An Assessment Model Study for Lean and Agile (Leagile) Index by Using Fuzzy AHP

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Email of Corresponding Author: lutfi.apiliogullari@leanofis.com
Available on SSRN-Elsevier
h3p://ssrn.com/link/12th-NCM-2018.html
12th NCMCONFERENCES
Ankara, 11-12 Sep, 2018
Ankara Yıldırım Beyazıt University
www.NCMConferences.com
An Assessment Model Study for Lean and Agile (Leagile) Index by
Using Fuzzy AHP
Lutfi Apiliogullari
Fenerbahce University, Industrial and System Engineering
Abstract
Lean and agile supply chains are the most important and valid supply chain strategies that are
accepted in today’s World. They can be used separately or together in business processes. However,
some of these companies cannot use lean and agile manufacturing characteristics as it should be
due to lack of knowledge or mis-directioning. As a result of this they are not able to come to
competitive levels. This study was done for the aim of supporting companies to analyze their current
situation, understand the missing and set a course for improvements in the direction of lean and
agile criteria. Especially for small-scale manufacturing industry, it is thought to contribute with
regards to understand very well what the lean / agile criteria’s are, notice where missing’s are in
processes, take an action against them and be competitive by keeping dynamic the cycle of
improvement and rendering this process continuously in order to be more efficient and competitive.
Keyword: Leagile Index, Lean and Agile Index
Introduction
Market and customer expectations are varying with and incredible speed due to technology,
globalization and socio-cultural reasons. With each passing day, customers are demanding
more personalized products [1] and as a result of this product variety increase and product
lifetime is reduced [2]. The price factor cannot be sufficient alone in preference process [25]
ability of speed and flexibility in supply chain is vitally important for a company [4]. All
these factors have pushed the supply chain strategies from static situation to being dynamic.
Lots of academician and practitioners did researches on lean and agile production strategies
and these opinions were dominated that; one single model in supply chain strategies cannot
respond to all expectation [26].
Lean and agile supply chain strategies are the most important and valid supply chain
strategies, which can be used either separately or together in business processes [5]. Many
companies have been adapting supply chain processes to lean and agile strategies. However,
some of these companies cannot use lean and agile manufacturing characteristics as it should
Electronic copy available at: https://ssrn.com/abstract=3300608

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be due to lack of knowledge or mis-directioning. As a result of this they are not able to come
to competitive levels. In order to be competitive it is extremely essential to have efficient
operational processes. In order to reach this aim, lean and agile supply chain strategies should
be understood very well and applied properly[4].
Literature Review
The main purpose of Lean is reducing lead-time and to improve cash flow in a positive way
by eliminating activities which create no value (losses and variables) in customer value chain
with improvement activities [28]. Therefore, lean manufacturing philosophy [29] that
indigenizing value, value chain, flow, pull and perfection approach is considered as a quite
effective competitive tool especially in cases that demand is predictable, order amount is high
and product variety is not too much in market [6]. However, in counter discourse situations as
unpredictable demand, very high variety and high diversity, lean manufacturing philosophy
have been criticized that is unable to respond all the needs by many researchers such as
Gunasekeran (1999) and Christopher (2002).
The most important factor of agility is change [4] and its most fundamental characteristic is
flexibility. Agility can be defined as “an ability of quick and efficient responding for products
designed by customers in a competitive environment where uncertain and unpredictable
changes occurred continuously”[6]. There are any other definitions that support this
definition in literature. While Christopher (2000), described the agility as “an ability of
organizations to adapt changes in the mean of volume and variability on the market
conditions which are changing and demands are unpredictable “by emphasizing flexibility
factor, Brown and Bessand (2003) defined as an ability to quick and effective responding
against unexpected variability.
Agility should not be compared with being lean. Many researchers like Jamee-Moore (1997),
Yusuf (2002), Christopher (2000) indicate that lean manufacturing philosophy is not effective
in the face of uncertainty and unpredictable changes and the main reason of this is a
requirement that there is no reserve at the mean of stock or capacity in in lean manufacturing
principle against changes. While lean manufacturing is a system, which is fighting against
losses, keeping stock or putting reserve capacity aside which is seen as defense mechanism
against variability is seen as a paradox for lean manufacturing systems [2].
It is generally come to a phenomenon in literature about lean and agile manufacturing; while
lean is comprehended as” supplying the needs terrifically in a needed time”, agility is defined
as “being first, being fast and being the best”[8]. In other words, while lean focuses on
eliminating losses, producing and consigning standard and stable products with minimum
costs, agile manufacturing focuses on delivery time beyond costs with a structure that
respond quickly to innovative products in unpredictable market [7,9,11,16].
Many application is made in the sense of lean and agile supply chain strategies but there is
not an evaluation or assessment system in their hands that clinch the argument like which
strategy is proper for which situations, how current situation analyses is done, how to
determine where they reach as a result of improvements, etc. Although there are relatively

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more studies about leanness in literature, little studies exist about lean and agile production
together (Leagile).
Table 1
Literature studies on Lean & Agile Index
Author Subject Method Content
Lin Agility index in the supply
chain
Fuzzy Process integration, collaboration,
information sharing, market sensitivity
Yang ve Li Multi-grade fuzzy assessments Ability evaluation of mass
customization product
manufacturing
Enterprise organization management agility,
product design agility, manufacturing
agility
Raj Leagile Evaluation of
Manufacturing Organization
Fuzzy AHP Production rate, cycle time, cost
Narasimhan Evaluation of performance
metrics of Leagile supply chain
Fuzzy AHP Organizational, operasyonel, customer
services, flexibility
Doolen and
Hacker
A Review of Lean Assessment
in Organizations
Exploratory Study Shop floor, manufacturing equipment,
supplier relationships, customer
relationship, workforce
Torng Lin Agility evaluation using fuzzy
logic
Fuzzy agility evaluation approach Integration, competence, team building,
technology, quality, market, partnership
Azadeh Leanness assessment and
optimization
Fuzzy cognitive map Management responsibility, manufacturing
management, technology, strategy
Miller and
Carpinetti
Supply chain performance
management
Fuzzy SCOR Model
Methods
This study consists of six phases. In the first phase, lean and agile manufacturing criterias are
selected from previous studies and experts opinions. Dematel method is used for
determination and prioritization purposes of relationship between the criteria in in this study.
Extended Fuzzy AHP model, suggested by Chang, was used for defining the weights of the
criteria’s of model in phase three. The model was applied to a company in phase four and
current lean / agile index of the company was determined. In order to test the reliability of
the model, lean and agile principles were performed systematically in phase five and lean /
agile index of company was calculated again; both results were compared in phase six.
Dematel Method
Dematel method (The decision Making Trial and Evaluation
Laboratory) has been developed with the aim of using in
jumbled and complex problem solving by Science and human
relations programme in Geneva Battelle Institute. It is used
within the scope of defining relation levels and weights of
criteria’s.
Step_1: Relations between criteria’s are determined
according to a defined scale by an expert group. Direct
relation matrix, Z, is calculated by averaging of assessment
obtained by expert opinion. Step_2: Normalized relation
matrix is obtained from Eq.2 depending on Direct Relation
Matrix Z. Step_3: After normalized relation matrix is
obtained, total relation matrix is calculated using Eq.7.
Step_4: Sum of columns of total relation matrix (R) sum of
the lines (C); sender (R-C) and receiver (R+C) calculations
Figure 1. Dematel Method

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are done. Step_5: Weight factor for each criteria is calculated using Eq. 9 from R and C
parameters. After this calculation, gross weight is calculated using Eq.10.
Fuzzy AHP Method
In this study, extended fuzzy AHP algorithm introduced by Chang is used. The most
important reason of selecting this method is being more practical than other fuzzy AHP
methods and it is very similar to classical AHP. Disadvantage of this method is that it uses
triangular fuzzy numbers. When literature studies are examined, Chang’s dimensional
analysis approach is the most preferred and accepted fuzzy AHP method.
is the set of criteria, m dimension analysis value is obtained by
expression for each criteria. Here, values show parameters, l, m and u
shows triangular fuzzy numbers.
Chang’s extended fuzzy AHP algorithm is like as,
Step 1: fuzzy artificial magnitude values is defined with using equation below (for ith
criteria)
(11)
Value is calculated as below with fuzzy sum of number of j=1,2...m order analysis
value.
(12)
In order to get value, fuzzy sum is done on , (j=1,2,...,m) values.
(13)
After this step, the inverse of the vector is calculated.
(14)
Step 2: Probability of occurrence M2 = (l2, m2, u2) ≥ M1 = (l1, m1, u1) is determined by the
function below.
M2 = (l2, m2, u2) and M1 = (l1, m1, u1) including triangular (convex) numbers;
Membership function of triangular (l, m, u) number is specified as below.

22 NCMCONFERENCES
(15)
M1 = (l1, m1, u1) and M2 =(l2, m2 , u2) are ordinate of triangular fuzzy numbers. In the
other words, it is the value of membership function. In order to compare M1 and M2, both
values should be calculated.
Figure 2. Fuzzy triangular numbers
Step 3: being bigger of a convex fuzzy number probability degree than convex number
can be defined as below (i=1,2...k).
(16)
(17)
For k=1,2…n, and k ≠ j If it is taken as , weight vector is;
(18)
Here, Ai is consisting of n element. (i 1,2,...,n) while weight vector is normalized, W vector
that is not a fuzzy
(19)
Experimental Study /Factor elimination with Dematel
Dematel method was used to define criteria used in model. It is asked twelve experts in
ceramic, automotive and textile sector to make scoring according to relation levels of
criteria’s. While defining relation levels; 0: no effect, 1: less impact 2: medium effect 3:

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strong impact 4: much strong impact scale was used. Direct relation matrix, Z, is obtained by
averaging of assessment obtained by expert opinion.
After this step, D: direct relation matrix and T: total relation matrix is obtained, R and C
values are calculated. Local (W) and gross weight (GW) of criteria’s is calculated with the
help of Eq.9 and Eq.10 and GW<0.35 was not included in the model. The structure of the
model was identified as factor and sub-factor by grouping criteria’s creating model.
Model / Factor - Sub Factors Weight Calculation with Fuzzy AHP
Fuzzy AHP method was used to define the weights of factors and sub-factors used in the
model. The weights of factors and sub-factors are defined by binary comparison with
themselves first factors than sub-factors. For comparisons, one-to-one interviews were
conducted with eleven middle and senior managers. Last matrixes are obtained as a result of
studies which group of five experts work on with the result repeating the most.
Different scales were found for different applications in literature. In this study, scale shown
in below which has been defined by Chang was used.
Table 2
Linguistic scale and fuzzy scale
Leagile Index / Lean Factor Weight Calculation with Fuzzy AHP
Only Lean main factor’s calculations are not shown due to space limit.
Table 3
Lean Main Factors: W (0.08, 0.36, 0.31, 0.16, 0.08)
Y1 Y2 Y3 Y4 Y5
Y1 1,00 1,00 1,00 0,40 0,50 0,67 0,40 0,50 0,67 0,67 1,00 1,50 1,00 1,00 1,00
Y2 1,50 2,00 2,50 1,00 1,00 1,00 0,67 1,00 1,50 1,50 2,00 2,50 1,50 2,00 2,50
Y3 1,50 2,00 2,50 0,67 1,00 1,50 1,00 1,00 1,00 0,67 1,00 1,50 1,50 2,00 2,50
Y4 0,67 1,00 1,50 0,40 0,50 0,67 0,67 1,00 1,50 1,00 1,00 1,00 0,67 1,00 1,50
Y5 1,00 1,00 1,00 0,40 0,50 0,67 0,40 0,50 0,67 0,67 1,00 1,49 1,00 1,00 1,00
The gross weight scale of the factors and sub-factor are obtained after fuzzy-AHP
methodology. Gross weight was calculated factor weight multiply by sub-factor weight.
Linguistic scale Triangular fuzzy scale Triangular fuzzy reciprocal scale
Just equal (1, 1, 1) (1, 1, 1)
Moderately important (2/3, 1, 3/2) (2/3, 1, 3/2)
Strongly important (3/2, 2, 5/2) (2/5, 1/2, 2/3)
Very strongly important (5/2, 3, 7/2) (2/7, 1/3, 2/5)
Extremely important (7/2, 4, 9/2) (2/9, 1/4, 2/7)

24 NCMCONFERENCES
Table 4
Lean Factors / sub-factors weights
Lean Factor Weight N Sub Factors Weight Gross Weight
Y1: Work Place Organization 0,08 Y1.1 Standard Work 0,450 0,038
Y1.2 5S 0,343 0,029
Y1.3 Visual Management 0,207 0,017
Y2: JIT 0,36 Y2.1 Flow 0,302 0,108
Y2.2 Pull 0,302 0,108
Y2.3 Heijunka 0,244 0,088
Y2.4 SMED 0,153 0,055
Y3:Total Quality Control 0,31 Y3.1 Quality at the Source 0,210 0,065
Y3.2 Jidoka 0,267 0,083
Y3.3 Poka Yoke 0,194 0,060
Y3.4 Production Quality 0,194 0,060
Y3.5 Supplier Development 0,135 0,042
Y4: Total Productive Maintenance 0,16 Y4.1 Autonom. Maintenance 0,316 0,051
Y4.2 Planned Maintenance 0,384 0,062
Y4.3 One Point Lesson 0,185 0,030
Y4.4 Quick Response 0,114 0,019
Y5: Continious Improvement 0,08 Y5.1 Cont. Improvement 0,302 0,025
Y5.2 Mngm. by Objectives 0,153 0,013
Y5.3 Problem Solving Skills 0,302 0,025
Y5.4 Multi Skilled Operators 0,244 0,020
Table 5
Agile Factors / sub-factors weights
Agile Factors Weight N Sub Factors Weight Gross Weight
C1: Market Sensitivity 0,30 C1.1 Demand Driven Manf. 0,278 0,082
C1.2 Product and process opt 0,074 0,022
C1.3 PLM 0,052 0,015
C1.4 Innovation 0,208 0,062
C1.5 Modular design 0,242 0,071
C1.6 ECR / CPFR 0,146 0,043
C2: Strategic Supplier Relationship 0,26 C2.1 Supplier searching and evaul 0,230 0,061
C2.2 Supplier Development 0,187 0,049
C2.3 VMI 0,134 0,035
C2.4 Process Int./ Suppliers 0,270 0,071
C2.5 Supplier Flexibility 0,064 0,017
C2.6 Effective com. with supplier. 0,116 0,031
C3: New Product Introduction 0,14 C3.1 Concurrent Engineering 0,316 0,046
C3.2 Rapid Prototyping 0,200 0,029
C3.3 Outsourcing 0,248 0,036
C3.4 Skilled Personnel 0,237 0,034
C4: Manufacturing Flexibility 0,30 C4.1 Process reliability 0,135 0,040
C4.2 Production Strategies 0,267 0,079
C4.3 Rapid Decision Making 0,194 0,057
C4.4 Flexible Process 0,194 0,057
C4.5 Invest in People 0,210 0,062
Apply Model to Firm / Leagile Index
Six-answer scale was used during the implementation of the model to company. A competent
team that was created within the company evaluated the criteria’s. Criteria; in the absence
(NA:0), very low (VL:0,2), low (L:0,4), medium (M:0,6), high (H:0,8) and very high (VH:1)
scale was used. Given answers were multiplied by criteria GW to find criteria index and lean
/ agility index were calculated separately with total sum.

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Implementation of the Lean and Agile principle to firm
QWE Company that study was carried operates in plastic injection industry. QWE records an
average annual turnover 65 million $ by producing for totally seventeen customers. LKSV is
QWE’s customer which QWE make the most turnover with 47 different SKU and annually
10 million $ turnover.
One of the customers, LSVS’s product family that consist of totally 47 products was
identified as pilot study area in order to improve the effectiveness of study and adopt the
principles of lean supply chain intensely to processes. It is utilized from continuous
improvement cycle methodology consists of totally five steps for observation, evaluation and
analysis process.
1) Current state mapping: Value stream mapping (VSM) method is used for current state
analysis of entire process of selected product family from suppliers to product
shipment. VSM is a process that everybody understands by depicting with using
standard symbols of all stream (process- materials and knowledge) beginning from
suppliers to product shipment for selected product family.
2) Future state mapping: Value stream mapping (VSM) method is used for future state
analysis of entire process of selected product family from suppliers to product
shipment.
3) Improvement road map: It is a preparation process of necessary improvement action
plans that should be needed to come from current situation to future situation.
4) Implementation: It is a process that carrying out kaizen which specified in
improvement road map one-to-one in the field.
5) Check result: It is a checking whether desired target is achieved or not as a result of
kaizen application, planning a new kaizen application if results are far from the target
and applying process.
Current state value stream mapping for LKSV was mapped within the scope of continuous
improvement methodology and the following findings are obtained as a result of current state
analysis.
• Annual average revenue is about 10 million $ and it is obtained from 47 different
products.
• Some of the products deliver regularly, some deliver one or two times a year.
• Planning of production is done intuitively in the light of forecast information which
accuracy is quite low and get from the customers.
• Production processes are managed with make to stock strategy for all products and
on-time delivery rate is about 80%.
• The cost of end product in stocks is totally 2,500,000 $ (120 days). The 50% of this
figure waits in stocks less than 30 days, 30% is 30-360 days and 20% of this figure

26 NCMCONFERENCES
waits more than 360 days. Products that not want by costumers anymore wait as
obsolete product in end product stocks and its costs is about 52,000 $.
• There are approximately 650,000$ cost of semi-finished product (31 days) in WIP
stocks, 2,100,000$ cost of raw material (126 days) in RM stocks and 1,100,000 $ cost
of operating materials inventory. Expiration dates for 10% of raw material stocks and
5% of operating materials have passed.
• Critical equipment OEE levels are about 60%, the level of internal processing fire rate
is approximately 7%.
• Production equipment’s constantly work, if there is capacity, production is carried out
more than order. Under normal conditions, 70% of capacity is sufficient to meet the
expectations of customers.
As a fist step the company organization structured was converted from silo type to value
stream management model in order to segment customer’s base on their needs. By this, each
value stream segment established direct link to customers. By using their dedicated team
members (Market sensitivity). This also improved the organization communication and fast
decision-making ability such as taking quick decision, effective communication. New product
ideas were started to plan by identified team with providing consensus, product life cycle
started to analyse and improvements were done in product and promotion optimization
(especially packaging issues).
In second step some analytical forecasting, data capturing tools are started to use in order to
analyse real demand. The company become a position to analyse the real time sales data over
the link between company and customers. Planning phase was conducted wit the attendance
of key customers and detailed feasibility analysis process was started in order to introduce
right product for the market.
The products were separated into groups according to ABC analysis in order to define which
production modes will be used, the ABC analysis was conducted for the products. Make to
stock mode for A, postponement (configure to order) mode for B and make to order mode for
C products were implemented. Building end-to-end pull system for group A group products
were not produced till finished product stock levels of these group products is minimum
level(avoid over production). This helped the company improve the flexibility and speed. To
make this system sustain, strategic supplies relationship process were developed by
implementing supplier quality team, reducing the number of suppliers, implanting vendor
managed inventory concept for some stock items and inviting the suppliers product
development phase. Some components and materials production were outsourced in order to
increase the concentration of the company staff. New technology such as fast prototyping,
flexible automation systems and simulation software were started to use together with
concurrent engineering techniques in new product introduction phase.

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Figure 3. Value Stream (before and after)
Standard work instructions for selected product family in every area, 5S applications and
developing visual management standards were conducted in order to establish base
requirement of lean manufacturing. JIT system was implemented. Process Island was
removed. Line-balancing studies were conducted in order to provide one-piece flow in
production processes. Process was managed with supermarket and kanban system where flow
cannot be achieved. Production was planned in small lot pieces and frequently returns of
product (sequencing and level loading) system were implemented. SMED activities
conducted some key equipment to reduce the set-up time, increase flexibility. TPM (Total
Productive Maintenance) process was started with application of autonomous maintenance
and planned maintenance. In order to attack the problems quick response teams were
establishing. Total quality control systems such as Jidoka, process control and poka yoke
application were implemented. An academy was established for technical and private
development of production personnel.
Re-Calculation of the Firm Leagile Index with the Proposed Model
The Leagile Index of the firm was (0.265, 0.439). After implementation new index
was calculated as (0.562, 0.589)

28 NCMCONFERENCES
Figure 4. Firm Leagile Index (before and after)
Results and Discussions
In this study, an assessment model was developed in order to determine what extent the
companies are adapt the lean and agile manufacturing process strategies to their operational
processes. The reliability of the model was tested with an actual application and it was proven
that the model give accurate results.
The model touches many main topics that can affect the performance of the organizations
because it contains both lean and agile parameters. By this means, it is possible that
companies are able to evaluate their processes extensively, distinguish weak points of the
operational processes, make improvement plans with this model and identify which areas and
what extent they develop as a result of actions they take.
Model can be adapted to many companies especially mass-production oriented in
manufacturing sector. Using the model by other companies will be enabling to compare their
performances with other companies. However, it should be used by competent person in
order to produce accurate result with the model. In the case of having people who are not
proficient in lean and agile strategies within the enterprise, it is much more proper that
adaptation of the model is done with an expert counselor or an institution.
It is not possible that this model can respond totally accurate to all strategy in the case of
applying different production strategies according to their product or market structure of all
companies. For example, the production dynamics of machine production industry and
continuous glass manufacturing industry are not the same. In general, the model is based on
mass production enterprises. Therefore, building assessment models that are on the basis of
industry or production strategies will be useful for further studies.
The model imports data with intuitive decision making in general terms. An assessment
model based on KPI (Key Performance Indıcators) may be built in order to identify lean and
agility index in a similar concept. But for this, sector-specified and benchmark KPI’s
1.0
0.2 0.4 0.6 0.8 1.0
0.2
0.4
0.6
0.8
(0.265, 0.439)
(0.562, 0.589)
Lean
Agile
VL L M H VH
Lean Index
VL L M H VH
Agile Index
Before After
Before After

29
identification is required. In this way, companies examine Leagile index results in a different
dimension by entering the numeric values of current performance over standard KPIs.
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An Assessment Model Study for Lean and Agile (Leagile) Index by Using Fuzzy AHP