A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing)

Document Type : Case study

Authors

1 Iran University of Science and Technology

2 Industrial & Manufacturing Engineering Department Florida State University,USA

Abstract

Training and adaption of employees are time and money consuming. Employees’ turnover can be predicted by their organizational and personal historical data in order to reduce probable loss of organizations. Prediction methods are highly related to human resource management to obtain patterns by historical data. This article implements knowledge discovery steps on real data of a manufacturing plant. We consider many characteristics of employees such as age, technical skills and work experience. Different data mining methods are compared based on their accuracy, calculation time and user friendliness. Furthermore the importance of data features is measured by Pearson Chi-Square test. In order to reach the desired user friendliness, a graphical user interface is designed specifically for the case study to handle knowledge discovery life cycle.

Keywords

Main Subjects


Beach, L. R. (1990). Image Theory: Decision Making in Personal and Organizational Contexts. European Journal of Operational Research, 47(2), xv,  254 p. doi: 10.1016/0377-2217(90)90287-l
Berthold, M. R., & Diamond, J. (1995). Boosting the performance of rbf networks with dynamic decay adjustment. Advances in neural information processing systems, 521-528.
Boles, J. S., Dudley, G. W., Onyemah, V., Rouziès, D., & Weeks, W. A. (2012). Sales force turnover and retention: A research agenda. Journal of Personal Selling & Sales Management, 32(1), 131-140.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees: CRC press.
Chalkiti, K., & Sigala, M. (2010). Staff turnover in the Greek tourism industry: A comparison between insular and peninsular regions. International Journal of Contemporary Hospitality Management, 22(3), 335-359.
Chang Youzheng, G. M. (2008). Data Mining to Improve Human Resource in Construction Company. International Seminar on Business and Information Management, 1(19), 275 - 278
Chapman, P., et al. (2000). CRISP-DM 1.0 Step-by-step data mining guide.
Chien, C.-F., & Chen, L.-F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280-290. doi: 10.1016/j.eswa.2006.09.003
Cho, V., & Ngai, E. W. (2003). Data mining for selection of insurance sales agents. Expert systems, 20(3), 123-132.
Clark, P., & Niblett, T. (1989). The CN2 induction algorithm. Machine learning, 3(4), 261-283.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Cottini, E., Kato, T., & Westergaard-Nielsen, N. (2011). Adverse workplace conditions, high-involvement work practices and labor turnover: Evidence from Danish linked employer–employee data. Labour Economics, 18(6), 872-880. doi: 10.1016/j.labeco.2011.07.003
DeConinck, J. B., & Johnson, J. T. (2009). The effects of perceived supervisor support, perceived organizational support, and organizational justice on turnover among salespeople. Journal of Personal Selling & Sales Management, 29(4), 333-350.
Fan, C.-Y., Fan, P.-S., Chan, T.-Y., & Chang, S.-H. (2012). Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Systems with Applications, 39(10), 8844-8851. doi: 10.1016/j.eswa.2012.02.005
Hancock, J. I., Allen, D. G., Bosco, F. A., McDaniel, K. R., & Pierce, C. A. (2013). Meta-analytic review of employee turnover as a predictor of firm performance. Journal of Management, 39(3), 573-603.
Holtom, B. C., Mitchell, T. R., Lee, T. W., & Eberly, M. B. (2008). 5 Turnover and Retention Research: A Glance at the Past, a Closer Review of the Present, and a Venture into the Future. The Academy of Management Annals, 2(1), 231-274.
Iverson, R. D., & Pullman, J. A. (2000). Determinants of voluntary turnover and layoffs in an environment of repeated downsizing following a merger: an event history analysis. Journal of Management, 26(5), 977-1003. doi: 10.1016/s0149-2063(00)00065-9
Lavrač, N., Kavšek, B., Flach, P., & Todorovski, L. (2004). Subgroup discovery with CN2-SD. The Journal of Machine Learning Research, 5, 153-188.
Linoff, G. S., & Berry, M. J. (2011). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management: Wiley.
Liu, Z., Zuo, M. J., & Xu, H. (2012). Parameter selection for Gaussian radial basis function in support vector machine classification. Paper presented at the Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2012 International Conference on.
Martinsons, M. G. (1997). Human resource management applications of knowledge-based systems. International Journal of Information Management, 17(1), 35-53.
Min, J. H., & Lee, Y.-C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614.
Ongori, H. (2007). A review of the literature on employee turnover. African Journal of Business Management, 049-054.
Racz, S. (2000). Finding the Right Talent Through Sourcing and Recruiting. STRATEGIC FINANCE -MONTVALE-, 38-44
Rekesh, A., & Remekrishnen, S. (1994). Fast Algorithms for Mining Association Rules. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, 487
Sexton, R. S., McMurtrey, S., Michalopoulos, J. O., & Smith, A. M. (2005). Employee turnover: a neural network solution. Computers & Operations Research, 32(10), 2635-2651. doi: 10.1016/j.cor.2004.06.022
Specht, D. F. (1990). Probabilistic neural networks. Neural networks, 3(1), 109-118.
Valle, M. A., Varas, S., & Ruz, G. A. (2012). Job performance prediction in a call center using a naive Bayes classifier. Expert Systems with Applications, 39(11), 9939-9945. doi: 10.1016/j.eswa.2011.11.126
Wang, X., Wang, H., Zhang, L., & Cao, X. (2011). Constructing a decision support system for management of employee turnover risk. Information Technology and Management, 12(2), 187-196.
Zimmerman, R. D. (2008). Understanding the impact of personality traits on individuals'turnover decisions: A meta‐analytic path model. Personnel Psychology, 61(2), 309-348.