IRJET- Preventive Maintenance of Utility Equipments at MANMUL, Gejjalagere Ma...
PhD Presentation Rajnish Kumar 2014
1. Improving the Procurement Process of
a Locomotive Manufacturer:
A Quantitative Approach
Rajnish Kumar
PhD Scholar Enroll No 301748
Supervisor: Prof S.K. Sharma
Dept of Mechanical Engineering, IIT (BHU), Varanasi
1
2. Introduction
Overview
The locomotive manufacturer is a production Unit
under Ministry of Railways.
Manufactures Diesel Locomotives, mainly of two types
ALCO (being phased out) and
EMD (Now called HHP, high Horse Power Locos).
Total Budget for 2011-12 – Rs 3265 crore
TARGET – 275 locos, out of which 215 are HHP type.
2
3. Production Trend
Due to Supply Chain constraints, the unit was compelled to reduce
Railway Board’s target from 215 HHP locos to 185.
3
22
39
59
80
110
150
190
126
147
163
177
148
117
69
148
186
222
257 258
267
259
0
50
100
150
200
250
300
2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12
No.ofLocomotives
HHP ALCO
4. Scope for improvement
• The tender process for all items is
common.
• Scientific classification of items is absent
• Even very standard items get out of stock
• Vendor/supplier evaluation and
development methods are subjective
4
5. Scope for improvement…2
• Without passing on the benefit of long
term contracts, in practice same vendors
remain in system for years
• Well trained technical staff involved with
supply coordination
• High level of inventory- about 3.5 months
5
6. Objective
Three parts of work
1. Suggest scientific classification of items using
a quantitative model, and suggest types of
contract
2. Estimate Optimal number of suppliers
3. Formulate methodology of supplier evaluation
for both existing and new suppliers
6
7. Approach and Methodology
Literature review has been done in supply chain
research and the focus is on:
1. Strategic sourcing – Kraljic model has been taken as
foundation of analysis
2. Optimal number of suppliers – using the probability
for supply chain disruption due to global or local
event and quantity discount
3. Supplier evaluation and rating methods – Taguchi
Loss Function, Analytic Hierarchy Process (AHP)
and Technique for Order Preference by Similarity to
Ideal Solution (TOPSIS) techniques have been used
7
8. Salient features of Procurement system
• Production Plan is received 2 years in advance, but
ordering is done on a yearly basis assuming lead time to
be about 6-12 months
• All types of material/items are procured in the same
manner
• The purchase organization is headed by a
CONTROLLER OF STORES
• Supplier/Vendor assessment, approval and development
is with DESIGN Department under Chief Mechanical
Engineer.
8
9. Salient features of Procurement system…..
• There is a Material Control Organization (MCO) whose
function is to generate indents
• MCO also follows up material availability once Purchase
Orders are placed.
• MCO is under administrative control of Controller of
Stores(COS).
• The Technical Evaluation of offers in Tenders is done by
DESIGN department for most of Loco items.
9
10. Salient features of Procurement system…..
Unscientific system of tendering
• In the same class of items, for each item there are separate vendors
and tender is done for all, increasing work volume without any value
addition to supply chain.
Item class Total types No. of Vendors
Gaskets 99 8
Angles 46 9
Bracket and
bracket assembly
27 16
Bush and bushing 25 15
10
Note: The manufacturer has started work on this already
11. Salient features of Procurement system…..
Issues with number of approved Suppliers
• It can be seen that number of approved suppliers is not adequate
and thus ordering has to be done on unapproved/new suppliers
Part-I Vendors as per Composite Vendor Directory DLW
Type of Product
Number of Part-I Vendors Total
ITEMS
NIL 1 2 > 2
HHP
191 1102 780 185 2258
ALCO
(OLD getting Phased out) 335 610 375 139 1459
HARDWARE
33 422 234 153 842
RAW MATERIAL
59 52 20 0 131
TOTAL
618 2186 1409 477 4690
% of TOTAL
ITEMS 13% 47% 30% 10%
What should be the optimal size of supplier base?
11
12. The decision variables
– Disruptions in supply
– Failure Rate
– Price
– Complexity/Technological intensiveness
Seminal Paper
Kraljic, P., (1983), Purchasing must become supply management,
Harvard Business Review, 16(5), 109-117
12
PART I
Classification of material/items
14. Definition of Variables
Factor Code Definition
Disruptions in
supply
S Number of loco days lost compared to total
days lost in the Decision Period by all
suppliers for the subject item,
expressed as %
Failure Rate F % failed in the total supply of the item by
all suppliers
Price P Average price of item expressed as % of
highest priced.
Complexity/
Technological
intensiveness
C Scale 1 - 100
These values will be plotted on a graph
14
15. Model for classification
The C, S, F and P
values are plotted
and polygon is
formed.
The coordinates of
the centroid would
define the category
of the item.
For example in this
case the item is a
bottleneck item
15
16. Calculation of Region
For the polygon that is
formed by the four
variables, the points
are
Using these values in the
equation for calculation of
centroid of polygon, we get
x0
y3
x2
y1
Area of
Polygon
Note: These are absolute values
16
17. P, xo C, y1 S, x2 F, y3
ITEM RATE
Total loss
in Loco
days
(3 years)
Price Complexity
Supply
Disruption
Failure
Rate A Cx Cy
Category
AC-AC
Traction
System
25836115 2211 100 95 7.49 3 5266.99 30.84 30.67 I
TCC 19061099 1035 74 90 3.51 6 3709.58 23.42 28.00 I
Alternator 7500000 247 29 100 0.84 10 1642.62 9.40 30.00 I
Traction
Motor
2671481 4390 10 95 14.87 15 1386.60 -1.51 26.67 II
Draft Gear 153618 222 1 80 0.75 8 59.25 -0.05 24.00 II
Union
Elbow
153618 33 1 10 0.11 5 5.30 0.16 1.67 I
Master
Controller
126575 885 0 90 3.00 5 165.67 -0.84 28.33 II
Elbow 20847 168 0 10 0.57 15 8.12 -0.16 -1.67 III
TM Air
Duct Boot
20847 48 0 10 0.16 20 3.65 -0.03 -3.33 III
L O
Manifold
15447 200 10 10 0.68 14 128.13 3.11 -1.33 IV
17
Some Examples for Classification of item
18. Part II
Optimal number of Suppliers
• Rationalization of supplier base is the first step
towards developing long term relationships.
Rationalization and reduction are not the same
things.
• It is a tight rope walk, as small supply base gives
rise to risk of supply disruptions.
• A large supply base raises the fixed costs and the
ordered quantity per supplier reduces due to which
suppliers may not extend price benefit to
manufacturer.
18
19. SEMINAL PAPER: Berger, P. D., Gerstenfeld, A., and Zeng A.Z. (2004), How Many
Suppliers are best? A Decision-Analysis approach, Omega: The International
Journal of Management Science, 32(1), 9-15
Pg is the probability of Global event
Si is the probability of Local event for supplier i
The model assumes,
For i≠j, Si and Sj are independent,
And Pg , Si are independent events
The probability that supplies from supplier i are disrupted, failed (f) is,
Model for estimating, n*
optimal number of suppliers
19
20. There are certain conditions:
•The number of suppliers is chosen from i= 1 to n.
•There are two conditions, all suppliers fail, or some fail.
•If all suppliers fail, there is a loss to company denoted by Lt
•The cost of operating a supplier, i is C(i), i=1,2,….,n.
•The Expected Total Cost (E) from the system when only one
supplier is used
Derivation of formula
20
•The Expected Total Cost (E) from the system when n suppliers are there,
21. Derivation of formula, MODEL-B by Berger
For determining the optimal size of suppliers, we have to
compare cost of operating n+1 suppliers to n suppliers.
Consider that the cost of maintaining a supplier is a linear
function of n,
Then C(n) is given by the following expression:
C(n) = u + v(n), u is fixed cost and v is variable cost
Using these functions, the formula arrived at is,
21
22. Derivation of formula – adding QUANTITY DISCOUNT to
Model-B
22
Where,
A is the cost of item
θ is a parameter for highest discount and is estimated as 0.05
for present study
λ is a variable for rate of decrease in discount
n is the number of suppliers
23. Derivation of formula – FINAL
23
The procedure is to increase, n till E (n+1)- E
(n) >0, which means that it is costlier to operate
(n+1) suppliers than n. The number n thus
obtained will be the optimal number of
suppliers n*.
24. Application to OEM’s case
24
v θ λ Pg S Lt
2000 0.05 0.4 0.05 0.1 300000
Result
A, cost of item in Rs n*, optimal number of suppliers
5000 3
10000 3
50000 3
100000 2
200000 2
500000 2
1000000 2
5000000 1
10000000 1
25000000 1
25. Sensitivity Analysis
25
In order to authenticate the model the sensitivity
test must be carried out. This is carried out to
notice whether the model is following natural rules
or not.
There are two situations:
• Parameters common to Model-B and the
present model: compare n*
• Parameters unique to the present model: no
comparison
26. 0
1
2
3
4
0 0.05 0.1 0.15
Global probability of failure
n*
n*(Berger)
0
1
2
3
4
5
0.00 0.10 0.20 0.30 0.40
Probability of supplier's failure
n*
n*(Berger)
0
1
2
3
4
5
0 10000 20000 30000 40000 50000 60000
Loss due to supply disruption
Hundreds
n*
n*(Berger)
0
1
2
3
4
0 20000 40000 60000
Cost of maintaining a supplier
n*
n*(Berger)
Sensitivity Analysis
Compare present model and model-B
27. 0
1
2
3
4
0 5000 10000 15000 20000 25000 30000
Price of item
Thousands
n*
n*
0
1
2
3
4
0 0.02 0.04 0.06 0.08
% discount offered by supplier
n*
n*
0
1
2
3
4
0 0.5 1 1.5
Rate of decrease in % discount
n*
n*
Sensitivity Analysis
Only this model
28. • Based on past research and analysis of systems at 20 major
manufacturers, four salient criteria have been found important in
supplier evaluation, namely,
– QUALITY
– ON-TIME DELIVERY
– PRICE
– SERVICE
• Therefore, in this study, these four valuable criteria are incorporated
into the Taguchi loss function, then combined into a total loss under
a weighted consideration via an Analytic Hierarchy Process (AHP).
• The TOPSIS (Technique for Order Preference by Similarity to Ideal
Solution) method which is popular in literature has been employed
to determine the final ranking of the suppliers.
28
Part III
Supplier Selection using Taguchi Loss Function
and Multi Criteria Decision Making techniques
29. Taguchi Loss Function
The two functions used
It is preferred to maximize the result, and
the ideal target value is infinity
HIGHER-IS-BETTER
The ideal target value is defined as
zero
SMALLER-IS-BETTER
29
L (y) = k /(y)2 L (y) = k (y)2
According to Taguchi, if a characteristic measurement is the same as the
target value, the loss is zero. if it is on lower or upper specification limit, loss is
100%.
30. Decision Variables for selecting a supplier
Criteria Target Value Range
Specification
limit
Quality 0% 0-5% 5% rejection
Lower the
better
Delivery 0 0-15 15 days
Lower the
better
Price Lowest 0-10% 10% higher
Lower the
better
Service 100% 100%-50% 50% lower
Higher
the better
NOTE: The loss is estimated by assuming that if 5% items are rejected there is
100% loss in Quality criteria, or
If supply is delayed by 15 days there is 100% loss in Delivery criteria etc.
30
31. Calculation of loss coefficient
Criteria
Taguchi
Function
Specification
limit
Loss
(assuming 100% loss at
spec limit)
Value of k
Quality ky2 5% rejection 100=k x (0.05)2 40000
Delivery ky2 15 days 100=k x (15)2 0.4444
Price ky2 10% higher 100=k x (0.10)2 10000
Service k/y2 50% lower 100=k / (0.50)2 25
Using these values of k, TAGUCHI LOSS of each supplier is computed.
31
32. Outputs of Taguchi Loss for suppliers
Characteristics of Suppliers
Supplier
Quality
% rejection
Delivery
delay in
days
Price
compared
to lowest
Service
level
judgment
A 3% 5 0% 85%
B 3% 6 6.50% 75%
C 2% 7 8.40% 80%
D 4% 2 4.20% 65%
These values in the matrix
will be used to calculate
the Taguchi Loss for each
Supplier, criteria wise,
using the value of loss
coefficient calculated
before.
But before this the
relative importance of
each criteria will be
found using AHP.
32
33. Using AHP to find relative
importance of criteria
• AHP – Analytic Hierarchy
Process is a well
established process to
assist the decision maker’s
judgment concerning the
relative importance of
criteria. It was developed
by Saaty in 1970s.
• There is a scale 1 to 9
lowest to highest relative
importance
Criteria compared with
Quality
Delivery
Price
Service
Criteriatobecompared
Quality 1 3 7 9
Delivery 1/3 1 3 5
Price 1/7 1/3 1 1/3
Service 1/9 1/5 3 1
SUM of
Column
Values
1.59 4.53 14.00 15.33
33
34. Normalization of Matrix and calculation of weights
• CI is the Consistency Index = (λ(max) -n)/n-1
• CR is the Consistency Ratio which should be less than 10% and,
• CR = CI/RI, RI is the Random Index which is a standard table depending on n, number of
criteria
Normalized Matrix
Normalized
Principal Eigen
vector
Quality 0.63 0.66176 0.5 0.58696 62%
Delivery 0.21 0.22059 0.21429 0.32609 22%
Price 0.09 0.07353 0.07143 0.02174 8%
Service 0.07 0.04412 0.21429 0.06522 8%
λ 0.9896 1.0039 1.1161 1.1563 4.266
Principal Eigen value,
λ(max)
n, number
of criteria 4 CI 0.089
CR 9.8% Consistency
34
n 1 2 3 4 5 6 7 8 9 10
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49
35. Integrated model for Supplier Ranking
Taguchi loss function provides the loss to system
by individual supplier
AHP provides the pair wise
comparison of four criteria.
Weightage of each criterion
Quality – 62%, Delivery – 22%, Price – 8%
and Service – 8%.
For final ranking, method called TOPSIS is
used.
35
36. Integrated model for Supplier Ranking
The TOPSIS method was introduced by Hwang and Yoon,
1981.
Behzadian et al., 2012 have found that it has been applied
to many applications ranging from manufacturing to
purchasing, health, safety, energy, human resource
management, chemical engineering, water resources
management and other areas.
The method finds the solution closest to ideal and farthest
from the worst scenario. It is simple and easy to use.
36
37. TOPSIS
(Technique for Order Preference by Similarity to Ideal Solution)
TOPSIS is based on the
concept that the chosen
alternative should have the
shortest geometric distance
from the positive ideal
solution and the longest
geometric distance from the
negative ideal solution
37
Distance to ideal and anti-ideal point
38. TOPSIS
Steps
38
The best satisfied alternative can now be decided according to the rank order of
alternatives with relative closeness. Closest to 1 is best.
Step 6 : Calculate the relative closeness to the ideal solution
Step 5 : Calculate the separation measures for each alternative from the ideal solution
and the negative-ideal solution
Step 4 : Determine Positive Ideal and Negative Ideal solutions
Step 3 : The weighted normalized decision matrix is now constructed.
Step 2 : Now the normalized decision matrix is constructed.
Step 1 : The decision matrix is constructed with m alternatives evaluated in terms of n
criteria.
39. Application of TOPSIS
• Taguchi Loss by each supplier is estimated
39
Quality
Q
Delivery
D
Price
P
Service
S
Value of
Taguchi
constant, k
k= 40000 k=0.4444 k=10000 k=25
Loss k×(value)2 k×(value)2 k×(value)2 k/(value)2
Supplier Taguchi Loss Taguchi Loss Taguchi Loss Taguchi Loss
A 36 11.11 0.00 34.60
B 36 16.00 42.25 44.44
C 16 21.78 70.56 39.06
D 64 1.78 17.64 59.17
40. Application of TOPSIS
After a certain number of steps, the Weighted normalized decision
matrix is created.
The positive ideal (lowest loss) and negative ideal (highest loss)
are marked in this matrix
40
Q D P S
Weights→
Supplier↓ 0.62 0.22 0.08 0.08
A 0.2678 0.0835 0 0.0306
B 0.2678 0.1202 0.0402 0.0393
C 0.1190 0.1637 0.0671 0.0345
D 0.4762 0.0134 0.0168 0.0523
Positive
Ideal
Negative
Ideal
wrv jijij
rij = normalized element
vij= weighted element
w= weight
41. 41
v- Negative Ideal
v* Positive Ideal
Separation measure for Negative ideal solution
Separation measure for Positive ideal solution
Separation measure of the values in each cell of weighted normalized matrix is calculated
(column wise) and summated in last column for finding the supplier’s total separation
(v - vi
*
)2
Q D P S sum
𝑆𝑖
∗
= 𝑣𝑖𝑗 − 𝑣𝑗
∗ 2
𝑚
𝑗=1
A
0.02214 0.00492 0.00000 0.00000 0.02706 0.16450
B
0.02214 0.01142 0.00161 0.00008 0.03525 0.18776
C
0.00000 0.02259 0.00450 0.00002 0.02711 0.16465
D
0.12754 0.00000 0.00028 0.00047 0.12830 0.35818
(vi
-
- v)2
Q D P S sum
𝑆𝑖
−
= 𝑣𝑖𝑗 − 𝑣𝑗
− 2
𝑚
𝑗=1
A
0.04340 0.00643 0.00450 0.00047 0.05480 0.23410
B
0.04340 0.00189 0.00072 0.00017 0.04618 0.21490
C
0.12754 0.00000 0.00000 0.00032 0.12786 0.35757
D
0.00000 0.02259 0.00253 0.00000 0.02512 0.15851
42. 42
Ci
* Ranking
A 0.587315 2
B 0.5337 3
C 0.68471 1
D 0.306774 4
The final step is estimation of Relative Closeness to Ideal, Ci
* for each
supplier, ranking depends on closeness of this value to 1.
The lesser the separation measure from positive ideal, the better
The table depicts the value of Ci
* and ranking of suppliers.
Final ranking
43. Assessment of New Supplier
Capability factors Code
Quality systems of the supplier Q
Financial capability of the supplier F
Production facilities and capacity P
Business volume/amount of past business V
Technological capability/R&D capability T
Supplier’s proximity/geographic location G
43
44. AHP Matrix
Relative Importance of Capability Factors
Q F P V T G
Q 1 3 5 6 8 9
F 1/3 1 2 3 6 7
P 1/5 1/2 1 3 4 5
V 1/6 1/3 1/3 1 3 5
T 1/8 1/6 1/4 1/3 1 5
G 1/9 1/7 1/5 1/5 1/5 1
sum
(col)
1.936 5.143 8.783 13.53 22.2 32
Quality systems of
the supplier
Q
Financial capability of
the supplier
F
Production facilities
and capacity
P
Business
volume/amount of
past business
V
Technological
capability/R&D
capability
T
Supplier’s
proximity/geographic
location
G
44
45. Estimation of Relative Importance
Normalization of Matrix
Q F P V T G
REL
IMP
Q 0.516 0.583 0.56926 0.443 0.36 0.281 51%
F 0.172 0.194 0.2277 0.222 0.27 0.219 19%
P 0.103 0.097 0.11385 0.222 0.18 0.156 12%
V 0.086 0.065 0.03795 0.074 0.135 0.156 8%
T 0.065 0.032 0.02846 0.025 0.045 0.156 5%
G 0.057 0.028 0.02277 0.015 0.009 0.031 4%
λ
0.991 0.999 1.04679 1.092 1.187 1.294 6.610
principal
Eigenvalue
n 6 CI 0.122
CR 9.8% Consistency
45
Quality systems
of the supplier
Q
Financial
capability of the
supplier
F
Production
facilities and
capacity
P
Business
volume/amount of
past business
V
Technological
capability/R&D
capability
T
Supplier’s
proximity/geograp
hic location
G
RI = 1.24 for n=6
46. Final Score sheet of new supplier
46
• The score sheet assigns 10 marks to each criterion. After the tabulation of
these scores, the following Table will be used to calculate the weighted
score of the supplier, and then according to the need suppliers may be
selected for inclusion into the purchase process.
Criterion Q F P V T G Total
weighted
score
Weight 0.51 0.19 0.12 0.08 0.05 0.04
Supplier Score Score Score Score Score Score
A
B
C
47. Recommendations
Strategic Sourcing
• For each of the FOUR defined class of items a different procurement method
47
Strategic
Items
Incentive to supplier by having 3 year contracts and long
term i.e. 10 year commitment. Assured business is the
key.
Development
Items
Request for Proposal must be floated, globally and
locally.
After RFP is floated and list of potential suppliers is
received, a development tender will be floated to get the
exact price and delivery schedule.
Bottleneck
Items
Limited Tendering to approved suppliers, and the goal
should be developing them to enter into long term
agreements. For such items, supplier training and
motivation is must.
Normal Items Limited tenders to approved suppliers. This class of
suppliers should be graduated to long term relationships.
48. There should be the following classes of suppliers:
• Approved – The first 3 of the ranked suppliers from past
performance record. They should have supplied atleast 10%
of the net procured quantity for the period under
consideration.
• Enlisted – Under active consideration, especially if it is found
that 3 suppliers cannot meet the demand or one of the
suppliers is degraded in ranking.
• Potential suppliers – Those who have supplied in past but
not ranked due to less than 10% of net procured quantity for
the period under consideration.
Recommendations
48
49. Optimal sizing of supplier base
• There is an unscientific distribution of number of suppliers for
various groups of items at OEM. In many cases for same
group of items they are 10 or more suppliers, each approved
only for a particular item. For each class only 3 suppliers.
Incidentally, work has already started on this issue
Institutional arrangement for Supplier Development
• The Material Control Organization (MCO) at OEM is having
requisite manpower and technical expertise to carry out this
work. At present they are doing non-technical function of
chasing the suppliers.
This recommendation has found acceptance and MCO is being redesigned.
Recommendations
49
50. Benefits estimated
OPTIMIZING THE SUPPLIER BASE will reduce the cost
of items by 2-5% as due to increased volumes, the
suppliers will reduce the price.
By focusing on supplier issues, the POTENTIAL LOSS due
to supply disruptions can be reduced, which will mean
reduction in financial loss due to non availability of loco.
A loco has earning potential of Rs 3.0 lakh per day.
50
51. Benefits estimated…2
SAVING MANPOWER- THE COSTLIEST ASSET
More than 100 trained technical personnel are actively
involved in purchase order execution. Much of this work is
routine and can be made redundant by streamlining the
process. This manpower can be effectively utilized
elsewhere where there is no alternative to technical work.
REDUCTION IN INVENTORY by 2 months will mean a
financial gain of Rs 40-70 crore annually by way of
reduction in loss due to interest on capital.
51
52. Contribution of Work
A. Quantitative model for classification of items to decide
on sourcing strategy – Four Categories defined.
B. Estimation of optimal number of suppliers considering
the probability of Global and Local event, Quantity
discounts.
C. Quantitative model for supplier evaluation for enlisting
on approved list.
D. Scientific system for assessment of new suppliers’
capability based on 6 attributes. (Perceived Fairness)
E. Procedure for enlisting of supplier in Supplier/Vendor
Directory.
52
53. Observations
• There is a wide gap in research and
practice in the field of supplier selection.
• It is important to formulate practicable,
simple models which can be understood
by all stakeholders in the system.
53
54. Seminal Papers/References
About 96 research papers were consulted
And supplier evaluation systems of 20 companies analyzed.
Seminal Papers
• Chai, J., Liu, J.N.K., Ngai, E.W.T. (2013), Application of decision-making techniques in
supplier selection: A systematic review of literature, Expert Systems with Applications: An
International Journal, Volume 40 Issue 10, 3872-3885
• Gelderman, C. J., Van Weele, A. J. (2005), Purchasing Portfolio Models: A Critique and
Update, Journal of Supply Chain Management, 41, 19–28
• Ho, W., Xu, X., Dey, P. K. (2010), Multi-criteria decision making approaches for supplier
evaluation and selection: A literature review. European Journal of Operational Research,
202(1), 16–24
• Kraljic, P., (1983), Purchasing must become supply management, Harvard Business Review,
16(5), 109-117
54
55. Seminal Papers/References
• Liao, C., Kao, H., (2010), Supplier selection model using Taguchi loss function, analytical
hierarchy process and multi-choice goal programming, Computers and Industrial
Engineering, Volume 58, Issue 4, 571-577
• Sarkar, A., Mohapatra P.K.J., (2009), Determining the optimal size of supply base with the
consideration of risks of supply disruptions, International Journal of Production Economics,
Volume 119, Issue 1, 122-135
• Berger, P. D., Gerstenfeld, A., and Zeng A.Z. (2004), How Many Suppliers are best? A
Decision-Analysis approach, Omega: The International Journal of Management Science,
32(1), 9-15
• Taguchi, G., Chowdhury S., Wu, Y., (2004), Taguchi's Quality Engineering Handbook, Wiley,
ISBN: 978-0-471-41334-9, Hardcover
• Behzadian, M., Khanmohammadi O. S., Yazdani, M., and Ignatius, J. (2012), A state-of the-
art survey of TOPSIS applications, Expert Systems with Applications, 39(17), 13051-13069
• Saaty, T. L. (1990), How to make a decision: the analytic hierarchy process, European journal
of operational research, 48(1), 9-26.
55
56. Research Papers
• ACCEPTED PAPER
– “Classification of items and purchasing strategy using modified Kraljic matrix – A
case study of Locomotive Manufacturer in India”, International Conference on
Agile Manufacturing, IIT(BHU), Varanasi, December 16-19,2012, co-authored
with Prof S.K.Sharma
• COMMUNICATED PAPERs
– “Applying modified Berger's model considering quantity discount to estimate the
optimal number of suppliers for a Locomotive manufacturer” in Annals of
Operations Research, Springer, co-authored with Prof S.K.Sharma
– “Supplier Selection Criteria and Methodology: Practice vs. Research” in
International Journal of Logistics Management, EMERALD co-authored with Prof
S.K.Sharma
• Paper under process
– “Integrated approach for Supplier Selection using Taguchi Loss Function, AHP
and TOPSIS” co-authored with Prof S.K.Sharma
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