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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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