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Business Intelligence (BI) initiatives are challenging tasks, implying significant costs in their implementation. Therefore, organizations have adopted prudent policies requiring a financial justification. A business-driven methodology is recommended in any BI project initiative, project scoping and planning being vital for the project success. A business-driven approach of a BI project implementation starts with a feasibility study. The decision-making process for large projects is very complicated, and will not be subject of this paper. Having in mind a middle-sized BI project, a feasibility study based on the Monte Carlo simulation method will be conducted. A SaaS BI initiative versus a traditional one will be taken into consideration.
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Professor Mihaela I. MUNTEAN, PhD
West University of Timisoara
Associate Professor Cornelia MUNTEAN, PhD
West University of Timisoara
Abstract: Business Intelligence (BI) initiatives are challenging tasks,
implying significant costs in their implementation. Therefore, organizations have
adopted prudent policies requiring a financial justification. A business-driven
methodology is recommended in any BI project initiative, project scoping and
planning being vital for the project success. A business-driven approach of a BI
project implementation starts with a feasibility study. The decision-making process
for large projects is very complicated, and will not be subject of this paper. Having
in mind a middle-sized BI project, a feasibility study based on the Monte Carlo
simulation method will be conducted. A SaaS BI initiative versus a traditional one
will be taken into consideration.
Keywords: Business Intelligence (BI), Software as a Service (SaaS), Monte
Carlo method, BI project feasibility, Total Cost of Ownership (TCO), Return on
Investment (ROI), Internal Rate of Return (IRR)
JEL classification: C02, C88, G17, L21, L86, M15
1 Business Intelligence
1.1 Adding Value to Businesses
Business Intelligence (BI) is unanimous considered the art of gaining business
advantage from data (Ghilic-Micu, 2008); therefore, BI systems and infrastructures
must integrate disparate data sources into a single coherent framework for real-time
reporting and detailed analysis within the extended enterprise. Gaining into the
business/organisation by understanding the company′s information assets, like
customer’s information, supply chain information, personnel data, manufacturing
data, sales and marketing activity data as well as any other source of critical
information, BI tools have the power to make informed decisions more effectively
(Negash, 2003). Including aggregation, analysis, and reporting capabilities, BI
solutions transform data into a high-value insight that allows managers to make
more timely and informed decisions. Without any doubts, business decisions are
only as good as the information on which they are based (Manjunath, 2011).
Looking inside the business and at the environment in which they operate,
managers are able to fundament the most productive and profitable decisions. Only
optimizing performance, an enterprise can survive and remain an important
Mihaela I. Muntean, Cornelia Muntean
competitor in the changing market, constantly taking advantage of the raising
opportunities, risking and being flexible at new multiple demands (Kaplan, 1996).
Having as a main goal productivity and profitability, BI initiatives help decidents
in solving business problems for maximizing the business value (Negash, 2003).
Subordonated to performance management at operational and strategic level, the
actual Business Intelligence approaches consolidate the corporate management
Also the solution to a business problem is a process that includes Business
Intelligence. BI, by itself, is rarely the complete solution to the problem
(Jamaludin, 2011). Therefore, BI tools must understand the process and how to be
part of it.
Based on the company’s information assets, the Business Intelligence value
chain represents a „From DATA To PROFIT“ approach and is recommended to
ground any performance management program (Muntean, 2011). BI applications
take data that is generated by the operations of an enterprise and translate that data
into relevant and useful information for consumption by people throughout the
enterprise. Further, the obtained valuable knowledge supports any decision-making
processes in order to achieve profit. According to (Porter, 1980), a value chain is a
systematic approach to examine the development of competitive advantage,
consisting of a series of activities that create and build value. Business Intelligence
can be described as a value proposition that helps organisations in their decision-
making processes.
Succesful implementation of performance management relies on technology
platforms that sustain the whole BI value chain. Some literature references
((Brohman, 2000), (McKnigts, 2004), (Mukles, 2009)) analyse the value delivered
by BI solutions. Aberdeen Group defines the BIPM AXIS (Business Intelligence
Performance Management AXIS) and provides an objective vendor assesment
looking at the provider’s history of Value Delivered (Y-axis) and their Market
Readliness (X-axis) (Hatch, 2009). In all situation „Value delivered“ implies the
knowledge created with respect to the introduced BI value chain.
1.2 Analyzing a BI project feasibility
Many organizations are in front of most competitive economic environments,
where, in order to survive, they must reduce costs all the time and adopt the most
intelligent business strategies, for increasing revenues and improving asset
The investment into a corporate IT project, like the implementation of a
Business Intelligence approach or any other Enterprise Information Systems’ view,
can be profitable for the investor, if certain aspects are taken into consideration.
„Building the ROI is a key component of ensuring that the project is focused on the
right areas and the company’s investment is justified“. (Oco, Inc., 2007). A robust
framework for ROI analysis is recommended, a framework that is capable to help
companies, justify and measure the benefits of the IT project.
With respect to the introduced BI value chain, the value created and delivered
for the organization’s shareholders will be quantified, by identifying the
Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte
Carlo Method
opportunities to increase revenue, lower costs and improve asset utilization. BI
system implementation success measures rely on process performance (budget,
time schedule) and infrastructure performance (system quality, information quality,
system use).
Delphi experts consider that BI system implementation is a continual
information improvement program to leverage decision support (Yeoh, 2008). A
business-driven methodology is recommended in any BI project management
approaches, project scoping and planning being vital for the project success.
According to a Delphi expert „the success of 90percent of the BI projects is
determined prior to the first day“. A well-communicated scope, realistic
expectations and time-lines and an appropriate budget will be conclusive (Yeoh,
A business-driven approach of a BI project implementation starts with a
feasibility study. The decision-making process for large projects is very
complicated, and will not be subject of this paper. Having in mind a middle-sized
BI project, a feasibility study based on the Monte Carlo simulation method will be
conducted. According to (Gonzalez, 2009), project management best practices
recommend the most suitable probabilistic, statistical and simulation tools for the
project analysis.
2 Monte Carlo Method
2.1. Theory fundaments
Today, the concept “Monte Carlo Method” has become something very
unspecific, because you can find Monte Carlo methods in almost every domain,
from medicine to economy and from chemistry to regulating the flow of traffic. It’s
obvious that the way these methods are applied varies substantially from field to
field and there are dozens of subsets of Monte Carlo in each of these fields. Finally,
to call something a Monte Carlo experiment all you need to do is use random
numbers to examine some problem (Woller, 1996). Upon the whole, Monte Carlo
methods allows us to examine more complex systems than we otherwise can
(Mode, 2011).
The Monte Carlo method relies on using random occurrences for
approximation calculi. The beginnings of Monte Carlo methods can be related to
the year 1873, when Hall published a paper about the determination of number Pi
by means of Buffon’s needle. PERCENTBuffon's needle problem asks to find the
probability that a needle of length a will land on a line, given a floor with equally
spaced parallellines a distance d apart (Weisstein, 2002). Actually, the innermost
crux of the method consists in revealing the association which could be established
between some thorough deterministic phenomena and some random experiments.
The Monte Carlo method developed systematically starting with the second
world war, when it was used at the blanketing of the atom bomb, in conjunction
with direct modelling of probabilistic problems regarding the random diffusion of
Mihaela I. Muntean, Cornelia Muntean
neutrons from a fissile material. The possibility of applying Monte Carlo methods
to deterministic problems was first announced by E. Fermi, J. Von Neumann, S.
Ulam and put forth by them hard upon the second world war.
At bottom, the Monte Carlo method is a method of computational disposal of
mathematical problems, based on the modelling of random variables.
We presume z to be a random variable. We perform n independent
experiments so that each should end with a value of z (we can imagine that in every
experiment, simply and solely, the value of z is measured). This process of
constructing for z a number of n values x1, x2, …, xn represents the modelling of the
random variables, and the values xi are called the achievments of z.
If it is about studying real phenomena, then the modelling of random variables
connected with them is called simulation.
The main procedure of elaborating a Monte Carlo method for solving a
problem consists in reducing this problem to the determination of mean values.
Rather, for calculating the approximate value of a scalar a (which could be the area
of a surface, the root of an equation, the value of a definite integral etc.) we must
find a random variable z, so that we can have z med = a. Then, by modelling z, that is
building n achievments for it x1, x2, …, xn, we will consider:
We want to make obvious the method by considering that we would like to
estimate the area SA of a plane bounded surface A (Postaru, A., 2004). To figure
this out we will fix on a rectangle D with the area SD which should enclose A
(Figure 1).
Figure 1. Area SA estimation (A -a plane bounded surface included in D)
In D we choose randomly n points. We name with n(A) the number of points that
got in A. Certainly if n is great-sized, then:
Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte
Carlo Method
Hence, we can determine the estimation:
In other words we can calculate the deterministic value
by using a known value
which we multiply through the incidence
, a random variable which
represents the number of favorable events
related to the total number of
events n , produced by the experiment of generating random points in the area D.
In this example the random variable z is by default present and has two possible
values: SD if the point gets in A and 0 if the point gets in D\A. We can easily verify
that: zmed = SA,
and thus:
Based on this reasoning the Monte Carlo method has four parts:
1. The definition of a domain of possible entries;
2. The construction of the probabilistic model of the real analysed process
3. The generation of random entries with a given distribution law and the
execution of deterministic computations with the random generated entries;
4. The use of the statistical estimation theory for aggregating the results.
Especially due to the third item in the above list, the Monte Carlo methods
lend oneself best to be approached with computer programs and tend to be used
especially when it is impossible to calculate an exact result with a deterministic
algorithm (Wang, 2010). In economy, the Monte Carlo methods are especially
useful for modelling phenomena with uncertain entries, such as risk evaluation in
business, feasibility studies, financial forecasting, portfolio analysis and much
more (Evans, 2009).
2.1 Evaluating BI projects. Establishing a general theoretical approach
Nowadays, organizations have adopted more prudent policies requiring a
financial justification for nearly every IT initiative, including Business Intelligence
system implementation. Therefore, a feasibility analysis is determinant in the
decision of going further with a BI initiative. The precision and reliability of the
feasibility analysis relies on the information used in the analysis. Based on the
input data, the financial condition and performance of the investment will be
evaluated and forecastings will be made. Expected return and expected risks will
ground the final financial decision (Björnsdóttir, 2010).
Best practises (Matson, 2000; Helfert, 2001; Park, 2002; Lee, 2009) show how
financial feasibility analysis should be conducted. A project can be considered
Mihaela I. Muntean, Cornelia Muntean
financially viable if an economic return to the investors at least equal to that
available from other similarly risky investments is predictable, and additionally an
„attractive interest rate of return“ has (Bennett, 2003). Therefore, for justifying a
Business Intelligence initiative the following indicators have been taken into
- the Return on Investment (ROI) is a profitability ratio that evaluates the
benefits of a project; it indicates how much will be obtained at the end of
the project for each invested monetary unit; and
- the Internal Rate of Return (IRR) calculates the inherent discount rate or
investment yield rate produced by the project.
With respect to the introduced Monte Carlo simulation method, a general
approach for evaluating BI projects will be established (Figure 2). Inputs, that will
ground the indicators calculation, are vital. These are in fact uncertain values up to
a point and will be modelled using random variables. According to the estimates
provided by experts, probability distributions will be associated with the uncertain
inputs grounding the predictions for the considered time period.
Figure 2. Establishing ROI & IRR for Business Intelligence initiatives
Financial feasibility calculations need to be done with care and the
complexity of the calculations depends on the number of different aspects that need
to be considered. The type of the BI initiative (Software as a Service - SaaS
approach or a traditional BI implementation) is determinant for the project, having
a direct influence on the Total Cost of Ownership (TCO). Obviously, a BI SaaS
alternative implies a lower TCO than a traditional BI implementation (Oco, Inc.,
2007). According to the previous indicated reference, the TCO is build by BI/PM
Application License, Data Integration License, System Integration Costs, Database
License, Infrastructure/Hardware Costs, Internal IT Personnel Costs, Training cost
and Support/Subscription Fees. „For small- and mid-sized companies, a SaaS BI
implementation can yield a lower TCO and a more compelling ROI“ is the
conclusion of the experts from Oco (Oco, Inc., 2007); nowadays, various Cloud BI
Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte
Carlo Method
initiatives, in fact SaaS approaches, are gaining advantage over the traditional ones,
lower costs being the main reason for this phenomena (Reyes, 2010).
2.3 Business Intelligence as a shared service
SaaS is a model of software delivery that allows companies to deliver
solutions to its customers in a hosted environment over the Internet (Joha, 2012).
„SaaS is generally associated with business software and is typically thought of as
a low-cost way for businesses to obtain the same benefits of commercially
licensed, internally operated software without the associated complexity and high
initial cost“ (Hurbean, C., 2010). Aspects like: 1 low cost of entry; 2 the
responsability is on the vendor; 3 less risky investment; 4 vendors must provide
a secure data environment; 4 the worls is flat; 5 Saas is safer; 6 SaaS products
are automatically backed up; 7 SaaS vendors innovate faster; 7 - SaaS is more
stable, especially for SMEs; 8 packaging and pricing, have been recognized by
the IT specialists community ((Jakovljevic, 2006), (Peterson, 2012)) as general
characteristics of the SaaS family. All major analysts, including IDC, Garnter, and
Forrester, predict for the SaaS BI market a major growth through 2013 (Neubarth,
Comming back to the TCO for a BI initiative, „all the upfront and ongoing
fees associated with the BI project implementation should be taken into
consideration“ (Oco, Inc., 2007). Based on the proposed general approach (Figure
2), TCO will be calculated as part of a concret feasibility analysis regarding a BI
project proposal for a midsized Limited Liability Company (LLC), that will ground
the practical study case in paragraph 3. Figure 3a shows a TCO calculation for a
SaaS BI initiative vs. a traditional BI implementation in Figure 3b.
Figure 3a. TCO calculation for a SaaS BI initiative
Mihaela I. Muntean, Cornelia Muntean
Figure 3b. TCO calculation for a traditional implementation
As expected, the implementation year has a huge cost and the next eight
years have also much greater costs then the SaaS variant. This because ETL (Data
Integration License), System Integration Costs, Database License and
Infrastructure/ Hardware Costs are not zero and because the internal IT Personnel
costs are much higher for a traditional BI solution implementation comparing with
the SaaS alternative.
3 Practical Case Study
The most powerful argument when implementing a BI solution is the
substantial growth of visibility over business performance (Mircea, 2012). The
greatest restriction that limits the adoption of a BI solution is the existence of a
limited organizational culture (Nicolau, 2009).
In Romania, the local business culture related to BI is not so developed, only
few managers have invested in BI initiatives. Recording to the last year statistics,
less than 10 percent from the eligible firms acquired a BI solution (Edelhauser,
2011). In the nowadays Romanian business environment, small and medium sized
enterprises proved to be a major source of innovation, flexibility and growth
(Raşca, 2007), (Voicu, 2009), (Păunescu, 2012). It is also encouraging that
entrepreneurs begin to identify the advantages brought by the BI systems in
supporting decision-making processes (Mircea, 2008). Based on a request
formulated by a Romanian midsized LLC, a complete feasibility analysis of a
desired BI initiative has been conducted. The demarche was deployed according to
the defined theoretical approach (Figure 2) and a convenient, popular Monte Carlo
simulation based tool, like @RISK6, was used.
3.1 Establishing predictions for the inputs
Without any BI initiative, based on the real figures of the company for the
last couple of years, the scenarios are those presented in Figure 4.
Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte
Carlo Method
Figure 4. No BI initiative. Prediction of the operational profit
Based on historical data and/or expert judgment, a distribution function for the
annual sales growth rate is introduced.
Predictions for Year 1, for example, have in mind a Pert distribution (Figure
5) with a certain base percentage and a provisioned Min…Max range for possible
extreme situations. Based on these assumptions, the probable evolution scenario
together with the pessimistic and the optimistic one will be deployed. In a similar
way, adequate distribution functions for the next years have been chosen.
Figure 5. Pert distribution function for Sales growth rate in Year 1
Having in mind this state of art (Figure 4), the desired Business Intelligence
initiative will be introduced under the form of a SaaS alternative (Figure 6).
Mihaela I. Muntean, Cornelia Muntean
The predicted sales growth rate has suffered some adjustments regarding the
considered Base, Min and Max assumption; the Pert distribution remains in
actuality. The adoption of a SaaS BI initiative is supposed to increase the sales
considerable and to diminish the personnel costs due to the increased operational
efficiency. Personnel costs savings are presumed to be enclosed in a range from a
minimum of 5% to a maximum of 14 percent and a base value of 10 percent
(Figure 6), a triangular distribution function being associated (Figure 7).
Figure 6. SaaS BI initiative. Prediction of the operational profit/ ROI / IRR
Figure 7. Triangular distribution function for the personnel costs savings
Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte
Carlo Method
All previously established outputs (Figure 2) are calculated. A mean ROI
value of 5,71 and a mean IRR of 95 percent has been obtained after 10.000
iterations executed during the Monte Carlo simulation process.
The simulation was also performed for a traditional BI initiative (Figure 8). As
expected, the results are not encouraging.
Figure 8. Traditional BI initiative. Prediction of the operational profit/ ROI /
IRR after eight years of implementation
3.2 Analysing and interpreting the results
The two main outputs ROI (Figure 9) and IRR (Figure 10) will be analyzed
based on a histogram, respectively a graph with cumulative descending
distribution. Interactions are possible moving the sliders over the diagrams in order
to identify the probability to obtain a certain output value (ROI or IRR).
In our case, according to Figure 9, the probability to obtain a ROI smaller
than 1.5 is 1.6 percent, fairly sufficient for the company to go ahead with the
project investment.
The graph in Figure 10 indicates a mean value for the IRR of 93.84 percent
and a 2.3 percent probability to obtain an IRR smaller than 20 percent; 20 percent
for IRR is generally accepted to be fairly sufficient for a new project investment in
a Romanian company (Popescu, 2009). The probability to get losses is lower than 1
percent, but not zero. This result is a direct consequence of the fact that the
minimum sales rate was presumed to be below 100 percent for the second, third
Mihaela I. Muntean, Cornelia Muntean
and fourth year.
Nevertheless, if the minimum sales growth rate can be increased to 100
percent, the risk of the project vanishes for good at all.
Figure 9. SaaS BI initiative. Results histogram for ROI
Figure 10. Saas BI initiative. Result graph for IRR
The mean values for ROI and IRR being profitable, the recommendation of a
SaaS BI initiative as an advisable solution will be reinforced.
When considering the second variant, that is adopting a traditional BI solution,
it is necessary to calculate the IRR and ROI similar to the SaaS variant, but for a
few more years instead of just four. Even considering eight years instead of four,
the investment is far too big for a midsized company and the probability to obtain
losses is impermissible high. The result histogram for the obtained ROI is shown in
Evaluating a Business Intelligence Solution. Feasibility Analysis Based on Monte
Carlo Method
Figure 11, and the graph with cumulative descending distribution for the IRR is
shown in Figure 12.
Figure 11. Traditional BI initiative. Results histogram for ROI
Figure 12. Traditional BI initiative. Result graph for IRR
Here, the probability to get a ROI below 1.5 is 18.7 percent, a pretty big value
(in contrast to 1.6 percent, as it was for the SaaS implementation), and even worse
is the probability of 85.1 percent (compared with 2.3 percent when adopting the
SaaS variant) to get an IRR smaller than 20 percent. Although the mean value
obtained for the ROI (2.6) is not definitely bad and the mean value of nearly 12
percent obtained for the IRR is also acceptable, the probability of getting losses is
much too high in this case. A traditional BI initiative is not an option for the
considered company.
Mihaela I. Muntean, Cornelia Muntean
4. Conclusions and future work
Business Intelligence is the process for increasing the competitive advantage
of a company by intelligent use of available data in decision-making. Only a
revolutionary solution, like a Business Intelligence initiative, can solve the
complex issues faced when evaluating decision support applications and ensure the
availability of any business-critical information.
Small and medium sized firms have demands for BI solutions, needing
systems that take into account users involved in operational actions, not only top
managers, using scorecards, key performance indicators, analytical grid, dashboard
analysis. But a rigorously feasibility analysis should be performed before starting
any BI initiative. To avoid losses, a carefully monetary analysis is necessary.
Therefore, a general theoretical approach will be proposed; outputs like ROI and
IRR will be determined based on the specified input values and their predictions
over the considered time period. Using Monte Carlo simulation techniques,
pessimistic, probable and optimistic scenarios are deployed.
The theoretical considerations have been applied to a concrete study case on a
Romanian LLC. Predictions of the inputs have been established, simulations have
been fulfilled and results have been analysed. As expected, the SaaS BI initiative
can be implemented with almost no risks at all.
Future researches have in mind an extended theoretical unitary approach of
further financial indicators in order to improve the proposal’s capabilities. Thereby,
the necessary support for evaluating BI initiatives will be guaranteed. This is an
essential first step in helping firms, in particularly Romanian small and medium
sized organizations, to become competitive by accumulating the right business
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... Amaç Gider sınıflandırma TSOM hesaplama Araç [19] Bulut bilişimin ekonomik boyutunu araştırmak Yatırım gideri ve operasyon gideri n yıllık TSOM = n yılda amorti edilmiş yatırım giderleri + n yıl için operasyon giderleri var [20] Hizmet olarak yazılım için maliyet hesaplanması Giderin oluştuğu aşamaya göre n yıllık TSOM = edinme ve gerçekleştirim maliyetleri + n yıl için operasyon giderleri + tasfiye maliyetleri var [21] Bilgi sistemleriyle ilgili TSOM'nin hesaplanması ve maliyetin ilgili iş birimlerine dağıtılması Sabit, marjinal ve kullanım gideri TSOM = sabit, marjinal ve kullanım giderleri toplamı var [22] Bir sistemin mimari tasarımı ve kullanılacak teknoloji ile ilgili karar verilmesi Giderin oluştuğu aşamaya göre n yıllık TSOM = gerçekleştirim, işletim ve destek giderleri toplamı var [23] İş zekası uygulamasının hizmet olarak yazılım (SaaS) olarak alınmasının ve yerel altyapıda barındırılmasının karşılaştırılması Gider sınıfına göre TSOM = tüm maliyet kalemlerinin yıllık toplamları (amortisman göz önüne alınmıyor) var [24] Barındırma hizmeti için özel ve kamusal bulut kullanımının karşılaştırılması Gider sınıfına göre n yıllık TSOM = n yıl için amorti edilmiş yatırım giderleri + n yıl için operasyon giderleri (amortisman için çizgisel olmayan bir oran kullanılıyor) yok [25] Barındırma hizmeti için yerel altyapının ve kamusal bulut kullanımının karşılaştırılması ...
... Gider sınıfına göre yok yok [30] Bir uygulamanın hizmet olarak yazılım (SaaS) olarak alınmasının, hizmet olarak altyapı (IaaS) üzerinde barındırılmasının ve yerel altyapıda barındırılmasının karşılaştırılması Yatırım gideri ve operasyon gideri n yıllık TSOM = n yıl için yatırım giderleri + n yıl için operasyon giderleri yok bir araçtır [23]. Bu bilgi birikiminin çözümlenmesi sonucunda literatürde değişen gereksinimlere göre genişletilebilir bir TSOM hesaplama çerçevesinin olmadığı tespit edilmiştir. ...
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Giderek daha fazla sayıda organizasyonun faaliyetleri bilgi sistemlerine bağımlı duruma gelmektedir. Bu da bilgi sistemlerini hayatımızın önemli bir bileşeni durumuna getirmektedir. Küreselleşme ve zorlaşan rekabet nedeniyle, rekabet avantajı elde etmek ya da bunu korumak için stratejik maliyet yönetimi vazgeçilmez bir konuma gelmiştir. Bu iki eğilim, bilgi sistemlerinin toplam sahip olma maliyetlerinin (TSOM) hesaplanmasını önemli kılmaktadır. Bu doğrultuda, bir organizasyonun gereksinimleri doğrultusunda genişletilebilecek bir TSOM hesaplama çerçevesi bu çalışma kapsamında sunulmuştur. Böylece mühendislerin uygun maliyetli bilgi sistemleri geliştirmelerine ve/veya mevcut sistemlerin maliyetlerini azaltmak için farklı çözümler uygulamalarına destek olunması hedeflenmiştir. Çerçevenin, uluslararası bir şirkette kullanımını anlatan ve nasıl genişletilebileceğine dair bir örnek oluşturan vaka çalışması ile geçerlilik denetimi yapılmıştır. Vaka çalışması sonucunda TSOM hesaplama sürecinin organizasyon içinde standart hale getirilmesinin çeşitli faydaları gözlemlenmiştir: (1) farklı kişiler tarafından aynı çerçeve kullanılarak oluşturulan kullanıcı başına TSOM’ler birbirleriyle karşılaştırılabilmiştir; (2) aynı amaç için ayrı yöntem, araç ve gider sınıfları oluşturma gereksinimi ortadan kalkmıştır ve TSOM hesaplama süreci daha etkili ve verimli hale gelmiştir.
... Both business intelligence [18][19][20] and corporate sustainability [21][22][23] are two themes that have been highly studied in scientific literature. Business intelligence is an umbrella term for various business managing processes based on well informed decisions, which lead to high performance levels within organizations. ...
... We reinforce our opinion that the management of corporate sustainability implies the use of business intelligence methods and tools for analyzing the financial, environmental, and social dimensions of the business [24]. Previous research by the author [6,20] has been focused on some theory and practice issues in business intelligence, with the BI value chain being introduced in terms of a value proposition [26]. Raw data is extracted from different data sources, further integrated into a multi-dimensional data model, which is nowadays usually stored in an "in-memory database". ...
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Business intelligence (BI) is an umbrella term for strategies, technologies, and information systems used by the companies to extract from large and various data, according to the value chain, relevant knowledge to support a wide range of operational, tactical, and strategic business decisions. Sustainability, as an integrated part of the corporate business, implies the integration of the new approach at all levels: business model, performance management system, business intelligence project, and data model. Both business intelligence issues presented in this paper represent the contribution of the author in modeling data for supporting further BI approaches in corporate sustainability initiatives. Multi-dimensional modeling has been used to ground the proposals and to introduce the key performance indicators. The démarche is strengthened with implementation aspects and reporting examples. More than ever, in the Big Data era, bringing together business intelligence methods and tools with corporate sustainability is recommended.
... Starting with extracting the variables from the collected data that consisted of, (student information system from registration office, with their grades along with their biography information from the residence center) Fig. 1, to populate warehouse system that can be used by decision makers for performance measurement [30]; [31] and decision support. ...
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Evolution in managing information systems and the big datasets that increasing every minute, generates the vital demand competition to explore the maximum benefits of the „big data‟ creating in higher education institutes . Business intelligence (BI) and learning analytics (LA) are nowadays draw attention in business and society and being one of the most competitive advantages themes and profitable areas of interest in industry and organizations, including higher education institutions. Business intelligence Predictive analytics systems, using BI tools have established substantial effect on tactical transformation, decision support and informing trends‟ predicting. Learning analytics is working to deploy the worth of business intelligence in the academic context arena of education and training as it influences students‟ success, retention and explore enhancement to improve satisfaction. These concepts extent the process from envisioning the problem to applying learning analytics techniques to a particular situation, achieving insight to help deploy the results to improve decision-making. This paper is based on a real dataset in a case study grounded in “Computer Science and Information Technology” college in Sudan university of science and technology (SUST), and focuses on investigating the potential of applying Business Intelligence approaches toward an expressive analysis of the organization‟s and student‟s experience, by making use of “business intelligence enhanced learning analytics framework”, showing challenges and opportunities. It provides valuable insights into build a profound knowledge about students‟ experience, so as to assess the situation in teaching- learning process, by identifying weaknesses to be considered through practical institutional responses and, prepares for smart informative supervision. The findings explored that, how technology captured data of students‟ performance for prediction, to identify at risk students, for the purpose of consulting and withholding them before being drop out, investigate the states and provinces that have very small number of students, the effect of number of students per college/department on average GPA, the individual student details are showed, such as his personal profile and his results in his complete educational life and the completion rate for graduated students is calculated. Moreover, how that constructions can take the form of statistical outlines, models, KPIs, insightful and interactive dashboards and relationships, offering a grand challenge for technology enhance learning (TEL). Keywords: Business intelligence; learning analytics; case study, higher education institutes ;educational management
... As described previously, through gaining business advantage from data, these tools help manage information, leading executives to more informed decisions [35]. A wide array of solutions is available, and new players are constantly entering the market [36]. ...
Conference Paper
With a shift from the purchase of a product to the delivery of a service, cloud computing has revolutionized the software industry. Its cost structure has changed with the introduction of Software as a Service (SaaS), resulting in decreasing variable costs and necessary amendments to the software vendors’ pricing models. In order to justify the gap between the software’s price and the incremental cost of adding a new customer, it is essential for the vendor to focus on the added value for the client. This shift from cost-to value-based pricing models has so far not been thoroughly studied. Through literature review and expert interviews, a conceptual model for customer-centric SaaS pricing, especially Business Intelligence & Business Analytics tools, has been developed. The model has then been initially validated by discussions with the top five software players in this realm and builds a strong basis for further theoretical inquiry and practical application. © IFIP International Federation for Information Processing 2014.
This chapter aims to highlight the digital technologies that are designed to complement the operation of lean manufacturing. Firstly, Industry 4.0 has been explained that helps to complement Lean Manufacturing to gain continuous improvement, better customer satisfaction and improved operational efficiency. The results of combining digital technologies with Lean Manufacturing yield the concept of “Lean Industry 4.0”. Secondly, blockchain occurs as a disruptive innovation to resolve the problems of lack of an integrated lean management system across the supply chain network, which is also discussed. Thirdly, the Radio Frequency Identification (RFID) system is analyzed, and its ability to offer high levels of accurate, real-time information, decrease time-consuming activities and labor cost while increasing product visibility and operational speed is covered. Fourthly, Artificial Intelligence (AI) and robotics are also discussed with the ability to deal with complexity, increase productivity and efficiency with the automatic system, and decrease production costs. Finally, other non-common yet useful tools are mentioned to give a comprehensive view of the application of digital technologies with Lean Manufacturing, including automated guided vehicles (AGVs), virtual stimulation (VS), and cybersecurity. To consolidate our findings, two case studies are presented to give realistic viewpoints of digital technology adoption from two giant firms in the textile and apparel industry, namely Uniqlo and H&M. The findings of a survey based on Vietnam’s fashion and textile industries on the use of technology such as RFID is also included in this chapter.KeywordsIndustry 4.0BlockchainRFIDArtificial intelligenceRoboticsAutomated guided vehicles
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More and more organizations are being run dependent on information systems. This makes information systems a pivotal component of our lives. Because of globalization and harsh competition, strategic cost management has become essential to keep or gain competitive advantage. These two trends make the investigation of Total Cost of Ownership (TCO) for information systems crucial. To this end, a systematic mapping study (SMS) is presented to identify the use of TCO in information systems context. A summary of the findings after analyzing and synthesizing 75 relevant publications are as follows: (1) an increased interest in TCO for information systems is observed over the years; (2) 76% of the selected publications lack validation and evaluation; (3) the main motivation behind the 72% of the publications is reduction of TCO; (4) essential means of reducing TCO are cloud computing, SaaS model, and multi-tenancy; (5) TCO calculations are also generally made to compare cloud-based infrastructures with in-house infrastructures and SaaS model with on-premise software; (6) TCO is an important criterion in making investment decisions for information systems such as ERP, CRM.
Purpose – The purpose of this paper is to investigate the development of software pricing, following the advent of cloud-based business intelligence&analytics (BI&A) Software.Avalue-based conceptual software model is developed to ignite and structure further research. Design/methodology/approach – A two-step research approach is applied. In step one, the available literature is screened and evaluated, and this is followed by ten semi-structured expert interviews. With that input, a conceptual software pricing model is designed. In step two, this model is validated and refined through discussions with representatives of the five leading business intelligence suites. Findings – The paper sheds light on the value perception of customers and suggests a clear focus on the interaction between customers and vendors, and less on technical issues. The developed customer-centric, value-based pricing framework helps to improve pricing techniques and strategies. Research limitations/implications – The research is focused on the pricing strategy of software houses and excludes differentiations of technical specifications and functionalities. Practical implications – The research can support practitioners in the field of BI&A in rethinking their pricing methods. Placing the customer at center stage can lead to lower customer churn rates, higher customer satisfaction and more pricing flexibility. Originality/value – This empirical study reveals the importance of a customer-centric pricing approach in the specific case of BI&A. It can also be applied to other fast-developing sectors of the software industry.
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The aim of the article is to present cognitive challenges in the area of management. Researchers and reflective managers still work on the identity of management belonging to the social sciences. The paper depicts the connections between cognitive problems (from the epistemological point of view), management methodology and social practice. Management sciences are parts of historical discourse and because of that epistemological and methodological levels have an impact on social practice. The main concern of this paper is the role of the management scientist, consultant and teacher. The analysis suggests that academic teacher and researcher are social roles with a character that can be called universal. Practitioner is associated rather with pragmatic aspect of management science. Practitioners are often regarded as managers, but their roles in the organisation might as well be non-managerial.
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Based on the literature review, significant benefits have been identified out of the implementation of Business Intelligence. However, risks have been also discovered, and they were mainly connected with an improper change management during the process of the BI systems implementation. Further direction for a development of BI system has been discussed, focusing in particular on maturity models available in the literature. The paper highlights the fact that maturity models currently available in the literature do not take comprehensively into account all aspects of the development of BI in organizations. Therefore, there is a need for further research in this field of science.
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Data warehouse acting as a decision support systems, Data warehouses standardize the data across the organization so that there will be one view of information. Data warehouses can provide the information required by the decision makers. Developing a data warehouse for educational institute is the less focused area as educational institutes are non-profit and service oriented organizations. In present day scenario where education has been privatized and cut throat competition is prevailing, institutes needs to be more organized and need to take better decisions. Educational institute's enrollments are increasing as a result of increase in the number of branches and intake. Today, any reputed Institute's enrollments count in to thousands. The management challenges include meeting diverse student needs, increased complexity in academic processes. The complexity of these challenges requires continual improvements in operational strategies based on accurate, timely and consistent information. The cost of building a data warehouse is expensive for any educational institution as it requires data warehouse tools for building data warehouse and extracting data using data mining tools from data warehouse. The present study provides an option to build data warehouse and extract useful information using data warehousing and data mining open source tools.
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The aim of the paper is to study the use of the advanced management methods in the 2010 year in Romania. The research results were obtained with the use of a questionnaire, and our purpose was to demonstrate some hypothesis concerning identify the effect that implementation of ERP and BI applications in all functions of an organization has over the management method and the IT&C based decision. The originality of this article consists in the study realized in computer based advanced management methods implementation. The study is limited to the SIVECO companies portfolio. The purpose of the study was to demonstrate some hypothesis concerning the relationship between the size of the organization, the management method used, and the role of IT&C in decision making. The practical value of this study consists in the measurement of the impacts of contingency factors, including size, and in the assessment of the ERP systems success. The results demonstrate that the relationship between firm size and ERP success is moderated by IT assets.
Business Intelligence (in the present material we will use only the short form – BI) and intercultural management are considered, but also framed in the category "of the last arrived" (in February 1994 the BI was formally defined in France) management science sub domains (it is used the singular form of the term, because in this context it is mentioned the fundamental sub domain and not the variety of approaches whom it can be assimilated to management-case in which it will be used the plural form: the term of the "management sciences"), resultant of some generalizing tries to propose solutions of success of organizations. Although still fulfills a resumative function regarding the organization of ideas, of its theoretical enunciations and not only, BI represents a discipline in continuous development (mostly because of its intentional and practical elements). These anticipative research will try to propose solutions (which can present interest in the enterprises daily practice, but also in the science) for the difficulties encountered in the enrolling of the BI’s activities and practices in the Romanian enterprises, keeping in mind the employees and the managers position towards the BI occurred fallowing an deduced behavior by the Romanian cultural peculiarity.
Software as a Service (SaaS) can be viewed as the fourth wave of outsourcing. SaaS is a relatively new type of service delivery model in which a service provider delivers its services over the web to many users on a pay per use or period basis. In the scarce literature available, the SaaS business model is almost always analyzed from the perspective of the service provider perspective, and rarely from the user organization. Using the unified business model conceptual framework, two case studies are investigated to understand the design choices underlying the SaaS business model from the user organization perspective. The analyses on the business model dimensions provided insight into the differences between the case studies and helped to identify eight discriminating design choices that are important when designing SaaS business models. These include the (1) SaaS service characteristics, (2) SaaS value source, (3) SaaS user target group, (4) data architecture configuration and tenancy model, (5) SaaS governance and demand/supply management core competencies, (6) cloud deployment model, (7) SaaS integration and provider strategy and the (8) SaaS pricing structure. An appeal is made for more research into the impact of cloud business models.
The implementation of a BI system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of CSFs for management reference. This article represents a first step of filling in the research gap. The authors utilized the Delphi method to conduct three rounds of studies with 15 BI system experts in the domain of engineering asset management organizations. The study develops a CSFs framework that consists of seven factors and associated contextual elements crucial for BI systems implementation. The CSFs are committed management support and sponsorship, business user-oriented change management, clear business vision and well-established case, business-driven methodology and project management, business-centric championship and balanced project team composition, strategic and extensible technical framework, and sustainable data quality and governance framework. This CSFs framework allows BI stakeholders to holistically understand the critical factors that influence implementation success of BI systems.
Considering the demands of knowledge society each organization must became an intelligence organization. Business Intelligence (BI) helps achieving this goal. The large variety of Business Intelligence solutions on the market, the difficult process to select one of them and evaluate the impact of the selected solution on the organization leads to the need of creating a strategy to help organizations chose the best solution for investment. Taking into account that every organization must spend its money carefully, and every solution that does not provide a rapid impact on the basic business is not considered a viable solution, the efficiency of its use must be correctly evaluated and demonstrated based on evaluation criteria, but also through monetary analyses (more difficult to achieve). We suggest and explain the main steps in order to choose the right solution and to evaluate it and we present a case study of the integration in Romanian public institutions of a Business Intelligence solution for acquisitions.
Based on the authors' extensive teaching, research and business experiences, this book reviews, discusses and integrates both theoretical and practical aspects of financial planning and forecasting. The book is divided into six parts: Information and Methodology for Financial Analysis, Alternative Finance Theories and Their Application, Capital Budgeting and Leasing Decisions, Corporate Policies and Their Interrelationships, Short-term Financial Decisions, Financial Planning and Forecasting, and Overview. The theories used in this book are pre-Modigliani-Miller Theorem, Modigliani-Miller Theorem, Capital Asset Pricing Model and Arbitrage Pricing Theory, and Option Pricing Theory. The interrelationships among these theories are carefully analyzed. Meaningful real-world examples of using these theories are discussed step-by-step, with relevant data and methodology. Alternative planning and forecasting models are also used to show how the interdisciplinary approach is helpful in making meaningful financial management decisions.