Continuous Improvement Introduction to Measure Module 1.08

1. Continuous Improvement Introduction to Measure Module 1.08 Lean Sigma Associates Ltd.

2. Contents • Why do we need to measure? • What exactly are we measuring? • Output Indicators • Input Indicators • How will we measure • Data Types • Data Attributes • Collection methods

3. Objectives • Identify Output and Input Indicators that effect CTQ’s • Understand the types of data • Continuous vs. Discrete • Collecting some data (Check Sheets)

4. Where have we been? Define Measure Analyse Improve Control Concepts/Tools: • VOC • Affinity Diagram • CTQ’s • SIPOC • Charter • Process Map • Key Define Takeaways: • We have identified problems/opportunities • We have launched a project and put together a team • We understand our customer wants/desires • We have translated these wants into a measureable • We have mapped the process

5. Where are we going? Define Measure Analyse Improve Control Concepts/Tools: • Data Types • Operational Definition • Baseline • Data Collection • Key Measure Takeaways: • We can identify types of data • We have defined the operational definition of what we measure • We have determined the baseline for how well we are doing • We have a plan on how we will collect the data

6. DMAIC Process & Tools • Data Attributes • Variation • Sample Size • DCP • Defining the measure • SIPOC • Charter • SMART • Surveys • Customer Data • Affinity Diagram • Process Map • VA/NVA Analysis • Run Chart • Pareto Chart • Histogram • Fishbone • Is/Is Not • Brainstorm • Solution Selection Matrix • Fishbone • Tree • 5 Why’s • Action/ Timing plans • Risk Analysis • Standardise

7. Why Do We Need to Measure? • You don’t know what you don’t know. • Making decisions without data is like testing detonation fuses with a hammer. It is all up to chance! NOOOOOOOO!!!!! Do you like the odds?

8. Data Helps Us . . . • Separate what we think is happening from what is really happening • Confirm or disprove preconceived ideas and theories • Establish a baseline of performance • See the history of the problem over time • Measure the impact of changes on a process • Identify and understand relationships that might help explain variation • Control a process (monitor process performance) • Avoid “solutions” that don’t solve the real problem 5 3 2 6 1 8 7 Data converts organisational issues (I think) into a quantitative definable problem (it is)

9. Critical Relationships VOC CTQ’s Y f (x) f (x)1 The input to f (x) The input to the Y The wants of the customer The wants of the customer which can be measured The measurable output of the process Critical path thinking to understand and quantify the inputs which deliver the desired output Critical path thinking to understand and quantify customer wants Continuous Improvement care point Customer care point

10. “X” & “Y” Variables Y = f ( X1 + X2 + X3 + . . . . . . . . . Xn ) Output Input/Process Final Score in Basketball Game First Quarter Score Second Quarter Score Third Quarter Score Fourth Quarter Score Overtime Score = + + + + Front Desk Courtesy Check In Ease Room Comfort Room Service Check Out Ease Customer Satisfaction = + + + + Application Data Entry Time Credit & Collateral Check Time Risk Assessment Time Review & Approval Time Loan Service Time Loan Process Cycle Time = + + + + Input measures must directly relate to the Y measure, if they do not you then need to find a more relevant input indicator.

11. Measuring Processes X - PREDICTOR (Leading) MEASURES Y - RESULTS (Lagging) MEASURES (X) (X) (Y) Input Process Output • Arrival Time • Accuracy • Cost • Key Specs • Customer Satisfaction • Total Defects • Cycle Time • Cost Profit Time Per Task In-Process Errors Labor Hours Exceptions How well do these… …predict these?

12. Categories of Performance Product or Service Features, Attributes, Dimensions, Characteristics Relating to the Function of the Product or Service, Reliability, Availability, Taste, Effectiveness - Also Freedom from Defects, Rework or Scrap (Derived Primarily from the Customer - VOC) Quality Cost Process Cost Efficiency, Prices to Consumer (Initial Plus Life Cycle), Repair Costs, Purchase Price, Financing Terms, Depreciation, Residual Value (Derived Primarily from the Business - VOB) Speed Lead Times, Delivery Times, Turnaround Times, Setup Times, Cycle Times, Delays (Derived equally from the Customer or the Business – VOC/VOB) • 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

13. Output-Process-Input Measures Helpful Hints • Use your SIPOC diagram and sub-process maps to help select measures and ensure “balance.” • Output measures can be taken before or after the Service is delivered to the patient. • Examples: Errors on a form prior to mailing, # of patient complaints, etc. • There are usually more “options” for Process measures than Output or Input measures. • A team must get Output measure(s) “up-front” to baseline the process. • Begin at least one Process and/or Input measure early in the project to help get some initial data for Analyse.

14. Translating VOC Voice of the Customer After Clarifying,the Key Issue(s) Is... Customer(s) Requirements • Good customer requirements: • Are specific & measurable (and the method of measurement is specific) • Are related directly to an attribute of the product or service • Don’t have alternatives and don’t bias the design toward a particular approach or technology • Are complete and unambiguous • Describe what, not how Remember This From VOC????

15. Exercise: Identify Potential Output Indicators Objective Identify a list of potential input and output indicators that evaluate the extent which the process meets CTQs Instructions: Review the SIPOC below or the one for your project and determine what input and output indicators would tie to a customer requirement Internal/external. Time:10 Minutes

16. Types of Data Continuous Discrete Measured on a continuum Count or categories • Objective • Time • Money • Weight • Length • Subjective • Satisfaction • Agreement • Extent • Type of error • Objective • Count defects • # approved • # of errors • Type of document • Subjective • Yes / No • Categories • Service performance rating (good, poor) • Satisfaction • Agreement • Classifying data is important because it will: • Provide a choice of data display and analysis tools • Provide performance or cause information • Determine the appropriate control chart to use • Determine the appropriate method for calculation of the improvement or capability of the process

17. Examples of Data Examples Type/How Obtained Continuous:(or “variables”) Measuring instrumentor a calculation Service: Elapsed time to complete transaction,average length of phone calls NHS: Elapsed cycle time, drug purity, oxygen flow, weight, length, speed Both: Budget vs. actual (dollars); average customer satisfaction score; amount purchased Discrete:Percentage orproportion Count occurrencesand non-occurrences Service: Proportion of late applications, incorrect invoices NHS: Proportion of breaches, secondary infections, damaged items, late shipments Both: Proportion of associates absent, incomplete forms Discrete:CountCount occurrencesin an area ofopportunity Service: Number of applications, errors, complaints,etc. NHS: Number of computer malfunctions, machine breakdowns, accidents Discrete:Attribute Observation Service: Type of application, type of request NHS: Type of treatment Both: Type of customer, type of method used (new vs. old), location of activity (city/state) Discrete:OrdinalObservation orRanking Both: Customer rating (1=very satis/ 5= very dissatis); day of week (MTWRF), date, time order

18. Exercise: Types of Data Objective: Practice identifying different types of data. This information is important for knowing both how to collect the data and how to analyse it. Instructions: Label the following with the appropriate type of data. If more than one may apply, describe how. You may work in pairs. Be prepared to share your answers with the whole group. Time: 10 minutes.

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20. Exercise: Answers Data Type Lateness of a delivery Continuous1 Defective insterments Discrete: Percentage or Count2 Overdue accounts Continuous3 Amount of recycled materials Continuous Equipment breakdowns Discrete: Percentage or Count2 Cycle time Continuous Lost patients Discrete: Percentage or Count2 Errors on reports Discrete: Count Changes to a schedule or plan Discrete: Count Percent of a report that needs to Discrete: Percentage be reworked

21. The road to…... 1) Define (Practical Business Issue) Delivery Problem S I P O C VOC CTQ’s Cost Parts Survey Focus groups Interviews Quality Service Y Measurable Data Collection Plan (DCP) Charter 2) Measure Problem Statement Representative Contextual Sufficient Reliable - Data Collection - Sampling Strategy Goal Statement ID Stakeholders Business Case - MSA (Gauge R&R)

22. Remember Data Helps Us . . • Separate what we think is happening from what is really happening • Confirm or disprove preconceived ideas and theories • Establish a baseline of performance • See the problem over time • Avoid “solutions” that don’t solve the real problem 1 2 8 7 3 6 5 You do not know what you do not know and making decision on the unknown is paramount to chance at best!

23. The Importance of Measure • To understand your process and the variation which can cause your output to fluctuate (Defective), one has to measure the process. • To understand output variation we need to measure the input variables to find the defect. • With good reliable data, you can make better decisions than without data or bad data. Remember, if your reacting to an output you are reacting when it is too late!

24. Asking Questions???? • Asking questions is one of the primary ways of collecting data. • Asking the right questions guides you to collecting the right data • Deduction from the right data makes the decision “Elementary”

25. What questions do you need to ask? It is essential that you have a grasp of the knowledge requirements of your project thus: 1) What do you want to know? 2) How do you want to represent what you want to know, i.e. chart or other visual tool? 3) What type of tool will generate the results you seek? 4) What type of data is needed to fit your tool selection, continuous or discrete? 5) Where can you obtain the required data type?

26. Who to Ask, Where to Look? • Along with deciding what to ask, one must decide who to ask and where to look. • There may be multiple measurement systems • People • Data Source • Is there only one data repository? Ask enough questions of enough people to understand what is going on

27. Data Attributes Representative • Full range of actual process conditions is seen in the data • Are Saturdays representative of every sales day of the week? Contextual • Collected along with other information about what is happening throughout the process (who, what, where, when) • Are higher sales in spring due to all departments or just some? Sufficient • Enough data so any patterns you see are likely to be real • Does one warm weekend in March indicate the start of Spring Season? Reliable • Actually represents the process of interest collected in a manner that is repeatable and reproducible • If we were to do an inventory count in lighting, how many of us would get the same counts? What if we each did it twice? Additional information in the Appendix

28. Some thoughts about data • You have no data 30 samples to set base line • You have historical data > 100 • Demonstration of improvement will depend on the type of data and the precision you require to demonstrate change • You are uncertain of the data new or old (Meaning until you have validated it) use it with speculation.

29. Population an entire group of objects that have been made or will be made. highly unlikely we can ever know the true population parameters. the average time to treat 20,000 sq. ft of grass. all registered voters in the U.S. Sample the group of objects actually measured. a sample is usually a subset of the population of interest. the average time to treat 20,000 sq. ft of grass today. a survey of 1000 voters. Population Vs. Sample “Population Parameters” m=Populationmean s=Populationstandarddeviation Population “Sample Statistics” X = Sample mean s=Samplestandarddeviation Sample

30. Population Vs. Sample Why use samples? • Sampling is: • Collecting a portion of all the data • Using that portion to draw conclusions (make inferences) • Why use samples?Because looking at all the data may be: • Too expensive • Too time-consuming • Destructive (e.g., taste tests)

31. Check sheets Simple data collection form which helps determine howoften something occurs. Concentration Diagrams Pictorial check sheet which helps you mark where something occurs or the type of problem. Tools to Help You Collect Data Missing Service Request Information Name Address Telephone Type of Service Needed Monthly or Quarterly Customer Existing Customer

32. Concentration Diagrams • A concentration diagram is a data collection form where you write directly on a picture of an object, form, or work area. Expense Report Name: _Tom Rodgers _______ Week ending ___________ 20__ Jan 21 03 • The most important aspect of a concentration diagram is that it lets you quickly see where problems physically appear and how they cluster. • Advantages include being easy to fill out and not requiring a lot of words. Date Project Code Hotel Trans Meals Misc Total Comments R R R R E E E E E M M T M F E E E E R R E E E E E R R R E A E A E E A E E E E E E A E E A E E E E A E A E Totals E: Entry missing R: Receipt missing M: “Misc.” not explained T: “Trans” not explained A: Arithmetic error

33. Distribution Check Sheet Time of Day Totals Incoming Patients

34. Review and Transition • In Identifying Measures we learned: • Identify Output and Input Indicators that effect CTQ’s • Creating an operational definitions for Measure • Understand the types of data and attributes • Data Collection • In Basic Statistics, we will learn: • To distinguish between two types of data • How to calculate central tendency • Evaluate distribution based on location, shape, spread and consistency • Distinguish the types of variation

35. Appendix

36. Representative • For conclusions to be valid, the samples must be representative. • The data you collect fairly represents all the data • No systematic differences between the data you collect and the data you don’t collect • Every item stands a known and usually equal chance of being included • Statisticians call this “avoiding bias.”

37. Representative • Being representative in your data collection strategy is one of the most important aspects of creating a data collection plan. • If you only collect data on a location in Southern California can you make any inference towards the same process in in Maine? The idea of representative simply means that the population is fairly represented in your sample

38. Contextual • Contextual data are collected to provide information on the context in which individual attitudes • behaviour, or other experiences take place • Contextual makes the primary measure make more sense • offers supporting data which might help in determining the few x’s which have the greatest influence on the Y output. What was the situation when the primary metric was collected? • Example: Cycle time is the primary metric, but time of day the process, who collected it, how was it captured, the day of the week, month of the year, and which location are important attributes to the CTQ of service deliver within 2 hours

39. Questionable Data?? • Bias in a sample is the presence or influence of any factor that causes the population or process being sampled to appear different from what it actually is.

40. Biased?? So what are these statistics telling me? What is wrong with what they are telling me? Is there any Bias which has been introduced?

41. Sufficient Sufficient • Enough data so any patterns you see are likely to be real. • There has to be enough data to make statistical inference • If we are measuring a monthly measure, how many consecutive months must we have to determine patterns? • Is it better to have a weekly or daily measure? • But what does that do to the required sample size?

42. Practicality • Sampling in some cases can either be too slow, costly, biased or non-contextual which regardless of how many samples you have makes your conclusions invalid. • Two basic rules: • At the very minimum to establish a base line you will need to collect 30 samples • Verify the sampling strategy with your Project Lead or Stat Guru prior to collecting any data as even with good work and intention you might collect the information incorrectly or introduce bias

43. Reliability • The last element about data requirements is that it is reliable. Simply put, you have to prove the integrity of the data. • A Measurement System Analysis (MSA) may be required before you can make any conclusion regarding your data. • MSA will be covered in the following module ‘MSA Attribute’. However, understand that if you can't prove that you have good data is the same as making decisions with bad or no data at all. • With an MSA you can actually confirm the size of the issue

44. How Reliable? • In determining reliability outside of conducting an MSA, asking the following questions will provide some key insights as to the reliability of the data. • Do all data collectors have the same operational definition? • Is the data collected from the same data pool? • How is the data transferred? • Is the data collected for the same time period? • Is the data collected by the same people over time? • If you cannot answer these questions satisfactorily, it is even more doubtful that your data will be reliable.