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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.
Journal of Industrial and Intelligent Information Vol. 2, No. 1, March 2014
©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:
= [∑( ) ]
Journal of Industrial and Intelligent Information Vol. 2, No. 1, March 2014
©2014 Engineering and Technology Publishing 3
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
Alternative 3 Alternative 3 Alternative 3
Alternative 4 Alternative 4 Alternative 4
Alternative 5 Alternative 5 Alternative 5
Alternative 6 Alternative 6 Alternative 6
Alternative 7 Alternative 7 Alternative 7
Selecting the best material/component
Journal of Industrial and Intelligent Information Vol. 2, No. 1, March 2014
©2014 Engineering and Technology Publishing 4
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
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[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]
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industrial case study,” International Journal of Production
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[7]
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[10]
D.-H. Byun, “The AHP approach for selecting an automobile purchase model,”
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[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.
Journal of Industrial and Intelligent Information Vol. 2, No. 1, March 2014
©2014 Engineering and Technology Publishing 5
[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.