This document describes a study that uses the analytic hierarchy process (AHP) and analytic network process (ANP) to help solve supplier selection problems for a textile company. The methodology involves using AHP to identify tangible and intangible criteria, and ANP to analyze interdependencies between criteria. Pairwise comparisons are made between goals, criteria, sub-criteria and alternatives. Supermatrix calculations are performed to determine the best alternative, which in this case was Outlet B. The study aims to provide a real-world solution for a jeans manufacturer facing declining sales and market share.
ANP-GP Approach for Selection of Software Architecture StylesWaqas Tariq
Abstract Selection of Software Architecture for any system is a difficult task as many different stake holders are involved in the selection process. Stakeholders view on quality requirements is different and at times they may also be conflicting in nature. Also selecting appropriate styles for the software architecture is important as styles impact characteristics of software (e.g. reliability, performance). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how to integrate these elements in the architecture. Selecting the best style is difficult because there are multiple factors such as project risk, corporate goals, limited availability of resources, etc. Therefore this study presents a method, called SSAS, for the selection of software architecture styles. Moreover, this selection is a multi-criteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, we suggest an improved selection methodology, which reflects interdependencies among evaluation criteria and alternatives using analytic network process (ANP) within a zero-one goal programming (ZOGP) model. Keywords: Software Architecture; Selection of Software Architecture Styles; Multi-Criteria Decision Making; Interdependence; Analytic Network Process (ANP); Zero-One Goal Programming (ZOGP)
An Analytic Network Process Modeling to Assess Technological Innovation Capab...drboon
To handle swift changes in global environment, Technological Innovation Capabilities (TICs) is one crucial and unique strategy to increase firms’ competitiveness. This research proposed a systematic framework of TICs assessment by employing Analytic Network Process (ANP) method for solving the complicate decision-making and assessing the interrelationship among various evaluation factors, whereas the relative important weight data were provided by industrial experts based on pair-wise comparison. With the novel TIC assessment model, high-level managers could easily gain management information to rationalizes the decision-making process based on the most important criteria which affect the firms’ competitive advantages and the highest priority factors which were needed to be handled. The last section also displayed the application of TICs assessment on three Thai automotive parts firms, as case study.
ANP-GP Approach for Selection of Software Architecture StylesWaqas Tariq
Abstract Selection of Software Architecture for any system is a difficult task as many different stake holders are involved in the selection process. Stakeholders view on quality requirements is different and at times they may also be conflicting in nature. Also selecting appropriate styles for the software architecture is important as styles impact characteristics of software (e.g. reliability, performance). Moreover, styles influence how software is built as they determine architectural elements (e.g. components, connectors) and rules on how to integrate these elements in the architecture. Selecting the best style is difficult because there are multiple factors such as project risk, corporate goals, limited availability of resources, etc. Therefore this study presents a method, called SSAS, for the selection of software architecture styles. Moreover, this selection is a multi-criteria decision-making problem in which different goals and objectives must be taken into consideration. In this paper, we suggest an improved selection methodology, which reflects interdependencies among evaluation criteria and alternatives using analytic network process (ANP) within a zero-one goal programming (ZOGP) model. Keywords: Software Architecture; Selection of Software Architecture Styles; Multi-Criteria Decision Making; Interdependence; Analytic Network Process (ANP); Zero-One Goal Programming (ZOGP)
An Analytic Network Process Modeling to Assess Technological Innovation Capab...drboon
To handle swift changes in global environment, Technological Innovation Capabilities (TICs) is one crucial and unique strategy to increase firms’ competitiveness. This research proposed a systematic framework of TICs assessment by employing Analytic Network Process (ANP) method for solving the complicate decision-making and assessing the interrelationship among various evaluation factors, whereas the relative important weight data were provided by industrial experts based on pair-wise comparison. With the novel TIC assessment model, high-level managers could easily gain management information to rationalizes the decision-making process based on the most important criteria which affect the firms’ competitive advantages and the highest priority factors which were needed to be handled. The last section also displayed the application of TICs assessment on three Thai automotive parts firms, as case study.
Inventory Optimization in a Market-Driven World - 27 APR 2015Lora Cecere
Executive Overview
Growth is slowing and the complexity in today’s supply chain is unprecedented. As a result, within a company, inventory management is often a hot issue. Shrinking inventory spins off a one-time, and highly desirable, cash windfall. In most industries there is a connection between market capitalization and inventory management. This drives pressure to reduce inventory and question existing practices. However, while companies are quick to ask questions, they often make the wrong judgements about inventory strategies. The goal of this report is to improve this dialogue.
Most companies have invested in many inventory optimization solutions over the last decade. Within the company, there is mounting frustration about the failure of these projects to actualize and maintain targets. What most companies fail to realize is that the technology strategy needs to be worked in concert with supply chain strategy. Often we find while companies improve inventory levels through the deployment of inventory technologies, operational decisions to widen the item master or lengthen the supply chain will undermine the project targets.
There are many drivers of inventory, and the management of inventory levels requires discipline and a cross-functional focus. It is a story of people, process, and technology. Let’s start with people. Today, fewer than 5% of companies have an end-to-end focus (as defined from the customer’s customer to the supplier’s supplier), and most companies lack alignment and balance. The largest gaps between are between operational and commercial groups. (Cecere L. , Three Techniques to Improve Organizational Alignment, 2013). As companies close the organizational gap, progress is made on inventory. Likewise, when it comes to balance, 68% of organizations surveyed lack balance in Sales and Operations Planning between the commercial groups (the “S”) and the operational groups (the “OP), When balance is achieved, the organization rates itself as more agile, and aligned, and there is an 11% improvement in inventory turns (Cecere L. , Research in Review, 2014).
Supply chain processes are now over 30-years old. While there is a generalized belief that maturity of supply chain processes has improved inventory turns, as can be seen in Figure 2, the improvements in cash-to-cash have primarily been driven by lengthening payables. In industries like beverage, pharmaceuticals, consumer packaged goods and medical device, the industry averages have gone backwards (inventory turns have decreased not increased). Only the food and apparel industries have posted double-digit improvements in inventory turns. Why? Food and apparel are largely regional supply chains which are maturing. They lag consumer packaged goods in supply chain maturity. While consumer packaged goods companies are more mature, they are more global. The rise of the global multinational has greatly impacted inventory requirements.
Inventory Optimization in a Market-Driven World - 27 APR 2015Lora Cecere
Executive Overview
Growth is slowing and the complexity in today’s supply chain is unprecedented. As a result, within a company, inventory management is often a hot issue. Shrinking inventory spins off a one-time, and highly desirable, cash windfall. In most industries there is a connection between market capitalization and inventory management. This drives pressure to reduce inventory and question existing practices. However, while companies are quick to ask questions, they often make the wrong judgements about inventory strategies. The goal of this report is to improve this dialogue.
Most companies have invested in many inventory optimization solutions over the last decade. Within the company, there is mounting frustration about the failure of these projects to actualize and maintain targets. What most companies fail to realize is that the technology strategy needs to be worked in concert with supply chain strategy. Often we find while companies improve inventory levels through the deployment of inventory technologies, operational decisions to widen the item master or lengthen the supply chain will undermine the project targets.
There are many drivers of inventory, and the management of inventory levels requires discipline and a cross-functional focus. It is a story of people, process, and technology. Let’s start with people. Today, fewer than 5% of companies have an end-to-end focus (as defined from the customer’s customer to the supplier’s supplier), and most companies lack alignment and balance. The largest gaps between are between operational and commercial groups. (Cecere L. , Three Techniques to Improve Organizational Alignment, 2013). As companies close the organizational gap, progress is made on inventory. Likewise, when it comes to balance, 68% of organizations surveyed lack balance in Sales and Operations Planning between the commercial groups (the “S”) and the operational groups (the “OP), When balance is achieved, the organization rates itself as more agile, and aligned, and there is an 11% improvement in inventory turns (Cecere L. , Research in Review, 2014).
Supply chain processes are now over 30-years old. While there is a generalized belief that maturity of supply chain processes has improved inventory turns, as can be seen in Figure 2, the improvements in cash-to-cash have primarily been driven by lengthening payables. In industries like beverage, pharmaceuticals, consumer packaged goods and medical device, the industry averages have gone backwards (inventory turns have decreased not increased). Only the food and apparel industries have posted double-digit improvements in inventory turns. Why? Food and apparel are largely regional supply chains which are maturing. They lag consumer packaged goods in supply chain maturity. While consumer packaged goods companies are more mature, they are more global. The rise of the global multinational has greatly impacted inventory requirements.
Value chain methodology: Potential use by the Ethiopian Livestock Feed (ELF) ...ILRI
Presented by Getachew Legese (EIAR) at the inception meeting for the ‘Fodder and feed in livestock value chains in Ethiopia’ project, ILRI, Addis Ababa, 21-22 February 2012
2015 MHI Annual Industry Report – Supply chain innovation – Making the imposs...Leon Eymael
This year’s annual MHI Industry Report, developed in collaboration with Deloitte, delves more deeply into supply chain challenges with a specific lens on how technology innovation can help illuminate the path to the supply chain of the future.
The report show research findings of existing sales and distribution practices of a few leading food companies and discusses ways to improve the sales through revision in sales and distribution policies
Business Driven Information Systems 8th Edition by Paige Baltzan solution man...ssuserf63bd7
https://qidiantiku.com/solution-manual-for-business-driven-information-systems-8th-edition-by-paige-baltzan.shtml
Full download please contact u84757(at)protonmail(dot)com or qidiantiku(dot)com
https://qidiantiku.com/solution-manual-for-business-driven-information-systems-8th-edition-by-paige-baltzan.shtml
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
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.
2
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
4
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
6
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
7
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.
8
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
9
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
10
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
11
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
12
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
13
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
15
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.
16
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.
17
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.
18
Editor's Notes
To solve the above representation, we first took all the relationship between all criteria in a matrix
form, and deployed the values of each according to all other as per our findings.
After finding the relationship of the top goal with all its criteria, we get a priority matrix. This represents the dependency of our goal on each of the criteria. After finding the priority matrix, we next find the relationship of each criterion with one another.