On the discovery of business processes orchestration patterns

COORDINSPECTOR is a Software Tool aiming at extracting the coordination layer of a software system. Such a reverse engineering process provides a clear view of the actually invoked services as well as the logic behind such invocations. The analysis process is based on program slicing techniques and the generation of, System Dependence Graphs and Coordination Dependence Graphs. The tool analyzes Common Intermediate Language (CIL), the native language of the Microsoft .Net Framework, thus making suitable for processing systems developed in any .Net Framework compilable language. COORDINSPECTOR generates graphical representations of the coordination layer together with business process orchestrations specified in WSBPEL 2.0

The perceived potential of business social networking sites

Business social networking is a facilitator of several business activities, such as market studies, communication with clients, and identification of business partners. This paper traduces the results of a study undertaken with the purpose of getting to know how the potential of networking is perceived in the promotion of business by participants of the LinkedIn network, and presents two main contributions: (1) to disseminate within the business community which is the relevance given to social networking; and (2) which are the social networks best suitable to the promotion of business, to support the definition of strategies and approaches accordingly. The results confirm that LinkedIn is the most suitable network to answer the needs of those that look for professional contacts and for the promotion of business, while innovation is the most recognized factor in the promotion of business through social networking. This study contributes to a better understanding of the potential of different business social networking sites, to support organizations and professionals to align their strategies with the perceived potential of each network.

The importance of quality in the consolidation of textile in Minho

The textile industry has a long tradition in Portugal and it is one of the most important sectors, despite the current economic crisis. It has always assumed a prominent role in terms of employment and a relevant position within the Portuguese economy. The lack of quality and the lower prices that other countries offer causes the loss of clients. Quality is a main tool to survive nowadays in the textile sector. To undertake our analysis, we made use of an existing database where 55 firms belonged to the textile industry, namely to the manufacturing sector. A new survey was created based on the original survey and was sent to 5 firms. Besides the survey, we also sent a few questions to the firms in order to retract more information about the actually situation in our country, concerning the textile industry. Several tables, graphs and pie charts were made to help shed light on our findings. This research was conducted in order to determine the importance of quality in the consolidation of textile firms in the north of Portugal. Most firms in our sample feel that quality improvement, business benefits, mobilizing employees’ knowledge and business image were important and that competition is very intense and is mainly by price and not by differentiation of product or service. The quality program has contributed to improve their competitive position and the improvement of their overall performance. The majority of the firms in our sample undertake TQM measures for quality purposes to meet customer expectations and prevent errors. Of all firms surveyed, the quality is certainly very important for its survival.

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.