Reconfiguring the Supply Network Using Current Performance Data

Reconfiguration of the supply chain network from time to time is essential for businesses to retain their competitive edge. This paper presents a methodology for reconfiguration of an existing supply chain network. The methodology is characterized by two decision levels. In the first level, the current network performance is evaluated and efficient practices are identified. In the next level, a model that incorporates efficient practices is developed to reconfigure the network. This integrated methodology allows for decision maker (DM) input throughout the process. The methodology has been implemented and tested in the reconfiguration of an outbound petroleum supply chain network for CountryMark Cooperative, Inc. In this case study, Data Envelopment Analysis (DEA) is used to analyze current operations and an integer programming (IP) model that incorporates efficiency metrics is developed for selection of distribution facilities and allocation of resources to the facilities. Use of this methodology can lead to improved operations and reduced operating expenses.     

The Determinants of Blue CollarWage Rates: An Analysis of a DataSet Incorporating Observed Job Characteristics

There is wide agreement that one of the major problems in the estimation of wage equations, and in testing theories of wages, is the quality of the data available. In particular, the data used in almost all studies involve indirect or unsatisfactory measures of a variety of relevant factors, including working conditions. In this paper we exploit an unusual data set that includes measures of characteristics of jobs generated by observation (rather than self report, or through inference from the Dictionary of Occupational Titles). With these data we estimate a wage equation in which compensating differentials play a significant role in wage determination and, once working conditions and other characteristics are effectively controlled, all but one measure of previous labour market experience had no effect on the wage rate.     

Designing a non-inferiority study in kidney transplantation: a case study

In organ transplantation, placebo-controlled clinical trials are not possible for ethical reasons, and hence non-inferiority trials are used to evaluate new drugs. Patients with a transplanted kidney typically receive three to four immunosuppressant drugs to prevent organ rejection. In the described case of a non-inferiority trial for one of these immunosuppressants, the dose is changed, and another is replaced by an investigational drug. This test regimen is compared with the active control regimen. Justification for the non-inferiority margin is challenging as the putative placebo has never been studied in a clinical trial. We propose the use of a random-effect meta-regression, where each immunosuppressant component of the regimen enters as a covariate. This allows us to make inference on the difference between the putative placebo and the active control. From this, various methods can then be used to derive the non-inferiority margin. A hybrid of the 95/95 and synthesis approach is suggested. Data from 51 trials with a total of 17,002 patients were used in the meta-regression. Our approach was motivated by a recent large confirmatory trial in kidney transplantation. The results and the methodological documents of this evaluation were submitted to the Food and Drug Administration. The Food and Drug Administration accepted our proposed non-inferiority margin and our rationale.     

Eliciting expert judgements about a set of proportions

Eliciting expert knowledge about several uncertain quantities is a complex task when those quantities exhibit associations. A well-known example of such a problem is eliciting knowledge about a set of uncertain proportions which must sum to 1. The usual approach is to assume that the expert's knowledge can be adequately represented by a Dirichlet distribution, since this is by far the simplest multivariate distribution that is appropriate for such a set of proportions. It is also the most convenient, particularly when the expert's prior knowledge is to be combined with a multinomial sample since then the Dirichlet is the conjugate prior family. Several methods have been described in the literature for eliciting beliefs in the form of a Dirichlet distribution, which typically involve eliciting from the expert enough judgements to identify uniquely the Dirichlet hyperparameters. We describe here a new method which employs the device of over-fitting, i.e. eliciting more than the minimal number of judgements, in order to (a) produce a more carefully considered Dirichlet distribution and (b) ensure that the Dirichlet distribution is indeed a reasonable fit to the expert's knowledge. The method has been implemented in a software extension of the Sheffield elicitation framework (SHELF) to facilitate the multivariate elicitation process.     

Subjective Welfare, Well-Being, and Self-Reported Food Hypersensitivity in Four European Countries: Implications for European Policy

This study estimates the effects of food hypersensitivity on individuals?? perceived welfare and well-being compared to non-food hypersensitive individuals. Study respondents were recruited in the Netherlands, Poland, Spain and UK. The difference in welfare between food hypersensitive respondents and those asymptomatic to foods was estimated using a subjective welfare approach, including income evaluation. Well-being was measured using the Cantril Ladder-of-Life Scale, and health status using the Self-Perceived Health Scale. The difference in well-being, welfare and health status between participant groups was explained further using a number of background variables. No significant within-country differences in welfare between food hypersensitive respondents and respondents asymptomatic to foods were found. In terms of well-being, adult food hypersensitive respondents and their spouses reported significantly less happiness than respondents and their spouses asymptomatic to foods in the Netherlands and Poland. In Spain, the spouses of the food hypersensitive respondents were significantly less happy than respondents aymptomatic to foods. The well-being of children did not significantly differ between groups. The degree of severity of food hypersensitivity was negatively related to overall health status. In Poland, food hypersensitive respondents reported worse health status compared to asymptomatic respondents. In Spain, the converse was true. Food hypersensitive respondents were generally less happy with their life as a whole than respondents asymptomatic to foods, presumably because they experienced more negative effects, which were not related to perceived health status.     

Elicitation of multivariate prior distributions: A nonparametric Bayesian approach

In the context of Bayesian statistical analysis, elicitation is the process of formulating a prior density f(·)$f(·)$ about one or more uncertain quantities to represent a person's knowledge and beliefs. Several different methods of eliciting prior distributions for one unknown parameter have been proposed. However, there are relatively few methods for specifying a multivariate prior distribution and most are just applicable to specific classes of problems and/or based on restrictive conditions, such as independence of variables. Besides, many of these procedures require the elicitation of variances and correlations, and sometimes elicitation of hyperparameters which are difficult for experts to specify in practice. Garthwaite et al. (2005) discuss the different methods proposed in the literature and the difficulties of eliciting multivariate prior distributions. We describe a flexible method of eliciting multivariate prior distributions applicable to a wide class of practical problems. Our approach does not assume a parametric form for the unknown prior density f(·)$f(·)$, instead we use nonparametric Bayesian inference, modelling f(·)$f(·)$ by a Gaussian process prior distribution. The expert is then asked to specify certain summaries of his/her distribution, such as the mean, mode, marginal quantiles and a small number of joint probabilities. The analyst receives that information, treating it as a data set D   with which to update his/her prior beliefs to obtain the posterior distribution for f(·)$f(·)$. Theoretical properties of joint and marginal priors are derived and numerical illustrations to demonstrate our approach are given.     

Hypothesis group testing for disjoint pairs

Classical group testing (CGT) is a widely applicable biotechnical technique used to identify a small number of distinguished objects from a population when the presence of any one of these distinguished objects among a group of others produces an observable result. This paper discusses a variant of CGT called group testing for disjoint pairs (GTAP). The difference between the two is that in GTDP, the distinguished items are pairs from, not individual objects in, the population. There are several biological examples of when this abstract model applies. One biological example is DNA hybridization. The presence of pairs of hybridized DNA strands can be detected in a pool of DNA strands. Another situation is the detection of binding interactions between prey and bait proteins. This paper gives a random pooling method, similar in spirit to hypothesis testing, which identifies pairs of objects from a population that collectively have an observable function. This method is simply to apply, achieves good results, is amenable to automation and can be easily modified to compensate for testing errors. M.A. Bishop is supported by AFOSR FA8750-06-C-0007. A.J. Macula is supported by NSF-0436298, AFOSR FA8750-06-C-0007.     

A two-stage procedure to pool information across quantile levels in linear quantile regression

In linear quantile regression, the regression coefficients for different quantiles are typically estimated separately. Efforts to improve the efficiency of estimators are often based on assumptions of commonality among the slope coefficients. We propose instead a two-stage procedure whereby the regression coefficients are first estimated separately and then smoothed over quantile level. Due to the strong correlation between coefficient estimates at nearby quantile levels, existing bandwidth selectors will pick bandwidths that are too small. To remedy this, we use 10-fold cross-validation to determine a common bandwidth inflation factor for smoothing the intercept as well as slope estimates. Simulation results suggest that the proposed method is effective in pooling information across quantile levels, resulting in estimates that are typically more efficient than the separately obtained estimates and the interquantile shrinkage estimates derived using a fused penalty function. The usefulness of the proposed method is demonstrated in a real data example.     

Investigating the implementation of SME-friendly policy in public procurement

Public sector employees with responsibility for purchasing are under increasing pressure to implement small and medium enterprise (SME)-friendly policies. Such policies are intended to make it easier for SMEs to compete for and win public sector contracts. In spite of the socio-economic importance of this issue, there remains a dearth of evidence on what is happening in practice. Using primary survey data from 271 public buyers in Ireland, this paper examines the extent to which SME-friendly procurement policy is being implemented and the individual and organization factors that affect implementation. The findings reveal a gap between what government policy recommends public buyers and their organizations should do to facilitate SMEs versus what they are actually doing. Policy familiarity, procurement involvement, organization size and the maturity of the procurement function are shown to be positively associated with the implementation of SME-friendly policy. For managers and legislators, the findings underline the need to invest in the human capital dimension of public procurement if policy implementation rates are to improve.