An Association Prediction Model: GECOL as a Case Study

Nowadays, there exists a lot of information that can be handled from business transactions and scientific data and information retrieval is simply no longer enough for decision-making. In this paper will supervised machine learning technique is applied to the mine data warehouse for Enterprise Resource Planning (ERP) of the General Electricity Company of Libya (GECOL). This technique has been applied for the first time on the data of production, transportation and distribution departments. These data are in the form of purchase and work orders of operational material strategic equipment spare parts. This technique would extract prediction rules in order to assist the decisionmakers of the company to make appropriate future decisions more easily and in less time. A supervised machine learning technique has been adopted and applied for the mining data warehouse. A well-known software package for data mining which is referred to as WEKA tool was adopted throughout this work. The WEKA tool is applied to the collected data from GECOL. The conducted experiments produce prediction models in the form set of rules in order to help responsible employees make the suitable, right and accurate future decision in a simple way and inappropriate time. The collected data were preprocessed to be prepared in a suitable format to be fed to the WEKA system. A set of experiments has been conducted on those data to obtain prediction models. These models are in the form of decision rules. The produced models were evaluated in terms of accuracy and production time. It can be concluded that the obtained results are very promising and encouraging.