## 4 Discussion
However, at this point, we can only discuss research results, even when referring to the validation of results on a large number of cases. Nonetheless, we can gather and summarize the achieved results, which are highly promising:  
Order Size Prediction Model: It turns out that according to the ultimately adopted solution, obtaining an order plan for 10 analytes, or even 11 analytes (which could potentially be more), takes a fraction of a second. Despite the fact that preparing the models took many weeks and included selecting the methodology, developing profiling, clustering, selecting the forecasting algorithms and aggregation methods, determining the hyperparameter values of the models, and, finally, developing the equations of the models themselves, these ready-made prediction models can be calculated as a set of equations with a sliding time window for prepared time series, and obtaining results takes less than a second--the same as updating a spreadsheet. For these finished models, computation time is an insignificant boundary condition. Obtaining the order plan for analytes can be achieved extremely quickly with such a tool. The consumption balance of reagents for testing can be calculated as a direct ratio, where one unit of reagent is consumed per test, so the order plan is simply a comparison of the forecasted monthly order numbers for each analyte. Some reagents are consumed in quantities above 30,000 units per month, while others are consumed in quantities below 2000 per month. Therefore, calculating the MAPE for each separately and then averaging it, the average error will be only 2.16%, indicating the precision of the models. These forecasts (for each analyte for 6 months), treated separately, constitute the inventory and order plan. We can indicate the precision and effectiveness of the models, but the realization of savings generated by reducing the level of reagent inventory based on these forecasts could only be calculated after implementing these methods into the company's logistics procedures, but currently, we do not have data on this. Calibration Necessity Analysis Model: The error that was particularly important in this situation, namely False Negative, indicates that the model predicts that calibration is unnecessary when it is actually required. This error in the best model is 1.5%, while the total accuracy of the model is 98.6%. Even estimating with a large margin of error (much higher than that quoted for network models) at the level of 10%, we can predict 90% out of 11% of the average number of tests performed, which constitutes quality control, calibrations, and repeats. However, at this stage, it cannot be determined whether this knowledge can be utilized every time by foregoing unnecessary calibrations and thus saving time and materials, as these decisions also depend on legal and procedural considerations. Analyzer Work Time Optimization Model: In the case of analyzer overload, the parameter assumed a 5% reduction in the analyzer's working time under maximum load. In the tests, each analyzer showed a reduction in time of over 10%. On average, the working time under maximum load was reduced by 11.98%. On average, this group of analyzers performs approximately 23,000 tests per day. After implementing the optimization, the number of tests performed without delays can even exceed 25,000 tests per day.