Sharad K. Maheshwari

Benchmarking quality practices in India, China and Mexico

Presents results of a quality‐management survey conducted among manufacturing companies in India, China, and Mexico. The results show that the majority of the manufacturers in the three countries are well aware of modern quality‐management concepts and philosophies. The comparisons of the survey results among the three countries show that Mexican companies generally are doing better than Indian and Chinese companies in terms of quality performance, quality improvement efforts, application of the ISO 9000 certifications, and adoption of the “quality is free” philosophy. Chinese companies show the least understanding of quality‐management principles among the three nations surveyed. In comparison to the survey results in developed countries such as Japan, the USA, Canada, and Germany, the responses from India and Mexico are comparable; the responses from China are somewhat inferior to those from the other countries.

Benchmarking Quality Management Practices in India

The main objective of this research is to study the quality management practices of Indian manufacturing organizations. Presents the results of a survey conducted among the chief operating officers of medium‐and large‐size Indian manufacturing companies. Presents an analysis of the survey results. The survey investigated several quality management‐related issues including adherence to the “quality is free” philosophy, causes of poor quality, quality performance, efforts to improve quality and status of ISO 9000 certification among the Indian companies. Compares also quality practices in India with industrialized nations such as Japan, USA, Germany and Canada.

Multivariate Statistical Analysis of Public Transit Bus Driver Distraction

This paper examines the efficacy of a multivariate statistical modeling approach to analyze public transit bus driver distraction data collected through a self-administered driver survey. The distracting activities were classified into four risk zones according to distraction risk indices derived from distracting ratings, distracting durations, and driver perception of risks. A multinomial logistic regression model was formulated for highly risky distracting activities, using levels of distraction as the categorical dependent variable and correlating it with categorical and continuous independent variables responsible for the distraction. Results revealed that the common sources of distraction were due to passenger-related activities, which match two-thirds of simulated validation outputs. On-site route observations and discussions with transit staff revealed mixed results. The model could be used to identify drivers at highest distraction risk from their demographic backgrounds, as well as driving schedules. The transit agency can use the results to implement relevant policies and training programs to mitigate distraction and improve transit performance.

Improving performance of public transit buses by minimizing driver distraction

This paper explores the problems of distracted driving for bus drivers at a local transit agency and uncovers factors that may cause the distractions. Data was collected on sources of driver distraction and perceived risks associated with a particular distracting activity along with potentially related independent variables like location, driving hours/week, age, gender, and driving experience. The seven highest distracting activities were categorized into three risk zones using a risk range system derived from the average distracting rating, average distracting duration, and driver’s perception of risk. Multinomial logistic regression was utilized to model each risk zone distracting activity using levels of distraction as the dependent variable and correlating it with the factors as independent /predictor variables. A stepwise procedure included all the selected factors in the model initially; non-significant factors were eliminated until a good fit was achieved with significant factors. The model’s goodness of fit was statistically tested and further verified graphically. The multinomial logistic regression outputs were analyzed for all seven risk zone distracting activities. Due to space limitation, an analysis of the highest risk distracting activity involving passenger using mobile phone is included in the paper. The results revealed that the common sources of driver distractions were due to passenger-related activities. The male drivers are more likely than female drivers to get distracted by passengers, while female drivers are more likely to get distracted by the ticket machine than male drivers. Older drivers are less likely to get distracted by the ticket machine and passenger-related activities, although more driving experience increased the likelihood of distraction by passengers and ticket machines. The drivers with higher weekly driving hours are less likely to get distracted by ticket machines and climate controls. The recommendations made on the basis of the results could be used as a potential training tool to mitigate driver distraction and improve bus transit performance.

Simultaneous evaluation and selection of strategies for loading and controlling machines and material handling system in FMS

Abstract In this research, two operational-level problems in FMS, machine loading and controlling, are considered simultaneously. Much of the past research has addressed these problems independently. However, overall performance of the system is dependent on both loading and control strategies. Furthermore, most work considers a unique system and thus general conclusions cannot be drawn from the research. This paper focuses on identifying the set of most significant factors which contribute to the performance of the system at the operational level for an FMS. These factors include loading and control strategies as well as system parameters such as buffer sizes and number of pallets. A procedure is developed for the evaluation and selection of operational-level strategies based on overall system performance. The proposed method incorporates an integer programming loading model, discrete-event simulation model at the control level and analysis of variance (ANOVA). The implementation of the procedure is illu...

A Methodological Approach For Studying Public Bus Transit Driver Distraction

The increase in bus transit ridership along with the proliferation of personal electronic control and communication gadgets is causing more distractions for the drivers. For transit vehicles, some distractions are caused by factors beyond the driver’s control such as operating additional equipment, attending to passengers, and communicating with the operations center. Several driver distraction studies have been conducted for personal vehicles and commercial vehicles. But bus transit driver distraction has received limited attention in the literature even though bus transit accidents may cause more injuries due to larger number of passengers. Hence, their distraction is not clearly understood; furthermore, no established methodology is available to conduct a detailed study at a transit agency because of inadequate research in the field. The objective of this paper is to present a detailed modular research framework for studying bus transit driver distractions. The framework provides a transit agency with a set of standardized methodologies for studying distraction over a wide range of cost and time intervals. An agency may choose one or more modules to suit their study requirements. The modules for data collection, analysis, validation, and interpretation and usage of results are designed on the basis of in-depth studies and tests at transit agencies in the Commonwealth of Virginia. The paper provides a detailed process and a set of guidelines to study bus transit driver distraction which will make it easier for any transit agency to conduct such a study. The results of the bus transit driver distraction studies could be used for training bus drivers to mitigate distraction and assist state and city governments to formulate effective regulations to control distracted driving.