Eva Ventura

Logistics-production, logistics-marketing and external integration: Their impact on performance

Highly competitive environments are leading companies to implement Supply Chain Management (SCM) to improve performance and gain a competitive advantage. SCM involves integration, co-ordination and collaboration across organisations and throughout the supply chain. It means that SCM requires internal (intraorganisational) and external (interorganisational) integration. This paper examines the Logistics-Production and Logistics- Marketing interfaces and their relation with the external integration process. The study also investigates the causal impact of these internal and external relationships on the company’s logistical service performance. To analyse this, an empirical study was conducted in the Spanish Fast Moving Consumer Goods (FMCG) sector.

Logistics‐production, logistics‐marketing and external integration

Purpose – This paper examines the logistics‐production and logistics‐marketing interfaces and their relation with the external integration. The study also investigates the causal impact of these internal and external relationships on the companys logistical performance.Design/methodology/approach – An empirical study was conducted in the Spanish FMCG sector and the theoretical model was subjected to analysis using SEM.Findings – The generic results derived from this study are: Internal and external integration influence each other. Integration in the logistics‐marketing interface does not lead to reductions in costs, stock‐outs and lead‐times, while the integration achieved in the logistics‐production interface does improve these performance measures, if there is no external integration. The external collaboration among supply chain members does always contribute to improving firms’ logistical performance.Research/limitations/implications – The study has some limitations: other important members of the g...

Supply Side Interventions and Redistribution

We evaluate the effect on welfare of shifting the burden of capital income taxes to labour taxes in a dynamic equilibrium model with heterogeneous agents and constant tax rates. We calibrate and simulate the economy; we find that lowering capital taxes has two effects: it increases efficiency in terms of aggregate production and it redistributes wealth in favour of those agents with a low wage/wealth ratio. When the parameters of the model are calibrated to match the distribution of income in terms of the wage/wealth ratio, the redistributive effect dominates, and agents with a high wage/wealth ratio would experience a large loss in utility if capital income taxes were eliminated.

Using composite estimators to improve both domain and total area estimation

In this article we propose using small area estimators to improve the estimates of both the small and large area parameters. When the objective is to estimate parameters at both levels accurately, optimality is achieved by a mixed sample design of fixed and proportional allocations. In the mixed sample design, once a sample size has been determined, one fraction of it is distributed proportionally among the different small areas while the rest is evenly distributed among them. We use Monte Carlo simulations to assess the performance of the direct estimator and two composite covariant-free small area estimators, for different sample sizes and different sample distributions. Performance is measured in terms of Mean Squared Errors (MSE) of both small and large area parameters. It is found that the adoption of small area composite estimators open the possibility of 1) reducing sample size when precision is given, or 2) improving precision for a given sample size.

An Empirical Evaluation of Small Area Estimators

This paper compares five small area estimators. We use Monte Carlo simulation in the context of both artificial and real populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated weights: one that assumes homogeneity of within area variance and squared bias and one that uses area-specific estimates of variance and squared bias. In the study with real population, we found that among the feasible estimators, the best choice is the one that uses area-specific estimates of variance and squared bias.

SCM Research Methodologies: Employing Structural Equation Modeling

Supply Chain Management research very often involves an analysis of relationships among abstract concepts. For this type of analysis, Structural Equation Modeling (SEM) is a very powerful technique because it combines measurement models (confirmatory factor analysis) and structural models (regression analysis) into a simultaneous statistical test. The objective of this paper is to show how SEM can be employed in theory testing. We will also describe a process regarding its implementation and show an example of a research paper based on this methodology.

A note on measurement error and Euler equations: An alternative to log-linear approximations

Empirical research based on panel data has to pay special attention to measurement errors. Utility maximization often yields nonlinear decision rules in which measurement errors enter in a multiplicative way. The usual strategy to deal with them consists of taking log-linear approximations of the equations to estimate. The expression to be estimated then includes a new error component and the estimators could be biased and inconsistent. We describe one particular parameterization that avoids linearizing the equation we want to estimate.

Lyfe-cycle effects on household expenditures: A latent-variable approach

Using data from the Spanish household budget survey, we investigate life- cycle effects on several product expenditures. A latent-variable model approach is adopted to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the measurement error in income. The proposed methodology also takes care of the case in which product expenditures exhibit a pattern of infrequent purchases. Multiple-group analysis is used to assess the variation of key parameters of the model across various household life-cycle typologies. The analysis discloses significant life-cycle effects on the mean levels of expenditures; it also detects significant life-cycle effects on the way expenditures are affected by income and family size. Asymptotic robust methods are used to account for possible non-normality of the data.

On the performance of small-area estimators: Fixed vs. random area parameters

Most methods for small-area estimation are based on composite estimators derived from design- or model-based methods. A composite estimator is a linear combination of a direct and an indirect estimator with weights that usually depend on unknown parameters which need to be estimated. Although model-based small-area estimators are usually based on random-effects models, the assumption of fixed effects is at face value more appropriate.Model-based estimators are justified by the assumption of random (interchangeable) area effects; in practice, however, areas are not interchangeable. In the present paper we empirically assess the quality of several small-area estimators in the setting in which the area effects are treated as fixed. We consider two settings: one that draws samples from a theoretical population, and another that draws samples from an empirical population of a labor force register maintained by the National Institute of Social Security (NISS) of Catalonia. We distinguish two types of composite estimators: a) those that use weights that involve area specific estimates of bias and variance; and, b) those that use weights that involve a common variance and a common squared bias estimate for all the areas. We assess their precision and discuss alternatives to optimizing composite estimation in applications.

Improving small area estimation by combining surveys: new perspectives in regional statistics

A national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devised. We investigate such methods and assess them by a Monte Carlo study. We explore how a complementary survey can be exploited in small area estimation. We use the context of the Spanish Labour Force Survey (EPA) and the Barometer in Spain for our study.