A Decision Framework to Select Alternative based on Lean ...B. Analytical Hierarchy Process (AHP)...

A Decision Framework to Select Alternative

based on Lean manufacturing Concepts in Design

Processes

Prapawan Pangsri Faculty of Industrial Technology, Valaya Alongkorn Rajabhat University under the Royal Patronage,

Phathumthani, Thailand

Email: [email protected]

Abstract—Today’s manufacturing and business

environments requires critical levels of competition to

survive in markets. Manufacturers have to create new

strategies for improving organizational performance. This

research aims to provide decision frameworks in product

design and process improvements based on lean

manufacturing principles and multi-criteria decision

making concepts. The results can select the best alternative

to optimal in quality, cost and flexibility.

Index Terms—lean manufacturing, analytical hierarchy

process, multi criteria decision method

I. INTRODUCTION

Lean manufacturing is a process management

philosophy meant to provide superior quality products for

more customers at a significantly lower price and

contribute to a more prosperous society. lt is important to

build a company production system based on this

philosophy. Lean manufacturing has endeavored to

rationalize production by completely eliminating waste in

the production process, build quality into processes,

reduce costs, improve productivity, and develop

integrated techniques that will contribute to corporate

operations. Lean manufacturing focuses on separate

value-added from non-value added activities and

eliminates the root causes and costs of non-valued

activities. As lean manufacturing spreads around the

world, it has outgrown not only the auto industry, but also

the whole manufacturing sector, taking root in areas as

diverse as logistics and distribution, services, retail,

healthcare, construction, maintenance, and even

government.

In the past, environmental perspectives of

manufacturing have not been very competitive and

production yielded low volume. Operations were worker

driven and product diversity at markets were low.

Currently, manufacturing technology has been

continuously developed to support a changing market and

customer requirements. Manufacturers have implemented

automated machinery and computer driven production

lines, instead of workers, which help to improve

processing and increase the number of products, to

achieve the customer needs. The concept of continuous

improvement should be practiced in organizations to

achieve customer satisfaction with products or services.

The growing trends lead consumers to expect high quality

products that meet quality standards. Many organizations

provide strategies for continued improvement by

determining a target for increasing productivity quality

and cost savings. They are aware that development

programs can be applied to improve product designs and

processes as a known technique for gaining a competitive

advantage.

II. THEORY AND LITERATURE REVIEW

A. Lean Manufacturing

Lean manufacturing approaches have gained

considerable importance in recent years. Wide research

has reported the implementation of lean practices yields

positive impacts on manufacturing performance. Lean

manufacturing is a concept that reduces waste from

processes and aims to improve quality standards. The

goals of lean manufacturing concepts consist of three

parts: reducing cost by eliminate waste, creating

conditions to guarantee product quality, and creating a

worksite with operators in minds to maintain profit,

quality first and operators keep in mind about workplace.

First, reducing costs by eliminating waste requires

constant efforts to reduce costs to maintain continuous

profits in manufacturing. The prime way to reduce costs

is to produce only those products determined by sales in a

timely fashion, to restrain excessive manufacturing and to

eliminate all waste in manufacturing methods. There are

various ways to analyze and implement cost reduction,

from the start of design phases all the way through to

manufacturing and sales. One of the goals of lean

manufacturing is to locate waste pragmatically in each

process and then eliminate it. It is possible to uncover a

very large amount of waste by observing employees,

equipment, materials and organization in the actual

production lines from the perspectives of the process

itself and the actual work involved. Some types of waste

are obvious, but others are hidden. Waste never improves

Journal of Industrial and Intelligent Information Vol. 2, No. 1, March 2014

©2014 Engineering and Technology Publishing 1doi: 10.12720/jiii.2.1.1-5

Manuscript received May 14, 2013; revised June 22, 2013.

value; it only increases cost. The thorough elimination of

waste leads to greater employee self-respect and to major

cost reductions by preventing unneeded losses.

A functional classification of the various elements that

only raise costs in production activities are the following

seven types of waste:

Waste of defect product repair

Waste of overproduction

Waste of waiting

Waste in delivery

Waste in processing

Waste of inventory

Waste of motion

Figure 1. Seven wastes of lean manufacturing

Second, conditions to guarantee product quality have

developed various ways to support the commitment to

"build quality into processes. This principle gives each

operator the responsibility to check quality thoroughly at

every stage of work within the process, and brings

product inspection directly into the process so that good

products flow to the following process and defects are

extracted at that point. Each operator must be aware that

the following processes must never send a defective

product to downstream customers. Thirdly, companies

must create a worksite with operators in mind.

Many researchers studied the benefits of lean concepts.

Fawaz A. Abdulmalek et.al. (2007) analyzed the benefits

of lean manufacturing and value stream mapping using

simulation models that compare before and after process

improvement[1]. T.Melton made changes and measured

benefits of lean tools in their case study. One of quality

improvement is Six Sigma concept that help

manufacturing to guaranty products and processes by

identifying the cause of defects to minimize variations in

processes and business performance. It uses quality

management tools including statistical processes, process

capability and follows the DMAIC methodology[2]. Lean

manufacturing has many tools to help continuous

improvement processes such as value stream mapping,

standardizing work, pull systems, kaizen etc. Y.-H. Lian

and H. Van Landeghem (2007) developed value stream

mapping for use in simulation[3]. Richard B. Detty and

Jon C. Yingling (2000) developed simulation models

using arena software and Siman V language for lean

principles in assembly systems to assist lean

manufacturing decisions in shop floors of consumer

electronic products. They found good benefits of lean

manufacturing in simulations[4]. Chen, Joseph C. et.al

(2008) proposed value stream mapping (VSM) and

Kaizen in small manufacturing in US. The method used 5

whys to find root causes, Taguchi experimental design to

find the optimal machine parameter that reduce variation

in plasma cutting process and rabbit chasing increased

system flexibility to reduce inventory between work

stations[5]. Muzammil M. Bepari and Nilesh Vedak

(2012) created value stream mapping (VSM) to provide a

continuous flow of material Bullwhip analysis. Swim

lane diagram provides the richer information on who does

what at particular manufacturing cells. Pareto analysis

gives the results in the continuous improvements in the

quality and combines the operations[6].

The 3P theory is a part of lean manufacturing in design

approaches that emphasizes production, preparation and

process. The advantages are a cross-functional team

approach, rapid testing of ideas and the embedding of

lean manufacturing principles into processes and product

designs. It focuses on eliminating waste through product

and process designs to meet customer requirements in the

least wasteful way and minimizes equipment costs or

design processes to enable one-piece flow.

Figure 2. The advantage of 3P

The methodologies of 3P event are described below.

Step1: Define Product or Process Design

Objectives/needs; team need to understand the core

customer needs and breakdown to components part or

materials.

Step 2: Diagramming; draw diagrams from the start

from raw material processes to finished products.

Step 3: Find Alternatives; create sketches of the

product or process, including materials, machines, tools

etc. Each of the sketches is evaluated and the best is

chosen.

Step4: Build, Present, and Select Process Prototypes;

to bring selected sketches to life as prototypes and to test

and refine concepts.

Step 5: Hold Design Review; Once a concept has been

selected for additional refinement, it is presented to a

larger group (including the original product designers) for

feedback.

Step 6: Develop Project Implementation plan: the

project is selected to proceed; the team selects a project

implementation leader who helps determine the schedule,

process, resource requirements, and distribution of

responsibilities for completion.


©2014 Engineering and Technology Publishing 2

B. Analytical Hierarchy Process (AHP) and the

Technique for Order Preference by Similarity to

Ideal Solution (TOPSIS)

1) The Analytic Hierarchy Process (AHP) is a

structured technique for dealing with complex

decisions. Rather than prescribing a "correct"

decision, the AHP helps the decision makers find

the one that best suits their needs and their

understanding of the problem. It was developed by

Thomas L. Saaty in the 1970s and has been

extensively studied and refined since then. The

AHP provides a comprehensive and rational

framework for structuring a decision problem, for

representing and quantifying its elements, for

relating those elements to overall goals, and for

evaluating alternative solutions. It is used around

the world in a wide variety of decision situations in

fields such as government, business, industry,

healthcare and education. Several firms supply

computer software to assist in using the process.

The AHP calculation is as follows:

Consider n elements to be compared, ….. and

denote the relative ‘weight’ (or priority or significance) of

with respect to by and form a square matrix

A=( ) of order n with the constraints that ⁄ , for

, and , all . Such a matrix is said to be a

reciprocal matrix.

The weights are consistent if they are transitive, that is

= for all . Such a matrix might exist if

the aij is calculated from exactly measured data. Then find

a vector ω of order n such that Aω = λω . For such a

matrix, ω is said to be an eigenvector (of order n) and λ is

an eigenvalue. For a consistent matrix, λ = n .

For matrices involving human judgment, the condition

= does not hold as human judgments

inconsistent to a greater or lesser degree. In such a case

the ω vector satisfies the equation = and

≥ . The difference, if any, between and n is an

indication of the inconsistency of the judgments. If

= then the judgments have turned out to be consistent.

TABLE I. THE SAATY RATING SCALE

Intensity

of

importance

Definition Explanation

1 Equal importance

Two factors contribute equally to the objective

3 Somewhat more

important

Experience and judgment

slightly favor one over the other.

5 Much more important

Experience and judgment strongly favor one over the

other.

7 Very much more important

Experience and judgment very strongly favor one over the

other. Its importance is

demonstrated in practice.

9 Absolutely more important

The evidence favoring one over the other is of the highest

possible validity

2,4,6,8 Intermediate

values

When compromise is needed

TABLE II. INDEX OF CONSISTENCY FOR RANDOM JUDGMENTS

1 2 3 4 5 6 7 8 9 10

0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

Finally, a Consistency Index can be calculated

from . That needs to be assessed

against judgments made completely at random and Saaty

has calculated large samples of random matrices of

increasing order and the Consistency Indices of those

matrices. A true Consistency Ratio is calculated by

dividing the Consistency Index for the set of judgments by

the Index for the corresponding random matrix. Saaty

suggests that if that ratio exceeds 0.1 the set of judgments

may be too inconsistent to be reliable. In practice, of

more than 0.1 sometimes have to be accepted,

⁄ . A of 0 means that the judgments are perfectly

consistent[7].

2) The technique for order preference by similarity to

ideal solution (TOPSIS) is a widely accepted

multiple criteria method to identify solutions from

a finite set of alternatives The basic principle is that

the chosen alternative should have the shortest

distance from the ideal solution and the farthest

distance from the negative-ideal solution in a

geometrical. The main steps of the TOPSIS

algorithm are as follows[8].

Step 1: Calculate the normalized decision matrix. The

normalized value rij is calculated as:

∑ ⁄

Step 2: Calculate the weighted normalized decision

matrix. The weighted normalized value is calculated as:

for

Step 3: Determine the ideal and negative-ideal solution:

Ideal solution: =( * ,… ,

*), where

* =(max

( ) if € ; min ( ) if € ')

Negative ideal solution: = ( ,… , ), where ' =

(min ( ) if € ; max ( ) if € ')

Let be the set of benefit attributes or criteria

is the set of negative attributes or criteria

Step 4: Calculate the separation measures, using the n-

dimensional Euclidean distance. The separation of each

alternative from the ideal solution is given as:

* = [∑( )

]

Similarly, the separation from the negative ideal

solution is given as:

= [∑( ) ]



Step 5: Calculate the relative closeness to an ideal

solution. The relative closeness of the alternative with

respect to is defined as:

⁄

Step 6: Rank the preference order.

In many cases, applied methods of multi criteria

decision making to selected the best alternative for

instance in construction company applied analytical

hierarchy process for decide to select the best contractor

of project[9] for deciding on car purchase[10], used

analytic hierarchy process (AHP) and multi-objective

programming for selected the best cost driver for activity

based costing approach [11] and measured the financial

performance of firm in Taiwan’s service industry by

TOPSIS method[12]. Otherwise, healthcare could apply

analytical hierarchy processes in quality service and

developed model for quality structure by using Monte

Carlo simulations to identify the priority of leading

attributes. This method helps cost saving or profit

increases[13].

III. METHODOLOGY

This research interested in 3P concept to design product

and process. It needs to select the best alternative to create

prototypes based on lean manufacturing concepts so this

research focuses on a decision making method.

Figure 3. Research framework

The methodologies following the step as below;

Step 1: select products to design and collect customer

requirements.

Step 2: draw diagrams of process flows, starting from

raw materials through the end processes.

Step 3: evaluate criteria and create alternatives for

processes such as material, machine, tools and fixture etc.

Step 4: decision making process by using analytical

hierarchy process (AHP) and the technique for order

preference by similarity to ideal solution (TOPSIS) to

select the best alternative.

Step 5: select the best alternative to consider and create

prototype of processes and products by project teams.

Figure 4. Research methodology

This research proposed decision making frameworks by

using the analytical hierarchy process (AHP) and

techniques for order preference by similarity to ideal

solution (TOPSIS). They are examples of multi criteria

decision methods.

IV. ILLUSTRATIVE EXAMPLE

In this case, selected packaging processes were one

process a hair treatment manufacturer used to design new

product, and are demonstrated as follows.

Step 1: company sets project teams using cross

functional departments and selected one product to study

by considering customer demands.

Step 2: project teams created process flow charts of this

product and determined process alternatives for

material/components, machines, methods and tools.

TABLE III. PROCESS ALTERNATIVES

Items Process alternatives

1 2 3 4 5 6 7

Material/component * * * * * * * Machine * * * * * * * Method * * * * * * * Tools * * * * * * *

Step 3: Created structures and evaluated criteria in each

item based on lean manufacturing principles illustrated in

Toyota production systems, for instance lowest cost,

highest quality and flexibility. For example, methods

selected the best alternative for material/components by

considering about seven types of material/components for

processes.

Figure 5. Decision structure of material / component

The weight of each criterion was assessed by analytic

hierarchy processes then the result assigned to processes

helped evaluate the best alternative by the technique for

order preference by similarity to ideal solution method.

TABLE IV. WEIHGT FOR EACH CRITERION

Criteria Weight

Lowest cost 0.657

Highest quality 0.242

Flexibility 0.101

The selection method has shown the priority of process

alternatives for material/components that came from

brainstorming by project teams. It found alternative 3 was

the material type to prepare for use in a new product. The

other topics, including machine, method and tools, use the

Level 1 : Goal

Level 2 : Criteria Lowest cost Highest quality Flexibility

Level 3 : Alternative Alternative 1 Alternative 1 Alternative 1

Alternative 2 Alternative 2 Alternative 2






Selecting the best material/component



same method to select alternatives. Team project will

create prototypes of products based on this result then get

feedback from other function before implementation.

TABLE V. WEIGHTS FOR MATERIAL/COMPONANT ALTERNATIVES

Process alternative Weight

Alternative 1 0.727

Alternative 2 0.668

Alternative 3 0.860

Alternative 4 0.652

Alternative 5 0.747

Alternative 6 0.596

Alternative 7 0.140

TABLE VI. PROCESS ALTERNATIVE SELECTION

Item Alternative

selection

Material/component Alternative 3

Machine Alternative 1

Method Alternative 6

Tools Alternative 3

V. CONCLUSION

For research result will get the information for design

and process development approach including

material/component, machine, method and tools etc. It

provides a structured process to ensure that people,

processes, and technology are optimized by bringing lean

manufacturing principles in product and process design

phases and meet customer requirements. So this method

helps improve performance of new or exists products and

processes, including improvements to cost, process, time

and quality by create a superior manufacturing system and

becoming a leader in markets.

ACKNOWLEDGMENT

This research was work conducted while working at the

faculty of Industrial Technology, Valaya Alonkorn

Rajabhat University under the Royal Patronage. The

researchers would like to express their appreciation to

Valaya Alonkorn Rajabhat University under the Royal

Patronage and office of the Higher Education Commission

for financial support.

REFERENCES

[1]

F. A. A. J. R. Abdulmalek, “Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A

process sector case study,”

International Journal of Production

Economics, vol. 107, pp. 223-236, 2007.

[3]

Y. H. Lian and H. Van Landeghem, “Analysing the effects of

Lean manufacturing using a value stream mapping-based

simulation generator,”

International Journal of Production Research, vol. 45, pp. 3037-3058, 2007.

[4]

R. B. Detty and J. C. Yingling, “Quantifying benefits of

conversion to lean manufacturing with discrete event simulation: A case study,”

International Journal of Production Research, vol.

38, pp. 429-445, 2000.

[5]

J. C. Chen, Y. Li, and B. D. Shady, “From value stream mapping toward a lean/sigma continuous improvement process: An

industrial case study,” International Journal of Production

Research, vol. 48, pp. 1069-1086, 2008.

[7]

G. Coyle, Practical Strategy Open Access Material. AHP: Pearson

Education, 2004.

[8]

S. Percin, “Evaluation of third-party logistics (3PL) providers by using a two-phase AHP and TOPSIS methodology,”

Benmarking,

vol. 16, pp. 588-604, 2009.

[9]

K. M. A.-S. Al-Harbi, “Application of the AHP in project management,” International Journal of Project Management, vol.

19, pp. 19-27, 2001.

[10]

D.-H. Byun, “The AHP approach for selecting an automobile purchase model,”

Information & Management, vol. 38, pp. 289-

297, 2001.

[12]

L. Y. C. A. P.-C. Huang, “The strategic selecting criteria and

performance by using the multiple criteria method,” iiisci.org.

Prapawan Pangsri work as Lecturer at Faculty of

Industrial Technology, Valaya Alonkorn Rajabhat

University under the Royal Patronage, Thailand .Graduated Master degree of Science

(Industrial Management) from King Mongkut’s

Institute of Technology Ladkrabang and Bachelor degree of Science (Production Technology) from

Khon Kean University, Thailand.



[2] T. Melton, “The benefits of Lean manufacturing: What Lean thinking has to offer the process industries,” Chemical

Engineering Research and Design, vol. 83, pp. 662-673, 2005.

[6] M. M. B. A. N. Vedak, “The lean ahead – for continuous improvements,” International Conference on Technology and

Business Management, 2012.

[11] M. J. S. A. T. Garvin, “Using the analytic hierarchy orocess and

multi objective programming for selection of cost drivers in activity based costing,” Operation Research, vol. 100, pp. 72-80,

1999.

[13] T.-H. Hsu and F. F. C. Pan, “Application of monte carlo AHP in

ranking dental quality attributes,” Expert Systems with Applications, vol. 36, pp. 2310-2316, 2009.

A Decision Framework to Select Alternative based on Lean ...B. Analytical Hierarchy Process (AHP)...

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