Cognitive decision making in smart industry

Abstract

A smart industry integrates ubiquitous sensing capabilities of Internet of Things (IoT) with industrial infrastructure in order to automate various industrial operations. The data collected by IoT system in smart industry can be used to replace manual employee evaluation system where there are ample chances of biasness. This paper proposes a model for automated performance evaluation of employees in a smart industry. The model uses the data collected by embedded sensors in smart industrial system to identify various industrial activities of employees. The identified activities are then classified as positive, negative and neutral activities. In addition, an employee is said to be participating in an activity if employee and activity are co-located. Therefore, the model collects the location data of every employee using GPS devices and calculates the participation of each employee in each of the identified positive, negative and neutral activities based upon the location data. The information hence obtained is then used to draw cognitive decisions for employees using game theory. The experimental study compares the proposed model with manual employee evaluation system and the results depict performance improvement of proposed model over manual system. The impact of automated system on employees is then evaluated both experimentally and mathematically. The results show that the correct evaluation of employees by the model effectively motivates employees in the favor of industry. Thus, the proposed model effectively and efficiently automates cognitive employee evaluation system and decision making process in smart industry.