Warehouse management systems find routes that will minimise the distance covered between storage and picking points. Parameters can be adjusted to apply various strategies that will boost efficiency levels while still ensuring that goods are handled correctly. For example, surplus stock can be stored overhead in areas that are accessed less frequently. It can then be moved in just a few steps to a picking location that is easier to get to and used for replenishment and for picking customer orders made up of small parts, which is much more common. Adding a warehouse management system to a storage strategy ensures that the storage and picking locations for goods are located near to one another. Another strategy involves goods being stored in an automated warehouse area and then being moved to the picking point as required. If goods are then picked manually, this is known as goods-to-person picking.
Examples of common strategies that are supported by digital warehouse management include:
Using ABC values as the basis for storage, with fast movers being put in prominent positions, so they can be accessed with speed and ease.
Positioning lightweight items on top of heavy ones and taking this into account when allocating picking locations and arranging storage to ensure that picking is efficient.
Condensing items in specific order groups within a manual storage system to keep the area to be called at for the picking order as narrow as possible or storing them apart when an automated storage system is in use to ensure that the equipment is utilised equally where possible.
Customer orders are collated in a warehouse management system and condensed into groups based on their staging times and locations, which mostly correspond to delivery tours. Orders are split on the basis of the areas in which the goods they include are stored and can be pooled together again at a later stage. These bundles of goods can be processed directly or as part of a multi-stage process in batches.
Orders are released when the control center or a rule-based automated system decides that an order needs to be processed soon. This usually happens on the basis of information on the staging time and the latest possible start time, which will have been recalculated taking into account employee and system capacity. Orders need to be released prior to the order start, which occurs when an order starts to be processed.
Before starting orders, the system should always be used to check that there are enough unfilled orders to guarantee that employee capacity and machine resources are distributed evenly. If too many orders are started at once, this can lead to bottleneck situations – especially in the case of automated systems that guarantee high output with capacity being distributed evenly, but also when manual systems are used. If too few orders are released despite there being further unfilled orders to deal with, the workflow will be less effective and employees and resources will not be deployed at full capacity.
Control centers can be supported by digital systems that display and compare process indicators such as the current system capacity, the usage of available resources, and the system performance. Key indicators include the number of orders still to be completed, the progress status of orders being processed, and the number of orders that have already been processed. These figures can be used to estimate the effort that is still required to achieve the target set for order processing.
When working with automated systems, it is also possible to visualise all of the automated processes in a supervisory control and data acquisition (SCADA) module, which helps staff in the control center to avoid bottleneck situations and perform troubleshooting. With integrated systems, allow for a drilling down into the individual elements of the system and then using the warehouse management system to access the units behind the goods being stored and transported.
Peter Totz works as Director Business Consultancy at SSI SCHAEFER.
His career began as a project engineer, data analyst and simulation specialist in Graz. With intermediate steps in production planning and as a logistics consultant, he worked as a senior consultant and project manager for many years. Later, he was responsible for business development in Latin America before taking over the lead of the globally active Business Consultancy Group.