Operations Management - IIvkteaching.weebly.com/uploads/1/4/3/9/14393508/om_s02e12.pdf ·...
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Session 12
Vinay Kumar Kalakbandi
Assistant Professor
Operations & Systems Area
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Operations Management - II Post Graduate Program 2015-17
Agenda
• Course updates
– Projects
– Quiz and Mid Term evaluation
• Recap
• Hank Kolb, Quality Assurance case
• Quality control tools
• Statistical Quality Control
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Hank Kolb, Director, Quality Assurance
• What are the causes of the quality problems in
the Greasex line?
• What should Hank do?
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The process view
• The Red Bead experiment
– https://youtu.be/ckBfbvOXDvU
• Who is responsible for bad quality at the White
bead factory?
• How is this situation similar to the Hank Kolb
case?
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Total Quality Management
Quality
System
Role of Top Management
Tools & Techniques Employee Involvement Training & Team Work
Seven QC tools
• Cause-and-effect diagram with action cards
• Check sheet
• Histogram or stem-and-leaf plot
• Pareto chart
• Defect concentration diagram
• Scatter diagram
• Run charts & Control charts
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Cause and Effect Diagram A generic representation
Materials Method
Machine Man
Quality
Ca use Effect
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Cause and effect diagrams
• What is its use?
– Enables a team to focus on the content of a
problem
– Creates a snapshot of collective knowledge and
consensus of a team; builds support for solutions
– Focuses the team on causes, not symptoms
– It is an effective tool that allows people to easily see
the relationship between factors to study processes,
situations, and for planning.
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Crowd task
• Develop a cause and effect diagram for the
factors that could
– Annoy a faculty while taking a class at IIM Raipur
– Annoy a student while in class at IIM Raipur
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CEDAC
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Checksheet
• Creates easy-to-
understand data
• Builds, with each
observation, a clearer
picture of the facts
• Patterns in the data
become obvious quickly
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Histogram An example
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Pareto Diagram An example
Defect concentration diagram
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Defect concentration diagram
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CONTROL CHARTS
The road to Six Sigma
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Variability: Dispersion in output
• Natural causes: Non-controllable; inherent
variability in the system, noise, usually minor
• Assignable causes: Controllable, bring about a
fundamental change on the nature of the
process, causes considerable impact on quality
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Process control charts
• Information: Monitor process variability over
time
• Control limits: Average + z Normal variability
– z = 3
• Decision Rule: Ignore variation outside
“abnormal”
• Errors: Type 1 and Type 2
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Types of Data
• Variable data
– X-bar and R charts
– Time, customer satisfaction scores
• Attribute data
– p-charts and c-charts
– Good/bad, yes/no, number of errors!
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Rest of the discussion
• Different charts have different purposes
• Constructing a control chart
• Knowing when things have gone wrong
• Process capability
• Six sigma!
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Constructing a control chart
• Decide what to measure and count
• Collect sample data
• Calculate and plot control limits on the control
chart
• Determine if data is in control
• If non-random variation is present, fix the
problem and recalculate control limits.
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Coefficients for computing LCL and
UCL in X-bar and R charts*
Sample size (n) A2 D3 D4
2 1.880 0 3.268
3 1.023 0 2.574
4 0.729 0 2.282
5 0.577 0 2.114
6 0.483 0 2.004
7 0.419 0.076 1.924
8 0.373 0.136 1.864
9 0.337 0.184 1.816
10 0.308 0.223 1.777
Source: Juran, J.M. and F.M. Gryna, (1995), “Quality Planning and Analysis”, Tata McGraw-Hill, 3rd Edition, New Delhi, pp 385.
Utility of X-bar and R chart
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Utility of X-bar and R chart
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Output
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Other charts
• P-charts
– Calculate percentage defectives in a sample
– an item is either good or bad
– Based on binomial distribution
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Other charts
• c Charts
– Count number of defects in an item
– Based on poisson distribution
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Performance variation patterns
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Process capability
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From Control to improvement
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Sigma statistics
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THANK YOU
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