Combined AHP-ANP approach to evaluated supply chain of electronic business

1. A combined AHP-ANP approach
to solve supplier selection
problem for a textile company
Presented By:
Abhishek Tuli
Ankit Karir
Under the guidance of Dr. Rohit Singh-
Assistant Professor ( Operations and Supply
chain Management)

2. Purpose:
 To solve the issues in supply chain network with the application of
Operations Management models.
 To provide the real time solution to a jeans manufacturing firm that
witnessed the decline in sales and market share, which was once
highly successful and highly competitive in the denim jeans market.
Methodology:
This problem includes both tangible and intangible criteria therefore
analytic hierarchy process (AHP) is accepted as the methodology and
to identify the interdependency between these criteria analytic network
process (ANP) is used.
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3. Literature Review 3

4. Analytic Hierarchy Process (AHP)
 The analytic hierarchy process
(AHP) is a structured technique for
organizing and analysing complex
decisions, based on mathematics
and psychology.
 It was developed by Thomas L.
Saaty in the 1970s.
 It has particular application in
group decision making and is used
around the world in a wide variety
of decision situations in fields such
as business, industry, healthcare
and education.
 ANP is used to check
interdependency among the criteria Source: Wikipedia
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5. Representation of Goal, Criteria and Sub- criteria 5

6. Thomas Saaty’s Nine point Scale:
Intensity of
importance
Definition Explanations
1 Equal importance Two activities contribute equally to the objective
3 Weak importance of one over another Experience and judgment slightly favor one
activity over another
5 Essential or strong important Experience and judgment strongly favor one
activity over another
7 Dominated importance An activity is favoured very strongly over another;
its dominance demonstrated in practice
9 Absolute importance The evidence favouring one activity over another is
of the highest possible order of affirmation
2,4,6,8 Intermediate values between the two
adjacent judgment
When compromise is needed
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7. Top Goal
Availability of store
per 1000 people
Market Reputation
Inventory
management
Financial Capacity
Availability of store
per 1000 people
1 3 5 3
Market Reputation 0.33 1 4 5
Inventory
management
0.20 0.25 1 3
Financial Capacity 0.33 0.2 0.33 1
1.86 4.45 10.33 12.00
Normalised Matrix
Top Goal
Availability of store
per 1000 people
Market Reputation
Inventory
management
Financial Capacity Priority Matrix
Availability of store
per 1000 people
0.54 0.67 0.48 0.25 0.49
Market Reputation 0.18 0.22 0.39 0.42 0.30
Inventory
management
0.11 0.06 0.10 0.25 0.13
Financial Capacity 0.18 0.04 0.03 0.08 0.08
Top Goal v/s Criteria
Consistency < 0.1
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8. Estimating Consistency Ratio
Step 1: Multiply each value in the first column of the
pairwise comparison matrix by the relative priority
of the first item considered. Same procedure for
other items. Sum the values across the rows to
obtain a vector of values labeled “weighted sum.”
Step 2: Divide the elements of the vector of weighted
sums obtained in Step 1 by the corresponding
priority value.
Step 3: Compute the average of the values computed in
step 2. This average is denoted as lmax.
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9. Estimating Consistency Ratio
Step 4: Compute the consistency index (CI):
Where n is the number of items being compared
Step 5: Compute the consistency ratio (CR):
Where RI is the random index, which is the consistency index
of a randomly generated pairwise comparison matrix. It
can be shown that RI depends on the number of elements
being compared and takes on the following values.
1n
nλ
CI max



RI
CI
CR 
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10. Random Index
 Random index (RI) is the consistency index of a
randomly generated pairwise comparison matrix.
 RI depends on the number of elements being
compared (i.e., size of pairwise comparison matrix)
and takes on the following values:
n 1 2 3 4 5 6 7 8 9 10
RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49
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11. Relationship of various
criteria with themselves
Availability of store
per 1000 people
Market
Reputation
Inventory
management
Financial
Capacity
Market Reputation 1 3 0.2
Inventory
management
0.33 1 0.2
Financial Capacity 5 5 1
6.33 9 1.4
Availability of store
per 1000 people
Market
Reputation
Inventory
management
Financial
Capacity
Priority
Matrix
Market Reputation 0.16 0.33 0.14 0.21
Inventory
management
0.05 0.11 0.14 0.10
Financial Capacity 0.79 0.56 0.71 0.69
Market Reputation
Availability of store per
1000 people
Inventory
management
Financial Capacity
Availability of store per
1000 people
1 5 0.33
Inventory management 0.2 1 3
Financial Capacity 3 0.33 1
4.2 6.33 4.33
Normalised Matrix
Market Reputation
Availability of store per
1000 people
Inventory
management
Financial Capacity
Priority
Matrix
Availability of store per
1000 people
0.238095238 0.789889415 0.076212471 0.368066
Inventory management 0.047619048 0.157977883 0.692840647 0.299479
Financial Capacity 0.714285714 0.052132701 0.230946882 0.332455
Consistency < 0.1
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12. Relationship of various
criteria with themselves
Inventory
management
Availability of store per
1000 people
Market
Reputation
Financial
Capacity
Availability of store
per 1000 people
1 5 3
Market Reputation 0.2 1 2
Financial Capacity 0.33 1
1.53 6
Normalised Matrix
Inventory
management
Availability of store per
1000 people
Market
Reputation
Financial
Capacity
Priority
Matrix
Availability of store
per 1000 people
0.65 0.77 0.50 0.64
Market Reputation 0.13 0.15 0.33 0.21
Financial Capacity 0.22 0.08 0.17 0.15
Financial Capacity
Availability of store per
1000 people
Market
Reputation
Inventory
management
Availability of store per
1000 people
1 3 4
Market Reputation 0.33 1 5
Inventory management 0.25 0.2 1
1.58 4.20 10.00
Normalised Matrix
Financial Capacity
Availability of store per
1000 people
Market
Reputation
Inventory
management
Priority Matrix
Availability of store per
1000 people
0.63 0.71 0.40 0.58
Market Reputation 0.21 0.24 0.50 0.32
Inventory management 0.16 0.05 0.10 0.10
Consistency < 0.1
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13. Relation between
criteria and sub-criteria
Availability of store per
1000 people
Urban Location Rural Location
Urban Location 1 6
Rural Location 0.17 1
1.166666667 7
Normalised Matrix
Availability of store per
1000 people
Urban Location Rural Location
Priority
Matrix
Urban Location 0.86 0.86 0.86
Rural Location 0.14 0.14 0.14
Inventory
management
Product Variety Product Availabilty
Product Variety 1 3
Product Variability 0.33 1
1.33 4.00
Normalised Matrix
Inventory
management
Product Variety Product Availability
Priority
Matrix
Product Variety 0.75 0.75 0.75
Product Variability 0.25 0.25 0.25
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14. Relation between
criteria and sub-criteria
Market
Reputation
Staff involvement Brand Value Promotional Offers
Staff involvement 1 0.2 0.1428
Brand Value 5 1 0.33
Promotional
Offers
7 3 1
13 4.2 1.4728
Normalised matrix
Market
Reputation
Staff involvement Brand Value Promotional Offers
Priority
Matrix
Staff involvement 0.077 0.048 0.097 0.074
Brand Value 0.385 0.238 0.224 0.282
Promotional
Offers
0.538 0.714 0.679 0.644
Financial Capacity Expansion plans Infrastructure
Expansion plans 1 5
Infrastructure 0.2 1
1.2 6
Normalised Matrix
Financial Capacity Expansion plans Infrastructure Priority Matrix
Expansion plans 0.83 0.83 0.83
Infrastructure 0.17 0.17 0.17
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15. Supermatrix
The mathematics performed in this
research may not be familiar to
every reader. Due to the dynamics
and complexity of real life, there is
no best method that can solve all
daily decision problems, but ANP, to
some extent, proves out to be quiet
effective, helping out with finding the
best alternative.
Source: The analytic network process (ANP) approach to
location selection: a shopping mall illustration
Eddie W.L. Cheng, Heng Li and Ling Yu Department of
Building and Real Estate, The Hong Kong
Polytechnic University, Hong Kong
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16.  As can be seen from the average value of the three columns, Outlet B
gets the maximum weightage of being selected as the best possible
choice as the retail outlet for our company.
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17. Conclusion & Future Scope
 This study applied dynamic ability, theoretical perception and the
proficiency-capability relationship to construct and examine the
relationships among the four latent factors.
 The study we conducted was for a firm belonging to a textile
sector but that doesn’t restricts its scope to a particular
industry/sector.
 It can be applied to any industry/situation/sector where a
manager has to take a decision between various available
choices.
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18. Reference
 Moody P.E., “Decision Making: Proven Methods for Better
Decisions”, Mc-Graw & Hill, New York, 1983.
 Saaty, Thomas L. (1996), Decision Making with Dependence and
Feedback: The Analytic Network Process, RWS Publications, 4922
Ellsworth Avenue, Pittsburgh, PA.15213.
 Safar Fazli and Azam Masoumi, “Assessing the vulnerability of
supply chain using Analytic Network Process approach”, International
Research Journal of Applied and Basic Sciences, Vol. 3, pp. 2763-
2771.
 Saaty, Thomas L. (1997), the Analytic Network Process, RWS
Publications, 4922 Ellsworth Avenue, Pittsburgh, PA 15213.
 Tran, L.T. Knight, C.G. O’NEILL, R.V. SMITH, “Integrated
Environmental Assessment of the Mid-Atlantic Region with Analytical
Network Process”, Environmental Monitoring and Assessment 94,
Kluwer Academic Publishers, 263-277, 2004.
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