# Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators
## 3 Human and Robot Factors Influencing the Production Process  
There are many human performance models [34] used for a prediction of human behaviour in a different task, domain, or system. In industrial practice, the human factors are often neglected. Figure 3: The schema of the main machining cell. Figure 2: Engine head production process. For example, in computer software used for production processes simulation the human factor is not sufficiently modelled. Simple mechanistic models of human resources are used, and people are treated as quasi-technical elements of the production system. They should operate in the same way as a machine, but in practice, the human behaviour is unpredictable because of personal individuality and human errors. This can help to explain why simulation models do not correspond with the reality as it would be expected [35]. Therefore, other methods for human and robot modelling are under development including the discrete event simulation (DES) method [36], Petri nets [37], multi-flexible-links modelling [38] or artificial intelligence [34]. The variability of human operators can be modelled by varying the operation times (e.g., as the simulation time increases, these increase). Robots can make work faster and more regular than human operators, but how fast a robot can work is related to the type of task. In some cases, human workers are very fast because of innate manual skills. On the other hand, the robot must be told exactly what to do. There are several methods for planning robot movement, described in [39; 40]. These methods are based on the well-known MTM (method time measurement) or on the traditional time study concept [41] and can be used for comparing the relative abilities of robots and humans. Dedicated computer software for robot movement planning can be used (ABB Robot Studio, FANUC Roboguide, KUKA SimPro, and others) as well as advanced software for human ergonomic modelling (Human Jack Simulator). The outcome of each technique is a set of time values that can be used to compare human and robot productivity [36]. An employee can work 8 h per day with a break for rest. On the other hand, robots can theoretically work 24 h per day without any breaks, but human supervision of the production process is required. Also, precise planning and scheduling of the robot work are necessary for better performance [18]. From time to time, applying changes of tools and reprogramming also require participation of an operator. Moreover, a robot requires periodic maintenance service and inspection before each automatic run. Automated and robotized production lines usually work very well, but sometimes problems with failures can occur. A failure of any elements of the manufacturing line can cause some production disturbances or a production stoppage of the whole factory. Therefore, the reliability of the components plays a key role for the productivity and utilization of the manufacturing system [42]. In some cases, a fallible operator can be replaced, but a fallible robot requires repair. The first type of robots (Unimate), has failure-free uptime equal to _MTBF_ (mean time between failures) = 500 h [40]. The work [43] shows the results of a study on reliability of the next generation of robots in the industrial environment. The reliability of the first robot generation is represented by a typical bathtub curve with high infant mortality rate. The next generation of robots was characterized by exponential distribution with _MTBF_ of about 8000 h. Nowadays, robot manufacturers declare an average of _MTBF_ = 50,000 \(\div\) 60,000 h or 20 \(\div\) 100 million cycles of work [2]. However, robot equipment is often custom made and may, therefore, be more unreliable than the robot itself.