a study on methodologies and tool used in six … · dmedi (define, measure, explore, develop and...

International Journal of Exploring Emerging Trends in Engineering (IJEETE)

Vol. 02, Issue 04, JUL-AUG, 2015 Pg. 189-201 WWW.IJEETE.COM

ISSN – 2394-0573 All Rights Reserved © 2015 IJEETE 89

A STUDY ON METHODOLOGIES AND TOOL USED IN SIX SIGMA

1Deepak,

2Dr. Rohit Garg,

3Somvir Arya

1M.Tech Research Scholar, IIET, Kinana, Jind,Haryana

2 Director, GNIOT, NOIDA

3 Head of Department of Mechanical Engineering, IIET , Kinana,Jind,Haryana

ABSTRACT: Six Sigma is a formal methodology

for analyzing, measuring, improving and then

controlling or “locking in” processes. This

statistical approach reduces the occurrence of

defects to a Six Sigma level - less than four

defects per million from a three sigma level or

66,800 defects per million. Six Sigma, a statistics-

based, comprehensive methodology that aims to

achieve nothing less than perfection in every

single company product and process. Six Sigma is

a highly disciplined process that focuses on

delivering and developing near-perfect products

and services consistently. Reduction of variation

to achieve very small standard deviations so that

almost all of your products or services meet or

exceed customer expectations is the purpose of

Six Sigma. A flexible and comprehensive system

for achieving, maximizing and sustaining

business success. Six Sigma is uniquely driven by

close understanding of customer needs, statistical

analysis, disciplined use of facts, and diligent

attention to improving, managing and reinvesting

business processes.

Keywords: six sigma tools.

I. INTRODUCTION Six Sigma is a disciplined, data-driven

methodology for eliminating defects in any

process. Within Six Sigma Tools and

methodology deal with overall costs of quality,

both tangible and intangible parts, trying to

minimize it, and in the same time, increasing

overall quality level contributing to company

business success and profitability. Success of Six

Sigma is measured in financial terms, Defects per

Million Opportunities, Customer Satisfaction, and

Performance of Internal Work Processes and in

Suppliers’ Performance. Six Sigma is in essence a

structured way of solving problems in an existing

process based on analysis of real process data, i.e.

facts

Six Sigma is a rigorous and a systematic

methodology that utilizes statistical analysis and

information (management by facts) to measure

and improve a company’s operational

performance, practices and systems by

identifying and preventing ‘defects’ in

manufacturing and service-related processes in

order to exceed and anticipate expectations of all

stakeholders to accomplish effectiveness.

(Tonner, 2003)

Six Sigma is a toolkit and program for improving

quality in manufacturing processes. A

methodology which aims to reduce variations in a

process. (Prewitt, 2003)

Six-Sigma, a set of techniques and tools for

process improvement, was developed by

Motorola in 1986. Six-Sigma addresses the major

root causes and guarantees the desired results,

both in terms of improvement and time span. This

enhancement approach delivers results of

productivity, profitability and quality

improvements based on its highly valuable

approach (Chandra, A. (2009). Six-Sigma is

adopted by many industries because of its proven

benefits in increased profitability and reduction in

cost especially for medium scale industries.

Manufacturing sector is on the top in

implementing Six Sigma with 69% contribution

followed by IT (Information Technology)

industries (Desai, D.A.,2008). Sigma (σ) is a

Greek letter that represents the standard deviation

of a sample population in statistics. When

measuring process capability, the standard

deviations between the process mean and the

nearest specification limit is designated in sigma

units. The greater the sigma value, more number

of standard deviations fit between the mean and

the nearest specification limit.



ISSN – 2394-0573 All Rights Reserved © 2015 IJEETE Page 190

“One Sigma” is a very high degree of variability

( i.e. 7 “mistakes” out of 10 opportunities)

“Six Sigma” is a very low degree of variability

(i.e. 3.4 “mistakes” out of one million

opportunities). This translates into 99.99966%

perfection.

II. SIX SIGMA METHODOLOGIES

Main focus of Six Sigma is to improve all key

processes of manufacturing setup and takes

quality as a function of processes and reduce the

rejection rate. Six Sigma mainly uses two main

methodologies one is called Define, Measure,

Analyze, Improve and Control, usually known as

DMAIC and other is Define, Measure, Analyze,

Design and Verify, known as DMADV. DMADV

is used for creating new processes to produce

products with minimum defect rate. Both the

methodologies are based on Edwards Deming’s,

Plan- Do-Check-Act cycle.

Some Other methodologies that are being used

during six sigma implementation are given as.

CDOC (Conceptualize, Design, Optimize and

Control)

DCCDI (Define, Customer, Concept, Design

And Implement)

DMADOV (Define, Measure, Analyze,

Design, Optimize and Verify)

DMEDI (Define, Measure, Explore, Develop

and Implement)

DCDOV (Define, Concept, Design, Optimize

and Verify)

IIDOV (Invent, Innovate, Develop, Optimize

and validate)

IDOV (Identify, Design, Optimize and

validate)

III THE SIX SIGMA DMAIC

METHODOLOGY

The most well known and most widely used

methodology in Six Sigma is The DMAIC

methodology. Most companies begin

implementing Six Sigma using the DMAIC

methodology, and later add the DFSS (Design for

Six Sigma, also known as DMADV or Define,

Measure, Analyze, Design, Verify)

methodologies when the organizational culture

and experience level permits.

The Six Sigma is not a completely new

foundation. The roots of Six Sigma as a

measurement standard can be traced back to Carl

Frederick Gauss (1777-1855) who introduced the

concept of the normal curve. It can be thought of

as a roadmap for problem solving and

product/process improvement. The purpose of

this phase is to clarify the goals and value of a

project. The Define phase and the beginning of

the Measure phase are mostly qualitative.

Sometimes quantitative data from process

evaluations are used. A problem to be solved

needs to be formulated from people’s

experiences.

The Six Sigma methodology itself is built from

concepts introduced by W. Edwards Deming- P-

D-C-A, or Plan-Do- Check-Act - which describes

the basic logic of data-based process

improvement (Pande et al., 2000). The Six Sigma

DMAIC (Define, Measure, Analyze, Improve,

and Control) methodology is based on Deming’s

PDCA idea. The DMAIC methodology is

considered to be a newer approach to Six Sigma

and is sometimes referred to as the “Breakthrough

Approach” developed by Mikel Harry and

Richard Schroeder (2000) (Gupta, 2004).

3.1.1 DEFINE STAGE OF THE SIX SIGMA

The Define stage of the Six Sigma methodology

and philosophy is the beginning of the spectrum

for a Six Sigma project. The purpose of this step

is to identify potential projects, to define and

select a project and to set up the project team.

Gryna (2001) specified five general steps of the

define stage, they are summarized as:

1. To Identify the Potential Projects: This stage

includes the screening and selection of projects.

The opportunities that will increase customer

satisfaction and reduce COPQ are the focus of

this stage.

2. Evaluate Projects: The evaluation of projects

includes a review which goes from an analysis of

the scope and benefit to an assessment of factors

to help set priorities.

3. Select Project: This is about selection of the

project. The initial project should be a successful

one. This is because a successful project is a form

of evidence to the project team that the process

works and helps to build momentum to future

endeavours.




4. Mission Statement for Project and Prepare

Problems: A mission statement is based on the

problem statement but it provides direction to the

project team. Establishing of a problem statement

brings to the forefront what it is while allowing

seeing a planned outcome.

5. Selection and Launch of Project Team:

Generally, a project team has a sponsor, a

recorder, a leader, team members and a

facilitator. to develop a charter that defines what

the team will do and how the team will function

is an option that may help in this step.

This Define phase essentially sets the tone for the

entire design project where the design problem is

defined by the management, projects which are

consistent are nominated with overall business

strategy and selected based on benefits (De Feo et

a l, 2002). Pareto principle is A way to assess the

potential projects. The Pareto Principle states that

a few contributors to the cost are responsible for

the bulk of the cost. These vital few contributors

need to be identified so that quality improvement

resources can be concentrated in those areas.

3.1.2 MEASURE

One of the Six Sigma methodologies is The

Measure phase which identifies key process

characterized and product parameters and

measures the current process capability. This

phase also concentrates on key customers and

their critical needs . The steps in this stage as

outlined by Gryna (2001) include:

1. Verify the project need and Measure the

baseline performance: It helps in justifying the

time spent on the project as well as helping to

overcome the resistance to accepting and

implementing a remedy. It is a good idea to

confirm the size of the problem in numbers

because it allows for a clear view of the problems

that you have to deal with.

2. Documentation of the Process: Using tools

such as process flow diagrams or process maps

are useful in this stage. Documentation of the

process allows for others to see the problems

you’re dealing with.

3. Data Collection Plan: This stage involves

quantification of symptoms and the formulation

of theories and outline of symptoms.

4. Measurement System Validation: Variation

comes in many different ways, from the process

itself or even from the measurement system.

Validating the measurement can involve such

things as accuracy, repeatability, reproducibility,

linearity and stability.

5. Process Capability Measurement: Knowing the

initial process capability helps to define the work

to be done in the analysis and improve phases to

achieve a capability at the six sigma level.

Process capability refers to the inherent ability of

a process to meet the specification limits for a

product.

In the planning aspects of operations it is very

important that the processes will be able to meet

the specifications. A good reason for being able

to quantify process capability is to be able to

compute the ability of the process to hold product

specifications. To use the process capability

measurements is one way of ensuring that the

process can meet specifications . Planners try to

select processes with the six sigma process

capability well within the specification width; a

measure of this relationship is the capability ratio

(Cp). It is useful to have a capability index that

reflects both variation and the location of the

process average; such an index is Cpk, because

the average is often not at the midpoint. The

higher the Cp, the lower the amount of product

outside specification limits, if the average is equal

to the midpoint of the specification range, then

Cpk is equaled to Cp, most capability indexes

assume that the quality characteristic is normally

distributed (Gryna, 2001).

3.1.3 ANALYZE

To identify the causes of variation and process

performance this phase of the Six Sigma

paradigm essentially analyzes the past and current

performance data. Selection a high-level design

from several different alternatives and develop

detailed requirements against which a design will

be optimized is the main purpose of this phase is

to (De Feo, 2002). The steps of this again as

stated by Gryna (2001) include:

1. Collection and Analysis of Data

2. Develop and Test Theories on Sources of

Cause & Effect Relationships and Variation

A large part of the Analyze phase is to be able to

test the theories of management controllable

problems. To do this would require the use of the




facts, rather than opinions to reach conclusions

about the causes of a quality problem. The factual

approach not only determines the true cause but

also helps to gain agreement on the true cause by

all of the parties involved (Gryna, 2001). Ways to

test theories that have been developed are to

collect new data. It requires data to be collected

in the new processes that have been developed in

order to see how well it is doing as compared to

the processes before. Some measures that can be

done include (Gryna, 2001):

Measurement following non-controlled

operations

Measurement at intermediate stages of a

single operation

Measurement of additional or related

properties of the product or process

Study of worker methods

In analyzing the errors of processes and

procedures there will no doubt be some errors

that are attributable to the way things are done.

There are also human errors that management

will have to contend with. However, not all errors

can be blamed on the processes or even the

machines being used. There are in general four

types of errors that can be attributable to workers;

they include inadvertent errors conscious errors,

technique errors, and communication errors

(Gryna, 2001).

3.1.4 IMPROVE

In this stage, the team must be ready to veer back

and forth between far out ideas along with the

details of executing a plan (Pande et al., 2002).

This phase of Six Sigma essentially designs a

remedy, proves its effectiveness and prepares an

implementation plan. The steps as outlined by

Gryna (2001) include:

1. Remedy Design: This step identifies

customers, defines their needs and proves the

effectiveness of the remedy. The remedy

designed must fulfil the original project mission,

particularly with respect to meeting customer

needs.

2. Prove Effectiveness of the Remedy: There are

two main steps that can be taken to prove the

remedy. Either by have a final evaluation under

real world conditions and a preliminary

evaluation of the remedy under conditions that

simulate the real world. Before any remedy is

accepted, it must be proven.

3. Evaluation Alternative Remedies: The remedy

selected should make an improvement on the

original problem and it should optimize both

company costs and customer costs. Reviewing the

remedies given, assess which ones would have

the largest impact and which of these are viable.

4. If necessary, Design Formal Experiments to

Optimize Process Performance: The designing of

the experiments can include production

experiments, evaluating suspected dominant

variables, exploratory experiments to determine

dominant variables, response surface experiments

and simulation.

5. Deal with Resistance to Change: Resistance to

change is very common in this type of

implementation, but a way to deal with this

resistance is to educate the people involved in the

change.

6. Transfer the Remedy to Operations: This stage

includes changes in staffing and responsibilities.

Additional equipment, materials and supplies

along with extensive training may be involved.

Transferring the remedy to operations may

include revisions in operating standards and

procedures.

A useful tool in the Improve phase is the use of

evolutionary operations or EVOP. The use of

EVOP introduces small changes into variables

according to a planned pattern of changes, these

changes are small enough to avoid a detour from

the status quo but large enough to gradually

establish which variables are important. EVOP is

based on the concept that every manufactured lot

has the information which can be used to

contribute about the effects of process variables

on a quality characteristic (Gryna, 2001).

Giving detail to the designing of experiments

would allow easier conformance to quality in the

future. The Six Sigma approach makes the use of

the Design of Experiments (DOE) as an important

part of its processes. Experiments can have

numerous objectives, and the best strategy

depends on the objective. Using DOE is like

setting a concrete plan to conduct the experiment.

DOE allows for establishing the important

variables that affect quality.




3.1.5 CONTROL

The Control phase is the last phase of the Six

Sigma methodology is where the designing and

implementation of certain activities to hold the

gains of improvement occur. Statistical Process

Control (SPC) is a technique for applying

statistical analysis to monitor, measure, and

control processes where the major component is

the use of the control charting methods (Wortman

et al, 2001). SPC is something that can be used in

this phase. The use of control charts has many

benefits. When a control chart shows that a

process is within specification limits and in

control, it is often possible to eliminate the costs

relating to inspection. The Control phase refers to

the process used to meet standards consistently.

The steps according to Gryna (2001) are:

1. Document the Improved Process and Design

Controls: Control during operations is done

through use of a feedback loop which is a

measurement of actual performance, comparison

with the action on the difference and standard of

performance.

2. Validate the Measurement System: This step

could include new measurement devices, the

collection of new data and additional training for

process personnel. After setting up the

measurement system for the improved process, it

must be evaluated and made capable.

3. Determine the Final Process Capability: The

process changes implemented should be

irreversible. Essentially, this step ensures that the

process capability gained can be held during

normal operating conditions.

4. Implement and Monitor the Process Controls:

The steps mentioned above are used to monitor

the processes and product performance. In this

step, all of the remedies are implemented into the

operations. Implementing and monitoring the

improved process is the final step in a quality

improvement project.

According to Gryna (2001), the control process is

in the nature of a feedback loop, control involves

a sequence of steps: choose the control subject,

measure actual performance, establish standards,

establish measurement of performance, compare

actual measured performance to standards and

take action on the difference. Pande et al. (2002)

states that the main purpose of the Control phase

is quite simple: “once the improvement’s been

made and results documented, continue to

measure the performance of the process routinely,

adjusting its operation when the data clearly

indicates you should do so or when the

customer’s requirements change.”

IV.COMMONLY USED QUALITY

CONTROL (QC) TOOLS IN SIX SIGMA

Significant number of quality assurance and

quality management tools are available and

selecting an appropriate tool is not always an easy

task. Seven basic quality tools used in Six-Sigma

methodologies are:

1. Flow chart

2. Pareto diagram

3. Check sheet

4. Control chart

5. Histogram

6. Scatter plot

7. Cause-and-effect diagram.

1. Cause and Effect Matrix:-

A cause-and-effect matrix — sometimes called

a C&E matrix for short — helps you discover

which factors affect the outcomes of your Six

Sigma initiative. It provides a way of mapping

out how value is transmitted from the input

factors of your system (the Xs) to the process or

product outputs (the Ys). With these relationships

visible and quantified, you can readily discover

the most-influential factors contributing to value.

Cause and Effect Matrix is a viable tool which

provides the maximum amount of information.

The Key Process Output Variables (KPIV) is

scored according to their importance while the

Key Process Input Variables (KPIV) is scored in

terms of their relationship to key outputs. In the

Matrix, a factor of importance for each parameter

score is rank ordered and every listed input

parameter is correlated to every output parameter.

Finally, a total value for each parameter is

obtained by multiplying the rating of importance

with value given to parameters and adding across

for each parameter. The KPIV are listed on the

left-hand side while the KPOV are listed on the

top right hand side of the diagram. In some cases,




the KPIV from one process are the KPOV for the

next process. For example, moisture content and

operator unawareness.. The results of the Cause

and Effect Matrix are further analyzed with the

Pareto Diagram. The Pareto Diagram (also known

as 20/80 principle) helps in prioritizing the

different categories taken into account for further

analysis like Failure More Effective Analysis

(FMEA) (A. Kumaravadivel, U. Natarajan,

2011). The KPOV are rank ordered in accordance

with the number of points from the Cause and

Effect Matrix. This table below shows the key

process input variable and key process output

variable relations related to the foundry process:

Importance

Estimation as

Scale for Process

From Customer

3 3 3 5 5 5 5 3 5 3 1 3 1 5 3 3 3

S.

no.

1 2 3 4 5 6 7 8 9 1

0 11

1

2

1

3

1

4

1

5

1

6

1

7

KPOV

KPIV

Mis

s ru

n

cold

sh

ut

Hot

Tea

rs

Cra

ck

Shri

nkag

e

Sla

g I

ncl

usi

on

Core

Shif

t

Tim

e O

F C

ycl

e

San

d I

ncl

usi

on

Sca

bs

Cuts

and W

asher

Mould

Shif

t

Sw

ells

Blo

w H

ole

s

Pouri

sity

and P

in

Hole

s

War

pag

e

Dir

t

Tota

l

1 Unskilled operator 3 1 0 0 0 3 0 3 3 3 0 0 3 0 0 3 0 70

2 Improper handling

of core on line 3 3 0 0 0 3 0 3 3 0 0 0 3 0 0 3 0 69

3

Core box is not

mounted properly

on machine

0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 3 0 15

4

Venting problem

of bed plate

bottom slab

0 0 1 1 3 0 0 0 3 0 0 0 3 3 3 1 0 44

5

Trolley

maintenance not

adhered

0 0 0 0 0 1 0 3 1 0 0 0 1 0 0 0 0 20

6 Less scratch

hardness of core 0 0 0 0 0 3 0 0 1 0 0 0 1 0 0 0 5 36

7 Strainer core /

filter core not used 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 25

8 Sand quality poor 0 0 0 0 0 5 0 0 0 0 0 0 0 3 0 0 0 40

9

Blow candle

problem in end

core

3 0 0 1 0 5 0 0 0 0 3 3 0 1 0 0 0 93

10

Locator broken

problem in top

slab core

0 0 3 5 3 1 0 1 3 0 0 0 0 0 0 0 0 62

11 Porosity problem

machine 0 0 0 0 0 3 0 0 5 3 3 3 3 1 3 3 3 96

12 Broken blow

candle of machine 0 0 0 0 0 0 0 0 5 0 0 0 0 0 3 0 0 34

13 Mounting of sand 0 0 0 0 0 0 0 0 3 0 0 0 0 0 1 0 0 18




magazine not ok

in machine

14

Core unloading

problem on the

machine

0 0 1 1 0 0 0 0 5 0 0 0 0 0 0 0 0 33

15 Sand mixture not

ok 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 5 3 48

16 Variation in wash

viscosity 1 1 0 0 0 0 0 0 1 0 0 0 0 0 3 0 0 20

17 No rubber seal in

the crank core box 0 0 0 0 0 0 0 0 1 0 3 0 0 0 1 0 0 11

18 Gas leakage

problem 0 0 1 1 0 3 0 0 1 3 3 0 3 3 3 0 0 67

19 Poor Design of

Crank Core 0 0 3 0 0 5 0 1 0 3 0 3 0 3 0 5 0 85

20 Core ejection

problem 0 0 0 0 0 5 0 0 0 0 0 0 0 3 0 0 0 40

21 No dimple mark

for screw 0 0 0 0 0 5 0 0 5 0 0 0 0 0 0 0 0 50

22 End core fitment

problem 1 3 5 1 1 1 5 0 1 0 1 1 0 0 0 3 1 88

23 Jig handling

problem at line 3 3 0 0 3 1 3 0 1 0 1 3 0 0 0 0 3 77

24 Design Parameters 1 1 0 0 3 1 1 0 3 0 0 1 0 0 0 0 1 52

25 Fifo is not

maintained 3 3 0 0 0 0 3 3 5 0 3 0 0 0 0 0 0 73

26 Uneven shop floor

problem 5 5 0 0 0 3 0 0 3 0 0 0 0 0 1 3 3 71

27

Go ,No go gauge

is not qualified in

bed plate assembly

0 0 0 5 3 0 0 1 3 0 0 3 0 0 5 3 0 91

28 Bad tyre of trolley 0 0 0 0 1 0 0 1 0 0 0 0 0 0 3 0 0 17

29 Wash stick on

Boss area 0 0 0 0 0 0 0 0 1 0 0 0 0 0 3 0 0 14

30 Poor handling core

assembly 0 0 0 3 5 0 0 5 0 0 0 0 0 5 5 0 0 95

31 Uneven Stripping 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 15

32 Improper Raming 0 0 5 5 0 0 0 3 3 5 0 0 0 0 0 0 0 73

33 Insufficient

Turbulence 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 50

34 Insufficient

permeability 0 0 0 0 0 0 0 0 0 0 0 0 0 5 3 0 0 34

35 Moisture Content 0 0 0 0 0 5 0 3 0 0 0 0 0 3 5 0 0 73

36 Operator

Unawareness 0 0 0 0 0 3 0 0 5 0 0 0 0 0 0 0 0 40

Total 6

9

60

57

115

110

330

60

84

360

51

17

51

17

150

135

96

57

2. Flow diagram




It is a collective term for a diagram representing a

flow or set of dynamic relationships in a system.

The term flow diagram is also used as synonym of

the flowchart, and sometimes as counterpart of the

flowchart. When it comes to conveying how

information data flows through systems (and how

that data is transformed in the process), data flow

diagrams (DFDs) are the method of choice over

technical descriptions for three principal reasons.

1. DFDs are easier to understand by technical and

nontechnical audiences

2. DFDs can provide a high level system overview,

complete with boundaries and connections to other

systems

3. DFDs can provide a detailed representation of

system components1

DFDs help system designers and others during

initial analysis stages visualize a current system or

one that may be necessary to meet new

requirements. Systems analysts prefer working

with DFDs, particularly when they require a clear

understanding of the boundary between existing

systems and postulated systems. DFDs represent

the following:

1. External devices sending and receiving data

2. Processes that change that data

3. Data flows themselves

4. Data storage locations

DFDs consist of four basic components that

illustrate how data flows in a system: entity,

process, data store, and data flow.

Entity

An entity is the source or destination of data. The

source in a DFD represents these entities that are

outside the context of the system. Entities either

provide data to the system (referred to as a source)

or receive data from it (referred to as a sink).

Entities are often represented as rectangles (a

diagonal line across the right-hand corner means

that this entity is represented somewhere else in the

DFD). Entities are also referred to as agents,

terminators, or source/sink.

Process

The process is the manipulation or work that

transforms data, performing computations, making

decisions (logic flow), or directing data flows

based on business rules. In other words, a process

receives input and generates some output. Process

names (simple verbs and dataflow names, such as

“Submit Payment” or “Get Invoice”) usually

describe the transformation, which can be

performed by people or machines. Processes can be

drawn as circles or a segmented rectangle on a

DFD, and include a process name and process

number.

Data Store

A data store is where a process stores data between

processes for later retrieval by that same process or

another one. Files and tables are considered data

stores. Data store names (plural) are simple but

meaningful, such as “customers,” “orders,” and

“products.” Data stores are usually drawn as a

rectangle with the righthand side missing and

labeled by the name of the data storage area it

represents, though different notations do exist.

Data Flow

Data flow is the movement of data between the

entity, the process, and the data store. Data flow

portrays the interface between the components of

the DFD. The flow of data in a DFD is named to

reflect the nature of the data used (these names

should also be unique within a specific DFD). Data

flow is represented by an arrow, where the arrow is

annotated with the data name.

Some Guidelines About Valid and Non-

Valid Data Flows

Before embarking on developing your own data

flow diagram, there are some general guidelines

you should be aware of.

Data stores are storage areas and are static or

passive; therefore, having data flow directly from

one data store to another doesn't make sense

because neither could initiate the communication.

Data stores maintain data in an internal format,

while entities represent people or systems external

to them. Because data from entities may not be

syntactically correct or consistent, it is not a good

idea to have a data flow directly between a data

store and an entity, regardless of direction.

Data flow between entities would be difficult

because it would be impossible for the system to

know about any communication between them.

The only type of communication that can be

modeled is that which the

system is expected to know or react to.

Processes on DFDs have no memory, so it would

not make sense to show data flows between two a

synchronous processes (between two processes that




may or may not be active simultaneously) because

they may respond to different external events.

Therefore, data flow should only occur in the

following scenarios:

Between a process and an entity (in either

direction)

Between a process and a data store (in either

direction)

Between two processes that can only run

simultaneously.

Here are a few other guidelines on developing

DFDs:

Data that travel together should be in the same

data flow

Data should be sent only to the processes that

need the data

A data store within a DFD usually needs to

have an input data flow

Watch for Black Holes: a process with only

input data flows

Watch for Miracles: a process with only output

flows

Watch for Gray Holes: insufficient inputs to

produce the needed output

A process with a single input or output may or

may not be partitioned enough

Never label a process with an IF-THEN

statement

Never show time dependency directly on a

DFD (a process begins to perform tasks as soon

as it receives the necessary input data flows)

Example of data flow diagram:

Foundry process flow diagram

3. PARETO CHART

A bar chart used to separate the “vital few” from

the “trivial many.” These charts are based on the

Pareto Principle which states that 20 percent of the

problems have 80 percent of the impact. The 20

percent of the problems are the “vital few” and the

remaining problems are the “trivial many.” The

PARETO procedure creates Pareto charts, which

display the relative frequency of quality-related

problems in a process or operation. The

frequencies are represented by bars that are ordered

in decreasing magnitude. Thus, a Pareto chart can

be used to decide which subset of problems should

be solved first or which problem areas deserve the

most attention.

Pareto charts provide a tool for visualizing the

Pareto principle,_ which states that a small subset

of problems tend to occur much more frequently

than the remaining problems. In Japanese industry,

the Pareto chart is one of the “seven basic QC

tools” heavily used by workers and engineers.

Ishikawa (1976) discusses how to construct and

interpret a Pareto diagram. Examples of Pareto

diagrams are also given by Kume (1985) . You can

use the PARETO procedure to

construct Pareto charts from unsorted raw data

(for instance, a set of quality problems that

have not been classified into categories) or

from a set of distinct categories and

corresponding frequencies

construct Pareto charts based on the percentage

of occurrence of each problem, the frequency

(number of occurrences), or a weighted

frequency (such as frequency weighted by the

cost of each problem)

add a curve indicating the cumulative

percentage across categories

construct side-by-side Pareto charts or stacked

Pareto charts

construct comparative Pareto charts that enable

you to compare the Pareto frequencies across

levels of one or two classification variables.

For example, you can compare the frequencies

of problems encountered with three different

machines for five consecutive days.

highlight the “vital few” and the “useful many

categories by using different colours for bars

corresponding to the n most frequently




occurring categories or the m least frequently

occurring categories.

Highlight special categories by using

different colours for specific bars

create charts using either a high-resolution

graphics device or a line printer

annotate charts created on graphics devices

save charts created on graphics devices in a

graphics catalogue for subsequent replay

display sample sizes on Pareto charts

display frequencies above the bars

define characters used for features on plots

produced on line printers

save information associated with the

categories (such as the frequencies) in an

output data set

restrict the number of categories displayed to

the n most frequently occurring categories

Both the chart and the principle are named after

Vilfredo Pareto (1848-1923), an Italian economist

and sociologist. His first work, Cours d’Économie

Politique (1895-1897), applied what is now termed

the Pareto distribution to the study of income size.

yJuran originally referred to these categories as the

“trivial many”; however, because all problems

merit attention, the term “useful many” is

preferable. Refer to Burr (1990).

Stage wise core rejection data of 100 cores rejected

Conclusion: 63 % cores rejected at core making

and core assembly supply to line

V.FISHBONE DIAGRAM

The Fishbone diagram (also called the Ishikawa

diagram) is a tool for identifying the root causes of

quality problems. It was named after Kaoru

Ishikawa, a Japanese quality control statistician,

the man who pioneered the use of this chart in the

1960's (Juran, 1999).

The Fishbone diagram is an analysis tool that

provides a systematic way of looking at effects and

the causes that create or contribute to those effects.

Because of the function of the Fishbone diagram, it

may be referred to as a cause-and-effect diagram

(Watson, 2004).

34

29

20

12

5 34

63

83

95 100

0

10

20

30

40

50

60

70

80

90

100

0

5

10

15

20

25

30

35

40

Stage 3 core Making

Stage 9 core supply to line

Stage 7 core assembly

Stage 4 core Transportation

Stage 5 & 6 core washing and

drying p

erc

en

t (c

on

trib

uti

on

in %

)

Re

ject

ion

of

core

s in

no

.

stages




Fishbone (Ishikawa) diagram mainly represents a

model of suggestive presentation for the

correlations between an event (effect) and its

multiple happening causes. The structure provided

by the diagram helps team members think in a very

systematic way. Some of the benefits of

constructing a Fishbone diagram are that it helps

determine the root causes of a problem or quality

characteristic using a structured approach,

encourages group participation and utilizes group

knowledge of the process, identifies areas where

data should be collected for further study (Basic

Tools for Process Improvement, 2009).

The design of the diagram looks much like the

skeleton of a fish. The representation can be

simple, through bevel line segments which lean on

an horizontal axis, suggesting the distribution of

the multiple causes and sub-causes which produce

them, but it can also be completed with qualitative

and quantitative appreciations, with names and

coding of the risks which characterizes the causes

and sub-causes, with elements which show their

succession, but also with other different ways for

risk treatment. The diagram can also be used to

determine the risks of the causes and sub-causes of

the effect, but also of its global risk (Ciocoiu,

2008).

Usually, the analysis after Fishbone diagram

continues with other representation and

establishing treatment priorities methods.

A lopsided diagram can indicate an over-focus in

one area, a lack of knowledge in other areas, or it

can simply indicate that the causes are focused in

the denser area. A sparse diagram may indicate a

lack of general understanding of the problem or

just a problem with few possible causes (Straker).

The repartition of the causes and sub-causes on

the diagram must meet some relevance,

membership or timeline criteria, but they can be

put in any preference order or even random

(Ciocoiu, 2008). After accepting the diagram,

which must be stated in a decisional document

(decision, minute, agreement etc.), follows the risk

analyze of the elements in the diagram and then to

the establishment of a plan for treatment or risk

operation of the components (causes) and of the

risk (global) of the characterized event (the effect)

Fig Fish bone diagram for the foundry industry




VI. ADVANTAGES AND DISADVANTAGES

ADVANTAGES

• Fishbone diagrams permit a thoughtful analysis

that avoids overlooking any possible root causes

for a need.

• The fishbone technique is easy to implement

and creates an easy-to-understand visual

representation of the causes, categories of causes,

and the need.

• By using a fishbone diagram, you are able to

focus the group on the ʺbig pictureʺ as to possible

causes or factors influencing the problem/need.

• Even after the need has been addressed, the

fishbone diagram shows areas of weakness that

once exposed - can be rectified before causing

more sustained difficulties.

DISADVANTAGES

• The simplicity of a fishbone diagram can be

both its strength and its weakness. As a

weakness, the simplicity of the fishbone diagram

may make it difficult to represent the truly

interrelated nature of problems and causes in

some very complex situations.

• Unless you have an extremely large space on

which to draw and develop the fishbone diagram,

you may find that you are not able to explore the

cause and effect relationships in as much detail as

you would like to. (WBI Evaluation Group

(2007), Needs Assessment Knowledge Base)

CONLUSION

Six Sigma is a disciplined, data-driven

methodology for eliminating defects in any

process. Within Six Sigma Tools and

methodology deal with overall costs of quality,

both tangible and intangible parts, trying to

minimize it, and in the same time, increasing

overall quality level contributing to company

business success and profitability. It is a very

broad field itself.

Six Sigma is a step-by-step business

improvement strategy used to drive out waste,

improve profitability, to improve the efficiency

and effectiveness and reduce quality costs and of

all operations that meet or even exceed

customers’ needs and expectations. Six Sigma is

a toolkit and program for improving quality in

manufacturing processes. A methodology which

aims to reduce variations in a process. A Six

Sigma DMAIC methodology is used to

understand the root causes and management of

recalls and also analyze the costs in a consumer

products supply chain. There are many variables

in supply chain, so it is essential for

manufacturers to have procedures in place to

prevent failures that result in a product recall.

REFERENCES

1.Tonner, C., (2003), “Six Sigma”, iSixSigma,

Available at: http://www.isixsigma.1

com/dictionary/Six_Sigma-85.htm.

2.Prewitt, E., (2003), “Six Sigma Comes to IT:

Once Confined to Manufacturing Groups, the

Quality Improvement Program called Six Sigma

is now being used to Clean Up IT’s Act”, CIO,

Vol. 16 No. 21, pp. 87-92.

3. Chandna, P. and Chandra, A. (2009), “Quality

tools to reduce crank shaft forging defects: an

industrial case study”, Journal of Industrial and

Systems Engineering, Vol.3 No. 1, pp. 27-37.

4. Desai, D.A. (2008), “Improving productivity

and profitability through Six Sigma: experience

of a small-scale jobbing industry”, International

Journal of Productivity and Quality Management,

Vol. 3 No. 3, pp. 290-310.

5.Pande et al., (2002), The Six Sigma Way Team

Field book: An Implementation Guide for

Process Improvement Teams, McGraw-Hill

Professional, New York, NY.

6.Wortman, B. et al., (2001), The Certified Six

Sigma Black Belt Primer - First Edition, Use -

Fourth Edition, McGraw-Hill, New York, NY.

7..Basic Tools for Process Improvement. (1995,

May 3). Retrived December 20, 2009, from

Balanced Scorecard Institute:

http://www.balancedscorecard.org/Portals/0/PDF

/c-ediag.pdf

8.Ciocoiu, C. N. (2008). Managementul riscului.

Teorii, practici, metodologii. Bucharest: ASE.

9.Ilie, G. (2009). De la management la guvernare

prin risc. Bucharest: UTI Press & Detectiv.

10.Juran, J. M. (1999). Juran's Quality Handbook

(5th Edition). McGraw-Hill.

http://www.balancedscorecard.org/Portals/0/PDF/c-ediag.pdf

http://www.balancedscorecard.org/Portals/0/PDF/c-ediag.pdf




11.Straker, D. (n.d.) Cause-Effect Diagram.

Retrived January 10, 2010, from QualityTools:

http://syque.com/quality_tools/toolbook/cause-

effect/cause-effect.htm

12.Watson, G. (2004). The Legacy Of Ishikawa.

Quality Progress 37(4) , 54-47.

13.WBI Evaluation Group (2007), needs

assessment knowledge base)

14. Gryna, (2001), Quality Plannind and

Analysis (From Product Development Through

Use), 4th

ediyion, New York: Mc-Graw-Hill.

http://syque.com/quality_tools/toolbook/cause-effect/cause-effect.htm

http://syque.com/quality_tools/toolbook/cause-effect/cause-effect.htm

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