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QUALITY CONTROL & QUALITY ASSURANCE:INSPECTION, Control Chart

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Production and Operations Management ­MGT613
VU
Lesson 27
QUALITY CONTROL & QUALITY ASSURANCE
Quality Control or QC as it is popularly referred as "is concerned with quality of conformance of a
process". The prime purpose of QC is to assure that the processes are performing in an acceptable
manner. Organizations accomplish QC by monitoring process outputs using statistical techniques. The
practical and pragmatic QC based Operations Strategy for a service or manufacturing organization
would focus on the principle of quality in design.
Learning Objectives
1.
Introduction to Quality Control and Assurance
2.
Phases of Quality Control
3.
Elements of Control Process
4.
How control charts are used to monitor a process and the concepts that underlie their use.
5.
Use and interpret control charts.
6.
Use of run tests to check for non randomness in process output.
Phases of Quality Assurance
Phases of Quality Assurance
Inspection
Quality built
Inspection and
Before / after
into the
Corrective action
production
process
during production
Pro
Continuous
Acceptance
ces
improvement
sampling
s
The most
The least
progressive
progressive
INSPECTION
Inspection is an important strategy, in its simplest form, is any method or device or tactics used to
minimize defects in products or services being offered to the customers. As Operations Manager we
should be able to identify the following four questions while considering Inspection process.
1. How Much/How Often
2. Where/When
3. Centralized vs. On-site
4. Whether to inspect Variables or Attributes.
An important thing to remember is that No inspection is necessary for low value, high volume products
like common items like common pins, erasers or pencils while automated inspection is necessary for
high value items. Automated inspection may be necessary for even high value, low volume items as
well. The word volume here refers to quantity.
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Input
Transformatio
Output
Acceptance
Acceptance
Process
sampling
sampling
control
INSPECTION COSTS FOR HOW MUCH/HOW OFTEN
The graph on the next page shows the relationship between amount of inspection required and
costs incurred in carrying out such inspection.
1. With increase in Inspection activities the cost of undetected defectives decreases.
2. With increase in inspection activities the cost of inspection increases.
We need to observe for Total cost function curve which shows high costs at no inspection and gradually
comes down and reaches a minimum value at the optimal amount of inspection and then start increases.
C
O
S
T
Optimal
Amount of Inspection
Inspection Costs for How Much/How Often
1. Where to Inspect in the Process
2. Raw materials and purchased parts ( DO not purchase poor quality products)
3. Finished products ( Poor products returned by customers can also lead to additional shipping
costs)
4. Before a costly operation ( Do not waste Resources of Man, Material and Machine)
5. Before an irreversible process ( Pottery, Ceramics, Tiles, PC chips, glass filaments)
6. Before a covering process ( Before painting, plating and assembly)
EXAMPLES OF INSPECTION POINTS IN SERVICE INDUSTRY
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Production and Operations Management ­MGT613
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We cannot have same inspection points for the service industry, infact we need to pay attention to the
type of industry or business in which a service organization competes. Please refer to the table on the
next page and note the difference in characteristics and location of inspection points.
Type of
Inspection
Characteristics
business
points
Fast Food
Cashier
Accuracy
Counter area Appearance, productivity
Eating area
Cleanliness
Building
Appearance
Kitchen
Health regulations
Safe, well lighted
Hotel/motel  Parking lot
Accuracy, timeliness
Accounting
Appearance, safety
Building
Waiting times
Main desk
Supermarket Cashiers
Accuracy, courtesy
Deliveries
Quality, quantity
CENTRALIZED VS ONSITE INSPECTION
1. Inspection of Ships, Nuclear Plants, Petroleum Refinery, Chemical Plant equipments for cracks,
brittle fracture etc both external and internal inspection.
2. Lab tests include blood tests, material testing
QUALITY CONTROL IN TERMS OF STATISTICAL PROCESS CONTROL:
We now focus on the idea of Quality Control in terms of Statistical Process Control, for this we need to
define
Statistical Process Control: Statistical evaluation of the output of a process during production
Quality of Conformance: A product or service conforms to specifications
Which Characteristics can be controlled: Only those characteristics which can be counted or
measured.
Main Task of QC: is to distinguish random from non random variability, because non random
variability indicates that the process is out of control
Control Chart
Control Chart: A time ordered plot representative sample statistics obtained from an on going
process (e.g. sample means)
Purpose: to monitor process output to see if it is random
Upper and lower control limits define the range of acceptable variation
CONTROL CHART & STATISTICAL PROCESS CONTROL
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Production and Operations Management ­MGT613
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Out of
Abnormal variation
contro
due to assignable
UC
Mea
Normal
variation
LC
Abnormal variation
due to assignable
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15
Sample
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 consists of the following important stages.
1. Define
2. Measure
3. Compare
4. Evaluate
5. Correct
6. Monitor results
Variations and Control
·Random variation: Natural variations in the output of a process, created by countless minor factors
Also called COMMON/ CHANCE. INHERENT and part of the process. E.g. Difference between old
and new machines.
·Assignable variation: A variation whose source can be identified
SAMPLING DISTRIBUTION
The variability of a sample statistic can be described by its SAMPLING DISTRIBUTION. The goal of
sampling is to determine whether non random /assignable/ correctable sources of variation are present in
the output of the process. E.g. Soft drinks bottle are never 250 ML. slight differences among the mean.
SAMPLING DISTRIBUTION
Sampling
distribution
Process
distribution
Mean
NORMAL DISTRIBUTION
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σ = Standard deviation
-3σ
-2σ
+2σ
+3σ
Mean
95.44%
99.74%
CONTROL LIMITS
Sampling
distribution
Process
distribution
Mean
Lower
Upper
contro
contro
l
l
CONTROL CHARTS
A control chart is a time ordered plot of sample statistics.
It is used to distinguish between random variability and non random variability.
The basis of control chart is sample distribution which essentially describes random variability.
Theoretically any value is possible as the distribution extends to infinity.
99.7% of all values will be within + 3 standard deviations
Control Charts
We draw a line at + 3 and call it control chart limits and observe any value beyond this to be out
of limits.
Control Chart limits are the dividing lines between random deviations and mean of the
distribution and non random deviations and mean of the distribution.
The limits that separate random variations from non random variations is known as UCL and
LCL.
A sample statistic that falls between UCL and LCL suggests ( does not proves) randomness and a value
outside suggests ( does not proves) no randomness.
SPC Errors
Type I error: Concluding a process is not in control when it actually is or concluding that no
randomness is present when it is only randomness that is present.
Type II error: Concluding a process is in control when it is not that no randomness is not present
when it is present.
Type I Error
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α/
α/
Mean
LC
UC
α = Probabilit
y
OBSERVATIONS FROM SAMPLE DISTRIBUTION
UCL
LCL
1
2
3
4
Sample number
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
MEAN AND RANGE CHARTS
(process mean is
shiftingupward)
Sampling
Distribution
UCL
Detects shift
L
C
L
UCL
R-chart
Does not detect shift
x-Chart
L
C
L
Sampling
Distribution
(process variability is increasing)
UCL
Does not
x-Chart
LC
reveal increase
L
UCL
R-chart
Reveals increase
L
C
L
CONTROL CHART FOR ATTRIBUTES
p-Chart - Control chart used to monitor the proportion of defectives in a process
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c-Chart - Control chart used to monitor the number of defects per unit
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
1. Variables
2. 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 Runs
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Counting Above/Below Median Runs
(7
B A
A
B
A
B
B
B A
A
Counting Up/Down Runs
(8
U
U
D
U
D
U D U U
Underlining each runs helps in counting
IN case of Ups and Down the first value does not receives either a U or D because nothing precedes it.
PROCESS CAPABILITY
Tolerances or specifications is the range of acceptable values established by engineering design
or customer requirements
Process variability: is the natural variability in a process
Process capability: is the process variability relative to specification
Process Capability is thus more importantly related to our discussion of Quality Control and Quality
Assurance and we will take up three cases in detail to understand this important concept.
1. In Case A we observe that process specifications and output are matched.
2. In Case B process variability is well within the process specification and output.
3. In Case C, we need to check whether a process is capable of meeting specifications and not just
use a control chart.
Lower
Upper
Specificatio
Specificatio
A. Process variability
matches specifications
Lower
Upper
Specificatio
Specificatio
B. Process variability
Lower
Upper
well within specifications Specificatio Specificatio
Process Capability
Case C, A manager in case C can take the following steps.
1. Redesign the process to obtain the desired output.
2. Use an alternative process to obtain the desired output.
3. Retain the current process but attempt to eliminate output using 100 percent inspection
4. Examine the specifications to see if they are necessary or can be relaxed
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Process Variability is the key factor in Process Capability. It is measured in terms of process standard
deviation. Process capability is considered to be + 3 Standard Deviations from the process mean. E.g.
An insurance company provides a service of registering a new membership ( filling of form) in 10 mins,
acceptable range of variation around the time is + 1 minute, the process has a standard deviation of
0.5min.It would not be capable because + 3 SDs would be + 1.5 Mins, exceeding the specification of +
1 minute.
Process Capability Ratio
Specification width
Process capability ratio, Cp =
Process width
Upper specification ­ lower specification
Cp=
6σ
3 SIGMA AND 6 SIGMA QUALITY
Upper
Lower
specification
specification
1350 ppm
1350 ppm
1.7 ppm
1.7 ppm
Process
mean
+/- 3 Sigma
+/- 6 Sigma
Improving Process Capability
1. Simplify
2. Standardize
3. Mistake-proof ( Poka Yoke)
4. Upgrade equipment
5. Automate
Taguchi Loss Function
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Traditional
cost
Cost
Taguchi
cost
Lowe
Uppe
Target
r
r
Limitations of Capability Indexes
1. Process may not be stable
2. Process output may not be normally distributed
3. Process not centered but Cp is used
OPERATIONS STRATEGY WRT Q/C
It is neither necessary nor desirable to use Control charts for every production process.
Some processes are highly stable and do not require Control Charts.
Managers should use Control Charts on processes that go out of control.
Use control Charts for new processes till they obtain stable results.
Judicious use of SPC will ensure detection of departures from randomness in a process.
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Table of Contents:
  1. INTRODUCTION TO PRODUCTION AND OPERATIONS MANAGEMENT
  2. INTRODUCTION TO PRODUCTION AND OPERATIONS MANAGEMENT:Decision Making
  3. INTRODUCTION TO PRODUCTION AND OPERATIONS MANAGEMENT:Strategy
  4. INTRODUCTION TO PRODUCTION AND OPERATIONS MANAGEMENT:Service Delivery System
  5. INTRODUCTION TO PRODUCTION AND OPERATIONS MANAGEMENT:Productivity
  6. INTRODUCTION TO PRODUCTION AND OPERATIONS MANAGEMENT:The Decision Process
  7. INTRODUCTION TO PRODUCTION AND OPERATIONS MANAGEMENT:Demand Management
  8. Roadmap to the Lecture:Fundamental Types of Forecasts, Finer Classification of Forecasts
  9. Time Series Forecasts:Techniques for Averaging, Simple Moving Average Solution
  10. The formula for the moving average is:Exponential Smoothing Model, Common Nonlinear Trends
  11. The formula for the moving average is:Major factors in design strategy
  12. The formula for the moving average is:Standardization, Mass Customization
  13. The formula for the moving average is:DESIGN STRATEGIES
  14. The formula for the moving average is:Measuring Reliability, AVAILABILITY
  15. The formula for the moving average is:Learning Objectives, Capacity Planning
  16. The formula for the moving average is:Efficiency and Utilization, Evaluating Alternatives
  17. The formula for the moving average is:Evaluating Alternatives, Financial Analysis
  18. PROCESS SELECTION:Types of Operation, Intermittent Processing
  19. PROCESS SELECTION:Basic Layout Types, Advantages of Product Layout
  20. PROCESS SELECTION:Cellular Layouts, Facilities Layouts, Importance of Layout Decisions
  21. DESIGN OF WORK SYSTEMS:Job Design, Specialization, Methods Analysis
  22. LOCATION PLANNING AND ANALYSIS:MANAGING GLOBAL OPERATIONS, Regional Factors
  23. MANAGEMENT OF QUALITY:Dimensions of Quality, Examples of Service Quality
  24. SERVICE QUALITY:Moments of Truth, Perceived Service Quality, Service Gap Analysis
  25. TOTAL QUALITY MANAGEMENT:Determinants of Quality, Responsibility for Quality
  26. TQM QUALITY:Six Sigma Team, PROCESS IMPROVEMENT
  27. QUALITY CONTROL & QUALITY ASSURANCE:INSPECTION, Control Chart
  28. ACCEPTANCE SAMPLING:CHOOSING A PLAN, CONSUMER’S AND PRODUCER’S RISK
  29. AGGREGATE PLANNING:Demand and Capacity Options
  30. AGGREGATE PLANNING:Aggregate Planning Relationships, Master Scheduling
  31. INVENTORY MANAGEMENT:Objective of Inventory Control, Inventory Counting Systems
  32. INVENTORY MANAGEMENT:ABC Classification System, Cycle Counting
  33. INVENTORY MANAGEMENT:Economic Production Quantity Assumptions
  34. INVENTORY MANAGEMENT:Independent and Dependent Demand
  35. INVENTORY MANAGEMENT:Capacity Planning, Manufacturing Resource Planning
  36. JUST IN TIME PRODUCTION SYSTEMS:Organizational and Operational Strategies
  37. JUST IN TIME PRODUCTION SYSTEMS:Operational Benefits, Kanban Formula
  38. JUST IN TIME PRODUCTION SYSTEMS:Secondary Goals, Tiered Supplier Network
  39. SUPPLY CHAIN MANAGEMENT:Logistics, Distribution Requirements Planning
  40. SUPPLY CHAIN MANAGEMENT:Supply Chain Benefits and Drawbacks
  41. SCHEDULING:High-Volume Systems, Load Chart, Hungarian Method
  42. SEQUENCING:Assumptions to Priority Rules, Scheduling Service Operations
  43. PROJECT MANAGEMENT:Project Life Cycle, Work Breakdown Structure
  44. PROJECT MANAGEMENT:Computing Algorithm, Project Crashing, Risk Management
  45. Waiting Lines:Queuing Analysis, System Characteristics, Priority Model