This master’s thesis describes the concept of the fourth industrial revolution and the importance of technology associated with it. A key element of I4.0 is the CPS, which integrates smart machines, storing systems and production facilities to provide insight into the past, present and future course of the production process. In addition to the CPS, a large role have 9 pillars that are described in the paper. As a result of the implementation of I4.0 technology, appeared the concept of smart factories which use ICT to manage production and business processes with the aim of achieving competitiveness in the market. The main characteristics of smart factories are connectivity, autonomy, optimization, transparency, proactivity and agility. It’s possible to implement lean manufacturing tools on industry 4.0 in order to remove production bottlenecks, continuously improve production processes and introduce profitable planning and scheduling strategy methods. The gearbox assembly line was taken as an example of the implementation of industry 4.0 elements. This thesis is balancing the assembly line so that the workloads are distributed as evenly as possible between the WSs. At each assembly station, the duration of the assembly process was measured using a stopwatch by snap back method. The lean philosophy has been applied in the sense that it strives to do as much as possible with as little effort and loss as possible. The experiment was performed in two ways, by "manual approach" and "I4.0 approach", and three iterations were performed. Throughout the I4.0 element balancing process, they have demonstrated operational advantages of the I4.0 equipment such as faster information flow, reduced paper sheet consumption and ease of manufacturing processes. The biggest drawback is that I4.0 functional equipment depends on the workers. In the last chapter of this master’s thesis, multicriteria decision making was performed using the Visual PROMETHEE program. Of the seven elements of I4.0, it was necessary to determine which were the most preferred according to the previously established criteria. The weighting criteria varied across 5 scenarios which influenced the preferences of the alternatives. The results are presented graphically and the influence of the weight change is explained.