## 3 Presentation of the Proposed Model
### Sub-Modules of the Presented Model
#### 3.3.6 Product Classification and Allocation Module  
Another module is the classification and simulation one, consisting of three steps: product classification, placement planning and order-picking simulation. In the product classification step, the user can select one of the classification methods, such as the association search algorithm, ABC analysis combined with XYZ analysis, or solo ABC analysis, as well as a model linking them to the COI Index and the author's algorithm for the relationships search. For the last two methods, a user should also specify the criterion of analysis--product popularity, number of units sold, weight or volume. In the simulation settings, a user only has to select the product classification model used together with the criterion and then specify the number of pick lists to be included in the simulation. Other simulation settings are defined in the program's initial settings--the ANN structure generation module and core system settings--so a user does not need to re-enter them. Once the selection is confirmed, the program simulates successively calculating the time to go to the rack and retrieve a specified number of items from it. The result of the simulation is the order-picking time, the penalty resulting from the need to change the order of products on the pick-up truck (additional time) and the total order-picking time with the penalty. In addition, the program provides statistical picking data, from the average stock level through the total number of items in the order, and warehouseman movement speed up to a number of items in the list, as well as total weight and volume. ABC analysis is the most commonly used analysis to classify products. Classic ABC analysis allows for dividing items into three groups with a percentage share of A--80%, B--15%, C--5%. Variations of this analysis can be found. They isolate more groups in a properly adjusted percentage share. ABC analysis is a one-criterion analysis so it is not possible to take several input parameters simultaneously into account. However, it is possible to do several analyses, taking another as a criterion feature, and then to synthesize the results by taking the appropriate weight for each criterion (analysis result) [29]. The mentioned analysis is most often performed according to the criteria [30; 31; 32]:  
* sales value or gain from sales;
* the frequency of picking;
* delivery size;
* weight and volume. ABC analysis is often compared with the Pareto-Lorenz classification, whereby class A, consisting of 20% of all items, generates 80% of the total value of the set. XYZ analysis allows for supplementing the ABC analysis with an additional criterion by making a classification within already isolated groups. The criterion most commonly used in XYZ analysis is the regularity of consumption based on historical sales data. Unlike ABC analysis, where the popularity of the product or the number of units sold is analyzed, the XYZ analysis allows independent evaluation based on an individual factor for each product. The basic assumptions are the same for both of the aforementioned analyses, e.g., the period used for the calculation must be common to all products and the compliance of the period and measuring units must be ensured. XYZ analysis is performed in the following steps:  
**Step 1**--A compilation of data from equal periods. However, the greater the number of periods, the better the results will be;  
**Step 2**--A calculation of the average number of units sold (7) for each product:  
\[\overline{w}_{t}\;=\;\frac{\sum_{j=1}^{n}w_{ij}}{n} \tag{7}\]  
**Step 3--**A calculation of standard deviation (8):  
\[s_{i}\ =\ \sqrt{\frac{\sum_{j=1}^{n}\left(w_{ij}-\overline{w}_{t}\right)^{2}}{n-1}} \tag{8}\]  
**Step 4--**A calculation of the coefficient of variation (9) for each product:  
\[V_{i}=\frac{s_{i}}{\overline{w}_{t}} \tag{9}\]  
**Step 5--**Sorting out the product list in ascending order by the value of the coefficient of variation \(V_{i}\). **Step 6--**Defining limit values for each group.