Boğaziçi University
Department of Management Information Systems
MIS 463 Decision Support Systems for Business
PROJECT FINAL REPORT
A DSS FOR STORE LOCATION SELECTION
Project Team No: 9
Zeynep Belya Akoğuz
Atilla Orgunmat
Eda Şen
Melisa Zenginkuzucu
Instructor : Aslı Sencer Erdem
İstanbul - December, 2010
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Table of Contents
I.1 The Decision Environment .................................................................................................... 3
I.2 Mission of Project .................................................................................................................. 4
I.3 Scope of Project ..................................................................................................................... 5
I.4 Methodology.......................................................................................................................... 5
II. LITERATURE SURVEY .......................................................................................................... 5
II.1 Location Attractiveness .......................................................................................................... 11
II.2 Psychographic Fit ............................................................................................................... 12
II.3 Demographic Fit ................................................................................................................. 13
II.4 Behavioral Fit ..................................................................................................................... 17
II.5 Constraints .......................................................................................................................... 19
IV. DEVELOPMENT OF THE DSS............................................................................................ 20
IV.1 DSS Architecture .............................................................................................................. 20
IV.2 Technical Issues ................................................................................................................ 21
IV.3 Model and Algorithms ...................................................................................................... 21
IV.4 User Interface and Reports ............................................................................................... 26
V. ASSESSMENT: ....................................................................................................................... 31
PROJECT PLAN ...................................................................................................................... 31
MASTER PLAN ....................................................................................................................... 31
VI. CONCLUSION....................................................................................................................... 32
APPENDIX ................................................................................................................................... 33
REFERENCES ............................................................................................................................. 34
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I. INTRODUCTION
I.1 The Decision Environment
Selecting a location for the stores of a clothing company can be a headache if the market is
unfamiliar, the demographic factors are different than the decision makers’. A mistake made
while entering a new market can have crucial consequences. Thus, we wanted to develop a
decision support system mainly help executives of clothing companies that directly sell their
products to end users and take positioning and profitability as their main criteria for location
selection and since it is a global trend think globally while expanding and take important
decisions every day.

Store selection is a complex decision often made without proper planning or sufficient
information. Store selection, since the qualitative data are as important as the quantitative data
includes managerial decisions and expertise. The most important aspect of location decision is
to assure that all factors that could possibly have any bearing on the decision are considered
carefully.
To select the right location; supply chain management, demographic related data and other
specific product/service attributes must be comprehensively examined.
In our project we examine clothing industry with respect to brand’s selections of location.
Opening a new store decision is made by the chief sales executive. When a clothing company
opens up a new business, they should decide the store’s location by considering the expected
profits, brands positioning and image.
The location selection decision is a onetime process. In this process there are two main
considerations profit expectations and brand positioning/image. The profit expectations are
shaped by the location which includes two subtitles;
 demographic attributes
 cost
The demographic attributes consists of;
 age interval
 income/consumption level,
 gender,
 education degree,
 occupation,
 wealth,
 population density.
The cost subtitle consists of;
 average store rent,
 average distribution cost,
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The second main consideration, location attractiveness includes these subsections;
 competition,
 magnet,
 convenience.
The other subtitles psychographic and behavioral fit;
 activities, interests and opinions,
 values and lifestyle,
 brand loyalty,
 price consciousness.
Since there are a lot of qualitative data, a manager decision based on experience/intuition must
be made. The chief sales executive will make the decision upon the relative changes in the
criteria which make the complexity of decision process very high.
There are also several bottlenecks in the decision making process and constraints. First of all,
this kind of clothing companies mostly have more than one brand under the umbrella and thus
they need to define each brand’s characteristics very clearly and distinctively. But on the other
hand, we need to define the strategies that we are focusing with each brand besides the
characteristics. For instance, if we are focusing on expanding to a new market with our luxury
brand, we only open our stores for middle class customers to the most profitable locations to
generate the cash that we will need and don’t concentrate on cash generation with our luxury
brand and focus more on opening new stores everywhere. These strategies lead us to constraints
and it is easier when the strategies and brand characteristics are clearly stated. But on the other
hand, the most crucial constraint is of course making profit from each store at each location. In
this case the cost and the three subtitles that make up the total cost which are average store rent,
distribution and advertisement costs are main constraints.
Currently, the companies use a more generalized system of selecting location while they are
expanding to a new country, but a more specific system for exact location selection within places
in cities is not much common.
A wrong decision in terms of forecasted profitability may make us lose a big amount of money
and reputation. Besides profitability, the other main criteria which is positioning can have bad
consequences when there is a misleading decision. For instance, once a luxury brand store is
opened to a location which mostly middle class citizens with low income levels are living the
positioning of this brand get affected badly and the general profit making lowers.
This program makes up a very big part of the expansion process of a clothing brand to a new
market which is full of surprises. Especially for a company with foreign directors, being
acquainted with the realities of one country and selecting right locations for its stores is a very
hard decision. The background information about the country and cities, even each location
within cities and the demographical structure of the country must be known and it takes a lot of
time for an executive to learn all these. At this point, our decision support system eases the job of
management by offering them right locations for right stores and maximizes profit while also
deals with better positioning.
I.2 Mission of Project
The choice of a store location has a profound effect on the entire business life. It is a complex
process to choose the most suitable store location for a brand since there are lots of attributes of a
brand affecting the site selection. This project aims to design and develop a decision support
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system for store location selection to find the most appropriate store location for a brand to
realize full market potential. This decision support system helps entrepreneurs to choose the best
location to open a store for a brand by giving less effort and spending less time.
Our project will decrease the time and effort people spend while choosing the most convenient
store location for a brand. It will also provide some benefits such as speed to market by knowing
where to go first, better allocation of your resources and faster return on investment by focusing
on the most profitable locations.
I.3 Scope of Project
The scope of this project is limited with profit expectations from a brand and positioning of this
brand. These two main criteria will determine the best location for a brand. We will also limit the
scope by eliminating current stores opened on locations by the brand. We will assume that it is
the first time we open a store on the location. In our AHP model, cost will be a constraint.
I.4 Methodology
For modeling our decision support system, we decided to use the Analytic Hierarchy Process
(AHP) as our methodology. Our project topic is location selection for brands. The aim is to reach
to optimum solution according to different alternatives, which covers expected profit, right brand
positioning and right image. The system will list various alternatives and chief sales executive is
expected to make an appropriate decision based on these alternatives.
While making a location selection for a brand, some number of criteria is determined and
evaluated, and the rank of each criterion differs for each brand. Therefore, we need a
methodology and reach a decision which will be a best location selection for a brand, so our
decision selection is best match with AHP model.
II. LITERATURE SURVEY
Location, Location, Location is a Channel 4 property show. The reality show follows Kirstie
Allsopp and Phil Spencer as they try to find the perfect home for a different set of buyers each
week.
Location selection is usually the most important decision in the process of opening up a new
store as it is in buying a house. The decision almost by itself can bring success or failure
(Karadeniz, 2009).
A good location can attract a large number of customers which is the first step of selling for a
brick and mortar store (Kuo, Chi & Kao, 1999).
Since selection of a location for a store is generally a one-time decision, the evaluation must be
made thoroughly.
Store location selection process in essence related with target market, rivals and costs
(Karadeniz, 2009).
For a particular store, the attributes below are important in the selection of a location process
(KUO et al., 1999):
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Figure 1: Evaluation Factors in the Selection of a Location Process
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Below there are some key independent variables regarding shopping frequency and store choice
which are effective on location selection (Pan & Zinkhan, 2005):
Figure 2: Key Independent Variables Effective on Location Selection
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Researches show that while some customers are keenly aware of the retailer’s objective which
may be providing high product quality some other group of customers can be satisfied with
average quality. Besides quality, perceived selection of product or service to offer highest utility
can be a variable for customers (Birtwistle, Clarke & Freathy, 1998).
There are some brand/product attributes which are effective on location through customer profile
(Birtwistle et al., 1998):
Price
Generally higher than
average (H)
Generally average (A)
Product selection
Providing good variety
(G)
Providing limited variety
(L)
Staff
Giving better than
average service (B)
Giving worse than
average service (W)
Generally below
average (L)
Product quality
Generally higher than
average (H)
Generally reasonable
(A)
Generally below
average (L)
Table 1: Brand/Product Attributes Effective on Location
For clothing companies there are at least six different customer sub-segments each having
special requirements of a store (Birtwistle et al., 1998). In location selection these sub-segments
should be thoroughly investigated.
A research shows that consumers with higher levels of involvement in fashion have less priceconscious, and they are more willing to pay more for fashion apparel items (Magie & Young,
2009). So price perception is another factor on location selection.
Significance of the effect of store which may be determined by brand culture is another effective
attribute. The associations between perceptions of customers’ of a store attributes, education and
age are also observed (Paulins & Geistfeld, 2003).
By reviewing the literature and discussing the issue with the marketing expert Associate
Professor Aslıhan Nasır, we have encountered with numerous variables which can be affective
on store selection decision.
Due to the limitation of time, people and resources, our study will be examining the constraints
and variables below:
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Figure 3: The Constraints and Variables of Store Location Selection
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II.1 Location Attractiveness
In our project, people interested in opening an apparel store have to choose one of the
predetermined streets of Istanbul. In the location selection problem, it is necessary to take
location attractivenes into consideration. In our project, location attractiveness consists of three
aspects. These are competition, magnet, and convenience.
II.1.1 Competition
In a journal article about selecting convenience store location; Kuo, Chi, and Kao (2002)
mention that the competitive stores will attract part of the consumers and reduce relatively the
number of consumers going to the sampled store. Competition is a significant aspect of location
attractiveness and one separate criteria are considered in this competition aspect: the number of
competitors.
According to a journal article about multicriteria selection for a restaurant location; Tzeng, Teng,
Chen, and Opricovic (2002) describe the number of competitors as the number of similar
restaurants in the vicinity. In our case, the number of competitors refers to the number of similar
apparel stores in the neighborhood. We assume that high number of competitors is an obstacle
for opening a store in the current location. If the number of competitors is low, then it is suitable
for the brand to open a store there.
II.1.2 Magnet
Magnet is an important aspect of location attractiveness, since people usually choose busy places
for shopping. Kuo et al. (2002) state that magnet aspect divides into five dimensions: These are
crowd point, culture and education organization, relaxation, government & business
organization, vehicle maintenance. The following table shows what these dimensions consist of:
Crowd Point
Culture &
Education
Organization
Relaxation
Hospital
School
Recreation Center
Market
Studying Center
Department Store
Hotel
Library
KTV, club
Restaurant
Cinema
Temple
Park
Government &
Business
Organization
Financial
Organization
Office Building
Vehicle
Maintenance
Gas Station
Parking Area
Garage
Table 2: Five Dimensions of Magnet Aspect
In our project, we will use crowd point, relaxation, and pedestrian volume as our criterias for
magnet aspect. We will obtain the number of markets, hotels, and restaurants for crowd point.
We will also search for the number of department stores and cinemas for relaxation criteria.
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Tzeng et al. (2002) support that the people passing by the restaurant are also potential customers
of the restaurant, so the pedestrian volume is very important to the restaurant. Pedestrian volume
criteria is also so important for apparel store location, since pedestrians passing by the store
when it is open are potential customers of the apparel store.
II.1.3 Convenience
In the location selection problem, it is also necessary to take convenience into consideration.
Two separate criterias are considered in this convenience aspect: parking space and
transportation.
According to a journal article about multicriteria selection for a restaurant location, Tzeng et al.
(2002) support that more parking spaces can attract more customers to dine in the restaurant. It is
also true for apparel store location, since some apparel customers are also likely to drive their
own cars. In our project, we take streets of Istanbul as location choices. Therefore, parking
spaces gain more importance if we take parking and traffic problems of Istanbul into account.
Tzeng et al. (2002) also state that “convenience to mass transportation refers to the number of
bus routes close to the restaurant, more precisely the number of bus routes within 500m around
the restaurant, with more bus routes indicating greater convenience.” Therefore, if there are more
bus routes close to store location, it is more suitable fort he brand to open a store there.
II.2 Psychographic Fit
There are many different groupings and definitions of psychographic segmentation variables.
While some resources take all factors such as opinions, life styles etc. Seperately some resources
prefer grouping them and creating a common segment. In the case of location selection decision
support system, we are addressing a region in which potential consumers representing certain
segments live in. Since we are addressing a broad group of consumers and that most of the
seperate factors are actually interdependent in terms of psychographic segmentation, we will take
VALS (Values and Life Styles) and AIOs (Activities, Interests and Opinions) analysis as a
starting point.
In most of the resources consumers are classified under seven different categories according their
values and life styles. These categories with explanations are stated below
(http://www.markmedia.org.uk/psychographic_segmentation.htm):
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Self actualisers
Focused on people and relationships,
individualistic and creative, enthusiastically
exploring change, 'in a framework of nonprescriptive consideration for others'.
Innovators
Self-confident risk-takers, seeking new and
different things, setting their own targets to
achieve.
Esteem seekers
Acquisitive and materialistic, aspiring to what
they see are symbols of success, including
things and experiences.
Strivers
Contented conformers
Attach importance to image and status, as a
means of enabling acceptance by their peer
group, at the same time holding onto
traditional values.
Want to be 'normal', so follow the herd,
accepting of their circumstances, they are
contented and comfortable in the security of
their own making.
Traditionalists
Risk averse, guided by traditional behaviours
and values, quiet and reserved, hanging back
and blending in with the crowd.
Disconnected
Detached and resentful, embittered and
apathetic, tending to live in the 'ever-present
now'.
Table 3: Categories according to Values and Lifestyles
AIOs



Activities - hobbies, vacations, sports
Interests - fashion, politics, job
Opinions – social issues, politics, products
II.3 Demographic Fit
Demographics cover the characteristics of human population. There is a close relationship
between people and the brand. That means that without people we cannot talk about any brands,
so when we talk about a brand, we should add people in any case. The location selection of a
brand is one the case that we need to consider people first, so we need some data on people’s
characteristics to be able reach an optimum solution for the location selection. We will deeply
explain age interval, income/consumption level, gender, education degree, occupation, wealth,
social class and population density respectively, then their relations with the location selection of
a brand will be emphasized and the relation will be examined to make it clearer.
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II.3.1 Age Interval
While determining on the location of a brand, age interval is one of important criterions. It can
change according to brands and same brand may want to reach different age intervals in different
locations. According to different age ranges, people are categorized and new person types are
defined. According to some experts in marketing and retail division states that there are 3 range
intervals and these are 18-25, 25-35 and 35+ and each age interval has a person type that are
young student, professionals and older fashionable respectively and addition to this information
each age interval has a purchase type that are limited purchases, buying larger range and
aspirational respectively (Quinn et al., 2007).
We decided to add new age interval and these are 14-18, 18-25, 25-35,35-65 and 65+ because
according to our observation, children have already started to make shopping alone when they
start to attend high school and the number is increasing in time. In addition, 35+ cover a great
number of people and we preferred to divide this age interval into two and add 35-65 and 65+.
Two academicians, made research on older apparel consumers, think that the number of 65 and
over year old people has increased and they have a large amount of income and wealth (Moye &
Giddings, 2002). Hence, it is an accurate way to differentiate them and create a new age interval.
II.3.2 Income/Consumption Level
Nowadays, location selection for a brand has gained more importance and all brands need to
spend time on it while launching their business. In addition, competition in the market is another
issue that increases the importance of the location selection. Selected location even makes a
brand one step forward from its competitors. Mustafa Karadeniz (2009), the director of Naval
Science and Engineering Institute, believes that people’s income level and population details are
two of big concerns while deciding on a location of a brand. Furthermore, there is a
questionnaire that was developed by some experts in computers and it was applied on experts.
This questionnaire includes:
“1.business development department managers of CVSs:
2.business development department working staffs of CVSs:
3.professional consultants:
4.lecturers, and
5.CVS related institution researchers” (Kuo et al., 2002).
‘CVS’ is an abbreviation of ‘convenience store’ (Kuo et al., 2002). In this questionnaire, there
are many evaluation factors. ‘Income level’, ‘Consumption level’ and ‘Population growth’ are
taken as important evaluation factors. In addition, according to the result of this questionnaire, all
factors have a weight and their weights are great enough to prefer to consider them while making
a selection on location of a brand. If we compare three evaluation criterions, we can observe that
consumption level has the highest weight and then income level and population growth
respectively by considering the results of the questionnaire survey.
When we compare convenience stores and apparel ones, we can say that they are different from
each other, yet we can observe that apparel ones have the same evaluation factors.
II.3.3 Gender
There are obvious differences between men and women while making shopping. Two experts in
shopping orientation made some researches to clarify the differences according to gender and
they states that “[i]n most cases, the results support the expected gender-related differences with
respect to the constructs included in our model. For example, women are significantly more
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“shopping for fun” oriented than men, irrespective of whether they purchase clothes for
themselves or for their partner. Conversely, men are significantly more “quick shoppers” in both
purchasing situations when compared to women” (Hansen & Jensen, 2009).
According to the target customers of each brand, gender preference change. One apparel brand
can target only women or men or both. When launching a new location for a brand, we should
consider the number of targeted consumers, so when there are alternative locations, we need to
check the gender division of the locations while making selection.
II.3.4 Education Degree
Life style is impacted from educational levels and it is one of the important people’s
characteristics. Two academicians made an empirical study and they divided people into 3
categories in terms of ‘apparel shopping behavior’, and according to this study people’s
educational level is divided into subcategories that are ‘highest educational levels’, ‘high
educational level’ and ‘lowest educational level’ (Preez & Visser, 2003).
Five-education level was defined to use them for this project. These are High School Degree,
Bachelor’s Degree, Master’s Degree, Doctorate Degree and Other. ‘Other’ education level
covers all people who have education level under high school degree. Instead of three levels, we
preferred to use these categories and they give us more detailed information about people’s
education levels.
Retailer should consider the education levels of their customers. Other two academicians at the
Ohio State University believe that “[t]he more educated the customer base, the more selective
one’s customers may be. While education levels are independent of family income and
individual purchasing power, this is an area that would benefit from further exploration due to
the market surveyed (Paulins & Geistfeld, 2003).”
II.3.5 Occupation
There is a close relationship between shopping and occupational status. Two academicians at
Ohio University reported that if people have professional careers, they prefer to spend less time
while doing shopping. When we consider non-professional people they spend more time
shopping (Paulins & Geistfeld, 2003). Occupation is also an important criterion while deciding
on the location of an apparel store because if the brand defines its target market and wants to
reach customers who have professional careers, we need to consider the locations where
customers can reach easily and less time can be enough to do shopping from this store.
II.3.6 Wealth
Wealth is another important criterion. We need to combine this data with occupation, education
and income to specify people’s social classes. Wealth is independent from income level.
II.3.7 Social Class
Occupation, education, income and wealth determine people’s social class. We will go through
an actual example of identifying one’s social class or at least get a closer look at America’s
system. The New York Times provides a web page to see how class works. By entering a profile,
this tool can tell us what social class your entry falls under. Hence, we entered a familiar profile.
First, we selected the occupation that is related to our study at our university: system analyst,
computer software engineers. Education: a Bachelor’s Degree. Income: the standard $100,000
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for the computer based job. (up to $100.000 per year) Wealth: the amount that is typically
amassed by someone in his/her middle age. ($100.000 to $500.000)
When we make these choices we get this Figure 1 that shows an interactive graphic from the
New York Times entitled “How Class Works” (http://www.nytimes.com/).
Figure 4: An example from the New York Times entitled “How Class Works”
My selected choices show that:
Occupation: 77th percentile
Education: 91st percentile
Income: 93rd percentile
Wealth: 85th percentile
Average: 86th percentile
We need to collect education, income, occupation and wealth data for a location and then after
entering the data that we have, we can get a meaningful result for social class of majority people
in a location. While deciding on location alternatives, it is significant information to use it as a
criterion.
II.3.8 Population Density
Population density is a measurement of population per unit area or unit volume. It is commonly
represented as people per square mile, which is derived simply by dividing total area population/
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land area in square miles. Density can be computed for any area- as long as one knows the size
of the land area and the population within that area. The population density of cities, states,
entire continents, and even the world can be computed.
Cities with high population densities are considered to be overpopulated. While selecting a
location to launch the business for a brand, it is significant to consider the population density. If
in a location population density is high and population covers our target customers, this
evaluation makes this location one step forward form other alternatives.
II.4 Behavioral Fit
II.4.1 Brand Loyalty
Figure 5: Loyalty Typology Based on Attitude and Behavior
Loyalty is one of the most crucial concepts for brands. The research on the value of loyal
customers over the past decade has generally focused on the direct impact of loyal customers on
the firm. That is, the major focus has been on the direct revenue stream resulting from retaining a
customer and keeping him/her satisfied (e.g. Blattberg and Deighton, 1996; Heskett et al., 1997;
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Reichheld, 1993, 1996; Schlesinger and Heskett, 1991). Further researches shows that customer
loyalty. Keeping these in mind, we include brand loyalty as a factor defining the segment of our
customers. Brand loyalty is connected with the affordability to a specific brand and the consumer
satisfaction from the brand.
II.4.1.1 Four Loyalty Levels
Table shows four loyalty archetypes based on the cross-classification of attitudinal and
behavioral loyalty levels: high (true) loyalty, latent loyalty, spurious loyalty, and low (or no)
loyalty. (4) Customers with high or true loyalty are characterized by a strong attitudinal
attachment and high repeat patronage. They almost always patronize a particular company or
brand and are least vulnerable to competitive offerings.
Those with latent loyalty exhibit low patronage levels, although they hold a strong attitudinal
commitment to the company. Their low patronage may occur because they do not have sufficient
resources to increase their patronage or because the company's price, accessibility, or distribution
strategy may not encourage them to become repeat customers.
Customers with spurious or artificial loyalty make frequent purchases, even though they are not
emotionally attached to the brand. (They may even dislike it even though they continue to make
purchases.) The high patronage levels of spuriously loyal customers can be explained by factors
such as habitual buying, financial incentives, convenience, and lack of alternatives, as well as
factors relating to the individual customer's situation.
Finally, the low-loyalty group exhibits weak or low levels of both attitudinal attachment and
repeat patronage. Spurious and low-loyalty groups are highly volatile and susceptible to
incursions from competitors.
II.4.2 Price Consciousness
Price consciousness is considered consumers’ trade off between prices and product quality, staff
service and product selection. Research runs a specific company oferating at clothing retail
sector showed that grouping consumers under segments in consideration with the factors stated
above gives us six alternatives: “Choice oriented”, “Value for Money”, “Service Oriented”,
“Selection Value”, “Price Conscious”, Price and Quality conscious”(Birtwistle, Clarke &
Freathy, 1998).
Group 1 customers were distinguishable by their service-oriented emphasis, with the utility score
indicating the perceived high benefits of this attribute. These customers shop because they could
get “good service [from] helpful and friendly staff” and they are “happy with past purchases”.
Group 2 customers are characterised by the importance they placed on achieving value for money
– whilst this group gave the highest utility of product quality, they had a preference for paying
below average prices. Typical comments from such customers are “provides good quality
clothing for fairly reasonable prices”, and they are “. . . reasonably cheap, and I don’t mind
paying more money for better quality clothes”. These views contrasted with that of customers
from Group 3, who were more overtly quality-oriented; the neutral price utility showing that
they were prepared to pay to achieve this level of quality. Typically, they decide to shop from a
specific brand because of the “good quality garments”, its “general layout” and because it is
perceived to be a “respectable company”. The main factor important to customers in Group 4
was a shift in importance to the overall range – with a combined emphasis on selection value.
This indicates a subtle difference from customers in Group 2, in that this group preferred to pay
lower or average prices, but placed a stronger emphasis on the selection available rather than just
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quality alone. People in this group noted that the store provided products which were of “good
quality, not too expensive [offering] . . . different styles” and the provision of “clothes [that are]
fashionable, up to date and of good quality”, with “good variety in merchandise” and “good for
leisure and office wear”. Group 5, however, were the most price conscious of all customer
groups, preferring to pay lower than average prices, whilst looking for a good selection of
products. Typical comments were “I like the quality of products and prices [which] are generally
reasonable, [and the stores have] a good selection and staff are pleasant”. By contrast, Group 6
customers placed little emphasis on price, and were more choice and quality conscious. A typical
statement from this group was although the variety is limited and prices are higher than average,
the quality is good”.
II.5 Constraints
II.5.1 Cost
II.5.1.1 Average Store Rent
The average of store rent’s in a definite area. This constraint is a crucial one since the financial
sustainability of a brand is needed to be maintained by each new store it opens. Costs can differ
a lot according to the location and the size of the store. For instance while relatively large stores
in Taksim square starting from can go up to 30.000 TL, stores in for instance regions such as
Maltepe start from 1000. In this constraint the best way to get the data for the decision support
system is demanding a direct entry without giving cost intervals since the range is too large
(http://www.emlak.net/isyeri/kiralik-magaza/turkiye/istanbul).
II.5.1.2 Average Distribution Cost
Distribution can be a really costly factor in today’s world where stores and the factories are a lot
far from each other. In addition, transportation among big cities are as well costly and
problematic because of high population and high number of vehicles on the streets. That’s why
brands choose the location of their stores not only looking at the costs resulting from the rent but
also the distribution channels and cost to the certain place. Distribution costs are mainly
considered under two subconsepts: These are the costs resulting from keeping the material in the
warehouse which is mainly the warehouse rent and the transportation costs from the warehouse
to the store. In our case we will only consider transportation costs since whether the new store is
opened or not rent for the warehouse would be paid. The transportation costs are calculated
taking into consideration a few factors such as the frequency of transportation, the mean of
transportation and the distance between the warehouse and the store etc. This cost should also be
entered by the manager of the firm to the decision support system since it is impossible for our
program to calculate the cost by taking into consideration all these factors.
These two factors give us the total cost of a store(due to its location) and they can be considered
as one single factor.
II.5.3 Existing Store
This factor is only concerned with the existence of one of our stores belonging to the same brand
in the definite trade zone. The existence of another store usually reduces the customer traffic in
one store since it spreads the number of total customers. That’s why we will take two inputs as
“Yes” and “No”. If the answer is “Yes”, than that location will automatically be eliminated from
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choices. If the manager wants to open a second store on the same street, s/he should select “No”
as answer. This will be explained in the interface.
IV. DEVELOPMENT OF THE DSS
IV.1 DSS Architecture
Figure 7: DSS Architecture
Store location selection DSS system needs information from helps chief sales executives,
managers on opening up a new store. First the constraints are gathered from a company and the
unrealistic locations are eliminated by the system. Then the chief sales executive asked to make a
pair wise evaluation on the criteria which are found critical on our literature review. A report
retrieved from customer data which are gathered from different locations by using surveys are
presented to chief sales executive to get a further pair wise evaluation this time on locations with
respect to the criteria.
The comparisons will be evaluated by using the AHP method and the best choice as a result of
the weighted averages on the pair wise evaluations will be presented as an outcome of the DSS
system.
In our DSS system we try to find best apparel store in İstanbul for a company. To accomplish
that, first executive evaluates criteria by pair wise comparison and the priority vector is
20
calculated. After the first comparison the survey results which are held on an predetermined
formatted excel sheets are loaded to DSS. As a third step the manager enters constraints to the
system. The location options which are not appropriate eliminated in this step. After the
elimination process the executive makes a second pair wise evaluation on locations, streets, in
İstanbul by using the same criteria one by one and proceeds to calculation of the approximation
to find the most suitable location to open up the store.
IV.2 Technical Issues
 Access 2007 - to keep survey data and summary of the data.
 Visual Studio 2005 - the platform for system.
 C# language - to code DSS
 Justinmind Prototyper – Mockup design
IV.3 Model and Algorithms
IV.3.1 Model
DSS for location selection is based on pair wise evaluation of location criteria and constraints.
User first chooses some location alternatives and enters some constraints to the system. After an
elimination of some alternatives system requires comparison about criteria. After the criteria
comparison system provides pair wise comparisons of location alternatives of a given criteria
and requires a comparison for the alternatives. The comparisons are made with the grades
ranging from 1-9.
IV.3.1.1 Goal
DSS helps decision maker to select the best location for an apparel store by comparing the
relevant location and target market attributes thoroughly.
IV.3.1.2 Criteria
The DSS system has the hierarchy tree below:
21
Figure 8: Hierarchy Tree of Criteria
22
Criteria
1. Location attractiveness
1.1. Competition
1.2. Magnet
1.3. Convenience
2. Customer segment fit
2.1. Psychographic fit
2.2. Demographic fit
2.3. Behavioral fit
IV.3.1.3 Constraint
1.
Cost
2.
Existing store
DSS for location selection is based on pair wise evaluation of location criteria and
constraints. User first chooses some location alternatives and enters some constraints to
the system. After an elimination of some alternatives system requires comparison about
criteria. After the criteria comparison system provides pair wise comparisons of location
alternatives of a given criteria and requires a comparison for the alternatives. The
comparisons are made with the grades ranging from 1-9.
IV.3.2 Algorithms
1. System asks for constraints are cost, competition/Target market population, and
existing store.
2. User records constraints.
3. System checks data if there is an error with an explanation about the data system
gets back to step 2.
4. System gets the input.
5. System eliminates the location alternatives based on constraints entered.
6. System displays possible locations list and asks user to choose minimum 2
maximum 7 options
7. User chooses the location alternatives to open up a new store
8. User records the data.
9. System checks data if there is an error with an explanation about the data system
gets back to step 6.
10. System gets the input.
11. System displays the criteria page to the user and it requires the user to compare
the criteria according to their branch and the level on the hierarchy. The criteria
are:
a. Location attractiveness
i. Competition
ii. Magnet
iii. Convenience
b. Customer segment fit
i. Psychographic fit
ii. Demographic fit
iii. Behavioral fit
23
12. User compares the criteria with each other according to their level of importance
by using a range from 1-9 which means equally important to extremely more
important.
13. User records the data.
14. System checks consistency. If there are some consistency errors system gets
back to step 11 by informing the user about problems with explanations about
the cause of the error or errors.
15. System gets input.
16. System displays every data for locations relevant to the lowest level criteria at
step 11 to retrieve pair wise comparisons.
17. User compares the locations through criteria with each other according to their
level of importance by using a range from 1-9 which means equally important to
extremely more important.
18. User records the data.
19. System checks consistency. If there are some consistency errors system gets
back to step 11 by informing the user about problems with explanations about
the cause of the error or errors.
20. System gets the input.
21. System displays the best location for a new store.
How comparison is done
DSS for location selection is done by using the AHP method. In AHP grades ranging
from 1-9 is used for comparison of criteria and alternative.
Figure 9: Ranking Scale for Criteria and Alternatives
First the user is asked to compare criteria and then system shows possible locations and
asks for comparisons for all criteria. As a final step system calculates the weight and
suggests the best location choice for an apparel store.
24
How calculation is done
AHP calculations are done by using comparison grades for criteria and alternatives
relative to criteria. In our model, each criterion which are related and on the same
hierarchy level constitutes a matrix.
Every matrix’s columns are normalized buy dividing each column element to sum of
each column then for each row, average row is calculated. The vector which consists of
the average of row’s elements is called priority vector.
After finding the priority vector the consistency of result is checked.
To check the consistency:
1. Criteria matrix is multiplied with priority vector.
2. The every element of result vector is divided to every element of priority vector.
3. Average of the elements, lambda max is found.
4. By using the formula  (lambda max-#criteria) / (#criteria-1) consistency index
(CI) is calculated.
5. By using the table, the is the upper row order of the random matrix, and the
lower row is the corresponding index of consistency for random judgments (RI)
the consistency ratio is calculated by the formula:
CR = CI/RI
6. If CR<0.1 then the evaluations are considered consistent
Location
attractiven
ess
Customer
segment
fit
Locati
on
attracti
veness
x
Custome
r
segment
fit
x
Eigen
Vector
(x)
Ax
=
lamda *
x
Ax/x
x
x
x
x
x
x
x
x
AVG
lamda
max=
using the formula CI= (lamda max - n) / (n-1)
CI=
x
Looking at the Random Index table we find that RI= x for size n
matrix
CR = CI/RI so
CR<0.1
CR
x
so consistency check is ok
Table 6: Example of Calculation Screen for Criteria
25
x
When calculating the weights for an alternative after finding the lowest level weights
the results are calculated in a bottom-up manner by multiplying each value with the
weight of the upper hierarch weight to find weighted values from the lowest level
criteria point of view for each alternative.
Age
Interval
Location
X
Location
Y
x
Eigen
Vector
(x)
x
Location
X
x
Location
Y
x
Ax
=
lamda *
x
Ax/x
x
x
x
x
x
x
AVG
lamda
max=
using the formula CI= (lamda max - n) / (n-1)
CI=
x
x
Looking at the Random Index table we find that RI= x for size n
matrix
CR = CI/RI so
CR<0.1
CR
x
so consistency check is ok
Table 7: Example of Calculation Screen for Alternatives According to a Ariterion
IV.4 User Interface and Reports
IV.4.1 User Interface
26
Figure 10: Criteria Comparison Screen
27
Figure 11: Age Interval Comparison Screen
28
Figure 12: Age Interval Chart Screen
29
IV.4.2 Reports
Figure 13: Location Alternative Order Report
30
V. ASSESSMENT:
PROJECT PLAN
PROJECT
PLAN
Forming Group
1
2
3
4
5
6
7
x
x
x
x
x
x
x
WEEK
8
9
10
11
12
x
X
x
x
x
x
x
x
x
x
x
13
x
Deciding on Project
Topic
x
Dividing Tasks
among Team
Members
Making Project
Proposal
Presentation
Making Literature
Survey
Preparation for
Mid-Presentation
Starting to Model
DSS Project
Programming
x
x
x
x
Designing User
Interfaces
Testing & Final
Report
x
x
x
MASTER PLAN
Project Code 09
Project Title DSS : Location Selections for Brands
Team Members Atilla Orgunmat, Eda Sen, Melisa Zenginkuzucu, Zeynep Belya Akoguz
Phase
Team Formation
Project Proposal
Presentation
Literature Review (Library,
Web, former studies)
Development of the model
Mid-report
Presentation
Data Collection and
Organization
Coding interfaces
Validation (Optional)
Final Report
Presentation
Planned
Start
Finish
Actual
Start
Finish
Complete
%
Problems
4 Oct
4 Oct
4 Oct
8 Oct
100
-
10 Oct
15 Oct
13 Oct
18 Oct
100
-
19 Oct
19 Oct
18-20 Oct
100
-
19 Oct
1 Nov
9 Nov
26 Oct
8 Nov
15 Nov
22 Nov
100
100
100
-
19 Nov
23 Nov
22 Nov
100
-
25 Nov
30 Nov
22 Nov
05 Dec
100
-
1 Dec
14 Dec
22 Nov
27 Dec
100
-
14 Dec
15 Dec
100
-
7 Dec
27 Dec
27 Dec
100
-
24 Dec
29 Dec
29 Dec
100
-
31
8 Nov
VI. CONCLUSION
Selecting a location for apparel stores is a big concern. We made a literature survey and
got many criterions and constrains that will help us to reach more optimal solution for
location of the store to launch the business. There are many criterions and these are
location attractiveness, psychographic fit, demographic fit and behavioral fit. In
addition, for each criterion we have some sub criterions to evaluate the factors to get the
desired result. Furthermore, there are some constraints that are called cost and existing
store. We obtained all these evaluation criterions by reading many academic articles and
while modeling our project we will use them to choose a location for an apparel brand
and then we will continue with programming part and the project will be completed
after all these entire stages. Before starting programming part, user interfaces were
designed to make it easy to understand how this system looks like and how it works.
32
APPENDIX
33
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