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

Introduction

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.