A Study of the Role Played by Manufacturing Strategic Objectives and Capabilities in Understanding the Relationship between Porter's Generic Strategies and Business Performance

An appropriate alignment between business strategy, manufacturing strategic objectives and manufacturing capabilities reportedly influences business performance positively. However, few papers empirically analyse this proposition for the case of Porter's generic strategies of cost leadership and differentiation, and none jointly considers all four elements. This paper integrates strategies, capabilities and performance in a single model and proposes that both manufacturing competitive priorities and capabilities, articulated in terms of cost and flexibility, are essential for explaining the link between generic business strategies and business commercial and financial performance. Within this analytic framework, we test whether companies that emphasize one business strategy rather than another achieve a better alignment and superior performance. The analyses conducted with a sample of 148 Spanish manufacturers provide general support for these propositions and contribute to a deeper understanding of the role played by functional strategies in understanding the outcomes of business strategy.     

Influence analysis in multivariate linear general models

In this paper we obtain several influence measures for the multivariate linear general model through the approach proposed by Muñoz-Pichardo et al. (1995), which is based on the concept of conditional bias. An interesting charasteristic of this approach is that it does not require any distributional hypothesis. Appling the obtained results to the multivariate regression model, we obtain some measures proposed by other authors. Nevertheless, on the results obtained in this paper, we emphasize two aspects. First, they provide a theoretical foundation for measures proposed by other authors for the mul¬tivariate regression model. Second, they can be applied to any linear model that can be formulated as a particular case of the multivariate linear general model. In particular, we carry out an application to the multivariate analysis of covariance.     

New Goodness-of-Fit Tests Based on Sample Quantiles

In this article power divergences statistics based on sample quantiles are transformed in order to introduce new goodness-of-fit tests. Quantiles of the distribution of proposed statistics are calculated under uniformity, normality, and exponentiality. Several power comparisons are performed to show that the new tests are generally more powerful than the original ones.     

Influence diagnostics in a multivariate normal regression model with general parameterization

Patriota and Lemonte [24] introduced a quite general multivariate normal regression model. This model considers that the mean vector and the covariance matrix share the same vector of parameters. In this paper we present some influence assessment for this model, such as the local influence, total local influence of an individual and generalized leverage which are discussed. Additionally, the normal curvatures for local influence studies are derived under some perturbation schemes.     

A time series model with random coefficients

There are many time series applications where an experi­menter observes the simultaneous responses of several sub­systems over time. In these instances one is often not interested in the parameters of individual subsystems, but rather in an overall characterization of the system in question. Under the assumption that subsystems are independent and first order autoregressive, the present paper presents two methods for estimating the distribution of the subsystem coefficients.     

On-line bin packing with restricted repacking

Semi-on-line algorithms for the bin-packing problem allow, in contrast to pure on-line algorithms, the use of certain types of additional operations for each step. Examples include repacking, reordering or lookahead before packing the items. Here we define and analyze a semi-on-line algorithm where for each step at most k items can be repacked, for some positive integer k. We prove that the upper bound for the asymptotic competitive ratio of the algorithm is a decreasing function of k, which tends to 3/2 as k goes to infinity. We also establish lower bounds for this ratio and show that the gap between upper and lower bounds is relatively small.