### The Economic Efficiency of the \"Pull\" and \"Push\" Strategy and Optimization Models  
A given strategy does not have to be related to either a competitive strategy or the specifics of a given manufacturing industry, but is an individual choice of a given company, as evidenced by the results of various studies. For example, a study of supply chain strategies used by light vehicle manufacturers in South Africa showed that manufacturers use both Lean and Agile supply chain strategies [16]. The problem is, of course, very complex, because the efficiency of production and logistics systems is not a matter of simply choosing between Pull and Push, or a combination of these systems, but of managing them properly. For example, since the execution of production processes has a large impact on energy consumption, [PERSON] et al. have developed, among other things, a matrix-geometric method of effective Make-to-Order process management to increase energy efficiency [17]. In fact, the Make-to-Order system, if it is to be effective, requires an efficient process management system, also including inventory management. Therefore, research is being conducted on this problem, an example of which is the Cyber-Physical Logistics System (CPLS) developed by [PERSON] et al. [18]. Above all, however, it requires proper management of processes throughout the supply chain and appropriate cooperation with suppliers. This problem was addressed by [PERSON] et al. [19], who formulated a control model that took into account the dynamic responses of customers to the joint implementation of these strategies in order to minimize the cost of disruption. This model took into account the inventories in this system, which is also worth emphasizing in the context of the considerations carried out in this article, as even if the effect of the Pull system is to reduce the level of inventories, it does not mean their complete elimination. Pull and Push strategies can be used even in the same company to manage different product groups. Such a view is presented, for example, by [PERSON] et al. [20], who gave the example of a chemical company that applied the MTS or MTO strategies to specific product groups based on demand volume and variability. However, according to the author of this article, even in the case of standard products, these two strategies can be implemented and are alternatives to each other. It is for such a case that simulations have been carried out, the results of which are presented in this article. For decision-making purposes and process management in various systems, various models were developed. For example, [PERSON] et al. [21] presented a flow time estimation procedure for dynamically setting lead times in MTO systems, using an artificial neural network. The problem of selecting a particular strategy using optimization models is widely described in the literature, which presents both general-purpose models as well as those relating to specific industries [22; 23; 24; 25; 26]. Both cost and quality aspects are important in every strategy. A study carried out by [PERSON] and [PERSON] [27] to compare the effectiveness of the MTS model and the MTA (Make-to-Availability approach) showed that the MTA model can effectively meet customer requirements. Surprisingly, however, the cost resulted from the need to keep inventory at relatively high levels. It is also worth noting that the authors used the category of \"opportunity cost\", which was taken into account by [PERSON] et al. [28] in their model, which considered various options for inventory control strategies. The optimal solution minimized the total costs (the costs of production, inventory holding and lost sales). The problem of customer service is particularly important in the MTO (Pull) system, and it is particularly challenging for \"Make-to-Order companies\" to achieve a high delivery reliability.