## 3 Results
### Setting of Buffer Input Parameters Using Authors' Own Methodology  
The last set of simulation experiments was applied with the setting of buffer parameters according to the authors' own methodology, which was described in Section 2.2. Calculations of basic inventory parameters and dimensioning of individual buffer zones for a sample item are summarised in Table 12. \begin{table}
\begin{tabular}{l c} \hline \hline
**Green zone (resulting value)** & **180** \\ \hline GZ1 & 180 \\ \hline GZ2 & 75 \\ \hline GZ3 & 50 \\ \hline
**Yellow zone (resulting value)** & **150** \\ \hline
**Red zone (resulting value)** & **113** \\ \hline Red base & 75 \\ \hline Red safety & 38 \\ \hline
**TOG (top of green)** & **443** \\ \hline
**TOY (top of yellow)** & **263** \\ \hline
**TOR (top of red)** & **113** \\ \hline
**Order spike threshold (OST)** & **56.5** \\ \hline
**Order spike horizon (OSH)** & **6** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Buffer setting after input factors’ objectification. Figure 14: Simulation outputs (main effects plot of the input parameters). Figure 15: Simulation outputs (multifactor analysis). 1. Default buffer setting (Section 3.1): This method assumes setting the parameters of the strategic stack using the original calculation methodology according to [PERSON] and Smith. 2. Buffer setting objectification (Section 3.2): This method used a series of simulation experiments to improve the default buffer setting. The objectification of parameters took place in two phases. In the first phase, a one-factor analysis (parameters LTF, VF, and DOC) was carried out. Based on the results of the single-factor analysis and using the Taguchi method, a series of simulation experiments was planned and implemented in the second phase as part of the multifactor analysis. 3. Buffer setting optimisation (Section 3.3): The third method used the original methodology of sizing the parameters of the stack, as proposed by the authors of the article. This methodology combines procedures for statistical analysis of historical data (determination of safety stock) and inventory management policies, working with the determination of reorder levels and the maximum stock level. According to the [PERSON] and Smith methodology [1], using the proprietary strategic stack size optimisation methodology of the sample item described in Section 2.2 results in:  
* improvement in inventory management parameters in comparison to the default buffer setting,
* an increase in the average service level (from 99.55% to 99.94%),
* reducing the dispersion of real service level values,
* a decrease in average stock of 8.9%,
* a reduction in total inventory costs by EUR 2283,
* reducing the dispersion of the fair values of the total cost of inventories. Objectification of buffer parameters using simulation (one or more factor analysis) brought comparable results to optimisation using the authors' own methodology, except for a lower average service level. However, the objectification of parameters required the implementation of a large number of simulation experiments, which were necessary to find the best combination of input factors and buffer parameters. In our case, these were:  
* fifteen series of simulation experiments in the single-factor analysis phase,
* nine series of simulation experiments in the multifactor analysis phase. The use of the proposed methodology for optimising buffer parameters dramatically reduced the labour of searching for the best solution, because it removed the need to