Typically, order picking is the most expensive element of a distribution centre operation. Traditionally it was difficult to automate due to the different product types, required flexibility and fluctuating volumes.
E-commerce is putting a huge pressure on this process. The typical quantities being picked are small, and due to the faster delivery proposition to customers, the available time is compressed, making it hard to balance the workload over a shift, a day, or a week. Additionally, most E-commerce operations have seen a rapid increase in the number of products being offered.
In a manual operation, this typically involves operators walking long distances to pick a relatively small number of products. The cost per line is typically significant, but additionally the revenue per line is quite small.
Opportunities to reduce costs
There are several options to mitigate the costs. Broadly they fall into three categories:
- The first option is to increase the number of orders that are picked by an individual operator at the same time. This can be through the same basic process, typically involving a trolley with multiple orders. It is a simple way to reduce the walking time per pick, but is limited by what the operator can physically move – the size and weight of the trolley. It can also be achieved through a two-stage process: an initial batch pick followed by a sort. The benefit is that the initial pick is much more productive, but it does require a second process, which is costly and increases the time to process an order.
- The second option is to remove activities beyond the actual order picking. This can include the automatic movement of the completed order once the actual picking is completed, using a conveyor or automatic guided vehicle. The benefit is that this can typically be done in an existing operation without major redesign to the process or building layout.
- The third option is to automate the process. Most picking automation reverse the traditional person-to-goods logic to a process where the product comes to the picker. The benefit is that the operator does not spend time moving around the warehouse but is purely focused on picking. There are a number of technology options available, such as a miniload or shuttle system (typically with products stored in totes or boxes) or an automatic guided vehicle that delivers a ‘storage unit’ to the picker.
Improving the end-to-end process
In order to assess the best way forward, it is important to understand the total process. The picking process almost certainly is linked to the storage solution, and particularly for E-commerce it is likely to feed into a packing and shipping process.
It is key to be clear on the overall objective. In general, this will be a mixture of meeting the required service levels and cost targets. E-commerce’s consistent drive to faster deliveries is putting a lot of pressure on the distribution operations, and picking is typically a time-critical task.
One of the effects of the faster deliveries is that it becomes much harder to smooth the volumes. As a consequence, the capacity requirement is based on a peak that is getting higher. This has a direct consequence for automated operations, which typically contain a higher fixed cost for this capacity. There are three options to deal with this. The simple, but expensive option is to provide capacity for the absolute peak, meaning the site runs at a lower utilisation throughout the year. The second option is to relax the service offering at peak, either by capping the volumes or by extending the service proposition. The third option is to provide low operating costs capacity for the majority of the time and complement it with a less efficient, but flexible capacity to deal with the peaks.
Using data analytics to improve order picking
Order picking is an area where data analytics can be leveraged for significant productivity benefits. Product level data, including usage at SKU level, can be leveraged to minimise travel time (either for the order pickers in a Person-to-Goods system, or equipment in a Goods-to-Person system). Order line data can help understand which products are likely to be combined in an order and would benefit from being stored in adjacent areas.
Analytics can also be used to better prioritise and schedule workload. By removing formal pick waves, and through continuous prioritisation of available work based on planned despatch time, stock positioning, resource availability and congestion, capacity can be better utilised and costs can be reduced.
Overall, it is clear that order picking is changing rapidly. The requirements for faster and lower costs operations, with higher service levels will continue to drive technology – both in terms of physical movement of people and products and in terms of data analytics.