AN INTEGRATED PROCESS IMPROVEMENT APPROACH USING THE SECOND-GENERATION QUALITY TOOLS IN JMP

JMP 11 added new features and improvements to CCB and MSA. INTRODUCTION BACKGROUND AND MOTIVATION We began developing the 2nd generation Quality tools with JMP 10 when we added two new platforms: Control Chart Builder (CCB) Measurement Systems Analysis (MSA) JMP 11 added new features and improvements to CCB and MSA. JMP 12 introduced the new Process Capability platform. Careful thought was given to the design of the platforms. We consulted with Don Wheeler, and they are heavily influenced by his methods and thinking.

All quality platforms should be easy to use and easy to interpret! INTRODUCTION Central ideas All quality platforms should be highly visual and make the identification of root causes more intuitive. There should be no need for data table manipulations to explore different charts or subgrouping. The charts should be dynamic and produce the most likely chart based on the inputs rather than requiring the use of a list of chart types. MSA and process capability analyses should relate to the way the SPC is practiced at the user’s organization. All quality platforms should be easy to use and easy to interpret!

Aster Cheese Company manufactures ten varieties of cheese. Case study aster cheese company Aster Cheese Company manufactures ten varieties of cheese. They have many quality issues that need to be monitored or studied. These examples will demonstrate the central ideas used in development and highlight the connections between our new quality tools. Examples will demonstrate: The central ideas used in development The connections between these 2nd generation Quality/SPC tools.

Infers type of chart based on data column modeling type and subgroups. Control chart builder Features Drag-and-drop interface can create many types of charts all in one platform. Infers type of chart based on data column modeling type and subgroups. Supports visualization of nested process data. It is easy to nest subgroups in a chart without having to return to the data table. Includes Process Capability Chart when spec limits are added.

Control chart builder demo Aster’s SPC plan to monitor percent moisture content in their White Vermont Cheddar Cheese: Worker tests the % moisture content after the cheddaring and pressing process twice a day, morning and evening. Target % moisture content is 37%. Need Individual & Moving Range Chart Monitor defects from sensory evaluation – A sensory team tests 50 samples of cheddar cheese from each day’s batch of cheese for January. They determine how many of the samples are defective in flavor or texture. There is a nonconformance issue on January 21 and January 22. Monitor moisture content – They test the %moisture content of their cheddar cheese twice a day, morning and afternoon. This is the data for the second half of January. They want the % moisture content to be at 37%. Each sample is recorded with a date and time stamp.

Process capability Central ideas and features Analysis should reflect the type of control chart used in the SPC program. A process can be subgrouped by grouping columns or a constant subgroup size, reflecting X-Bar control charts. The potential performance and past performance should be available alongside each other. Both within and overall variation are computed. Within variation analyzes potential performance. (also known as short-term variation) Overall variation analyzes past performance. (also known as long-term variation) Most reports and graphs are available for both types of variation.

Increase ease of use, efficiency, and consistency. Process capability Central ideas and features Increase ease of use, efficiency, and consistency. Modernized graphs. Allow user to select and color the data table values that are outside of spec. Present same Individual Detail Report in Process Capability and Control Chart Builder platforms. Replaces former Capability platform. Former Capability platform still available via scripting.

Process capability demo Aster’s process capability study for final product weight of all ten cheese lines. Studied 4 samples from 30 lots for 10 cheese lines. Each cheese line had lower and upper spec limits and targets. Process Capability report within Control Chart Builder for Canadian Cheddar Cheese. Studies the weight in grams of all 10 cheese lines. The study consisted of 4 samples from 30 lots for each cheese. Each cheese has an upper and lower spec limit as well as target. They are saved in the column properties. Capability Box Plots – I can see that Pepper Jack, Canadian Cheddar, and White American cheese all have products weights out of spec. Monterey Jack and White Vermont Cheddar appear to be pretty off target. Select and color out of spec values. Goal Plot – Show the three processes that are not capable and the 2 that are off target. These are using overall sigma. Turn on the within sigma points. Show that Canadian cheddar overall and within points are far apart. Could mean an unstable process. Show the Individual Detail Reports. Go through a few of them, especially Canadian Cheddar. We need to investigate if this is an unstable process. Go to CCB and create an Xbar and S chart for Canadian Cheddar. Show how the process has drift and is unstable. Show the same Process Capability report.

Measurement systems analysis (MSA) Evaluating the measurement process (EMP) The Measurement Systems Analysis (MSA) platform was designed to implement Don Wheeler’s Evaluating the Measurement Process (EMP) methodology. EMP gives visual information and results that are easy to interpret. EMP emphasizes the effect of measurement ability on SPC: “Given this measurement system, how well can we correctly identify process changes?” EMP classification system Shift Detection Profiler

Measurement systems analysis (MSA) EMP Classification system Classifies a measurement system by its ability to detect changes in the production process. EMP Classification System Classification Probability of Warning* First Class 0.99 – 1.00 Second Class 0.88 – 0.99 Third Class 0.40 – 0.88 Fourth Class 0.03 – 0.40 * Probability of warning for a 3s shift within 10 subgroups with Test 1.

Measurement systems analysis (msa) The Shift Detection Profiler Quantifies the measurement system’s ability to detect process shifts. Uses the results of the MSA study to help the user make data-based decisions on control chart planning and use. Allows the user to explore how changes in warning rules and subgroup sizes impact their ability to detect process shifts while keeping the false detection rate reasonably small. Allows the user to explore “what if” scenarios of how process and measurement system changes impact their ability to detect process shifts.

Measurement Systems analysis (MSA) demo Aster’s MSA study on measuring the pH of Colby Jack cheese before brining. 6 Operators measured the pH of 10 samples of Colby Jack cheese 4 times. 1. Average chart – shows measurement averages for each operator and cheese. In this study it is good for the points to look “out of control” because it shows that we can measure each cheese. 2. Standard Deviation chart – shows the variability for each operator and cheese combination. We want these to look in control because we want the operators to be measuring the cheeses consistently. 3. Show EMP Results – Note that the system is second class with and without bias. Note what this means in the legend. We can an even more accurate picture with the Shift Detection Profiler. Show Variance Components – these are the estimates used in the shift defection profiler Show Shift Detection Profiler – Gives the probability of the process control chart (individuals by default) giving a warning within 10 subgroups of a one sigma shift in the pH of the Colby Jack cheese. If I slide the Part Mean Shift all the way to the right, it gives me a 3 sigma shift. Show how we can easily increase the probability of detecting a shift by increasing the subgroup size to 2. If that isn’t possible, I could also add more rules. The best thing would be to try to reduce the test-retest error (within) if possible.

Coming in JMP 13: Non-normal distributions in Process Capability. summary Understanding how your measurement systems, statistical process control program, and process capability assessments fit together is key to improving and maintaining quality. Coming in JMP 13: Non-normal distributions in Process Capability. Questions?