Statistical Process Control - · PDF fileStatistical Process Control ... o The process is in statistical control and produces 100% ... • People can utilized these tools with minimal

Statistical Process ControlCreation and Interpretation of Control Charts

Douglas B. Brown, Ph.D.

IVT Validation Week (Coronado, CA)

October 18 – 20, 2016

Disclaimer

The contents of this presentation represent

the opinions and views of the speaker and do

not necessarily represent any opinions or

views from Charles River Laboratories, its

subsidiaries, or any employee associated

with Charles River Laboratories.

2

The “Process” Lifecycle

3

• Stage 3 begins with the completion of the process or productvalidation.

• The purpose of this stage is the continual assurance that the validatedprocess is performing as it is intended (i.e., designed) throughout theentirety of the process’s operation.

• In other words, we want the process to stay in a controlled state.

Why should we be concerned about

Process Control?Referring to the 2011 Guidance for Industry on Process Validation…

Grace E. McNally, Senior Policy Advisor for the U.S. Food and Drug Administration, asked 3 questions during a May 2011 presentation concerning the Lifecycle Approach in the context of Process Validation:

1. Do I have confidence in my manufacturing process? Or, more specifically, what scientific evidence assures me that my process is capable of consistently delivering quality product?

2. How do I demonstrate that my process works as intended?

3. How do I know my process remains in control?

4

What is Control?The Four Process States• The ideal state

o The process is in statistical control and produces 100% conformance.

o The process has proven stability and target performance over time.

o It is predictable and its output meets expectations.

• The threshold stateo The process is in statistical control but produces the

occasional nonconformance.

o Although predictable, this process does not consistently meets expectations.

• The brink of chaos stateo The process is not in statistical control, however, it is not

producing defects.

o The process is unpredictable, but the outputs of the process still meet the requirements.

o The lack of defects leads to a false sense of security, however, as such a process can produce nonconformances at any moment. It is only a matter of time.

• The state of chaos o The process is not in statistical control and produces unpredictable levels of

nonconformance (e.g., Out of Specification)

5

Overview

• Statistical Process Control

• Creating Control Charts

• Establishing Zones/Limits

• Identifying Outliers in the Baseline (Background)

• Rules for “Out of Control” State

• Establishing Alert Flags

• Analyzing Control Charts

6

Statistical Process Control

• Statistical Process Control (SPC) is a method

of quality control which uses statistical methods to

compare (or evaluate) what is currently

happening to what has occurred historically.

• SPC can be applied to ANY process where the

"conforming product" output (i.e., product meeting

some type of specification(s)) can be measured.

• Monitoring and controlling a process (or system)

ensures that the operation is performing at its full

potential with minimal waste (or defects).

7

What Do I Monitor? • Identify the nature, source, and

extent of the variability within

your process, product, or

production (e.g., people,

equipment and instruments,

facilities and environment,

materials and reagents).

8

• Some degree of variability is inherently present in every process.

• Know AND understand the impact of the variability on the

effectiveness of your process.

• Controlling (and especially monitoring) the variability begins at the

initial stages of the validation lifecycle and is ongoing as the

process is used throughout Stage 3 until the process is

discontinued.

Statistical Process Control Techniques

• Graphical techniques are helpful in troubleshooting issues

related to quality.

• These techniques are often referred to as “Basic Tools of

Quality”.

• People can utilized these tools with minimal formal training in

statistics and they include:

o Histograms

o Scatter Diagrams

o Cause and Effect Diagrams

o Defect Concentration Diagrams

o Pareto Charts

o Check Sheets

o Control Charts

9

Histograms – I

• The purpose of a histogram is to graphically summarize

the distribution of a univariate data set (i.e., one variable

set of data). The histogram graphically shows the

following:

o Center (or location) of the data

o Spread (or the scale) of the data

o Skewness (or asymmetric

distribution) of the data

o Presence of potential outliers

o Presence of multiple modes in the

data

10

Histograms – II

The most common form of the histogram is obtained by

splitting the range of the data into equal-sized bins (called

classes). Then for each bin, the number of points from the

data set that fall into each bin are counted.

• Vertical axis: Frequency

(i.e., counts for each bin)

• Horizontal axis: Response

variable

11

Scatter Diagrams (or Plots)

• A type of diagram or plot using coordinates to

display values for typically 2 variables for a data

set.

• Typically,

– The vertical axis (Y-axis) is the

variable response

– The horizontal axis (X-axis) is

usually some variable we

suspect may be related to the

response

12

Cause and Effect Diagrams• Also know as Ishikawa or fishbone diagrams, identifies the potential

factors which may be a cause to an overall defect.

• Each cause or reason for imperfection is a source of variation.

• Causes are usually grouped into major categories to identify sources of

variation.

o Equipment: Any instruments, computers,

tools, etc. required to accomplish the job

o Process: How the method/procedure is

performed and the specific requirements for

doing it, such as policies, procedures, rules,

regulations and laws

o People: Anyone involved with the process

o Materials: Raw materials, parts, etc.

o Environment: The conditions, such as

location, time, temperature, and culture in

which the process operates

o Management: Physical and

Financial support

o Measurements: Data generated

from the process that are used

to evaluate its quality

13

Defect Concentration Diagrams

• A graphical tool that is used when analyzing the

causes of a product (or part) defect.

• It is a drawing of the product (or process) with all

relevant views displayed, onto which the

locations and frequencies of various defects

shown.

14

Pareto Charts and Check Sheets

• A Pareto chart is a bar chart that displays the relative

importance of problems in a format that is easy to

interpret.

o The most important problem is represented by the tallest bar.

o Generally, 80 % of the problems stem from 20% of the possible

causes (J.M. Juran).

• A check sheet is a tool for

collecting data for Pareto chart.

o Check sheets are simply a form

used to collect data; specifically, a

tally sheet of nonconformities

15

Control Charts – General Uses

• A control chart is a graph used to study how a process (or system) changes over time.

• Data are plotted chronologically.

• The most common application for using control charts is to monitor process stability and control.

• A less common use, although some might argue a more powerful application of employing control charts, is as an analysis tool for identifying “significant” variation.

16

Basic Variation Types

• When a process is stable and in control, it displays “common cause variation”.– This is variation that is inherent to the process

(e.g., instrument/equipment, reagents/materials, human beings)

• A process is in control when, based on past experience, it can be predicted how the process will vary (within limits) in the future.

• If the process is unstable, then the process displays “special cause variation” (i.e., non-random variation from an external factor(s)).

17

Basic Elements of a Control Chart

1. A control chart begins with a graphing (plotting) data

chronologically.

2. A Central Line (i.e., average or mean) is added as a visual

reference for detecting shifts or trends. This is also referred to as

the process location.

3. Upper and Lower Control Limits (UCL and LCL) are computed

from available data and placed equidistant from the Central Line.

This may also be labeled as the process dispersion.

18

Controlled Variation

• Controlled variation is characterized by a

stable and consistent pattern of variation over

time, and is associated with common causes.

• A process operating with controlled variation

has an outcome that is predictable within the

bounds of the control limits.

19

Uncontrolled Variation

• Uncontrolled variation is characterized by variation

that changes over time and is associated with special

causes.

• The outcomes of this process are UNprEDictAble.

20

Control Chart Limits

• Control chart limits are set based on the normal

distribution of the process generated data in which

– 68.3% of the data is within ±1 standard deviation from the

average.

– 95.4% of the data is within ±2 standard deviations from the

average.

– 99.7% of the data is within ±3 standard deviations from the

average.

• As such, plotting data that is not normally distributed

may signal an unexpectedly high rate of false alarms.

21

Control Chart and Normal Distribution

• Note that process capability and process stability are two different things...BUT they are related.

• A process should be stable (allowing only common cause variation to exist) and capable of consistently meeting defined specification before process control is addressed.

22

Control Limits v. Specification Limits – II

23

•The control limits assist personnel in

identifying changes which may (or may not)

lead the process or system into an “Out of

Specification” state.

•The “Out of Specification” state exists

when data (i.e., results) occur outside the

established specification limits; limits that

are determined externally by the client or

developer.

•A process is defined as being “Capable”

when the control limits are within or on the

specification limits (see Top Figure).

•A process is defined as being “Not

Capable” when one or both of the control

limits are outside of the specification limits

(see Bottom Figure).

What Type of Control Chart Should I Use?

• Variable Control Charts (measuring how much)

– X and Rm – Individual and moving average

– X and R – Median and range

– X-bar and S – Mean and SD

– X-bar and R – Mean and range

• Attribute Control Charts (counting how many)

– C Chart – Number of defects in sample

– U Chart – Number of defects per item

– NP Chart – Number of defective items

– P Chart – Proportion of defective items

24

Outline




• Identifying Outliers in the

Baseline (Background)




25

Control Charting • There are many statistical software packages in market

(e.g., SAS, JMP, MiniTab, Tableau) Pro – many are excellent, easy to use software packages

Pro – many have pre-loaded charts and analyses (from simple to complex/advanced statistical analyses)

Con – packages can run from about $1000 to over $10,000 per year for a single license

Con – some software require an annual renewal fee, too

• If monetary constants prevent you from using this powerful tool, then you can use Microsoft Excel to create your own charts. Pro – you already own it and there are no user/licensing constraints

Con – it will require some work (time) and maintenance where marketed software do not

26

Background Information – I

• Using Microsoft Excel* (MS Excel), open a new

workbook (i.e., spreadsheet).

• Label columns with pertinent tracking information.

*Microsoft Office Excel 2007 is used throughout this presentation

27

Background Information – II

• Set the top 2 or 3 items

that are important for

determining the system’s

(or process’s) stable.

• Label/title the next set of

columns with the data to be

collectedo Positive

and/or

negative

controls

o Standard

curve points

o Concentration

points

o Flow rates

o Peak times

28

Background Information – III

• All data being analyzed

should use the SAME

equipment/instrument settings

or parameters

• Entered data (replicate points) may be used to

generate additional information for future evaluation

(e.g., mean, range)

29

Creating a Control Chart – I

• Highlight the

background

(historical) data

and dates

associated with

the selected data

to be plotted.

• Select the “Insert” tab

• Select the “Scatter” icon

• Select the “Scatter with Straight Lines and Markers”

30

Creating a Control Chart – II

• The Move Chart window will appear

o Select “New sheet” option

o Enter a title for your new worksheet

• The scatter plot

will appear within

the

spreadsheet

• Select the

“Design” tab and

then the “Move

Chart Location”

icon

31

Creating a Control Chart – III

• Select the “Design” tab and

then the “Select Data” icon

o The “Select Data

Source” window appears

o Select the “Edit”

button.

o The “Edit Series”

window will appear.

Enter:

Series Name

X-values = Time

Y-values = Item to be

trended

32

Creating a Control Chart – IV

• Delete the “Legend” entry from

the graph

• Add the vertical axis label

o Select the “Layout” tab

o Select the “Axis Titles”

icon

o Select the “Primary Vertical

Axis Title” menu option

o Select the “Rotated Title”

menu option

o Select the “Axis Title” and

type in a suitable title for

the y-axis (repeat for x-axis)

33

Outline









34

Establishing the Background Baseline – Ia

• Ideally, 50 data points should be used to

establish the background baseline.

• The baseline is established from data collected

under the current or established testing or

operating process (or system).

o The final procedure or process used during the

qualification and validation procedure is performed

under the same conditions

o These data points may be used to establish the first set

of data points for which the control chart zones and

alert flags are established.

35

Establishing the Background Baseline – Ib• If 50 data points are not available, then follow the suggested

guidelines, or use another justified system. Begin with a baseline containing a minimum of 10 data points (if possible).o After at least 30 total data points are acquired, adjust the baseline to use

the first 20 data points.

o After at least 40 total data points are acquired, adjust the baseline to use the first 30 data points.



• No baseline adjustments are needed unless the process (or system) changes with respect to the most recent validated (or established) state.

NOTE: If the process or system CHANGES, then all previous data should not be used and the trending must begin again.

However, if a Lot Test (or something equivalent) can demonstrate that the change does not affect the process or system, then the trending may continue as established (i.e., a new background baseline does not need to be created).

36

Establishing the Control Charting Zone Limits – I

• Create a table with basic

information

o Control Chart Baseline Count

(cell AB10) =COUNT(M9:M58);

results in a numeric value of 50.

o Baseline Mean 156250 CT (cell

AB11)

=ROUND(AVERAGE(M9:M58),2);

results in a mean value which is

rounded to two places past the

decimal point (i.e., the hundredths

place).

o Baseline Std Dev 156250 CT (cell

AB12)

=ROUND(STDEV(M9:M58),3);

results in a mean value which is

rounded to three places past the

decimal point (i.e., the thousandths

place).

• Additional information specific for a

procedure or process should be

recorded.

37

Establishing the Control Charting Zone Limits – II

o First Data Date (cell

AB16) =MIN(E9:E200);

the earliest date in which

data is being collected;

formula includes a large

range to accommodate

data to be added in the

future.

o Latest Data Date (cell

AC16) =MAX(E9:E200);

the latest (most recent)

date in which data is

being collected; formula

includes a large range to

accommodate data to be

added in the future.

38

Establishing the Control Charting Zone Limits – III

o Control Chart Zone

Limits

Zone C – GREEN ZONE;

data points are ±1 SD from

the mean (CL). Between the Upper Limit

Control 1 (UCL1) and Lower

Limit Control 1 (LCL1)

Zone B – YELLOW ZONE;


the mean (CL). Between UCL1 and UCL2

Between LCL1 and LCL2

Zone A – ORANGE ZONE;


the mean (CL). Between UCL2 and UCL3

Between LCL2 and LCL3

Out of Control – RED ZONE; data

points are MORE THAN ±3 SD from the

mean (CL). Above UCL3

Below LCL3

39

Establishing the Control Charting Zone Limits – IV

o Center Line (CL)

Cells AB18,AC18

Mean value for data set

Why are 2 cells required? Two

points are needed to make a

line.

=ROUND(AVERAGE(M9:M58

),2)

o UCL1 (cells AB19,AC19)

=ROUND(AB11+(1*AB12),2)


=ROUND(AB11+(2*AB12),2)


=ROUND(AB11+(3*AB12),2)

oLCL1 (cells AB23,AC23)=ROUND(AB11-(1*AB12),2)



40

Establishing the Control Charting Zone Limits – V

o Range Center Line (CL)

Cells AB28,AC28

Mean Range Value for data set

=ROUND(AVERAGE(O9:O58)

,2)

SD Range Value for data set

=ROUND(STDEV(O9:O58),3)


=ROUND(AB28+(1*AB29),2)


=ROUND(AB28+(2*AB29),2)


=ROUND(AB28+(3*AB29),2)

oLCL1 (not calculated)By default, the LCL1 is zero

The LCL1 range is the zone between 0

and the CL

41

Establishing the Control Charting Zone Limits – VI

• Click on the plot and then

select the “Select Data”

option.

• The “Select Data

Source” window appears

• Select the “Edit” button

42

Establishing the Control Charting Zone Limits – VII

• The “Edit Series” window

appears.

• Enter an appropriate “Series

name:”

• Select the “Series X values:”

and enter the date range

o =‘Background_Current

Data’!$AB$16:$AC$16

• Select the “Series Y values:” and enter the date range

o CL: =‘Background_Current Data’!$AB$18:$AC$18

o UCL1: =‘Background_Current Data’!$AB$19:$AC$19

o UCL2 : =‘Background_Current Data’!$AB$20:$AC$20

o UCL3 : =‘Background_Current Data’!$AB$21:$AC$21

o LCL1: =‘Background_Current Data’!$AB$23:$AC$23

o LCL2 : =‘Background_Current Data’!$AB$24:$AC$24

o LCL3 : =‘Background_Current Data’!$AB$25:$AC$25

43

Establishing the Control Charting Zone Limits – VIII

• Click a line and then

select the “Format Data

Series” option.

• The “Format Data

Series” window appears

o Select the “Line Color”

option to change the color

of the line

o Select the “Line Style”

option to change the

appearance of the line

Repeat steps as needed

44

Establishing the Control Charting Zone Limits – IX

• Click a line and then select

the “Add Data Labels”

option.

o The numerical value for the line

appears on both ends of the line

o Delete the 1st (closest to Y-axis)

• Click on the data label and

select the “Format Data

Labels” option.

• The “Format Data Labels”

window appears.

o Select “Right” under the “Label

Position” heading

Repeat steps as needed

45

Adjusting the Plot Axis

• Both the X- and Y-

axis may be

adjusted

• Click on the X-axis

and select the

“Format Axis”

option

• Date conversions

o 1 month = 30.4375

days

o 1 week = 7.024 days

• Calendar Dates

o 01JAN2016 (ddMMMyyyy) = 42370 (generic numerical value)

• Adjust Minimum, Maximum, Major unit, Minor unit values as needed

• Adjust Y-axis as needed; following steps above as a guide

46

Control Chart Plot – Background Baseline & Data

Notice within

the “Baseline”

region there is

one (1) point

located in the

“Out of

Control” Zone

OPTIONAL...

Insert->Text

Box->click on

graph...type

text and

color fill box

47

Outline









48

Identifying Outliers in the Background Region – I

• When setting up the control charting zones, it is

important to evaluate data points that appear to be

significantly far away from the rest of the plotted

background data (but what is significant?)

• The Tukey Method is used to evaluate and identify

potential outliers.

• The Tukey Method sets the cutoff-values based on the

Interquartile Range.

• The data set may be visualize as a “Box and Whisker”

plot (which may be created using MS Excel).

49

Identifying Outliers in the Background Region – II

• Use the “Quartile” function to set the limitso Minimum (MIN) Value =QUARTILE(M9:M48,0)

o First (1st) Quartile =QUARTILE(M9:M48,1)

o Median (MED) Value =QUARTILE(M9:M48,2)

o Third (3rd) Quartile =QUARTILE(M9:M48,3)

o Maximum (MAX) Value =QUARTILE(M9:M48,4)

• These values are used to calculate the Low and HighExtreme Outlier Limits (or Far Lower Fence and FarUpper Fence, respectively).

50

Identifying Outliers in the Background Region – III

• IQR is the difference between

the 3rd and 1st Quartile values

=(M4-M2)

• 3IQR = Multiple IQR by 3

=3*(M4-M2)

• Low Extreme Outlier Limit is

the 3IQR – 1st Quartile value

=M2-(3*(M4-M2))

• High Extreme Outlier Limit is

the 3IQR + 3rd Quartile value

=M2+(3*(M4-M2))

51

Identifying Outliers in the Background Region – IV

• For Range Charts, only the High Extreme Outlier Limit

is created.

• In Column N, outliers for Control Charts may be identified

after the data points are entered by comparing the mean

value to the outlier criteria.

• =IF(OR(M32<$M$6,M32>$M$7),"OUTLIER Exist","")

52

Identifying Outliers in the Background Region – IV

• The statement

“OUTLIER

Exist” will

ONLY appear

when 1 of 2

logical

conditions is

met.

• The value in cell M32 (24.73) is LOWER than the value for

“LO Extreme Outliers:” M32<$M$6

• The value in cell M32 (24.73) is HIGHER than the value for

“HI Extreme Outliers:” M32>$M$7

53

Identifying Outliers in the Background Region – V

• The value in

cell M32

(24.73) is

greater than

the high

extreme

outlier limit

(23.92)

• “OUTLIER Exist” appears in the adjacent cell.

• If neither logical condition 1 nor 2 is met, then the adjacent cell

in Column N will remain blank.

54

Identifying Outliers in the Background Region – VI

• Once a data point has been identified as an outlier, a

determination to keep or exclude the data point from

the population set must be made.

• Including extreme outlier data points may lead to...

1. “Masking” of data that otherwise would be flagged as the

system is trending toward an “Out of Control” state.

2. Falsely flagging data as trending towards an “Out of

Control” state when, in fact, the system is displaying its

normal variation range.

55

Identifying Outliers in the Background Region – VII

• Removing an extreme outlier data point WILL CHANGE: o Baseline count, mean, standard deviation

o CL, UCLs, LCLs

o Box and Whisker Plot

o Low and High Extreme Outlier Limits

56

Identifying Outliers in the Background Region – VIII

• After removing the high extreme outlier data point: o The Control Chart Zones and labels are changed

o Data that might be trending toward and “Out of Control” state is

now “unmasked”.NOTE: It is good practice to annotate your plots, especially when data has been

removed for any reason.

57

Identifying Outliers in the Background Region – IX

58

Outline




• Identifying Outliers in the Baseline

(Background)




59

Rules for Determining “Out of Control” State - I

• Alert Flags (or Rule Violations) indicate that the

procedure, process, or system is not behaving

as established (or validated).

• When the current collected data are plotted the

the development of an “Out of Specification”

state may begin to materialize.

• Alert Flags are used to identify drift within a

system and/or some other not normal sudden

occurrence.

60

Rules for Determining “Out of Control” State - II

• SPC control limits (and later alert limits) are established by identifying

the 3-sigma level, both high and low (already discussed).

• Once a process is brought under control and the 1, 2, and 3-sigma

limits established, the sensitivity of the control chart for detecting drifts

before the 3-sigma control limit is reached, may now become

apparent.

• The decision-making criteria first was popularized by the Western

Electric Company (abbreviated as WECO … an American electrical

engineering and manufacturing company primary serving AT&T from 1881 – 1996).

• The WECO Rules were first implemented in the 1920's.

• These quality control guidelines were codified in the 1950's and form

the basis for many other rule sets.

61

Rules for Determining “Out of Control” State - III

• While there are many common individual rules in different industries

throughout the world, many industries have developed their own variations to

the WECO Rules. o Nelson Rules – The Nelson rules were first published in the October 1984 issue of the Journal

of Quality Technology in an article by Lloyd S Nelson.

o AIAG Rules – The (AIAG) Automotive Industry Action Group control rules are published in the

their industry group “Statistical Process Control Handbook”.

o Juran Rules - Joseph M. Juran was an international expert in quality control and defined these

rules in his “Juran's Quality Handbook”, McGraw-Hill Professional; 6 edition (May 19,

2010), ISBN-10:0071629734

o Hughes Rules

o Duncan Rules – Acheson Johnston Duncan was an international expert in quality control and

published his rules in the text book “Quality control and industrial statistics” (fifth edition). Irwin,

1986.

o Gitlow Rules - Dr. Howard S. Gitlow is an international expert in Sigma Six, TQM and SPC.

His rules are found in his book “Tools and Methods for the Improvement of Quality”,

1989, ISBN-10: 0256056803 .

o Westgard Rules – The Westgard rules are based on the work of James Westgard, a leading

expert in laboratory quality management . They are considered “Laboratory quality control

rules”. (www.quinn-curtis.com/spcnamedrulesets.htm)

62

Rules for Determining “Out of Control” State - IV

Rule 1 Flag. Out of Control (above UCL3 OR below

LCL3)

The most recent data point plots outside the either the HI or LO 3-

sigma limit.

Rule 1:

63

Rules for Determining “Out of Control” State – V

Rule 2 Flag. Zone A–HI (UCL2 UCL3) OR Zone A–LO

(LCL3 LCL2)

Two (2) of the three (3) most recent data points plot within Zone A

AND on the same side (either HI or LO).

Rule 2:

64

Rules for Determining “Out of Control” State – VI

Rule 3 Flag. Zone A/B–HI (UCL1 UCL3) OR Zone

A/B–LO (LCL3 LCL1)

Four (4) of the five (5) most recent data points plot within 2- or 3-

sigma of mean, AND on the same side (either HI or LO).

Rule 3:

65

Rules for Determining “Out of Control” State – VII

Rule 4 Flag. Nine (9) out of the last nine (9) datapoints plot on the same side of the centerline (CL)

The chance that a data point falls on the same side of the CL (ormean value) as one before it is 1 out of 2 (or 50%).

The probability that the next data point fall on the same side again is1 out of 4 (or 25%).

The probability that the next data point fall on the same side again is1 out of 8 (or 12.5%). Rule 4:

The probability that nine (9) data points fall on the same side is 1 out of 512 (or about 0.2%).

66

Rules for Determining “Out of Control” State – VIII

Rule 5 Flag. Eight (8) out of the last eight (8) data

points DO NOT fall within 1-sigma of the centerline

(CL) AND the data points occur on BOTH sides of the

centerline (CL)

Rule 5:

67

Rules for Determining “Out of Control” State – IX

Rule 6 Flag. Seven (7) consecutive data points are

either increasing ↑ or decreasing ↓

The probability that seven data points fall on the same side is 1 out of

128 (or about 0.8%).

This is also known as “Trending”.

Rule 6:

68

Rules for Determining “Out of Control” State – X

Rule 7 Flag. Fourteen (14) consecutive data points

alternating direction

This is also known as “Oscillating”.

Rule 7:

69

Outline









70

Creating an Alert Flag Spreadsheet – I

• Begin by typing your selected rules at the top of

the spreadsheet.

• For quick reference and good practice for

displaying/discussion

71

Creating an Alert Flag Spreadsheet – II

• Entered data from a previous worksheet may be linked to

appear on this new worksheet.

72

Creating an Alert Flag Spreadsheet – III

• The “ZONE Value for (MEAN)” is determined to assist with the evaluation of the

(mean) data point value compared to previous recorded (mean) data points.

• By utilizing MS Excel’s IF/THEN function, mathematical equations can be created

to assist in determining if a process is drifting or trending toward an “Out of

Control” state.o If the value is greater than the UCL3 limit, then a value of 1,000,000 is assigned.

o If the value is less than the UCL3 limit, but greater than the UCL2 limit, then a value of 100,000

is assigned.

o If the value is less than the UCL2 limit, but greater than the UCL1 limit, then a value of 10,000

is assigned.

o If the value is less than the UCL1 limit, but greater than the CL, then a value of 1,000 is

assigned.

o If the value is less than the CL, but greater than the LCL1 limit, then a value of 100 is assigned.

o If the value is less than the LCL1, but greater than the LCL2 limit, then a value of 10 is

assigned.

o If the value is less than the LCL2, but greater than the LCL3 limit, then a value of 1 is assigned.

o If the value is less than the LCL3, then a value of 0 is assigned.

73

Creating an Alert Flag Spreadsheet – IV

• =IF(F10="","",IF(F10>'Background_Current Data

(I)'!$AB$21,1000000,IF(F10>'Background_Current Data





(I)'!$AB$24,10,IF(F10>'Background_Current Data (I)'!$AB$25,1,0))))))))

• In this example, “F10” is the cell

containing the value being tested

against the conditions listed

above and the control charting

zone limits for this example are

found on the worksheet titled

'Background_Current Data (I)’

• The reported value appears in

the adjacent cell (e.g., cell G10)

NOTE: While there may be other methods (e.g., formulas, calculations) to relate a current data point to

previous ones, this is just one possible approach.

74

Creating an Alert Flag Spreadsheet – V

• For example, the Rule 1 Flago The MS Excel formula for this flag is:

=IF(F10="","",(IF(OR(VALUE(G10)=0,VALUE(G10)=1000000),"Rule 1 Flag","")))If the value is equal to either 0 or 1,000,000, then the data

point is labelled with a “Rule 1 Flag”.

This means that the data point is more than three (3) standard deviations away from the centerline (CL).

The “Rule 1 Flag” will appear in Column H (see next figure) in the same row as the cell containing the data point being evaluated.

NOTE: Due to the lengthy formulas, additional explanations for Rule Flag 2 through 7 are not described.

75

Creating an Alert Flag Spreadsheet – VI

• Using the assigned “ZONE Value for MEAN”, the conditions for signalling an alert flag may be now be assessed.

• More than one alert flag may be triggered if the conditions for a particular alert flag are satisfied.o If there is no value in the cell being evaluated, then no alert flag is signalled.

o If the cell value does not meet (or trigger) the alert limit(s), then no alert flag is signalled.

76

Annotating the Control Chart Plots (optional)

• Use colored circles

and boxes along

with text to identify

the specific alert

flags.

• Note/annotate when

critical or key

material changes

(e.g., new dilutions

of reference/testing

standards, new lot

material and/or

same reagent from

different vendor is

introduced)

77

Outline









78

Analyzing the Control Chart Plots & Tables – I

• In the table,

there are

many “Out of

Control” alerts

associated

with Analyst F.

• If this analyst

is removed,

then a

different

picture

develops.

79

Analyzing the Control Chart Plots & Tables – II

• A clear shift for this

High Standard Curve

point can be seen

beginning around

December 2014.

• With a little more

investigation, it was

discovered that a

dilution error was made

in the creation of a

material used for the

creation of this assay

standard curve.

• This root cause for the

drift associated with this

assay was masked by

issues associated with

Analyst F.

80

Analyzing the Control Chart Plots & Tables – III

• Notice what happens with the removal of Analyst F.

• For the High Standard Curve point, the Range Control

Chart Limits shift and the Alert Flags disappear (though

this may not always happen).

81

Analyzing the Control Chart Plots & Tables – IV

• Notice what happens with the removal of Analyst F.

• For the Low Standard Curve point, some of the Alert

Flags disappear and other issues materialize.

82

Analyzing the Control Chart Plots & Tables – V

• Again, for the Low Standard Curve point, notice what

happens with the removal of Analyst F.

• Clearly, the impact from Analyst F can be seen.

83

Douglas B. Brown, Ph.D.Research Scientist, Methods Development and Validations

Charles River Laboratories, Biologics Testing Solutions

Malvern, PA

610-407-1071

[email protected]

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