Integrating environmental and economic life cycle analysis in product development: a material selection case study

Purpose Achieving sustainability by rethinking products, services and strategies is an enormous challenge currently laid upon the economic sector, in which materials selection plays a critical role. In this context, the present work describes an environmental and economic life cycle analysis of a structural product, comparing two possible material alternatives. The product chosen is a storage tank, presently manufactured in stainless steel (SST) or in a glass fibre reinforced polymer composite (CST). The overall goal of the study is to identify environmental and economic strong and weak points related to the life cycle of the two material alternatives. The consequential win-win or trade-off situations will be identified via a Life Cycle Assessment/Life Cycle Costing (LCA/LCC) integrated model. Methods The LCA/LCC integrated model used consists in applying the LCA methodology to the product system, incorporating, in parallel, its results into the LCC study, namely those of the Life Cycle Inventory (LCI) and the Life Cycle Impact Assessment (LCIA). Results In both the SST and CST systems the most significant life cycle phase is the raw materials production, in which the most significant environmental burdens correspond to the Fossil fuels and Respiratory inorganics categories. The LCA/LCC integrated analysis shows that the CST has globally a preferable environmental and economic profile, as its impacts are lower than those of the SST in all life cycle stages. Both the internal and external costs are lower, the former resulting mainly from the composite material being significantly less expensive than stainless steel. This therefore represents a full win-win situation. As a consequence, the study clearly indicates that using a thermoset composite material to manufacture storage tanks is environmentally and economically desirable. However, it was also evident that the environmental performance of the CST could be improved by altering its End-of-Life stage. Conclusions The results of the present work provide enlightening insights into the synergies between the environmental and the economic performance of a structural product made with alternative materials. Further, they provide conclusive evidence to support the integration of environmental and economic life cycle analysis in the product development processes of a manufacturing company, or in some cases even in its procurement practices.

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.

Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal

In face of the current economic and financial environment, predicting corporate bankruptcy is arguably a phenomenon of increasing interest to investors, creditors, borrowing firms, and governments alike. Within the strand of literature focused on bankruptcy forecasting we can find diverse types of research employing a wide variety of techniques, but only a few researchers have used survival analysis for the examination of this issue. We propose a model for the prediction of corporate bankruptcy based on survival analysis, a technique which stands on its own merits. In this research, the hazard rate is the probability of ‘‘bankruptcy’’ as of time t, conditional upon having survived until time t. Many hazard models are applied in a context where the running of time naturally affects the hazard rate. The model employed in this paper uses the time of survival or the hazard risk as dependent variable, considering the unsuccessful companies as censured observations.

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.