An empirical assessment of the operational performance through internal benchmarking: a case of a global logistics firm

This article solves an operational performance measurement problem of a global logistics firm through an internal benchmarking tool. The intended impact is to enable logistics firms to form a deeper understanding of their own internal processes and metrics. The methodology of this in-depth action research involves a sequential approach with a series of interviews, questionnaire-based surveys, operations data collated through observations and process mapping yielding real-world data. A series of statistical tests are conducted to analyse the collated data. Strategic priorities of the firm are integrated with the firm’s operational performance to ascertain the effective performance by considering both the tangible and intangible measures. The outcomes inform both practitioners and academics how the firm could improve its freight forwarding business’s profitability by ensuring that its operations meet the prioritised criteria. The ‘best practice’ derived from internal benchmarking forms an intermediate step towards external benchmarking. The outcomes facilitate investigating the current business strategy, the standard operating procedures and the scope of improving those.     

Benchmark Dose Analysis via Nonparametric Regression Modeling

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose‐response modeling. It is a well‐known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low‐dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal‐response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap‐based confidence limits for the BMD. We explore the confidence limits’ small‐sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty.     

Tests of symmetry with one-sided alternatives in three-way contingency tables

For a two-dimensional contingency table of probabilities, the concept of symmetry around the main diagonal is well defined. Statistical hypothesis test of symmetry versus positive bias have also been explored. For tables of higher (three or more) dimensions, however, different concepts of symmetry are available. In this study, we consider statistical inference procedures of symmetry in partial tables versus various biases in three-dimensional tables. We find the maximum likelihood estimates of the cell probabilities and the asymptotic distribution of the likelihood ratio test statistic in each case. Simulation studies are used to investigate the sizes and powers of the tests. The methodologies developed are applied on real data sets.     

On the cohen-sackrowitz estimator of a common mean

The paper reconsider certain estimators proposed by COHENand SACKROWITZ[Ann.Statist.(1974)2,1274-1282,Ann.Statist.4,1294]for the common mean of two normal distributions on the basis of independent samples of equal size from the two populations. It derives the ncecessary and sufficient condition for improvement over the first sample mean, under squared error loss, for any member of a class containing these. It shows that the estimator proposded by them for simultaneous improvement over botyh sample means has the desired property if and only if the common size of the samples is at least nine. The requirement is milder than that for any other estimator at the present state of knolwledge and may be constrasted with their result which implies the desired property of the estimator only if the common size of the samples is at least fifteen. Upper bounds for variances if the estimators derived by them are also improved     

Bayesian inference for circular distributions with unknown normalising constants

Very often, the likelihoods for circular data sets are of quite complicated forms, and the functional forms of the normalising constants, which depend upon the unknown parameters, are unknown. This latter problem generally precludes rigorous, exact inference (both classical and Bayesian) for circular data.Noting the paucity of literature on Bayesian circular data analysis, and also because realistic data analysis is naturally permitted by the Bayesian paradigm, we address the above problem taking a Bayesian perspective. In particular, we propose a methodology that combines importance sampling and Markov chain Monte Carlo (MCMC) in a very effective manner to sample from the posterior distribution of the parameters, given the circular data. With simulation study and real data analysis, we demonstrate the considerable reliability and flexibility of our proposed methodology in analysing circular data.     

Bayesian estimation for the Pareto income distribution

The Bayes estimators of the Gini index, the mean income and the proportion of the population living below a prescribed income level are obtained in this paper on the basis of censored income data from a pareto income distribution. The said estimators are obtained under the assumptions of a two-parameter exponential prior distribution and the usual squared error loss function. This work is also extended to the case when the income data are grouped and the exact incomes for the individuals in the population are not available. The method for the assessment of the hyperparameters is also outlined. Finally, the results are generalized for the doubly truncated gamma prior distribution. Now deceased.     

Revisiting the outsourcing debate: two sides of the same story

Although outsourcing of organisational functions has become a popular practice amongst organisations, the literature provides a myopic and simplistic view of the phenomenon by focusing on either single functions or taking monadic views of a single party. The design of our study overcomes these weaknesses by including the following features: a range of organisational functions were included; the unit of analysis was the outsourcing contract; the level of analysis was at the dyadic level where both service providers and receivers presented their views and the approach was a qualitative one. In practice, this involved interviews with managers in service receiving and providing organisations that were in charge of 10 separate outsourcing arrangements. Using agency theory as a theoretical anchor, we contribute to the outsourcing debate by highlighting its complexity and areas of convergence and divergence between service receivers and providers, thereby suggesting a contingent approach towards implementation of outsourcing arrangements.     

Combined estimate of a location parameter

In this paper, we revisit the problem of combining estimates of location considered by Cohen (1976). Our results unify and strengthen the results of Cohen (1976), Bhattacharya (1981) and Akai (1982).