Theory of constraints (TOC) production and manufacturing performance

This paper is based on an empirical study of the relationship between Theory of Constraints (TOC) production and operational performance in manufacturing plants. The study uses a survey questionnaire to collect data from a sample of 61 European firms which have implemented the TOC approach. Analysis of variance (ANOVA) technique and regression models have been employed to test the research hypotheses. The results detect many differences and similarities in adoption of TOC practices across the countries and suggest that manufacturing managers should consider adopting some TOC practices instead of others. In particular the Drum-buffer-rope methodology, the development of a Master Production Schedule based on constraints and the use of Non-constraint resources with excess capacity are among the most important practices to enhance competitive performance of manufacturing plants

Constraints to Strategy Implementation and their Influence on Business Performance: the Case of a Waste Management Logistics Company

Waste management companies in developing countries often have to contend with a plethora of factors that inhibit their business performance. The primary objective of this study was to investigate the influence of constraints to strategy implementation on the business performance of a waste management logistics company in South Africa. The study was triggered by the lack of previous research focusing on constraints to strategy implementation in the waste management sector. The study employed a quantitative approach using the cross sectional survey design in which data were collected from 309 employees of a waste management logistics company based in Gauteng Province. Seven constraints to strategy implementation; namely, management practices, human resource capabilities, customer service, external orientation, internal communication, innovation and employee motivation were identified through Exploratory Factor Analysis. Pearson correlations showed that business performance is negatively affected as and when each constraint becomes more prevalent. Regression analysis showed that all constraints were statistically significant. To academics, the study provides current insights on factors impacting on business performance in waste management organisations. Management practitioners may improve the levels of business performance through structural adjustments of the seven constraints identified in this study. The study may be used as a reference point in the diagnosis of business performance related challenges in companies operating within the waste management sector.&nbsp

Financial capital, constraints, partners, and performance: An empirical analysis of Indonesia SMEs

Indonesia's small and medium enterprises (SMEs) are considered the backbone of the national economy. However, the fact that SMEs still contribute less to the national gross domestic product (GDP) in terms of value-added, need to be addressed. While previous studies mainly focused on financial (access) constraints as one of the major constraints faced by small enterprises which affect their growth and performances, this study aims to extend the relationship between capital and financial performance of Indonesia SMEs with the moderating effect of financial constraints and partners. This study is different from others as it uses a bigger panel dataset which is about 4.36 million SMEs in Indonesia and is the first to explore the role of financial partners comprehensively. Moreover, the panel regression model with geographic analysis unit uses as a data analysis method. The results of the study show that financial capital has a positive and significant effect on the financial performance of SMEs. Furthermore, while the moderation role of financial partners on the relationship between financial capital and financial performance of Indonesia SMEs was failed to prove, the negative moderation effect of financial constraints was able to prove in this study

L1-Penalized quantile regression in high-dimensional sparse models

We consider median regression and, more generally, quantile regression in high-dimensional sparse models. In these models the overall number of regressors p is very large, possibly larger than the sample size n, but only s of these regressors have non-zero impact on the conditional quantile of the response variable, where s grows slower than n. Since in this case the ordinary quantile regression is not consistent, we consider quantile regression penalized by the L1-norm of coefficients (L1-QR). First, we show that L1-QR is consistent at the rate of the square root of (s/n) log p, which is close to the oracle rate of the square root of (s/n), achievable when the minimal true model is known. The overall number of regressors p affects the rate only through the log p factor, thus allowing nearly exponential growth in the number of zero-impact regressors. The rate result holds under relatively weak conditions, requiring that s/n converges to zero at a super-logarithmic speed and that regularization parameter satisfies certain theoretical constraints. Second, we propose a pivotal, data-driven choice of the regularization parameter and show that it satisfies these theoretical constraints. Third, we show that L1-QR correctly selects the true minimal model as a valid submodel, when the non-zero coefficients of the true model are well separated from zero. We also show that the number of non-zero coefficients in L1-QR is of same stochastic order as s, the number of non-zero coefficients in the minimal true model. Fourth, we analyze the rate of convergence of a two-step estimator that applies ordinary quantile regression to the selected model. Fifth, we evaluate the performance of L1-QR in a Monte-Carlo experiment, and provide an application to the analysis of the international economic growth.

The Interplay Between Families and Schools: Immigrant and Native Differentials in Educational Outcomes

We examine the effects of school context on educational outlooks and outcomes of the children of immigrants, in comparison with natives in Spain, an under-represented case in the international literature and a fast growing immigration destination in Europe. Using two sources of hierarchical data, 2011 Chances Survey and the 2010 Secondary Schooling National Evaluation Survey, which cluster students across schools, we investigate the factors that contribute to the formation of long term educational careers. To start with we analyze performance from both an objective (test scores in mathematics) and subjective perspective (estimation by children and also their parents of whether individual school results will allow them to proceed to tertiary education). Then we turn our attention to the adjusted educational expectations (controlled for prior performance) of children. Our results reveal the different way that school context works for immigrant and native origin children. Our multilevel regression analysis finds significantly worse school results among immigrants (test scores). Although immigrant children themselves understand the constraints that such disadvantage imposes on their future educational careers, immigrant parents seem to hold on to a rather unrealistic position. This parental optimism in turn seems to boost the career expectation of immigrant children independent of school effects. Thus while school context determines the performance of immigrant origin students to a greater extent than those of natives, the opposite is true for expectations. The formation of aspirations is more family-oriented among immigrants, and thus more positive, than among natives