Lean Six Sigma Green Belt Training

Lean Six Sigma Green Belt Training

UNIT 2 Measure

Measure – Learning Objectives
At the conclusion of this unit, you will be able to: Create detailed process map/flowchart. Develop a data collection plan to gather initial data. Stratify data to facilitate understanding. Revise the problem statement based on data. Assess financial impact of your project. Ref Unit 2-1

Measure – Major Activities
DEFINE MEASURE ANALYZE IMPROVE CONTROL Create Detailed Process Map Revise Problem Statement Assess Financial Impact Gather Initial Data Stratify Data Overall objective: Narrow the improvement opportunity to a specific problem statement. Ref Unit 2-2a

Measure – Key Deliverables/Commonly Used Tools
Detailed process flowchart Upstream metrics/ Targets/Specification limits Data collection plan Operational Definitions Metric stratification Defect definitions Process sigma Revised problem statement Statement of cost/benefit Flowchart/Process maps Line graph/Run chart Data collection worksheet Checksheet Concentration diagram Pareto chart Histogram Control chart Cost/benefit analysis Ref Unit 2-2b

DEFINE MEASURE ANALYZE IMPROVE CONTROL Create Detailed Process Map Revise Problem Statement Assess Financial Impact Gather Initial Data Stratify Data Ref Unit 2-3a

Process Mapping Benefits
Provides a picture Promotes consistency Fosters teamwork Stimulates ownership Highlights inefficient and missing activities Gets everyone singing on the same page Ref Unit 2-3b

Functional Flowcharts
“Swimlanes” Who is available Who has knowledge As-is vs. Should be Ref Unit 2-4

Common Flowchart Symbols
An oval is used to show the inputs to start the process, or to show the output at the end of the process. A rectangle or box is used to show process activities. Each box should have only one arrow coming in, and one going out. Words in a box should begin with an “action word”, e.g., receives, sends, completes, etc. An arrow is used to show the direction or flow of process activities. A diamond is used to show where decisions occur in the process. Decisions have one arrow in and two arrows out (yes/no). A small circle with a letter or number identifies a break in the same page. A rectangle or box with dashed lines is used to list clarifying information. Ref Unit 2-5

See Process Mapping Tips
Flowcharting Steps Identify and name the process Identify process boundaries Determine process level List process roles Identify activities for each role Review and validate See Process Mapping Tips Ref Unit 2-6/7

Develop a detailed functional flowchart of your project’s process.
Activity Develop a detailed functional flowchart of your project’s process. Ref Unit 2-9

DEFINE MEASURE ANALYZE IMPROVE CONTROL Gather Initial Data Create Detailed Process Map Revise Problem Statement Assess Financial Impact Stratify Data Why do we need data? Where does the data come from? Ref Unit 2-10

Collect Outcome Metric Data
Process Suppliers Customers Products Services Inputs Outputs Outcome Metrics Is process acceptable? Ref Unit 2-11

Do you know the name of this tool?
The Gap Good Do you know the name of this tool? Titles Data over time Good arrow Target info. Time on X axis Source info. What does it tell us? Ref Unit 2-12

Line Graph/Run Chart Helps identify cycles, shifts and trends in process performance Highlights gaps between process targets and actual performance Clarifies before and after changes in process performance Aids in predicting future performance Ref Unit 2-13

Upstream Metrics are measures of performance within a process.
Suppliers Customers Products Services Inputs Outputs Upstream Metrics Outcome Metrics May want to relate this to SIPOC covered earlier. Upstream Metrics are measures of performance within a process. Ref Unit 2-14

Sources of Upstream Metrics
(cont’d.) Sources of Upstream Metrics Inputs from suppliers Outputs from sub-processes Handoffs from one employee or department to another Key decisions Inspection points Y = f(x) Ref Unit 2-15a

Process Metrics and Targets Example
Critical Customer Requirement Service Characteristic Outcome Metric Target Upstream Metric “I want my policy issued quickly.” Turn Around Time % of policies not issued on time <2% not on time Time to complete underwriting <2 working days Operational Definitions: On time means within 5 working days starting the day after the policy is received from agent. Time to complete underwriting begins at receipt of policy from Sales, ends when underwriting approved Ref Unit 2-15b

Process Metrics and Flowchart
Sales Sales Marketing Customer Sales/Marketing Manager Clerk Engineer Rep Receive Send RFP Send RFP RFP Log RFP Want No No Update Assign to Quote Cust. . File A RFP ? Yes Upstream Metric Prepare Add Proposal Pricing U1 No No Specs Log/Give to Mgr. OK ? Yes Yes A Price Price No No OK OK ? ? Outcome Metric Prepare “No Quote” Letter Yes Yes Approve Approve Approve Approve O1 O1 Proposal Proposal Proposal Proposal Receive Send Response Response Ref Unit 2-16

Create Line Graph for Outcome Metric & Define Upstream Metrics/Targets
Activity Create Line Graph for Outcome Metric & Define Upstream Metrics/Targets Ref Unit 2-17

Overview of Data Collection
Information to be Covered What types of data will you need? How can you ensure the data is accurate? How much data is necessary? What tools are useful for data collection? Ref Unit 2-18a

Planning for Data Collection
Overall Approach Plan - Who, What, When, Where, and How the data will be collected (4Ws and 1H) Ensure - data integrity Analyze – compare first samples taken with what is needed Adjust - data collection plan as needed Ref Unit 2-18b

Types of Data Continuous data: variables, measurable, e.g. time, distance, weight, money. Attribute data: discrete, counted; measures presence or absence of a characteristic, e.g. good/bad # of defects. Ref Unit 2-19

Activity Analyze Types of Data Ref Unit 2-20

Ensure Data Integrity Precise/Accurate – Data from the measurement method or device does not vary much from the actual value Repeatable – Repeated measurements of the same item or characteristic by the same person lead to the same result Reproducible – Two or more people measuring the same characteristic in the same way get the same result Stable over time – Data and measurements taken at different times by different people are done in the same way (the measurement system doesn’t change over time) Ref Unit 2-21

Operational Definitions
Characteristics Specific Clear Measurable Reflects customer Perspective Givens Test and refine the definition after it is first developed Training is required to ensure that results from using the definition are consistent Definition An operational definition is a precise description of how to get a consistent value for the characteristic you are trying to measure. Ref Unit 2-22

Operational Definitions
(cont’d.) Defect – process output that is not acceptable to the customer; not within specification Defect Opportunity – A point where a CCR could be missed each time something moves through a process An opportunity is a place where a defect can reasonably occur The number of defect opportunities is related to the complexity of the process. The number of defect opportunities per unit must stay constant before and after DMAIC improvement Ref Unit 2-23

Create operational definitions for your Outcome & Upstream Metrics.
Activity Create operational definitions for your Outcome & Upstream Metrics. Ref Unit 2-24

Characteristics of Useful Data
Relates to the problem you’re studying – the data can be directly linked to the Preliminary Problem Statement Answers questions about current process performance – when is it occurring, who is involved, where is it occurring, what is happening, how is it happening Provides information about related conditions – a defect occurred on Thursday; on Thursday we were short-staffed; also on Thursday the system was down for 2 hours Cost effective – the value of the data collected is worth more than the cost of collecting the data How Much Data to Collect Ref Unit 2-25

Develop a Sampling Plan
Sampling is collecting some of the data from a process to make inferences about all of the data. Population -“N” Usually unknown Sample – “n” Take a sample To make inferences about… Ref Unit 2-26

(cont’d.) Reliable Samples The data is representative of the population Every item has a known and usually equal chance of being included There are no systematic differences between the data you collect and the data you do not collect Unreliable Samples Samples are only collected when/where convenient The sample collection method has a pattern The process changes during data collection Faulty measurements Only a portion of people you need data from respond to your request for information Ref Unit 2-27

(cont’d.) What does sample size depend upon? Desired sampling error – how precise you want to be (± 4%) Desired confidence level – how confident that true population rate falls between selected error rate (95%) Variation of the population – characteristics, make-up Size of the population – larger populations, larger sample Calculations and other information related to determining sample size is covered in BlackBelt training. Ref Unit 2-28

Tools for Collecting Data
Gaps between process targets and actual performance Cycles and trends in process performance The use of tools in data analysis is critical – they help you visualize! Ref Unit 2-29a

Frequency Plot Checksheet
Checksheets Report Preparation Confirmation Checksheet Step Done? Completion Data Planned date Actual date Planned duration Project completed Client review & approval Final report, draft Final report review Final report revisions Desktop publishing of report Final report submission 6-12 6-17 6-30 7 -12 7-21 7-28 7-30 6-26 7-6 8-2 Actual duration 5d 13d 12d 9d 7d 2d 10 15d N/A Notes Cust requested changes Client personnel on vacation Minor changes requested Source: X files Correct cashier error OK check Checkout Line Delays Cashier Date Reason Frequency Comments Price check needed No cashier available Register out of tape Not enough money Forgot item Wrong item Manager assistance needed Other Wendy May 19 Source: X files Frequency Plot Checksheet Package Weight 16.0 16.1 16.2 16.3 16.4 16.5 16.6 16.7 16.8 Weight in ounces Source: X files Source: X files Ref Unit 2-29b/30

Concentration Diagram
Ref Unit 2-33

Implementation Interpretation Checksheets (cont’d.)
Are all appropriate, high potential categories of data being considered? Is the right data being gathered? Does everyone understand how to fill out the checksheet? Has management clearance been given? Do the people being observed understand why the checksheet is being used? Interpretation Look for trends and patterns in the data Look for something unusual Other tools can be used with the Checksheet to help interpret the data, e.g., line graph, pareto chart, etc. Extract summary data by sub-totaling, or stratifying the data Ref Unit 2-32

Create or review the data collection plan for your project.
Activity Create or review the data collection plan for your project. Ref Unit 2-34

Activity (cont’d.) Ref Unit 2-35
Data Collection Plan Project ________________________ How measured1 What questions do you want to answer? Data Operational Definition and Procedures What Measure type/ Data type Related conditions to record2 Sampling notes Notes 1) Be sure to test and monitor any measurement procedures/instruments. 2) “Related factors” are stratification factors or potential causes you want to monitor as you collect data. What is your plan for starting data collection? (attach details if necessary) How will the data be displayed? (Sketch below) How/where recorded (attach form) Ref Unit 2-35

DEFINE MEASURE ANALYZE IMPROVE CONTROL Gather Initial Data Create Detailed Process Map Revise Problem Statement Assess Financial Impact Stratify Data Ref Unit 2-36a

What is Stratification?
Stratification is classifying and separating data into related groups: Stratify Outcome Metric data sources of the gap Goal of stratification is a search for significance Use stratification to organize resources Stratification helps identify the value of the improvement Ref Unit 2-36b

Stratify By Asking Questions
% Not Issued on Time Good % of Policies Not Issued On Time 0% 2003 2004 Target = <2% 1st Qtr. 2nd Qtr. 3rd Qtr. 4th Qtr. 5% 10% 20% 25% 15% GAP Source: X Files 2005 Period When did the data spike, then go down? What are reasons for the data to cycle? Is it possible to track where the policies that took the longest were written? Who wrote the policies that were not issued on time? How did it happen? Answer Questions with Data Ref Unit 2-37

Pareto Chart non-Medical Exam Policies not issued on time 20 40 60 80 100 120 140 Missing Info. on Application Unable to contact agent with question Insufficient follow up Lack of premium Second requirements Delayed by delivery Large amount coverage System error 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% N=149 Source: X files Ref Unit 2-38

Interpreting Pareto Chart
Isolate the significant few from the trivial many – example: 80% in first bar. The first category (bar) does not have to contain 80% of the total under analysis. A flat or somewhat level pareto chart usually means examine the data further. Stratify the data in different ways (frequency, severity, impact) in order to identify the most significant category. Ensure that each category has the same opportunity to contribute. Ref Unit 2-40

How else could this data be stratified?
Pareto Practice How else could this data be stratified? Reasons Proposals Take Longer Than 3 Days To Present to Manager for Approval 10 20 30 40 50 60 70 80 Waiting for price info. service specs. Not aware of deadline Researching service info. RFP not assigned Other # Late Proposals 100% n = 80 0% 50% Sales & Marketing Dept. Jan – Jun, 2003 Ref Unit 2-41

Complete Pareto analysis with data provided
Activity Complete Pareto analysis with data provided Ref Unit 2-42a

Activity – Answer 1 Ref Unit 2-42b XYZ Loan Processing 10/23-27/01 48
40 30 20 10 XYZ Loan Processing N = 48 12 Type of Error 10/23-27/01 Number of Defects 7 6 3 4 Not legible Signature Calculation Late submittal Approval Missing info. Other 100% 50% 25% 75% Source: Loan Dept. 10/23-27/99 S. Martinez Ref Unit 2-42b

XYZ Loan Processing - Cost of Defects Extended Cost - Type of Defect
Activity – Answer 2 Source: Loan Dept. 10/23-27/99 S. Martinez XYZ Loan Processing - Cost of Defects Extended Cost - Type of Defect Not legible Approval Late submittal Signature Calculation Other Missing info. $3008 2500 2000 1500 1000 N = $3008 1158 Co$t of Defects 469 456 408 350 63 104 500 100% 50% 25% 75% Ref Unit 2-42c

Multi-level Stratification
Ref Unit 2-44

Histogram Avg. Time Request to Patient Pickup Ref Unit 2-45a
Avg. Request to Pickup Time (in minutes) at Holy Cross Hosp. Taken by Transporter Dispatcher Jan-Feb, 2004 Ref Unit 2-45a

Histogram/Frequency Chart
Claims Processing (Hours) Frequency Total 1:00-2:30  3 2:31-4:00  12 4:01-5:30  16 5:31-7:00  11 7:01-8:30  6 8:31-10:00  1 10:01-11:30 TOTAL 50 Ref Unit 2-45b

Histogram Interpretation
Focus on the center of the data distribution Look at the data spread Consider the typical histogram shapes Ref Unit 2-47

(cont’d.) Nothing unusual. Try to find causes of variation (data spread). Determine if data is centered properly. Stratify the data to avoid two populations of data; create two histograms. Recalculate and draw the histogram again. Investigate “outliers”. Determine why the data is not centered near the average. Determine why part of the data is missing. Check your calculations. Collect more data if necessary. CLASSES OF DATA FREQUENCY (y) (x) Normal Bi-modal Comb Skewed Precipice Ref Unit 2-48/49

(cont’d.) Would you change anything? Ref Unit 2-50

(cont’d.) How would you proceed? Ref Unit 2-51

Understand Data Variation
Expect Variation in Everything Target Target Too early Too early Too late Too late Defects Defects Reduce Variation Delivery Time Delivery Time Spread of variation is too wide compared to customer specifications Spread of variation is narrow compared to customer specifications Less Variation is Desired Ref Unit 2-52

Measures of Central Tendency
Measure Definition: Example: Mode: The most frequently occurring value in the data set (12,14,16,17,14,11,18,17,24,26,23,27,14,14) Ordered Data = (11,12,14,14,14,14,16,17,17,18,23,24,26,27) Mode = 14 Median: The value that falls exactly in the middle of the ordered data set, half of the numbers fall above, and half below. (11,12,14,14,14,14,16,17,17,18,23,24,26,27) Median = ( )/2 = 33/2 Median = 16.5 Mean: The sum of the data set values, divided by the number of values in the data set. (11,12,14,14,14,14,16,17,17,18,23,24,26,27) Ref Unit 2-53

Activity – Calculating Central Tendency
The data below are a sample of days of absence over a year in a department. Determine the mean, median, and mode of this sample: 1, 2, 3, 3, 4, 5, 7, 9, 10, 11, 120 Mode = Median = Mean = If this data set were handed to you to analyze, what might be your reaction? 3 5 15.9 Answer to question: Data is skewed, which would effect mean. Perhaps median is better representation of central tendency. Ref Unit 2-54

Measures of Dispersion
Measure / Definition Example Range - The difference between the highest and lowest (greatest and smallest) values. (12,14,16,17,14,11,18,17, 24, 26, 23, 27, 14, 14) Ordered Data = (11,12,14,14,14,14,16,17,17,18, 23, 24, 26, 27) Range: (27 – 11) = ________ Standard Deviation (sd) – The calculated variation between each data point in the set and the mean. (3,5,7,9,11,12,4,5,13,1,16) Ordered Data = (1,3,4,5,5,7,9,11,12,13,16) Xbar = ________ s = 4.73 Note: Standard deviation calculations are covered in BlackBelt training. 16 7.82 Ref Unit 2-55

The Empirical Rule 68.26% of all data should fall within a distance of ±1sd from the mean 95.45% within a distance of ±2sd from the mean 99.73% with a distance of ±3sd from the mean Ref Unit 2-56

Activity – Predicting Process Output
Assuming the average number of days to process a new insurance policy is 5, and the standard deviation is 1. How many days can you expect it to take to process a new insurance policy? In other words, within what range would you expect to find 99.73% of the data? 99.73% confidence level: Xbar = 5 s = 1 Upper range limit = 5+(3x1) or 8 Lower range limit = 5 - (3x1) or 2 95.45% confidence level: Xbar = 5 s = 1 Upper range limit = 5+(2x1) or 7 Lower range limit = 5 - (2x1) or 3 To ensure they get it, you may want to ask them what range of data is with 2 sd of the average. Ref Unit 2-57

Causes of Variation Special Cause: something different happening at a certain time or place Common Cause: always present to some degree in the process Ref Unit 2-58

Control limits - calculated from data = ± 3 sd
Control Charts Data points plotted over time UCL - Upper Control Limit 20 30 40 50 60 70 80 90 100 Process Metric J A S O N D F M 10 Control limits - calculated from data = ± 3 sd Center Line, mean, or Xbar LCL - Lower Control Limit Data patterns are formed by two types of variation… Ref Unit 2-59

Common Cause Variation
Control Charts (cont’d.) Common Causes Inherent in any system Process with only common causes: stable, predictable, in control Special Causes Special, specific, assignable causes. Process with common causes plus factors that induce variation above that inherent to the system: unstable, unpredictable, not in statistical control 6 Signals that special causes are present Common Cause Variation 10 20 30 40 50 1 3 5 7 9 11 13 15 17 19 21 23 25 Special Cause 10 20 30 40 50 1 3 5 7 9 11 13 15 17 19 21 23 25 Ref Unit 2-60

6 Signals of Special Cause
Control Charts (cont’d.) 6 Signals of Special Cause Point Outside Control Limit Any point on or outside the limits is abnormal and requires investigation Run Seven points continually on one side of the center line is abnormal, Also, 10 out of 11, 12 out of 14 or 16 out of 20 points on one side of the center line is abnormal Trending Seven points in a continuous upward or downward direction is abnormal Ref Unit 2-61a

6 Signals of Special Cause
Control Charts (cont’d.) 6 Signals of Special Cause Approaching CL (hugging) Most points within center line and 1.5σ* indicates mixing of data from different populations, making the control limits too wide and stratification necessary Cycling Any repeated up and down trend is abnormal and requires investigation Approaching Control Limits 2 of 3 points outside 2σ (σ= standard deviation) line is abnormal Ref Unit 2-61b

Goal of Process Improvement
Control Charts (cont’d.) Goal of Process Improvement Notice that the control limits are more narrow after variation is reduced. Can you explain why? Answer to question: The range of the data is more narrow; the range of the data is how the control limits are calculated. Additional information related to control charts is covered in BlackBelt training. Ref Unit 2-62

Control Limits, Specifications & Targets
Control limits (VOP) Calculated from the data Describe what the process is capable of achieving Specification limits (VOC) Set by the customer, management, or other requirements Describe what you want a process to achieve Target lines (VOB) Based on objectives external to the process Not statistically calculated (not shown) 70 80 90 100 110 120 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 UCL LCL Upper Spec Lower Ref Unit 2-63

Possible Process Conditions
LSL USL Not In Control ( Not Stable) In Control (Stable) Not meeting Customer Specifications (Not Capable) Meeting Customer Specifications (Capable) Customer Specification Control Limit Voice of the Process Voice of the Customer Ref Unit 2-64

Activity Stratify Upstream Data to Search for Significant Contributors to the GAP in the Outcome Metric Ref Unit 2-65

DEFINE MEASURE ANALYZE IMPROVE CONTROL Create Detailed Process Map Revise Problem Statement Assess Financial Impact Gather Initial Data Stratify Data Ref Unit 2-66a

Revise Problem Statement
Data you have collected and stratified so far focuses your project on the most significant reasons for the performance gap. Ref Unit 2-66b

(cont’d.) DO Make sure problem is of size/scope that is solvable State the problem in quantitative measurable terms Ref Unit 2-67a

(cont’d.) State a preconceived idea of what the cause(s) may be Imply a particular solution Affix blame on a person or group DO Not Ref Unit 2-67b

(cont’d.) Narrow the Focus Preliminary Problem Statement Revised Problem Statements are more Focused New products miss target launch dates 45% of the time. Nine out of the last ten new bond funds missed target launch dates by an more than three months. 20% of late payments come from product XYZ customers 85% of product XYZ customers in the U.S. account for 75% of late payments. Storage personnel are involved in 75% of back strain cases, Storage personnel were lifting something when 92% of the back strain cases occurred. Ref Unit 2-68

Identify good problem statements
Activity Identify good problem statements Ref Unit 2-69

Calculate Process Sigma
What is Process Sigma? A reflection of the variation in your process Based on the “Yield” - how much of your process output is within specification, e.g., acceptable to the customer Requires operational definitions for both defects and defect opportunities Calculated for the outcome metric(s) Related to the revised problem statement Determined using a table Ref Unit 2-70a

Compute First Time Right yield (see Lean Six Sigma Overview, page 16) % YIELD = 1 – (defects / total defect opportunities) X 100 Look up % YIELD in a Process Sigma Table (Appendix) Example: Assume 100 units, 5 opportunities, 7 defects % YIELD = (1 – 7 / (5 x 100)) X 100 % YIELD = (1 – (7 / 500) X 100 % YIELD = (1 – .014) X 100 % YIELD = 98.6%, and 3.7 Process Sigma Ref Unit 2-70b

Process Sigma Target If process sigma is greater than 3.0, set a 2x defect improvement goal e.g., 1,000 DPMO to 500 DPMO. If process sigma is less than 3.0, set a 10x defect improvement goal e.g., 1,000 DPMO to 100 DPMO. Ref Unit 2-71

Activity Develop a Revised Problem Statement, Calculate Process Sigma, Establish an Improvement Target Ref Unit 2-72

DEFINE MEASURE ANALYZE IMPROVE CONTROL Create Detailed Process Map Revise Problem Statement Assess Financial Impact Gather Initial Data Stratify Data Ref Unit 2-73

Cost of Poor Quality (COPQ)
COPQ is the sum of costs to maintain or satisfy customer requirements after a defect has occurred. Understanding process COPQ will help identify the financial impact of the project. Get help, if needed. Ref Unit 2-74

Types of Costs Cost of Poor Quality (cont’d.)
Conformance (Value-Adding) Appraisal costs (necessary, value adding) Prevention costs Non-conformance (Non-Value- Adding) Appraisal costs (not needed, non-value- adding) Internal failure costs External failure costs Ref Unit 2-75

Cost of Poor Quality Appraisal Costs - May be Non-Value- Adding or Value-Adding Maintenance/calibration of inspection/test equipment or procedures Materials/supplies used in inspection/test Incoming inspection/test In-process inspection/test Final inspection/test Product/service audits Ref Unit 2-76

Cost of Poor Quality (cont’d.) Failure Costs – Non-Value Add Internal
External Inspections or tests Rework Avoidable process losses Down time/Delays Sorting Failure analysis Repair Waste Complaint handling/adjustment Returned items Warranties and Allowances Lost business Lost sales Lost customers Link “Waste” to discussion on waste that occurs in Analyze Step. Ref Unit 2-77

Cost of Poor Quality (cont’d.) Prevention Costs – Value Adding
New product review Process quality planning Process control Quality system audits Training Supplier review and qualification Fool proofing so defects cannot be made, e.g., automatic seat-belt restraints Ref Unit 2-78

Project Benefits Cost of Poor Quality (cont’d.)
See calculations from Define step. Add newly identified benefits. See list in workbook. Ref Unit 2-79

Estimate of Project Annual Financial Impact
Cost of Poor Quality (cont’d.) Financial Impact Summary Estimate annual impact Indicate if increasing stream of benefits Save worksheets Estimate of Project Annual Financial Impact Benefit total: (+) COPQ total: Conformance (forecast of value-adding costs) (-) Non-conformance (forecast of non-value-adding cost reductions) Project total: Ref Unit 2-80

What costs are Involved in Your Project?
Activity What costs are Involved in Your Project? Ref Unit 2-81

Measure Step Tollgate Questions
Does the process map clearly convey responsibility for each activity? What is the plan for data collection? How has the data been documented? How was the data stratified to identify any significant reasons for the preliminary problem statement? What is the revised problem statement? What is the forecast financial impact of the project? Ref Unit 2-82

Unit Summary Having completed this unit, here are some questions you should be able to answer: What are two types of data? List four sources of data to be addressed in a data collection plan. Name three tools that can be used to collect and stratify data? What should not be included in a revised problem statement? What should? What are three types of costs to be addressed in a COPQ assessment? How is a control chart different from a run chart? Ref Unit 2-83a

Project Components: MEASURE
Overall objective: Narrow the improvement opportunity to a specific problem statement. Plot Outcome Defect Data Stratify Data Revise Problem Statement From Define Step Create Detailed Process Map Frequency Plots Run Chart F 1 3 4 2 Calculate Performance Pareto Chart DPMO_____ Yield _____ Sigma _____ Goal _____ Collect Upstream Data Check Sheet Develop Data Collection Plan A Mary John Sally Jim B C E Understand Variation Assess Financial Impact (COPQ) UCL LCL Data Collection Plan Project ________________________ What questions do you want to answer? Data Operational Definition and Procedures What Measure type/ Data type How Measured 1 Related conditions to record 2 Sampling notes How/where recorded (attach form) Control Chart To Analyze Step Ref Unit 2-83b

Lean Six Sigma Green Belt Training

Similar presentations

Presentation on theme: "Lean Six Sigma Green Belt Training"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Lean Six Sigma Green Belt Training

Similar presentations

Presentation on theme: "Lean Six Sigma Green Belt Training"— Presentation transcript:

Similar presentations

About project

Feedback