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UNCLASSIFIED / FOUO National Guard Black Belt Training Module 20 Data Collection UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUOCPI Roadmap – Measure 8-STEP PROCESS 6. See 1.Validate 2. Identify 3. Set 4. Determine 5. Develop 7. Confirm 8. Standardize Counter- the Performance Improvement Root Counter- Results Successful Measures Problem Gaps Targets Cause Measures & Process Processes Through Define Measure Analyze Improve Control TOOLS •Process Mapping ACTIVITIES • Map Current Process / Go & See •Process Cycle Efficiency/TOC • Identify Key Input, Process, Output Metrics •Little’s Law • Develop Operational Definitions •Operational Definitions • Develop Data Collection Plan •Data Collection Plan • Validate Measurement System •Statistical Sampling • Collect Baseline Data •Measurement System Analysis • Identify Performance Gaps •TPM • Estimate Financial/Operational Benefits •Generic Pull • Determine Process Stability/Capability •Setup Reduction • Complete Measure Tollgate •Control Charts •Histograms •Constraint Identification •Process Capability Note: Activities and tools vary by project. Lists provided here are not necessarily all-inclusive. UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUO Learning Objectives Determine what to measure and why Prepare plans to collect output, process and/or input data Apply sampling techniques, as needed Construct forms and test data collection procedures Refine data collection Implement data collection plan UNCLASSIFIED / FOUO 3
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UNCLASSIFIED / FOUO What Is a Measure? A quantified evaluation of characteristics and/or level of performance based on observable data Examples include: Length of time (speed, age) Size (length, height, weight) Dollars (costs, sales revenue, profits) Counts of characteristics or “attributes” (types of customer, property size, gender) Counts of defects (number of errors, late checkouts, complaints) UNCLASSIFIED / FOUO 4
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UNCLASSIFIED / FOUO Why Measure? Establish the current performance level (baseline) Determine priorities for action – and whether or not to take action Substantiate the magnitude of the problem To gain insight into potential causes of problems and changes in the process Prevent problems and predict future performance To gain knowledge about the problem, process, customer or organization UNCLASSIFIED / FOUO 5
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UNCLASSIFIED / FOUO National Guard Black Belt Training Determine What to Measure UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUO What Do We Need to Know? The first step in the creation of any data collection plan is to decide what you need to know about your process and where to find measurement points What data is needed to “baseline” our problem? What “upstream” factors might affect the process/problem? What do we plan to do with the data after it has been gathered? Do we have a balance between Output and Input/Process measures? UNCLASSIFIED / FOUO 7
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UNCLASSIFIED / FOUO Deciding “What and Where” Process Input Output Preparing the SIPOC diagram and a more detailed process map can help a team select its measures Choosing good measures requires a clear understanding of the definitions of and relationships between Output, Process, and Input measures UNCLASSIFIED / FOUO 8
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UNCLASSIFIED / FOUO “X” and “Y” Variables Y = f ( X1 + X2 + X3 + . . . . . . . . . Xn ) Output Input/Process Final Score in First Second Third Fourth = + + + + Overtime Basketball Quarter Quarter Quarter Quarter Game Score Score Score Score Score Customer = Front Desk Check In Room Room Check Out + + + + Satisfaction Courtesy Ease Comfort Service Ease Loan Process Application Credit & Risk Review & Loan Service Cycle Time = Data Entry + Collateral + Assessment + Approval Time + Time Time Check Time Time Generally, you can influence some of the Xs but not all. CPI projects will generally address those Xs which can be influenced and which have the greatest impact on Y. UNCLASSIFIED / FOUO 9
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UNCLASSIFIED / FOUO Measuring Business Processes X - PREDICTOR Y - RESULTS (Leading) MEASURES (Lagging) MEASURES (X) (X) (Y) Input Process Output • Arrival Time • Customer • Accuracy Satisfaction • Cost • Total Defects • Key Specs • Cycle Time • Cost Profit Time Per Task How well do these (Xs)… In-Process Errors …predict this (Y)? Labor Hours Exceptions UNCLASSIFIED / FOUO 10
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UNCLASSIFIED / FOUO Categories of Performance Metrics Developing Input, Process and Output metrics around the Voice of the Customer (VOC) and Voice of the Business (VOB) process performance needs is a good starting point for determining what to measure Product or Service Features, Attributes, Dimensions, Characteristics Relating to the Function of the Product or Service, Reliability, Availability, Quality Taste, Effectiveness - Also Freedom from Defects, Rework or Scrap (Derived Primarily from the Customer - VOC) Process Cost Efficiency, Prices to Consumer (Initial Plus Life Cycle), Repair Cost Costs, Purchase Price, Financing Terms, Depreciation, Residual Value (Derived Primarily from the Business - VOB) Lead Times, Delivery Times, Turnaround Times, Setup Times, Cycle Speed Times, Delays (Derived equally from the Customer or the Business – VOC/VOB) Service Service Requirements, After-Purchase Reliability, Parts Availability, Service, and Safety Warranties, Maintainability, Customer-Required Maintenance, Product Liability, Product/Service Safety Ethical Business Conduct, Environmental Impact, Business Risk Stewardship Management, Regulatory and Legal Compliance UNCLASSIFIED / FOUO 11
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UNCLASSIFIED / FOUO Output Measures Referred to as “Y” data. Output Metrics quantify the overall performance of the process, including: How well customer needs and requirements were met (typically Quality & Speed requirements), and How well business needs and requirements were met (typically Cost & Speed requirements) Output measures provide the best overall barometer of process performance Focus on one Primary Output (Y) metric at a time. Use Secondary Y metrics to “keep you honest” Example: If the Primary Y is to improve cycle time, the Secondary Y could monitor defects to make sure they also improve or at least don’t get worse! UNCLASSIFIED / FOUO 12
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UNCLASSIFIED / FOUO Typical Output Measures Possible Output Process Type Output (Y) Measures Metal chemistry/thickness/ Ammo propellant weight/ballistics Product/ Number of missing/incorrect Manufacturing Dining-in place cards, seating time, Ceremony delivery time, accuracy (food/beverage order) Cycle time, accuracy (# of Re-enlistment errors), completeness (# of Service/ Papers items missing) Transactional/ Administrative Delivery timeliness, Anthony’s accuracy, temperature Pizza UNCLASSIFIED / FOUO 13
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UNCLASSIFIED / FOUO X and Y Metrics Suppliers Inputs Process Outputs Customers • Billing Dept. staff Billing Process • Delivered • Customer Invoice database • Shipping information • Order information Input Metrics Process Metrics Output Metrics • Accuracy of • System responsiveness • Rework % at each step • Invoice accuracy database info. • Accuracy of order info. Quality • Staff expertise • Accuracy of shipping • System up-time info. • Time to receive order info. • # of process steps • Invoice cycle time Other Metrics • Time to receive shipping • Time to complete invoice • Invoices information • Time to deliver invoice Speed processed per • Delay time between steps month and variability • # of billing staff • # of process steps • Cost/invoice Cost UNCLASSIFIED / FOUO 14
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UNCLASSIFIED / FOUO National Guard Black Belt Training Develop Data Collection Plan UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUO Exercise: Data Collection Collect Height Data UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUOTypes of Data Continuous / Variable – Any variable measured on a continuum or scale that can be infinitely divided into recognizable parts. Primary types include time, dollars, size, weight, temperature, and speed. Any metric that can be continuously divided by 2 and the metric still makes sense is a continuous metric. Continuous Data is always preferred over Discrete or Attribute Data. Discrete / Attribute – A count, proportion, or percentage of a characteristic or category. Service process data is often discrete. Continuous/Variable Discrete/Attribute • Cycle time • Late delivery • Cost or price • Gender • Length of call • Region/location • Temperature of rooms • Room type UNCLASSIFIED / FOUO 17
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UNCLASSIFIED / FOUO The Objective: Data Collection Plan Let’s see how a Data Collection Plan is developed Data Collection Plan Performance Operational Data Source How Will Who Will When Will Measure Data Be Collect Data Data Be Sample Size Stratification Factors Definition & Location Collected Collected Developed earlier 2 3 4 5 6 1 How will data be used? How will data be displayed? Examples: Examples: Identification of Largest Contributors Pareto Chart Identifying if Data is Normally Distributed Histogram Identifying Sigma Level and Variation Control Chart Root Cause Analysis Scatter Diagrams Correlation Analysis UNCLASSIFIED / FOUO 18
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UNCLASSIFIED / FOUO Step 1. Stratification Factors What are the ways you need to look at the data? Data Stratification - Capturing and use of characteristics to sort data into different categories (also known as “slicing the data”) Used to: Provide clues to root causes (Analyze) Verify suspected root causes (Analyze) Uncover times, places where problems are severe (“vital few”) Surface suspicious patterns to investigate UNCLASSIFIED / FOUO 19
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UNCLASSIFIED / FOUO Stratification Factors Factors Examples What Complaints, Defects When Month, Day Where Region, City Department, Who Individual If you do not collect stratification factors “up front,” you might have to start all over later. On the other hand, seeking too many factors makes the data more difficult and/or more costly to collect. UNCLASSIFIED / FOUO 20
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UNCLASSIFIED / FOUO Stratification Matrix Key Steps Fill in the Output measure Y Fill in the key stratification questions you have about the process in relationship to the Y List out all the levels and ways you can look at the data in order to determine specific areas of concern Create specific measurements for each subgroup or stratification factor Review each of the measurements (include the Y measure) and determine whether or not current data exists Discuss with the team whether or not these measurements will help to predict the output Y, if not, think of where to apply the measures so that they will help you to predict Y UNCLASSIFIED / FOUO 21
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UNCLASSIFIED / FOUO Stratification Matrix 2 3 4 Questions About Process Stratification factors Measurements X Variables Does data exist to support these measurements ? (Y/N) 5 Will these measurements (Output Y) help to predict Y? (Y/N) 1 6 UNCLASSIFIED / FOUO 22
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UNCLASSIFIED / FOUOStratification Matrix Example - Checkout 2 3 4 Questions About Process Stratification factors Measurements X Variables Does data exist Does the number # adjustments / day to support adjustment vary over time? By time period these # adjustments last year measurements 2 ? 3 4 (Y/N) Is there a difference by % of adjustments / associate By employee 5 type of employee? # of adjustments by new vs. exp. Employees Total adjustments Will these at checkout measurements help to predict Is there a difference by (Output Y) # adjustments by room size Y? (Y/N) type of customer? By type # adjustments by 1 customer segment 6 Does the amount of adjustments vary from one # adjustments in North East location to another? By location # adjustments in South # adjustments in Midwest UNCLASSIFIED / FOUO 23
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UNCLASSIFIED / FOUO Step 2. Developing Operational Definitions Operational Definitions apply to MANY things we encounter every day. For example, all the measurement systems we use (feet/inches, weight, temperature) are based on common definitions that we all know and accept. Sometimes these are called “standards.” Other times, our operational definitions are more vague. For example, when someone says a loan is “closed” they might mean papers have been sent, but not signed; another person might mean signed but not funded; a third person might mean funded but not recorded. While here we are focused on operational definitions in the context of measurement, the concept applies equally well to “operationally defining” a customer requirement, a procedure, a regulation, or anything else that benefits from clear, unambiguous understanding Learning to pay attention to and clarify operational definitions can be a major side benefit of the CPI process UNCLASSIFIED / FOUO 24
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UNCLASSIFIED / FOUO Defining “Operational Definitions” What it is... A clear, precise description of the factor being measured Why it is critical... So each individual “counts” things the same way So we can plan how to measure effectively To ensure common, consistent interpretation of results So we can operate with a clear understanding and with fewer surprises UNCLASSIFIED / FOUO 25
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UNCLASSIFIED / FOUODeveloping Operational Definitions From General to Specific: Step 1 – Translate what you want to know into something you can count Step 2 – Create an “air-tight” description of the item or characteristic to be counted Step 3 – Test your Operational Definition to make sure it is truly “air-tight” Note: Sometimes you will need to do some “digging” up-front to arrive at good operational definitions. It is usually worth the effort!! UNCLASSIFIED / FOUO 26
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UNCLASSIFIED / FOUO Step 3. Data Sources Key Question: Does the data currently exist? Existing Data – Taking advantage of archived data or current measures to learn about the Output, Process, or Input This is preferred when the data is in a form we can use and the Measurement System is valid (a big assumption and concern) New Data – Capturing and recording observations we have not or do not normally capture May involve looking at the same “stuff,” but with new Operational Definitions This is preferred when the data is readily and quickly collectable (it has less concerns with measurement problems) UNCLASSIFIED / FOUO 27
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UNCLASSIFIED / FOUO Key Considerations: Existing vs. New Data Existing vs. New Considerations Is existing or “historical” data adequate? Meet the Operational Definition? Truly representative of the process, group? Contain enough data to be analyzed? Gathered with a capable Measurement System? Cost of gathering new data Time required to gather new data The trade-offs made here, I.e. should the time and effort be taken to gather new data, or only work with what we have, are significant and can have a dramatic impact on the project success UNCLASSIFIED / FOUO 28
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UNCLASSIFIED / FOUOStep 4. How will Data Be Collected? Check Sheets The workhorse of data collection Enhance ease of collection Faster capture Consistent data from different people Quicker to compile data Capture essential descriptors of data “Stratification factors” Need to be designed for each job UNCLASSIFIED / FOUO 29
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UNCLASSIFIED / FOUO Data Collection Forms – Check Sheets Check sheets are convenient for gathering data Data sheets allow: Faster, more accurate capture Consistent data from different people Quicker, easier compilation Capture essential descriptors of data Designed for each different data gathering situation The data may then be analyzed UNCLASSIFIED / FOUO 30
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UNCLASSIFIED / FOUO Get Data You Can Use As you set up Check Sheets... Prepare a spreadsheet to compile the data Think about how you will do the compiling (and who will do it) Consider what sorting, graphing, or other reports you will want to create Continuous or Discrete Data? Adequate level of discrimination and accuracy? Adjust check sheet as needed to ensure usable data later But do not make data harder to collect UNCLASSIFIED / FOUO 31
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UNCLASSIFIED / FOUO Constructing Check Sheets 1. Select specific data and factors to be included 2. Determine time period to be covered by the form Day, Week, Shift, Quarter, etc. 3. Construct form Be sure to include: Clear labels Enough room Space for notes 4. Test the form! UNCLASSIFIED / FOUO 32
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UNCLASSIFIED / FOUO Check Sheet Tips Include name of collector(s) (first and last) Reason/comment columns should be clear and concise Use full dates (month, date, year) Use explanatory title Consider lowest common denominator on metric Minutes vs. Hours Inches vs. Feet Test and validate your design (try it out) Do not change form once you have started, or you will be “starting over!” UNCLASSIFIED / FOUO 33
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UNCLASSIFIED / FOUO Types of Check Sheet: Frequency Plot Shows “distribution” of Frequency of Repairs July items or occurrences 1 2 X X X X X X X 3 X X X X X along a scale or ordered 4 X X X X X 5 X X X X 6 X X 7 8 X X X X quantity 9 X X X X X X 10 X X X X 11 12 X X X X X X X X Helps detect unusual patterns in a population – 13 X 14 X X X 15 16 17 X X X X X X X X X X X or detect multiple populations 18 X X X X X X X X 19 X X X X 20 X 21 X X X X X Gives visual picture of 22 23 X X X X X X X X X 24 X X X X X X X 25 26 27 X X X X X X X X X X X X “average” and “range” 28 X X X X X 29 X X 30 X X X X X X X X 31 X X X X X X UNCLASSIFIED / FOUO 34
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UNCLASSIFIED / FOUO Types of Check Sheets: Standard Week of: 6/26 Collected by: Kevin Regan Repair Complaint Repair Call Date Call Time Initials Notes TV Smk Det Thrmstat RemCon Shower Window Time 30-Jun 8:00a EJS X X 10 min 28-Jun 8:15a MWT X 1 hr 27-Jun 7:00p MWT X 15 min 26-Jun 6:30p KLC X 2 hrs 28-Jun 5:45p PP X 30 min 30-Jun 6:00a KR X 40 min 1-Jul 8:15p DRT X 4 hrs Replaced part 1-Jul 8:20p ECS X 2 hrs Not in stock 28-Jun 9:35a MWT X 1 hr 29-Jun 9:40a KLC X 30 min 29-Jun 5:15p EJS X 45 min 29-Jun 5:20p KR X 15 min UNCLASSIFIED / FOUO 35
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UNCLASSIFIED / FOUO Types of Check Sheets – Traveler Traveler Checksheet Awards Approval Process Awardee: __________________________________________________ Award type: □ PCS □ Other ___________________________ Proposed award date: ________________________________________ Recommender’s division: □ G-1 □ G-2 □ G-3 □ G-4 □ Other __________ Time begun; Time Process step Defects found completed Fill out forms Approve recommendation Schedule presentation UNCLASSIFIED / FOUO 36
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UNCLASSIFIED / FOUO Types of Check Sheets – Confirmation Example: Power Steering project tracking UNCLASSIFIED / FOUO 37
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UNCLASSIFIED / FOUO Check Sheet Takeaways A check sheet is an easy way to collect data in order to observe trends and identify improvement priorities Mistake-proof data collection by using check boxes, tallies, or choices that can be circled (reduce any writing to an absolute minimum – or none at all!) Remember to include those who understand the process and those who will actually use the check sheet in the design of the check sheet. This is very important for success! UNCLASSIFIED / FOUO 39
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UNCLASSIFIED / FOUOStep 5. Who Will Collect the Data? Considerations: Familiarity with the process Availability/impact on job Rule of Thumb – If it takes someone more than 15 minutes per day it is not likely to be done Potential Bias Will finding “defects” be considered risky or a “negative?” Benefits of Data Collection Will data collection benefit the collector? UNCLASSIFIED / FOUO 40
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UNCLASSIFIED / FOUO Preparing Collectors Be sure collectors: Give input on the check sheet design Understand operational definitions (!) Understand how data will be tabulated Helps them see the consequences of changing Have been trained and allowed to practice Have knowledge and are unbiased UNCLASSIFIED / FOUO 41
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UNCLASSIFIED / FOUO Step 6. Sampling Sampling is using a smaller group to represent the whole population (the foundation of “statistics”) Benefits: Saves time and money Allows for more meaningful data Simplifies measurement over time Can improve accuracy UNCLASSIFIED / FOUO 42
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UNCLASSIFIED / FOUO Sampling Considerations Time Cost Accuracy Units Processed Cost to Collect Per Day Data UNCLASSIFIED / FOUO 43
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UNCLASSIFIED / FOUO Sampling Types Population – Drawing from a fixed group with definable boundaries. No time element. Customers Complaints Items in Warehouse Process – Sampling from a changing flow of items moving through the business. Has a time element. New customers per week Hourly complaint volume Items received or shipped by day UNCLASSIFIED / FOUO 44
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UNCLASSIFIED / FOUO Population or Process Sampling Of primary importance in a Lean Six Sigma measurement effort is to clarify if you are engaged in Population or Process sampling Most traditional statistical training focuses on sampling from populations – a group of items or events from which a representative sample can be drawn. A population sample looks at the characteristics of the group at a particular point in time. Quality and business process improvement tends to focus more often on processes, where change is a constant UNCLASSIFIED / FOUO 45
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UNCLASSIFIED / FOUO Population or Process Sampling In process sampling, you measure characteristics of things or characteristics as they pass through the process, and observe changes over time Any data you collect that has “time order” included can be examined as either a population or a process – however, the size of the sample analyzed might need to be different Given a choice, process data gives more information, such as trends and shifts of short duration. Process sampling techniques are the foundation of process monitoring and control. UNCLASSIFIED / FOUO 46
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UNCLASSIFIED / FOUO Sampling Methods/Strategies The big pitfall in sampling is “bias” – i.e., select a sample that does NOT really represent the whole. The sampling plan needs to guard against bias. Different methods of sampling have different advantages and disadvantages in managing bias. Judgment As it sounds – selecting a sample based on someone’s knowledge of the process, assuming that it will be “representative.” Judgment guarantees a bias, and should be avoided. Convenience Also just like it sounds – sampling those items or at those times when it is easier to gather the data. (For example, taking data from people you know, or when you go for coffee.) This is another common (but ill-advised) approach. UNCLASSIFIED / FOUO 48
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UNCLASSIFIED / FOUO Sampling Strategies Best Methods: Random Best approach for population situations. Use a random number table or random function in Excel or other software, or draw numbers from a hat. Systematic Most practical and unbiased in a process situation. “Systematic” means that we select every nth unit, or take samples at specific times of the day. The risk of bias comes when the timing of the sample matches a pattern in the process. UNCLASSIFIED / FOUO 49
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UNCLASSIFIED / FOUO Sampling Strategies Considerations Should we stratify first? ... Focus on one group within the process or population? Ensure adequate representation from various segments of the population or process? Does it “feel right?” Sampling needs to fit common sense considerations Confront and manage your biases in advance UNCLASSIFIED / FOUO 50
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UNCLASSIFIED / FOUO Key Sampling Terms/Concepts Sampling Event – The act of extracting items from the population or process to measure Subgroup – The number of consecutive units extracted for measurement at each Sampling Event (A “subgroup” can be just one!) Sampling Frequency – Applies only to process sampling; the number of times per day or week a sample is taken (i.e., sampling events per period of time) These are the key elements to be included in the sampling plan: what we will “extract,” how many we will take at a time, and how often we will take a sample. UNCLASSIFIED / FOUO 51
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UNCLASSIFIED / FOUO Population Sampling Steps Building the “Sampling Plan” 1. Develop an initial profile of the data 2. Select a sampling strategy 3. Determine the initial sample size 4. Adjust as needed to determine minimum sample size UNCLASSIFIED / FOUO 52
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UNCLASSIFIED / FOUO Sampling – Initial Data Profile Population size (Noted as “N”) As you begin preparing the Sampling Plan, you first need to determine the rough size of the total population Stratification factors If you elect to conduct a stratified sample, you need to know the size of each subset or stratum What precision result do you need? Next, you need to define the level of precision needed in your measurement. Precision notes how tightly your measurement will describe the result. For example, if measuring cycle time, your sample will be affected by whether you want precision in days (e.g. estimate is within +/- 2 days) or hours (estimate is within +/- 4 hours). Precision is noted by the variable “d” or D. The sample size goes up very rapidly as the precision is tightened. UNCLASSIFIED / FOUO 53
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UNCLASSIFIED / FOUO Sampling – Initial Data Profile The last step in your initial profile is to estimate the variation in the population Continuous data requires an estimate of the “standard deviation” of the variable being measured Continuous data: How much does the characteristic vary? (estimated standard deviation) Discrete data requires an estimate of “P,” the proportion of the population that contains the characteristic in question Discrete data: What proportion contains the characteristic? UNCLASSIFIED / FOUO 54
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UNCLASSIFIED / FOUO Sampling – Sampling Strategy Random or systematic? How will we draw the sample? Who will conduct the “sampling event?” How will we guard against bias? Most representative vs. time, effort, and cost No differences between what you collect and what you do not collect UNCLASSIFIED / FOUO 55
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UNCLASSIFIED / FOUO Sampling Some Final Tips ... When you want to ensure representation from different groups or strata, prepare a separate sampling plan for each group Be sure to maintain the time order of your samples/subgroups so you can see changes over time Common sense is a useful tool in sampling Help is available if you need it! UNCLASSIFIED / FOUO 56
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UNCLASSIFIED / FOUO Test, Refine and Implement Ensuring “Quality” Measurement Measurement is rarely perfect – especially at first Even good measurement can go “bad” As you use data, lessons might include ... How to simplify measures Other stratification factors needed Ways to improve collection forms Other measures to investigate UNCLASSIFIED / FOUO 57
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UNCLASSIFIED / FOUOOperational Definitions Template Define each of the Key Input, Output, Process Metrics from your SIPOC that you are going to collect data on (via the Data Collection Plan) as well as any other terms that need clarification for the data collectors and everyone else on the team. Examples: Award Process PLT: The time from when a Director submits the Award recommendation to the time when the employee is presented the Award in a ceremony. Number of Claims Processed: The number of Claims processed per weekday (M-F). Total Hours Worked: The total number of hours worked in the facility including weekends and holidays. Number of Personnel: The total number of military and civilian personnel working (not including contractors). Include other unique terms that apply to your project that require clear operational definitions for those collecting the data and for those interpreting the data. Required UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUO Data Collection Plan Template Performanc Operational Data How Will Data Be Who Will When Will Sampl Stratificati How will e Measure Definition Source and Collected Collect Data Be e Size on Factors data be Location Data Collected used? 1 Ability to update X – Steps to In DEPMS By counting steps Name ASAP 1 None To find VA, BNVA, projects and update projects NVA build tollgate reviews - Example - 2 Ability to update X – Tollgate In DEPMS By determining % of Name ASAP 40 None To determine projects and template slides activity steps identified in consistency with build tollgate that match POI “Introduction to _____” POI reviews modules in POI that are adequately addressed in templates 3 Easy Access to X – Availability of In DEPMS By determining the Name ASAP 63 None To determine LSS tools and LSS tools and percentage of tools, with availability of tools references references their references, listed on and references DMAIC Road Map slides that can be found in PS 4 Easy Access to X – Steps In DEPMS By counting # steps Name ASAP 37 None To find VA, BNVA, LSS tools and required to find required to find the tools NVA references tools and and their references references Required Deliverable UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUO Exercise: Data Collection Objective Create a data collection plan for the GGAs Budget Department Instructions Include: 1. Key input, process and output metrics 2. Operational definitions 3. Data collection methods Time = 30 Minutes UNCLASSIFIED / FOUO 61
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UNCLASSIFIED / FOUO Takeaways Know what to measure and why Create a plan to collect output, process and/or input data Construct forms and test data collection procedures using appropriate data sampling methods Refine data collection Collect the data Analyze the data UNCLASSIFIED / FOUO 62
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UNCLASSIFIED / FOUO What other comments or questions do you have? UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUO National Guard Black Belt Training Appendix Sample Size Calculations UNCLASSIFIED / FOUO
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UNCLASSIFIED / FOUO How Many Do We Need to Count? Factors in Sample Size Selection: Situation: Population or Process Data Type: Continuous or Discrete Objectives: What you will do with results Familiarity: What you guess results will be Certainty: How much “confidence” you need in your conclusions Determine What to Measure and Data Collection UNCLASSIFIED / FOUO 65
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UNCLASSIFIED / FOUO Three Factors Drive Sample Sizes Three concepts affect the conclusions drawn from a single sample data set of (n) items: Variation in the underlying population (sigma) Risk of drawing the wrong conclusions How small a Difference is significant (delta) Risk Variation Difference UNCLASSIFIED / FOUO 66
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UNCLASSIFIED / FOUO Three Factors: Variation, Risk, Difference These 3 factors work together. Each affects the others. Variation: When there’s greater variation, a larger sample is needed to have the same level of confidence that the test will be valid. More variation diminishes our confidence level. Risk: If we want to be more confident that we are not going to make a decision error or miss a significant event, we must increase the sample size. Difference: If we want to be confident that we can identify a smaller difference between two test samples, the sample size must increase. UNCLASSIFIED / FOUO 67
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UNCLASSIFIED / FOUO Determining Minimum Sample Size Minimum sampling size from a population or a stable process can be estimated from the following formulas: Continuous Data Sample Size For continuous data: 2 1.96 s n= D Where: n = minimum sample size required s = estimate of standard deviation of the population or process data D = level of precision desired from the sample in the same units as the “s” measurement 1.96 = constant representing a 95% confidence interval UNCLASSIFIED / FOUO 68
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UNCLASSIFIED / FOUO Determining Minimum Sample Size Discrete Data Sample Size For discrete or proportion data: 2 1.96 n= P(1 P) D Where n = minimum sample size P = estimate of the proportion of the population or process which is defective D = level of precision desired from the sample in units of proportion 1.96 = constant representing a 95% confidence interval The highest value of p(1-p) is 0.25 or p=0.5 Benefits of Continuous Data Usually requires a smaller sample More information for stratification and root cause analysis UNCLASSIFIED / FOUO 69
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UNCLASSIFIED / FOUO Formula for Small Populations Making adjustments in the minimum sample size required/needed for small populations: Both sample size formulas assume: a 95% confidence interval a small sample size (n) compared to the entire population size (N) If n/N is greater than 0.05, the sample size should be adjusted to: n n finite = n 1+ N The proportion formula should only be used when: nP 5 UNCLASSIFIED / FOUO 70
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UNCLASSIFIED / FOUO Formula for Small Populations Example: Processing CAC applications Given: The sample size formula shows that you need a minimum sample size of 289 You have only processed 200 units Solution: The correct minimum sample size would be: n 289 n finite = = = 118.2 or 119 - minimum sample size required n 289 1+ 1+ N 200 UNCLASSIFIED / FOUO 71
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UNCLASSIFIED / FOUOMinimum Sample Size – Continuous Example Example: Sample Size Calculation – Continuous A Lean Six Sigma team samples a contracting process to determine the average processing time and wishes to estimate the average time within one day. Based on previous sampling, the team has estimated the standard deviation of the current contract process time as 4 days. What is the minimum sample size required to be able to estimate the average with the required precision? 2 1.96s n= D 1.96 4 2 n= = 62 contracts 1 UNCLASSIFIED / FOUO 72
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UNCLASSIFIED / FOUO Minimum Sample Size – Discrete Example Example: Sample Size Calculation – Discrete Another Lean Six Sigma team determines the minimum sample size required for the proportion of DPW, Department of Public Works, service contracts that require rework at the approval meeting. From interviews, the team has concluded that approximately 25% of the contracts contain errors and require rework. They wish to determine the % requiring rework within 5%. 2 1.96 n = .25(1 .25) .05 n =(1536.64)(.1875) = 289 contracts UNCLASSIFIED / FOUO 73
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UNCLASSIFIED / FOUO Exercise: Sample Size Objective: Determine the appropriate sample size Instructions: Use the pizza delivery example. The pizza is scheduled for the time the customer requests delivery. The customer requirement is +/- 10 minutes from the scheduled delivery time Estimated s = 7.16 minutes and D = 2 minutes Estimated number of defects is 30% ( P = 0.30; D =5%) Determine the minimum sample size for both continuous and discrete data UNCLASSIFIED / FOUO 74
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UNCLASSIFIED / FOUO Exercise: Sample Size Objective: Determine the appropriate sample size Instructions: Select one output indicator for your process Determine the type of data (continuous / discrete) Continuous - estimate “s” and D Discrete - estimate D and P Determine the minimum sample size required UNCLASSIFIED / FOUO 76
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UNCLASSIFIED / FOUO Exercise: Sample Size Formula Objective: Determine the appropriate sample size formula to use Instructions: At your tables determine the right formula (proportion/discrete or continuous) to use and calculate the sample size for each situation 1.Estimate the average cycle time within 2 hours. The estimated standard deviation is 8 hours. What is the minimum number to sample? 2.A team collected 100 observations to determine the proportion defective. They found 20% to be defective. How accurately can they estimate the proportion defective? 3.You have a customer survey with 2 categorical questions and 8 interval statements. You estimate that at least one option of a categorical question will be answered by approximately 50% of the respondents and you want to be able to detect a difference within ± 5%. For the continuous statements you want to be able to detect a difference of at least ½ a point. The highest estimated standard deviation for any of the statements is 1.2. You expect the response rate to be 25%. How many surveys do you have to send out and how many completed surveys do you need returned? UNCLASSIFIED / FOUO 77
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UNCLASSIFIED / FOUO Answers to Sampling Exercise 2 2 1.96s 1.96(8) 1. Continuous n= = = 62 D 2 2 1 . 96 2. Discrete/Proportioned n = p (1 p ) D 2 1.96 100 = .2(1 .2) D2 D = .08 or 8% 2 1.96 3. Discrete Calculation n = .5(1 .5) = 385 .05 2 Continuous 1.96(1.2) n= = 23 .5 Must send out 4* minimum sample or 4*385 = 1,540 UNCLASSIFIED / FOUO 78
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