Assignment module

This GUI leads the decision‐maker towards the assignment issue by the definition of the appropriate location to assign to a generic SKU in the forward area. Considered the horizon of analysis (e.g. the same chosen for allocation analysis or different as for the sample of Figure 45), the user classifies SKUs according to a set of proposed criteria or metrics (i.e. index based policies) rather than assessing the correlation among SKUs (i.e. correlated based policies) through a clustering approach. Both opportunities compute a ranked list of SKUs (eventually of clusters of SKUs) responding to particular criteria (e.g. popularity, turn, order closing), to be properly matched with a list of locations, according to the procedure presented in Chapter 3.

Figure 46. Assignment GUI

Figure 46 shows the Assigment module as appears to the decision‐maker. On the left side two calendar panels allow to establish the interval of analysis, whilst the right side of the interface is composed by a TabControl with three subsequent TabPages. The first TabPage refers to the Index based assignment policies. The command named Calculate Index Matrix implements the previously described method SKUIndexMatrixCreate() belonging to Demand class. Once the metric is selected a list of SKUs is given sorted in accordance with the ranking rule illustrated in Section 3.2.3.1. In the proposed sample of Figure xxx the index adopted is Popularity and the SKUs are accordingly ranked from the highest requested SKU to the least. The second TabPage is devoted to the correlated assignment policies based on clustering approach. The GUI illustrated in Figure 47 presents all the commands and functionalities for the implementation of the clustering techniques. At first, the user select the period of analysis as for the index based assignment rules. Then, define the similarity index (i.e. chosen between McAuley and Accorsi & Maranesi) to adopt and the clustering algorithm (i.e. chosen among Slink, Clink and Upgma) through the related Combo Boxes. The five factors composing the Accorsi & Maranesi metric, also named Picking Oriented Index (POI) are configurable through proper check box, labeled x1, x2, x3,

x4 and x5. The DST realizes the clustering and actuates the similarity cut‐off based on the threshold

percentile (i.e. 20 in the sample) or threshold value methods. Finally in the third step of the process, the decision maker decides the rules for the sorting of created clusters.

  The graph in Figure 47, reports the value of similarity of the progressive SKUs, which enter into a cluster. Obviously, the trend of such curve is descendent, but its shape bright an important insight about the effectiveness of clustering in fitting the demand profile. Figure 47. Correlated assignment GUI The third input TabPage of the assignment module gives the opportunity to the user to define the most convenient storage locations within the zone are. This setting depends on the routing strategies (i.e. return or traversal) and on the site of receiving and shipping docks, assuming the former as the point where the picking tour begins, and the latter as the point where the tour ends. Figure 48 shows such more than twenty combinations for site of receiving and shipping docks (e.g. corner, middle, bottom‐up, distributed), affects the single‐command distance to access to a generic location. This distance (i.e. measured in millimeters) reports the path travelled by the picker starting form the receiving dock, achieving each base module, and then going to the shipping dock. This is assumed as the metric to rank the storage locations according to the grade of convenience.

In the Output TabPage the DST assign the sorted SKUs to the most convenient locations in the forward area, in a sort of greedy matching. Each SKU holds the number of location required to store the quantity set with the allocation step. Once the appropriate location in forward area is assigned to a specific SKU, the bulk area is accordingly arranged by the adoption of greedy heuristics to reduce the distance between an item and its reserve.

Figure 48. Assignment layout GUI

Results of the assignment module are store into the database, through the button , and detailed illustrated as bird view of the designed warehouse zone. Once the assignment module ends, the zone is completely configured and its characteristics are stored in the database available for zoning, routing, batching and benchmarking modules. The bird view, proposed in Figure 49, is a frame shot of SKUs locations, where each SKU is differently coloured and storage details (e.g. location code, item code, number of cartons per item) are summarized on the right panel by a simple click.

The DST proposes this virtual 2D layout representation through a TabControl where each TabPage is a level of storage for both forward and reserve area.

At this step, the decision‐maker might also realize a comprehensive 3D CAD layout of the configured scenario through the previously illustrated interface. The results of this interface is reported as sample in Figure 50, which shows how the rack infrastructure is fill by SKUs in the defined quantities and locations. By considering real commercial racks, the decision‐maker obtains a ready‐to‐print version of the designed warehouse useful for warehouse builders as well as warehouse operators responsible for put away and picking activities.