LSSG Green Belt Training Measure: Finding and Measuring Potential Root Causes

DMAIC Six Sigma - Measure Objectives Identify Inputs and Outputs Determine key inputs and outputs for the process and measures to be analyzed Measure Process Capability Collect data and compare customer requirements to process variation Revise Charter Validate project opportunity and perform charter revision Measure Control Analyze Improve Define

Agenda for Measure Types of Measures/Setting Targets Data Collection and Prioritization, MSA SPC, Control Charts Process Capability

Measures Purpose of measurement: Performance of a process vs. Expectations Objective Lose 13 Pounds in 3 months Secondary Objective Lose 1 Pound per week Driver(s) Calories consumed less Calories burned Critical Success Factors (Drivers Run 4 miles/day and consume less than 1500 calories/day Must measure both the result (Y) and the drivers (Xs). Measure daily – to determine if CSFs are met, and to make adjustments to plan. Select Measures “SMART” Objectives Clear operational definitions E.g. Losing Weight

LSS Measurement Measurement vs. Control Causes/ Effects Measurement Measurement Plan Data Operational Definitions and Procedures What data type? How measured? What conditions? By who? Where measured? What sample size? How to ensure consistency of measurement? What is the data collection plan? Measurement vs. Control Causes/ Effects Measurement System Control System Use: To identify important factors for data collection, to identify factors that influence the output Control Measurement Goal: to identify, collect and report key inputs and outputs of business processes Control Goal: to proactively control and manage process performance rather then to simply measure the past Historical data Current data Measurement is not control! So, what is it?

Setting Targets Set Targets Objective/Meaningful Management-employees collaboration Team goal compatible with value stream objective Balanced Score Card Perspectives Financial Customer Internal Process Learning & Growth

Agenda for Measure Types of Measures/Setting Targets Data Collection and Prioritization, MSA SPC, Control Charts Process Capability

Data Collection and Prioritization Some Collection Tools Customer Survey Work / Time Measurement Check Sheet Some Prioritization Tools Pareto Analysis Fishbone Diagram Cause and Effect Matrix

Work Measurement Goals of Work Measurement Scheduling work and allocating capacity Motivating workers / measuring performance Evaluating processes / creating a baseline Determining requirements of new processes Work Sampling Data collected over a period of time (e.g., a shift, a day, etc.) Assumption that breaks, delays, etc. occur normally No need for special equipment or training No need to make adjustments to observed times due to operator bias or allowances (lunch, breaks, etc.)

Time Studies Typically using stop watches For infrequent information - estimates OK Measure person, machine, and delays independently Medium Duration - not too short; not too long Eliminate Bias - Compute Standard times from observed times

Time Study: Calculations Step 1: Collect Data (Observed Time) Step 2: Calculate Normal Time from Observed Time, where: Step 3: Calculate Standard Time from Normal Time, where:

Time Study: Numerical Example A worker was observed and produced 40 units of product in 8 hours. The supervisor estimated the employee worked about 15 percent faster than normal during the observation. Allowances for the job represent 20 percent of the normal time for breaks, lunch and 5S. Determine the Standard Time per unit.

Data Analysis Tools Scatter Diagram Run Chart Histogram Control Chart Can be used to illustrate the relationships between factors such us quality and training Can be used to identify when equipment or processes means are drifting away from specs Histogram Control Chart Frequency Data Ranges Can be used to display the shape of variation in a set of data Use to identify if the process is predictable (in control)

Cause and Effect Diagram Material Method Environmental Man Machine Effect

Pareto Charts Root Cause Analysis 80% of the problems may be attributed to 20% of the causes

Continuous Improvement Process Orlando Remanufacturing And Distribution Center

Phase 1: Internal Kickbacks Equipment To Be Remanufactured Tear Down And Wash Remanu- facture Reassembly Final Clean-up QA Unit Not OK To Customer Five Most Common Reasons For Returns From QA Missing/ Wrong Part Dirt/Rust Defective Part Leaks Poor Insulation Impact of Reasons for Returns from QA - Weighted Average Weighted Avg. = % Occurring X Defect Cost (0-10, Based on Time to Repair) Leaks Dirt/Rust Stainless Steel Missing/ Wrong Part Defective Part Poor Insulation

Why Dirt? (Fishbone) Dirt Machines Best Tools for $$? Measurement Lack of Communication QA to IT Rework Rinse Training Attention to Detail Poor Lighting Dust/Humidity Space Limitations Tools for $$ Cleansing Compounds Larger Wire Brushes Environment Dirt Machinery Materials Methods Man Measurement Machines Best Tools for $$? Measurement QA Manager Fixes Some Things Without Informing the Technicians Environment Dust/Humidity Poor Lighting Space Limitations Methods Reworking Steel after Valves are Installed Need to Rinse Parts off after Sandblasting Materials Cleansing Compounds Need Larger WireBrushes People Need More Training More Attention to Detail – Do it Right First Time

No Leak Testing Prior to QA Identify Most Occurrences Why Leaks? (Fishbone) No Leak Testing Prior to QA Quality Check Don’t Crimp Properly Use Wrong Clamps Poor Lighting High Temperature Reengineer Rims “O” Rings Old Bad Tubing Environment Leaks Machinery Materials Methods Man Measurement Forget to Connect Mishandle Units Identify Most Occurrences Environment High Temperatures Poor Lighting Methods Check Units for Ways They Could Leak Does Testing Create Leaks? Materials Bad Tubing “O” Rings Too Old (Dry) People Use Wrong Clamps Don’t Crimp Properly Forget to Connect Machines Need Rims That Make it Easier to Install Tubing Measurement No Testing for Leaks Prior to QA Which Mfr./Model Leak the Most?

Variation Analysis Variation is Present in All Processes! Most variation without “special” causes will be normally distributed Environment Machinery Materials Man Measurement Output Variation is typically classifiable into the 6 M’s Methods Variation is additive Variation in the process inputs will generate more variation in the process output Variation is Present in All Processes!

Measurement System Analysis (MSA) Goal - To identify if the measurement system can distinguish between product variation and measurement variation Some key dimensions Accuracy Precision Bias Key dimensions Stability Consistency; in statistical control; no special causes Discrimination Precision/ability to distinguish between values of a measure Bias Differences in measurements due to other variables, such as different instruments, different operators, different locations, time of day, day of week, etc. Accuracy Difference between observed value and a target (standard) value Repeatability Precision or test/retest error; the variation between successive measurements with no changes to other factors (man, machine, materials,…) Reproducibility Difference in measurement average by different operators measuring the same characteristic Tools: Gage R&R, DOE, Control Charts

Agenda for Measure Types of Measures/Setting Targets Data Collection and Prioritization, MSA SPC, Control Charts Process Capability

SPC vs. Acceptance Sampling Acceptance Sampling: Used to inspect a batch prior to, or after the process Take Sample Receive Lot Meet Criteria? Accept Reject Rework /Waste Send to Customer Yes No Statistical Process Control (SPC): Used to determine if process is within process control limits during the process and to take corrective action when out of control 400 420 440 460 480 500 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LCL UCL

Statistical Process Control UCL LCL Process in Statistical Control Process not in Statistical Control Statistical process control is the use of statistics to measure the quality of an ongoing process A Process is in control when all points are inside the control limits A Process is not in control when one or more points is/are outside the control limits Special Causes

When to Investigate UCL LCL 1 2 3 4 5 6 In Control Even if in control the process should be investigated if any non random patterns are observed OVER TIME UCL LCL Trend - Constant Increase/Decrease UCL LCL 1 2 3 4 5 Close to Control Limit 1 2 3 4 5 6 UCL LCL 5 10 15 20 Cycles UCL LCL 1 2 3 4 5 6 Consecutive Points Below/Above Mean

Control Chart Development Steps Collect Data 2 INPUTS OUTPUT X’s Y’s Identify Measurement 1 Improve Process 4 A B C D Defects Start Eliminate Special Causes Reduce Common Cause Variation Improve Average Determine Control Limits 3

Frequently Used Control Charts Attribute: Go/no-go Information, sample size of 50 to 100 Defectives p-chart, np-chart Defects c-chart, u-chart Variable: Continuous data, usually measured by the mean and standard deviation, sample size of 2 to 10 X-charts for individuals (X-MR or I-MR) X-bar and R-charts X-bar and s-charts

SPC Attribute Measurements -2 -1 1 2 3 -3 Z- VALUE is the number of Standard Deviations from the mean of the Normal Curve Normal Distribution: Z-Value m Z p-Chart Control Limits percentage defects (mean) Standard deviation of p Z Number of standard deviations n Number of observation per sample (i.e., sample size) UCL Upper control limit LCL Lower control limit

p-Chart Example Calculate the sample proportion, p, for each sample Calculate the average of the sample proportions Calculate the sample standard deviation Calculate the control limits (where Z=3) Plot the individual sample proportions, the average of the proportions, and the control limits

SPC Continuous Measurements X-bar, R Chart Control Limits Chart Limits R Chart Limits Shewhart Table of Control Chart Constants

SPC Continuous Measurements UCL X-bar Chart LCL Sample Range -0.15 0.05 0.25 0.45 0.65 0.85 1.05 1.25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Sample LCL UCL R Chart

Proper Assessment of Control Charts Find special causes and eliminate If special causes treated like common causes, opportunity to eliminate specific cause of variation is lost. Leave common causes alone in the short term If common causes treated like special causes, you will most likely end up increasing variation (called “tampering”) Taking the right action improves the situation

Quarterly Audit Scores Did something unusual happen? 1 2 3 4 5 6 Quarter Score

Quarterly Audit Scores What do these lines represent? 1 2 3 4 5 6 Quarter Score

Quarterly Audit Scores Now what do you think? 1 2 3 4 5 6 Quarter Score

Agenda for Measure Types of Measures/Setting Targets Data Collection and Prioritization SPC, Control Charts Process Capability

Process Capability Introduction “Voice of the Process” (The “Voice of the Data”) Based on natural (common cause) variation Tolerance limits (The “Voice of the Customer”) Customer requirements/Specs Process Capability A measure of how “capable” the process is to meet customer requirements Compares process limits to tolerance limits

Process Capability Scenarios natural variation specification A specification natural variation C specification natural variation B specification natural variation D

Process Capability Index, Cpk Capability Index shows if the process is capable of meeting customer specifications 50.00 ± 4.00 Mean = 50.50 Stdev = 1.5 Find the Cpk for the following: A process has a mean of 50.50 and a variance of 2.25. The product has a specification of 50.00 ± 4.00

Interpreting the Cpk Cpk > or = 0.33 Capable at 1 * * Processes with Cpk < 1 are traditionally called “not capable”. However, improving from 1 to 2, for example, is extremely valuable.

Calculating Yield Traditional Yield (TY) Task 1 Task 2 Task 3 Task 4 Task 5 96 units 4 rwk 98 units 2 rwk 95 units 5 rwk 90 units 10 rwk 100 units Traditional Yield (TY) Rolled Throughput Yield (RTY): another way to get “Sigma” level Some examples can be found at: https://oasis.northgrum.com/general/docs/advanced/AdvancedProcessCapability2-Yield.ppt#3 The Hidden Factory = TY - RTY The Hidden Factory = 0.96-0.77 =0.19 Traditional Yield assessments ignore the hidden factory!

Six Sigma Quality Level Six Sigma results in at most 3.4 DPMO - defects per million opportunities (allowing for up to 1.5 sigma shift). Is Six Sigma Quality Possible? IRS Tax Advice DPMO 1,000,000 Doctor Prescription Writing Restaurant Bills 93% good 100,000 Airline Baggage Handling 99.4% good 10,000 Payroll Processing 99.98% good 1,000 Link to: http://www.mtech.edu/nchci/Rural Conference/presentations/safety IT.ppt http://www.ehcca.com/presentations/HIPAA4/3_07.ppt 100 Domestic Airline Flight Fatality Rate (0.43PMM) 10 1 1 2 3 4 5 6 SIGMA Source: Motorola Inc.

Six Sigma Quality Six Sigma Shift The drift away from target mean over time 3.4 defects/million assumes an average shift of 1.5 standard deviations With the 1.5 sigma shift, DPMO is the sum of 3.39767313373152 and 0.00000003, or 3.4. Instead of plus or minus 6 standard deviations, you must calculate defects based on 4.5 and 7.5 standard deviations from the mean! Without the shift, the number of defects is .00099*2 = .002 DPMO. Z 4.5 6.0 7.5 P(<Z) 0.99999660232687 0.99999999901341 0.99999999999997 1 - P(<Z) 0.00000339767313 0.00000000098659 0.00000000000003 * 1,000,000 3.39767313373152 0.00098658770042 0.00000003186340

Quality Levels and DPMO Defects per million opportunities Assumes 1.5 sigma shift of the mean Sigma Level DPMO (Defects per million opportunities) Reduction from previous sigma level 1.0 697672   2.0 308770 55.74% 3.0 66811 78.36% 4.0 6210 90.71% 5.0 233 96.25% 6.0 3.4 98.54% Regardless of the current process sigma level, a very significant improvement in quality will be realized by a 1-sigma improvement!

Is Six Sigma Quality Desirable? 99% Quality means that 10,000 babies out of 1,000,000 will be given to the wrong parents! One out of 100 flights would result in fatalities. Would you fly? What is the quality level for Andruw Jones?