## 3 Results--Effectiveness of Pull and Push Strategies in Production and Distribution
### Economic Efficiency of Pull and Push Systems  
Based on the results obtained from the above simulations, calculations of the economic efficiency of each strategy can be conducted. The assumptions (data) are shown in Table 9. When calculating the costs, we made every effort to utilize data on the processes and the costs of these processes occurring in economic practice--e.g., in Poland. The calculation results for the Gaussian distribution are shown in Tables 10-12. They take into account the parameters of shipments; the value of goods; the resulting costs of transportation, inventory maintenance, and storage; and the cost of lost sales. Differences in total costs depend on the delivery strategy used but also on the type of product, because the size and tonnage impact the costs of warehousing and transportation. The criterion for evaluating the effectiveness of the system is the \"total cost\", which includes the costs of inventory, storage, transportation, and the costs of \"lost sales\". The costs of lost sales are derived from the level of inventory availability from previous simulations (Table 1). Thus, if the logistical customer service measured by such a parameter is 98.96% for the \"Pull daily\"/one DC variant., i.e., the company loses 1.04%. The costs of lost sales are calculating in the following way for \"Food\":  
No of items \(\times\) Price of a commodity \(\times\) 1.04% =  
32,000,000 [pcs./year] \(\times\) 1 [EUR/item] \(\times\) 0.04% = 332 EUR/year  
which is 11.23% of the \"total cost\" (Table 10). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strategy** & \multicolumn{3}{c}{**Stand. Dev. of**} & \multicolumn{3}{c}{**No of DCs**} \\  & **Average Sales** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Pull daily & 3\% & 769 & 769 & 769 & 769 & 769 & 769 \\ Push daily & 3\% & 554 & 554 & 554 & 554 & 554 & 554 \\ Push weekly & 3\% & 548 & 548 & 548 & 548 & 548 & 548 \\ Pull daily & 6\% & 955 & 955 & 955 & 955 & 955 & 955 \\ Push daily & 6\% & 561 & 561 & 561 & 561 & 566 & 566 \\ Push weekly & 6\% & 559 & 559 & 559 & 559 & 559 & 559 \\ \hline \hline \end{tabular} Source: own calculations.  
\end{table} 
Table 8: Impact of distribution strategies on the production level (Gamma distribution). Yearly sales [items/year] = 40,000. \begin{table}
**Commodity** & **Food** & **Garment** & **Electronics** \\ \hline No of items [pcs./year] & 32,000,000 & 2,000,000 & 3,200,000 \\ Weight of a commodity [kg/item] & 1.00 & 2.00 & 3.00 \\ Weight of a commodity [tons/pellet] & 0.80 & 0.10 & 0.24 \\ Value of a commodity [EUR/item] & 0.6 & 18 & 30 \\ Price of a commodity [EUR/item] & 1 & 30 & 50 \\ \hline \hline \end{tabular} \
\begin{tabular}{c c c} \hline \hline Road transport (Pull) & 0.069 & 0.556 & 0.232 \\ Road transport (Push) & 0.066 & 0.529 & 0.221 \\ Rail transport (Push) & 0.013 & 0.106 & 0.044 \\ Distances [km] & 800 & 800 & 800 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline Capital costs in inventories & 10\% & 10\% & 10\% \\ Warehousing [EUR/item/day] & 0.001 & 0.020 & 0.013 \\ \hline \hline \end{tabular} Source: own assumptions.  
\end{table} 
Table 9: Assumptions and input data for calculations of economical effectiveness. For this group of products, the share of these costs increases with the number of warehouses in the distribution network and is obviously higher with larger sales fluctuations. As expected, these costs will be higher for more expensive products (Table 12), which provides a justification for including them in cost calculations. For \"Food\" (the cheapest) and with a 5% standard deviation, the lowest total costs occur with regular (\"Push every week\") deliveries to six warehouses by rail transport. If