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## Continuous Improvement

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**Continuous Improvement**Bill Pedersen, PE**Distort the System**Downsizing example Reduced inventory example Distort the figures End of Quarter Example There are 3 ways to get better numbers: • Improve the System!!!!!!**Continuous Improvement isn’t just about improving, it is**about improving an organization’s ability to improve.**“Speed is everything. It is the indispensable ingredient**in competitiveness.” • Jack Welch, GE CEO**There is always one basic goal. Any ideas what that might**be? • SIMPLIFY!!!!!!!!!!!!!! • Start with the basic building blocks. • Improve on that. • Repeat - forever.**Seven Step Procedure**• Define the Opportunity • Study the Current Situation - Key measures, measurement system, R&R. • Cause Analysis - Pareto and Fishbone Diagrams, Run Charts, Control Charts. • Experiment with the Process - Cpk, EZs, DOE • Check Results - new Cpk. • Standardize - SOPs, TPDs. • Communicate the gain - Improvement Record.**Applications of Continuous Improvement**• Product Redesign Example • Plant Operation and Scheduling Example • Machine Utilization Example**LEADERSHIP!**• Sir Ernest Shackleton • Johnsonville Sausage • Merck & Co., Inc.**What is SPC?**• My Definition: The use of statistical tools to promote continuous improvement and consistently produce high quality parts. • Management by Data**Motivation for Using SPC**• SPC is one of the most important continuous improvement tools. • Variation is the enemy. SPC allows you to identify and eliminate causes. • Gets people involved with process. • Provides hard numbers by which to judge performance. • Excellent source of statistical data to incorporate into designs.**Use Continuous Data whenever possible - more information.**As Quality improves, sample size increases dramatically. Sample size such that n x p >= 5 Data Types • Continuous Data • AttributeData • Time • Temperature • Dimensions • Go/No Go gage • Pass/Fail • Number of defects • Above/Below Setpoint**Basis of SPC**68.26% 95.46% 99.73%**Central Limit Theorem**• Justification of assuming a normal distribution: • The averages of independently distributed random variables is approximately normal, regardless of the distributions of the individual samples.**SPC System Tools**• Many times SPC is thought of as control charts. They are only one component of SPC. • It it the continuous improvement system and methodology that makes this work.**Common Causes of Variation**Are always present. Several small contributors add up to the whole common cause variation. Examples - weather, machine rigidity, incoming material variation. Special Causes of Variation NOT always present - sporadic in nature. Typically have a larger effect on variation than any single common cause. Examples - broken air conditioner, worn bearing, incorrect material. Variation**Sources of Variation - 5 M’s**• Machines • Method • Man • Materials (Incorporates environment usually) • Measurement**Measurement System**• Must verify the measurement system is precise and accurate. Precise Accurate**Repeatability and Reproducibility or Gage Capability**• VarianceTotal = Varianceproduct + Variancemeasurement • Variancemeasurement = Variancerepeatability + Variancereproducibility • Repeatability - Can the same operator get consistent results? • Reproducibility - Can two operators get the same results?**Control Charts**• Many Types, the most common- • Xbar and R, average and range • Xbar and mR, average and moving range • Xbar and S, average and variation of sample • p chart, proportion defective • np chart, number of defects/sample • c chart, number of defectives/sample • u chart, u=c/n => average defects/unit**Primary Goal of Control Charts**Elimination of Variation**Primary Problem with Control Charts**Charting for the Sake of Charting**Control Limits**• Control limits are generally set at +/- 3 sigma from the mean, (both estimated from samples) • example - xBar, R chart • UCL = Xdoublebar + A2*R • LCL = Xdoublebar - A2*R • Central Limit Theorem - The averages of independently distributed random variables is approximately normal, regardless of the distributions of the individual samples.**Out of Control Conditions**• A process that is in control only has common cause variation present. • An out of control process has special cause variation present as well. • Common cause variation is random in nature. If there is a pattern present, it is assumed to be due to a special cause.**Out of Control Conditions - ctd**• Any point outside of control limits. • Eight points in a row on one side of centerline. • Seven points in a row steadily increasing or decreasing. • Fourteen points in a row alternating up and down. • Two of Three in a row outside of 2 sigma, (same side of centerline). • Four of Five in a row outside of 1 sigma, (same side of centerline). • NOTE: You will see variations in these rules. The difference is based on statistical confidence desired.**Xbar, R chart example - ctd**• Sample sizes determined to be subgroups of three, with sampling frequency every order. • Will hopefully be able to reduce this later on. • Need to determine control limits. • Set up a run chart to gather data. • After 25 samples, use that to calculate control limits. • Xdoublebar +/- A2*R • Range Chart: • UCL = D4*Rbar • Centerline = Rbar • LCL = D3*Rbar**Xbar, R chart example - ctd**• Action plan for out of control conditions is well documented and operators have been trained. • Try it and see what happens. • After you fix the bugs, then try it again. • Be sure to check the chart personally every shift, (multiple times at first). Take action when necessary. This is extremely important to the operators, not to mention the process.**What to do with all this data?**• Impress your friends. • Pareto - 80/20 rule. Work on the most significant special cause.**Overadjustment**• This is usually the result of a lack of understanding that variation is random in nature. • Perfect example - Budgets. • Spend it or lose it. • Matching next years budget to this years spending. • Result is a steadily increasing trend away from centerline. The rich get richer, the poor get poorer.**Capability Index - Cpk**• Control charts have control limits. What were they based on? • The actual variation from the process. • Specification limits determine which product is acceptable. They have nothing to do with the control limits whatsoever. • Cpk is used to predict the percentage out of specification based on the estimated process average and variation.**Cpk - ctd**• Cp = (USL - centerline)/(UCL - centerline) • or centerline minus lower limit. • Cpk = min(Cp) • In other words if the Spec Limits just happened to be equal to the Control Limits, then Cpk = 1.0**Six Sigma**• Cp = 2.0 • Cpk = 1.5 due to shifting of the process by 1.5 standard deviations over time. • Result - one sided distribution failure of 3.4 ppm. • Application - electronics.**Improving a Process that is in Control**• Design of Experiments. • Machine modifications. • Method Changes, (SOPs). • Material Changes. • Manpower Changes, (training plan). • Measurement changes are not generally advised, but don’t always rule it out, especially if the process has been running in control for quite some time.**Seven Step Procedure - revisited**• Define the Opportunity • Study the Current Situation - Key measures, measurement system, R&R. • Cause Analysis - Pareto and Fishbone Diagrams, Run Charts, Control Charts. • Experiment with the Process - Cpk, EZs, DOE • Check Results - new Cpk. • Standardize - SOPs, TPDs. • Communicate the gain - Improvement Record.