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.
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)
290 Neeraj Grover, Virender Chahal, Narender Kumar, Mohit and Pardeep
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
Analyzing Agility of an Indian Manufacturing Enterprise (A Case Study) 291
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
292 Neeraj Grover, Virender Chahal, Narender Kumar, Mohit and Pardeep
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
Analyzing Agility of an Indian Manufacturing Enterprise (A Case Study) 293
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
294 Neeraj Grover, Virender Chahal, Narender Kumar, Mohit and Pardeep
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 )
LINGUISTIC VARIABLES FUZZY TRIANGULAR VALUES
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)
LINGUISTIC VARIABLES FUZZY TRIANGULAR VALUES
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
Analyzing Agility of an Indian Manufacturing Enterprise (A Case Study) 295
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)
296 Neeraj Grover, Virender Chahal, Narender Kumar, Mohit and Pardeep
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)
Analyzing Agility of an Indian Manufacturing Enterprise (A Case Study) 297
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)
298 Neeraj Grover, Virender Chahal, Narender Kumar, Mohit and Pardeep
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)
Analyzing Agility of an Indian Manufacturing Enterprise (A Case Study) 299
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
300 Neeraj Grover, Virender Chahal, Narender Kumar, Mohit and Pardeep
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
Analyzing Agility of an Indian Manufacturing Enterprise (A Case Study) 301
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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