## 5 Discussion  
The presented text consists only of selected information from a very extensive project on maintenance optimization, and accurate records of machines and equipment in the selected company. It was a long-term project which can be schematically divided into four stages. The first stage is characterized by an analysis of implementation options, database creation, user programs, diagnostic programs and outputs, management, and maintenance staff training. The second phase involved data collection and coordination of specialists (data analysts). The input data was arranged as sequential files (indexed, tree-hierarchical, and networked). The maintenance data files contained maintenance objects, drawings, stored spare parts and supplies, type standards of work, etc. The third phase was the process of pilot data analysis. The fourth phase included project implementation, compilation of outputs and analyses, integration into the existing information system, and updating in case of changes and for practical use. Simple models such as second degree planar curves, rational polynomial function, general polynomial, etc., were used to move from heuristics-based approaches to maintenance, based on calculations from both current and historical data. The main problems of the solution were the differentiation of machines and equipment according to their importance in the production process, inclusion in the appropriate level of maintenance labor intensity (13 levels in total), determination of machine shifts, and standards of annual repair activities. The parameters were objectified on the basis of the created knowledge-based system, knowledge base using forward chaining based on data-driven reasoning (creation of a modular knowledge base including heuristics and exact knowledge), the inference mechanism, interface, explanatory module, and the knowledge acquisition module. It can be stated that the maintenance of machines and their efficiency was massively conditioned by the processing of maintenance data, database platform (Firebird, MySQL, MS SQL, Oracle, etc. ), performance of the hardware, actual maintenance, and proper use of standardized query language SQL (Structured Query Language). Specifically, the optimization meant finding the optimum time between maintenance interventions, individual determination of the maintenance content based on real-time information about the technical condition, and refining the maintenance work technology for a particular machine. After introducing the new maintenance system, it is rightfully expected to improve maintenance planning, cost-effectiveness, and other intangible benefits. According to various literary sources, time after implementation could possibly be reduced by up to 30% [20; 23]; specific information from the pilot project was only from one workshop (mechanical machining workshop) from the company where the project was implanted and is 7%, as shown below. Along with the current complexity, extent, automation, and concentration of production machines, the probability of failure increases inevitably. Complexity (number of elements) and extent (number of systems: control, regulation, electronic, mechatronic, electrical, mechanical, hydraulic, pneumatic, etc.) depend on their specific connection (serial, parallel, combined) and degree of automation (without automation elements, using the operator = hands on, partial automation = hands off, conditional  
Figure 11: Annual maintenance plan for group of importance II and IIIautomation = eyes off, high automation = mind off, complete auto-automation, mechanical or software robotization of the machine). As for the concentration of machines, this relates to the number of different machines that have different maintenance requirements (in terms of the number and complexity of operations, maintenance staff qualifications, maintenance time, etc.). The number of elements of a particular machine is decisive, as far as the number of possible failures is concerned.