An application of multivariate planning methods in determining KPIs of rock disintegration process

KPIs (Key Performance Indicators-KPIs) are quantifiable values that reflect the critical success factors of operation, enterprise companies. Choice of these parameters depends on the type of system being examined. As an example of the technological process in finding the optimal mode, KPI respectively, is a process of rock disintegration. The theoretical basis is the energy of disintegration theory, specific volume work of disintegration (the amount of energy used to disengage the unit volume of rock), depending on the downforce, speed and drilling speed. Finding the optimum on the experimental stand is using the multidimensional planning method, which allows the use of the model - MISO (example several indicators) derived from the measured values in the process of the disintegration of rocks - KPI. KPIs vary between companies and industries, depending on their priorities or performance criteria. A company must establish its strategic and operational goals and then choose the KPIs which best reflect those goals. Execution of each experiment is associated with a certain degree of heuristic activity; therefore a logical structure of planning experiments differs from abstract mathematics. A mathematical theory of an experiment creates a formal logical flow of experiments to study an issue, which is formulated by mathematical means. A fundamental axiom of planning experiments is the criterion of optimality. This criterion can be either static or dynamic. Static experiments are those for which the optimal distribution of data points in the space of independent variables is found. Dynamic experiments include an examination strategy of questions on the whole. Dynamic tasks should be monitored as a change in the space of independent variables. For further procedure it is necessary to select the most advantageous area for the models that are the most commonly used polynomials to be studied by examining the results of the geometrical shape. We are talking about planning stud- experiments using the model surface. The resulting interpretation is obtained by combining the results of local tasks solved in the direction of the gradient model examined.