Intelligent agents and risk based model for supply chain management.
Smeureanu, Ion ; Ruxanda, Gheorghe ; Diosteanu, Andreea 等
Reference to this paper should be made as follows: Smeureanu, I.;
Ruxanda, G.; Diosteanu, A.; Delcea, C.; Cotfas, L. A. 2012. Intelligent
agents and risk based model for supply chain management, Technological
and Economic Development of Economy 18(3): 452-469.
JEL Classification: C02, D22.
1. Introduction
Nowadays, the enterprise environment is heavily influenced by the
evolution of the IT&C technologies. The main impact of these changes
is on the collaborative nature of the economic activity. This results in
an increased level of competition at a global scale. Therefore, adapting
and efficiently taking advantage of these changes represents a real
challenge for the top and medium management. The enterprise steering
committee has to permanently be updated with the latest discoveries so
that to obtain maximum satisfaction.
In the context of dynamic business, maximizing and optimizing
business performance is a critical requirement for profitability. The
new economy considers enterprise interoperability along with efficient
knowledge management strategies as one of the main prerequisites for
improving the level of economic performance.
All the transformations that occur in the enterprise economic
environment are closely connected and have a great influence over the
globalization trend the economy is heading towards. The global market
can be characterized by increased levels of interoperability that
reflect into the integration of multiple and different information
systems which are able to share, manipulate and combine knowledge so
that to facilitate the enterprise collaboration process. Having given
all the above facts, we can conclude that the very frequently used term
"integrated company" is not of great interest anymore.
Presently, it is substituted by collaborative information systems
composed of business networks that are linked to independent partners
that provide individual services and goods.
The changing economic conditions and the emerging global economy
create a context in which companies that quickly and accurately evaluate
new market opportunities and create innovative products and services can
perform. Fully integrated traditional enterprises are being replaced by
business networks in which every company is specialized in certain
services or products (Gosain et al. 2005), (Nissen, Sengupta 2006).
In the economic systems that are based on enterprise
interoperability, the business flow is more complex than in the
non-collaborative ones. The business processes that compose the flow
have to be designed as to take into account both the internal and the
external economic environments. While the internal enterprise
environment is composed of: activities such as marketing, production
etc., actors (all the persons involved in the activities) and resources
that characterize and assure its main functions, the external one
consists of actions and behaviors that are part of the collaboration
flow and establish the enterprise place and role in the global economic
sector in which it performs. The supply chain activity is one of the
most representative examples of enterprise collaboration and
interaction.
In order to efficiently model and coordinate transactions in such
complex economic systems, advanced collaborative software business
solutions are required. The distributed nature of the collaborative
economic environment can be represented and modeled by using web
services and intelligent agents that interact with each other. According
to Papazoglou, Kratz (2007) this requires that several web service
operations or processes attain transactional properties reflecting
business semantics, which are to be treated as a single logical (atomic)
unit of work that can be performed as part of a business transaction.
This paper presents a software agent based framework architecture
for boosting performance in supply chain management applications. The
framework is based on agent interaction and neuronal networks. The
purpose of such a platform is to develop flexible business applications
for SCM transactions modeling in collaborative and distributed economic
systems. The interaction between agents is limited by a cybernetic model
that takes into account several constraints one of the main being
bankruptcy risk potential of the peer partner company.
The first section of the article consists of a short literature
review so that to establish the place of our research in the current
international research trends. The following section describes the
economic model that is applied. The last section presents the main
elements of the multi-agent supply chain framework and how the economic
model is integrated.
The innovative features of our proposal are the flexible agent
search, the use of location and semantic annotation and also the use of
the economic model that considers bankruptcy risk as one of the
variables when evaluating the possible future business partner.
2. Agent and semantic web service technologies applied in business
flows
When business processes cross over the boundaries of the
organization they become more difficult to model and the software
applications that are to be implemented in this situation are very
complex. There are some main elements that have to be taken into
consideration Papazoglou, Kratz (2007):
--Establish a common interaction language between distributed and
distinct systems: unanimously agreed communication and business
standards, common trading documents format, etc.
--Business protocols for message exchange that specify: flow of
messages representing business activities among trading partners
(without requiring any specific implementation mechanism). Collectively,
business process protocols and associated data format and message
exchange standards provide the means for automated, system-to-system
exchange of data and messages between trading partners.
Nowadays, there are true businesses networks composed of
independent partners that offer to the others their specific services or
products. Therefore, we can assume that companies become product and
customer oriented structures. In the context of dynamic business,
maximizing and optimizing business performance is a critical requirement
for profitability.
Software agents are complex autonomous software entities that
behave and interact with each other in order to collaboratively fulfill
the purpose of the entire multi-agent system. One important feature of
multi-agent systems refers to their ability of decomposing complex
problems into more easily manageable sub problems. This feature is
particularly useful for representing supply chains as networks composed
of autonomous business that negotiate, manufacture and distribute
products and services, Bodea, Mogos (2007).
Using multi-agent systems in collaborative supply chain management
systems implies modeling communication and cooperation between agents in
order to allow demand-supply processes. As shown in Di et al. (2009)
agents can communicate and interact with each other through ontology
language.
The demand for flexible, adaptive multi-agent behavior has
increased the challenges inherent in the design of multi-agent systems
Bond, Gasser (1988). As a result, the attempt to automate the
collaboration process between enterprises has been amplified. The payoff
for meeting these amplified challenges is the creation of more capable,
more robust multi-agent systems.
In general, adapting the organization of a multi-agent system
enables agents to overcome problems or improve performance by changing
the pattern of information, control, and communication relationships
among agents as well as the distribution of tasks, resources, and
capabilities. For example, agents may be able to overcome agent failure
(by restructuring collaborative decision-making to exclude failed
agents), communication failure (by allowing agents awaiting orders to
eventually take initiative), and under-performance (by allowing agents
to establish new collaborations that may work better) Martin, Barber
(2006).
Intelligent software agents have been used in enterprise
independent software systems integration process, not only to assure an
approach for functional integration, but also to facilitate the use of
business intelligence and collaboration among enterprises for their
communication, interaction, cooperation, pro-activeness, and autonomous
intelligent decision making.
In order to achieve the objectives of the current enterprise
interoperability trend, we propose a framework that combines web
services and software agents so that to provide an efficient service
selection, retrieval, composition and integration.
Multi-agent systems are closely connected to web service technology
because they represent interoperable, portable and distributed
solutions. Agents and web services may be related in different ways:
agents use web services, web services are in fact agents or agents are
composed of, deployed as, and dynamically extended by web services
Martin et al. (2005).
3. Supply chain management platform based on agents'
interaction
This paper illustrates an agent based software platform for
modeling supply chain management application in the modern
collaborative, knowledge based economic environment. Agents'
behavior simulates human interaction, communication and negotiation
processes.
The proposed framework enables the implementation of multi-agent
supply chain systems that allow customers to easily identify suppliers
that meet their business needs. All participants in the supply chain are
modeled using software agents.
Similar to the real world business they represent on the internet,
each agent has inputs and outputs. Outputs represent the services and
products the agent offers, their price levels and technical features.
Inputs consist in raw material, required-services or sub-assemblies
needed in order to manufacture a specific output product or in order to
provide a specific service.
In order to choose the best supplier for each input a series of
features have to be taken into account. These features are combined in
an evaluation function that is used to compute a score for each
potential business supplier. Every agent will have its own evaluation
function for selecting the most suitable business partners. The function
is parameterized, so that each agent can choose different importance
weights for the constraints according to its preferences.
In our paper, before applying the selection function we perform a
classification of the companies based on their risk of bankruptcy. For
the selection function we consider as variables: distance, price,
technical features. This parameters can change based on the purpose of
each business application. In this manner we assure an increased level
of flexibility to our solution. In order to evaluate and predict the
bankruptcy risk, we developed a wavelet neuronal network based model.
3.1. Bankruptcy Prediction Model
In order to classify the identified suppliers into secure and
non-secure ones we developed a binary classification model that is based
on wavelet neuronal networks. Due to the competitive exchange economy,
each firm can be seen as a producer or consumer looking to maximize
either its profit or utility Rezvani, Analoui (2010). The algorithm has
several stages that will be presented in detail below.
Stage 1: Prerequisite phase--data collection
This stage consists in creating a training set for the next phases
of the algorithm. The set consists of forty five companies that are
grouped into profitable, (thirty two companies) and less profitable ones
(thirteen). We used the "profitable companies" term as a
general term, these companies being characterized by sustainable growth
and "proficient" economic strategy demonstrated by the values
of certain indicators. For this we consider ten indicators that will be
analyzed and in the variable selection phase will be selected only the
ones that qualify to be part of the binary classification model. The
computations are developed in MATLAB 7.9.0 and we took into account
several data sources such as: BVB (2011), ANAF (2011), Mfin (2011),
ListaFirme (2011), KTD (2011).
Stage 2: Data mining and variables selection stage
The data mining process consists of identifying a list of financial
indicators that can be used to predict bankruptcy. Furthermore, over
this stage we compute the values for indicators. Predictive variables
selection is made in three steps:
Step 1: The bankruptcy prediction literature was reviewed and a
number of 50 variables were selected, these variables representing the
characteristics of profitability, liquidity, activity, stability,
growth, trend, liability, structure, etc., see Table 1.
Step 2: Among them, eight variables were retained based on their
availability on finding online necessary data;
In order to obtain the necessary data from the Internet, a web
crawling application was developed. The application takes as input the
CUI (fiscal code) and crawls the sites that were presented above. The
web crawler module not only looks for data over the Internet, but also
computes the financial indicators within the identified set based on the
available data. For the training data set composed of 45 companies the
application was able to identify data only for 10 indicators which will
be presented in detail. Figure 1 presents a print screen from the web
crawling application based on a single company search.
If no Fiscal code is entered the application crawls for the
companies that we considered as a training data set. The results are
displayed in Figure 2.
The following eight variables are the ones that manifested the
great availability in finding their values through web crawling:
--X1--Debt ratio (DR);
--X2--Quick assets to total assets (QA2TA);
--X3--Return on equity (ROE);
--X4--Net profit to equity (NPE);
--X5--Financial expenses to sales (FE2S);
--X6--Earnings per share (EPS);
--X7--Current assets turnover (CAT);
--X8--Profit margin (PM).
[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]
Step 3: Using feature selection on standardized data for a specific
significance level, a number of three variables are retained and used in
the proposed model.
As Guyon, Elisseeff (2003) have identified the potential benefits
of feature selection:
--facilitating data visualization and data understanding;
--reducing the measurement and storage requirements;
--reducing training and utilization times;
--defying the curse of dimensionality to improve prediction
performance.
The feature selection algorithm is suited for training wavelet
neuronal networks. In order to determine the most important features, we
train a sample neural network using all the eight features presented at
the previous step as shown in Figure 3. The network consists of three
layers: input layer, hidden layer and output layer. All the nodes in
each layer are fully connected to the nodes in the next layer. The input
layer has 8 nodes corresponding to the features taken into
consideration. The hidden layer has m nodes and the output layer
contains only one node, representing the classification. The network is
trained using the considered firms in order to determine the weights
between the nodes. We consider [W.sub.ij] the weight of a connection
between the input node i and the hidden node j (i = 1 ... [n.sub.p]; j =
1 ... [n.sub.h]) and [w'.sub.j] the weight of the connection
between the hidden node j and the output node. In our case the number of
input nodes, [n.sub.p] equals 8, the number of variables taken into
consideration.
[FIGURE 3 OMITTED]
The most important features are the one for which the input has the
highest absolute influence on the output. We can calculate the impact of
the input node i on the output of the hidden node j using the following
formula in which we divide the weight of the hidden to output link with
the relevance of the i to j connection over all the inputs to hidden
node j:
[w.sup.*]ij = [absolute value of [[w.sub.ij]]/[[n.sub.p].summation
over (i=1)][absolute value of [w.sub.i]]] * [absolute value of
[w'.sub.i]] (1)
For each input node we compute the importance indicator:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
(1) and (2) can be combined in a single equation:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
Based on the importance indicator, the following three variables
were selected:
--X1--Debt ratio; (stability);
--X7--Current assets turnover; (activity);
--X8--Profit margin; (profitability).
Debt ratio calculated as:
DR = TL/TA, (4)
where: TL = Total liabilities; TA = Total assets.
DR indicates what proportion of debt a company has relatively to
its assets. It is categorized into the solvency and stability ratios
category. Solvency may reveal the some firms' failure causes rooted
in its financing policy. This ratio also gives an idea over
company's potential risks in terms of debt load and has a negative
relation with bankruptcy occurrence.
Current assets turnover (CAT) ratio determined by dividing the
sales to the current assets is an efficiency ratio. A higher efficiency
can be translated through higher profitability and better liquidity and
finally to lower default risk. Etemadi et al. (2009)
CAT = TS/CA, (5)
where: TS = Totals sales; CA = Current assets.
Profit margin (PM) calculated as net profits divided by sales or as
net income divided by revenues. A higher level of this ratio means
better coverage production costs and a lower default risk.
PM = NP/TS, (6)
where: NP = Net profit; TS = Total sales.
For the both considered categories (profitable and less profitable)
and based on the initial data sample (forty five companies), the mean
values of the X1, X7 and X8 indicators are calculated and their values
could be found in Table 2. These are going to be used in the next step,
which deals with the binary classification of new firms.
Graphically, the mean values of the classification variables for
each of the two representative sets are drawn in Figure 4.
In the variable selection phase we have identified a set of three
variables that are best representing the situation in which a company
can be found. Using the identified variables, we can pass to the next
step: using a wavelet network classification model for predicting the
future stage in which a firm could be found: profitable or less
profitable.
Stage 3: Wavelet neural network classification model
Many approaches currently exist for predicting the corporate
financial distress, usually based on discriminant analysis using linear
functions. Such approaches have the disadvantage that the patterns need
to be linearly separable and the samples must follow a multivariate
normal distribution. Other approaches involve binary classification
models Min, Jeong (2009) that involve the calculation of a similarity
degree between a newly considered firm and the two considered sets in
which a firm could be found. Neural networks emerged as a successful way
to overcome such limitations Chauhan et al. (2009). They are built from
many interconnected processing elements called neurons. The weights
associated with the connections between the neurons model the
input-output characteristics. Neural networks are used in Cimpoeru
(2011) for credit risk assessment. Wavelet neural networks emerged as an
approach that does not depend on the set of initial conditions used when
defining the neural network structure. Figure 5 presents a
representation of a wavelet function using Wolfram Mathematica.
An individual wavelet function is defined by the following formula:
[[psi].sup.a,b](x) = 1/[square root of [absolute value of [sigma]]]
* [psi]([x - b]/a). (7)
Several methods to train wavelet neural networks--WNN exist
including Differential evolution and Threshold accepting trained WNN
Chauhan et al. (2009). A back propagation approach for training a normal
neural network is presented in Ruxanda (2010).
[FIGURE 5 OMITTED]
In order to train the network we use the following steps:
Step 1: We first assign random values for the connections between
the input nodes and the hidden nodes and between the hidden nodes and
the output node, [w.sub.ij] [member of] (0,1); [w'.sub.j] [member
of] (0,1).
Step 2: The output of a sample [V.sub.k], k = 1,..., np, where np
is the number of samples, is calculated with the following formula:
[V.sub.k] = [nhn.summation over (j=1)][w'.sub.j] *
f([nin.summation][w.sub.ij] * [x.sub.ki] - [b.sub.j]/[a.sub.j]), (8)
where: k = 1,..., np; nin = number of input nodes and nhn = number
of hidden nodes.
Step 3: Reduce the error of prediction based on the training data
set by adjusting the weights [w.sub.ij], [w'.sub.j], j and the
values for a, b. The error is computed as follows:
E = [square root of [np.summation over (k=1)]([V.sub.k] -
[[??].sub.k]/[V.sub.k])] (9)
Step 4: Return to Step 2 until the error E is as small as possible
and the training phase can be considered completed.
The resulting WNN will have three input nodes corresponding to the
selected values X1, X7 and X8 as shown in Figure 6.
[FIGURE 6 OMITTED]
The stages of the bankruptcy prediction and classification
algorithm presented above are summarized in Figure 7.
[FIGURE 7 OMITTED]
3.2. Trading agents interaction process
For example, consider, a manufacturer that develops Web service
based solutions to automate the order and delivery business functions
with its suppliers as part of a business transaction. The transaction
between the manufacturer and its suppliers may only be considered as
successful once all products are delivered to their final destination,
which could be days or even weeks after the placement of the order, and
payment has ensued.
According to Fox et al. (2000) a supply chain usually consists of
suppliers, factories, warehouses, distribution centers, and retailers
through which raw materials are acquired, transformed, and delivered to
customers.
Although creating specialized agents for these categories implies
several benefits we consider that this approach limits flexibility and
the overall capacity of the system to model the real business entities.
Taking into account that a participant in a supply chain might belong to
several of the above mentioned categories and offer multiple services
and products we have chosen to implement generic trading agents.
Intelligent agents are used in Xiaolong et al. (2010) for trading in the
construction projects field.
When evaluating offers, agents will first classify the providers
based on their profitability by using the algorithm presented above. If
a certain supplier is considered safe, the following function is used to
evaluate the best offer Cotfas et al. (2010).
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (10)
where: m = number of suppliers; n = number of technical features;
[o.sub.j] = number of possible values for the feature j; [p.sub.lj] =
importance of feature j for trading agent l; [[alpha].sub.jk] = possible
values of the technical feature j. [M.sub.lj]: {[[alpha].sub.jk]; j =
1...n,k = 1..[o.sub.j]} [right arrow] {1...10} = function evaluating the
optimality of characteristic j for trading agent l.
If one mandatory rule is not met, the offer is rejected and will no
longer be taken into consideration during the evaluation process. Based
on previous experience or user input, each agent maintains a list of
trusted and mistrusted agents. Importance for the above mentioned
parameters can be defined using the dedicated Supplier's Web
Interface.
All agents can and usually perform several roles in the supply
chain. In Figure 8 the agent called Trader 1 is a supplier for the agent
Customer from whom it receives a service or a product request. If Trader
1 cannot complete the request by itself he can request additional
services or subassemblies to other agents becoming thus a customer.
Trader 2, 3 and 4 are possible suppliers for Trader 1, but they also can
become customers for other agents. If a trader agent cannot supply the
requested service or product in the requested conditions, it returns a
void offer.
Ontology Web Language (OWL) was used to semantically describe the
products and services and also their technical characteristics. All
configurations are done using the trader agents' web interface.
[FIGURE 8 OMITTED]
Although location is considered an important aspect, many existing
frameworks fail to use it at the full potential or omit it completely.
We consider location import as it might have a big influence on
transport costs and risks involved. It also helps us narrow the search
when the requested service or product can be delivered only in certain
areas. GPS coordinates are stored for all agents involved. Besides, for
agents offering distribution services, GPS coordinates for the delivery
areas are also stored as geographic shapes (polygon) using Geographic
Markup Language (GML) standard format as defined by Open Geospatial
Consortium (OGC). Using a standard format will allow us to integrate the
framework with other applications like the one described in Dumitrescu
et al. (2010).
3.3. Identifying the best offer by using intelligent agent
interaction
The communication between agents is based on OWL ACL messages
compliant with FIPA standards. The steps that are taken into account are
presented below Chauhan et al. (2009).
Step 1: All Domain Directory Facilitator (DDF) agents register
themselves with the main Directory Facilitator (DF) agent. Domain
Directory Facilitator agents will keep information about service from a
certain domain. A trader agent first queries the DF agent in order to
obtain the available DDFs and then, it registers its services and
products with the corresponding ones.
As shown in Figure 9, the framework is implemented in a distributed
manner that allows having multiple DF agents running on different
machines. Communication between the distributed components is
implemented using web services for better interoperability across
heterogeneous computing environments. The Web Service Agent (WSA) agent
manages the translation between ACL messages and web service calls and
constantly maintains a list of DF agents on other machines.
[FIGURE 9 OMITTED]
Step 2: As the supply chain is a customer demand-driven system, the
flow starts from the customer's incoming orders. The customer agent
first queries the DF and DDF agents on all machines for the list of
supplier agents that provide a certain service or product.
Step 3: The customer agent sends its demand to the seller agents
discovered in the previous step. The demand specifies the requested
delivery addresses using GPS coordinates, the requested products, there
technical features, quantity and maximum delivery date.
Step 4: Each contacted supplier agent analyzes the customer's
demand and respond in case of match. First it compares the geographic
position of the customer with its points of sales covered surface. Next,
the seller agent compares the request products or services with his own
data described using OWL. Using OWL proved to be a good solution to
overcome data heterogeneity. Inference was implemented using Jena to
better understand the customer's request. If the seller agent
can't provide the requested service or product by itself (ex: it
needs to buy subassemblies or raw materials), it will need to find
suppliers. Thus, the seller agent becomes a customer for other seller
agents and the process repeats starting with Step 2 (Figure 8).
Step 5: The customer agent continues to communicate and negotiate
with the agents that positively respond. The offer evaluation takes into
consideration both mandatory rules and the evaluation function
(bestOffer). More than one seller agent might be selected if necessary
in order to meet the required quantity. If the customer agent is not the
initial customer, but a seller agent searching for necessary
sub-assemblies or services, the algorithm continues with the selection
of the best seller offer in the previous recursion step. The algorithm
ends when the best seller offer evaluation is done by the initial
customer agent.
As shown above, the algorithm is implemented in a recursive manner
that allows each seller agent to become a customer agent if needed. This
approach guarantees a high degree of flexibility in generating the
supply chain. The customer agents can take into consideration a set of
predefined rules when selecting suppliers like shown in Figure 10.
[FIGURE 10 OMITTED]
Additional reasoning capabilities can be added to seller agent in
order to enable the seller agents to adapt prices according to market
situation so it can maximize its revenues by selling at appropriate
prices.
4. Conclusions
This paper presents and agent based framework that can be used for
modeling Supply Chain Management application in the process of
identifying the most convenient suppliers. The frameworks has an
evaluation function for the best offer that is very customizable in the
sense of enabling business application developers to take into account
various types of constraints. Furthermore, this software solution also
incorporates a bankruptcy prediction module based on neuronal networks
that pre-classify potential suppliers based on their risk of going
bankrupt, thus reducing the dimensions of the search space.
Acknowledgement
This article is one of the results of the research activity carried
out under the frame of the project "Doctoral Program and PhD
Students in the education research and innovation triangle". This
project is co-funded by European Social Fund through the Sectorial
Operational Program for Human Resources Development 2007-2013,
coordinated by The Bucharest University of Economics.
doi: 10.3846/20294913.2012.702696
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Ion SMEUREANU has graduated the Faculty of Planning and Economic
Cybernetics in 1980, as promotion leader. He holds a PhD diploma in
"Economic Cybernetics" from 1992 and has a remarkable didactic
activity since 1984 when he joined the staff of Bucharest Academy of
Economic Studies. Currently, he is a full Professor of Economic
Informatics within the Department of Economic Informatics and the dean
of the Faculty of Cybernetics, Statistics and Economic Informatics from
the Academy of Economic Studies. He is the author of more than 16 books
and an impressive number of articles. He was also project director or
member in many important research projects. He was awarded the Nicolae
Georgescu-Roegen diploma, the award for the entire research activity
offered by the Romanian Statistics Society in 2007 and many others.
Gheorghe RUXANDA. PhD in Economic Cybernetics, is Full Professor
and PhD Adviser within the Department of Economic Informatics and
Cybernetics, The Bucharest Academy of Economic Studies. He graduated the
Faculty of Economic Cybernetics, Statistics and Informatics, Academy of
Economic Studies, Bucharest (1975) where he also earned his
Doctor's Degree (1994). Had numerous research visits, as follows:
Columbia University--School of Business, New York, USA (1999), Southern
Methodist University (SMU), Faculty of Computer Science and Engineering,
Dallas, Texas, USA (1999), Ecole Normale Superieure, Paris, France
(2000), Reading University, England (2002), North Carolina University,
Chapel Hill, USA (2002). He is full professor of: Multidimensional Data
Analysis (Doctoral School), Multidimensional Data Analysis (Master
Studies), Modeling and Neural Calculation (Master Studies). Fields of
Scientific Competence: evaluation, measurement, quantification, analysis
and prediction in the economic field; econometrics and
statistical-mathematical modeling in the economic-financial field; data
mining, multidimensional statistics and multidimensional data analysis;
pattern recognition and neural networks; risk analysis and uncertainty
in economics; development of software instruments for
economic-mathematical modeling.
Scientific research activity: over 35 years of scientific research
in both theory and practice of quantitative economy and in coordinating
research projects; 50 scientific papers presented at national and
international scientific sessions and symposia; 65 scientific research
projects with national and international financing; 70 scientific papers
published in prestigious national and international journals in the
field of economic cybernetics, econometrics, multidimensional data
analysis, microeconomics, scientific informatics, out of which 9 papers
being published in ISI--Thompson Reuters journals; 17 manuals and
university courses in the field of econometrics, multidimensional data
analysis, microeconomics, scientific informatics; 31 studies of national
public interest developed within the scientific research projects.
Andreea DIOSTEANU holds a PhD diploma in the field of economics,
specialized in informatics (artificial intelligence) applied in
economics. She has graduated the Faculty of Economic Cybernetics,
Statistics and Informatics in 2008 as promotion leader, with an average
of 10. She is conducting research in Economic Informatics at Bucharest
Academy of Economic Studies and she was also a pre-Assistant lecturer
within the Department of Economic Informatics and .NET programmer
(technical project manager) at TotalSoft. She participated in many
student competitions both at national and international level obtaining
a lot of first and second prizes. The most important competitions she
was finalist in were Microsoft International Imagine Cup Competition,
Software Design section (national finalist); Berkley University and IBM
sponsored ICUBE competition where she qualified for the South Eastern
Phase-Novatech. Furthermore, she also obtained the "N.N
Constantinescu" excellence scholarship in 2007-2008 for the entire
student research activity. She is the author of several articles
published in national and international journals. Andreea obtained a
full scholarship for the entire PhD program supported by the European
Structural Funds under project POSDRU/6/1.5/S/11. She also attended a
research stage at the Artificial Intelligence Group from Tor Vergata
University of Rome.
Camelia DELCEA holds a PhD diploma in the Economic Cybernetics and
Statistics Department at the Bucharest University of Economics. She
holds a degree in Economics from the University of Economics, Bucharest
(2008) and a teaching certificate from the same university. Her research
activity is in the area of modeling and forecasting of a firm's
"diseases" using the concepts and methods offered by
multi-agent systems and includes more than 25 articles, an international
IEEE Paper Award (IEEE GSIS Nanjing, China, 2009) and a full scholarship
for the entire PhD program supported by the European Structural Funds
under project POSDRU/6/1.5/S/11.
Liviu Adrian COTFAS holds a PhD diploma and a graduate of the
Faculty of Cybernetics, Statistics and Economic Informatics. He is
currently conducting research in Economic Informatics at Bucharest
Academy of Economic Studies and he is also an Academic Assistent within
the Department of Economic Informatics. Amongst his fields of interest
are geographic information systems, genetic algorithms and web
technologies. Liviu Adrian a full scholarship for the entire PhD program
supported by the European Structural Funds under project
POSDRU/6/1.5/S/11.
Ion Smeureanu (1), Gheorghe Ruxanda (2), Andreea Diosteanu (3),
Camelia Delcea (4), Liviu Adrian Cotfas (5)
Faculty Cybernetics, Statistics and Economic Informatics, Bucharest
Academy of Economic Studies, 010552 Bucharest, Romania
E-mails: (1) smeurean@ase.ro (corresponding author); (2)
ghrux@ase.ro (3) andreea.diosteanu@gmail.com; (4)
camelia.delcea@yahoo.com; (5) liviu.cotfas@ase.ro
Received 21 February 2012; accepted 14 April 2012
Table 1. Indicators used in financial distress Delcea (2010),
Delcea, Scarlat (2010)
Category Indicator
Profitability Gross income to sales
Earnings before interest and tax to total assets
Return on total asset
Financial expenses to liabilities
Cost of sales * Cost of sales growth ratio
Net income to total interest
Profit margin
Cost of sales to net sales
Financial expenses and normal profit to total assets
Financial expenses growth rate to assets
Non-operatory expenses growth rate to assets
Net profit to equity
Activity Payables turnover
Inventory growth rate to sales
Current assets turnover
Fixed assets turnover
Inventory turnover
Total assets turnover
Total assets turnover * sales growth rate
Liquidity Solvency ratio
Window coefficient
Cash flow to total liabilities
Stability Debt ratio
Long term debt ratio
Quick ratio
Net worth to total assets
Cash and cash equivalents to current liabilities
Growth Growth rate of primary business
Growth rate of total assets
Growth rate of sales
Total asset change ratio
Total asset growth rate
Interest increase ratio
Subsistence income to total assets
Trend Financial expenses growth
Structure The proportion of fixed assets
ratios
The proportion of current assets
The proportion of equity to fixed assets
The proportion of current liability
Liability Current ratio
Asset liability ratio
Ratio of cash to current liability
Equity to debt ratio
Ratio of liability to tangible net asset
Interest coverage ratio
Ratio of liability to market value of equity
Table 2. Means of the variables calculated for the two groups
considered in the model
Variable X1 X7 X8
Means of profitable firms 0.6745 1.4403 0.2428
Means of less profitable firms 0.7946 1.0955 0.0140
Fig. 4 Mean values representation
Profitable Less
Profitable
X1 0.6475 0.7946
X7 1.4403 1.0955
X8 0.2428 0.0141
Note: Table made from line graph.
