Determinates of Employee Voluntary Turnover and Forecasting in R&D Departments: A Case Study

employee voluntary turnover factors using logistic regression and forecasts employee tenure using a decision tree for four research and development departments in a large U.S organization. Company job title, gender, ethnicity, age and years of service significantly affect employee voluntary turnover behavior determined by logistic regression. The findings assist managers and human resource departments in specific employee retention strategies to reduce R&D departments’ voluntary turnover rate. The decision tree method built a five-level depth tree model with 17 nodes. This model has the lowest AIC value and the best performance in the validation dataset. Age at hire, jobtitle, division, and race are statistically significant factors to predict employee tenure. The most important variable is age at hire located in the decision tree’s first, third, and fourth nodes. Classification rules assist managers and human resource departments in quickly predicting employee tenure and in making hiring decisions

Analysis of acoustic emission data for bearings subject to unbalance

Acoustic Emission (AE) is an effective nondestructive method for investigating the behavior of materials under stress. In recent decades, AE applications in structural health monitoring have been extended to other areas such as rotating machineries and cutting tools. This research investigates the application of acoustic emission data for unbalance analysis and detection in rotary systems. The AE parameter of interest in this study is a discrete variable that covers the significance of count, duration and amplitude of AE signals. A statistical model based on Zero-Inflated Poisson (ZIP) regression is proposed to handle over-dispersion and excess zeros of the counting data. The ZIP model indicates that faulty bearings can generate more transient wave in the AE waveform. Control charts can easily detect the faulty bearing using the parameters of the ZIP model. Categorical data analysis based on generalized linear models (GLM) is also presented. The results demonstrate the significance of the couple unbalance

Application of fractal algorithms of coastline echo’s generation on marine radar simulator

Background: Marine radar simulator is a useful approach endorsed by International Maritime Organization (IMO) to train the seafarers on how to operate marine radar equipment and use marine radar equipment for positioning and collision avoidance in laboratory. To fulfill all of the marine radar simulator training requirements, a high performance simulator is necessary. However, imperfections with currently available marine radar simulators require simulator developers to make improvements. Case Description: In this study, improved fractal algorithms (random Koch curve, fractional Brownian motion, and Weierstrass-Mandelbrot function) are applied to generate natural-looking radar echoes on a marine radar simulator. Discussion and evaluation: From the results of the simulations, we can observe that the structures of the coastline echoes generated by improved fractal algorithms, especially by fractional Brownian motion algorithm, outperform the echoes generated by conventional method in representing a natural coastline feature. Conclusions: Based on evaluations from a panel of experienced mariners, we conclude that the coastline echoes simulated by fractal algorithms better represent a natural coastline feature than those generated by conventional methods

Investigation on Additive Manufacturing as an enabler for reshoring manufacturing activities

The recent phenomenon known as re-shoring, has gained momentum among developed countries. It is also evident that the new generation of technologies such as Additive Manufacturing (AM) and intelligent robotics can affect the manufacturing location decision. This study aims to investigate how AM can help companies to re-shore manufacturing activities. Three in-depth case studies are conducted where AM is used as primary manufacturing approach to reduce the number of suppliers and shorten the supply chain. The results show that companies can reduce transportation, lead-time, inventory and substantially improve customisation, meanwhile accommodate product changes as well as process changes in production

A systematic literature review on the stochastic analysis of value streams

Value stream analysis is a very useful approach in the identification of non-value adding wastes and developing a systemic plan for achieving process improvement. However, traditional value stream mapping fails in considering the inherent variability of processes, hence reinforcing improvements that might not lead to significant results. In this sense, the uncertainties associated with value streams become an issue that can be curbed with the integration of stochastic methods. By conducting a systematic literature review, this research evaluates the level of integration of stochastic methods into value stream analysis and identifies those stochastic methods that are widely adopted to address uncertainties in the value stream analysis. Results from the review indicate that the application of the existing stochastic methods into value stream analysis is still at its infancy and is not systematically integrated. In addition, the few studies that consider stochasticity of value streams weakly examines the effect that uncertainty sources entail on each other

Key Establishment for Layered Group-based Wireless Sensor Networks

Establishing pairwise keys for sensors is crucial to secure communications in Wireless Sensor Networks (WSNs). Several key pre-distribution schemes have been proposed to establish pairwise keys, but they are not scalable and suffer from a dramatic degradation of security when the number of compromised sensor exceeds a threshold. In this paper, we propose a Layered Group-based Key Establishment (LGKE) scheme, in which a logic top layer ensures the scalability through an Exclusion Basic System (EBS) technique while a logic low layer adopts a group-based key preload scheme to ensure the security. The security analysis and quantitative evaluation show the superiority of LGKE with regard to scalability, resilience, robustness and communication overhead

Effective precedence constrained scheduling in a make-to-order environment

The make-to-order assembly lines have gradually replaced the traditional mass production assembly lines. These assembly lines follow a \u27make-to-order\u27 production policy, which are featured with a short production lead time, small number of working stations and highly skilled workers. In order to maximise the throughput under the resource (machine, labour and time) constraints, the problem of minimising makespan with general precedence constraints is addressed in this paper. A mathematical model of the problem is presented and a new heuristic, genetic job-oriented list scheduling (GJLS) is developed. Computational experiments indicate that the proposed algorithm outperforms the existing Graham\u27s list scheduling (GLS)