Techniques for Improving Process and Product Quality in the Wood Products Industry: An Overview of Statistical Process Control

Transcription

1 1 Techniques for Improving Process and Product Quality in the Wood Products Industry: An Overview of Statistical Process Control Scott Leavengood Oregon State University Extension Service The goal: $

2 2 Outline: History & Philosophy of SPC What is SPC? Management issues Benefits of SPC SPC in a nutshell Process capability analysis History of SPC 1920 s: : Walter Shewhart of Bell Telephone Labs develops SPC as an economical means to control quality in manufacturing 1930 s: : U.S. telecommunications industry is world standard for quality 1940 s: : During WWII, Bell Labs personnel train armament manufacturers to use SPC 1940 s s:: Use of SPC following the war: U.S. economy intact, SPC disappears Japanese devastated, W. Edwards Deming arrives

3 3 History of SPC 1960 s-1970 s 1970 s: : Quality techniques blossom in Japan 1980 s: : The world begins to recognize Made in Japan as a sign of quality. In U.S., automotive and electronics industry lead the quality revolution 1990 s: : SPC now recognized by most U.S. industries as a vital quality management tool. In wood products industry, principal use is size control in primary sawmills Philosophy of SPC Variation is the enemy and it is inevitable Inherent, common, random Special, assignable Real quality improvement comes from defect prevention,, not defect detection- You can t inspect quality into a product Prevent defects by monitoring, controlling & reducing variability Reduce scrap and rework = increased productivity, increased profits, increased employee morale Continuous process improvement to reduce variability

4 4 Continuous Process Improvement: Tools- brainstorming cause and effect diagrams Deming s 14 points of TQM process flow diagram scatter diagrams designed experiments trend charts nominal group technique process teams check sheets Pareto analysis histogram process capability SPC Statistical Process Control- What is it? Statistical- With the help of numbers or data Process- we study the characteristics of our process... Control- in order to make it behave the way we want it to behave. Source: The Statistical Quality Control Handbook, Western Electric Company, 1956

5 5 A change of mindset- Product Quality Control Activities to evaluate and regulate quality following production ( inspect and reject/ rework ) ProcessProcess Quality Control Activities to ensure a quality product is produced during manufacturing through defect prevention Who needs to understand and commit to SPC? Everyone in the plant management production engineering and design sales Giving one person all of the responsibility for SPC will not work

6 6 A partial list of benefits of SPC Increase profits by: reduce scrap reduce rework increase production Improve employee morale Improve customer confidence Better understanding of process Documents and tracks improvement One aspect towards attaining ISO 9000 certification If SPC is the answer, What was the question? How capable is the process of meeting customer (internal or external) expectations? or, is the supplier s product meeting specs.? What is the distribution of process output? centering, range or spread, likelihood of an extreme value What is causing the variability? When is it reasonable to get tough with employees?

7 7 If SPC is the answer, What was the question? Can we afford to minimize the variability? Over time, how can we be sure the process hasn t changed? When should we tinker with the process and when should we leave it alone? Statistics- Who Needs It??? Statistics- The Science of Variation In God We Trust- All Others Must Use Data Answers to, If SPC is the Answer, What was the Question? : process capability? process distribution? causes of variability? afford to minimize variability? changes over time? to tinker or not to tinker? s = f( z)= n ( x x) n 1 z 2 2 e π i=

8 8 Sources of Variation: 4M s and OE Machine Material Method Measurement Operator Environment Variation- Random, chance, constant, common, unknown causes the rhythm of the process Assignable, special causes something has changed

9 9 Assignable Causes of Variation: The Backbone of SPC Examples of things that may be assignable causes of variation: machine troubles (damaged saw teeth, plugged blowpipe, etc.) faulty measuring device operator overcontrol worker fatigue drastic changes in raw material SPC in a nutshell : Describe the distribution of a process Estimate the limits within which the process operates under normal conditions Determine if the process is stable. Sample the output of the process and compare to the limits Decide: process appears to be OK, leave it alone, or there is reason to believe something has changed and look for the source of that change Continuous process improvement

10 10 Distribution of Process Ouput Neither accurate, nor precise Precise, but not accurate Precise and accurate Distribution of Process Output Histograms: Frequency distribution- a tally of process output falling into specific categories Histogram- a pictorial representation of the frequency distribution Frequency Strength (psi)

11 11 SPC in a nutshell : Describe the distribution of a process Estimate the limits within which the process operates under normal conditions Determine if the process is stable. Sample the output of the process and compare to the limits Decide: process appears to be OK, leave it alone, or there is reason to believe something has changed and look for the source of that change Continuous process improvement Estimating Process Parameters: Central tendency- arithmetic mean ( average ) Dispersion- range or standard deviation Frequency Strength (psi)

12 12 Estimating Process Parameters: Process centering mean or average, X = n i= 1 xi n (an estimate of the population mean, µ) Why isn t the average alone, good enough? Target X = 6.00 X = 6.00 We need a way to quantify the spread in the data

13 13 Estimating Process Parameters: Process spread : standard deviation, s = n i = 1 ( x i X ) n 1 2 range, R = X X max min (both are estimates of the population standard deviation, σ) Do 2 little numbers really tell us that much? Wouldn t it be nice, if knowing only the mean and standard deviation, we could predict the amount of product that will be produced within the specs. or any given range? For example, with a mean of 3.5 inches, and a standard deviation of 0.05 inches, how much product can we expect to produce that is 4 inches or larger? To do this, you need an equation to describe the distribution.

14 14 Exponential Weibull ( λx ) ( ) = λe F x ( ) ( ) = 1 F x e x α β How about one of these equations? ( ) Gamma α F x = λ α 1 λ x e x Γ( α ) Normal 1 Fx ( ) = σ 2π e ( x µ ) 2 2 2σ Determining the limits : The Normal Distribution Frequency to - to + + µ - 3σ µ µ + 3σ3

15 15 The Normal Distribution Relative Frequency (%) Mark on chip So what? By knowing the mean and standard deviation, and that the numbers are approximately normally distributed, we know everything we need to know about the distribution- it is predictable IF- we can somehow be sure that the process is stable

16 16 SPC in a nutshell : Describe the distribution of a process Estimate the limits within which the process operates under normal conditions Determine if the process is stable. Sample the output of the process and compare to the limits Decide: process appears to be OK, leave it alone, or there is reason to believe something has changed and look for the source of that change Continuous process improvement Is the Process Stable? or in SPC lingo, Is the process in statistical control?

17 17 Statistical Control The term in control in SPC does not have entirely the same meaning as in common usage: The process is out of control!!! A process is described as in control when a stable system of chance causes seems to be operating. (Grant & Leavenworth, Statistical Quality Control) The affect of control on the process In control - a stable system of chance causes seems to be in operation. The variability of the process is reasonably predictable. It is likely to be unprofitable to search for assignable causes of variability.

18 18 The affect of control on the process Out of control - assignable causes of variability must be present. A little investigation is suggested to find and eliminate those causes. Statistically rare occurrences are indications of lack of control How about a little poker?

19 19 Either this guy s cheatin or I ve just witnessed a statistically rare event A statistically rare event- an outcome with a very small probability of occurrence ( the odds are against it ) In manufacturing, an outcome that is statistically rare usually signals the presence of assignable causes of variation How can you know what is statistically rare and what is commonplace? SPC

20 20 What is a statistically rare occurrence Obtaining a sample value that is very far away from the average is a highly unlikely event In SPC, we usually define very far away as 3σ 99.73% µ - 3σ µ µ + 3σ3 Determining the state of control: The Control Chart Graphical view of the process over time- - central tendency - spread - trends - changes UCL X Time LCL

21 Time Time 21 The Control Chart: (a.k.a- The Shewhart Control Chart) Developed by Dr. Walter A. Shewhart of Bell Telephone Labs in the 1920 s Emphasis was on developing procedures to economically control quality of manufactured product Mathematical and statistical theory provides the foundation UCL What the Control Chart is intended to do: LCL LCL X

22 22 Control Charts: Two different broad categories: attributes fraction defective (go/ no-go data) defect counts variables measurement data Control Charts for Attributes p charts- counts of percent nonconforming in a sample (sample size may vary) np charts- counts of number of items nonconforming (requires uniform sample size) c charts- counts of nonconformities per unit (sample size may be 1) u chart- counts of nonconformities per unit area

23 23 Control Charts for Variables Process centering X (mean) individuals Process spread s (standard deviation) R (range) R charts are quite common due to ease of calculations (R = x max -x min) SPC in a nutshell : Describe the distribution of a process Estimate the limits within which the process operates under normal conditions Determine if the process is stable. Sample the output of the process and compare to the limits Decide: process appears to be OK, leave it alone, or there is reason to believe something has changed and look for the source of that change Continuous process improvement

24 24 Determining the state of control: Interpreting the Control Chart UCL X Time LCL UCL X Time LCL To be useful, control charts should be updated, interpreted, and actions taken in real time

25 25 SPC in a nutshell : Describe the distribution of a process Estimate the limits within which the process operates under normal conditions Determine if the process is stable. Sample the output of the process and compare to the limits Decide: process appears to be OK, leave it alone, or there is reason to believe something has changed and look for the source of that change Continuous process improvement Outline: History & Philosophy of SPC What is SPC? Management issues Benefits of SPC SPC in a nutshell Process capability analysis

26 26 Is the process capable of meeting specifications? Process Capability Analysis: Process capability- A measure of process potential by relating the spread of the process to the specification width Many customers are setting capability index requirements for their suppliers A visual look at process variation LSL µ (Target) USL

27 27 Capability Indices: C p = USL LSL 6 σ$ C pl = X LSL 3 σ$ C pu = USL X 3 σ$ { } C = min C, C pk pl pu Process Capability- An Example Customer specifications are 3.20 inches ± 0.10 LSL = 3.10 inches, USL = 3.30 Process operates at an estimated mean ( X) of 3.15 inches and estimated standard deviation ( σ$ ) of 0.05 inches Cp = 6005 (. ) = Cpl = = (. ) Cpu = { } = (. ) Cpk = min Cpl, Cpu = 0333.

28 28 Process Capability- What does this example tell us? C p < 1.0, process variability too high (process currently incapable of meeting specs.) C pk < 1.0, currently producing excessive defects C pl C pu, process is currently off-center. (C pl < C pu process is centered too close to the LSL) Excessive defects will be produced (below the LSL) pu, About how much defective product will be made? Process mean = 3.15 inches Standard deviation = 0.05 inches defects 15.9% LSL 3.20 USL 3.10 X

29 29 What if we could shift the process mean to 3.20 inches? Process mean = 3.20 inches Standard deviation = 0.05 inches defects 2.28% defects 2.28% LSL New X = 3.20 USL What if we could reduce the process standard deviation to 0.03 inches? Process mean = 3.20 inches Standard deviation = 0.03 inches defects defects 0.043% 0.043% LSL New X = 3.20 USL

30 30 Summary History & Philosophy of SPC What is SPC? Management issues Benefits of SPC SPC in a nutshell Process capability analysis