An Assessment Model Study for Lean and Agile (Leagile ... · An Assessment Model Study for Lean and Agile (Leagile) Index by Using Fuzzy AHP Lutfi Apiliogullari Fenerbahce University,

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Ankara,11-12Sep,2018 Ankara Yıldırım Beyazıt University

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AnAssessmentModelStudyforLeanandAgile(Leagile)IndexbyUsingFuzzyAHP

LutfiApiliogullariFenerbahce 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

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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





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.


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(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:





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)


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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.





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


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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.





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)


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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

LeanIndex

VL L M H VH

AgileIndex

Before After

Before After





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. References

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An Assessment Model Study for Lean and Agile (Leagile ... · An Assessment Model Study for Lean and Agile (Leagile) Index by Using Fuzzy AHP Lutfi Apiliogullari Fenerbahce University,