Abstract

The Enterprise Resource Planning system (ERP) has been pointed out as a new information systems paradigm. However, achieving a proper level of ERP success relies on a variety of factors that are related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation success based on organizational profiles has been discussed. As with the need to create the expectations of organizations of ERP, an expert system was developed by exploiting the Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP success. The expert system role is in preparation to obtain data from the new enterprises that wish to implement ERP, and to predict the probable system success level. To this end, factors of organizational profiles are recognized and an ANN model is developed. Then, they are validated with 171 surveyed data obtained from Middle East-located enterprises that experienced ERP. The trained expert system predicts, with an average correlation coefficient of 0.744, which is respectively high, and supports the idea of dependency of ERP success on organizational profiles. Besides, a total correct classification rate of 0.685 indicates good prediction power, which can help firms predict ERP success before system implementation.

Abstract

When cranial bone needs to be removed or lost, subsequent reconstruction of the defect is necessary to protect the underlying brain, correct aesthetic deformities, or both. Cranioplasty surgical procedures are performed to correct the skull defects requiring reconstruction of form and function. Personalized cranial implants can repair severe injuries to the skull can be done through This study presents the optimization of cranial titanium implants. A total of sixty different models were subjected to a simulation by Finite Element Analysis (FEA) applying the mechanical properties of a grade 5 titanium alloy (Ti6Al4V) implant material. The material was subjected to intracranial pressure (ICP) conditions, with a typical range (10 mm Hg) and twelve fixation points in the boundary conditions. An artificial neural network (ANN) was created to connect the designs, obtaining maximum displacements. Optimal designs were obtained using a generalized reduced gradient that minimizes the amount of material, maintaining as a restriction a maximum displacement of 0.1 mm for the 5th to 95th percentiles, which represent the group of individuals under study.

Abstract