Training and Consulting Services
The Research Group offers a wide range of seminars, training and other consulting services to aid organizations seeking methods for improving the quality and productivity of their current working environment. Tailoring our services to the unique needs of an individual group helps ensure immediate relevance and cost effectiveness. Whenever practical, we incorporate job related projects into each seminar to encourage even more rapid implementation of Quality concepts and methods. Feedback from our clients demonstrates that significant improvements and savings have been generated for their companies as a result.
Courses
We offer the following courses taught by UW faculty. Please contact the Research Group for further details. E-Mail us at shsteiner "AT" uwaterloo.ca or call Stefan Steiner (519) 888-4567 x36506.
Please note that most courses are conducted in-house. Please contact the Research Group for further information and prices.
| Introduction To Data Mining
(2 Day Course) More and more companies have enormous databases that may contain undiscovered but useful information. Data Mining is the search for this information using statistical models and computational techniques. This one day course will illustrate the state of the art, using real data from direct marketing, drug discovery and industrial control problems. |
| Design of Experiments
(2 or 3 Day Course) A designed experiment is a special type of process study that involves changing one or more process characteristics to investigate their effects. |
| Forecasting in Quality / Productivity
Improvement (2 Day Course) Forecasting plays a central role in business decision making. Accurate forecasts are needed when making decisions about investments, resource allocations, schedules and inventory levels. |
| Statistical Engineering
(3 Day Course) Statistical Engineering is a combination of statistical strategies and tools carefully selected to efficiently solve chronic problems in high volume manufacturing. Statistical Engineering attempts to exploit observational data from your existing process to "home-in" on the root cause of problems. |