## Case Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variant** & **Area of Storage (sqm)** & **Count** & **Mean** & **Homogeneous Groups** \\ \hline I & 2188 & 1000 & 1438.5 & x \\ II & 7149 & 1000 & 1673.3 & & x \\ III & 12.491 & 1000 & 1685.7 & & x \\ IV & 17.225 & 1000 & 1587.2 & & x \\ V & 22.021 & 1000 & 1357.7 & x & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Multiple range tests for picking time (sec) by area of storage (m\({}^{2}\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Area of** & **Average Picking** & **Standard** & **Coeff. of** & & & \\
**Storage (m\({}^{2}\))** & **Time (sec)** & **Deviation** & **Variation** & **Minimum** & **Maximum** & **Range** \\ \hline
2188 & 1438 & 643.6 & 44.74\% & 198.0 & 3753.0 & 3555.0 \\
7149 & 1673 & 718.5 & 42.93\% & 303.0 & 4342.0 & 4039.0 \\
12.491 & 1685 & 716.8 & 42.52\% & 290.0 & 4549.0 & 4259.0 \\
17.225 & 1587 & 665.1 & 41.90\% & 265.0 & 3905.0 & 3640.0 \\
22.021 & 1357 & 529.8 & 39.02\% & 272.0 & 3224.0 & 2952.0 \\ Total & 1548 & 670.8 & 43.32\% & 198.0 & 4549.0 & 4351.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary statistics for picking time (sec). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Mean Square** & **F-Ratio** & \(p\)**-Value** \\ \hline Between groups & 8.44107 \(\times\) 10\({}^{7}\) & 2.11027 \(\times\) 10\({}^{7}\) & 48.68 & 0.0000 \\ Within groups & 2.16545 \(\times\) 10\({}^{9}\) & 433,523 & & & \\ Total (Corr.) & 2.24986 \(\times\) 10\({}^{9}\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of ANOVA analysis. efficiency of its operation as a result of the application of product classification. In this case, it is more rational to reduce the costs of warehouse operation by reducing the storage area. On the other hand, when conducting simulations for medium and large warehouses (Variant IV and V), it was found that the best results were obtained when the products were distributed in the warehouse based on ABC analysis (weight) and the analysis of ABC and COI (weight). The worst results were obtained using the ABC analysis combined with the COI Index according to the popularity of products--the median value of the picking time was 1797 s, and the range of results was 8610 s. It can therefore be concluded that large warehouses and products with low susceptibility to damming loads are best suited to methods where the decisive criteria are the volumes and weights of products, such as the COI Index. On the other hand, the use of the presented method allows the warehouse to operate more efficiently, by approximately 16.8%. This is a result more than twice as good as that which can be achieved with the use of classical classification methods, regardless of the adopted criterion. Due to the lower labor consumption and relatively high speed of computing, this method can be successfully used in warehouses, where the quantitative share of individual products and the assortment often change. Both the method of searching for links and the use of artificial neural networks can be successfully implemented for practical applications. The developed method has a comprehensive approach, i.e., it allows taking into account the relationships between products, based on the orders. The analyses have proven that there is a statistical dependence of picking time on warehouse area size (Figure 12). The simulations indicate that the picking time decreases for warehouse sizes larger than 10.000 m\({}^{2}\). For small warehouses, the product choosing method for allocation plays a secondary role.