comprehend “the whole story.”
Using StatSoft’s multivariate solutions, patterns emerge that were not previously apparent. From these patterns, automatic alerts are generated, which indicate much earlier that a problem is developing. Once the alert is generated, the anomaly can be analyzed in real time to determine if it is the component or some other factor in the system that is causing the problem.
Warranty Process Flow: Early Warning Detection and Root Cause Analysis System
ETL: Data Access
Problem: Data resides in disparate databases requiring data connections, aggregation, and alignment across multiple databases and data historians. Getting to the data is a manual process, and engineers spend too much time producing reports.
Solution: Build data access, automated process monitoring, and root cause analysis templates only once and do away with manual data retrieval. Free up engineers’ time for higher value task resulting in process improvement and reduced warranty claims.
Traditional SPC Analysis
Problem: How to implement effective SPC monitoring that is responsive to small changes in (warranty) trends?
Solution: Use cumulative sum charts, exponentially weighted moving average charts, and runs tests to simultaneously monitor hundreds or thousands of components and subcomponents
Modeling: Failure Modes + Component Life
Problem: How to link warranty issues to manufacturing parameters and product testing? How to implement successful strategies for driving down warranty repair costs?
Solution: Predictive modeling techniques can identify the key patterns relating manufacturing and product testing data to warranty claims; those predictive models can then be used in “what-if” (scenario) analyses to identify cost-effective solutions to drive down warranty costs.
Root Cause Analysis
Problem: What are the most important variables that impact product quality and reliability in the field, and drive warranty cost? How to quickly diagnose root causes when new failure modes are reported in the field?
Solution: Diagnose complex issues quickly by applying automated root cause analyses. Quickly identify the critical manufacturing parameters and inputs where additional resources are needed to drive warranty cost down.
Effective Multivariate Process Monitoring
Problem: Exceptional component/product reliability is the result of a complete understanding of the interactions among numerous manufacturing parameters, supplier inputs, etc. When reviewing a process sequentially, one parameter at a time, important interactions will be overlooked.
Solution: Find anomalies and patterns in high-dimensional data through the implementation of multivariate and model-based process monitoring. Find manufacturing quality problems early and before they show up in standard control charts.
Ad Hoc Engineering Analytics; Text Mining of Warranty Claims
Problem: How to find emerging problems and new patterns in warranty data? How to avoid the expense of trained engineers reading large numbers of warranty reports in order to classify them, and to detect new problems?
Solution: Apply advanced automatic text mining methods to classify and cluster warranty claim reports; then use ad-hoc drill-down methods to detect emerging trends.
STATISTICA Enterprise/QC Receives Top-Notch Real-world Review from VISTEON Automotive