# Application of Logistic Regression for Production Machinery Efficiency Evaluation
## 1 Introduction  
Market success of a manufacturing company is shaped primarily by the demand for the manufactured products and the rate of return on capital employed [1; 2]. Poor machine efficiency and frequent downtimes can lead to a reduction in production levels, resulting in lost market opportunities, increased operating costs and reduced profits [3]. It is therefore necessary to apply appropriate methods and tools to support management and to organize maintenance services in an adequate manner to ensure that the production system operates at the assumed levels of productivity and efficiency [4]. Effectiveness is an important element in the analysis of the production process [5; 6; 7], often considered in scientific publications. It is assessed on the basis of various measures. In practice, numerous mathematical models and tools are used to support the assessment of the performance of machinery. The most frequently used measures for analyzing the efficiency of technical facilities are those resulting from three general models of operation assessment, i.e., the reliability model, the operational efficiency OEE (overall equipment effectiveness) model and the organizational and technical KPI (key performance indicators) model [8]. In addition, methods and tools for its evaluation can be classified in five main areas, i.e., operational, market, financial, technical or dynamic [9; 10]. Particularly important from the point of view of machinery efficiency diagnostics is operational efficiency, and the research in this area focuses primarily on the search for opportunities to reduce the consumption of production resources. These include analysis of labor productivity growth, cost reduction, minimization of losses and shortening of production cycles. Studies available in the literature indicate the application of a number of methods and tools in this area, such as methods of productivity and profitability indicators, analysis of efficiency and degree of work stations' utilization, cost calculation of activities, study of spatial efficiency of production organization and economic evaluation of the production structure [11]. Maintaining the company's machinery stock at an appropriate level requires continuous monitoring and evaluation of the adopted effectiveness indicators. A number of companies have MES (manufacturing execution system) systems in place, which enable ongoing control of the above parameters. There are also companies (including those examined by the authors) that do not have such software and therefore proper evaluation of efficiency parameters is difficult. Such analyses are supported by mathematical tools and methods, which also include modeling with the use of logistic regression, as presented in this article. The subject of this research was a plastics manufacturing company, while the main objective was to evaluate the effectiveness of the production process based on selected factors that may significantly affect the level of machinery efficiency. The analysis was carried out on the basis of information on the performance of the company's production system recorded from 1 September 2015 to 31 August 2017. Monitoring the effectiveness of utilization of the available machinery allows production reserves or waste in the processes underway to be identified [12; 13; 14]. The basis for successful assessment is an appropriate selection of measures and indicators. The analysis of literature made it possible to distinguish those which were of the greatest importance both in theoretical and industrial-practical aspects. Three general models should be distinguished:  
* operational efficiency model OEE (overall equipment effectiveness),
* reliability model,
* organizational and technical model--KPI [15]. Within the operational efficiency model, a frequently employed parameter (which was monitored in the examined entity as well) is the overall equipment effectiveness (OEE) indicator, which is widely described in the literature [16; 17; 18; 19].