### Case Studies of Pull and Push Strategies  
The literature also contains interesting case studies of companies that have implemented these strategies. Here are some examples:  
A company from the plastic packaging sector classified products according to the ABC analysis of the production and used the mixed Pull system, where items in categories A and B should be in supermarkets (Push-MTS) and products in category C should be made according to requests (Pull-MTO) [44]. It is worth noting that this resulted not only in a change in distribution strategy, but also necessitated a reorganization of production processes at the factory.  
The need to combine these strategies and use combinations of them for different products postulated by the authors cited above is also supported by case studies from other industries--such as agricultural equipment manufacturing ([PERSON], [PERSON] 2012) [45].  
When implementing Pull, there is a need to increase the efficiency of this system so that deliveries to customers can be made efficiently. However, the use of such a strategy can be enabled by the fact that customers are willing to wait for their product. An example of such a strategy is Dell, which was the first company to introduce a Configure-to-Order (CTO) model in which customers can customize computers according to their requirements. The company introduced new products and technologies faster than its competitors. However,shipping times have lengthened (about 7-14 days) because computers are produced after an order is placed [46].  
This brings up an aspect that is probably not often mentioned in publications--namely, the logistics aspect. For even if a company actually manages to increase the flexibility of its production system--there remains the question of shipping these goods to customers. In the Push system, the products are already produced; they can be loaded and combined with other products to make the best use of the cargo space of the means of transport. In the Pull/MTO system, such possibilities are already limited. Moreover, in the context of the ever-increasing interest in ecology, it seems that MTS/Push should be the preferred strategy. Similar problems to those in the production field also occur at a later stage, i.e., the physical distribution of goods.  
An abundance of research, including models and decision-making tools, demonstrates the complexity of these problems. They also show that there is no single universal optimization model. The model should be adapted to a specific problem.  
The question also arises here as to whether the efficiency of a given strategy depends not only on how the stocks in these warehouses are replenished, but also on how many warehouses there are. The level of stock in warehouses, the level of logistics customer service, and the costs of transport to these warehouses are also influenced by the degree of centralization of this network (number of warehouses). Therefore, the question arises as to whether the two decision problems should be considered together. It is difficult to find scientific studies that take these two decision-making problems into account together. For this purpose, a simulation model of such a system was developed to study the impact of different strategies on the efficiency of logistics and, to some extent, production processes.  
The literature on the subject and the opinions of company managers and case studies of companies lead to two conclusions. First, the choice of a given production or distribution strategy has a significant impact on the costs of production and logistics processes, and consequently on the financial results of companies that implement given strategies. Secondly, customers expect companies to adapt to their changing requirements, which are difficult to predict. In response, their suppliers tend to use strategies such as \"MTO\" and \"Pull\" and avoid building up stocks. This inspired the research presented in this article, which examined the economic efficiency of these strategies.