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DATA GATHERING. What is Data ? Data is a numerical expression of an activity. Conclusions based on facts and data are necessary for any improvement. -K. Ishikawa If you are not able to express a phenomenon in numbers, you do not know about it adequately. - PowerPoint PPT Presentation
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• What is Data ?
Data is a numerical expression of an activity.
Conclusions based on facts and data are necessary for any improvement.
-K. Ishikawa
If you are not able to express a phenomenon in numbers, you do not know about it adequately.
-Lord Kelvin
DATA GATHERINGDATA GATHERING
TYPES OF DATATYPES OF DATACONTINUOUS DISCRETE
Measurable
e.g. :Length, Temperature
Subjective Assessmente.g. :Score in a beautycontest
Countablee.g. :Number of defects
Data if properly collected
• Least influenced by individual biases
• Could be subject to critical analysis
• Generally beyond language barriers and therefore universal in expression.
WHAT IS THE DIFFERENCE WHAT IS THE DIFFERENCE BETWEENBETWEEN
A SHAFT DIAMETER
THE NUMBER OF SHAFTS REJECTED FOR OVERSIZE
DIAMETER
The diameter of a
shaft can take any
value ever after the
decimal point e.g..
19.055, 19.0516
etc..
Data related to this
type of parameters
are called
Continuous data.
The number of shaft rejected has necessarily to be a whole number. e.g.. 0, 2, 7, 10 numbers rejected etc..
Data related to this
type of parameters are
called Discrete data.
HOW DO YOU DISTINGUISH BETWEEN HOW DO YOU DISTINGUISH BETWEEN
CONTINUOUS AND DISCRETECONTINUOUS AND DISCRETE
CONTINUOUS DISCRETE
•They are real numbers
•Normally, they are measured values
•They can not take a single value. There is an area associated with it
•They are continuous
•Requires less sample size
• They are whole numbers
• Normally, they are counted values
• They can take only ‘Zero’ or non-fractional positive values
• They are in steps of ‘1’• Requires more sample
size to have the more precision
WHICH OF THE BELOW ARE WHICH OF THE BELOW ARE CONTINUOUS AND DISCRETE CONTINUOUS AND DISCRETE
DATA?DATA?• Width of sheet
• No. of liners thinned
• Tubes rejected by Go- Nogo Gauge
• Diameter of Piston
• Height of a Man
• Sheet thickness
• Out of 100 sheets the numbers that meet the thickness 4 0.9
• Time taken to process a purchase order
• No. of bugs in a program
OBJECTIVES OF DATA COLLECTIONOBJECTIVES OF DATA COLLECTION• To know and quantify the status
• To monitor the process
• To decide acceptance or rejection
• To analyse and decide the course of action
HOW TO COLLECT DATA ?HOW TO COLLECT DATA ?
• Define the purpose
• Decide the type of analysis
• Define the period of data collection
• Is the the required data already available ?
FOR PROPER DATA COLLECTION...FOR PROPER DATA COLLECTION...
• Proper sampling procedure
• Proper choice of instruments
• Calibration of instruments used
• Availability of standards for sensory characteristics
• Adequate lighting and other test/inspection facilities.
• Record all relevant information
• No two things in nature are alike. • This is also true for manufactured products. • This dissimilarity between two products for the
same characteristic is called variation.
• The variation may be or can be made to be so small so as to make the product SEEM similar.
• When we say that 2 things are similar we actually mean that it is not possible to measure the variation present within the accuracy of the existing measuring equipment.
• Variation between 2 products are compared for SIMILAR features or characteristics.
WHAT IS VARIATION ?WHAT IS VARIATION ?
• Variations among pieces at the same time
• Variations across time
TYPES OF VARIATIONTYPES OF VARIATION
This man wants to reach his work place by 6.55 a.m.. But he can not do so, exactly at 6.55 a.m. daily. Sometimes he reaches earlier (but almost never before 6.50 a.m.). Sometimes he reaches later (but almost never after 7.00 a.m.). WHY ?
6.50 6.55 7.00
6.55 a.m. 5 minutes.
OF CERTAIN FACTORS WHICH• Affect the time he takes • He cannot control• Vary randomly
e.g. The traffic you encounter under normal course of travel
THE VARIATION THAT OCCURS DUE TO THESE KIND OF FACTORS IS CALLED INHERENT VARIATION OR COMMON CAUSE VARIATION OR WHITE NOISE.e.g.. m/c vibration,tool wear etc.
THIS IS BECAUSE....THIS IS BECAUSE....
UNDER NORMAL UNDER NORMAL SCHEME OF OPERATIONSCHEME OF OPERATION
InherentVariability(white noise)
Aimed value
Minimum deviation
Maximum deviation
6.30
TODAY HE IS EARLY !
WHY ?
PROBABLY BECAUSE :• His watch was running fast.• He got a lift.• His bus driver took a
shortcut.• He stayed over in the
colony.• He had some important work
to be finished before 7.30.These causes are characteristic of a specific circumstance and do not occur in the normal scheme of actions.
Variation due to these types of reasons is called assignable or special cause variation or black noise
GRAPHICAL DISPLAY OF GRAPHICAL DISPLAY OF VARIABILITIESVARIABILITIES
InherentVariability
Assignable Variability
Assignable Variability
TOTAL VAR I A B I L I T Y
Assignable Variability
Assignable Variability
Aimed Value
CASE I
CASE II CASE III
(Black noise)
COMMON PROBLEMS WITH COMMON PROBLEMS WITH MEASUREMENTSMEASUREMENTS•Problems with the measurements themselves
1.Bias or inaccuracy: The measurements have a different average value than a “standard” method.
2.Imprecision: Repeated readings on the same material vary too much in relation to current process variation.
3. Not reproducible: The measurement process is different for different operators, or measuring devices or labs. This may be either a difference in bias or precision.
4. Unstable measurement system over time: Either the bias or the precision changes over time.
5. Lack of resolution: The measurement process cannot measure to precise enough units to capture current product variation.
DESIRED MEASUREMENT DESIRED MEASUREMENT CHARACTERISTICS FOR CHARACTERISTICS FOR
CONTINUOUS VARIABLESCONTINUOUS VARIABLESGood accuracy if
difference is small
Standard value
Observed value
Data from repeated measurement of
same item
Good repeatability if variation is small *
1. Accuracy
The measured value has little deviation from the actual value. Accuracy is usually tested by comparing an average of repeated measurements to a known standard value for that unit.
2. Repeatability
The same person taking a measurement on the same unit gets the same result.
3. ReproducibilityOther people (or other instruments or labs) get the same result you get when measuring the same item or characteristic.
* Small relative to a) product variation
andb) product tolerance (the width of the product specifications)
Data from Part X
Data Collector 1
Data Collector 2
Good reproducibility if
difference is small *
Data from Part X
DESIRED MEASUREMENT DESIRED MEASUREMENT CHARACTERISTICS FOR CHARACTERISTICS FOR
CONTINUOUS VARIABLESCONTINUOUS VARIABLES
4. StabilityMeasurements taken by a single person in the same way vary little over time.
Time 1
Time 2
Good stability if difference is
Small*
Observed value
Observed value
* Small relative to a) product variation
andb) product tolerance (the width of the product specifications)
DESIRED MEASUREMENT DESIRED MEASUREMENT CHARACTERISTICS FOR CHARACTERISTICS FOR
CONTINUOUS VARIABLESCONTINUOUS VARIABLES
5. Adequate Resolution
There is enough resolution in the measurement device so that the product can have many different values.
5.1 5.2 5.3 5.4 5.5X X
XXX
XXXXXX
XXX
XX
Good if 5 or more distinct values are observed
DESIRED MEASUREMENT DESIRED MEASUREMENT CHARACTERISTICS FOR CHARACTERISTICS FOR
CONTINUOUS VARIABLESCONTINUOUS VARIABLES
IMPROVING A IMPROVING A MEASUREMENT SYSTEMMEASUREMENT SYSTEM•A measurement system consists of
• Measuring devices
• Procedures
• Definitions
• People
•To improve a measurement system, you need to
• Evaluate how well it works now (by asking “how much of the variation we see in our data is due to the measurement system?”).
• Evaluate the results and develop improvement strategies.
NOTE ON CALIBRATING NOTE ON CALIBRATING MEASUREMENT EQUIPMENTMEASUREMENT EQUIPMENT• Measurement instruments should only be recalibrated when they show
special cause evidence of drift. Otherwise, variation could be increased by as much as 40%. This is because adjusting for true common cause variation adds more variation (Deming’s rule 2 of the funnel).
Measurements taken from stable instrument
Measurements taken with stable instrument recalibrated before each reading