As we know from a few years, the basic characteristics of ERP systems are: modular-design, central common database, integration of the modules, data transfer between modules done automatically, complex systems and flexible configuration. Because this, is obviously a parallel approach to design and implement them within parallel algorithms, parallel calculus and distributed databases. This paper aims to support these assertions and provide a model, in summary, what could be an ERP system based on parallel computing and algorithms.ERP Systems, Modeling, Parallel Calculus, Incremental Model

Loredana MOCEAN

English

Research Papers in Economics

128    Informatica Economică vol. 15, no. 3/2011 
 
 
The Modeling of the ERP Systems within Parallel Calculus 
 
Loredana MOCEAN, PhD 
Babes – Bolyai University of Cluj – Napoca 
Business Information Systems Department 
loredana.mocean@econ.ubbcluj.ro 
 
As we know from a few years, the basic characteristics of ERP systems are:   
modular-design, central common database, integration of the modules, data transfer between 
modules done automatically, complex systems and flexible configuration. Because this, is 
obviously a parallel approach to design and implement them within parallel algorithms, 
parallel calculus and distributed databases. This paper aims to support these assertions and 
provide a model, in summary, what could be an ERP system based on parallel computing and 
algorithms. 
Keywords: ERP Systems, Modeling, Parallel Calculus, Incremental Model 
 
Introduction 
Modeling is a method used in science and 
technique and consists in schematically 
reproduction of an object or system as a 
similar system or analogue with the scope of 
studying the properties and transformation of 
the original system. 
The incremental model which is possible in 
ERP design is shown in Figure 1 [1]. 
 
 
 
 
 
 
 
 
 
            …  …    … 
 
 
 
 
 
 
 
 
 
Fig. 1. The incremental model implemented on ERP systems 
 
One of the first methods of producing 
parallel programs consisted of parallelizing 
sequential programs with the biggest amount 
of scientific calculations.  
All loops express an intrinsic parallelism 
which can be exploited by dividing it into 
several simultaneous activities. As we see in 
Figure 1, it is very efficient to divide the 
programming work in parallel design of the 
components and parallel programming of the 
modules. 
A second possibility is to design the best 
parallel version from scratch. It will be 
considered whether it is a parallelism of data 
or of control [2].  
 
1 
  Preliminary education 
  Making the project team 
  Requirements analysis 
  Pick up of information 
  Hard dimension analysis 
 
 
 Elaboration of decision 
 ERP providers’ visits 
 Show solutions of 
providers 
 Project planning 
 Details planning 
 ERP instruction 
 
 
Design 
component1 
Implement 
component1 
Design 
component N  Implement 
component N 
Setup 
component1 
Post-
implementation 
support 
Setup 
component N 
Post-
implementation 
support 
 Informatica Economică vol. 15, no. 3/2011    129 
 
 
2 Purpose of the study 
This study will be a result of important 
researches to help determine the answers to 
important questions about ERP systems, 
modeling, parallel calculus and distributed 
databases because currently, there is no 
effective approach to modeling a prototype 
of ERP system based on parallel calculus or 
parallel programming. 
The purpose of this study is helpful for all the 
companies (commerce, programming, 
manufactures etc.) that desire to implement 
or upgrade the ERP basis system, to change 
the algorithms of implementation from linear 
algorithms to parallel algorithms, from 
simple databases to distributed databases, 
from linear computing to parallel computing. 
 
3 Related work 
“Enterprise Resource Planning (ERP) 
systems involve the purchase of pre-written 
software modules from third party suppliers, 
rather than bespoke (i.e. specially tailored) 
production of software requirements, and are 
often described as a buy rather  than build 
approach to information systems 
development”, tell us the authors of the paper 
[3]. It was a very important paper for us to 
think to the possibility of designing ERP 
systems in parallel mode. 
D. Robey et al. in paper [4] present a 
comparative case study of 13 industrial firms 
that had implemented an enterprise resource 
planning (ERP) systems. The algorithms 
which had been used were linear.  
In paper [5], the authors tell us” Making 
changes to ERP software code (called 
modification) is usually strongly discouraged 
by vendors and implementation consultants”. 
That’s why we try to see the implementation 
of a ERP system by mean of parallel 
programming and parallel calculus. 
Chen in paper [6] presents us briefly which 
are the secrets of a successfully 
implementation of a ERP system, the top 
management of each company is interested in 
these new information technology and 
philosophy and identify new opportunities in 
programming of such systems. 
The business benefits of Enterprise Resource 
Planning (ERP) systems are presented also in 
paper [7]. We think that a parallel 
implementation of an algorithm and using a 
distributed database, will be a real benefit for 
all the companies. 
“Over the past few years, firms around the 
world have implemented enterprise resource 
planning (ERP) systems to have a 
standardized information system (IS) in their 
respective organizations and to reengineer 
their business processes” [8]. But most of 
them implemented a classical ERP, with 
classic database and a unique administration 
of algorithms.  
If we try to implement an ERP within a 
parallel algorithm and parallel calculus, we 
must work in a complicate way but the 
satisfaction of the clients and the usability of 
the system will be for us a starting point to 
continue our research. 
 
4 The structure of an ERP system using 
parallel algorithms and parallel calculus 
“An enterprise resource planning (ERP) 
system is an attempt to create an integrated 
product that manages the majority of 
operations in a company. What is different 
about ERP systems is that they integrate 
across functions to create a single, unified 
system rather than a group of separate, 
insular applications.” [10] 
The characteristics of ERP systems: 
•  modular-design; 
•  central common database; 
•  modules are integrated, data transfer 
between modules is done automatically; 
•  they can be configured and adapted to 
the needs of business customers; 
•  are complex systems, flexible and offers 
best practices; 
•  most ERP systems can be accessed via 
the Internet. 
Integrating databases with the computer 
network has created a more powerful, 
explosive technology, whose development 
was done in steps of evolution. In applying 
parallel algorithms, we think that any ERP 
system could be structured as in Figure 2. 
 130    Informatica Economică vol. 15, no. 3/2011 
 
 
 
 
 
 
 
 
   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 2. A structure of an ERP system within parallel calculus with the help of P1 (processor 
1), P2, P3 etc. 
 
When we think to design a parallel ERP 
system, we must take in mind several 
important issues related to parallel 
processing: 
•  Construction of efficient parallel 
computers; 
•   Design of efficient parallel algorithms; 
•  The existence of metrics for performance 
evaluation of parallel algorithms; 
•  Programming languages for parallel 
processing; 
•  Parallel programming tools; 
•  The development of portable programs 
[11]. 
In the process of modernization, often firms 
require to modernize their applications less 
effective or even replace them with more 
efficient, which ensures that real-time and 
efficient decisions and therefore the 
management process.  
Currently, we consider that the 
implementation of integrated ERP systems 
with parallel algorithms and parallel 
programming is able to help improve the 
efficiency of firms.  
By making such systems company offering 
the possibility to integrate the new system to 
existing applications with new applications 
specific to the operating company. 
Next we focus on the Client module. In this 
way we will work with multiple tables, 
between them exits relationship. 
Tables used in the database are: 
a.  Table  Clients, with the following 
structure ( see table 1): 
 
Table 1. The structure of Clients table 
Filed  Type  Properties 
Code  AutoNu
mber 
Long Integer, 
primary key 
Type  L  0 for individual, 1 
for companies 
Name  Text  50 
Identificati
onCode 
Text  15 (CNP for 
individuals, CUI 
for companies) 
Identificati
onKey 
Text  10 (No of CI) 
Adress  Text  50 
ZipCode  Number  10 
IBAN  Text  24 
Processor 1: Supply 
Database 
DBSp 
P2: Sales 
DBS  P3: Accounting 
DBA 
P4: Marketing 
DBMk 
P5: Clients 
DBC 
Central processor (CP) 
Central database (CDB) 
P6: Management 
     DBMg 
P7: Manufacture 
DBMf 
P8: Human Resources 
DBHR 
 Informatica Economică vol. 15, no. 3/2011    131 
 
 
Bank  Text  30 
 
b.  Table Documents, with the following 
structure (see table 2): 
 
Table 2. The structure of Documents table 
Field  Type  Properties 
IdDocument  AutoNumber  Long 
Integer 
Date  Date/Time  Medium 
Date 
TypeDocument  Number  Integer 
CodeClient  Number  Long 
Integer 
CodeEmployee  Number  Long 
Integer 
IdProduct  Number  Long 
Integer 
Quantity  Number  Long 
Integer 
Price  Number  Long 
Integer 
 
c.  Table  Products, with the following 
structure ( see table 3): 
 
Table 3. The structure of Documents table 
Field  Type  Properties 
IdProduct   AutoNumber  Long 
Integer 
Name  Text  50 
Characteristics  Text  50 
InitialStoc  Number  Integer 
Price  Number  Long 
Integer 
 
At the level of serial processing we propose 
three queries that we rely on. 
 
1.  The query Sales Products 
SELECT Products.IdProducts, 
Products.Name, 
Products.Characteristics, 
Documents.TypeDocument 
FROM Products INNER JOIN Documents  
ON Products.Idproduct = 
Documents.Idproduct 
GROUP BY Products.IdProducts, 
Products.Name, 
Products.Characteristics, 
Documents.TypeDocument 
HAVING 
(((Documents.TypeDocument)="0")); 
 
2.  The query Output Quantity 
SELECT Products.IdProducts, 
Products.Name, Documents.Type, 
Documents.Date, 
Sum(Documents!Quantity) AS Canti 
INTO Output 
FROM Produse INNER JOIN Documents 
ON Products.IdProducts = 
Documents.IdProduct 
GROUP BY Products.IdProduct, 
Products.Name, Documents.Type, 
Documents.Date 
HAVING (((Documents.Type)="0") AND 
((Documents.Date)<=Date())); 
 
3.  The query Input Quantity  
SELECT Products.IdProducts, 
Products.Name, Documents.Type, 
Documents.Date, 
Sum(Documents!Quantity) AS Canti 
INTO Input 
FROM Produse INNER JOIN Documents 
ON Products.IdProducts = 
Documents.IdProduct 
GROUP BY Products.IdProduct, 
Products.Name, Documents.Type, 
Documents.Date 
HAVING (((Documents.Type)="1") AND 
((Documents.Date)<=Date())); 
 
A database query  can be run in parallel to 
increase the performance. The nature of the 
task, the database configuration, and the 
hardware equipment,, all determine the 
design for performing a task in parallel.  
These considerations are interrelated, and 
should be considered together when we work 
on the physical and logical design of a 
database.  
There are two types of query parallelism: 
inter-query parallelism and intra-query 
parallelism [12].  
Inter-query parallelism refers to the ability of 
the database to accept queries from multiple 
applications at the same time. Each query 
runs independently of the others. 
Intra-query  parallelism  refers to the 
simultaneous processing of parts of a single 
query, using either intra-partition parallelism, 
inter-partition parallelism, or both.  
At Inter-query parallelism level, we propose 
the development of three applications: 
 132    Informatica Economică vol. 15, no. 3/2011 
 
 
1.  At the level of processor P5 and P8 
The proposed application will deal mainly 
with employees; will include Human 
resources department and Accounting. The 
main query is determining sales per 
employee. 
 
 
Fig. 3. A first query between DBC and DBHR 
 
2.  At the level of processor P4  and P8 
Proposed application will handle the 
marketing services provided by this 
department, the number of clients made by 
each employee of the department of 
marketing and sales for each of them. 
 
Fig. 4. A second query between DBMk and DBHR 
 
3.  At the level of  processor P2, P7 and 
P8 
The application will try to determine total 
sales of production achieved. 
 
 Informatica Economică vol. 15, no. 3/2011    133 
 
 
 
Fig. 5. A third complex query between DBMf, DBS and DBHR 
 
At Intra-query parallelism level we aim to 
achieve the following queries: 
1.  The first query determines the 
products sold, input quantity, output quantity 
for each product. At serial processing level, 
the three queries have been previously 
described. At the level of parallel processing 
we will propose a parallel implementation of 
the three queries and finally the aggregation 
of the results in a new selection query. 
 
 
Fig. 6. An Intra-Query 
 
2.  The second query determines the 
number of documents issued for each 
customer and the employees issued 
document. 
 134    Informatica Economică vol. 15, no. 3/2011 
 
 
 
Fig. 7. An intra-query within three processors 
 
3.  The third query with two parameters 
run in this mode: the user enters the date as 
the first parameter, then the second parameter 
is client code, finally resulting all invoices to 
a customer at a certain date. 
 
Fig. 8. An intra-query within three processors 
 
5 Conclusions 
This paper aims to provide some solutions to 
the implementation of parallel computing and 
parallel algorithms in studying and 
implementing ERP systems, as well as 
theoretical researchers seeking solutions in 
these areas. 
The paper starts with some previous 
modeling results ERP systems wishing to 
research breast enlargement using 
parallelism. 
In the paper are given some examples of 
implementation of inter-query queries and 
intra-query, simple, selection query and 
parameters query. The implementing of 
detailed system will be the subject of further 
publication. 
 
 
 
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Loredana MOCEAN  has graduated Babes-Bolyai University of Cluj-
Napoca, the Faculty of Computer Science in 1993, she holds a PhD diploma 
in Economics from 2003 and she had gone through didactic position of 
assistant and lecturer, since 2000 when she joined the staff of the Babes- 
Bolyai University of Cluj-Napoca, Faculty of Economics and Business 
Administration. She is the author of more than 10 books and over 35 journal 
articles in the field of Databases, Data mining, Web Ser-vices, Web 
Ontology, ERP Systems and much more. 

A Comparison of Open Source ERP Systems”,

A Comprehensive Framework for Classifying the Benefits of ERP Systems”

About Modeling The Erp Systems”,

Agile Software Development,

An Undergraduate Curriculum

Baze de date distribuite,

Engineering an Academic Program

Evaluating Enterprise Resource Planning (ERP) Systems using an Interpretive Approach”,

Identification of Necessary Factors for Successful Implementation of ERP Systems”,

Learning to Implement Enterprise Systems: An Exploratory

Linkman Realizing Evidencebased Software Engineering,

Loredana MOCEAN has graduated Babes-Bolyai University of ClujNapoca, the Faculty of Computer Science in 1993, she holds a PhD diploma in Economics from

Modeling the Architecture for Component-Based Ecommerce System,

Planning for ERP systems, analysis and future trend”,

Putting the enterprise into the enterprise system”,

Research On A Parallel Algorithm Implementation Strategy

Sisteme informatice integrate, Universitatea Agora,

Solutii informatice integrate pentru gestiunea afacerilor,

Tailoring ERP Systems: A Spectrum of Choices and their Implications”, Available:

The relation of interface usability characteristics, perceived usefulness, and perceived ease of use to end-user satisfaction with enterprise resource planning (ERP) systems”, Available: http://www.sciencedirect.com/science/ar ticle/pii/S0747563203000748.

Version models for software configuration management”,

The Modeling of the ERP Systems within Parallel Calculus

http://revistaie.ase.ro/content/59/11%20-%20Mocean.pdf

The Modeling of the ERP Systems within Parallel Calculus

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