The composition of the customer orders to be served from the warehouse is crucial for the selection of the right storage and picking strategy and the associated technologies.
Key figures from the analysis of the order structure are e.g.
- Number of active SKUs per time unit
- Number of picks per order line
- Number of different order lines per order
and all this related to specific temperature zones, customer groups, etc.
As with other analyses, when analyzing the order structure, it is always advisable to look at the actual data distribution and not rely on one-dimensional statistical values such as arithmetic mean and variance.
The diagram above shows the distribution of order lines per order in an eGrocery fulfillment center. Although the median is just over 25, there are also a large number of significantly larger orders. These place particularly high demands on the sequencing capability of an automatic system, for example, since product sensitivity must be taken into account in the packing sequence and an order with a large number of order lines will fill several order bins.
The distribution of quantity per order line also has important implications for the logistics system. Among other things, it gives an indication of the picking efficiency to be expected in a manual system as well as the number of tote changes in goods-to-person picking stations.
Thus, the order structure influences the picking method (single-order picking, multi-order picking, batch picking), the design of goods-to-person systems and the selection of the AS/RS technology, the expected performance of pickers, the performance requirements of the AS/RS, and the design of picking stations. The analysis of the order structure is thus one of the analyses that absolutely must be carried out for warehouse analysis, optimization and planning. And again, it is imperative that the analyst understands the customer’s business and takes it into account in the analysis. The examples shown above, for example, include two customer groups with very different ordering behavior: Business customers and private customers. A separate analysis provides much more homogeneous results; an aggregation of key figures for order lines per order and quantities per order line, on the other hand, would distort the picture and hide insights for system optimization.
