Blended Lean Six Sigma Black Belt Training – ABInBev

Slides:



Advertisements
Similar presentations
Demystifying 6.
Advertisements

Fundamentals of Probability
Introductory Mathematics & Statistics for Business
Detection of Hydrological Changes – Nonparametric Approaches
BUS 220: ELEMENTARY STATISTICS
Overview of Lecture Partitioning Evaluating the Null Hypothesis ANOVA
Lecture 2 ANALYSIS OF VARIANCE: AN INTRODUCTION
Assumptions underlying regression analysis
Chi-Square and Analysis of Variance (ANOVA)
Comparing Two Groups’ Means or Proportions: Independent Samples t-tests.
Statistical Inferences Based on Two Samples
Chapter Thirteen The One-Way Analysis of Variance.
Statistically-Based Quality Improvement
Experimental Design and Analysis of Variance
Multiple Regression and Model Building
Blended Lean Six Sigma Black Belt Training – ABInBev ©2010 ASQ. All Rights Reserved. Answers to DDC Study “Improve”
Introduction to Lean. Benefits of Lean Why go Lean? Improvements in: –Customer service –Quality and efficiency –Staff morale –Internal communication and.
Commonly Used Distributions
Chapter 9A Process Capability and Statistical Quality Control
1 Manufacturing Process A sequence of activities that is intended to achieve a result (Juran). Quality of Manufacturing Process depends on Entry Criteria.
8-1 Quality Improvement and Statistics Definitions of Quality Quality means fitness for use - quality of design - quality of conformance Quality is.
Roberto lopez LSSMBB DMAIC project template.
© ABSL Power Solutions 2007 © STM Quality Limited STM Quality Limited Six Sigma TOTAL QUALITY MANAGEMENT 6 
Lean Six Sigma Knowledge of Lean 6σ Tools can help you in your daily work.
Total Quality Management BUS 3 – 142 Statistics for Variables Week of Mar 14, 2011.
Lean Six Sigma: A Vision
Paul Prunty The 7 Basic Quality Tools ~ The DMAIC Process Continuous Improvement and … To a hammer, everything’s a nail … How many tools do you have in.
Methods and Philosophy of Statistical Process Control
Lean Six Sigma A Methodology for Cultural Change and Continuous Process Improvement (CPI)
Benefits of Lean Manufacturing: To benefit from Lean Manufacturing, the processes must be maintained consistently and correctly. Everyone involved must.
Overview of Lean Six Sigma
Lean Six Sigma Black Belt Blended Learning Program Course Description Blended Learning FLEXIBLE: Class sessions can be 100% online or augmented with live.
Program Participants: Department Managers, Project Leaders, Senior officers, Black Belt candidates and anyone who desires an understanding of Lean Six.
“Safety is a Measure of Success”
1 © The McGraw-Hill Companies, Inc., 2006 McGraw-Hill/Irwin Technical Note 8 Process Capability and Statistical Quality Control.
Six Sigma at Boston Scientific Tuesday 12 September 2006 Steve Czarniak BSC Six Sigma: ASQ Meeting – 12 September 2006.
Explain Six Sigma Simply (Football story from SSDSI) Six Sigma Simplicity.
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Probability Distributions Chapter 6.
Value Analysis/ Flow Analysis
Welcome to Lean Six Sigma Green Belt Training
New Directions Learning & Development  All Rights Reserved. Lean Your Library: How Lean Six Sigma Strategies Can Improve Operations.
Statistical Review We will be working with two types of probability distributions: Discrete distributions –If the random variable of interest can take.
Process Capability and SPC
Success depends upon the ability to measure performance. Rule #1:A process is only as good as the ability to reliably measure.
Lean Manufacturing Chapter 15 pp June 29, 2012.
Analyze Improve Define Measure Control L EAN S IX S IGMA L EAN S IX S IGMA Chi-Square Analysis Chi-Square Analysis Chi-Square Training for Attribute Data.
Measure : SPC Dedy Sugiarto.
Process Characteristics
Hypothesis Testing. Why do we need it? – simply, we are looking for something – a statistical measure - that will allow us to conclude there is truly.
ENGM 620: Quality Management Session 8 – 30 October 2012 Process Capability.
1 Prof. Indrajit Mukherjee, School of Management, IIT Bombay Bill Smith’s findings…. Bill Smith’s paper concluded that if product was found defective and.
1/20/2016ENGM 720: Statistical Process Control1 ENGM Lecture 05 Variation Comparisons, Process Capability.
Department of Defense Voluntary Protection Programs Center of Excellence Development, Validation, Implementation and Enhancement for a Voluntary Protection.
DoD Lead Agent: Office of the Assistant Secretary of the Army (Installations and Environment) Department of Defense Voluntary Protection Programs Center.
Class Six Turn In: Chapter 15: 30, 32, 38, 44, 48, 50 Chapter 17: 28, 38, 44 For Class Seven: Chapter 18: 32, 34, 36 Chapter 19: 26, 34, 44 Quiz 3 Read.
Measure Phase Wrap Up and Action Items. Measure Phase Overview - The Goal The goal of the Measure Phase is to: Define, explore and classify “X” variables.
LESSON 4 Process Improvement – Lean
Lean Six Sigma Road Map Improvement Process Road Map
BINARY LOGISTIC REGRESSION
Statistical Quality Control, 7th Edition by Douglas C. Montgomery.
acceptable quality level (AQL) Proportion of defects
Welcome to my presentation
Basic Training for Statistical Process Control
Basic Training for Statistical Process Control
and its applications to improve animal welfare during transportation
The 7 Basic Quality Tools ~ The DMAIC Process
Statistical Thinking and Applications
Measure Phase Wrap Up and Action Items
Six Sigma (What is it?) “Six sigma was simply a TQM process that uses process capabilities analysis as a way of measuring progress” --H.J. Harrington,
Presentation transcript:

Blended Lean Six Sigma Black Belt Training – ABInBev Normal class introduction – “Welcome to the 3 Day session of the ASQ Lean Six Sigma Black Belt Blended training. We’re planning to cover several topics and areas during our time today. The scope and intent is to review some of the session materials that you have already completed—or in process of completing, to ensure that you’re comfortable with the pace of learning, the materials, and progress you have made to date on your project. Transition to next Week One Review ©2010 ASQ. All Rights Reserved.

Last Time We … Defined our project Leaned our processes Learned statistical techniques Validated our measurement system Created standard work Conducted hypothesis tests Practiced coaching Green Belts Let’s do a quick review before starting the new material.

Define Phase Created the project charter – the critical first step in any LSS project Collected Voice of the Customer High-level process map (SIPOC)

A complete lean system should have: Lean Defined A philosophy and set of methods where waste is identified continuously and eliminated passionately. A complete lean system should have: Techniques/Tools Management (mindset and accountability) Organizational Environment (skills, culture, and behaviors) Waste is any activity (or inactivity) that consumes resources and do not add value to the customer through the product or service.

Key Terms Value-adding activities transform the product in a way the customer would be willing to pay for. Value stream is a series of activities, both those that create value and those that do not, required to deliver a product or service. Value stream mapping is a tool used to document (map) all activities of a value stream to clearly see waste and variations.

8 Wastes The Eight Wastes EXCESS INVENTORY EXTRA PROCESSING DEFECTS EXCESS TRANSPORTATION WAITING EXCESS MOTION OVERPRODUCTION UNUSED CREATIVITY

5 S SORT involves sorting through the contents of the workplace and removing unnecessary items. STRAIGHTEN involves putting the necessary items in their place and providing easy access. SHINE involves cleaning everything, keeping it clean daily, and using cleaning to inspect the workplace and equipment for defects. STANDARDIZE involves creating visual controls and guidelines for keeping the workplace organized, orderly, and clean. SUSTAIN involves training and discipline to ensure that everyone follows the 5S standards.

Seven basic probability rules Basic Statistics Seven basic probability rules All events have a probability between 0 and 1. The sum of all possible probabilities of defined events is equal to 1.00. The probability of an event not occurring is equal to the probability of the event occurring subtracted from 1.0. If events are mutually exclusive, the sum of the probability of occurrence of these events is equal to 1. The probability of the joint occurrence of independent events is the product of the probability of each event. The probability of occurrence of either or both non-independent events is the sum of the probability of each independent events minus the probability of joint events. The probability of observing two dependent events is the product of the probability of the first event and the conditional probability of the second event, given that the first has occurred.

Your Turn 1 Assume a sample is selected from a normally distributed population with a mean of 12 oz and a standard deviation of .01 oz. What percent of the sample would be expected to have a value greater than 12.02? What percent of the sample would be expected to have a value less than 11.99? Ten percent of the sample would be expected to have X or less. What is X? Find the answers and draw graphs of the results using Minitab.

Your Turn 2 Assume a fair coin is flipped 100 times. What is the probability of exactly 50 heads? What is the probability of greater than 60 heads? Assume a process creates .01 defects per unit. One hundred units were sampled. What is the probability there will be: Zero defects? Two defects? Find the answers with Minitab.

Measurement Systems Analysis (Gage R&R) Observed Variation Actual Process Variation Measured Variation Long-Term Process Variation Short-Term Process Variation Variance Due to Instrument Variance Due to Operators Repeatability Calibration Stability Linearity Reproducibility Repeatability and reproducibility are typically the primary contributors to measurement error

Your Turn 3 A Green Belt conducted a gage R&R on a bottling line. He had three operators measure the temperature of the product at 20 locations in the line three times each. He sent the data in Temperature Measurement.mtw. What would you recommend?

Error Proofing Error proofing is a systematic approach for anticipating and detecting potential defects and preventing them from reaching the customer (internal or external) Proactive identification and prevention Prevent first Do not allow defects from going further Intentionally designed, not by chance At the source—close to the origination Inexpensive first Transparent to the operator Absolutely reliable

Defining Process Capability Principle: process capability is a ratio of process variability to design tolerance. Process width Design width It is measured in several ways.

Process Capability Terms Cp – Short-term process capability For a limited period of time (not including shifts and drifts) Does not consider process centering Also known as process entitlement Cpk – Short-term process capability index Does consider process centering Pp – Long-term process capability For an extended period of time (including shifts and drifts) Ppk – Long-term process capability index Here are some basic definitions for capability indices. Note the difference between the short and long term indices. [Briefly review.] See formulae on next page.

Cp = Capability Formulae Specification Width(s) Short-Term Process Width Cp = Pp = Cpk= Ppk= = Specification Width(s) Long-Term Process Width = Lesser of: or Lesser of: or

Your Turn 4 A Green Belt collected data on the fill volumes in cans. The specification limits are 11.995 to 12.005 with a target of 12. The data is in six pack spc. What is the capability of this process?

Documents layout and flow process sequence Also documents: Standard Work Documents layout and flow process sequence Also documents: • Standard W.I.P. • Quality checks • Safety precautions Posted in cell Includes visual work instructions for each operation

Hypothesis Testing Enables us to determine if there is a statistically significant difference between the characteristics of interest of samples of data.

Testing Method Selection Matrix There are many types of hypothesis tests we will learn to use depending on the type of data available for the inputs (Xs) and outputs (Ys). Variable Type Attribute Y Count Y Continuous Y Discrete X 1 or 2 Treatments Proportions 3 +Treatments Chi Square 1 or 2 Treatments Poisson 3 + Treatments Chi Square 1 or 2 Treatments T tests 3 +Treatments ANOVA Continuous X Logistic Regression Least Squares Regression

Your Turn 5 A Green Belt collected data on Gross Line Yield before and after a process change. Did the change make a statistically significant improvement (more is better) on Gross Line Yield? The data is in Gross Line Yield.mtw.

Let’s get started! What’s Next? Completing our set of hypothesis testing tools More lean tools Designed experiments Control plans Design for Six Sigma Let’s get started!

Blended Lean Six Sigma Black Belt Training – ABInBev Normal class introduction – “Welcome to the 3 Day session of the ASQ Lean Six Sigma Black Belt Blended training. We’re planning to cover several topics and areas during our time today. The scope and intent is to review some of the session materials that you have already completed—or in process of completing, to ensure that you’re comfortable with the pace of learning, the materials, and progress you have made to date on your project. Transition to next Answers to Week One Review Your Turn ©2010 ASQ. All Rights Reserved.

Your Turn 1 Solution 1 Calc>Probability Distributions>Normal This will give the probability of less than 12.02, which we will subtract from 1 to get the answer. Minitab returns Normal with mean = 12 and standard deviation = 0.01 x P( X <= x ) 12.02 0.977250 So .02275 would be greater than 21.02 oz.

Your Turn 1 Assume a sample is selected from normally distributed population with a mean of 12 oz and a standard deviation of .01 oz. What percent of the sample would be expected to have a value greater than 12.02? What percent of the sample would be expected to have a value less than 11.99? Ten percent of the sample would be expected to have X or less. What is X? Find the answers and draw graphs of the results using Minitab.

Your Turn 1 Solution 1 Graph Graph>Probability Distribution Plot

Your Turn 1 Solution 1 Graph

Your Turn 1 Solution 1 Graph

Your Turn 1 Solution 2 Graph Control+e to return to the last dialog box

Your Turn 1 Solution 2 Graph

Your Turn 1 Solution 3 Graph Control+e to return to the last dialog box

Your Turn 1 Solution 3 Graph

Your Turn 2 Assume a fair coin is flipped 100 times. What is the probability of exactly 50 heads? What is the probability of greater than 60 heads? Assume a process creates .01 defects per unit. One hundred units were sampled. What is the probability there will be: Zero defects Two defects Find the answers with Minitab

Your Turn 2 Solution 1 Calc>Probability Distributions>Binomial Binomial with n = 100 and p = 0.5 x P( X = x ) 50 0.0795892

Control+e to return to the last dialog box Your Turn 2 Solution 2 Control+e to return to the last dialog box This will return the probability of 60 or less. Subtract from 1 to get the probability of greater than 60. Binomial with n = 100 and p = 0.5 x P( X <= x ) 0.982400 The probability of greater than 60 heads is .0176.

Calc>Probability Distributions>Poisson Your Turn 2 Solution 3 Calc>Probability Distributions>Poisson .01 * 100 = 1 Poisson with mean = 1 x P( X = x ) 0 0.367879

Control+e to return to the last dialog box Your Turn 2 Solution 3 Control+e to return to the last dialog box Poisson with mean = 1 x P( X = x ) 2 0.183940

Your Turn 3 A Green Belt conducted a gage R&R on a bottling line. He had three operators measure the temperature of the product at 20 locations in the line three times each. He sent the data in Temperature Measurement.mtw. What would you recommend?

Your Turn 3 Solution Assistant>Measurement Systems Analysis

Your Turn 3 Solution

Your Turn 3 Solution This is a good measurement system.

Your Turn 4 A Green Belt collected data on the fill volumes in cans. The specification limits are 11.995 to 12.005 with a target of 12. The data is in six pack spc. What is the capability of this process?

Your Turn 4 Solution Assistant>Capability Analysis

Your Turn 4 Solution

Your Turn 4 Solution

Your Turn 4 Solution Process control in marginal and capability is poor

Your Turn 5 A Green Belt collected data on Gross Line Yield before and after a process change. Did the change make a statistically significant improvement (more is better) on Gross Line Yield? The data is in Gross Line Yield.mtw.

Your Turn 5 Solution Assistant>Hypothesis Test

Your Turn 5 Solution

Your Turn 5 Solution He can conclude a statistically significant improvement was made.