Applied Stochastic Models in Business and Industry

Lipovatz, Daphne; Mandaraka, Maria; Mourelatos, Alexandros

2000 Applied Stochastic Models in Business and Industry

doi: 10.1002/1526-4025(200004/06)16:2<85::AID-ASMB384>3.0.CO;2-D

Principal component analysis is integrated with canonical analysis to examine aspects of the competitiveness of two different sectors of the Greek manufacturing, i.e. the food and the beverage industries. Different measures of labour productivity, vertical integration, technological innovation and size of the firms which are considered as critical factors of industrial competitiveness are used in the application of the principal component analysis. Canonical analysis is then applied to correlate the variables of labour productivity with the other variables. In the case of the food and beverage integrated sector, the results of the principal component analysis pinpoint that there are two main principal components: (a) labour productivity and vertical integration, and (b) technological innovation and size. The first factor depicts the internal organizational, structural and production processes changes realized so that the competitiveness of the sector firms improves, whereas the second factor reflects the response of the sector to technological and growth trends. The two variables of labour productivity are affected by the degree of vertical integration and, at a lesser degree, by a common factor of the size of the firm and the level of investment for technological innovation. Copyright © 2000 John Wiley & Sons, Ltd.

Ugwuowo, F.I.; McClean, S.I.

2000 Applied Stochastic Models in Business and Industry

doi: 10.1002/1526-4025(200004/06)16:2<99::AID-ASMB385>3.0.CO;2-3

Manpower planning is an essential methodology for business and industry; it allows managers to make more efficient use of human resources. However, human behaviour is highly variable and it is therefore essential for manpower planning that population heterogeneity is successfully modelled. In this paper we review methods of incorporating population heterogeneity into manpower modelling. The analysis of differentials in a manpower system is emphasized since they are a source of aggregation error in stochastic models. Two strategies have been stressed, the use of observable sources of heterogeneity as they affect wastage, and the latent sources which cannot be identified precisely but are known to affect the key parameters of most models. Copyright © 2000 John Wiley & Sons, Ltd.

Møller, Christian Max

2000 Applied Stochastic Models in Business and Industry

doi: 10.1002/1526-4025(200004/06)16:2<111::AID-ASMB412>3.0.CO;2-1

The aim of the present paper is to make use of the modern theory of point processes to study optimal solutions for single‐item inventory. Demand for goods is assumed to occur according to a compound Poisson process and production occurs continuously and deterministically between times of demand, such that the inventory evolves according to a Markov process in continuous time. The aim is to propose a way of finding optimal production schemes by minimizing a certain expected loss over some finite period. There are holding/production costs depending on the stock level, and random penalty amounts will occur due to excess demand which is assumed backlogged. For simplicity we will not incorporate fixed costs. We give some numerical illustrations. Copyright © 2000 John Wiley & Sons, Ltd.

Nordmoe, Eric D.; Jain, Dipak C.

2000 Applied Stochastic Models in Business and Industry

doi: 10.1002/1526-4025(200004/06)16:2<127::AID-ASMB387>3.0.CO;2-A

Logit models have been widely used in marketing to predict brand choice and to make inference about the impact of marketing mix variables on these choices. Most researchers have followed the pioneering example of Guadagni and Little, building choice models and drawing inference conditional on the assumption that the logit model is the correct specification for household purchase behaviour. To the extent that logit models fail to adequately describe household purchase behaviour, statistical inferences from them may be flawed. More importantly, marketing decisions based on these models may be incorrect. This research applies White's robust inference method to logit brand choice models. The method does not impose the restrictive assumption that the assumed logit model specification be true. A sandwich estimator of the covariance ‘corrected’ for possible mis‐specification is the basis for inference about logit model parameters. An important feature of this method is that it yields correct standard errors for the marketing mix parameter estimates even if the assumed logit model specification is not correct. Empirical examples include using household panel data sets from three different product categories to estimate logit models of brand choice. The standard errors obtained using traditional methods are compared with those obtained by White's robust method. The findings illustrate that incorrectly assuming the logit model to be true typically yields standard errors which are biased downward by 10–40 per cent. Conditions under which the bias is particularly severe are explored. Under these conditions, the robust approach is recommended. Copyright © 2000 John Wiley & Sons, Ltd.

Nonell, Ramon; Thió‐Henestrosa, Santiago; Aluja‐Banet, Tomàs

2000 Applied Stochastic Models in Business and Industry

doi: 10.1002/1526-4025(200004/06)16:2<147::AID-ASMB386>3.0.CO;2-7

Classically principal component analysis is one of the most used techniques for exploring the multivariate association pattern of variables. On the other hand, conditioning is one of the most promising ideas for controlling the variability of observed data. Here we present a review of some conditioning methods from the analysis of residuals of a parametric model to the analysis of the local variation defined by means of a non‐oriented graph of individuals, this variation being defined from the deviation from a local mean or alternatively from the differences among contiguous vertices. We will compare these approaches and will show that under some conditions they give comparable results. Finally, we will present an example of application to illustrate the results previously stated. Copyright © 2000 John Wiley & Sons, Ltd.