Employee attrition can become a serious issue because of the impacts on the organization’s competitive advantage. It can become costly for an organization. The cost of employee attrition would be the cost related to the human resources life cycle, lost knowledge, employee morale, and organizational culture. This study aimed to analyze employee attrition using logistic regression. The result obtained can be used by the management to understand what modifications they should perform to the workplace to get most of their workers to stay. The data for the study were around four thousand employees, covering 261 days (one year working days) during 2015 — the data period between January and December. We use R for data integration, exploratory data analysis, data preparation, logistic regression, model evaluation, and visualization. The study has five steps: (1) data collection and business understanding, (2) data pre-processing, (3) exploratory data analysis, (4) model selection and training, and (5) test and evaluation of the model. The result of the study found eleven variables as key driving factors for employee attrition. It also showed that the model has 75 percent accuracy with 73 percent sensitivity and 75 percent specificity.

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Human-robot collaboration (HRC) has arisen as a promising technology to improve the productivity of assembly processes. This paper discusses an assembly line balancing problem (ALBP) where manual, robotic, or HRC operations may be considered decision alternatives. Each assembly process task may be operated either by a human operator, a robot operator, or an HRC. This possibility of shared functions between humans and robots may result in a hybrid manual-robotic assembly line. This problem’s mathematical model is developed based on the simple ALBP and modifying the idea of two-sided ALBP, with additional aspects related to resource alternatives of human, robot, or HRC, and robot’s tool-type for the operations. The problem is formulated analytically in a mixed-integer linear programming model with a cost-oriented objective function. The exact method can be applied to obtain an optimal solution.