Supply Network Structure,Visibility, and Risk Diffusion: A Computational Approach

Understanding and managing supply chain risks is a critical functional competency for today's global enterprises. A lack of this competency can have significant negative outcomes, including costly production and delivery delays, loss of future sales, and a tarnished corporate image. The ability to identify and mitigate risks, however, is complicated as supply chains are becoming increasingly global, complex, and interconnected. Drawing on the complex systems and epidemiology literature, and using a computational modeling and network analysis approach, we examine the impact of global supply network structure on risk diffusion and supply network health and demonstrate the importance of supply network visibility. Our results show a significant association between network structure and both risk diffusion and supply network health. In particular, our results indicate that small‐world supply network topologies consistently outperform supply networks with scale‐free characteristics. Theoretically, our study contributes to our understanding of risk management and supply networks as complex networked systems using a computational approach. Managerially, our study illustrates how decision makers can benefit from a network analytic approach to develop a more holistic understanding of system‐wide risk diffusion and to guide network governance policies for more favorable health level outcomes. The article concludes by highlighting the main findings and discussing possibilities of future research directions.     

A multi-treatment two stage adaptive allocation for survival outcomes

A multi-treatment two stage adaptive allocation design is developed for survival responses. Assuming noninformative random censoring, asymptotic p values of relevant tests of equality of treatment effects are used to derive the assignment probability of incoming second stage subjects. Several ethical and inferential criteria of the design are studied, and are compared with those of an existing competitor. Applicability and performance of the proposed design are also illustrated using a data arising from a real clinical trial.     

On an ethical cum optimal adaptive allocation design

In the present work, we formulate a two-treatment single period two-stage adaptive allocation design for achieving larger allocation proportion to the better treatment arm in the course of the trial with increased precision of the parameter estimator. We examine some properties of the proposed rule and compare it with some of the existing allocation rules and report substantial gain in efficiency with a considerably larger number of allocations to the better treatment even for moderate sample sizes.     

D–optimality of a class of saturated main–effect plans and allied results

This paper applies the theory of unimodular matrices to prove that all saturated main effect plans of an s₁ × s₂ factorial are equivalent from the point of view of D–optimality and are hence all D–optimal. The A– and E–optimal plans in this context have also been derived. An application in sequential experimentation has been considered     

Interval estimation of the population mean under model uncertainty: Robust versus empirical statistics

With reference to the problem of interval estimation of a population mean under model uncertainty, we compare approaches based on robust and empirical statistics via expected lengths of the associated confidence intervals. An explicit expression for confidence intervals arising from a general class of robust statistics is worked out and this is employed to obtain a higher order asymptotic formula for the expected lengths of such intervals. Comparative theoretical results, as well as a simulation study, are then presented.     

Regression Type Estimators Using Multiple Auxiliary Information

In this paper we consider a practical situation where information on two auxiliary variables related to the study variable is available at different levels. Following Kiregyera (1980, 1984) who has obtained a chain ratio-to-regression estimator and regression to regression estimator, we shall study several estimators that arise naturally in this context and compare them under the mean square error criterion. We extend these results to the case when multiple auxiliary information is available.     

On the optimality of rao's statistic

This paper establishes the asymptotic optimality of Rao's

test within a very wide class of tests that Includes the likeli hood ratio test and Wald's test. An expression for the defici¬ency of the tests in this class relative to Rao's test has also been obtained     

Admissibility in factorial designs

The notion of admissibility in factorial designs has been introduced. It has been shown that factorial designs which are binary and have a C-matrix with property A are admissible.     

A Bayesian adaptive design for two-stage clinical trials with survival data

A randomized two-stage adaptive Bayesian design is proposed and studied for allocation and comparison in a phase III clinical trial with survival time as treatment response. Several exact and limiting properties of the design and the follow-up inference are studied, both numerically and theoretically, and are compared with a single-stage randomized procedure. The applicability of the proposed methodology is illustrated by using some real data.