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SPC Basics Training V1 By Carlos Sanchez
The document outlines basic statistical process control (SPC) concepts and tools aimed at quality improvement in manufacturing processes. It covers important topics such as variation types, the significance of sampling versus continuous monitoring, control charts, and process capability metrics like Cp and Cpk. Emphasis is placed on the proactive detection of process changes to prevent product defects and ensure higher quality standards.
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Introduction to Statistical Process Control with key topics like Quality Improvement, Variation, SPC tools including Control Charts and Process Capability.
Explains the limitations of sampling in process control and emphasizes the need to assess how products meet specifications.
Details on assignable and common variation, ways to improve process control like SPC, Six Sigma, and Lean methods.
SPC as a tool to monitor process changes and its relation to the Normal Distribution and Sigma (standard deviation).
Illustration of how different sigma levels (1, 2, 3) impact product specification compliance.
Introduction to classical SPC tools, focusing on the use of Control Charts for process analysis.
Justification for using control limits in monitoring processes instead of merely relying on specifications.
Understanding how control limits relate to the Normal Curve and central tendency in data analysis.
Analyzing control charts to detect shifts, trends, and the significance of measurement stability.
Illustrative examples to explain process capability with respect to meeting specifications in practical scenarios.
Introduction to process capability indices Cp and Cpk, emphasizing their importance in evaluating process performance.
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SPC Basics Training V1 By Carlos Sanchez
- 1. Basic Statistical Process ControlTraining By Carlos Sanchez
- 2. 2 Content Quality Improvement& Statistics Variation What is Statistical Process Control? The Normal Distribution Let’s talk about Sigma SPC Tools Control Charts Process Capability
- 3. Why can’t wejust take Samples? 3 Process Control Final Product Sampling Inspection cost per unit is low Costs are Higher (Take sample, analyze, report, dispose sample) Inspection not destructive or detrimental to our products May be destructive or detrimental to our products Process can be adjusted, stopped, inspected and started up again at a reasonable cost Process control is not feasible (After the fact) If product is out of spec now we have a full tank/silo
- 4. But the productit’s in Spec! Meeting the specification is simply NOT ENOUGH, we need a way to know this: How close to the target spec. is our product? How spread out were the results? 4
- 5. Variation Assignable Variation:Variation caused by factors that can be clearly identified and possibly managed Common Variation: Random variation which is caused by the production process 5 Example: A poorly trained employee that creates variation in finished product output. Example: A particle classification process that always allows bigger particles to flow to the finished product
- 6. So, How dowe improve? More Money New & better equipment Flawless Raw Material Luck Reduce Common Variation…How? Observation, Observation…and more Observation Act on little changes observed Preventive Maintenance Statistical Process Control (SPC) Six Sigma Lean Design of Experiments 6 Continuous Improvement Tools
- 7. SPC in anutshell Minimize needless adjustments in the process (Tweaking) It’s a monitoring tool that lets us know when the process is changing BEFORE product becomes UNACCEPTABLE/Out Spec/ Unusable. It’s a prevention tool that allows to detect trends that could lead to defective products. (Early warning system) Final inspection does not assure quality; remember: “You can’t inspect quality into the product” Final Inspection is too late downstream 7 SPC quantifies variability and allows you to determine if a process changed
- 8. Two things toknow about the Normal Distribution SPREAD LOCATION: The Center of the curve is expressed as the AVERAGE This is where the target Specification is aimed at SPREAD or RANGE: The dispersion it is usually expressed as SIGMA8
- 9. Let’s talk aboutSigma 9 Sigma is just a fancy word for Standard Deviation, which tells us how far is a particular value from the average of the data set. +/- 1 sigma +/- 2 sigma +/- 3 sigma 64.25% 96.45% 99.73% This is where the infamous SIX SIGMA comes from, it means sending Product in spec. 99.73% of the time
- 10. Example 64 Tons 96 Tons 99.7Tons Imagine if an upside down bell curve could hold 100 Tons of Cement from a storage silo. If we are working at +/- 1 sigma only 64 Tons are in Spec. If we are working at +/- 2 sigma only 96 Tons are in Spec. If we are working at +/- 3 sigma almost all 100 Tons are in Spec. 10
- 11. 7 Classical SPCTools 11 Histogram Pareto Chart Control Chart Stratification Chart Cause Effect Chart Flow Chart Check Sheet For this initial Training we will focus on: Control Charts
- 12. Why use ControlCharts? Reduce variation by the systematic elimination of assignable causes Prevent unnecessary process adjustments (Tweaking) Visually diagnose the process by observing data patterns Find out what our process can do Provide immediate visual feedback Decide if continuing production is worthwhile 12
- 13. Types of ControlCharts Run Charts for variable data: Individual Chart Mean & Range Charts Std. Dev. Charts Attribute Charts 13 We will focus on these today
- 14. Why don’t wejust use the Specs. As our Limit? 14 Too late…It’s bad Upper Spec. Lower Spec. Target With limits we have a “cushion or safety net” before the S#$@%! Hits the fan!
- 15. So where shouldthese Control Limits be? 15 +/- 1 sigma +/- 2 sigma +/- 3 sigma 64.25% 96.45% 99.73% Where would you put a Control Limit?
- 16. How is achart related to the Normal Curve? 16 Upper Spec. Lower Spec. Upper Control Limit Lower Control Limit
- 17. Let’s tilt theChart and let the points fall! 17 Huh! That makes sense!
- 18. At the endit averages out! When the population is big, looking at individuals to detect trends is tricky… It’s been proven that when you look at averages these tend to behave like a Normal Curve Google this: Central Limit Theorem (It’s great for those sleepless nights) So from now on this training all example charts are based on Averages. This means that a “Point” in a control charts represents the “Average” value of a sample (Typical sample size varies from 3 to 5), I like 5, but heck, you can choose whatever size you want 18
- 19. Usefulness of lookingat Average & Range 19 UCL LCL UCL LCL R-chart x-Chart Detects shift Does not detect shift (process mean is shifting upward) Sampling Distribution
- 20. More Usefulness oflooking at Average & Range 20 UCL LCL UCL LCL R-chart x-Chart Does not Detects shift Detect shift (process variability is increasing) Sampling Distribution
- 21. If you reallywant to plot a chart by hand…Ok! 21 x Chart Control Limits UCL = x + A R LCL = x - A R 2 2 R Chart Control Limits UCL = D R LCL = D R 4 3 n A2 D3 D4 2 1.88 0 3.27 3 1.02 0 2.57 4 0.73 0 2.28 5 0.58 0 2.11 6 0.48 0 2.00 7 0.42 0.08 1.92 8 0.37 0.14 1.86 9 0.34 0.18 1.82 10 0.31 0.22 1.78 11 0.29 0.26 1.74 Average of all Averages Average Range Constants Sample Size
- 22. Let’s Analyze thatChart 22 Upper Spec. Lower Spec. Upper Control Limit Lower Control Limit Points out of Control Limits : Rule of thumb, if there are any point outside the Control limits should be investigated.
- 23. Let’s Analyze thatChart 23 Upper Spec. Lower Spec. Upper Control Limit Lower Control Limit Trends : Rule of thumb, if there are 7+ points in a row all higher or lower than the preceding point
- 24. Let’s Analyze thatChart 24 Upper Spec. Lower Spec. Upper Control Limit Lower Control Limit Shifts : Rule of thumb, if there are 5+ points in a row all higher or lower than target or Average, this means that the Average has SHIFTED
- 25. Let’s Analyze thatChart 25 Upper Spec. Lower Spec. Upper Control Limit Lower Control Limit Cycle : Rule of thumb, if there are 3+ similar peaks or valleys, this is typical of Machine wear, or dosage cycles…or Tweaking!
- 26. Let’s Analyze thatChart 26 Upper Spec. Lower Spec. Upper Control Limit Lower Control Limit Adherence to Center : Rule of thumb, if there are 7+ all smothering the average or target spec. This means that the measurement equipment is no longer capable of detecting significant variation. This is good, but it signals for improvement in the measurement system. Maybe the spec can be tightened.
- 27. Let’s Analyze thatChart 27 Upper Spec. Lower Spec. Upper Control Limit Lower Control Limit Erratic : Rule of thumb, if there are 6+ points shifting from one extreme of the chart to the other, borderline with the Control Limits, this shows that the process is not stable…When you see this pattern be alert for Non conforming product.
- 28. Let’s talk aboutAdjustment or Tweaking the Process 28 x xx xxxxxxx xx x xx xxxxxxx xxx x xxxxxxx xxx x xxxxxxx x x x xxx x xxx xxxxxx xx x xxxxx x xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xx x xxx xxxx x xxx x xxxxxxxx xxxxxxxx x x x x x x xx x x If your process is not capable, then there is a good chance that some of your sample will have values outside the specification. Chances are if you are not looking at a SPC control chart, you may be tempted to make an adjustment. Let's see what would happen. x xx xxxxxxx xx x xx xxxxxxx xxx x xxxxxxx xxx x xxxxxxx x x x xxx x xxx xxxxxx xx x xxxxx x xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xx x xxx xxxx x xxx x xxxxxxxx xxxxxxxx x x x x x x xx x x x xx xxxxxxx xx x xx xxxxxxx xxx x xxxxxxx xxx x xxxxxxx x x x xxx x xxx xxxxxx xx x xxxxx x xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xx x xxx xxxx x xxx x xxxxxxxx xxxxxxxx x x x x x x xx x x
- 29. Let’s try tounderstand Process Capability 29 Jim. Nice guy too, Works in Plant B as a research assistant, he lives 5 miles from work. In order to get to work he has to get through 5 traffic light onto Hwy 4 (which is frequently backed up by crazy skiers) to downtown Beachtree. There he has to find parking spot, sometimes a couple of blocks away. He is late to work quite frequently. Jack, Nice guy; works as a technician. He lives 10 miles away from the Plant A. In order to get to work he takes Hwy. 7 and gets off at the Beachtree exit and zips right into work. He never hits any traffic and there is no traffic light between his home and work. He's never late to work.
- 30. Process Capability Continued 30 8:068:128:007:547:487:42 Late to work Early to work Jack Arrives to work between 7:48 to 7:56 AM. Jim Arrives to work between 7:48 to 8:06 AM If we thought of being early or late to work as our specification, then we can say that Jack is Capable meeting the specification. Jim is Not Capable of meeting the specification. Tolerance
- 31. Let’s Calculate theirCapability to get on Time 31 Jack Arrives to work between 7:48 to 7:56 AM 99.7% of time. 6 sigma = 7:56 -7:48 = 8 min. Jim Arrives to work between 7:48 to 8:06 AM 99.7% of time. 6 sigma = 8:06 - 7:48 = 18 min. Tolerance = late - early Tolerance = 8:00 - 7:46 Tolerance = 14 minutes Capability = Tolerance 6 sigma Jack's Capability = 14 / 8 = 1.75 (Bill is capable) If Capability is > 1 then we can conclude that is capable of meeting the spec Jim's Capability = 14/18 = 0.78 (Jim is NOT capable)
- 32. Ok, lets getto Cp & Cpk…Say what? 32 Out Spec Tolerance Target Real Avg. Out Spec Cp: Measures the capability of the process to meet the Tolerance…just like Jack & Jim Cpk: Measures the capability of the process to meet the Target Spec. it looks at the likelihood of making product out spec. So the more “centered” the curve is, a better cpk you will get.
- 33. More formulas…But theyare short! 33 Cpk = The smallest of: Target - lower spec or Upper spec - Target 3 sigma 3 sigma Cp = Upper Spec. – Lower Spec. 6 sigma Criteria: Both Cp & Cpk should be AT LEAST > 1 Ideally > 1.33 Why? Just trust me on this one…
- 34.