## 1 Introduction
Many published papers are devoted to the standardisation and implementation of the DDMRP system in practice [4; 5; 6; 7; 8; 9; 10]. Publications comparing the DDMRP system with other alternative inventory planning and management systems are the second largest group of scientific articles. The DDMRP system, as a combined pressure/tensile system, is compared in individual works with pressure systems, such as MRP and MRP II [11; 12; 13; 14], with the Kanban tensile system [15; 16], as well as with alternative combination systems, such as CONWIP [17] and OPT [18]. Further work is devoted to individual elements of the DDMRP system. The authors [PERSON] et al. [19] and [PERSON] et al. [20; 21] addressed the issue of determining disconnection points and optimally positioning strategic inventories in the supply chain. [PERSON] et al. [21; 22] suggested using the MILP (Mixed-Integer Linear Programming) model to optimise strategic stack positions. The authors paid great attention to determining the parameters of the DDMRP system, whether from the point of view of the primary setting of buffers at disconnection points or from the point of view of their dynamic adjustments in the use phase. In their publication [23], [PERSON] and [PERSON] paid attention to optimising the level of safety stocks in the strategic storage systems of the DDMRP system. [PERSON] et al. [24] described dynamic adjustments to the parameter \"decoupled lead time\" that resulted in the dynamic adjustment of inventory levels in strategic stacks. The dynamic dimensioning of strategic reservoirs is also described in the work of [PERSON] et al. [25; 26], who used heuristic approaches based on type control (s(t); S(t)) with time-varying thresholds. [PERSON] et al. [27] discussed the dynamic determination of control parameters OST (order spike threshold) and OSH (order spike horizon). [PERSON] et al. [28] suggested the use of the MILP model in determining strategic reservoir parameters. In their publications [29; 30], authors [PERSON] et al. presented a procedure for applying genetic algorithms to determine DDMRP system parameters. In their publication, [PERSON] et al. [31] compared several approaches to sizing DDMRP system parameters from different authors and verified the results using dynamic simulation. The next part of the publications was devoted to the actual planning and management of inventory in the DDMRP system. In their case study, [PERSON] and [PERSON] [32] demonstrated the use of a prognostic model of simple exponential smoothing in inventory management in a DDMRP system applied in veterinary medicine. [PERSON] and [PERSON] [33] described a replenishment algorithm using a learning algorithm based on [PERSON]'s decision-making process. The authors [PERSON] et al. [34], [PERSON] et al. [35], and [PERSON] et al. [36] added part of capacity planning to the planning process in the DDMRP system. Just as unlimited capacities were considered in the MRP system, in the original DDMRP methodology [1], the authors did not consider capacity constraints. However, the capacity planning module represents an important part of inventory planning, especially if the stock in the strategic reservoir is replenished from its own production. [PERSON] et al. [37] compared inventory management and order prioritisation based on inventory levels in a strategic stack with classical priority rules (FIFO, Due Date, and ROP) used in manufacturing order management and prioritisation. As already noted in the literature review, a large group of authors paid attention to setting the parameters of the DDMRP system in their publications. The correct determination of the positions of strategic reservoirs and the dimensioning of their sizes are key prerequisites for the proper functioning of the system in inventory planning and management. It should be noted, however, that most of the authors deviated significantly from the original philosophy of the DDMRP system, as presented by [PERSON] and [PERSON] [1].