# Application of Logistic Regression for Production Machinery Efficiency Evaluation
## 4 Conclusions  
The reliability of machinery and equipment is an essential part of the proper functioning of a business. Modern technologies support the maintenance of an adequate level of readiness and suitability of the technical infrastructure. They not only facilitate production control and implementation of modern operating strategies, but also ensure continuous monitoring of processes and detection of any disturbances or failures. The activities carried out in this area boil down to balancing the maintenance of full operational efficiency and continuity of production and ensuring an acceptable level of costs of these activities. This is a difficult task, particularly in the case of older generation machinery stock, which is deprived of support from computerized production management systems--as the one presented in this article. In any case, however, the aim is to ensure that machines function perfectly without failures and that products are manufactured without defects. On the other hand, it is also important to ensure the efficiency of the equipment use and balanced workload, which proved to be a problem in the analyzed company. This was the reason for undertaking research in this field and the basis for mathematical analysis of the effectiveness of the manufacturing process. The lack of IT systems for controlling and monitoring the production process results in the inability to archive data on an ongoing basis, which makes it much more difficult to control processes, and sometimes it is conducive to abandonment thereof. Poor quality of recorded documentation imposes significant limitations on the use of mathematical tools as well; therefore, the authors wanted to present simultaneously that even in such a situation it is possible to create mathematical models that would improve the efficiency of the machinery stock. The available data proved to be sufficient to achieve the research objective, which was to formulate a model providing an unambiguous answer as to whether the efficiency of the equipment used in production is acceptable from the point of view of the assumptions made by the company (in this case 90%). This was made possible through the use of logistic regression, which, above all, does not require the meeting of assumptions made by other mathematical models, e.g., linear regression and general linear models. The advantage of this method is also the form of the dependent variable. The predictor here is a dichotomous variable and its values can be interpreted as the probability of an event occurring. The organization of production in the analyzed company has remained unchanged for many years. All machines work three shifts every day. Regardless of the orders placed and the market demand, full staff is employed. Machines are taken out of the process only in cases of random incidents. Lack of modification of the adopted procedures and control of the implemented processes results--as demonstrated in the research--in the process not being effective and the use of machinery not being optimal. The logistic regression model made it possible to identify the causes influencing machinery efficiency. It turned out that the load is not identical during every shift, the productivity is much lower during the first shift in comparison to the other shifts. Restructuring the shift system and limiting the production process to only two shifts or modifying the working time could increase the productivity of machinery, optimize the use of human resources and reduce the costs of the production process. The load on the individual machines also appeared to be disproportionate. Increased use of one piece of equipment may result in an increase in the frequency of breakdowns, increase the costs of repairs and reduce the total life of the equipment, so it would be advisable to evenly distribute production across all equipment. The reduction in productivity was also caused by a decline in the number of production orders.