The contribution of manufacturing to shortening design cycle times

Shortening design cycle times, without increasing design costs or sacrificing quality has become an important management goal. In this paper, we will provide a short overview of the literature on shortening design cycle times. Building on this, we want to establish how manufacturing and the manufacturing/R&D interface can contribute to shorter development cycle times. The European Manufacturing Futures database will be used to test some of the literature findings on a sample of large European Manufacturers.     

A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection

Several state‐of‐the‐art binary classification techniques are experimentally evaluated in the context of expert automobile insurance claim fraud detection. The predictive power of logistic regression, C4.5 decision tree, k‐nearest neighbor, Bayesian learning multilayer perceptron neural network, least‐squares support vector machine, naive Bayes, and tree‐augmented naive Bayes classification is contrasted. For most of these algorithm types, we report on several operationalizations using alternative hyperparameter or design choices. We compare these in terms of mean percentage correctly classified (PCC) and mean area under the receiver operating characteristic (AUROC) curve using a stratified, blocked, ten‐fold cross‐validation experiment. We also contrast algorithm type performance visually by means of the convex hull of the receiver operating characteristic (ROC) curves associated with the alternative operationalizations per algorithm type. The study is based on a data set of 1,399 personal injury protection claims from 1993 accidents collected by the Automobile Insurers Bureau of Massachusetts. To stay as close to real‐life operating conditions as possible, we consider only predictors that are known relatively early in the life of a claim. Furthermore, based on the qualification of each available claim by both a verbal expert assessment of suspicion of fraud and a ten‐point‐scale expert suspicion score, we can compare classification for different target/class encoding schemes. Finally, we also investigate the added value of systematically collecting nonflag predictors for suspicion of fraud modeling purposes. From the observed results, we may state that: (1) independent of the target encoding scheme and the algorithm type, the inclusion of nonflag predictors allows us to significantly boost predictive performance; (2) for all the evaluated scenarios, the performance difference in terms of mean PCC and mean AUROC between many algorithm type operationalizations turns out to be rather small; visual comparison of the algorithm type ROC curve convex hulls also shows limited difference in performance over the range of operating conditions; (3) relatively simple and efficient techniques such as linear logistic regression and linear kernel least‐squares support vector machine classification show excellent overall predictive capabilities, and (smoothed) naive Bayes also performs well; and (4) the C4.5 decision tree operationalization results are rather disappointing; none of the tree operationalizations are capable of attaining mean AUROC performance in line with the best. Visual inspection of the evaluated scenarios reveals that the C4.5 algorithm type ROC curve convex hull is often dominated in large part by most of the other algorithm type hulls.     

Social Employment of Welfare Recipients in Belgium: An Evaluation

In Belgium, welfare agencies receive a subsidy to employ welfare recipients for a period sufficiently long to entitle them to unemployment benefits. We investigate the effect of this programme on the exit rate from welfare. We propose a grouping/IV estimator of the programme effect that eliminates selection bias. The estimator is consistent, even if the selection into the programme depends on the average unobserved characteristics of welfare recipients in a region and in a welfare duration interval. Without correction for selectivity we find that the programme reduces welfare dependence, but after correction this conclusion is reversed.     

R&D and market structure: The impact of measurement and aggregation problems

Estimates on the relationship between concentration and R&D effort are shown to be sensitive to problems of aggregation and of adequate measurement of R&D in small firms. While estimates from the official R&D survey of the Netherlands show a highly significant linear relationship between concentration and R&D, comparable estimates from our R&D data base prove it to be insignificant. However, at a finer level of disaggregation and when our sample is split into sectors with low and high technological opportunities, we discover evidence of a quadratic relationship in the latter and evidence of a weak linear relationship in the former.