## 2 An Overview of Related Research
The high performance is related to the ability of DRP to anticipate changes, based on forecast information, in demand along the supply chain and release time-phased orders in anticipation of future requirements. DRP and Order Point replenishment strategies require very different information. According to [25], complex and extensive software information systems are necessary for DRP systems. This is explained by the various nodes in the supply chain or distribution network that must communicate with the central planning function. Additionally, this constraint is important when the network nodes are controlled by independent enterprises. Intelligent information sharing in supply networks is analysed in [26]. Order Point systems require less coordination of information. The system implementation is much easier if it uses only inventory and order information local to the upstream replenishment loop. Since this paper deals with the inventory control problem in the private pharmaceutical distribution company characterized by the transparency of information within the distribution network, we will review the DRP approach. According to [25], DRP is a time-phased replenishment approach, with inventory level monitoring and periodically realized delivery plans. In order to foresee requirements, companies use forecasting. Orders are realized, in accordance with inventory status and lead times, in a manner that minimizes inventory cost and, at the same time, prevents unnecessary shortages. The DRP system implies planned lead times and defined lot sizes. The fixed order quantity (FOQ) and lot-for-lot (LFL) lot-sizing rules, considered in this paper, are often used because they apply to each of the planning and control systems. FOQ and LFL ordering policy systems belong to a group of classical static inventory models. Some of the main assumptions of the model indicate the known total deterministic demand and order quantity that should be determined in a manner that minimizes stock-outs and the average level of inventories. As can be seen in [4; 27; 28; 29; 30; 31; 32], these classical inventory control models and their variations represent a starting point for understanding inventory dynamics, even in new books related to inventory control. The mentioned DRP logic can be analogous with material requirements planning (MRP) logic, where forecasts are made based on customer demand (often a retail echelon). Planning of the orders in this manner is similar to order realizing for independent demand items in accordance with an MRP concept. A detailed comparison of MRP and production and inventory control theory, including their similarities and differences, is described in [33]. According to [33], unlike MRP logic, the inventory control approach in cludes forecasting and up-to-date re-planning based on system state changes. MRP reflects real-world planning, but decision-making procedure becomes difficult after incorporation of real-life constraints, while inventory control systems incorporate the presumption of real-life decision-making. Production inventory systems are often characterized by the uncertainty of external demand. MRP is applicable when future demands are known, on the contrary of production inventory control model defined by known inputs, previous actions and rescheduling. [PERSON] et al. [33] proposed the integration of MRP, production, and inventory control logic in order to overcome their main disadvantages. Similarly, the authors of this paper propose the inter-linking of DRP and the modern control theory concepts aimed at the constitution of dynamic discrete inventory control models of the pharmaceutical distribution system in the real-world company. Some of the main disadvantages of the dynamic DRP model, which imply its improvement in order to solve the inventory control problem considered in this paper, can be defined as [25]:  
* DRP does not determine the lot size or safety stock. These decisions represent inputs to the process;
* DRP does not explicitly consider any costs.