Application of Data Mining Techniques for Measuring and Predicting Employee Performances in Automotive Industry
Keywords:
Data Mining, Employee Performance, HR Analytics, Clustering, Decision Tree, Predictive Analytics, Big DataAbstract
The growth of integrated information technology based enterprise systems and social media platforms are helping automobile industries to generate large amount of databases and huge unstructured data on various business functions. Employees performance management monitoring and assessment in an automobile company is an important HR function, as it is directly related with the business results of the organization. Application of HR Analytics by using data mining techniques on employees and various productivity related data is used for extracting hidden insights for predicting employee performances.
Data Mining is a systematic process of discovering knowledge and useful information by extraction meaningful data patterns, profiles and trends using pattern recognition technologies such as classification, clustering, regression, artificial intelligence, neural networks, association rules, decision trees, machine learning, genetic algorithm, nearest neighbour algorithm etc. Data Mining tools are used for measuring the performances of the employees for predicting the success or failure of an organization.
The present applications of smart manufacturing practices using Industry 4.0 framework in automobile industry is generating large amount of data to be classified under Big Data category, which needs special data mining techniques for extracting intelligence from the data pattern.
This research contributes by exploring the data clustering, decision tree and other data mining techniques for evaluating and predicting the employee performances for automobile industry by considering various employee performance factors. Every automobile company has its own productivity and strength which depends upon the performance capability of its employees.
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