# Application of Logistic Regression for Production Machinery Efficiency Evaluation
## 1 Introduction
The available studies most often present the theoretical aspects of its calculation and indicate the categories of losses that may occur during the process of machinery and equipment use in relation to ideal conditions [19; 20; 21]. Analyses are also available to demonstrate the practical implementation of this parameter in manufacturing companies [12; 22; 23]. The OEE index is a product of three components [23; 24], i.e., readiness and efficiency of machinery and quality of the manufactured products. It is therefore a general, comprehensive assessment, most often presented in percentage form. According to [PERSON] from the Japan Institute of Plant Maintenance [25; 26], OEE should remain at 85.41%, but it should be stressed that each enterprise operates in a specific environment; thus, this indicator will be different for each entity, depending on its size, profile and industry, and will not take on the same value in two different operating units [9; 27]. Therefore, in practice the above indicator has evolved into different forms of application depending on the sector in which a given entity operates, adjusting to the needs of the environment. The following indicators should be mentioned: OFE (overall factory effectiveness), OPE (overall plant effectiveness), OTE (overall throughput effectiveness), PEE (production equipment effectiveness), OAE (overall asset effectiveness) or TEEP (total equipment effectiveness performance) [21]. The reliability model allows measures in statistical terms to be determined, on the basis of a time analysis of the performance of technical facilities. In practice, these refer to the technical condition of machines, as well as to the activities of maintenance staff. These are MTBF (mean time between failures), MTTR (mean time to repair) or MTTF (mean time to failure) [15]. The organizational and technical KPI model includes a set of measures enabling a comprehensive assessment of the efficiency and effectiveness of the implemented processes. It includes 72 indicators classified in three areas: economic (e.g., total relative cost of maintenance), technical (availability of facilities for preventive works) and organizational (number of maintenance staff) [28]. In relation to the analyzed company, indicators associated with the operational effectiveness model, related to efficiency, will be preferable in the context of machinery stock management; therefore, they have become the subject of this analysis. Following the literature in this field [13; 29], it was assumed that efficiency in production processes is the quotient of the actual efficiency to the nominal efficiency, as specified in the following ratio (1):  
\[W_{Q}=\frac{Q_{r}}{Q_{O}}, \tag{1}\]  
where:  
\(W_{Q}\)--machinery operational efficiency indicator,  
\(Q_{r}\)--actual (achieved) efficiency (pcs./h),  
\(Q_{O}\)--theoretical efficiency, as defined in the technical documentation (pcs./h). Thus calculated, it indicates the degree of efficient use of the production line for each operation and, as such, indicates areas for improvement. Efficiency is most often presented in percentage form. This does not always allow for its quick and unambiguous assessment. In forecasting studies, it is assumed to be a quantitative variable, which limits the availability of some modeling methods. Therefore, with regard to the analyzed company, according to the authors, a better approach would be to analyze the efficiency from the individual point of view of each company by determining its satisfactory level and reacting only if it is not achieved. Numerous studies using logistic regression models with regard to machine maintenance are available in the literature. The main objective of the proposed tools is to assess the technical condition of technical objects along with reliability parameters [30; 31], predict upcoming failures [32; 33] and estimate the service life of machinery [34]. For example, [PERSON] and [PERSON] assessed the performance of an elevator door system in real time and identified the types of possible failures [30].