## 2 Literature Review of the Problem
Therefore, it should be stated that the production efficiency of resources is defined as the maximum amount of products that can be produced at a specified time [17]. It determines the potential of the company's resources (e.g., the production capacity of machines and equipment), which can be used at the same time. Most authors in the literature on the subject [18; 19; 20] relate to the concept of production efficiency only to analyze the potential possibilities of human work, while performance is a feature of all objects involved in the production process [21]. Due to the level of detail of production efficiency analysis, planning performance and effective efficiency are distinguished [22]. A detailed production efficiency analysis requires such a distinction, as it is the basis for its assessment at various analytical levels in the company [23]. Planned performance usually assumes ideal conditions that do not consider any production interference [24]. Planned performance does not consider the unproductive time related to intentionally planned downtime [25]. To calculate the planned performance of the entire production process, you must first determine the performance of individual operations. Because a given production operation is most often performed at several workstations, its performance is determined as the sum of the performance of individual positions. For example, in work [26]. Discusses aspects related to the effectiveness of production processes. Management concepts focused on increasing efficiency, such as Lean Manufacturing, TOC, TQM, Six Sigma, TPM and Kaizen philosophy were analyzed. Each of these concepts offers a set of tools and methods that allow to optimize manufacturing processes. Finally, specific organizational methods and techniques were discussed, such as Ishikawa charts, ABC analysis (Pareto-Lorenza method), smart method, technique 5 Why, Kanban cards and Gantt charts [27]. These tools allow systematic diagnosis and problem solving, which supports continuous improvement of production processes. In many publications related to the subject of quality management, you can find the approach that tools and techniques used to manage quality are extremely important in the support of development and improvement of the quality of final products that go to buyers [28]. The spectrum of methods and tools of assessment. The quality that is mentioned in the literature on the subject is wide [29]. Various quality management methods and tools can be used to monitor the entire production cycle from the design stage, through manufacturing to the control of the finished product. All tools and methods are characterized by planned, repetitive and scientific basis for the way they are used during their use [30]. The literature emphasizes that production efficiency is the key to maintaining competitiveness and profitability [31]. One way to improve efficiency is to support the decision to optimize data. The Industry 4.0 concept introduced a revolution in the approach to collecting and using data in production companies in the automotive sector. Thanks to this, the process of preparing, sending and archiving information has become much simpler and more available [32]. The main challenge that organizations face today is how to effectively find themselves in this thicket of information, to conduct effective Data management and to extract critical information from an endless data stream. Excess chaotic information can lead to the same as the same as they are completely lacking--the inability to make decisions efficiently [33; 34; 35]. Only effective response will improve production processes and increase the efficiency of devices to previously unattainable levels. To achieve this, the data themselves are not enough. As one of the researchers emphasizes, what is needed is essence--important, key information, maintained in a specific context and data extracted from the silos. This information must be available to conduct data analytics, which is a well-known concept of the so-called Big Data [36; 37; 38].