Boğaziçi UniversityDepartment of Management Information Systems

MIS 463 Decision Support Systems for Business

PROJECT FINAL REPORT

A DSS FOR STORE LOCATION SELECTION

Project Team No: 9Zeynep Belya Akoğuz

Atilla OrgunmatEda Şen

Melisa Zenginkuzucu

Instructor : Aslı Sencer Erdem

İstanbul - December, 2010

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Table of ContentsI.1 The Decision Environment.....................................................................................................3I.2 Mission of Project..................................................................................................................5I.3 Scope of Project......................................................................................................................5I.4 Methodology..........................................................................................................................5

II. LITERATURE SURVEY...........................................................................................................5II.1 Location Attractiveness...........................................................................................................11

II.2 Psychographic Fit................................................................................................................12II.3 Demographic Fit.................................................................................................................13II.4 Behavioral Fit......................................................................................................................17II.5 Constraints..........................................................................................................................19

IV. DEVELOPMENT OF THE DSS............................................................................................20IV.1 DSS Architecture...............................................................................................................20IV.2 Technical Issues.................................................................................................................21IV.3 Model and Algorithms.......................................................................................................21IV.4 User Interface and Reports................................................................................................26

V. ASSESSMENT:........................................................................................................................31PROJECT PLAN.......................................................................................................................31MASTER PLAN........................................................................................................................31

VI. CONCLUSION.......................................................................................................................32APPENDIX....................................................................................................................................33REFERENCES..............................................................................................................................34

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I. INTRODUCTIONI.1 The Decision EnvironmentSelecting 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,

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average distribution cost, 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 ProjectThe 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

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brand affecting the site selection. This project aims to design and develop a decision support 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 ProjectThe 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 SURVEYLocation, 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 Product selection Staff Product qualityGenerally higher than

average (H)Providing good variety

(G)Giving better than

average service (B)Generally higher than

average (H)Generally average (A) Providing limited variety

(L)Giving worse than

average service (W)Generally reasonable

(A)Generally below

average (L)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 price-conscious, 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 CompetitionIn 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 MagnetMagnet 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 PointCulture & Education Organization

RelaxationGovernment & Business Organization

Vehicle Maintenance

Hospital School Recreation Center Financial Organization Gas Station

Market Studying Center Department Store Office Building Parking Area

Hotel Library KTV, club Garage

Restaurant Cinema

Temple Park

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.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

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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 ConvenienceIn 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 ofbus 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 FitThere 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 non-prescriptive consideration for others'.

InnovatorsSelf-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.

StriversAttach importance to image and status, as a means of enabling acceptance by their peer group, at the same time holding onto traditional values.

Contented conformersWant to be 'normal', so follow the herd, accepting of their circumstances, they are contented and comfortable in the security of their own making.

TraditionalistsRisk averse, guided by traditional behaviours and values, quiet and reserved, hanging back and blending in with the crowd.

DisconnectedDetached 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 FitDemographics 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.

II.3.1 Age IntervalWhile 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

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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 GenderThere 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 “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).

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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 DegreeLife 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 OccupationThere 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 WealthWealth 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 ClassOccupation, 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 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)

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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 percentileEducation: 91st percentileIncome: 93rd percentileWealth: 85th percentileAverage: 86th percentileWe 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 DensityPopulation 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/ 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.

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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; Reichheld, 1993, 1996; Schlesinger and Heskett, 1991). Further

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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 ConsciousnessPrice 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 RentThe 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 CostDistribution 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 StoreThis 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 DSSIV.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

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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 ModelDSS 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 GoalDSS 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 CriteriaThe DSS system has the hierarchy tree below:

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Figure 8: Hierarchy Tree of Criteria

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Criteria1. Location attractiveness

1.1. Competition1.2. Magnet1.3. Convenience

2. Customer segment fit2.1. Psychographic fit2.2. Demographic fit2.3. Behavioral fit

IV.3.1.3 Constraint1. Cost2. 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 Algorithms1. 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 options7. User chooses the location alternatives to open up a new store8. 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 attractivenessi. Competition

ii. Magnetiii. Convenience

b. Customer segment fiti. Psychographic fit

ii. Demographic fitiii. Behavioral fit

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.

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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 doneDSS 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.

How calculation is done

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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

attractiveness

Customer

segment fit

Eigen Vector (x)

Ax = lamda * x

Ax/x

Location attractiven

ess

x x x x x

Customer segment

fit

x x x x x

AVG

lamda max=

x

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 x

CR<0.1 so consistency check is ok

Table 6: Example of Calculation Screen for Criteria

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

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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

Eigen Vector (x)

Ax = lamda * x

Ax/x

Location X

x x x x x

Location Y

x x x x x

AVG

lamda max=

x

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 x

CR<0.1 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

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Figure 10: Criteria Comparison Screen

Figure 11: Age Interval Comparison Screen

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Figure 12: Age Interval Chart Screen

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IV.4.2 Reports

Figure 13: Location Alternative Order Report

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V. ASSESSMENT:PROJECT PLAN

PROJECT PLAN

WEEK1 2 3 4 5 6 7 8 9 10 11 12 13

Forming Group xDeciding on Project Topic

x

Dividing Tasks among Team Members

x

Making Project Proposal Presentation

x

Making Literature Survey

x x x xPreparation for Mid-Presentation

x xStarting to Model DSS Project

x xProgramming x x x x x xDesigning User Interfaces

X x x x xTesting & Final Report

x x x x

MASTER PLAN

Project Code   09Project Title DSS : Location Selections for Brands

Team Members Atilla Orgunmat, Eda Sen, Melisa Zenginkuzucu, Zeynep Belya Akoguz

Phase Planned Actual Complete% Problems

Start Finish Start Finish

Team Formation 4 Oct 4 Oct  4 Oct 8 Oct 100  -

Project Proposal  10 Oct 15 Oct 13 Oct  18 Oct 100  -

Presentation 19 Oct 19 Oct 18-20 Oct 100 -Literature Review (Library, Web, former studies) 19 Oct 26 Oct      100  -Development of the model  1 Nov 8 Nov       100  -Mid-report  9 Nov 15 Nov 8 Nov 22 Nov 100  -

Presentation 19 Nov 23 Nov 22 Nov 100 -Data Collection and Organization  25 Nov 30 Nov   22 Nov  05 Dec 100  - 

Coding interfaces  1 Dec 14 Dec   22 Nov  27 Dec  100  -

Validation (Optional)  14 Dec 15 Dec       100  -

Final Report  7 Dec 27 Dec   27 Dec 100  -

Presentation  24 Dec 29 Dec   29 Dec  100  -

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VI. CONCLUSIONSelecting 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.

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APPENDIX

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