A SIMULATION ANALYSIS OF THE JAPANESE JUST-IN-TIME TECHNIQUE (WITH KANBANS) FOR A MULTILINE,MULTISTAGE PRODUCTION SYSTEM

The Japanese “just-in-time with kanban” technique reduces in-process inventory to absolute minimal levels, in concert with the Japanese belief that inventory is an unnecessary evil. Due to the success of Japanese firms that employ this type of system, American firms would like to import this technique and emulate Japanese successes. But this Japanese success may be attributable not only to the just-in-time with kanban technique but also to the production environment in which the technique is employed. This paper simulates the just-in-time with kanban technique for a multiline, multistage production system in order to determine its adaptability to an American production environment that might include such characteristics as variable processing times, variable master production scheduling, and imbalances between production stages. The results have practical implications for those firms considering adoption of the Japanese technique.     

Inferences on regression coefficients in a regression model under heteroscedasticity and robustness with respect to departure from normality

In this paper we consider a simple linear regression model under heteroscedasticity and nonnormality. A statistical test for testing the regression coefficient is then derived by assuming normality for the random disturbances and by applying Welch's method. Some Monte Carlo studies are generated for assessing robustness of this test. By combining Tiku's robust procedure with the new test, a robust but more powerful test is developed.     

A simulation study of the nearest neighbour analysis method of papadakis

The relative performances of randomised block, balanced lattice squares and Papadakis nearest neighbour analyses were compared on two simulated fields whose soil heterogeneity profiles were generated, one with a few evenly spaced contours and the other with many unevenly spaced contours. Four levels of random error were generated to simulate different proportions of random error and soil heterogeneity. Dummy treatments, corresponding to 7 x 7 and 11 x 11 balanced lattice squares were applied to the fields. The results from simulated experiments showed an interaction of error mean square (EMS) between size of experiment (7 x 7, 11 x 11) and levels of soil heterogeneity in the lattice analyses, but no such interaction in the Papadakis analyses. The Papadakis EMS decreased as random error decreased but at a rate depending on the map andthe ratio of soil heterogeneity to random error.     

Measuring Mutual Help Willingness and Criteria among Hong Kong People: Confirmatory Factor Analyses

Mutual help has been often found to be beneficial for people’s well-being in clinical settings. Research in the general population, however, is relatively limited, partly due to the lack of applicable measurement tools. The present study attempted to develop two scales to measure mutual help willingness and criteria and test their psychometric properties among a community sample of Hong Kong people (n = 951). Confirmatory factor analyses suggested that the hypothetical models fitted the data very well. Results also indicated that though the willingness to seek help and to give help was significantly correlated, Hong Kong people were generally more willing to give help than to seek help. Age had a negative effect on help-seeking willingness, which can not be explained by education level. A set of criteria were applied similarly when people decided on help-giving and help-seeking. Agreement with the criteria was negatively correlated with age but the associations became negligible when education level was controlled. Research and practical implications of the findings are discussed.     

Model-Based Classification via Mixtures of Multivariate t-Factor Analyzers

A model-based classification technique is developed, based on mixtures of multivariate t-factor analyzers. Specifically, two related mixture models are developed and their classification efficacy studied. An AECM algorithm is used for parameter estimation, and convergence of these algorithms is determined using Aitken's acceleration. Two different techniques are proposed for model selection: the BIC and the ICL. Our classification technique is applied to data on red wine samples from Italy and to fatty acid measurements on Italian olive oils. These results are discussed and compared to more established classification techniques; under this comparison, our mixture models give excellent classification performance.     

The servant identity: Influences on the cognition and behavior of servant leaders

This paper approaches the study of servant leadership by concentrating on the identity of servant leaders. An identity is important to one's sense of self and it influences the way a leader cognitively processes socially relevant information and exercises a particular leadership behavior in response to a situation. Unlike existing studies, which typically approach servant leadership as one of a number of possible leadership styles, and which merely describe its characteristics, this paper serves to explain the basis for individuals enacting this mode of leadership. This paper defines and elaborates on the servant identity by showing the psychological factors constituting it. Taking a socio-cognitive approach, the paper offers a theoretical framework for the servant identity. The framework encompasses the cognitive and behavioral disposition of leaders with servant identity and the organizational contexts that influence it. This new angle on servant leadership opens avenues for future research and practice.     

An Improved Boxplot for Univariate Data

The boxplot is an effective data-visualization tool useful in diverse applications and disciplines. Although more sophisticated graphical methods exist, the boxplot remains relevant due to its simplicity, interpretability, and usefulness, even in the age of big data. This article highlights the origins and developments of the boxplot that is now widely viewed as an industry standard as well as its inherent limitations when dealing with data from skewed distributions, particularly when detecting outliers. The proposed Ratio-Skewed boxplot is shown to be practical and suitable for outlier labeling across several parametric distributions.     

The impact of random machine unavailability on inventory policies in a continuous improvement environment

There are production situations where a production facility (e.g. a machine) is used intermittently to produce lot sizes of certain products. Upon completion of production run, the facility may not be available for a random amount of time due to several reasons, such as: the facility needs to be maintained and the maintenance time is random due to unforeseen circumstances; or that the facility is leased by different manufacturers and the demand for the facility is random. As a result of machine unavailability, stock-out situations might arise. This paper extends the work of Abboud et al . (2000, Computers and Operations Research , 27 , 335-351) by assuming learning and forgetting in production. A new mathematical model is developed with numerical examples and sensitivity analysis provided. Furthermore, this paper determines how the overall inventory cost is influenced by the nature of the random variable that represents the unavailability time of the production facility.