This course takes place over 2 full-day sessions. Training Dates: October 7-8 from 8:30 a.m. – 5:00 p.m.
Overview:
The key to improving process performance is the ability to understand, control and reduce variation. In this workshop, participants will learn how the monitoring and analysis tools of SPC can be used to achieve that goal. Going beyond the mere mechanics of SPC, this workshop will also guide participants through the steps needed to define a process and determine proper measurement techniques so that the right control chart is used in the right place at the right time.
By the end of the course, participants will be able to:
- Understand the role of SPC in the quality improvement process
- Identify key process and product characteristics
- Establish key characteristics for process monitoring
- Distinguish between variable and attribute data
- Create a meaningful data collection plan
- Use statistics to separate common and assignable causes of variation b
- Decide when a particular SPC tool is appropriate to use
- Collect data for that tool and convert the data into charts
- Interpret patterns and signals on the charts
- Perform a process capability study
- Use Cause and Effect diagrams and Pareto charts to diagnose process problems
- Use statistical software programs for data analysis and to produce informative statistical graphics
Who Should Attend:
Individuals and teams requiring a thorough understanding of the philosophy and tools of Statistical Process Control (SPC) in order to plan and implement successful SPC efforts their workplace.
Prerequisites:
Participants in this training course should have:
- A laptop computer loaded with MS Excel (version 2003 or later). They will also need to add in the Analysis ToolPak, a statistical package that comes with MS Excel.
- An SPC project in mind from their workplace.
- Experience using personal computers, especially using the Windows operating system. Basic math skills and be able to follow the order of operations for basic algebraic functions.
Cost: 2-day class
Location:
