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Cusum BasicsMel Alexander, ASQ Fellow, CQE

Tutorial: ASQ - Baltimore Section

January 13, 2004

Phone: 410-712-7426/work

E-mail: alexanderm@dfmc.org or

vze3gmwz@verizon.net

- Review how Cumulative Sum (Cusum) charts can detect small process shifts sooner than standard control charting schemes

- Cusum Background
- Why CUSUM are useful
- Examples where Cusum are used
- Ways of constructing Cusums
- Future Trends and Developments
- Q & A

- Roots began with Abraham Wald’s Sequential Probability Ratio Test (SPRT) in the late 1940s
- Cusums were introduced by E.S. Page in 1954
- Popularized by Jim Lucas (et al.) at DuPont in the 1970s and 1980s

Wald’s SPRT used a 3-way sequential sampling where samples of n 1 were taken at stages to:

(1) declare that a process is in control;

(2) find the process out-of-control;

(3) take additional observations

The advantage SPRT had over fixed-sized sampling was decisions regarding the two risks associated with shift detection and the size of the shift were determined in advance.

The two risks regarding shift detection are:

- finding false alarms (finding process shifts that did not occur)

- missing shifts that did occur (fail to detect process shifts that occurred)

With fixed-sized sampling, is usually specified first, while is computed for different process level values

Wald and G.A. Barnard (in 1945, 1946) showed that the acceptance and reject limit numbers (Ca and Cr, respectively) must satisfy the relationships: and

so that a decision interval boundary could be formed Ca < SPRT < Cr

- Cusums are more capable of detecting:
- small changes in process levels
- the start when processes drifts out-of-control

- Each Cusum point gives the cumulative history of processes
- small systematic shifts easily detected
- but large abrupt shifts detected faster with Shewhart charts

Cusums (Si) plot the cumulative sums of deviations of sample values (Xis) from a target value or aim (T) over time

Si = where

Xi = process output value of the i-th item or sample (sometimes = i-th mean may be used),

T = Target value or aim, T may be estimated with the in- control mean ,

n= number of samples collected, tested, or baselined

- Gil Culfari presented Bioprocess Protein% tutorial at the Sept. 2003 ASQ-Baltimore Section meeting (see http://www.asqbaltimore.org/gilcsep16.htm)
- ChemicalProcess industries (DuPont applied more than 10,000 cusums between 1980s-1990s)
- In Healthcare, cusums helped assess physicians’ clinical competence performing surgeries and managing hospital length of stay (los) by patients
- In business & finance, change-point analysis use cusums to monitor the impact of trade deficits on stock market portfolios and in handling product/service complaints by consumers

- (1) Tabular Approach (preferred method to easily implement with spreadsheet software)
- ARL - average number of samples/items/subgroups tested before an out-of-control signal is sent or shift is detected
- (2) V-mask proposed by G.A. Barnard in 1959
- Error probability considerations ( , , delta shift)
- (3) Fast Initial Response (FIR) proposed by Lucas and Crosier (Technometrics, 24, 199-205,1982)

Tabular One-Sided Decision Interval Cusum

Uses deviations above (below) the target T that is calculated as:

Upper Cusum: (Shi,i) = max[0, Shi,i-1 + Xi – (T - k)]

Lower Cusum: (Slo,i) = max[0, Slo,i-1 + (T- k) - Xi]

where starting values Shi,0 = Slo,0 = 0,

Next, we find parameter values K and H

K = reference value (a.k.a. allowance or slack) equal to some constant (multiple, coefficient) times - sigma,

i.e, standard deviation estimated from values, subgroup ranges, or average moving range.

Usually, K= 0.5 x Delta = where

Delta =the amount of shift from the target (T) we seek to detect. Usually, Delta equals sigma (= )

Xout = out-of-control value of the mean (= T + K )

The parameter H serves as a decision point (like a control limit) that works as follows:

H=4 or 5 indicates an out-of-control signal

whenever

Shi,i > H or Slo,i > H for sample item (or subgroup) i

Parameters H and K are designed to yield large Average Run Lengths (number of samples before an signaling an out-of-control condition) when process is on target, denoted as ARL(0).

As the process shifts by the size of Delta, the Average Run Lengths should be small, denoted as ARL(Delta).

Tables exist that show the relationship of ARLs to H, K, and Delta

V-mask (Error Probability) Approach

- The V-mask is the classical cusum two-sided scheme
- Estimates H and K from the error probabilities
- - finding false shifts
- - missing real shifts
- Interprets the cusum as a reverse SPRT (working backwards through past data)
- A sideways-shaped V placed a fixed distance after the last data point

Cusum Example with V-mask(Source http://www.itl.nist.gov/div898/handbook/pmc/section3/pmc323.htm)

- Data collected on 20 Sample means of size 4
- Each sample mean input as single data point
- Target of 325,
- Delta = = 0.6325,
- = 0.0027 (equivalent to Shewhart’s 3 )
- = 0.01

Cusum Example with V-mask cont.

- V-mask over last data point has cusum point below lower arm, indicating upward drift
- In-control ARL(0) 63 or 64
- Out-of-control ARL(Delta) 7

Cusum Example with V-mask cont.

- Moving V-mask backwards through past data helped find where shift-signal first occurred.
- First signal of upward shift took place at sample 14 since lower V-mask arm did not cross data at sample 13 (i.e., process was in control)

Fast Initial Response (FIR) at Headstart

- Introduced to increase cusum sensitivity upon startup
- Sets starting values of Shi,0 and Slo,0 to some nonzero value, say H/2 (a.k.a. 50 percent headstart)
- FIR detects out-of-control situations 40% faster than standard cusums
- Drifts to zero quickly for in-controlled processes

Cusums can be used to monitor process variabilty

- For Xis N(0, ), the standardized Xi is zi = (Xi - 0)/
- A new standardized quantity by Hawkins (JQT, 13, 228-231, 1981; JQT, 25, 248-261, 1993) is defined by:
- vi =
- Hawkins suggested that the vi s were more sensitive to variance changes than mean changes.

Cusums for monitoring process variabilty cont.

- So vi N(0,1), and the two-sided Scale Cusum is defined as:
- Shi,i = max(0, vi – k + Shi,i-1)
- Slo,i = max(0, vi – k + Slo,i-1) where
- Shi,i = Slo,i = 0.
- If either Shi,i > Hor Slo,i > H, then the process is declared out-of-control

Other Types of Cusums

- Cusums have been been studied on binomial and Poisson (attributes) and non-normal data.
- See Lucas(Technometrics, 27, 129-144, 1985); Ewan & Kemp (Biometrika, 47, 363-380, 1960); Wadsworth et al., Modern Methods for Quality Control and Improvement, (Wiley, 1986); Ryan, Statistical Methods for Quality Improvement, (Wiley, 1989); Bourke(1999, http://www.stat.fi/isi99/proceedings/arkisto/bork0597.pdf); and British Standards Institution (BS5703-4, 1997 or ISO/TR 7871:1997) for more information.

Cusum Limitations

- Short term drifts or erratic behavior in the process mean may not be detected.
- Not as effective in detecting large process shifts as Shewhart charts, but is corrected with a combined Cusum-Shewhart scheme, See Lucas(JQT, 14, 51-59, 1982) for more information.
- Since parameters that construct cusums depend much on the ratio of vertical and horizontal axes, this may require scales to be redrawn and redefined as more data are collected.

Cusum Future Trends and Developments

- Manhattan Control
- Dr. Juergen Ude of Australia modified Woodward and Goldsmith’s (Cumulative Sum Techniques, Oliver and Boyd for ICI, 1964) approaches to detect onset and duration of changes in manufacturing processes. Visit http://www.qtechinternational.com for more information

Statistical significance tests for relative changes are performed on adjacent local means that help identify new problems

Cusum Future Trends and Developments cont.

- Change-Point Analysis
- Wayne Taylor combined Cusum charting scheme with bootstraping (resampling) to detect changes on various kinds of data (time-ordered, non-normal, customer complaints, and data with outliers).
- Adaptive CUSUM that adjusts to signal one-step ahead forecasts of varying location shifts in deviations from target. See Sparks (JQT, 32, 157-171, 2000) for details.

Change-Point Analysis Example: Plot of US Trade Deficit Data Showing Changes in Background

For more information, visit Wayne Taylor’s web site: http://www.variation.com/cpa/tech/changepoint.html

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