With this blog article series, we provide you guidance through the jungle of technical innovations in logistics. The first article took a closer at Warehouse Picking (Humanoid) Robots. This article provides insights about another core innovation that Gartner defined in its Hype Cycle for supply chain execution technologies: warehouse resource planning and scheduling.
Planning and optimization of warehousing tasks
While warehouse picking (humanoid) robots are very tangible, warehouse resource planning and scheduling is a pure software innovation. The main concept is the application of predictive constraint-based planning and optimization of warehousing tasks. While manufacturing plants have been using these techniques for more than 30 years, warehouses mainly allocate tasks using rudimentary principles like task interleaving and wave planning.
A miniscule number of vendors offer forward-planning capabilities (such as labor and equipment planning) in their warehouse management system (WMS) solutions. As market factors drive the need for increased efficiency and labor costs lead to a high demand for expanded competencies, vendors are required to keep pace with the growing demand by including more advanced methods that consider resources, planning and constraints. Gartner places current market solutions as early-stage developments that are suitable for early adopters but will mature over time and will be used by a wider audience.
Results from Market Research
During our market research, we identified two main differences in the application of AI in WMS for predictive restricted resource allocation and optimization. The first approach is used to integrate AI into an overall WMS, while the second approach offers an individual add-on to the existing WMS. An example of an add-on is shown in the picture below:
Overview of the RedPilot Add-on Modules by RedPilot 
There is a wide variation in how AI is used in these WMSs. In some cases, predictive analysis can identify incoming regularities and the system reacts accordingly. In other cases, predictive analysis can consider and forecast external factors (e.g., transportation delays or return probabilities) to reduce their negative input and increase capabilities for, e.g., capacity utilization, batching or overall resource allocation. There are also other use cases: dynamic allocation of roles, pro-active prioritization, route optimization for forklift or other modes of transport and process optimization through camera or data input.
How CAMELOT rates this trend
Warehouse resource allocation and scheduling can lead to desired efficiency and provide predictions for upcoming events. However, our research shows that the available solutions are still at an early stage. The application of AI in WMS for resource management will shift operative and middle management from reacting to irregularities to acting: For example, by sending a picker to the packing area because picking outpaces packing. Additionally, the focus will shift from controlling and observation towards the latter.
Complex warehouses are recommended to consider the use cases as the first. However, all warehouse types and fields can include warehouse resource allocation and scheduling software in their digital warehouse target vision. The use of AI in WMS can improve not only resource allocation and process optimization, but also incident prediction. To apply this innovation, a clean database in combination with solid master data management is required.
Innovations like warehouse resource planning and scheduling have great potential, the solutions available so far are still at an early stage. Even in a later stage of market readiness, innovations like warehouse resource planning and scheduling still need human acceptance, especially considering the change efforts that have to go along with it. Thinking along the lines of possibilities and benefits for one’s own organization is likely to help with the definition of a suitable target vision.
To find out more on the possibilities in digital warehousing, we invite you to turn to CAMELOT´s market research on AI in warehousing. If you want to know more about additional ai-driven innovations or the integration of innovative technologies into your digital warehouse future vision, contact us.
We would like to thank Thomas Grill and Albert Peychal-Heiling for their valuable contribution to this article.