chapter 6: tqm tools and techniques. - unimap...

Chapter 6: TQM Tools and Techniques.

Mohd Zaizu Ilyas

At the end of this topic, you should be able to DEFINE and EXPLAIN

1.QFD,

2.Benchmarking,

3.Kanban,

4.JIT.

DESCRIBE, EXPLAIN and DEMONSTRATE 5.Quality Tools

Objectives

• Most decision point and root causes remain unclear until valid data are studied and analyzed.

• Collecting and analyzing data using total quality tools make the task easy for everyone.

Why need Quality tools?

• No matter where you fit into organization in future, you may use all or some of these tool and employers will serve you well for better prospects.

• This chapter will explain the most widely used of quality tools.

Why need Quality tools?

• The Seven Basic Tools of Quality is a designation given to a fixed set of graphical techniques identified as being most helpful in troubleshooting issues related to quality.

What is the Quality tools?

• The tools are:-

• 1) the Pareto charts

• 2) the Cause-and-Effect / Ishikawa diagram /Fishbone diagram

• 3) the Check Sheet

• 4) the Flow Chart

• 5) the Histogram

• 6) the Scatter Diagram

• 7) the Control Chart

What is the Quality tools?

• Pareto charts are useful for separating the important from the trivial.

• Named after Italian economist and sociologist Vilfredo Pareto (1848-1923). Was promoted by Dr.Josep Juran.

• Pareto charts are important because they can help an organization decide where to focus limited resources.

• On a Pareto chart, data are arrayed along an X-axis and a Y-axis.

1) Pareto Chart

Example

• In a factory, Only20% of problems will produce 80% of defects .

• 80% of defect’s cost will be assigned to only 20% of the total number of defect types occurring.

• So, 80% of defect costs will spring 20% of total cost element.

Example

• Pareto can show you where to apply your resources by “revealing few from the trivial many”..

• (Highlight few most important issues out of many)

Purpose of Pareto

Figure 15-1

Pareto Chart

Pareto Chart

• Figure 15-1 represents customers A,B, C, D, E and All others.

• 75% sales are from 2 customers; A,B

• All others include many more customers but brings insignificant sales (>5%)

• Which customers should be kept happy?

Pareto Chart

Pareto Chart Pareto Chart

Pareto Chart • Figure 15-2 shows sales of particular model of

automobile by age group of the buyers. • The manufacturer has limited budget in

advertising. • The chart reveals the most logical choice to

target to advertise. • Concentrating on advertising on 26-45 age

will result in the best return of investment. (75%)

• The significant few 26-45 age • The insignificant many are those under 26 &

above 45

Pareto Chart

Pareto Chart

• Figure 15-3

Pareto Chart

Pareto Chart

• Figure 15-3 shows 80% of the cost was related to 5 defect causes.

• All the other (about 30 more) were insignificant. • The longest bar ($70k) accounted for 40%, if solved,

immediate reduction in rework cost will happen. • After eliminate the longest bar, the team sorted data

again to develop level 2 Pareto Chart *Read page 484-489 for further understanding

Pareto Chart

Pareto Chart

• Figure 15-4

Pareto Chart

1. Select the subject of the chart 2. Determine what data to be gathered 3. Gather the data related to the quality problem 4. Make a check sheet of the gathered data, record the

total numbers in each category. 5. Determine total numbers of nonconformities,

calculate percentage each. 6. Select scales of the chart 7. Draw PARETO Chart from largest category to

smallest. 8. Analyze the chart

Steps in Constructing Pareto Chart

2) Cause and Effect Diagrams / Ishikawa Diagrams

• Use to identify and isolate causes of a problem. Developed by Dr. Kaoru Ishikawa. (1915-1989)

• Also called Ishikawa Diagram / Fishbone Diagram.

Cause and Effect Diagrams


• Benefits;

– Creating the diagram – enlightened, instructive process.

– Focus a group, reducing irrelevant discussion.

– Separate causes from symptoms

– Can be used with any problems


Cause and Effect Diagrams Cause and Effect Diagrams

1. Man (Operator)

2. Method

3. Measurement

4. Material

5. Machine

6. Environment

6 Common major factors in Ishikawa Diagrams

Ishikawa Diagram

MAN MACHINE ENVIRONMENT

MEASUREMENT MATERIAL METHOD

Problem A


• From figure 15-8, the spine points to the effect.

• The effect is the problem we are interested in. • The lower level factors affecting major factor

branch. • Check Figure 15-7 to see whether the major

causes can be identified.



• E,g: Machine soldering defects – Six major groupings of causes are;

• Solder machine itself

• Operators

• Materials

• Methods/procedures

• Measurement of accuracy

• Environment



• Normally created by teams to brainstorm the cause/effect.

• Completed diagram reveals factors & relationship which not been obvious.

• Some problems previously were isolated now can be identified.

• Therefore, further action shall be taken.

• Cause and Effect Diagrams serve as a reminder.


• The ‘5 Whys’ is a question-asking method used to explore the cause/effect relationships underlying a particular problem.

• Objective: To determine a root cause of a defect or problem.

• The technique was originally developed by Sakichi Toyoda (1867-1930)and was later used within Toyota Motor Corporation during the evolution of their manufacturing methodologies.

• Part of Toyota Production System activities.

FIVE WHYs

• My car cannot start. (the problem statement) • Why? - The battery is dead. (first why) • Why? - The alternator is not functioning. (second

why) • Why? - The alternator belt has broken. (third why) • Why? - The alternator belt was well beyond its useful

service life and has never been replaced. (fourth why)

• Why? - I have not been maintaining my car according to the recommended service schedule. (fifth why, root cause)

This example could be taken further to a sixth, seventh, or even greater level.

Example of 5 whys

1. Write down the specific problem. Describe it completely. It also helps a team focus on the same problem.

2. Ask Why the problem happens and write the answer down below the problem.

3. If the answer doesn't identify the root cause of the problem that you wrote down in step 1, ask Why again and write that answer down.

4. Loop back to step 3 until the team is in agreement that the problem's root cause is identified. Again, this may take fewer or more times than five Whys.

How To Complete The 5 Whys

Problem Statement: Customers are unhappy because they are being shipped products that don't meet their specifications.

1. Why are customers BEING SHIPPED BAD PRODUCTS? - Because manufacturing built the products to a specification that is different from what the customer and the sales person agreed to.

2. Why did manufacturing build the products to a different specification than that of sales? - Because the sales person expedites work on the shop floor by calling the head of manufacturing directly to begin work. An error happened when the specifications were being communicated or written down.

3. Why does the sales person call the head of manufacturing directly to start work instead of following the procedure established in the company? - Because the "start work" form requires the sales director's approval before work can begin and slows the manufacturing process (or stops it when the director is out of the office).

4. Why does the form contain an approval for the sales director? - Because the sales director needs to be continually updated on sales for discussions with the CEO.

5 Whys Examples

3) Check sheets

• Many organizations: They are :”DATA RICH, INFORMATION POOR”

• Check sheet can be a valuable tool in wide applications.

• Purpose: To make it easy to collect data for specific purposes or to convert into valuable information.

Check sheets

E.g; Weekly Summary of Shaft Dimensional Tolerance Results

Check sheet

• Figure 15-11 reports how the works being produced relates to the shaft length specifications.

• Machine setup limits 1.120 – 1.130 inches. Outside range waste!

• So Figure 15-12 is the check sheet set up to display useful information.

• It produces histogram.

Check sheets

4) Flow Chart

• A flowchart is a type of diagram that represents an algorithm or process, showing the steps as boxes of various kinds, and their order by connecting these with arrows

Flow Chart

http://en.wikipedia.org/wiki/Diagram�

http://en.wikipedia.org/wiki/Algorithm�

http://en.wikipedia.org/wiki/Process_(science)�

Flow Chart

Example of the flow chart symbol

5) Histograms

• Used to chart frequency of occurrence. (How often does something happen?)

• Commonly associated with processes: attributes and variables

DATA TYPES EXAMPLES

Attributes Has / has not

Good / bad

Pass / fail

Accept / Reject

Conform / non-conform

Variables Measured values (Dimension, weight, voltage, surface, etc.)

Histograms

• Attribute data: Go/no go information. • Variable data: measurement information. • Looking at Figure 15-14, we are using attributes data;

either they passed or they failed the screening. • But, it does not reveal about the process contributing

to the adjustment. • Also, does not tell the robust process. This is why

variables data is needed.

Histograms

Histograms

Date Accepted Rejected

11 11 1

12 12 0

13 11 1

14 12 0

15 12 0

Total 58 2

• Figure 15-14 : Shaft Acceptence: week of 7/11 (Spec: 1.120-1.130”)

• Example: textbook – page 500 BEAD EXPERIMENT

There are 900 white beads, 100 red beads =1000beads

1. The beads mixed thoroughly. 2. 50 beads are drawn at random. – Count how many red beads. –

Check mark is entered in histogram. 3. All the beads are put back into container and mixed again. 4. Repeat Step 1 Step 3 The process does not change, but the output changed! If these steps are taken over and over, Histogram as in Figure 15-15 will

occur

Histograms and Statistics

• Figure 15-15 and Figure 15-16


• The flatter and wider the frequency distribution curve, the greater the process variability.

• Taller and narrower the curve, the less process variability. – 2 things in process variability;

• Standard deviation, σ

• Mean, μ


• Mean is the sum of the observations divided by the number of observations

• Also describes the central location of the data in the chart,

– Standard Deviation describes the spread or dispersion of data. Calculating the Mean, μ μ=ΣX÷n X=product of the number of beads in a sample times the number

of samples containing the number of beads. *See Figure 15-17b, page 503 for further understanding.


μ =ΣX÷n =510 ÷ 100 =5.1


• Calculating Standard Deviation, σ

d = The deviation of any unit from the mean n = the number of units sampled.

From Figure 15-17c, n(100)

σ = 1.49, 2 σ = 2.99, 3 σ = 4.47


• Figure 15-18

Shapes of Histograms

Shape of Histograms

Figure 15-19 • Process A is much tighter, normal distribution,

favorable. • Process B greater variances. • C and D are not centered, skewed to left and right,

product will be lost. • F – someone has discarded. Take out the reject, and

only collect data within acceptable range. • G –the vendor has screened out the parts, took out

the best to other customers. • H – a proper normal distribution between upper and

lower limits. • I and J skewing! Significant loss of product… • K until P shifting… why???

6) Scatter Diagram

• A scatter diagram is a tool for analyzing relationships between two variables. One variable is plotted on the horizontal axis and the other is plotted on the vertical axis

• While the diagram shows relationships,

• It does not by itself prove that one variable causes the other.

Scatter Diagram

• Scatter diagrams will generally show one of six possible correlations between the variables:

Scatter Diagram

• 1) Strong Positive Correlation - The value of Y clearly increases as the value of X increases.

Scatter Diagram

• 2) Strong Negative Correlation - The value of Y clearly decreases as the value of X increases

Scatter Diagram

3) Weak Positive Correlation -The value of Y increases slightly as the value of X increases.

Scatter Diagram

4) Weak Negative Correlation - The value of Y decreases slightly as the value of X increases.

Scatter Diagram

5) Complex Correlation -The value of Y seems to be related to the value of X, but the relationship is not easily determined

Scatter Diagram

6) No Correlation -There is no demonstrated connection between the two variables

Scatter Diagram

7) Control Chart

Figure 15-27 shows a basic of control chart. Data stay between Upper Control Limit (UCL) and Lower Control Limit

(LCL)

Control Chart

• As long as the plots stay between the limits, and don’t congregate on 1 side or the other of the average line, the process is in STATISTICAL CONTROL.

• Common causes/chance: Small random changes in the process that cannot be avoided – but still in statistical control – Varying out of the centerline of the process – Result of the sum of numerous small resources of natural variation

that are always part of the process. – Eg; Setting on machines, environment, methods etc.

• Special causes/Assignable causes: Variations in the process that can be identified as having a specific cause. – A plot point breaks through UCL or LCL – OR there a several points in a row above/below the lines. – Result of the factors that are not part of the process and only occur is

special case. – Eg: New operator involve, electricity blackout, shipment faulty of

material etc.

Control Chart

• Only after the special has been identified, it should be corrected, and restart the process.

• How to correct? (By eliminating root cause)

• Control chart is usually operated under Statistical Process Control (SPC) – Chapter 18.

Control Chart

Control Chart - Statistical Process Control (SPC)

• What is SPC?

SPC is a statistical method of separating variation

resulting from special causes resulting from natural causes, to eliminate the special causes, and to establish and maintain consistency in the process, enabling process improvement.

Control Chart

• Common factors that can affect output are;

5M’s Machines and environment employed

Material used Methods (work instructions)

Measurements taken Manpower (People who operate the process)

If these factors are perfect; this means; 1. Environment facilitates quality work and there are no

misadjustments in the machines 2. No flaws in materials 3. Follow work instruction accurate and precisely 4. Accurate and repeatable measurements 5. People work with extreme care – follow instructions

extremely well

Control Chart

Control Chart

• From Figure 18-5, The average, is;

averagesubgroupx

groupssubofnumberkkxx

=

=

÷=∑x

067.10012,

8.1200

=

÷=

=

∑∑

xxSo

x

x)ofvalueMinxofvalue(Max RR

is R range, subgroup of Average

−=

÷=∑ kR

Average Range, is R

∑ =÷= 667.51268R,So

RAxLCL

RAxUCL

2

2

−=

+=

A2 is the confidence level for the data, the larger the value of A, the farther the control limits.

Control Chart

• From Figure 18-6;

2467.1667.522.0

08726.10667.578.1

3

4

=×==

=×==

RDLCL

RDUCL

R

R

And UCL and LCL for the values in R chart;

31023.98)667.531.0(067.10082377.101)667.531.0(067.100

=×−=

=×+=

x

x

LCLUCL

n=10, so UCL and LCL in x-bar chart is;

Figure 18-7 (a)

Figure 18-7 (b)

Control Chart • Suppose that we have been setting up a new process (not stable).

• It would look like in Figure 18-8

In Figure 18-8;

• Subgroup 7 was out of limits.

• Can we ignore?

• Because-control limit has been calculated with the data inclusive of special cause event. (E.g: result of untrained operator etc)

• We MUST determine and eliminate the cause.

• After eliminate it, flush out SUBGROUP 7 and recalculate the process average (x-bar) and the control limits.

• We will find narrower limit, Figure 18-9

NO!

Figure 18-9

• If still penetrates the new out of limits, repeat the same action.. Until the points are all well between the limits.

• X-bar chart is used to show the center of the process measurements (accuracy).

• R chart is to show the spread of the data (precision).

• Without Range, it would not be able to understand the PROCESS CAPABILITY of the chart.

Control Chart

• Stable process?? It is a process that exhibits only common variation.

• Advantages; 1. Management knows the process capability, so they can predict

cost well.

2. Productivity MAX, cost MIN

3. Management can measure effect faster and more reliable.

4. Got data if management wants to alter spec limits.

5. Stable process is basic requirement for process improvement efforts.

Control Chart-Advantages of a Stable Process

• A committee for developing QC tools affiliated with JUSE was set up in April 1972.

• Their aim was to develop QC techniques for use by managerial level and staff.

• In January 1977 the committee announced the results of its research in

7 NEW QC TOOLS

the form of a new set of methods called 'The Seven New QC Tools’.

The tools are:- • 1) Affinity Diagram • 2)Interrelationship diagram • 3)Tree diagram • 4) Prioritization Matrix • 5) Matrix Diagram

• 6) Process Decision Process Chart (PDPC)

• 7) Activity Network Diagram

7 NEW QC TOOLS

Quiz

• Define and Show examples (Diagrams picture) Rule 1 and Rule 2 to show the process is not in statistical control.

Statistical error: Type I and Type II

• Statisticians speak of two significant sorts of statistical error.

• Type I error: An incorrect decision to REJECT something when it is true. – False alarm

• Type II error: An incorrect decision to ACCEPT something when it is true. – Oversight

Common Use Control Chart for attribute data

(Counted values)

• P chart – No. of defects in samples of varying size as a percentage of fraction.

• (e.g anywhere defects can be counted)

• np chart- no. of defective pieces in samples of fixed size.

• C chart – No. of defects in a single product. (e.g: blemish, deform, scratches in one part)

• U chart – No. of defects per-unit area. (Carpet area, lenght)

Exercise

Exercise 2

Quality Function Deployment

• Defined as: – A systematic method for transferring customer

wants/needs/expectations into product and process characteristics

QUALITY FUNCTION DEPLOYMENT

Quality Function Deployment

Voice of the customer

House of Quality

QFD: An approach that integrates the “voice of the customer” into the product and service

development process.

House of quality

customer needs

engineering metrics

benchmarking on needs

target and final specs

technical correlations

relative importance

relationships between

customer needs and

engineering metrics

QFD & House of Quality

• Identify customer wants • Identify how the good/service will satisfy customer

wants • Relate the customer’s wants to the product’s hows • Identify relationships between the firm’s hows • Develop importance ratings • Evaluate competing products

Example QFD Facial Foam 100ml : • A : Nivea Visage (Biersdorf Hamburg) • B : L’Oreal (Paris) • C : Biore (Kao)

Facial Foam A Facial Foam B Facial Foam C

QFD Details

RM

House of Quality

RM

Tabel Score

To conclude : • Product A (Nivea Visage) Facial Foam has the highest

score among others. Means this product is the best chosen by customers.

Benefits Of QFD

• Customer Driven

• Reduces Implementation Time

• Promotes Teamwork

• Provides Documentation

Quality Function Deployment (QFD)

• QFD seeks to bring the voice of customers into the process of designing and developing a product or service.

• QFD can point out areas of strength as well as weaknesses in both existing or new products.

• When a company uses QFD, they stop developing products/services on their own interpretation.

Main benefits of QFD

1. Customer focused – QFD gives information which is then translated into a set of specific customer requirements.

2. Time efficient – Time is not wasted on developing features that have no value to customers.

3. Teamwork oriented – All decisions are based on consensus and involve in-depth discussion and brainstorming

4. Documentation oriented – QFD forces the issue of documentation. This document changes as new information gained. Having up-to-date information about customer requirements, will be very helpful.

chapter 6: tqm tools and techniques. - unimap...

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