ERP mainly relates to business management software which consists of several modules that are important for the growth and survival of different types of businesses and organisations. The integration of related busine...ERP mainly relates to business management software which consists of several modules that are important for the growth and survival of different types of businesses and organisations. The integration of related business applications in an organisation can bring significant advantages to the business by enabling faster supply chain management. ERP implementation enables firms to adapt and configure information flows and integrate business processes in order to enhance business performance. System implementation has been reviewed with adequate literature to significantly support the importance of arithmetic and logical concepts and basic errors in programming including typographical errors in a programme, identification of syntax errors and programme output process errors. It has further discussed the variables declaration, significance of modularization and necessity of supporting accessories along with the usability of library files and the importance of the use of frameworks. Enhancement of coding formatting and significance of coding syntax were also critically reviewed with sufficient literature to identify the system implementation failures in the ERP development process.展开更多
ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal f...ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal factors present during an implementation as predictors. Data for training and testing the models was from a cross section of firms that had implemented ERPs. ANFIS is compared with other prediction techniques, ANN and MLRA. The results establish that ANFIS is able to predict outcome well with an error (RMSE) of 0.277 and outperforms ANN and MLRA with errors of 0.85 and 0.86 respectively. This study is expected to provide guidelines to managers and academia to predict ERP outcomes ex ante, and thereby enable corrective actions to redirect ailing projects.展开更多
文摘ERP mainly relates to business management software which consists of several modules that are important for the growth and survival of different types of businesses and organisations. The integration of related business applications in an organisation can bring significant advantages to the business by enabling faster supply chain management. ERP implementation enables firms to adapt and configure information flows and integrate business processes in order to enhance business performance. System implementation has been reviewed with adequate literature to significantly support the importance of arithmetic and logical concepts and basic errors in programming including typographical errors in a programme, identification of syntax errors and programme output process errors. It has further discussed the variables declaration, significance of modularization and necessity of supporting accessories along with the usability of library files and the importance of the use of frameworks. Enhancement of coding formatting and significance of coding syntax were also critically reviewed with sufficient literature to identify the system implementation failures in the ERP development process.
文摘ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal factors present during an implementation as predictors. Data for training and testing the models was from a cross section of firms that had implemented ERPs. ANFIS is compared with other prediction techniques, ANN and MLRA. The results establish that ANFIS is able to predict outcome well with an error (RMSE) of 0.277 and outperforms ANN and MLRA with errors of 0.85 and 0.86 respectively. This study is expected to provide guidelines to managers and academia to predict ERP outcomes ex ante, and thereby enable corrective actions to redirect ailing projects.
