The Quality Gurus Edward Deming .


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Expense of value. Counteractive action costsAppraisal costsInternal disappointment costsExternal disappointment costsOpportunity costs. What is quality administration about?. Attempt to deal with all parts of the association keeping in mind the end goal to exceed expectations in all measurements that are essential to
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Quality Management " It costs a considerable measure to create an awful item. " Norman Augustine

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Cost of value Prevention costs Appraisal costs Internal disappointment costs External disappointment costs Opportunity costs

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What is quality administration about? Attempt to deal with all parts of the association keeping in mind the end goal to exceed expectations in all measurements that are vital to "clients" Two parts of value: features: more components that address client issues = higher quality freedom from inconvenience: less deformities = higher quality

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The Quality Gurus – Edward Deming Quality is "consistency and trustworthiness" Focus on SPC and factual apparatuses "14 Points" for administration PDCA strategy 1900-1993 1986

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The Quality Gurus – Joseph Juran Quality is "wellness for utilize" Pareto Principle Cost of Quality General administration approach and also insights 1904 - 2008 1951

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History: how could we arrive… Deming and Juran sketched out the standards of Quality Management. Tai-ichi Ohno applies them in Toyota Motors Corp. Japan has its National Quality Award (1951). U.S. what\'s more, European firms start to actualize Quality Management projects (1980\'s). U.S. sets up the Malcolm Baldridge National Quality Award (1987). Today, quality is a basic for any business.

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Technical Tools (Process Analysis, SPC, QFD) Customer Cultural Alignment What does Total Quality Management incorporate? TQM is an administration theory: ceaseless change authority advancement association improvement

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Design quality Dimensions of value Conformance quality Developing quality particulars Design Input Process Output

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A rationality and set of strategies organizations use to wipe out deformities in their items and procedures Seeks to lessen variety in the procedures that prompt to item deserts The name "six sigma" alludes to the variety that exists inside give or take six standard deviations of the procedure yields Six Sigma Quality

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Six Sigma Quality

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Define Customers, Value, Problem Statement Scope, Timeline, Team Primary/Secondary & OpEx Metrics Current Value Stream Map Voice Of Customer (QFD) Measure Assess determination/Demand Measurement Capability (Gage R&R) Correct the estimation framework Process delineate, Time obs. Measure OVs & IVs/Queues Analyze ( and settle the self-evident) Root Cause (Pareto, C&E, conceptualize) Find all KPOVs & KPIVs FMEA, DOE, basic Xs, VA/NVA Graphical Analysis, ANOVA Future Value Stream Map Improve Optimize KPOVs & test the KPIVs Redesign handle, set pacemaker 5S, Cell outline, MRS Visual controls Value Stream Plan Control Document handle (WIs, Std Work) Mistake verification, TT sheet, CI List Analyze change in measurements Value Stream Review Prepare last report Validate Project $ Validate Project $ Validate Project $ Validate Project $ Six Sigma Roadmap (DMAIC) Next Project Celebrate Project $

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Six Sigma Organization

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Quality Improvement Continuous Improvement Quality Traditional Time

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Plan Do Act Check Continuous change logic Kaizen: Japanese expression for constant change. A well ordered change of business procedures. PDCA: Plan - do - check - go about as characterized by Deming. Benchmarking : what do best entertainers do?

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Tools utilized for consistent change 1. Handle flowchart

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Performance Time Tools utilized for nonstop change 2. Run Chart

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Tools utilized for consistent change 3. Control Charts Performance Metric Time

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Machine Man Environment Method Material Tools utilized for consistent change 4. Circumstances and end results graph (fishbone)

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Tools utilized for constant change 5. Check sheet

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Frequency Tools utilized for persistent change 6. Histogram

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Tools utilized for consistent change 7. Pareto Analysis 100% 60 75% 50 40 Frequency half Percentage 30 20 25% 10 0% A B C D E F

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Summary of Tools Process stream outline Run graph Control diagrams Fishbone Check sheet Histogram Pareto investigation

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Case: shortening phone holding up time… A bank is utilizing a call voice-mail The primary objective as far as quality is "zero holding up time" - clients get an awful impression - organization vision to be neighborly and simple get to The question is how to break down the circumstance and enhance quality

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Customer B The present procedure Operator Receiving Party Customer A How would we be able to decrease holding up time?

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Absent accepting gathering Working arrangement of administrators Absent Too many telephone gets Lunchtime Out of office Makes client hold up Not at work area Absent Not giving getting gathering\'s directions Does not comprehend client Lengthy talk Does not know association well Complaining Takes an excess of time to clarify Leaving a message Customer Operator Fishbone chart investigation

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Reasons why clients need to hold up (12-day examination with check sheet)

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Frequency Percentage 87.1% 300 250 71.2% 200 49% 150 100 0% A B C D E F Pareto Analysis: reasons why clients need to hold up

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Ideas for development Taking snacks on three unique movements Ask all representatives to leave messages when leaving work areas Compiling an index where by staff\'s name shows up her/his title

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Percentage Frequency 100% 87.1% 300 71.2% Improvement 200 49% 100 100% 0% A B C D E F B C A D E F Results of actualizing the proposals … After Before…

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as a rule, how might we screen quality… ? By watching variety in yield measures! Assignable variety: we can survey the cause Common variety: variety that may not be conceivable to redress ( arbitrary variety , irregular clamor )

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Statistical Process Control (SPC) Every yield measure has an objective esteem and a level of "satisfactory" variety (upper and lower resistance limits) SPC utilizes tests from yield measures to appraise the mean and the variety (standard deviation) Example We need brew jugs to be loaded with 12 FL OZ ± 0.05 FL OZ Question: How would we characterize the yield measures?

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with a specific end goal to quantify variety we require… The normal (mean) of the perceptions: The standard deviation of the perceptions:

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Average & Variation case Number of pepperoni\'s per pizza: 25, 25, 26, 25, 23, 24, 25, 27 Average: Standard Deviation: Number of pepperoni\'s per pizza: 25, 22, 28, 30, 27, 20, 25, 23 Average: Standard Deviation: Which pizza would you rather have?

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High Incremental Cost of Variability Zero Lower Tolerance Target Spec Upper Tolerance Traditional View When is an item sufficient? a.k.a Upper/Lower Design Limits (UDL, LDL) Upper/Lower Spec Limits (USL, LSL) Upper/Lower Tolerance Limits (UTL, LTL) The "Goalpost" Mentality

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High Incremental Cost of Variability Zero Lower Spec Target Spec Upper Spec But are all "great" items approach? Taguchi\'s View "Quality Loss Function" (QLF) LESS VARIABILITY infers BETTER PERFORMANCE !

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Capability Index (C pk ) It demonstrates how well the execution measure fits the outline particular in light of a given resilience level A procedure is k s competent if

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Capability Index (C pk ) Another method for composing this is to ascertain the capacity list: C pk < 1 implies process is not fit at the k s level C pk >= 1 implies process is proficient at the k s level

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Accuracy and Consistency We say that a procedure is precise if its mean is near the objective T. We say that a procedure is predictable if its standard deviation is low.

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LTL UTL X Example 1: Capability Index (C pk ) X = 10 and σ = 0.5 LTL = 9 UTL = 11

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Example 2: Capability Index (C pk ) X = 9.5 and σ = 0.5 LTL = 9 UTL = 11 LTL UTL X

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Example 3: Capability Index (C pk ) X = 10 and σ = 2 LTL = 9 UTL = 11 LTL UTL X

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Example Consider the capacity of a procedure that puts pressurized oil in a vaporizer. The plan specs require a normal of 60 pounds for every square inch (psi) of weight in each can with an upper resistance point of confinement of 65psi and a lower resilience farthest point of 55psi. An example is taken from creation and it is found that the jars normal 61psi with a standard deviation of 2psi. Is the procedure proficient at the 3 s level? What is the likelihood of creating an imperfection?

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Solution LTL = 55 UTL = 65 s = 2 No, the procedure is not fit at the 3 s level.

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Solution P(defect) = P(X<55) + P(X>65) =P(X<55) + 1 – P(X<65) =P(Z<(55-61)/2) + 1 – P(Z<(65-61)/2) =P(Z<- 3) + 1 – P(Z<2) =G(- 3)+1-G(2) =0.00135 + 1 – 0.97725 (from standard ordinary table) = 0.0241 2.4% of the jars are deficient.

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Example (contd) Suppose another procedure has a specimen mean of 60.5 and a standard deviation of 3. Which process is more exact? This one. Which process is more predictable? The other one.

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Upper Control Limit Central Line Lower Control Limit Control Charts Control outlines let you know when a procedure measure is displaying unusual conduct.

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Two Types of Control Charts X/R Chart This is a plot of midpoints and ranges after some time (utilized for execution measures that are factors ) p Chart This is a plot of extents over the long haul (utilized for execution measures that are yes/no qualities )

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Statistical Process Control with p Charts When would it be a good idea for us to utilize p diagrams? At the point when choices are basic "yes" or "no" by assessment When the example sizes are sufficiently huge (>50)

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Statistical Process Control with p

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