Ergonomics and Kaizen as strategies for competitiveness: a theoretical and practical in an automotive industry

With increased international competitiveness in the automotive industry, came the concern of the companies save costs and lower production costs. For this purpose many ways are designed to reduce costs and waste of raw materials and reduce activities that do not aggregate value to manufacturing processes. In the early XVII appears the manufacturing system, which processes were hard with little concern for the health and safety of employees and conditions of the workplace. After the advent of the production system called lean manufacturing, a new paradigm in terms of production system capable of providing high levels of productivity and quality. It is based on waste elimination that occur during the production process. After began a new way of thinking, creating a culture of continuous improvement and lean process with no waste and reducing costs, without neglecting the welfare worker and improving the conditions of their work environment. This paper presents a reflection on the application of ergonomics in a lean production system of an automotive industry, using methodology based on the Kaizen (Continuous Improvement) to gain performance and improving the conditions of the workplace, also will be presented with positive and negative points in using this methodology in relation to ergonomics. The research will be conducted by collecting data 'in loco' and interviews with workers. Some studies show that in companies that are lean system and using the methodology of Kaizen, the results of product quality, levels of absenteeism and accidents are better than those obtained in companies that do not apply the same concept.     

Rényi Statistics in Directed Families of Exponential Experiments*

Rényi statistics are considered in a directed family of general exponential models. These statistics are defined as Rényi distances between estimated and hypothetical model. An asymptotically quadratic approximation to the Rényi statistics is established, leading to similar asymptotic distribution results as established in the literature for the likelihood ratio statistics. Some arguments in favour of the Rényi statistics are discussed, and a numerical comparison of the Rényi goodness-of-fit tests with the likelihood ratio test is presented.     

Rényi statistics for testing composite hypotheses in general exponential models

We introduce a family of Rényi statistics of orders r?∈?R for testing composite hypotheses in general exponential models, as alternatives to the previously considered generalized likelihood ratio (GLR) statistic and generalized Wald statistic. If appropriately normalized exponential models converge in a specific sense when the sample size (observation window) tends to infinity, and if the hypothesis is regular, then these statistics are shown to be χ²-distributed under the hypothesis. The corresponding Rényi tests are shown to be consistent. The exact sizes and powers of asymptotically α-size Rényi, GLR and generalized Wald tests are evaluated for a concrete hypothesis about a bivariate Lévy process and moderate observation windows. In this concrete situation the exact sizes of the Rényi test of the order r?=?2 practically coincide with those of the GLR and generalized Wald tests but the exact powers of the Rényi test are on average somewhat better.     

Testing hypotheses in truncated samples by means of divergence statistics

In this paper we consider the problem of testing hypotheses in parametric models, when only the first r (of n) ordered observations are known.Using divergence measures, a procedure to test statistical hypotheses is proposed, Replacing the parameters by suitable estimators in the expresion of the divergence measure, the test statistics are obtained.Asymptotic distributions for these statistics are given in several cases when maximum likelihood estimators for truncated samples are considered.Applications of these results in testing statistical hypotheses, on the basis of truncated data, are presented.The small sample behavior of the proposed test statistics is analyzed in particular cases.A comparative study of power values is carried out by computer simulation.     

On Some Properties of the Beta Normal Distribution

The beta normal distribution is a generalization of both the normal distribution and the normal order statistics. Some of its mathematical properties and a few applications have been studied in the literature. We provide a better foundation for some properties and an analytical study of its bimodality. The hazard rate function and the limiting behavior are examined. We derive explicit expressions for moments, generating function, mean deviations using a power series expansion for the quantile function, and Shannon entropy.     

Identification Using Stability Restrictions

This paper studies inference in models that are identified by moment restrictions. We show how instability of the moments can be used constructively to improve the identification of structural parameters that are stable over time. A leading example is macroeconomic models that are immune to the well‐known (Lucas (1976)) critique in the face of policy regime shifts. This insight is used to develop novel econometric methods that extend the widely used generalized method of moments (GMM). The proposed methods yield improved inference on the parameters of the new Keynesian Phillips curve.     

A test for uniformity based on informational energy

In this paper a test of fit for uniformity based on the estimated Informational Energy is proposed. The test usesm-step spacings. The test is shown to be a consistent test of the null hypothesis. Percentage points and power against different alternatives are calculated. Finally, our test is compared with other test of uniformity. This work was supported by the grant DGES BMF2000-0800.     

A Non‐parametric ANOVA‐type Test for Regression Curves Based on Characteristic Functions

This article studies a new procedure to test for the equality of k regression curves in a fully non‐parametric context. The test is based on the comparison of empirical estimators of the characteristic functions of the regression residuals in each population. The asymptotic behaviour of the test statistic is studied in detail. It is shown that under the null hypothesis, the distribution of the test statistic converges to a finite combination of independent chi‐squared random variables with one degree of freedom. The coefficients in this linear combination can be consistently estimated. The proposed test is able to detect contiguous alternatives converging to the null at the rate n ^{? 1 ∕ 2}. The practical performance of the test based on the asymptotic null distribution is investigated by means of simulations.