Using Lean Six Sigma to improve mobile order fulfilment process in a telecom service sector

This study proposes and follows a specific and systematic framework for implementing Lean Six Sigma (LSS) methodology in a telecom company in order to improve customer satisfaction by minimizing the company’s response time to customer requirements. The goal of this study was achieved by utilizing several LSS tools under five phases of the DMAIC methodology. Unlike previous studies in the telecom sector that used only qualitative method, in this study, both qualitative and quantitative methods were utilized to draw meaningful conclusions. As a result of the implementation of the LSS methodology, the average order fulfilment lead time for sales orders (SO) and value-added service (VAS) orders was reduced from 10.3 to 5.9 days and from 1.5 to 0.5 days, respectively. The reduction in lead time resulted in an increase in the sigma level for SO and VAS orders from 0.44 to 1.26 and from 0.73 to 2.66, respectively. These improvements were expected to lead to a financial benefit in savings of over $600,000 per year in operational costs, enhancements to customer experience and an increase in revenue generating opportunities. Moreover, this article enriches the existing literature on the application of LSS concept in the service industry, and helps the company to speed up the response to customer requirements.     

Optimum M-step,step-stress design with K stress variables

Most of the available literature on accelerated life testing deals with tests that use only one accelerating variable and no other explanatory variables. Frequently, however, there is a need to use more than one accelerating or other experimental variables. Examples include a test of capacitors at higher than usual levels of temperature and voltage, and a test of circuit boards at higher than usual levels of temperature, humidity, and voltage. M-step, step-stress models are extended to include k stress variables. Optimum M-step, step-stress designs with k stress variables are found. The polynomial model is considered as a special case, and a lack of fit test is discussed. Also a goodness-of-fit test is proposed and the appropriateness of using its asymptotic chi-square distribution for small samples is shown.     

Inclusion or transformation? An early assessment of an empowerment project for disabled people in occupied Palestine

Can disabled people become advocates in occupied Palestine? This article presents findings from a current three-year project. It examines the environmental, attitudinal and institutional barriers faced by disabled people and the activities being undertaken to challenge social discrimination. The article considers the project in relation to liberation theory and the struggle by young disabled Palestinians to achieve social inclusion – and eventually social transformation.     

The fever that hit Arab satellite television: audience perceptions of Turkish TV series

Turkish television drama series were locally produced products until the early 2000s. Since then, about 70 different titles have been broadcast to audiences in 40 countries, especially in the Middle East, where they comprise approximately 60% of the share in foreign programme broadcasts. This article explores the factors that led to such an attraction by considering the debates around the impact of transnational media products on Arab cultural identity. The authors conducted focus groups and interviews with audiences in Palestine and Egypt and with the producers/distributors of these TV series in Turkey. The data presented here comprise the results of one of the first field-based studies on the reception of Turkish media products in the region. Foremost, the Arab audience response to these Turkish TV series highlights the differences in conceptualisations of ‘modernity’ in the Arab World. Moreover, the data suggest that audiences do not necessarily consider transnational media products as culprits that corrupt cultural identity.     

Bayesian credit ratings: A random forest alternative approach

Cerciello and Giudici (2014 Cerciello, P., Giudici, P. (2014). Bayesian credit ratings. Commun. Stat. Theory Methods. 43:867–878.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) proposed a Bayesian approach to improve the ordinal variable selection in credit rating assessment. However, no comparison has been made with other methods and the predictive power was not tested. This study proposes an integrated framework of random forest (RF)-based methods and Bayesian model averaging (BMA) to validate and investigate the ordinal variable importance in evaluating credit risk and predicting default in greater depth. The proposed approach was superior to the Cerciello and Giudici method in terms of predictive accuracy and interpretability when applied to a European credit risk database.     

Discrete‐time survival trees

Tree‐based methods are frequently used in studies with censored survival time. Their structure and ease of interpretability make them useful to identify prognostic factors and to predict conditional survival probabilities given an individual's covariates. The existing methods are tailor‐made to deal with a survival time variable that is measured continuously. However, survival variables measured on a discrete scale are often encountered in practice. The authors propose a new tree construction method specifically adapted to such discrete‐time survival variables. The splitting procedure can be seen as an extension, to the case of right‐censored data, of the entropy criterion for a categorical outcome. The selection of the final tree is made through a pruning algorithm combined with a bootstrap correction. The authors also present a simple way of potentially improving the predictive performance of a single tree through bagging. A simulation study shows that single trees and bagged‐trees perform well compared to a parametric model. A real data example investigating the usefulness of personality dimensions in predicting early onset of cigarette smoking is presented. The Canadian Journal of Statistics 37: 17‐32; 2009 © 2009 Statistical Society of Canada