Multi-Agents Approach for Data Mining Based k-Means for Improving the Decision Process in the ERP Systems

Multi-Agents Approach for Data Mining Based k-Means for Improving the Decision Process in the ERP Systems

Nadjib Mesbahi (Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria), Okba Kazar (Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria), Saber Benharzallah (Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria), Merouane Zoubeidi (Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria), and Samir Bourekkache (Smart Computer Science Laboratory, University of Biskra, Biskra, Algeria)
Copyright: © 2015 |Pages: 14
DOI: 10.4018/IJDSST.2015040101
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Today the enterprise resource planning (ERP) became a tool that enables uniform and consistent management of information system (IS) of the company with a large single database. In addition, Data Mining is a technology whose purpose is to promote information and knowledge extraction from a large database. In this paper, an agent-based multi-layered approach for data mining based k-Means through the ERP to extract hidden knowledge in the ERP database is proposed. To achieve this, the authors call the paradigm of multi-agent system to distribute the complexity of several autonomous entities called agents, whose goal is to group records or observations on similar objects classes using the k-means technique that is dedicated the task of clustering. This will help business decision-makers to take good decisions and provide a very good response time by the use of multi-agent system. To implement the proposed architecture, it is more convenient to use the JADE platform while providing a complete set of services and agents comply with the specifications FIPA.
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Nowadays, companies operate in an environment that is increasingly complex and changing. Today, they face several problems: saturated markets, increased competitiveness, and customers more demanding and less loyal. One of the power factors in companies is integration of information technology and communication in their information systems tools, namely the enterprise resource planning (ERP).

First, an ERP is designed for very large companies; they are now implanted in SMEs Adam and O’Doherty (2000). These packages are constituted as a set of functional modules covering the domains of finance, human resources, logistics, production, marketing and sales, integrated around a single database (Philippe, 2006).

Some ERP offer a decision module allowing treatment of more or less sophisticated on the data basis of ERP in order to help executives to make the right decisions at the right time. This decision module provides the ability to use intuitively the wealth of ERP data and meet the needs of multiple functions simultaneously: production, logistics, finance, sales ...etc. In addition, the decision module facilitates the verification of the consistency of the data to ensure the quality of decision. It allowing all employees, whatever their profession, to take the pertinent decisions based on performance indicators and accurately track the targets set for all departments of the company.

Several approaches and techniques were used to achieve these decision-making objectives, but they remain insufficient. For this reason, the data mining technology and knowledge discovery (useful and unknown) are buried in large databases in order to enhance the information and extraction of new decision-making knowledge and / or forecast knowledge. Furthermore, the aim of data mining is to valorize data information in companies systems.

The birth of data mining is mainly due to the combination of two factors (Hébrail & Lechevallier, 2003):

  • 1.

    The exponential growth in business, data related to their activities (customer data, inventory, manufacturing, accounting ...) that it would not be good to ignore it because they contain key information on their strategic functioning for decision-making.

  • 2.

    The rapid advances in hardware and software.

Therefore, we suggest a multi-agent approach to integrate the data-mining module with ERP platform. We use the paradigm of multi-agent system and the data mining technique k-means, which is dedicated to the task of clustering, for discover useful knowledge hidden in the ERP database and to improve the quality of response times. Moreover, in our approach the integration of expert prior Knowledge in a data mining process is crucial for improving datasets preparation. In order to assurer this integration an ontology ERP is built by analyzing existing database ERP with the collaboration of expert users who play a central role. The design of this ontology is directed for facilitate the preparation of datasets.

In this overview, our work will be organized as follows:

A general introduction is followed by a presentation of emerging technologies such as ERP systems and data mining. We will describe later the related work, the proposed approach for extracting knowledge from ERP, and agents modeling of Data Mining based K-means. Then, we describe the JADE platform adapted to implement the proposed approach. Finally, a conclusion and perspectives of study will conclude this work.

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