Post on 26-Nov-2014
Quality Management
25-11-2010
Definition of Quality
• Conformance to the requirement – Phillip Crosby
• A predictable degree of uniformity and dependability at low cost and suited to the market – W. Edward Deming
• Fitness for use (Satisfies customer’s need) – Joseph M. Juran
• Fundamental to any quality program is the determination of quality specification and the cost of achieving those specifications.
• The quality specification of a product or service derive from decision and actions made relative to the quality of its design and the quality of its conformance to that design.
• Design Quality- It refers to the inherent value of the product in its market place and thus strategic decision for the firm.
• Conformance Quality- It refers to the degree to which the product or service design specification met.
Dimensions of Quality
• Performance
• Features
• Reliability/Durability
• Serviceability
• Aesthetics
• Perceived Quality
Phases of Quality Assurance
Acceptancesampling
Processcontrol
Continuousimprovement
Inspection of lotsbefore/afterproduction
Inspection andcorrective
action duringproduction
Quality builtinto theprocess
The leastprogressive
The mostprogressive
Inspection
• How Much/How Often
• Where/When
• Centralized vs. On-site
Inputs Transformation Outputs
Acceptancesampling
Processcontrol
Acceptancesampling
Co
st
OptimalAmount of Inspection
Inspection Costs
Cost of inspection
Cost of passingdefectives
Total Cost
Where to Inspect in the Process
• Raw materials and purchased parts
• Finished products
• Before a costly operation
• Before an irreversible process
• Before a covering process
Examples of Inspection PointsType ofbusiness
Inspectionpoints
Characteristics
Fast Food CashierCounter areaEating areaBuildingKitchen
AccuracyAppearance, productivityCleanlinessAppearanceHealth regulations
Hotel/motel Parking lotAccountingBuildingMain desk
Safe, well lightedAccuracy, timelinessAppearance, safetyWaiting times
Supermarket CashiersDeliveries
Accuracy, courtesyQuality, quantity
• Statistical Process Control: Statistical evaluation of the output of a process during production
• Quality of Conformance:A product or service conforms to specifications
Statistical Control
Control Chart
• Control Chart
– Purpose: to monitor process output to see if it is random
– A time ordered plot representative sample statistics obtained from an on going process (e.g. sample means)
– Upper and lower control limits define the range of acceptable variation
Control Chart
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
UCL
LCL
Sample number
Mean
Out ofcontrol
Normal variationdue to chance
Abnormal variationdue to assignable sources
Abnormal variationdue to assignable sources
Statistical Process Control• The essence of statistical process
control is to assure that the output of a process is random so that future output will be random.
Statistical Process Control
• The Control Process– Define– Measure– Compare– Evaluate– Correct– Monitor results
Statistical Process Control
• Variations and Control– Random variation: Natural variations in
the output of a process, created by countless minor factors
– Assignable variation: A variation whose source can be identified
Sampling Distribution
Samplingdistribution
Processdistribution
Mean
Normal Distribution
Mean
95.44%
99.74%
Standard deviation
Control LimitsSamplingdistribution
Processdistribution
Mean
Lowercontrol
limit
Uppercontrol
limit
SPC Errors
• Type I error– Concluding a process is not in control
when it actually is.
• Type II error– Concluding a process is in control when it
is not.
Type I and Type II Errors
In control Out of control
In control No Error Type I error
(producers risk)
Out of control
Type II Error
(consumers risk)
No error
Type I Error
Mean
LCL UCL
/2 /2
Probabilityof Type I error
Observations from Sample Distribution
Sample number
UCL
LCL
1 2 3 4
Control Charts for Variables
• Mean control charts
– Used to monitor the central tendency of a process.
– X bar charts
• Range control charts
– Used to monitor the process dispersion
– R charts
Variables generate data that are Variables generate data that are measuredmeasured..
Mean and Range Charts
UCL
LCL
UCL
LCL
R-chart
x-Chart Detects shift
Does notdetect shift
(process mean is shifting upward)
SamplingDistribution
x-Chart
UCL
Does notreveal increase
Mean and Range Charts
UCL
LCL
LCL
R-chart Reveals increase
(process variability is increasing)SamplingDistribution
Control Chart for Attributes
• p-Chart - Control chart used to monitor the proportion of defectives in a process
• c-Chart - Control chart used to monitor the number of defects per unit
Attributes generate data that are Attributes generate data that are countedcounted..
Use of p-Charts
• When observations can be placed into two categories.– Good or bad
– Pass or fail
– Operate or don’t operate
• When the data consists of multiple samples of several observations each
Use of c-Charts
• Use only when the number of occurrences per unit of measure can be counted; non-occurrences cannot be counted.– Scratches, chips, dents, or errors per item– Cracks or faults per unit of distance– Breaks or Tears per unit of area– Bacteria or pollutants per unit of volume– Calls, complaints, failures per unit of time
Use of Control Charts• At what point in the process to use
control charts
• What size samples to take
• What type of control chart to use
– Variables
– Attributes
Run Tests• Run test – a test for randomness
• Any sort of pattern in the data would suggest a non-random process
• All points are within the control limits - the process may not be random
Nonrandom Patterns in Control charts
• Trend
• Cycles
• Bias
• Mean shift
• Too much dispersion
Counting Above/Below Median Runs (7 runs)
Counting Up/Down Runs (8 runs)
U U D U D U D U U D
B A A B A B B B A A B
Counting RunsCounting Runs
NonRandom Variation
• Managers should have response plans to investigate cause
• May be false alarm (Type I error)
• May be assignable variation
• Tolerances or specifications
– Range of acceptable values established by engineering design or customer requirements
• Process variability
– Natural variability in a process
• Process capability
– Process variability relative to specification
Process Capability
Process CapabilityLowerSpecification
UpperSpecification
A. Process variability matches specifications
LowerSpecification
UpperSpecification
B. Process variability well within specifications
LowerSpecification
UpperSpecification
C. Process variability exceeds specifications
Process Capability Ratio
Process capability ratio, Cp = specification widthprocess width
Upper specification – lower specification6
Cp =
3
X-UTLor
3
LTLXmin=C pk
If the process is centered use Cp
If the process is not centered use Cpk
Limitations of Capability Indexes1. Process may not be stable
2. Process output may not be normally distributed
3. Process not centered but Cp is used
Example 8
Machine
Standard Deviation
Machine Capabilit
yCp
A 0.13 0.78 0.80/0.78 = 1.03
B 0.08 0.48 0.80/0.48 = 1.67
C 0.16 0.96 0.80/0.96 = 0.83
Cp > 1.33 is desirableCp = 1.00 process is barely capableCp < 1.00 process is not capable
Processmean
Lowerspecification
Upperspecification
1350 ppm 1350 ppm
1.7 ppm 1.7 ppm
+/- 3 Sigma
+/- 6 Sigma
3 Sigma and 6 Sigma Quality3 Sigma and 6 Sigma Quality
Improving Process Capability• Simplify
• Standardize
• Mistake-proof
• Upgrade equipment
• Automate
Taguchi Loss Function
Cost
TargetLowerspec
Upperspec
Traditionalcost function
Taguchicost function