An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software.
Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of expert’s opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation.
An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.
ERP is a integrated, consulate enterprise wide information system that combines all necessary business functions like production planning, purchase, inventory control, sales, finance, human resource. Organizations require ERP implementation for the purposes of customer-order integration, standardization of production process, reduction of inventory level and order preparation time, standardization human resources information. Today organizations operate in an economic environment where customer demands are continuously changing and increasing. In today, markets a great number of competitors are in places and competition is so fierce. Quality and cost do not suffice in competition and therefore new competition parameters are needed like delivery date in right time and customize product (Yusuf, Gunasekaran, & Wu, 2006).
These organizations strive to reduce total cost through supply chain, production cycle, and inventory. Additionally, they request increasing diversity of product, more accurate delivery dates and coordinating the supply and production effectively (Liao et al., 2007 and Xiuwu et al., 2007).
ERP software automates and integrates business processes and allows information sharing in different business functions. In addition that ERP software supports the finance, human resources, operations and logistic, sale and market in functions by through more effected and productive business process. At the same time it improves the performance of organization’s functions by controlling those (Hallikainen, Kimpimaki, & Kivijarvi, 2006). Although organizations can develop their own ERP software, other ones may prefer ready systems to shorten application cycle. The vendors sale ERP software that is developed on different operating system and database in the market. When the organizations prefer to buy ready systems, it is going to be very height cost (Verville & Halingten, 2003).
Group decision or single decision process has always some difficulties in decision making problems. For a group decision, there can be found some solutions to reduce a from a group decision to a single one in the literature. Satty (2001) has discussed some techniques suchlike geometric mean, arithmetic mean, linear homogeneity, agreement, pareto optimality, condorced paradoks. Although the techniques can be applied in many applications, a new group (i.e. project team) must be established in every practical application. However in this study, when a network is trained with using group decision it accommodates also group decisions. If there is a single decision maker, in a new decision problem (i.e. ERP software selection), a single person’ subjective decisions is taken as input of the ANN network, but the network predicts a result which is extracted from objectivity (i.e. synthesis of expert people decision).
Other important differences of the proposed model in this study are an establishing a new group is not required after completing for once in practical application, saving time and cost and removing difficulties of reducing from a group decision to a single decision.
