Validating Requirements and Improving Specifications with Telematics Data C1897
Topics: Advanced Technologies
Field failures cause high warranty expenses, perhaps the highest quality cost. Failures occur when new designs are introduced, existing products are sold in new markets, and product specifications don’t reflect actual product usage. Any mistake in product specifications affects the entire product development process and cascades through the supply chain.
New product requirements are developed using prior requirements, rely on customer surveys, use “expert” opinion, or are the result of compromises to meet timing or management direction. The resulting requirements may be excessive or insufficient. If excessive, then verification testing costs are too high; if insufficient, then product verification is inadequate.
This seminar teaches the student how to analyze development and field usage data with a focus on projecting to design life targets. Data needs to be collected from the customer. Today’s availability of wireless services makes this relatively easy and tomorrow’s internet of things (IOT) can provide the raw data for analysis. This seminar uses selected automotive telematics data collected by special modules installed in development, fleet, and retail vehicles.
Learning Objectives
By attending this seminar, you will be able to:
- Define different types of data in Telematics.
- Determine the need and ways to standardize data metrics
- Determine the best probability distributions to describe counting or continuous data for a group of vehicles
- Apply cumulative distributions to determine population percentiles and project data metrics to design life targets. Examples: Engine start/stop events and vehicle speeds
- Analyze state transitions for product that operate in a finite number of states.
- Analyze two-dimensional multivariate data. (Engine Speed vs. Engine Torque)
- Make comparisons of different vehicle categories. (Police vehicles vs. retail vehicles)
Who Should Attend
The course is designed for more senior personnel who validate requirements, develop test plans and verify conformance to requirements. These analytic methods will be useful to information technologists, reliability engineers, product engineers, quality engineers and management.
Prerequisites
Statistics and an engineering degree.
You must complete all course contact hours and successfully pass the learning assessment to obtain CEUs.
Dennis Craggs
Dennis Craggs has a Masters in Operations Research with a focus on quality and reliability, and a Masters in Engineer Mechanics. He is a professional engineer. For over 20 years, he was an ASQ CQE and CRE. He worked in the aerospace and automotive industries at NASA, Teledyne CAE, Ford and Chrysler. His positions include aerodynamicist, packaging engineer, fastener engineer, wheel/tire engineer, programmer, Quality and Reliability engineer, and statistical specialist. Dennis is now self-employed as a Quality, Reliability and Data Analytics consultant. Dennis worked with engineers and management through the product development process. He worked in Test Cycle Development to analyze vehicle Telematics data. He developed Matlab software to implement new analytic methods.
Dennis has presented at AEC, ASQ, ISSAT and SAE conferences. He wrote papers and/or presented on Telematics Data Analysis, Monte Carlo Simulation of Power Dissipation in an Engine Controller, and the Analysis of Hall Sensor Data. His article “Vehicle Telematics Data Analysis” earned the 2017 Cecil C. Craig award. He has written 30 articles on Telematics, Statistics, Process Capability Analysis, and other topics.
