## 2 Materials and Methods
### Verification of Proposed Methodology  
Methodology verification was carried out for a strategic stack of a sample item whose basic parameters were known (Table 1). It is a manufactured item with a medium lead time length and a medium level of variability (buffer profile = M, M, M). In addition to the data presented in the table, there are a series of historical data that are necessary for evaluating the variability of consumption, lead time, and delivery quantity (see Section 2.2.3). Parameters in the Table 1 have been determined based on the delivery terms of the item (MOQ and DOC), the analysis of historical consumption data (ADU), the inventory cost structure (ordering cost, carrying cost, and stockout cost), and the basic DDMRP system settings (DLT, LTF, and VF). The sample item was subjected to a series of simulation experiments based on simulating the process of generating delivery orders [43; 44; 45]. As part of the simulation, the following sets of experiments were carried out:  
1. Simulation of the process of generating delivery orders with the default setting of the buffer parameters according to [PERSON] and [PERSON]'s methodology [1] (Table 1). 2. Simulation of the impact of a change in the item output parameters on the efficiency parameters of the inventory replenishment process--one-factor analysis: 1. change of lead time factor (simulated LTF values: 0.1, 0.3, 0.5, 0.7, and 0.9), 2. change of variability factory (simulated VF values: 0.1, 0.3, 0.5, 0.7, and 0.9), 3. change of desired order cycle (simulated DOC values: 4, 6, 8, 10, and 12 days). 3. Simulation of the impact of a change in the item input parameters (LTF, VF, and DOC) on the parameters of the efficiency of the inventory replenishment process--multifactor analysis. 4. Simulation of the process of generating delivery orders with the setting of buffer parameters according to the methodology of the authors, described in Section 2.2. In the simulation of individual variants, the procedure for generating delivery orders according to the demand-driven planning methodology [1] was used. A simulation model was created that simulates random inputs to the simulation based on the probability distribution of input quantities found in the input statistical analysis:  
* random daily usage,
* random variations of lead time,
* random variations in delivered quantity. The order generation process itself was based on calculating the net flow equation [1]:  
\[\text{NFP}=\text{OH}+\text{OO}-\text{QD} \tag{21}\] \begin{table}
\begin{tabular}{l c} \hline \hline
**Average Daily Usage (ADU)** & **30** \\ \hline Buffer profile: & M, M, M \\ \hline -lead time factor (LTF) & 0.5 \\ \hline -variability factor (VF) & 0.5 \\ \hline Minimum order quantity (MOQ) & 50 \\ \hline Desired order cycle (DOC) & 8 \\ \hline Decoupled lead time (DLT) & 5 \\ \hline Ordering cost (EUR/order) & 1000 \\ \hline Carrying cost (EUR/(piece \(\times\) day)) & 2 \\ \hline Stockout cost (EUR/(piece \(\times\) day)) & 38 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic parameters of the example item. where:  
NFP--net flow position,  
OH--on hand (the quantity of stock physically available),  
OO--on order (the quantity of stock that has been ordered but not received),  
QD--qualified sales order demand (the sum of sales orders past due, sales orders due today, and qualified spikes). Within the simulation model, thresholds were set to:  
* order spike threshold: OST = 0.5 \(\times\) RZ,
* order spike horizon: OSH = DLT + 1. The duration of the simulation run was 1 calendar year (250 working days). Every day, the simulation model calculated a value called net flow position (NFP) using the net flow equation. The net flow position determines whether generating a new delivery order is recommended in the stack. If the position is below the upper limit of the yellow zone (TOY, top of yellow), then a quantity delivery order is generated, which is calculated as the difference between the net flow position and the upper limit of the green zone (TOG, top of green).