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This paper aims to identify key factors that impact operating room (OR) utilization and evaluate different scenarios on OR performance.

Five months of data were collected. stepwise regression and best subset models were used to select factors and generate regression model for OR utilization. We further used simulation to test the influence of case duration mean, case duration variation, scheduled utilization and first-case delay on OR utilization, OR cost inefficiency and patient wait time on the day of surgery.

The scheduled utilization, case cancellation and add-on cases were the most important factors identified in all models. The larger the case duration variation, the lower the OR cost efficiency and utilization, the longer the patient wait time. First-case delay and turnover times are not critical in OR utilization or cost efficiency.

OR management should focus on creating an effective way to manage case cancellation and add-on policy to tackle the change on the day of surgery. In addition, several weeks before the surgery, the management needs to consider how to schedule cases to fit the allocated OR time.

In complementary of current OR management, this research assists OR management by identifying the factors that would result in the most significant improvement on OR utilization.

This research is directed toward predicting constituent behaviors of university giving from its alumni. A regression modeling analysis of the alumni giving of a major state university is developed using best subsets regression. Based on an extension of this modeling effort a clustering of alumni giving patterns will be developed.

The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes.

A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity.

Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics.

An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation.

The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors.

Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices.

Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.

The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.

The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.

First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.

The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.

This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.

The purpose of the present study was to improve the fit of women’s bifurcated garments by developing an equation that can predict the crotch length accurately by using a few basic body measurements. This equation could provide a simple mass-customization approach to the design of bifurcated garments.

Demographic characteristics and easy-to-record body measurements available in the size USA database were used to predict the crotch length. Different methodologies including best subset regression, lasso regression and principal components regression were experimented with to identify the most important predictor variables and establish a relationship between the significant predictors and crotch length.

The lasso regression model provided the highest accuracy, required only five body dimensions and dealt with multicollinearity. The preliminary pattern preparation and garment fit tests indicated that by utilizing the proposed equation, patterns of customized garments could be successfully altered to match the crotch length of the customer, thereby, improving the precision and efficiency of the pattern making process.

Crotch length is a crucial measurement as it determines bifurcated garment comfort as well as aesthetic fit. The crotch length is usually estimated arbitrarily based on non-scientific methods while drafting patterns, and this increases the likelihood of dissatisfaction with the fit of the lower-body garments. The present study suggested an algorithm that could predict crotch length with 90.53% accuracy using the body dimensions height, hips, waist height, knee height and arm length.

The purpose of this paper is to present the factors associated with the employee barriers while implementing lean manufacturing within the small- and medium-scale enterprises (SMEs).

The structural equation modelling approach is employed to develop the initial model drawing a sample survey of 133 small and medium enterprises. The result of the study shows that the lack of well-trained and experienced staff, lack of knowledge about existing specialist, cultural resistance to change are acting as the employee barriers while implementing lean manufacturing in SMEs. Then, a further study has been conducted to develop the employee barrier model with these three factors and its contributing variables using specification search representing the sample of 117 small and medium enterprises using the following fit function criteria: Chi-square (C), Chi-square-df (C-df), Akaike information criteria, Browne-Cudeck criterion, Bayes information criterion, Chi-square divided by the degrees of freedom (C/df) and significance level (p).

The lack of well-trained and experienced staff, lack of knowledge about existing specialist, and cultural resistance to change with 19 associated elements were considered in the questionnaire. Specification search was carried out to build up the model on the collected data from 117 SMEs. The results of the specification search identified that these three factors with 15 key variables are significant to employee barrier while implementing lean manufacturing in SMEs.

The limitation of the study was that the sample size of the study was relatively small for further research, large sample size more than 117 are to be expected.

The present study has explored an unfocused area of lean implementation in small and medium enterprises. The results obtained from the study are expected to help researchers, academics, and professionals for the further studies in the domain of lean manufacturing.

To implement and understand the lean manufacturing system, government of the many countries around the world are helping and encouraging by providing financial assistance for training professionals and establishing professional associations. However, many industries are not successful in lean implementation. This research work provides to develop a strategy to tackle employee barriers for successful lean implementation.

Very little research has been carried out exploring employee barriers while implementing lean manufacturing in SMEs. This paper will provide value to academics, researchers and practitioners of lean by way of providing insight into significant employee barriers for lean implementation, especially in Indian industries.

Construction managers face many work-related stressors induced by unrealistic schedule expectations, tight budgets, and long hours. Over time, these stressors can result in both mental and physical exhaustion, a condition referred to as burnout. Early-career managers are a key worker demographic, as they represent the near-term future of the construction industry, yet they have a high risk for burnout. The purpose of this study is to explore the prevalence of burnout in new construction managers, and to identify which individual or work-related factors are associated with feelings of burnout.

Using the Maslach Burnout Inventory General Survey (MBI-GS), data from 146 early-career professionals (less than 10 years of experience) with construction management degrees in the USA were collected and analyzed using correlational and best subset regression techniques.

The results show that the early-career demographic in the USA experiences both the Emotional Exhaustion and Cynicism dimensions of burnout at comparable levels to prior studies with more mid-to-late career respondents. However, the Professional Efficacy dimension was significantly higher in early-career professionals than any other sample. No individual factors, such as gender, marital status, or number of children, were predictive of any dimension of burnout. Instead, only work-related factors including co-worker friendliness, opportunities for personal development and promotion, and the ability to control the work pacing were strongly associated with one or more dimensions of burnout.

This study is the first to explore burnout in the key early-career demographic for construction managers in the United States construction industry. This work provides evidence that organizational policies and culture have a greater efficacy in alleviating burnout in this demographic, when compared to the work–life balance of the individual.

The research is directed toward the prediction of operating income within the MetLife Insurance Company. The operating income of the firm is the amount of profit realized from a firm’s own operation, as opposed to net income. The econometric model is based on 10 years of quarterly data (2004–2014). The explanatory variables used in this modeling effort are (1) stock price, (2) long-term borrowing, (3) capital surplus, (4) free cash flow, (5), S&P average, (6) GDP, and (7) CPI.

Practitioners often face challenges in model development when establishing a relationship between the input and output variables and their optimization and control. The purpose of this paper is to demonstrate, with the help of a real life case example, the procedure for model development between a key process output variable, called the multi-stage flash evaporator efficiency, and the associated input process variables and their optimization using appropriate statistical and analytical techniques.

This paper uses a case study approach showing how multiple regression methodology has been put into practice. The case study was executed in a leading Indian viscose fiber plant.

The desired settings of the relevant process parameters for achieving improved efficiency have been established by appropriately using the tools and techniques from the Lean Six Sigma tool kit. The process efficiency, as measured by M3 of water evaporated per ton of steam, has improved from 3.28 to 3.48 resulting in satisfactory performance.

This paper will be valuable to many practitioners of Six Sigma/Lean Six Sigma and researchers in terms of understanding the systematic application of quality and optimization tools in a real world situation.

The aim of this study is to investigate wearing comfort of summer work uniforms judged by construction workers.

A total of 189 male construction workers participated in a series of wear trials and questionnaire surveys in the summer of 2014. They were asked to randomly wear two types of work uniforms (i.e. uniforms A and B) in the two-day field survey and the subjective attributes of these uniforms were assessed. Three analytical techniques, namely, multiple regression, artificial neural network and fuzzy logic were used to predict wearing comfort affected by the six subjective sensations.

The results revealed that fuzzy logic was a robust and practical tool for predicting wearing comfort in terms of better prediction performance and more interpretable results than the other models. Pressure attributes were further found to exert a greater effect than thermal–wet attributes on wearing comfort. Overall, the use of uniform B exhibited profound benefits on wearing comfort because it kept workers cooler, drier and more comfortable with less work performance interference than wearing uniform A.

The findings provide a fresh insight into construction workers’ needs for work clothes, which further facilitates the improvement in the clothing tailor-made design and the enhancement of the well-being of workers.