quality management what and why? tools for continuous improvement statistical process control...

Quality Management What and Why? Tools for Continuous Improvement Statistical Process Control Process Capability 6 Sigma Quality Taguchi’s View

What is Quality?

Performance - primary operating characteristics

Features - little extras Reliability - no failure in a given time span Conformance - meeting standards Durability - length of usefulness Serviceability - speed, ease of repair Aesthetics - pleasing to the senses Perceived quality - reputation

8 Dimensions of Quality

How is Quality Defined in Service?

What is Total Quality Management?

Top management involvement Customer driven quality standards Quality at the source Supplier-customer links

everyone has a customer Prevention orientation Continuous improvement

TQM: Philosophical Elements

What are the Costs Associated with Managing Quality?

Kaizen: Continuous Improvement

Standard-Maintaining System Performance standards are fixed unless

major breakthrough in technology occurs Continuous-Improvement System

Performance level should be continuously challenged and incrementally upgraded

Typically requires multifunctional work teams participative management decentralized decision making

The PDSA Cycle (Deming Wheel)

A P

DS

Plan a change ora test, aimed at improvement

Do - Carry out thechange or the test

Study theresults. What didwe learn? What went wrong?

Act -Adopt the change,or abandon it, or run through the cycle again

Source: The New Economics, Deming

Tools for Continuous Improvement Used extensively to improve quality Deceptively simple - workers at all

levels can use Yet, very powerful

Flowcharts, Check sheets, Pareto diagrams, Histograms, Cause-and-effect diagrams, Scatter Diagrams, Run Charts Control charts

Flow Chart A picture of a process that shows

the sequence of steps for a process Makes the process explicit Facilitates group understanding Identifies unknown or

misunderstood steps Can quickly illustrate problems and

solutions

Physician’s Office

Admitting FinancialPlanning

InpatientAccounting

Medicare

Medical Records

Unit

EmergencyDept

UtilizationManagement

Clinical Admin

Medicare Inpatient Billing Process at MassachusettsGeneral Hospital – High Level Flow Diagram(Source: Berwick, Godfrey, Roessner, 1990)

Selected Flow Diagram Symbols Operation/activity

Decision

Storage

Information flow

Flow

Check Sheets

Billing Errors

Wrong Account

Wrong Amount

A/R Errors

Wrong Account

Wrong Amount

Monday

Histograms Graphical representation of the variation in a set of data Provide clues about the characteristics of the population

e.g. bimodal pattern suggests two individual processes

Pareto Diagram To separate "vital few" from the "trivial many" 80/20 rule Histogram of data from the largest frequency to the smallest

Pareto Chart-Occurrences of Errors in Providing a Product to a Customer

0

10

20

30

40

50

60

Delivery Raw Fabrication Final Subassembly materials assembly

Cause-and-Effect Diagram Helps in understanding the possible causes of

the problem Problem is listed at one end of a horizontal line Branches are drawn to represent possible cause

PeopleMachine

Effect

MaterialMethod

Environment

Fishbone Chart-Delivery of Goods by Truck

Shipping Documents

Trucking

Leave atright time

Driver knowsroute

Latest traffic& road conditions

Truckmaintenance Delivery of

goods bytruck

Packinglist

Invoice

Rightinformation

Label stuckon well

Containerlabeling

Labellocation

Protectivepacking

Rightcontainer

Quantityin containerPacking

Scatter Diagram

Shows correlation between variables Variables can be from the cause-and-effect diagram

Hours of Training

02468

1012

0 10 20 30

Hours of Training

Def

ects

Control Charts Why bother?

Understanding Variability: An Experiment

Write small letter ‘a’ ten times with your “good” hand.

Now write it ten times with your other hand.

Understanding Variability

Process

Common Causes Special

Causes

Variation

Common Vs. Special Causes Common Causes: Random,

unidentifiable sources of variation

Special Causes: Variation causing factors that can be identified and eliminated

Statistical Process Control:

Conceptual Framework

Every process has variation Must differential between acceptable

and unacceptable variations Acceptable - - random or COMMON Unacceptable - non-random or SPECIAL

- assignable cause exists

A process is in control when all SPECIAL causes are removed.

The NormalThe NormalDistributionDistribution

-3 -2 -1 +1 +2 +3Mean

68.26%95.44%99.74%

= Standard deviation

Figure 7.5

Control ChartsControl Charts

UCL

Nominal

LCL

Assignable causes likely

1 2 3SamplesFigure 7.6

Using Control Charts for Using Control Charts for Process ImprovementProcess Improvement

Measure the process When problems are indicated, find

the assignable cause Eliminate problems, incorporate

improvements Repeat the cycle

Measure variables - continuous scale x-chart; R Chart

Measure attributes -for yes/no decisions proportion defective -- p-chart number of defects per unit -- c-chart

number of paint defects/sq yard

Types of Control Charts

p-chart, for the population proportion defective take a random sample, inspect plot the sample proportion defective compare with UCL and LCL to see

whether the process is in control

Control charts for attributes

Control ChartsControl Chartsfor Variablesfor Variables

West Allis IndustriesWest Allis Industries

Example 7.1


Sample Sample

Number 1 2 3 4 R x

1 0.5014 0.5022 0.5009 0.5027 0.0018 0.5018

2 0.5021 0.5041 0.5024 0.5020 0.0021 0.5027

3 0.5018 0.5026 0.5035 0.5023 0.0017 0.5026

4 0.5008 0.5034 0.5024 0.5015 0.0026 0.5020

5 0.5041 0.5056 0.5034 0.5047 0.0022 0.5045

R = 0.0021

x = 0.5027

Special Metal Screw

Example 7.1

=

_

Example 7.1


Control Charts – Special Metal Screw

R-Charts R = 0.0021

UCLR = D4RLCLR = D3R

Example 7.1


Control Charts - Special Metal Screw

R - Charts R = 0.0020 D4 = 2.2080

Control Chart FactorsControl Chart Factors

Factor for UCLFactor for UCL Factor forFactor for FactorFactorSize ofSize of and LCL forand LCL for LCL forLCL for UCL forUCL forSampleSample xx-Charts-Charts RR-Charts-Charts RR-Charts-Charts

((nn)) ((AA22)) ((DD33)) ((DD44))

22 1.8801.880 0 0 3.2673.26733 1.0231.023 0 0 2.5752.57544 0.7290.729 0 0 2.2822.28255 0.5770.577 0 0 2.1152.11566 0.4830.483 0 0 2.0042.00477 0.4190.419 0.076 0.076 1.9241.924

Example 7.1


Control Charts—Special Metal Screw

R-Charts R = 0.0021 D4 = 2.282D3 = 0

UCLR = 2.282 (0.0021) = 0.00479 in.LCLR = 0 (0.0021) = 0 in.

UCLR = D4RLCLR = D3R

Range Chart - Range Chart - Special Metal Special Metal ScrewScrew

Figure 7.9

Example 7.1



X-Charts

UCLx = x + A2RLCLx = x - A2R

==

R = 0.0021x = 0.5027=

Example 7.1


Control Charts - Special Metal Screw

R = 0.0020x = 0.5025

x - Charts


Control Chart FactorsControl Chart Factors

Factor for UCLFactor for UCL Factor forFactor for FactorFactorSize ofSize of and LCL forand LCL for LCL forLCL for UCL forUCL forSampleSample xx-Charts-Charts RR-Charts-Charts RR-Charts-Charts

((nn)) ((AA22)) ((DD33)) ((DD44))

22 1.8801.880 00 3.2673.26733 1.0231.023 00 2.5752.57544 0.7290.729 00 2.2822.28255 0.5770.577 00 2.1152.11566 0.4830.483 00 2.0042.00477 0.4190.419 0.0760.076 1.9241.924

Example 7.1



x-Charts

UCLx = 0.5027 + 0.729 (0.0021) = 0.5042 in.LCLx = 0.5027 – 0.729 (0.0021) = 0.5012 in.


==

R = 0.0021 A2 = 0.729x = 0.5027=

xx-Chart—-Chart—Special Metal Special Metal ScrewScrew

Figure 7.10

xx-Chart— -Chart— Special Metal Special Metal ScrewScrew

Figure 7.10

xx-Chart—-Chart—Special Metal Special Metal ScrewScrew

Figure 7.10

Measure the process Find the assignable cause Eliminate the problem Repeat the cycle


for Variables Using for Variables Using

Example 7.2

UCLUCLxx = = xx + + zzxx

LCLLCLxx = = xx – – zzxx

x = /n

==

==



Example 7.2



x = /n

==

==

Sunny Dale Bank

x = 5.0 minutes = 1.5 minutesn = 6 customersz = 1.96

=



UCLx = 5.0 + 1.96(1.5)/ 6 = 6.20 min

UCLx = 5.0 – 1.96(1.5)/ 6 = 3.80 min



x = /n

==

==

Sunny Dale Bank

x = 5.0 minutes = 1.5 minutesn = 6 customersz = 1.96

=

p-chart, for the population proportion defective take a random sample, inspect plot the sample proportion defective compare with UCL and LCL to see

whether the process is in control

Control charts for attributes

n

)p-(1 p = p

UCL = p + Z

LCL = p - Z

p

p

Attribute Measurements (P-Charts)

p = Total # of Defectives/Total Sample

Sample n Defects1 100 42 50 23 100 54 100 35 75 66 100 47 100 38 50 89 100 1

10 100 211 100 312 100 213 100 214 100 815 100 3

Example

1. Calculate the sample proportion, p, for each sample.Sample n Defects p

1 100 4 0.042 50 2 0.043 100 5 0.054 100 3 0.035 75 6 0.086 100 4 0.047 100 3 0.038 50 8 0.169 100 1 0.01

10 100 2 0.0211 100 3 0.0312 100 2 0.0213 100 2 0.0214 100 8 0.0815 100 3 0.03

2. Calculate the average of the sample proportions.

3. Calculate the standard deviation of the sample proportion ....

.02065= 91.6667

.04073)-04073(1=

n

)p-(1 p = p

0.04073=1375

56 = p

4. Calculate the control limits.

p + / - Z p

UCL = 0.10268LCL = -0.02122 (or 0)

0.04073 +/- 3(0.02065)

0

0.05

0.1

0.15

0.2

0 5 10 15

Observation

p

5. Plot the individual sample proportions, the average of the proportions, and the control limits ....

UCL

LCL

A single point outside limits Two consecutive points near a limit A five point trend toward a limit A run of five points above or below

the average

Erratic behavior

When To Take Action

Process Capability Used to assess the degree to which

the output of a process conforms to specifications

Natural spread of a process is defined as 6 sigma

Specification limits or tolerance limits (LSL, USL) or (LTL, UTL)

Natural Spread vs. Tolerance Spread

Natural Spread

Tolerance Spread

(a) (b)

(c)

Cp= Tolerance Spread/Natural Spread= (USL-LSL)/6sigma

• A cookie machine produces cookies with mean sugar content of 4.28; std of 0.122

• Tolerance Limits for the Product [3.98,4.98]

• Process Capability Ratio =

Process Capability Ratio

Process Capability Index, Cpk

Shifts in Process Mean ....

Cpk

= minX LSL

3 or

USL-X

3

3.914 4.6464.28

3.98 4.98

Example Mean of the Process = 4.28; STD = 0.122 Tolerance Limits for the Product

[3.98,4.98] Process Capability Ratio

=(4.98-3.98)/6*0.122 = 1.366 Process Capability Index =

= min{0.81, 1.91}=0.81

}122.0*3

28.498.4,

122.0*3

98.328.4min{

Why Six Sigma?

If natural spread = tolerance spread we will have:

> 20,000 wrong prescriptions/yr. > 15,000 babies accidentally dropped

each year by nurses and obstetricians 500 incorrect surgical operations

each week

Six Sigma Quality

X-Sigma Quality

Cp W/O shift in mean (ppm)

With shift in mean (ppm)

3 1 2,700 66,803

4 1.33 63 6,200

5 1.67 0.57 233

6 2 0.002 3.4

When Cp=2, it is called six-sigma qualityppm: parts per million

Building Process Capability

Division ManagementMobilize the Entire Organization

Marketing, Design,and EngineeringImprove Performance byIncreasing the Numerator

Manufacturing, Delivery,and ServiceImprove Performance by Decreasing the Denominator

Cpk

SOURCE: Smith (1990).

Is it enough to be "in spec" ? Robustness comes from consistency

Consistent deviation vs scattered deviation

More deviation from the target means greater quality losses Quality Loss Function: losses proportional

to the square of deviation

Robust Quality

Taguchi’s View of Variation

Traditional View Taguchi’s View

LowerTolerance

DesignSpec

UpperTolerance

Non

-con

form

ance

to

desi

gn c

ost

$$$

0

LowerTolerance

DesignSpec

UpperTolerance

Actual value->

Controlling Variation

Fear? Control?

Loss of Options?Can Variations be Controlled in Health Care?

Handcuffs?

Tool for Medical Field Care path: standardized guidelines for a

particular diagnosis or procedure High volume and resource use Caregivers now have a recipe to follow Interdisciplinary

merging the medical and nursing plans of care with those of other disciplines, such as physical therapy, nutrition, or mental health.

Benchmarks permit effectiveness assessment

Examples Massachusetts General Hospital

Coronary Artery Bypass Graft Surgery (CABG)

The University of Texas Medical Center Colon cancer

Shouldice Hospital Hernia surgery

quality management what and why? tools for continuous improvement statistical process control...

Documents

dimensions of quality

deming slide

smallest slide

managing quality

total quality management

flow chart

process explicit

ar errors wrong account