The increasing complexity of warehouse operations, particularly in tasks like picking, packing, and shipping, has led to longer training periods for new employees, and finding workers with the necessary skills has become more challenging.
So, what's the remedy - longer training and onboarding times for workers or AI-powered microservices guiding the workforce?
Addressing Labor Shortages and Operational Complexity in Warehousing
Warehouses today are grappling with rising costs, particularly in terms of their workforce, and with attracting and retaining personnel for tasks that may not always be challenging. Warehouse operations demand a level of precision and quality that is becoming harder to find within the available labor pool.
Furthermore, modern warehouses are experiencing greater variability in their daily workload, often with shorter lead times to complete tasks. This variability adds to the complexity of managing warehouse operations efficiently.
In response, companies are turning to AI-powered microservices which offer an attractive alternative, providing guidance to the human workforce, reducing the need for extensive training, and ensuring operational efficiency from day one.
The increasingly popular solution for optimizing warehouse operations – microservices - enables fast implementation and modification of functions without disturbing existing systems.
- Microservices allow for plug-and-play functionality in warehouse management systems (WMS), without the need for large upfront costs and enable businesses to adapt these services based on their operational needs, shifting from capital expenditure to operational expenditure through Automation-As-A-Service models.
- In scenarios where hardware automation falls short—such as operations with a large variety of stock-keeping units (SKUs) or complex pallet stacking—Microservices shine by enabling human-in-the-loop instructions and optimizations.
- Microservices can be used in test environments without integration, for proof testing, the "Proof-Of-Concept" phase. Within the existing WMS logic, workflows can be set up that decide whether to apply the app's logic in certain situations.
Case Study: Optimizing Pick & Pack Operations
A significant operational inefficiency encountered in warehouse operations was pallet building. Warehouse operators struggled to assemble stable pallets containing a variety of SKUs and SKU cases. This resulted in frequent restacking during picking, product damage, increased transportation costs, and a lengthy six-week training period for new employees to reach sufficient productivity levels.
However, with the assistance of software automation, we were able to calculate the ideal pallet stack for each order and determine the optimal position for each box on the pallet to achieve stability. This information is then visualized for the operator, along with the picking sequence.
Results
Based on the proof-of-concept (POC), it has been demonstrated that the palletization software results in a 7% increase in productivity. Operators no longer need to spend time choosing and restacking, thereby reducing the damage from tipping over to virtually zero and enabling operators to operate at full productivity in about 3 days.
Bottom Line
Microservices offer a viable solution to labor shortages in warehousing by optimizing operations and increasing productivity. By leveraging microservices, warehouses can adapt quickly to changing demands, minimize training periods, and reduce operational costs, ultimately ensuring smoother and more efficient operations amidst the ongoing labor shortage.