This study tests the validity of the Belgian Ooghe-Joos-De Vos (1991) failure prediction models (1 and 3 years prior to failure) across 18 different industries and different size classes. Firstly, the performance results and the trade-off functions reveal a wide range of performances for the different industries. However, we notice that the OJD models perform best for the classical manufacturing industries and financial services, while they show the worst performance results for the service industries and the no-industry category. Furthermore, when using new, industry specific cut-off points, the error rates of the models are significantly reduced. Secondly, the OJD model 1 year prior to failure seems to perform best for large companies and companies with complete form annual accounts. Finally, the performance differences between the various subgroups with respect to industry, size class and form of annual account of the model 3 years prior to failure the are much smaller than those of the model 1 year prior to failure.

Few studies have addressed the performance of smaller unquoted companies involved in take-overs, especially in the Continental European countries. Therefore this study addresses the post-take-over financial characteristics of privately held companies involved in 143 Belgian take-overs between 1992 and 1994. Specifically, this paper examines the financial performance of the acquiring firm after the take-over, using statistical analysis of industry-adjusted variables. Our findings show that following the take-over, the profitability, the solvency and the liquidity of most of the combined companies decline. This decline is also reflected in the failure prediction scores. With respect to the added value, take-overs are found to be accompanied by increases in the labour productivity, caused by the general improvement of gross added value per employee of Belgian companies in the last ten years and partly caused by laying off the target's workers. So it seems that, contrary to the general expectations and beliefs, take-overs usually do not seem to improve the acquirer's financial performance.

In this paper we elaborate on a Total Cost of Ownership supplier selection methodology that we have constructed using real life case studies of three different industrial components groups in a firm. These case studies are presented in this article. Analysing the value chain of the firm, data on the costs generated by the purchasing policy and on supplier performance are collected using Activity Based Costing (ABC). Since a spreadsheet cannot encompass all these costs, let alone optimise the supplier selection and inventory management policy, a mathematical programming model is used. For a specific component group the combination of suppliers is selected that minimises the Total Cost of Ownership. TCO takes into account all costs that the purchase and the subsequent use of a component entail in the entire value chain of the company. The TCO approach goes beyond minimising purchase price and studies all costs that occur during the entire life cycle of the item in the organisation. Possible savings of between 6 and 14% of the total cost of ownership of the current purchasing policy are obtained for the three cases. Keywords: Activity Based Costing, mathematical programming, supplier selection, purchasing