A creative solution for rapid and inexpensive model-making in product design processes

Rapid prototyping (RP) is an approach for automatically building a physical object through solid freeform fabrication. Nowadays, RP has become a vital aspect of most product development processes, due to the significant competitive advantages it offers compared to traditional manual model making. Even in academic environments, it is important to be able to quickly create accurate physical representations of concept solutions. Some of these can be used for simple visual validation, while others can be employed for ergonomic assessment by potential users or even for physical testing. However, the cost of traditional RP methods prevents their use in most academic environments on a regular basis, and even for very preliminary prototypes in many small companies. That results in delaying the first physical prototypes to later stages, or creating very rough mock-ups which are not as useful as they could be. In this paper we propose an approach for rapid and inexpensive model-making, which was developed in an academic context, and which can be employed for a variety of objects.

Utilização do método Multicritério AHP, na aquisição de um sistema ERP

The selection of an Enterprise Resource Planning (ERP) system is one of the most sensitive and highest impact processes in the area of information systems and technologies, because it supports and integrates the whole business of an organization. Hence the importance of deciding the best solution in order to contribute to the organization's competitiveness in a global and increasingly demanding market. Therefore, it is essential to provide tools to support decision making, turning complex and often intangible decisions into simple and quantifiable scenarios. This study addressed the adoption of the Analytical Hierarchy Process (AHP) multicriteria decision method to support the selection of an ERP system. The literature review was the source used to obtain the set of the most relevant criteria to be considered in this decision, which were subsequently validated through systematic application of various surveys of experts and people related to the field of ERP systems. To support the application of AHP, according to the model obtained in the study, it was developed a web application that will be available to the general public. The responsible for the acquisition of ERP systems can use it to easily apply the AHP method based on validated decision model. On the other hand, the web application can be used as a validation tool, allowing collecting data for future developments of the decision model.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.