1. Research Project Report
ON
“Launching New Brands in Sports & Casual Category
: A Strategic Perspective”
Submitted
By
Rishu Singh
Under the Supervision/Guidance
Of
C.C. Dr.Goutam Saha
IN PARTIAL FULFILLMENT OF THE POST GRADUATE DEGREE "MASTER OF FASHION
MANAGEMENT (MFM)"
Submitted to
Department of Fashion Management Studies (FMS)
National Institute of Fashion Technology (NIFT)
IDCO Plot No. 24, Chandaka Industrial Estate
Bhubaneswar, PIN - 751024
Ph. 91 674 2305700, Fax: 2304710
Web: www.nift.ac.in
May, 2016
2. DECLARATION
I, Rishu Singh hereby declare that the Project entitled ―“Launching New Brands in Sports & Casual
Category: A Strategic Perspective‖ submitted towards, partial fulfilment of the Degree of Master of
Fashion Management is my original work and no part of the project has been copied from any other reports
or any other work carried by someone else which has been submitted for any other degree/award. However,
any material taken from any other published source has been suitably referred and acknowledged at various
places.
Name: Rishu SIngh
Roll Number: MFM/14/106
Batch: 2014-2016
Centre BHUBANESWAR
Date:
Place: BHUBANESWAR
3. CERTIFICATE
This is to certify that the Project entitled “Launching New Brands in Sports & Casual
Category: A Strategic Perspective” submitted towards the partial fulfilment of the Degree of
Master of Fashion Management by Rishu SIngh is his/her original work under my guidance and
the results are based on the research done by him.
(Dr.Goutam Saha)
Name of Guide/Designation
Date:
Place: BHUBANESWAR
4. ACKNOWLEDGEMENT
I am grateful to NIFT for providing me an opportunity to do research work on ―Launching New Brands in
Sports & Casual Category: A Strategic Perspective‖. I express my whole hearted thanks to my guide
Dr.Goutam Saha, for his encouragement and moral support in organizing my work and giving me valuable
tips for making it presentable.
I am indebted to Mr Akash Sharma, Manager(e-commerce), my industry mentor who has guided and
supervised me throughout this study. I have no words to express my gratitude to her.
My thanks are also due to Mr Aayush Goenka, MD of Calzini Fashion Ltd. for his advice in collecting data
and other relevant information.
I will be failing in my duty if I do not mention the name of my CC Dr.Goutam Saha and other faculty
members for their help in my Degree Project
NAME: Rishu Singh
Master of Fashion Management
Date of submission:
5. OBJECTIVES
To study different strategic management models for launching new brands efficiently in e-commerce
market place
To provide informed decision about further investments in different brands for e-commerce.
To modify existing strategies as well as to make new strategies for upcoming new brands in e-
commerce market place.
Analyzing all the available brands at e-commerce market place in socks category w.r.t. prices,
discounts and socks
SIGNIFICANCE OF STUDY
The study will Calzini Fashion Ltd. to modify existing strategies as well as to make new strategies for
upcoming new brands in e-commerce market place.
METHODOLOGY
We analyzed Competitiveness of Indian Socks Market through Porter‘s 5 Forces Model.
Intra Company Brand Analyses is done through BCG, Directional Policy and Analytical Hierarchy
Process Matrix.
We have applied Concept of Central Tendency by analyzing Mean and Standard Deviation of the
existing data set of Stocks, Price and Discount.
6. TABLE OF CONTENT
LIST OF FIGURES......................................................................................................................................................................................7
LIST OF TABLES........................................................................................................................................................................................8
LIST OF ABBREVATION........................................................................................................................................................................9
ABSTRACT ......................................................................................................................................................................................1
1. INTRODUCTION ...................................................................................................................................................................3
1.1. GENERAL..................................................................................................................................................................................................3
1.2. ABOUT THE PROJECT.........................................................................................................................................................................5
1.2.1. GENERAL..............................................................................................................................................................................................5
1.2.1.1. PROBLEM DEFINITION..............................................................................................................................................................5
1.2.1.2. RESEARCH GAP..............................................................................................................................................................................5
1.2.1.3. RESEARCH OBJECTIVE...............................................................................................................................................................5
1.3. SPECIFICS ABOUT THE PROJECT..................................................................................................................................................6
1.4. PROJECT ELEMENTS ..........................................................................................................................................................................6
BRANDS UNDER CALZINI (E-COMMERCE)...........................................................................................................................................6
1.5. PROJECT RESULTS...............................................................................................................................................................................6
1.6. PICTURES OF SAMPLE SOCKS OF ‘MOJEME’ AND ‘STEP- SOCKS’...............................................................................7
1.7. SIGNIFICANCE OF THE STUDY ......................................................................................................................................................7
2. LITERATURE REVIEW ........................................................................................................................................................8
Application:..........................................................................................................................................................................................................9
Dimensions: ......................................................................................................................................................................................................10
Relative market share..........................................................................................................................................................................10
Market growth rate...............................................................................................................................................................................10
Limitation:.........................................................................................................................................................................................................10
Application:.......................................................................................................................................................................................................11
Dimensions:.......................................................................................................................................................................................................11
Attractiveness of a Market Segment..............................................................................................................................................11
Capability of the organization ........................................................................................................................................................11
Inferences from Directional Policy Matrix..........................................................................................................................................12
Limitation:.........................................................................................................................................................................................................12
General ...............................................................................................................................................................................................................12
Framework........................................................................................................................................................................................................13
Application........................................................................................................................................................................................................16
Limitation ..........................................................................................................................................................................................................17
3. SOCKS INDUSTRY ANALYSIS ......................................................................................................................................... 18
4. BCG MATRIX (E-COMMERCE) ....................................................................................................................................... 19
5. DIRECTIONAL POLICY MATRIX ................................................................................................................................... 21
6. ANALYTICAL HIERARCHY PROCESS .......................................................................................................................... 22
1.1. MATHEMATICAL MODEL ..............................................................................................................................................................22
7. FINDINGS ............................................................................................................................................................................ 25
8. CONCLUSION....................................................................................................................................................................... 26
9. COMPARATIVE ANALYSIS.............................................................................................................................................. 26
9.1. COMPARATIVE STUDY ..................................................................................................................................................................26
9.2. SETTING OF PRICE, DISCOUNT AND STOCK DISTRIBUTION ......................................................................... 27
9.3. INFERENCES DEDUCTED FROM THE STUDY......................................................................................................................27
10. RESULTS ........................................................................................................................................................................... 31
11. REFERENCE ...................................................................................................................................................................... 32
7. LIST OF FIGURES
FIGURE 1: STEP SOCKS SAMPLE ...................................................................................................7
FIGURE 2: MOJEME SOCKS SAMPLE..............................................................................................7
FIGURE 3 SOCKS INDUSTRY ANALYSIS.......................................................................................18
FIGURE 4 BCG MATRIX .............................................................................................................20
FIGURE 5 DIRECTIONAL POLICY MATRIX ...................................................................................21
FIGURE 6 AHP MATRIX..............................................................................................................25
FIGURE 7 STOCK DISTRIBUTION CHART .....................................................................................28
FIGURE 8 STOCK DISTRIBUTION FOR DIFFERENT PRICE POINTS..................................................29
FIGURE 9 STOCK DISTRIBUTION OVER DISCOUNT STRUCTURE...................................................30
8. LIST OF TABLES
TABLE 1 BCG MATRIX CALCULATION.......................................................................................20
TABLE 2 NORMALIZED CRITERIA COMPARISON MATRIX ...........................................................22
TABLE 3 CONSISTENCY TEST MATRIX........................................................................................23
TABLE 4 SUB CRITERIA MATRIX- AESTHETICS ...........................................................................23
TABLE 5 SUB CRITERIA MATRIX- COMFORT ..............................................................................23
TABLE 6 SUB CRITERIA TABLE- TECHNOLOGY ..........................................................................24
TABLE 7 SUB CRITERIA MATRIX- HEALTH.................................................................................24
TABLE 8 SUB CRITERIA MATRIX: BRAND PREMIUM...................................................................24
TABLE 9 WEIGHTED BENEFITS- COST TABLES ...........................................................................25
TABLE 10 PRICE POINTS .............................................................................................................26
TABLE 11 DISCOUNT STRUCTURE ..............................................................................................27
TABLE 12 STOCK DISTRIBUTIONS...............................................................................................27
TABLE 13 STOCK DISTRIBUTION FOR DIFFERENT PRICE POINTS .................................................28
TABLE 14 TABLE OF COEFFICIENT OF VARIANCE FOR DIFFERENT PRICE POINTS........................29
TABLE 15 STOCK DISTRIBUTION OVER DISCOUNT STRUCTURE..................................................30
TABLE 16 COEFFICIENT OF VARIATION FOR DIFFERENT DISCOUNT STRUCTURE ........................30
9. LIST OF ABBREVATION
S.NO. ABBREVATIONS FULL FORM
1. PO1 Pack Of 1
2. PO2 Pack Of 2
3. PO3 Pack Of 3
4. PO4 Pack Of 4
5. PO5 Pack Of 5
6. BCG Matrix Boston Consulting Group
7. DPM Directional Policy Matrix
8. AHP Analytical Hierarchy Process
10. 1
ABSTRACT
The growing number of multi-business companies (McKinsey & Company, 2008) incorporated in
recent decades has made it necessary for these same companies to manage and keep profitable
various Strategic Business Units (SBUs) – ―a grouping of functional units that have the
responsibility for profit (or losses) of part of the organization‘s core businesses‖ (Kerzner, 2009, p.
128) – that have become increasingly autonomous both strategically and in terms of functional
support (Chakravarthy & Henderson, 2007). However, various aspects of portfolio planning
models have been strongly criticized when used in diversified companies; in particular, because
they are subject to multiple free interpretations (Wind et al., 1983), additionally for the absence of
descriptive elements relative to the potential collaborative relationship between the various SBUs,
and for the lack of meaningful directives in terms of a concrete approach to dealing with the
competition (Gluck & Kauffman, 1979). In the past, also academic research began to focus on the
relationships between diversification strategies and company performance (Ramanujam &
Varadarajan, 1989), in particular in an attempt to show how companies characterized by a portfolio
of unrelated activities (conglomerates) or by a series of vertically integrated activities are less
productive than companies with a portfolio of correlated activities (Rumelt, 1982). Nevertheless,
even though some attempts to apply market analysis frameworks to the fashion business can be
found – e.g., the Ansoff matrix (Chiari, 2009, p. 46), there is no one particular study in the
literature that points out, through a methodological application, the potential of the GE/McKinsey
Matrix in the managerial sphere specific to companies that operate in the fashion industry, in terms
of decision-making support for strategies of both portfolio and competitive analyses. Indeed, it is
well known that there has been a worldwide trend in recent decades toward internationalization of
fashion companies (particularly in the luxury market); and the development of diversification
strategies based on an increase in the number of products and/or brands held in their portfolios
(Amatulli, 2009). These are strategies which are successful, in particular for multi-brands and
particularly in the long-term if accompanied by reasoning that is not purely financial, the correct
vision of the sectorial specificity and right balance between brand autonomy and integration
(Carcano & Rovetta, 2009). India is adding three Internet users every second and is already the
second-largest Internet market globally in terms of users, according to the report. India expect
Internet penetration to increase from 32 per cent in 2015 to 59 per cent in 2020, translating to a
near-doubling of the Internet user base. It estimates India will have almost 320 million online
shoppers by 2020 compared with 50 million in 2015. The top three online retail platforms
dominated the Indian ecommerce market in 2015 with a combined market share of 83 per cent.
Flipkart, including Myntra, maintained its top position with a 45 per cent market share, followed
by Snapdeal (ex-Freecharge) at 26 per cent and Amazon India at 12 per cent. Paytm had a 7 per
11. 2
cent share. At $13.8 billion, the GMV of the top three e-commerce companies exceeded that of the
top 10 offline retailers at $12.6 billion last year. Mobile is set to become the dominant channel,
with more than 500 million consumers already using phones. Smartphones will account for more
than 90 percent of handsets in the market by 2020. At the same time, it estimated that around 45
million people in India transacted online in 2015, representing only 14 percent of all those
connected: ―This means there is a huge opportunity for growth and the opportunity to incorporate
new technology for customer engagement, real-time data feedback, social engagement,
recommendations and 3-D fitting rooms.‖
There is a gap in existing research literature to apply strategic management in its Indian Fashion e-
commerce market. In this research paper we try to explain how AHP matrix along with GE matrix
can be used to strategise different brands under a company and how concept of simple Central
Tendency can be used as a powerful tool to set Price, Discounts and Distribute Stocks of any new
brands to be launched by a company.
In Socks Industry, aproximately 64% of market share of the branded socks at e-commerce market
place is dominated by Sports/Casual Category and at present Calzini Fashions Ltd do not have any
brand that caters the demand of this segment. Hence, to launch new brands under this segment for
e-commerce channel, a comparative study of brands available at e-retail along with Brand Strategy
Management Study needs to done, to strategise the upcoming brands as well as to take decisions
on pricing, discount and stock distribution.
12. 3
1. INTRODUCTION
1.1. GENERAL
The growing number of multi-business companies (McKinsey & Company, 2008) incorporated
in recent decades has made it necessary for these same companies to manage and keep profitable
various Strategic Business Units (SBUs) – ―a grouping of functional units that have the
responsibility for profit (or losses) of part of the organization‘s core businesses‖ (Kerzner, 2009,
p. 128) – that have become increasingly autonomous both strategically and in terms of functional
support (Chakravarthy & Henderson, 2007). In response to this complex issue, the classic and
still valid GE/McKinsey Matrix stands out among the various alternatives introduced, in
particular by consulting companies. However, various aspects of portfolio planning models have
been strongly criticized when used in diversified companies; in particular, because they are
subject to multiple free interpretations (Wind et al., 1983), additionally for the absence of
descriptive elements relative to the potential collaborative relationship between the various
SBUs, and for the lack of meaningful directives in terms of a concrete approach to dealing with
the competition (Gluck & Kauffman, 1979). In the past, also academic research began to focus
on the relationships between diversification strategies and company performance (Ramanujam &
Varadarajan, 1989), in particular in an attempt to show how companies characterized by a
portfolio of unrelated activities (conglomerates) or by a series of vertically integrated activities
are less productive than companies with a portfolio of correlated activities (Rumelt, 1982).
Nevertheless, even though some attempts to apply market analysis frameworks to the fashion
business can be found – e.g., the Ansoff matrix (Chiari, 2009, p. 46), there is no one particular
study in the literature that points out, through a methodological application, the potential of the
GE/McKinsey Matrix in the managerial sphere specific to companies that operate in the fashion
industry, in terms of decision-making support for strategies of both portfolio and competitive
analyses. Indeed, it is well known that there has been a worldwide trend in recent decades toward
internationalization of fashion companies (particularly in the luxury market); and the
development of diversification strategies based on an increase in the number of products and/or
brands held in their portfolios (Amatulli, 2009). These are strategies which are successful, in
particular for multi-brands and particularly in the long-term if accompanied by reasoning that is
not purely financial, the correct vision of the sectorial specificity and right balance between
brand autonomy and integration (Carcano & Rovetta, 2009). At the same time, the competitive
scenario has widened both in terms of market – with the new B.R.I.C. (Brazil, Russia, India, and
China) emerging markets – and in terms of competitors – with new firms coming out of markets
still developing their specialization or which were simply too far away previously (Jin, 2004;
Taplin & Winterton, 2004). This evolution of the fashion industry market points out the need,
13. 4
even more clearly, for organizations operating in this sector to know about and use to their best
advantage analysis tools that have already been proven effective and relatively rapid to use.
India is adding three Internet users every second and is already the second-largest Internet
market globally in terms of users, according to the report. India expect Internet penetration to
increase from 32 per cent in 2015 to 59 per cent in 2020, translating to a near-doubling of the
Internet user base. It estimates India will have almost 320 million online shoppers by 2020
compared with 50 million in 2015. The top three online retail platforms dominated the Indian
ecommerce market in 2015 with a combined market share of 83 per cent. Flipkart, including
Myntra, maintained its top position with a 45 per cent market share, followed by Snapdeal (ex-
Freecharge) at 26 per cent and Amazon India at 12 per cent. Paytm had a 7 per cent share. At
$13.8 billion, the GMV of the top three e-commerce companies exceeded that of the top 10
offline retailers at $12.6 billion last year. Mobile is set to become the dominant channel, with
more than 500 million consumers already using phones. Smartphones will account for more than
90 percent of handsets in the market by 2020. At the same time, it estimated that around 45
million people in India transacted online in 2015, representing only 14 percent of all those
connected: ―This means there is a huge opportunity for growth and the opportunity to
incorporate new technology for customer engagement, real-time data feedback, social
engagement, recommendations and 3-D fitting rooms.‖
CALZINI FASHIONS LTD is Founded in 1990 and is counted among the leading
manufacturers, traders and exporters of Organic Socks. It is an Indo-Swiss-Italian venture in
collaboration with Calzificio Kim SRL (Italy) and Intertrend AG (Switzerland) and a known
name in the world of fashion. Our product list is comprised of socks, caps and handkerchiefs.
Calzini's manufacturing facility is located in NOIDA. It is the first NSIC approved organic socks
manufacturing facility in India. The factory manufactures 3,50,000 pairs of socks each month
using state of the art fully computerized knitting machines. The company manufacture organic
socks from GOTS (Global Organic Textile Standards) certified organic cotton, strictly as per the
MHA (Ministry of Home Affairs) specifications for Organic Socks for paramilitary forces. All
the socks of the company are tested regularly at NABL approved laboratories and fully comply
with the MHA specifications for Organic Socks. The company sells its production into India and
also exports to buyers in Europe, UK, Middle east and Australia. Besides selling to retailers
under its own/licensed brands (Arrow, Calzini, Flying Machine, Hush Puppies, Savile Row Co);
The company also working as the socks vendor/partner to various brands and private labels
including Bata, Reliance, Spencer‘s, Metro Cash & Carry, Metro Shoes, SG Sport, etc.
In Socks Industry, aproximately 64% of market share of the branded socks at e-commerce
market place is dominated by Sports/Casual Category and at present Calzini Fashions Ltd do not
have any brand that caters the demand of this segment. Hence, to launch new brands under this
segment for e-commerce channel, a comparative study of brands available at e-retail along with
14. 5
Brand Strategy Management Study needs to done, to strategise the upcoming brands as well as to
take decisions on pricing, discount and stock distribution.
There is a gap in existing research literature to apply strategic management in its Indian Fashion
e-commerce market. In this research paper we try to explain how AHP matrix along with GE
matrix can be used to strategise different brands under a company and how concept of simple
Central Tendency can be used as a powerful tool to set Price, Discounts and Distribute Stocks of
any new brands to be launched by a company.
1.2. ABOUT THE PROJECT
1.2.1. GENERAL
1.2.1.1. PROBLEM DEFINITION
Possible modification in existing strategies of the brands under CALZINI
FASHIONS LTD serving e-commerce channel.
Provide necessary information required to launch new brands under Sports and
Fashion Socks Category for e-commerce market place.
No brand under CALZINI FASHIONS LTD was serving Sports and Fashion
Category, which constitutes of 64% of branded socks category.
1.2.1.2. RESEARCH GAP
There is a gap in existing research literature to apply strategic management in
its Indian Fashion e-commerce market. In this research paper we try to explain
how AHP matrix along with GE matrix can be used to strategise different
brands under a company and how concept of simple Central Tendency can be
used as a powerful tool to set Price, Discounts and Distribute Stocks of any
new brands to be launched by a company.
1.2.1.3. RESEARCH OBJECTIVE
To study performance of the existing brands under the Company and suggest
possible modifications in the strategies to further improve each brand
performance.
To study different strategic management models for launching new brands
efficiently in e-commerce market place.
To provide informed decision about further investments in different brands for
To research well-known and high rating socks brands available on top six e-
commerce portals for setting up Price and Discount for the brands to be launched
at e-commerce market place under Calzini Fashions Ltd.
15. 6
1.3. SPECIFICS ABOUT THE PROJECT
This project aims to provide all the necessary information required to launch new brands in
Sports/Fashion Category at e-commerce market place to CALZINI FASHIONS LTD. To serve
the purpose, Brand Extension theories are used to strategize existing as well as upcoming brands
under the company for e-commerce channel. For setting up Prices, Discounts and Stock
Distribution, six different e-commerce portals, namely Amazon, Flipkart, Jabong, Snapdeal,
Myntra and Paytm has been researched and concept of Central Tendency is used.
1.4. PROJECT ELEMENTS
BRANDS UNDER CALZINI (E-COMMERCE)
1.5. PROJECT RESULTS
Brand ‗MOJEME‘ is launched under Sports Socks category at e-commerce market place.
Brand ‗STEP SOCKS‘ is launched under Fashion/Casual Socks category on e-commerce
platform.
16. 7
1.6. PICTURES OF SAMPLE SOCKS OF „MOJEME‟ AND „STEP- SOCKS‟
Figure 1: Step Socks Sample
Figure 2: Mojeme Socks Sample
1.7. SIGNIFICANCE OF THE STUDY
This project will help CALZIINI FASHIONS LTD to modify its existing strategies for the its
brands for e-commerce channel as well as to decide on strategies for their upcoming brands at e-
commerce market place.
17. 8
2. LITERATURE REVIEW
A company offers a variety of product lines, each requiring a certain investment and promising a
certain return on that investment. In this view of operations, top management‘s role is to
determine the products (or businesses) that will comprise the portfolio and to allot funds to them
on some rational basis. A number of product portfolio models have appeared over the past
several years to assist management in this task. Examples are the growth/share matrix, the
business profile matrix, the business assessment array, and the directional policy matrix.
Framework for Design
Analysis of a product portfolio requires seven major steps:
1. Establishing the level and unit of analysis and determining what links connect them.
2. Identifying the relevant dimensions, including single-variable and composite.
3. Determining the relative importance of the dimensions.
4. To the extent that two or more dimensions are viewed as dominant, constructing a matrix
based on them.
5. Locating the products or businesses on the relevant portfolio dimensions.
6. Projecting the likely position of each product or business on the dimensions if (a) no changes
are expected in environmental conditions, competitive activities, or the company‘s strategies and
if (b) changes are expected.
7. Selecting the desired position for each existing and new product (as a basis for developing
alternative strategies to close the gap between the current and new portfolios) and deciding how
resources might best be allocated among these products.
The Role of Brand Portfolio Management
The proliferation of brands not only across the entire spectrum of a firm‘s products, but
also as a key firm asset, has made brand strategy a key element of corporate strategy. Central to
any brand strategy is brand portfolio management - the ability to organize all the firm‘s brands
into a coherent brand portfolio and manage the complex interrelationships among brands in these
portfolios. This process has become crucial for every company with multiple brands because the
objective is to ensure not only that individual brands are successful, but also that the firm‘s
overall group of brands is well coordinated and holistic. Well-managed brand portfolios create
advantages throughout the firm, from avoiding consumer confusion to ensuring internal
efficiency by preventing investment in overlapping product-development and/or marketing
efforts (Carlotti, Coe, and Perrey, 2004). The far-reaching impact of brand portfolio decisions on
a company‘s key economic measures highlights the importance of effective brand portfolio
management not only in a marketing program‘s success, but also in the overall success of a
18. 9
company (Morgan and Rego, 2006; Tybout, Calkins, and Kotler, 2005). Companies managing
brand portfolios must address two primary tasks: (1) optimizing the structure of the brand
portfolio so that existing brands meet consumer preferences and enhance the firm‘s performance,
and (2) adapting the firm‘s brand portfolio to changes in the market environment and in the
strategic direction of the firm. The first task requires constant monitoring of the brand portfolio
to avoid cannibalization among brands while enhancing the synergistic effects between a
company‘s brands. Adapting a competitive portfolio to the constantly changing business
environment requires that brand portfolio managers integrate strategic decisions and
environmental information while engaging in some form of brand portfolio restructuring. Three
fundamental options are available for brand portfolio restructuring: (a) reorganizing the portfolio
by repositioning brands, (b) rationalizing the portfolio through the deletion and/or divestiture of
existing brands, and/or (c) expanding the portfolio by adding new brands (Aaker, 2004). While
portfolio restructuring may occur using any option alone or in any combination, each option
presents the firm with distinctive issues and approaches to managing not only individual brands
but also the overall portfolio. Although all three brand-portfolio restructuring options are viable
and widely used, this research will focus exclusively on the third option: brand portfolio
expansion.
SELECTED BRAND PORTFOLIO
BCG GROWTH/SHARE MATRIX
Application:
To be successful, a company should have a portfolio of products with different growth
rates and different market shares. The portfolio composition is a function of the balance
between cash flows. High growth products require cash inputs to grow. Low growth
products should generate excess cash. Both kinds are needed simultaneously.
STAR QUESTION MARK
?
CASH COW
DOG
RelativeMarketShare
Market Growth
19. 10
Dimensions:
Relative market share
This indicates likely cash generation, because the higher the share the more cash will
be generated. As a result of 'economies of scale' (a basic assumption of the BCG
Matrix), it is assumed that these earnings will grow faster the higher the share. The
exact measure is the brand's share relative to its largest competitor.
Market growth rate
Rapidly growing in rapidly growing markets, are what organizations strive for; but, as
we have seen, the penalty is that they are usually net cash users – they require
investment. The reason for this is often because the growth is being 'bought' by the
high investment, in the reasonable expectation that a high market share will eventually
turn into a sound investment in future profits. The theory behind the matrix assumes,
therefore, that a higher growth rate is indicative of accompanying demands on
investment.
Limitation:
The apparent implication of its four-quadrant form is that there should be balance of
products or services across all four quadrants; and that is, indeed, the main message
that it is intended to convey. Thus, money must be diverted from `cash cows' to fund
the `stars' of the future, since `cash cows' will inevitably decline to become `dogs'.
There is an almost mesmeric inevitability about the whole process. It focuses
attention, and funding, on to the `stars'. It presumes, and almost demands, that `cash
cows' will turn into `dogs'.
No consideration of risk, No weighting of dimensions.
Stringent as dimensions are pre-defined.
20. 11
DIRECTIONAL POLICY MATRIX
Application:
The Directional Policy Matrix measures the attractiveness of a segment and the capability
of the organization to support that segment.
Dimensions:
Attractiveness of a Market Segment
Evaluating the attractiveness of a segment should include but not be limited to, these
variables:
Size of the segment (number of customers, units or $ sales)
Growth rate of the segment (a very important variable)
Profit margins of the segment to the sales organization
Ongoing purchasing power of the segment
Attainable market share given promotional budget, fragmentation of the market
and competitors‘ promotional expenditures
Required market share to break even.
Capability of the organization
Evaluating the capability of the organization to meet the needs of the segments should
include, but not be limited to, these variables analyzed against the competition:
Competitive capability of the organization against the marketing mix
(product/service, place, price and promotion)
Access to distribution channels
AVERAGE
WEA
K
AVERAGE
STRONG
UNATTRACTIVE ATTRACTIVE
Disinvest
Phase
Withdrawal
Cash
Generation
Phased
Withdrawal
Custodial
Growth
Growth
Leader
Leader
Try
Harder
Double
or QuitCompany’s
Competitive
Capabilities
Prospects of Profitability
21. 12
Capital and human resource investment required to serve the segment
Brand association of the organization in the eyes of the segment
Current market share/likely future market share.
Inferences from Directional Policy Matrix
The tactics for each sector descriptor are:
Leader – Focus your resources on segments in this sector.
Growth leader – Grow by focusing just enough resources here.
Cash Generator – Milk segments in this sector for expansion elsewhere.
Phased withdrawal – Move cash to segments with greater potential.
Custodial – Do not commit any more resources to segments in this sector.
Try harder –Determine if there are ways in which you can build your capability for
segments in this sector for low levels of cash.
Double or quit – Invest in your capability or get out of segments in this sector.
Divest – Liquidate or move assets used in segments in this sector as fast as you can.
Limitation:
The Shell directional policy matrix has been criticized on the grounds that, like the BCG
approach, it assumes that the same set of factors is universally applicable for assessing the
prospects of any product or business. Critics believe that the relevant factors and their
relative importance will vary both according to the firm‘s products and the individual
characteristics of each company. In addition, the matrix does not provide any guidelines on
how to implement the strategies suggested in each cell of the matrix.
ANALYTICAL HIERARCHY PROCESS
General
The analytic hierarchy process (AHP) is a structured technique for organizing and
analyzing complex decisions, based on mathematics and psychology. It was developed
by Thomas L. Saaty in the 1970s and has been extensively studied and refined since then.
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 government, business, industry,
healthcare, shipbuilding and education. Rather than prescribing a "correct" decision, the
AHP helps decision makers find one that best suits their goal and their understanding of
the problem. It provides a comprehensive and rational framework for structuring a
22. 13
decision problem, for representing and quantifying its elements, for relating those
elements to overall goals, and for evaluating alternative solutions.
Framework
A. Establishment of a structural Hierarchy
A complex decision is to be structured in to a hierarchy descending from an overall
objective to various criteria, sub criteria till the lowest level. The overall goal of the decision
is represented at the top level of the hierarchy. The criteria and the sub criteria, which
contribute to the decision, are represented at the intermediate levels. Finally the decision
alternatives are laid down at the last level of the hierarchy.
B. Establishment of comparative judgments
Once the hierarchy has been structured, the next step is to determine the priorities of
elements at each level. A set of comparison matrices of all elements in a level with to
respect to an element of the immediately higher level are constructed. The pair wise
comparisons are given in terms of how much element A is more important than element B.
The preferences are quantified using a nine – point scale that is shown inTable1.
C. Synthesis of priorities and measurement of consistency
The pair wise comparisons generate the matrix of rankings for each level of the hierarchy
after all matrices are developed and all pair wise comparisons are obtained, Eigen vectors
(relative weights) are obtained. Eigen Vector Method: Suppose we wish to compare a set of
‗n‘ objects in pairs according to their relative weights. Denote the objects by A1,A2,.....An
and their weights by w1,w2,.....wn.
A1 A2 ………………… An
A1 W1/w1 W1/w2 W1/wn
A2 W2/w1 W2/w2 W2/wn
:
:
An Wn/w1 Wn/w2 Wn/wn
Table 1 Matrix containing weights
The matrix shown in Table 1a) has positive entries everywhere and satisfies the reciprocal
property aji = 1/aij. It is called a reciprocal matrix. If we multiply this matrix by the transpose of
the vector wT = ( w1,w2,.....wn) we obtain the vector nw.
23. 14
Intensity of
Importance
Definition Explanation
1
Equal importance Two elements contribute equally to the property
3
Moderate importance of one
over another
Experience and judgment slightly favor one over
the other
5
Essential or strong
importance
Experience and judgment strongly favor one over
another
7 Very strong importance
An element is strongly favored and its dominance
is demonstrated in practice.
9
Extreme importance
The evidence favoring one element over another
is one of the highest possible order of affirmation
2,4,6,8 Intermediate values between
two adjacent judgments
Comprise is needed between two judgments
Reciprocals
When activity i compared to j is assigned one of the above numbers, the activity j
compared to i is assigned its reciprocal
Rational Ratios arising from forcing consistency of judgments
Table 2 Importance of Weights
Our problem takes the form Aw= nw. We started with the assumption that w was given.But if
we only had A and wanted to recover w, we would have to solve the system (A- nI) w = 0 in
the unknown w. This has a nonzero solution if n is an eigenvalue of A, i.e., it is a root of the
characteristic equation of A. But A has unit rank since every row is a constant multiple of the
i ,i=1,2,.....n of A are zero except one. Also it is known
that n column of A. These solutions differ by a multiplicative constant. However, this solution
is normalized so that its components sum to unity. The result is unique solution no matter
24. 15
which column is used. The matrix A satisfies the cardinal consistency property. The
consistency ratio is calculated as per the following steps i) Calculate the Eigen vector or the
max for each matrix of order n.ii) Compute the consistency index for
max – n ) / (n – 1) iii) The consistency ratio is
then calculated using the formulae CR = CI / RI ,where RI is a known random consistency
index obtained from a large number of simulation runs and varies depending upon the order
of the matrix .
Size of
matrix (n)
Random
consistency
index (RI)
1 0
2 0
3 0.52
4 0.89
5 1.11
6 1.25
7 1.35
8 1.4
9 1.45
10 1.49
Table 3 Consistency Index
The acceptable CR range varies according to the size of the matrix i.e. 0.05 for a 3 by 3
value of CR is equal to, or less than that value it implies that the evaluation within the matrix
is acceptable or indicates a good level of consistency in the comparative judgments
represented in that matrix. If CR is more than that acceptable value, inconsistency of the
judgments within the matrix has occurred and the evaluation process should be reviewed.
The Use of Pairwise Comparisons
One of the most crucial steps in many decision-making methods is the accurate estimation of
the pertinent data. This is a problem not bound in the AHP method only, but it is crucial in
many other methods which need to elicit qualitative information from the decision-maker.
Very often qualitative data cannot be known in terms of absolute values. For instance, "With
respect to Academics Criterion, what is the relative performance of EEE over ECE? "
Although information about questions like the previous one are Analytical Hierarchy Process
approach 867 vital in making the correct decision, it is very difficult, if not impossible, to
quantify them correctly. Therefore, many decision-making methods attempt to determine the
relative importance, or weight, of the alternatives in terms of each criterion involved in a
given decision-making problem. Pairwise comparisons are used to determine the relative
25. 16
importance of each alternative in terms of each criterion. In this approach the decision-maker
has to express his opinion about the value of one single pairwise comparison at a time.
Usually, the decision-maker has to choose his answer among 10-17 discrete choices. Each
choice is a linguistic phrase. Some examples of such linguistic phrases are: "A is more
important than B", or "A is of the same importance as B", or "A is a little more important than
B", and so on .The main problem with the pairwise comparisons is how to quantify the
linguistic choices selected by the decision maker during their evaluation. All the methods,
which use the pairwise comparisons, approach eventually, express the qualitative answers of a
decision maker into some numbers, which, most of the time, are ratios of integers. Since
pairwise comparisons are the keystone of these decision-making processes, correctly
quantifying them is the most crucial step in multi-criteria decision-making methods, which
use qualitative data. Pairwise comparisons are quantified by using a scale. Such a scale is a
one-to-one mapping between the set of discrete linguistic choices available to the decision
maker and a discrete set of numbers, which represent the importance, or weight, of the
previous linguistic choices. The scale proposed by Saaty is depicted in table 1. Others have
also proposed other scales.
Application
While it can be used by individuals working on straightforward decisions, the Analytic
Hierarchy Process (AHP) is most useful where teams of people are working on complex
problems, especially those with high stakes, involving human perceptions and judgments,
whose resolutions have long-term repercussions. It has unique advantages when important
elements of the decision are difficult to quantify or compare, or where communication among
team members is impeded by their different specializations, terminologies, or perspectives.
Decision situations to which the AHP can be applied include:
Choice – The selection of one alternative from a given set of alternatives, usually where
there are multiple decision criteria involved.
Ranking – Putting a set of alternatives in order from most to least desirable
Prioritization – Determining the relative merit of members of a set of alternatives, as
opposed to selecting a single one or merely ranking them
Resource allocation – Apportioning resources among a set of alternatives
The applications of AHP to complex decision situations have numbered in the
thousands, and have produced extensive results in problems involving planning, resource
allocation, priority setting, and selection among alternatives. Other areas have
included forecasting, total quality management, business process re-engineering, quality
26. 17
function deployment, and the balanced scorecard. Many AHP applications are never
reported to the world at large, because they take place at high levels of large organizations
where security and privacy considerations prohibit their disclosure.
Limitation
The AHP is included in most operations research and management science textbooks, and
is taught in numerous universities; it is used extensively in organizations that have
carefully investigated its theoretical underpinnings. While the general consensus is that it is
both technically valid and practically useful, the method does have its critics. Most of the
criticisms involve a phenomenon called rank reversal, discussed in the following section.
Rank reversal
Decision making involves ranking alternatives in terms of criteria or attributes of those
alternatives. It is an axiom of some decision theories that when new alternatives are added
to a decision problem, the ranking of the old alternatives must not change — that rank
reversal must not occur.
There are two schools of thought about rank reversal. One maintains that new alternatives
that introduce no additional attributes should not cause rank reversal under any
circumstances. The other maintains that there are some situations in which rank reversal
can reasonably be expected. The original formulation of AHP allowed rank reversals. In
1993, Forman introduced a second AHP synthesis mode, called the ideal synthesis mode,
to address choice situations in which the addition or removal of an 'irrelevant' alternative
should not and will not cause a change in the ranks of existing alternatives. The current
version of the AHP can accommodate both these schools—its ideal mode preserves rank,
while its distributive mode allows the ranks to change. Either mode is selected according to
the problem at hand.
Rank reversal and the AHP are extensively discussed in a 2001 paper in Operations
Research, as well as a chapter entitled Rank Preservation and Reversal, in the current
basic book on AHP. The latter presents published examples of rank reversal due to adding
copies and near copies of an alternative, due to intransitivity of decision rules, due to
adding phantom and decoy alternatives, and due to the switching phenomenon in utility
functions. It also discusses the Distributive and Ideal Modes of the AHP.
There are different types of rank reversals. Also, other methods besides the AHP may
exhibit such rank reversals. More discussion on rank reversals with the AHP and other
MCDM methods is provided in the rank reversals in decision-making page.
27. 18
Non-Monotony of some weight extraction methods
Within a comparison matrix one may replace a judgement with a less favourable
judgement and then check to see if the indication of the new priority becomes less
favourable then the original priority. In the context of tournament matrices, it has been
proven by Oskar Perron in, that the principal right eigenvector method is not monotonic.
This behaviour can also be demonstrated for reciprocal n x n matrices, where n > 3.
Alternative approaches are discussed in.
3. SOCKS INDUSTRY ANALYSIS
Porter's five forces analysis is a framework that attempts to analyze the level of competition
within an industry and business strategy development. It draws upon industrial organization (IO)
economics to derive five forces that determine the competitive intensity and therefore
attractiveness of an Industry. Attractiveness in this context refers to the overall industry
profitability. An "unattractive" industry is one in which the combination of these five forces acts
to drive down overall profitability. A very unattractive industry would be one approaching "pure
competition", in which available profits for all firms are driven to normal profit.
They consist of those forces close to a company that affect its ability to serve its customers and
make a profit. A change in any of the forces normally requires a business unit to re-assess
the marketplace given the overall change in industry information. The overall industry
attractiveness does not imply that every firm in the industry will return the same profitability.
Figure 3 Socks Industry Analysis
28. 19
4. BCG Matrix (E-COMMERCE)
The growth–share matrix is a chart to help corporations to analyze their business units, that is,
their product lines. This helps the company allocate resources and is used as an analytical tool
in brand marketing, product management, strategic management, and portfolio analysis. Some
analysis of market performance by firms using its principles has called its usefulness into
question.
Cash cows is where a company has high market share in a slow-growing industry. These units
typically generate cash in excess of the amount of cash needed to maintain the business. They
are regarded as staid and boring, in a "mature" market, yet corporations value owning them due
to their cash generating qualities.
Dogs, more charitably called pets, are units with low market share in a mature, slow-growing
industry. Though owning a break-even unit provides the social benefit of providing jobs and
possible synergies that assist other business units, from an accounting point of view such a unit
is worthless, not generating cash for the company. They depress a profitable company's return on
assets ratio, used by many investors to judge how well a company is being managed. Dogs, it is
thought, should be sold off.
Question marks (also known as problem children) are businesses operating with a low market
share in a high growth market. They are a starting point for most businesses. Question marks
have a potential to gain market share and become stars, and eventually cash cows when market
growth slows. If question marks do not succeed in becoming a market leader, then after perhaps
years of cash consumption, they will degenerate into dogs when market growth declines.
Question marks must be analyzed carefully in order to determine whether they are worth the
investment required to grow market share.
Stars are units with a high market share in a fast-growing industry.
The balanced portfolio has:
Stars whose high share and high growth assure the future.
Cash cows that supply funds for that future growth.
Question marks to be converted into stars with the added funds.
29. 20
Calculation:
Figure 4 BCG Matrix
This Matrix shows that Arrow is clearly Cash-Cow for the company for e-commerce channel
Calzini is somewhere between Cash-Cow and Star and Hush Puppies is Problem Child/Question
mark, which indicates strategies for Hush Puppies needs to be revised.
Market Growth
Arrow 1.146974789
Calzini 1.175230305
HP 1.196474513
Relative Market Share
Arrow 1
Calzini 0.299401188
HP 0.008892034
Table 4 BCG Matrix Calculation
30. 21
5. Directional Policy Matrix
The Directional Policy Matrix measures the attractiveness of a segment and the capability of the
organization to support that segment.
Attractiveness of a Market Segment
Evaluating the attractiveness of a segment should include but not be limited to, these variables:
Size of the segment (number of customers, units or $ sales)
Growth rate of the segment (a very important variable)
Profit margins of the segment to the sales organization
Ongoing purchasing power of the segment
Attainable market share given promotional budget, fragmentation of the market and
competitors‘ promotional expenditures
Required market share to break even.
Evaluating the capability of the organization to meet the needs of the segments should
include, but not be limited to, these variables analyzed against the competition:
Competitive capability of the organization against the marketing mix (product/service, place,
price and promotion)
Access to distribution channels
Capital and human resource investment required to serve the segment
Brand association of the organization in the eyes of the segment
Current market share/likely future market share.
Figure 5 Directional Policy Matrix
31. 22
6. Analytical Hierarchy Process
Analytical hierarchy process is a structured technique to manage complex decisions. It provides
a comprehensive and coherent approach to structuring the problem, quantifying its elements
related to the overall objectives and evaluating alternative solutions. It has been used in many
decisions in the field of economy, energy management, environmental, transport, agriculture,
industry and the military ones Structure of AHP method AHP method as a flexible model
for decision making, clarifying the issues which have several possible solutions. Decision by
AHP method can be divided into three different levels.
1.1. MATHEMATICAL MODEL
Calculation:
Criteria Comparison Matrix
Asthetics Comfort Technology Health Attributes
Brand
Premium
Asthetics 1.00 4.00 1.00 3.00 1.00
Comfort 0.25 1.00 0.20 0.33 0.25
Technology 1.00 5.00 1.00 3.00 3.00
Health Attributes 0.33 3.00 0.33 1.00 0.33
Brand Premium 1.00 4.00 0.33 3.00 1.00
Sum 3.58 17.00 2.86 10.33 5.58
Normalised Criteria Comparison Matrix
Asthetics Comfort Technology Health Attributes
Brand
Premium Weights
Asthetics 0.28 0.24 0.35 0.29 0.18 0.27
Comfort 0.07 0.06 0.07 0.03 0.04 0.06
Technology 0.28 0.29 0.35 0.29 0.54 0.35
Health Attributes 0.09 0.18 0.12 0.10 0.06 0.11
Brand Premium 0.28 0.24 0.12 0.29 0.18 0.22
1.00 1.00 1.00 1.00 1.00 1.00
Table 5 Normalized Criteria Comparison Matrix
Table 1 Criteria Comparison Matrix
34. 25
Brands Benefits Relative MRP
Arrow 0.69 2.50
Calzini 0.45 1.00
Hush Puppies 0.88 5.00
Figure 6 AHP Matrix
7. FINDINGS
Arrow Market Share needs to be increased to bring Arrow from Cash Cow to Star Segment.
If possible increase Calzini Market Share to bring Calzini position from Cash Cow to Star
Segment, so that premium can be charged.
Calzini needs to increase its benefits like Health Attributes, Aesthetics, Comforts to increase its
Competitiveness which will further enhance its position from Custodial to Growth Leader
Hush Puppies falls under Problem Child Category because its new to the company, hence further
investments should be made into this brand.
Profitability of the brand would further increase which will change Hush Puppies position from
Growth Leader to Leader
Maximum Selling Category in Socks is Sports/Fashion wear.
Calzini Fashions Ltd has no brand under Sports/Fashion Category.
Table 12 Weighted Benefits- Cost Tables
35. 26
8. CONCLUSION
New Brands to be launched under Sports and Fashion Category.
9. COMPARATIVE ANALYSIS
E-commerce Portals Selected for the Research are as follows:
1. Amazon
2. Flipkart
3. Jabong
4. Snapdeal
5. Myntra
6. Paytm
9.1. COMPARATIVE STUDY
Comparative Study of Pricing, Discount Structure and Stock is done according to pack size, of
the stock available on the above mentioned 6 online portals. This study is further used in making
decision regarding the price points, discount structure and stock distribution.
Following are the Price Points according to Pack Size that are taken into consideration for this
study:
MRP Range(INR)
MISSED PO1 <=289
NOT SURE PO1 =>290
MISSED PO2/3 <389
CFL RANGE PO2/3 390-489
CFL RANGE PO2/3 490-700
MISSED PO2/3 >700
MISSED PO4+ <=589
CFL RANGE PO4+ 590-789
CFL RANGE PO4+ 790-900
MISSED PO4+ >900
Table 13 Price Points
36. 27
Discount Structure according to Pack Size that are taken into consideration for this study:
Discount
Structure(%)
LOW <=29%
MED
30-
49%
HIGH =>50%
Table 14 Discount Structure
In the above two table Red Colour indicated the Price and Discount to be missed initially but
after the study the results were not in the favor of the initial decision and hence the modification
is made according to the study.
9.2. Setting of Price, discount and stock distribution
Setting of Price, discount and stock distribution is done through the comparative analysis using
Central Tendency and its consistency is checked via coefficient of variation which is less than
10% for each category.
9.3. INFERENCES DEDUCTED FROM THE STUDY
Stock Distribution over given 6 online portal:
E-commerce
Portals
Stock
Distribution%
Filpkart 15
Amazon 35
Jabong 11
Myntra 5
Snapdeal 22
Paytm 12
Table 15 Stock Distributions
37. 28
Figure 7 Stock Distribution Chart
Stock Distribution for different Price Points:
Price Points Stock%
PO1 <=289 11
PO1 =>290 11
PO2/3 <389 14
PO2/3 390-489 17
PO2/3 490-700 17
PO2/3 >700 4
PO4+ <=589 10
PO4+ 590-789 4
PO4+ 790-900 2
PO4+ >900 8
Table 16 Stock Distribution for different Price Points
38. 29
Figure 8 Stock Distribution for different Price Points
Above data is consistent and reliable because coefficient of variation is less than 10% for each
price point.
Table of Coefficient of Variation is as follows:
PO1 <=289 4.97
PO1 =>290 4.02
PO2/3 <389 2.25
PO2/3 390-489 2.21
PO2/3 490-700 3.56
PO2/3 >700 3.15
PO4+ <=589 4.44
PO4+ 590-789 3.93
PO4+ 790-900 1.02
PO4+ >900 6.25
Table 17 Table of Coefficient of Variance for different Price Points
39. 30
Stock Distribution for different Discount Structure:
Discount% Stock%
<30 19.93
30-49 22.01
>49 3.69
Table 18 Stock Distribution over Discount Structure
Figure 9 Stock Distribution over Discount Structure
Above data is consistent and reliable because coefficient of variation is less than 10% for each
discount category.
Table of Coefficient of Variation is as follows:
<=29% 3.4
7
30-
49%
8.2
0
=>50% 4.7
2
Table 19 Coefficient of Variation for different Discount Structure
40. 31
10. RESULTS
New brands launched:
Mojeme in Sports Category
Step Socks in Fashion/Casual Category
Mojeme Pricing is decided to be Premium, following Nike, Adidas and Reebok similar Pricing
and Discount Structure.
Step Socks Pricing is decided to be Value for Money, following Happy Socks similar Pricing
and Discount Structure
41. 32
11. REFERENCE
1. See George S. Day, ―Diagnosing the Product Portfolio,‖ Journal of Marketing,April 1977, p.
29.
2. Sidney Schoeffler, Robert D. Buzzell, and Donald F. Heaney, ―Impact of Strategic Planning
on Profit Performance,‖ HBR March–April 1974, p. 137.
3. Dan E. Schendel and G. Richard Patton, ―A Simultaneous Equation Model of Corporate
Strategy,‖ Management Science, November 1978, p. 1611; and Jean-Claude Larréché, ―On
Limitations of Positive Market Share-Profitability Relationships: The Case of the French
Banking Industry,‖ 1980 Educators’ Conference Proceedings (Chicago: American
Marketing Association, 1980), p. 209.
4. For a description of how GE uses environmental scenarios for this purpose, see Ian H.
Wilson, ―Reforming the Strategic Planning Process: Integration of Social and Business
Needs,‖ Long Range Planning, October 1974, p. 2.
5. See Derek F. Channon, ―Commentary on Strategy Formulation,‖ in Dan E. Schendel and
Charles W. Hofer, eds., Strategic Management (Boston: Little, Brown, 1979).
6. See ―Designing Product and Business Portfolios‖ by Yoram (Jerry) Wind and Vijay Mahajan
in Harvard Business Review (January 1981 issue)
7. Yana Kuzmina , ―Brand Portfolio Management and the Role of Brand Acquisitions ―
Louisiana State University and Agricultural and Mechanical College, August 2009
8. Michael Porter, Nicholas Argyres, Anita M. McGahan, "An Interview with Michael
Porter", The Academy of Management Executive
9. Fripp, Geoff.―BCG Matrix and the Experience Curve‖ Guide to the BCG Matrix
10. See ―making-good-strategic-choices-directional-policy-matrix‖, journal Thinking Factory
11. See ―Indian-e-commerce-market-set-for-fastest-growth‖, journal of Fibre2fashion, Feb, 2016
12. See ―Analytical Hierarchy Process approach – An application of engineering education‖,
Mathematica Aeterna, Vol. 2, 2012, no. 10, 861 - 878
13. See ―Strategic Analysis through the General Electric/McKinsey Matrix: An Application to
the Italian Fashion Industry‖, International Journal of Business and Management,
Vol. 6, No. 5; May 2011
