Analyzing Agility of an Indian Manufacturing Enterprise (A Case … · (AC26) FLEXIBLE WORK FORCE (AC31) MULTI – LINGUAL (AC32) EMPOWERED WORKERS (AC33) TOP MANAGEMENT SUPPORT (AC34)

International Journal of Theoretical and Applied Mechanics.

ISSN 0973-6085 Volume 12, Number 2 (2017) pp. 287-302

© Research India Publications

http://www.ripublication.com

Analyzing Agility of an Indian Manufacturing

Enterprise (A Case Study)

Neeraj Grover1, Virender Chahal2, Narender Kumar3, Mohit4 and Pardeep5 1. Assistant professor, Department of Mechanical Engineering ,N.C.C.E,Israna,

Panipat, Haryana, India 2. Research Scholar Department of Mechanical Engineering, Deenbandhu Chhotu

Ram University of Science and Technology, Sonipat, Haryana, India 3. Assistant professor, Department of Mechanical Engineering ,N.C.C.E,Israna,

Panipat, Haryana, India 4. Assistant professor, Department of Mechanical Engineering ,N.C.CE,Israna,

Panipat, Haryana, India 5. Assistant professor, Department of Mechanical Engineering,BPIT,Delhi, India

Abstract

The aim of this paper is to study Agile Manufacturing system and its

attributes. On the basis of these attributes evaluate the current Agility status

using fuzzy Logic of selected industry. On the basis of Agility, the level of

an industry can be predicted. Also in this work, the obstacles which are

restricting any industry to achieve higher level of agility can be calculated.

By removing/improving these obstacles, agility of an industry can be

improved. As evaluation technique, Fuzzy logic is being used. Agility will

be measured in terms of Fuzzy Agility Index (FAI).

Keywords: Agile Manufacturing, Agility, Fuzzy Logic, Fuzzy Agility Index.

INTRODUCTION

Agile manufacturing is an approach to manufacturing which is combination of Lean

manufacturing and flexible manufacturing system. This overall approach is

concentrated on meeting the needs to industry. It is a knowledge which is adds all the

techniques. They are basically:, TPM, TQM, Taguchi, JIT CIM, Agile Lean and

flexible,. This move toward is grow up towards industries during working in a highly

spirited surroundings, where little variations in routine and product delivery very large

difference in future a Industry continued existence and standing among customers.

mailto:[email protected]

288 Neeraj Grover, Virender Chahal, Narender Kumar, Mohit and Pardeep

LITERATURE REVIEW

Due to rapid globalization and faster growth of world market, there is always a need

of modification in the present system; these changes increase the efficiency and move

ahead in competitive market. These changes also advance the producer and relation

with customer. Frayret et. al. [2001] presents a strategic framework for designing

and operating agile networked manufacturing systems. This framework allows

collaboratively planning, controlling and managing day-to-day operations and

contingencies in a dynamic environment. Zhang et. al. [2000]. According to past

approach, manufacturing achievement and continued existence are flattering more and

harder to ensure. This fact is rooted in the appearance of a new commerce era as one

of its major individuality. Yusuf et. al. [1999] have identified the drivers of agility

and discussed the portfolio of competitive advantages that have emerged overtime as

a result of the changing requirements of manufacturing processes. The main driving

force of agility is change.

RESEARCH OBJECTIVE

After going through the literature review following objectives have been identified:

1. Evaluation of Agility Evaluate agility of that selected industry with the help of FAI (Fuzzy agility Index).

This evaluation is done with use of fuzzy Logic.

2. Recommendation After knowing the agility, the recommendation of result will take place. If industry

wants to improve, it should remove/improve obstacles.

METHODOLOGY

The methodology consists of the following steps. The basic steps of research

methodology shown in the block diagram.

Fig. 1 Block diagram of research methodology

Analyzing Agility of an Indian Manufacturing Enterprise (A Case Study) 289

ANALYSIS

On the basis of our methodology, analyze as follows:

i. Firstly Hierarchy structure is drawn.

Fig.2 Hierarchy structure of Agile Manufacturing System (Level 0, 1 & 2)

AGILE MANUFACTURING SYSTEM

STRATEGIES (AC1) SYSTEM(A

C2)

PEOPLE(AC3) TECHNOLOGIES(AC4)

CONCURRENT

ENGINEERING(AC11)

PHYSICAL LY

DISTRIBUTED

MANUFACTURING

SYSTEM (AC12)

MRP

(AC21)

ABC/ABM

(AC22)

CAD/CAE

(AC23)

ERP (AC24)

CIM (AC25)

KANBAN

(AC26)

FLEXIBLE WORK FORCE

(AC31)

MULTI – LINGUAL (AC32)

EMPOWERED WORKERS

(AC33)

TOP MANAGEMENT

SUPPORT (AC34)

FLEXIBLE PART

FEEDER (AC41)

FLEXIBLE

FIXTURINNG (AC42)

MULTI- MEDIA

(AC43)

INFORMATION

TECHNOLOGY (AC44)

FLEXIBLE

MANUFACTURING

SYSTEM (AC45)


ii. Select an industry. Select an industry XYZ.

iii. Formulate a questionnaire to implement fuzzy Logic.

On the basis of above drawn hierarchy structure, we can formulate our

Questionnaire, which is filled by experts of different departments of our

selected industry.

iv. Determine the appropriate linguistic scale to assess the performance ratings and importance weights of the agility capabilities.

Table 1: Linguistic Variables for Performance Rating, Importance

Weighting and FAI

PERFORMANCE RATING

(R )

IMPORTANCE

WEIGHTING ( W )

FUZZY AGILITY

INDEX (FAI)

NOT AT ALL (NL) NIL(NL) SLOWLY (S)

SMALL (SM) LOW (L) FAIRLY(F)

SOME (S) MEDIUM (M) AGILE (A)

LARGE (L) HIGH (H) VERY AGILE (VA)

VERY LARGE (VL) VERY HIGH (VH) EXTREMELY AGILE

(EA)

v. Measure the performance and importance of agility capabilities using

linguistic terms. Table 2: Measurement of Performance Rating & Importance Weighting in terms of

Linguistic Variables

ACi ACij ACijk Rijk Wijk Wij Wi

AC1 AC11 AC111 L VH H VH

AC112 L VH

AC113 SM M

AC12 AC121 L VH M

AC122 S H

AC123 VL VH

AC124 SM H

AC125 L H

AC2 AC21 AC211 VL VH VH H


AC212 SM H

AC213 S H

AC214 L H

AC23 AC231 NL NL H

AC232 VL H

AC233 L H

AC24 AC241 L H VH

AC242 VL VH

AC25 AC251 VL VH H

AC253 NL NL

AC254 NL NL

AC255 NL NL

AC256 NL NL

AC26 AC261 S M M

AC262 L M

AC263 L M

AC3 AC31 AC311 VL H H VH

AC312 S H

AC313 VL VH

AC314 NL VH

AC315 VL VH

AC316 L H

AC317 L M

AC318 VL H

AC319 L VH

AC31-10 S M

AC31-11 L VH

AC32 AC322 S M L

AC323 NL NL

AC33 AC331 L H H


AC332 L VH

AC333 VL VH

AC334 L M

AC335 L H

AC336 VL H

AC337 L H

AC338 VL VH

AC339 L H

AC34 AC341 VL VH VH

AC342 VL VH

AC343 VL VH

AC344 VL VH

AC345 L VH

AC346 VL VH

AC347 S H

AC348 L H

AC349 L VH

AC34-10 L VH

AC34-11 L VH

AC34-12 L VH

AC34-13 VL VH

AC34-14 L H

AC34-15 L H

AC34-16 S M

AC34-17 S M

AC34-18 L VH

AC34-19 S M

AC34-20 VL H

AC34-21 L H

AC34-22 S H


AC34-23 VL VH

AC34-24 S H

AC4 AC41 AC411 NL NL VH VH

AC412 S M

AC413 S H

AC414 L H

AC42 AC421 NL NL VH

AC422 S M

AC423 S H

AC424 L VH

AC43 AC439 L H M

AC43-10 NL NL

AC43-11 L H

AC43-12 NL NL

AC43-13 L H

AC43-14 SM L

AC44 AC441 VL VH M

AC442 L VH

AC443 L VH

AC444 L VH

AC445 L VH

AC446 VL H

AC447 L VH

AC45 AC451 L VH VH

AC452 SM M

AC453 SM VH

AC454 S VH

AC455 SM H


vi. Approximate the linguistic terms by fuzzy numbers. This is the step where we convert linguistic terms into some numeric values for

calculation. This can be done in many ways but we will use here only Fuzzy

Triangular Values. This conversion into numeric value is to be done on the basis of

some scale and it is to be done very carefully because whole result will depend on

these scales.

Table 3: Fuzzy Triangular Values of Linguistic Variables for Performance Rating

PERFORMANCE RATING ( R )

LINGUISTIC VARIABLES FUZZY TRIANGULAR VALUES

NOT AT ALL (NL) 0 0 3

SMALL (SM) 0 3 5

SOME (S) 2 5 8

LARGE (L) 5 7 10

VERY LARGE (VL) 7 10 10

Table 4: Fuzzy Triangular Values of Linguistic Variables for Importance Weighting

IMPORTANCE WEIGHTING ( W )


NIL(NL) 0 0 0.3

LOW (L) 0 0.3 0.5

MEDIUM (M) 0.2 0.5 0.8

HIGH (H) 0.5 0.7 1

VERY HIGH (VH) 0.7 1 1

Table 5: Fuzzy Triangular Values of Linguistic Variables for FAI

FAI (FUZZY AGILITY INDEX)


SLOWLY (S) 0 1.5 3

FAIRLY(F) 1.5 3 4.5

AGILE (A) 3.5 5 6.5

VERY AGILE (VA) 5.5 7 8.5

EXTREMELY AGILE (EA) 7 8.5 10


vii. Aggregate fuzzy ratings with fuzzy weights to obtain the FAI (Fuzzy Agility Index) of an enterprise.

FAI represents overall enterprise agility. Enterprise agility increases with increasing

FAI. Thus, the membership functions of FAI for level of agility. Here fuzzy weighted

average meaning, the fuzzy index of the agility 2-grade-capability ACij can measure

as

n n

ACij = ∑ (Wijk * ACijk) / ∑ Wijk ----------- (1)

k=1 k=1

Where ACijk and Wijk; respectively, represent the fuzzy performance rating and

fuzzy importance weight of the agility element capability.

Table 6: Linguistic terms approximated by Fuzzy numbers

ACi ACij ACijk Rijk Wijk Wij Wi

AC1 AC11 AC111 (5,7,10) (0.7,1,1) (0.5,0.7,1) (0.7,1,1)

AC112 (5,7,10) (0.7,1,1)

AC113 (0,3,5) (0.2,0.5,0.8)

AC12 AC121 (5,7,10) (0.7,1,1) (0.2,0.5,0.8)

AC122 (2,5,8) (0.5,0.7,1)

AC123 (7,10,10) (0.7,1,1)

AC124 (0,3,5) (0.5,0.7,1)

AC125 (5,7,10) (0.5,0.7,1)

AC2 AC21 AC211 (7,10,10) (0.7,1,1) (0.7,1,1) (0.5,0.7,1)

AC212 (0,3,5) (0.5,0.7,1)

AC213 (2,5,8) (0.5,0.7,1)

AC214 (5,7,10) (0.5,0.7,1)

AC23 AC231 (0,0,3) (0,0,0.3) (0.5,0.7,1)

AC232 (7,10,10) (0.5,0.7,1)

AC233 (5,7,10) (0.5,0.7,1)

AC24 AC241 (5,7,10) (0.5,0.7,1) (0.7,1,1)

AC242 (7,10,10) (0.7,1,1)

AC25 AC251 (7,10,10) (0.7,1,1) (0.5,0.7,1)


AC253 (0,0,3) (0,0,0.3)

AC254 (0,0,3) (0,0,0.3)

AC255 (0,0,3) (0,0,0.3)

AC256 (0,0,3) (0,0,0.3)

AC26 AC261 (2,5,8) (0.2,0.5,0.8) (0.2,0.5,0.8)

AC262 (5,7,10) (0.2,0.5,0.8)

AC263 (5,7,10) (0.2,0.5,0.8)

AC3 AC31 AC311 (7,10,10) (0.5,0.7,1) (0.5,0.7,1) (0.7,1,1)

AC312 (2,5,8) (0.5,0.7,1)

AC313 (7,10,10) (0.7,1,1)

AC314 (0,0,3) (0.7,1,1)

AC315 (7,10,10) (0.7,1,1)

AC316 (5,7,10) (0.5,0.7,1)

AC317 (5,7,10) (0.2,0.5,0.8)

AC318 (7,10,10) (0.5,0.7,1)

AC319 (5,7,10) (0.7,1,1)

AC31-10 (2,5,8) (0.2,0.5,0.8)

AC31-11 (5,7,10) (0.7,1,1)

AC32 AC322 (2,5,8) (0.2,0.5,0.8) (0,0.3,0.5)

AC323 (0,0,3) (0,0,0.3)

AC33 AC331 (5,7,10) (0.5,0.7,1) (0.5,0.7,1)

AC332 (5,7,10) (0.7,1,1)

AC333 (7,10,10) (0.7,1,1)

AC334 (5,7,10) (0.2,0.5,0.8)

AC335 (5,7,10) (0.5,0.7,1)

AC336 (7,10,10) (0.5,0.7,1)

AC337 (5,7,10) (0.5,0.7,1)

AC338 (7,10,10) (0.7,1,1)


AC339 (5,7,10) (0.5,0.7,1)

AC34 AC341 (7,10,10) (0.7,1,1) (0.7,1,1)

AC342 (7,10,10) (0.7,1,1)

AC343 (7,10,10) (0.7,1,1)

AC344 (7,10,10) (0.7,1,1)

AC345 (5,7,10) (0.7,1,1)

AC346 (7,10,10) (0.7,1,1)

AC347 (2,5,8) (0.5,0.7,1)

AC348 (5,7,10) (0.5,0.7,1)

AC349 (5,7,10) (0.7,1,1)

AC34-10 (5,7,10) (0.7,1,1)

AC34-11 (5,7,10) (0.7,1,1)

AC34-12 (5,7,10) (0.7,1,1)

AC34-13 (7,10,10) (0.7,1,1)

AC34-14 (5,7,10) (0.5,0.7,1)

AC34-15 (5,7,10) (0.5,0.7,1)

AC34-16 (2,5,8) (0.2,0.5,0.8)

AC34-17 (2,5,8) (0.2,0.5,0.8)

AC34-18 (5,7,10) (0.7,1,1)

AC34-19 (2,5,8) (0.2,0.5,0.8)

AC34-20 (7,10,10) (0.5,0.7,1)

AC34-21 (5,7,10) (0.5,0.7,1)

AC34-22 (2,5,8) (0.5,0.7,1)

AC34-23 (7,10,10) (0.7,1,1)

AC34-24 (2,5,8) (0.5,0.7,1)

AC4 AC41 AC411 (0,0,3) (0,0,0.3) (0.7,1,1) (0.7,1,1)

AC412 (2,5,8) (0.2,0.5,0.8)

AC413 (2,5,8) (0.5,0.7,1)


AC414 (5,7,10) (0.5,0.7,1)

AC42 AC421 (0,0,3) (0,0,0.3) (0.7,1,1)

AC422 (2,5,8) (0.2,0.5,0.8)

AC423 (2,5,8) (0.5,0.7,1)

AC424 (5,7,10) (0.7,1,1)

AC43 AC439 (5,7,10) (0.5,0.7,1) (0.2,0.5,0.8)

AC43-10 (0,0,3) (0,0,0.3)

AC43-11 (5,7,10) (0.5,0.7,1)

AC43-12 (0,0,3) (0,0,0.3)

AC43-13 (5,7,10) (0.5,0.7,1)

AC43-14 (0,3,5) (0,0.3,0.5)

AC44 AC441 (7,10,10) (0.7,1,1) (0.2,0.5,0.8)

AC442 (5,7,10) (0.7,1,1)

AC443 (5,7,10) (0.7,1,1)

AC444 (5,7,10) (0.7,1,1)

AC445 (5,7,10) (0.7,1,1)

AC446 (7,10,10) (0.5,0.7,1)

AC447 (5,7,10) (0.7,1,1)

AC45 AC451 (5,7,10) (0.7,1,1) (0.7,1,1)

AC452 (0,3,5) (0.2,0.5,0.8)

AC453 (0,3,5) (0.7,1,1)

AC454 (2,5,8) (0.7,1,1)

AC455 (0,3,5) (0.5,0.7,1)

By using the formulas in Eq. (1), the fuzzy index of the agility 2-grade-capability

ACij is obtained. For example, the fuzzy index of the agility 2-grade, agility AC11, is

calculated as

AC11 = [(5,7,10) * (0.7,1,1) + (5,7,10) * (0.7,1,1) + (0,3,5) * (0.2,0.5,0.8)] /

[(0.7, 1, 1) + (0.7, 1, 1) + (0.2, 0.5, 0.8)]

= (4.375, 6.2, 8.571)


Applying the same equation, other fuzzy indexes of agility 2-grade-capabilities ACij

and the agility 1-grade-capabilities ACi are obtained as listed in following Table.

Finally, applying Eq. (1) again, the FAI of XYZ is calculated as

FAIXYZ = [(4.3, 6.4, 8.6) * (0.7, 1, 1) + (5.5, 8, 8.4) * (0.5, 0.7, 1) +

(5.3, 7.4, 9.1) * (0.7, 1, 1) + (5.1, 6.9, 8.8) * (0.7, 1, 1)] /

[(0.7, 1, 1) + (0.5, 0.7, 1) + (0.7, 1, 1) + (0.7, 1, 1)]

= (5, 7.1, 8.7)

Table 7: Fuzzy Index of each Grade of Agility Capabilities

ACi ACij Rij Ri FAI FAI

(CRISP VALUE)

AC1 AC11 (4.4, 6.2, 8.6) (4.3, 6.4, 8.6) (5, 7.1, 8.7) 6.9

AC12 (4.1, 6.7, 8.6)

AC2 AC21 (3.8, 6.6, 8.3) (5.5, 8, 8.4)

AC23 (6.0, 8.5, 9.1)

AC24 (6.2, 8.8, 10)

AC25 (7, 10, 6.2)

AC26 (4, 6.3, 9.3)

AC3 AC31 (4.9, 7.1, 9) (5.3, 7.4, 9.1)

AC32 (2, 5, 6.6)

AC33 (5.8, 8.2, 10)

AC34 (5.3, 7.8, 9.5)

AC4 AC41 (3.3, 5.7, 8.2) (5.1, 6.9, 8.8)

AC42 (3.5, 5.9, 8.2)

AC43 (5, 6.5, 8.4)

AC44 (5.5, 7.8, 10)

AC45 (1.8, 4.4, 6.7)

viii. Match the FAI with an appropriate level. Once the FAI has been obtained, to identify the level of agility, the FAI can be further

matched with the linguistic label whose membership function is the same as (or


closest to) the membership function of the FAI from the natural-language expression

set of agility label (AL).

Several methods for matching the membership function with linguistics terms have

been proposed .There are basically three techniques:

(1) Euclidean distance method,

(2) Successive approximation, and

(3) Piecewise decomposition.

It is recommended that the Euclidean distance method be utilized because it is the

most intuitive form of human perception of proximity.

In this case the natural-language expression set AL = {EXTREMELY AGILE (EA),

VERY AGILE (VA), AGILE (A), FAIRLY (F), SLOWLY (S)} is selected for

labeling. Then, by using the Euclidean distance method, the Euclidean distance D

from the FAI to each member in set AL is calculated as:

𝑑(𝐹𝐴𝐼, 𝐴𝐿𝑖) = {∑ (𝑓𝐹𝐴𝐼(𝑥) − 𝑓𝐴𝐿𝑖𝑥∈𝑝 (𝑥))2}1/2

Table 8: Euclidean Distances

S.NO. ENTITIES EUCLIDEAN DISTANCE

1 D(FAI,EA) 2.77

2 D(FAI,VA) 0.55

3 D(FAI,A) 3.39

4 D(FAI,F) 6.83

5 D(FAI,S) 9.43

Thus, by matching a linguistic label with the minimum D, the agility index level of

the XYZ can be identified as ‘‘Very Agile’’, as shown in Figure 3.

Fig. 3 Linguistic Levels to Match Fuzzy-Agility-Index


CONCLUSION

The Agility of an industry is studied and calculated. Now a day in highly competitive

environment every industry wants to be extremely agile, so that it can:

Satisfy customers’ varying demand

Employees satisfaction

High Profit

Less Inventory

High Quality

Less time requirement

And so on

So to achieve all above stated point, industry need to be Extremely Agile. For this we

have to evaluate the current Agility status of industry with the help of fuzzy logic

(Fuzzy Agility Index, FAI). Then calculate the obstacles which are restricting it to be

Extremely Agile. When obstacles are known, then improve those obstacles to get

higher level of agility. This particular technique is not restricted to only a specific

industry; these can be implemented in any industry whether it is large scale or small

scale industry. Not only even industries, this can also be implemented in Hospitals,

Banks, Malls, Schools, and Colleges etc.

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Documents

Analyzing Agility of an Indian Manufacturing Enterprise (A Case … · (AC26) FLEXIBLE WORK FORCE (AC31) MULTI – LINGUAL (AC32) EMPOWERED WORKERS (AC33) TOP MANAGEMENT SUPPORT (AC34)