Loading in 2 Seconds...

Quality Management “ It costs a lot to produce a bad product. ” Norman Augustine

Loading in 2 Seconds...

- By
**Lucy** - Follow User

- 462 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about 'Quality Management “ It costs a lot to produce a bad product. ” Norman Augustine' - Lucy

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

“It costs a lot to produce a bad product.”Norman Augustine

- Prevention costs
- Appraisal costs
- Internal failure costs
- External failure costs
- Opportunity costs

What is quality management all about?

Try to manage all aspects of the organization in order to excel in all dimensions that are important to “customers”

Two aspects of quality:

features: more features that meet customer needs = higher quality

freedom from trouble: fewer defects = higher quality

The Quality Gurus – Edward Deming

- Quality is “uniformity and dependability”
- Focus on SPC and statistical tools
- “14 Points” for management
- PDCA method

1900-1993

1986

The Quality Gurus – Joseph Juran

- Quality is “fitness for use”
- Pareto Principle
- Cost of Quality
- General management approach as well as statistics

1904 - 2008

1951

- Deming and Juran outlined the principles of Quality Management.
- Tai-ichi Ohno applies them in Toyota Motors Corp.
- Japan has its National Quality Award (1951).
- U.S. and European firms begin to implement Quality Management programs (1980’s).
- U.S. establishes the Malcolm Baldridge National Quality Award (1987).
- Today, quality is an imperative for any business.

Tools

(Process Analysis, SPC, QFD)

Customer

Cultural

Alignment

What does Total Quality Management encompass?

- TQM is a management philosophy:
- continuous improvement
- leadership development
- partnership development

Dimensions of quality

Conformance quality

Developing quality specifications

Design

Input

Process

Output

A philosophy and set of methods companies use to eliminate defects in their products and processes

Seeks to reduce variation in the processes that lead to product defects

The name “six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs

Six Sigma Quality- Customers, Value, Problem Statement
- Scope, Timeline, Team
- Primary/Secondary & OpEx Metrics
- Current Value Stream Map
- Voice Of Customer (QFD)

- Measure
- Assess specification / Demand
- Measurement Capability (Gage R&R)
- Correct the measurement system
- Process map, Spaghetti, Time obs.
- Measure OVs & IVs / Queues

- Analyze (andfix the obvious)
- Root Cause (Pareto, C&E, brainstorm)
- Find all KPOVs & KPIVs
- FMEA, DOE, critical Xs, VA/NVA
- Graphical Analysis, ANOVA
- Future Value Stream Map

- Improve
- Optimize KPOVs & test the KPIVs
- Redesign process, set pacemaker
- 5S, Cell design, MRS
- Visual controls
- Value Stream Plan

- Control
- Document process (WIs, Std Work)
- Mistake proof, TT sheet, CI List
- Analyze change in metrics
- Value Stream Review
- Prepare final report

Validate Project $

Validate Project $

Validate Project $

Validate Project $

Six Sigma Roadmap (DMAIC)Next Project

Celebrate Project $

Do

Act

Check

Continuous improvement philosophy

- Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes.
- PDCA: Plan-do-check-act as defined by Deming.

- Benchmarking : what do top performers do?

Tools used for continuous improvement

1. Process flowchart

Man

Environment

Method

Material

Tools used for continuous improvement

4. Cause and effect diagram (fishbone)

Tools used for continuous improvement

5. Check sheet

Tools used for continuous improvement

7. Pareto Analysis

100%

60

75%

50

40

Frequency

50%

Percentage

30

20

25%

10

0%

A

B

C

D

E

F

- Process flow chart
- Run diagram
- Control charts
- Fishbone
- Check sheet
- Histogram
- Pareto analysis

Case: shortening telephone waiting time…

- A bank is employing a call answering service
- The main goal in terms of quality is “zero waiting time”

- customers get a bad impression

- company vision to be friendly and easy access

- The question is how to analyze the situation and improve quality

Working system of operators

Absent

Too many phone calls

Lunchtime

Out of office

Makes customer wait

Not at desk

Absent

Not giving receiving party’s coordinates

Does not understand customer

Lengthy talk

Does not know organization well

Complaining

Takes too much time to explain

Leaving a message

Customer

Operator

Fishbone diagram analysis

Reasons why customers have to wait

(12-day analysis with check sheet)

Percentage

87.1%

300

250

71.2%

200

49%

150

100

0%

A

B

C

D

E

F

Pareto Analysis: reasons why customers have to wait

- Taking lunches on three different shifts
- Ask all employees to leave messages when leaving desks
- Compiling a directory where next to personnel’s name appears her/his title

Percentage

Frequency

Frequency

100%

87.1%

300

300

71.2%

Improvement

200

200

49%

100

100

100%

0%

0%

A

B

C

D

E

F

B

C

A

D

E

F

Results of implementing the recommendations

…After

Before…

In general, how can we monitor quality…?

By observing

variation in

output measures!

- Assignable variation: we can assess the cause
- Common variation: variation that may not be possible to correct (random variation, random noise)

Statistical Process Control (SPC)

Every output measure has a target value and a level of “acceptable” variation (upper and lower tolerance limits)

SPC uses samples from output measures to estimate the

mean and the variation (standard deviation)

Example

We want beer bottles to be filled with 12 FL OZ ± 0.05 FL OZ

Question:

How do we define the output measures?

In order to measure variation we need…

The average (mean) of the observations:

The standard deviation of the observations:

Average & Variation example

Number of pepperoni’s per pizza: 25, 25, 26, 25, 23, 24, 25, 27

Average:

Standard Deviation:

Number of pepperoni’s per pizza: 25, 22, 28, 30, 27, 20, 25, 23

Average:

Standard Deviation:

Which pizza would you rather have?

Incremental

Cost of

Variability

Zero

Lower

Tolerance

Target

Spec

Upper

Tolerance

Traditional View

When is a product good enough?

a.k.a

Upper/Lower Design Limits

(UDL, LDL)

Upper/Lower Spec Limits

(USL, LSL)

Upper/Lower Tolerance Limits

(UTL, LTL)

The “Goalpost” Mentality

Incremental

Cost of

Variability

Zero

Lower

Spec

Target

Spec

Upper

Spec

But are all ‘good’ products equal?Taguchi’s View

“Quality Loss Function”

(QLF)

LESS VARIABILITY implies BETTER PERFORMANCE !

It shows how well the performance measure fits the design specification based on a given tolerance level

A process is ks capable if

Another way of writing this is to calculate the capability index:

Cpk < 1 means process is not capable at the ks level

Cpk >= 1 means process is capable at the ks level

We say that a process is accurate if its mean is close to

the target T.

We say that a process is consistent if its standard deviation

is low.

Example

- Consider the capability of a process that puts pressurized grease in an aerosol can. The design specs call for an average of 60 pounds per square inch (psi) of pressure in each can with an upper tolerance limit of 65psi and a lower tolerance limit of 55psi. A sample is taken from production and it is found that the cans average 61psi with a standard deviation of 2psi.
- Is the process capable at the 3s level?
- What is the probability of producing a defect?

Solution

P(defect) = P(X<55) + P(X>65)

=P(X<55) + 1 – P(X<65)

=P(Z<(55-61)/2) + 1 – P(Z<(65-61)/2)

=P(Z<-3) + 1 – P(Z<2)

=G(-3)+1-G(2)

=0.00135 + 1 – 0.97725 (from standard normal table)

= 0.0241

2.4% of the cans are defective.

Example (contd)

Suppose another process has a sample mean of 60.5 and

a standard deviation of 3.

Which process is more accurate? This one.

Which process is more consistent? The other one.

Central Line

Lower Control Limit

Control Charts

Control charts tell you when a process measure is exhibiting abnormal behavior.

- X/R Chart
- This is a plot of averages and ranges over time (used for performance measures that are variables)
- p Chart
- This is a plot of proportions over time (used for performance measures that are yes/no attributes)

Statistical Process Control with p Charts

When should we use p charts?

- When decisions are simple “yes” or “no” by inspection
- When the sample sizes are large enough (>50)

Statistical Process Control with p Charts

Let’s assume that we take t samples of size n …

Statistical Process Control with X/R Charts

When should we use X/R charts?

- It is not possible to label “good” or “bad”
- If we have relatively smaller sample sizes (<20)

Statistical Process Control with X/R Charts

Take t samples of size n (sample size should be 5 or more)

R is the range between the highest and the lowest for each sample

X is the average of the averages.

R is the average of the ranges

Statistical Process Control with X/R Charts

Statistical Process Control with X/R Charts

define the upper and lower control limits…

Read A2, D3, D4 from

Table TN 8.7

Example: SPC for bottle filling…

Calculate the average and the range for each sample…

Calculate the upper and lower control limits

Download Presentation

Connecting to Server..