## 3 Results--Effectiveness of Pull and Push Strategies in Production and Distribution
### Impact on Logistics-Production Potential  
The first parameter affected by these strategies was the level of stock availability in warehouses, which can be used as a measure of logistics customer service. The simulation results are presented in Table 1 and Figures 5 and 6. CDs were replenished in the following three ways:  
* \"Pull daily\" in quantities to meet the actual demand;
* \"Push daily\", also in small but fixed amounts, according to forecasts of demand;
* \"Push weekly\" in fixed amounts corresponding to the average weekly demand. The third option applies when, e.g., rail transport is used, which is less flexible than road transport, and deliveries are made on schedule. In the first step, simulations were carried out for a Gaussian distribution and standard deviations of 5% and 30% from the average demand. For small fluctuations in sales (5% standard deviation of average sales), deliveries in large quantities (\"Push weekly\") are the most effective in this regard. A high level of customer service is also ensured by \"Pushing\" goods daily from the plant to the warehouses. As the number of warehouses increases, the level of customer service drops very sharply in weekly deliveries, while in the \"Push daily\" method, it remains high. Daily \"Pulled\" deliveries result in a lower level of customer service. Only with six distribution centers can they offer a higher level of service than weekly \"Push\" deliveries. However, in most cases, the best customer service is associated with \"Pushing\" deliveries in small quantities (daily). The situation changes when demand is more volatile (standard deviation of 30% of average demand)--in all strategies and variants, the level of customer service measured by inventory availability is lower, but to varying degrees. Regardless of the number of CDs in the distribution network, the best inventory availability is again observed for daily \"Push\" deliveries (Figure 6). This time, a slightly lower but also high level of service is observed for daily \"Pull\" deliveries. In the case of weekly \"Push\" deliveries, the level of service is much lower. The results of these simulations are not surprising--as expected, with greater fluctuations in demand and therefore less predictability in demand, a more flexible system that adjusts the volume of deliveries to actual rather than forecast demand is more efficient from a customer service perspective. The differences between the levels of logistics service appear to be small. But first, they are due to the probability distributions assumed in the simulations and the data generated by the model. Since lower levels of service are observed in practice, there is a need to investigate what the actual probability distributions and sales fluctuations of companies are. The model does not take into account one more service level factor--on-time delivery. This is because it assumes that deliveries arrive on time. Taking into account the delays in deliveries to warehouses would result in either lower service levels or increased inventories. However, even without considering the above factors, even a small change in the level of logistics customer service can have a big impact on the efficiency of the system. As demonstrated later in the article, the deterioration of service by even a few percent can result in high costs of lost sales. Increasing that service by just a few percent as well can also result in a significant increase in costs--for example, inventory maintenance and warehousing. Table 2 and Figures 7 and 8 presents results of the simulation of the impact of different strategies on the level of stocks for the levels of service from Table 1.  
increase if sales fluctuations increase from 5% to 30%. In all strategies, too, inventory levels increase as the number of warehouses increases, which confirms (and explains) the existence of the benefits of the strategy of the \"centralization of stocks\"--better customer service can be achieved with less inventory.