Measuring operational excellence: an operational excellence profitability (OEP) approach

The pursuit of operational excellence in the manufacturing industry is at rise but its measurement still lacks of appropriate indicators to determine its financial benefits. The ambiguity is due to the impact arisen from manufacturing fluctuations such as price and cost, production mix and direct and indirect parameters variations. Manufacturing fluctuations distort the cost benefit of operational excellence. This paper, therefore, proposes the OEP (Operational Excellence Profitability) indicators to isolate the impact of manufacturing fluctuation and distinctly identify the payback of operational excellence strategies and initiatives through cost benefits of achieving higher efficiency and yield. The paper presents the conceptual and mathematical development of the proposed OEP indicators and the formulas used for their calculation. Hypothetical and industrial-based investigations and applications of the OEP indicators are conducted for their validation. The results obtained from the hypothetical exercise and industrial case suggest that OEP indicators can provide an effective cost-benefit analysis of operational excellence. This would contribute in providing manufacturing organizations with more complete information regarding the performance of their processes, which will allow their directors and managers to take better decisions related to the management and improvement of their processes.     

Introduction

Public Organization Review -     

A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models

When prediction intervals are constructed using unobserved component models (UCM), problems can arise due to the possible existence of components that may or may not be conditionally heteroscedastic. Accurate coverage depends on correctly identifying the source of the heteroscedasticity. Different proposals for testing heteroscedasticity have been applied to UCM; however, in most cases, these procedures are unable to identify the heteroscedastic component correctly. The main issue is that test statistics are affected by the presence of serial correlation, causing the distribution of the statistic under conditional homoscedasticity to remain unknown. We propose a nonparametric statistic for testing heteroscedasticity based on the well-known Wilcoxon''s rank statistic. We study the asymptotic validation of the statistic and examine bootstrap procedures for approximating its finite sample distribution. Simulation results show an improvement in the size of the homoscedasticity tests and a power that is clearly comparable with the best alternative in the literature. We also apply the test on real inflation data. Looking for the presence of a conditionally heteroscedastic effect on the error terms, we arrive at conclusions that almost all cases are different than those given by the alternative test statistics presented in the literature.     

Emerging Genetic and Epigenetic Mechanisms Underlying Pubertal Maturation in Adolescence

The adolescent transition begins with the onset of puberty which, upstream in the brain, is initiated by the gonadotropin‐releasing hormone (GnRH) pulse generator that activates the release of peripheral sex hormones. Substantial research in human and animal models has revealed a myriad of cellular networks and heritable genes that control the GnRH pulse generator allowing the individual to begin the process of reproductive competence and sexual maturation. Here, we review the latest knowledge in neuroendocrine pubertal research with emphasis on genetic and epigenetic mechanisms underlying the pubertal transition.     

Defining Governance in Latin America

Public Organization Review - This exploratory research aims to propose a conceptual definition of governance and to speculate on the state of governance in Latin America. Political instability,...     

The U.S. Health System and Immigration: An Institutional Interpretation1

We examine the institutions that comprise the U.S. health system and their relationship to a surging immigrant population. The clash between the system and this human flow originates in the large number of immigrants who are unauthorized, poor, and uninsured and, hence, unable to access a system largely based on ability to pay. Basic concepts from sociological theory are brought to bear on the analysis of this clash and its consequences. Data from a recently completed study of health institutions in three areas of the United States are used as an empirical basis to illustrate various aspects of this complex relation. Implications of our results for theory and future health policy are discussed.     

Describing poverty in an ultraperipheral region: The case of the Canary Islands

Based on data from the Survey on Social Conditions in the Canary Islands of the Instituto Canario de Estadística, this study analyzes poverty in Canary households by island of residence, age, employment and the educational level of the main provider. In addition to accepted objective measures, we also estimate poverty using subjective methods, in which the poverty line is defined or obtained by the opinion of household members of their own level of poverty.     

No reliable evidence for immediate noise-induced song flexibility in a suboscine

Urban Ecosystems - Vermilion flycatchers, a suboscine, sing songs with more elements in territories with higher urban noise levels. We tested the hypothesis that this pattern is achieved through...     

Trend estimation of multivariate time series with controlled smoothness

This paper extends the univariate time series smoothing approach provided by penalized least squares to a multivariate setting, thus allowing for joint estimation of several time series trends. The theoretical results are valid for the general multivariate case, but particular emphasis is placed on the bivariate situation from an applied point of view. The proposal is based on a vector signal-plus-noise representation of the observed data that requires the first two sample moments and specifying only one smoothing constant. A measure of the amount of smoothness of an estimated trend is introduced so that an analyst can set in advance a desired percentage of smoothness to be achieved by the trend estimate. The required smoothing constant is determined by the chosen percentage of smoothness. Closed form expressions for the smoothed estimated vector and its variance-covariance matrix are derived from a straightforward application of generalized least squares, thus providing best linear unbiased estimates for the trends. A detailed algorithm applicable for estimating bivariate time series trends is also presented and justified. The theoretical results are supported by a simulation study and two real applications. One corresponds to Mexican and US macroeconomic data within the context of business cycle analysis, and the other one to environmental data pertaining to a monitored site in Scotland.