One of the considered business opportunities that exploit the geographical aspect is the branch o ce expansion. However, this activity still has some problems to learn various location criteria, either in quantitative or qualitative form. Moreover, there is a need to analyze those criteria quickly to get a proper location priority that will be expanded. Therefore, this research develops a semantic technology of the form geospatial dashboard using analytic hierarchy processing (AHP) method that can display the distribution of location and its criteria in a map. Furthermore, it recommends the list of top priority locations will be expanded implicitly from the various kinds of data. This research takes a case study in one local company in Indonesia. At the end, this research shows that geospatial dashboard using AHP method is considered capable of producing accurate solution up to 90.48% to determine the priority order of locations to be expanded.
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Based on this survey result, we note that more users need technology that can support their location analytic skills in the form of geographic information and equipped by predictive analytic capability to predict which business steps that must be executed further and gives the semantic of given variety of data. This kind of technology brings us to the conclusion that the needs of an enterprise to look the appropriate data in the various type, such as qualitative and quantitative, and then process them into an obvious visualization are the sort of challenges that always be faced to the capability of BI and GIS technology.
The output of this research is geospatial dashboard as the form of visualization and geospatial analytics which uses Analytic Hierarchy Processing (AHP) to strengthen aspects of predictive analytics to determine the location of branch o ces to be built rst. This research takes a case study at Rekan Usaha Mikro Anda (RUMA) Company 3, which is one of the Micro nance Company that has some various types of product and some branch o ce locations in Indonesia. This company is feasible for a case study due to its main condition is t to the challenge of BI and GIS technology that is mentioned above. Their problem in terms of analyzing location data to expand their branch o ce was divided into two aspects, which are business and technology. In business aspect, they do not nd such a method that can calculate qualitative and quantitative data based on many criteria and some location alternatives. In technology aspect, they still try to look for the technology that can give recommendations to expand their branch o ce in the priority order of location. In following, this paper will use the involvement of Zone Expansion Manager (ZEM) of RUMA as a division that takes care to the management of branch o ce expansion in RUMA.
This paper is written and arranged to focus on the use of AHP to solve the problem of branch o ce expansion. The notion of geospatial dashboard will be introduced only as a tool which support the decision making process. This paper initially introduces the theoretical background of geospatial dashboard and AHP in section 2. Then, in section 3 we explain the methodology of this research, meanwhile in section 4 we provide the evaluation on the AHP method. At the end, we put the conclusion and future work of this research in the last section. 2
Here we introduce the theoretical background of geospatial dashboard and AHP.
Since the main subject of these two terminologies are business intelligence, then
the detailed explaination about business intelligence can be read in [
One thing that is currently needed for the non-technical user to understand the
data into the form of information, decision, and action is the translation of data
into a form of visualization. Technology that covers that needs is called data
visualization. According to [
According to [
To develop the architecture of geospatial dashboard, we can start from the
point of view of BI architecture [
According to [
The paper [
Every computed eigenvector gives us the relative ranking for our criteria or alternatives. The eigenvector calculation is computed by running this algorithm. 1. Raise the pairwise matrix to powers that are successively squared each time. 2. Calculate the row sums and the normalize it. 3. The computation is stop to iterate when the di erence between the normalized row sums in two consecutive calculations is smaller than an assigned value.
We want to choose which city that will be expanded based on the given criteria and the assigned objective. By following the previous explained steps to do the AHP cal culation, rst we need to de ne the objective, criteria, and cities. To make it general, we state that we have criteria C 1, C 2, and C 3. Here we treat the criterion C 3 as a quantitative criterion. Then, we have cities which are A1, A2, and A3. We construct these components into AHP tree structure which is depicted in Figure 1.
Fig. 1: Example of AHP Tree Structure Next, we do the second step by constructing pairwise comparison matrix and turn it a criteria ranking by eigenvector computation. The values in this matrix are obtained by the agreement between the domain experts.
C2 0:33
1 4:00
C3 2:00 1 0:25 A 1 =)
1 A These values state that criterion C 3 is the most important criterion. Similar to
A2 4:00
1 0:20
1 A = A1 0
1 A Therefore, we conclude that alternative A3 has the greatest value as the most important city to be expanded rst. 3
The methodology of this research is adopted based on the stages in the
development of geographic information system project ([
C3 A1 0 0:287 1 A2 @ 0:299 A A3 0:414 Since, the criterion C 3 is quantitative, then we only do the fourth step which is normalizing the alternatives value based on C 3.
Last, we compute the nal alternatives ranking priority by combining all the
alternatives ranking priority into a matrix and then multiply it by the criteria
ranking.
obtain the criteria ranking, we obtain the alternative ranking by running the
third step. First, we compute the alternative ranking based on criterion C 1.
Next, we compute the alternative ranking based on criterion C 2.
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terms of its architecture. The fourth stage is to implement the geospatial
dashboard and AHP from the design sketch in previous stage that will be integrated
with the AHP algorithm. Finally, the last stage is how to evaluate the result
from the implementation. The explanation of this research method is depicted
in the following Figure 2. The analysis to model this case study into AHP tree
structure as well as the implementation of geospatial dashboard and AHP will
be explained more detail in the following subsections.
Here, we analyze the needs of some components of AHP model, which are,
objective, criteria and alternatives. We take the case study of looking for the order
of priority of branch o ce locations that will be expanded as the objective of
AHP model. To analyze the criteria of decision making for the expansion of
branch o ce location, we adopt the structure of the criteria based on [
First, the type of target market criteria which is owned by RUMA was the data of amount of stalls and population density in a region. Next, the competitive aspect that is used to learn the competitors in the area around the site was the price of a competitor's product. Then, many types of operational activity of RUMA make us to focus on the number of criteria used, such that the top operational consideration taken for this research is the regional minimum wage. The criterion based on change factor is determined by some changes that will occur in an area, both physical and non-physical, which can a ect the increase of the number of potential customers for the company. In terms of RUMA's case study, we use the population of its business centers of Bank XYZ who had cooperation with RUMA in terms of nancial transactions for micro-enterprises. In addition to the above four types of criteria, we also take a more qualitative criterion. That criterion is the potential PT. RUMA to recruit the number of stalls as customers in a region.
Then, this research exploits the alternative component of AHP method by taking some cities/counties that were the current branch o ce location up to February 2014 which is the period when this research began rst time. There are 22 cities/counties taken into this research which were Bandung, Bogor, Bekasi, Ciamis, Canjur, Cilegon, Cirebon, Garut, Indramayu, Jakarta, Kendal, Klaten, Lebak, Majelengka, Semarang, Serang, Sukabumi, Sumedang, Tabanan, Tangerang, Tasikmalaya, and Yogyakarta. Last, all these components of AHP are structured into AHP tree (Figure 3). The name of this geospatial dashboard is \Sistem Ekspansi Lokasi Kantor Cabang PT. RUMA" (System of Expansion of Branch O ce Area at RUMA). Geospatial Dashboard has some features that are useful to facilitate RUMA, especially ZEM team to know the priority of branch o ce locations that are rst to be built using AHP approach. Some features of geospatial dashboard are described as follows: 1. Selecting the decision making criteria 2. Selecting the city that will be expanded 3. Running the calculations of AHP 4. Showing the distribution of each locations in a form of map After the user runs the AHP, the application will display a map with the position of the selected location. Each location will have a di erent colour marker which indicates the priority of that location to be expanded. There are three kinds of priority order. The rst priority which is indicated by red colour means a high priority. The second priority is interpreted with a yellow one means a medium priority, while the third priority indicates a low priority for a city with a green marker colour. Users can view detailed information of a city / county on the map by selecting the corresponding marker. 5. Showing the order of priority value of each branch o ce locations in the form of table.
The main purpose of this evaluation is to measure the accuracy of AHP method
used in geospatial dashboard. This evaluation compares the resulted priority
order of city from geospatial dashboard with the data of order of all branch
o ce location of RUMA based on the level of pro t per transaction of product.
This data was produced in the February 2014. For the simplicity, we name this
ordered data as a targeted list for the further experiment. We take this data
with a reason that the resulted list from geospatial dashboard is the order of
city/county with the pro t expectation from the top to the bottom based on
the criteria that we analyzed before. This comparison technique directly looks
into a correlation between two di erence lists of order, such that this technique
require such a statistic method that is so-called Spearman Rho [
The next step is how to determine the chosen criteria and city during the evaluation. We make two group of experiments that have di erent format of chosen criteria and city. Every group had 3 experiments. The rst group will use the chosen criteria based on some recommendations from having focus group discussion with ZEM of RUMA. The other group of experiments use criteria which is obtained by conducting a little experiment aided by spearman rho.
In this evaluation, every criterion would be divided into two groups that are basic criteria and non-basic criteria. Basic criteria are criteria that always become top consideration for ZEM of RUMA once analyzing a location to be expanded, while non-basic criteria are only supporting criteria. Therefore, every two groups of experiment will have di erent basic and non-basic criteria. According to focus group discussion with ZEM of RUMA, we have some basic criteria that are density of population (DP), density of stalls (DS), and price of competitor's product (P), while the non-basic criteria are regional minimum wage (RW), population of business center (PB), and potency of RUMA to recruit stalls (PR). Moreover, for another group of experiment we have some following steps to choose criteria based on the spearman rho method, which are: { Run 6 little experiments that only use one criterion in each of them. Recall that we only have 6 criteria in this research. For every experiment, choose all 22 cities and run the geospatial dashboard. { Once obtaining the result, compare it to the list of order of cites/counties based on the level of pro t per product transaction. In other words, we compare the rank of each city in these two lists. { Calculate this comparison by using spearman rho and in the end every little experiment will have an accuracy value. This accuracy value directly become a value for the used criterion in this little experiment. { From these little experiments, we have the basic criteria which are price of competitor's product (P), regional minimum wage (RW), and population of business centre (PB), while the non basic criteria are the rest.
For every group of experiments, we will do 8 sub-experiments in which the number 8 is obtained as the combination of three basic criteria and all subsets of non-basic criteria. We name the experiments in the rst group as A, B, and C, while in the second group we have D, E, and F. All sub-experiments will be treated by running geospatial dashboard, choosing all 22 cities, and use one of the chosen criteria.
However, there are di erent ways of comparing the resulted ordered list with the targeted list. For experiments A and D, we compare the similarity of rank of 22 cities between these lists. For experiment B and E we only choose rst 8 cities from the resulted ordered list of geospatial dashboard to be compared with their rank in the targeted list as a consideration that 8 cities are the rst priority of cities to be expanded in the middle term planning. For experiment C and F, similar to experiment B and E, but we only chose rst 4 cities because among 8 cities as the rst priority, there were only 4 cities to be observed rst in the short term planning of ZEM. Table 1 and table 2 show the result of this evaluation with the information of accuracy value with some chosen criteria. Based on table 1, we can conclude that in 1st experiment group which used the chosen criteria from having focus group discussion with ZEM, we can get at most 80.95% accuracy which only uses the rst 4 cities from the resulted list of geospatial dashboard for the comparison and the criteria used are density of population, density of stalls, and price of competitor's product. On the other hand, table 2 shows that we can get at most 90.48% accuracy which only uses the rst 8 cities from the resulted list of geospatial dashboard for the comparison and the criteria used are price of competitor's product, density of population, and density of stalls. Here we develop the geospatial dashboard based on analytic hierarchy processing technique for the expansion of branch o ce location by using a case study of Rekan Usaha Mikro Anda Company. First, we show how to analyze the component of AHP, such as objective, criteria, and alternatives. Then, we construct them into an AHP tree structure. Next, we implement the geospatial dashboard by introducing its featurs. Using the evaluation in terms of the accuracy of AHP method, we can get at most 80.95% of accuracy by using chosen criteria from focus group discussion with ZEM of RUMA and at most 90.48% of accuracy by using the criteria that are determined from spearman rho calculation.
Other research that can be developed from this research is the development of data warehouse or online analytical processing (OLAP) for the utilization of geospatial dashboard. With the similar situation in case study, this technology can provide data which is multidimensional, historic, or even real-time, so the decision maker viewpoint to a location data can be wide and varied. Some machine learning techniques to read and process the variety of data can be injected further into this application for the improvement process of decision making.