Tackle Pick Labor Challenges in Warehouses Using Optimization

Dr. Manjeet Singh, Research Director, DHL Supply Chain
Dr. Manjeet Singh, Research Director, DHL Supply Chain

Dr. Manjeet Singh, Research Director, DHL Supply Chain

Background

Labor costs for picking intensive fulfillment operations can be up to 80% of the total warehousing costs. A significant portion of these costs occur due to sub optimal labor utilization. There are three important functions which could be improved significantly from current practices to optimize labor utilization: wave planning, tasking, and labor allocation.

Challenges

• Wave Planning - waving in warehouses refers to grouping a predetermined number of orders together to be sent to the warehouse floor for processing. Waving large number of orders can createefficiencies; however, it presents challenges as well. Waves are released sequentially one after the other by the WMS (Warehouse management System). This leads to high idle time for operators between waves as lagging work needs to be completed prior to closing of the wave.

The opposite of sending large waves is single order streaming. It releases one order at a time after enough capacity is generated downstream. This approach helps in improving resource utilization and reduces the wait time. However, it invariably results in low pick density and therefore low pick productivity and low throughput.

• Tasking- tasking refers to the process of dividing a wave into discrete group of orders which will be picked in distinct pick assignments by multiple pickers.Generally WMS’ use FCFS (First Come First Serve) method to generate these tasks. Theseorders could have multiple itemsfrom various locations and aisles in the warehouse. WMS groups these orders together using FCFS, therefore, it does not take the proximity of items within different orders into consideration while creating each task. Invariably the assignments generated using FCFS methodology will generate few picks across many aisles. This leads to low pick density and longer pick paths for pickers when they travel to pick these tasks. Finally, it results in low pick productivities, high order cycles times, and low throughput in warehouses.

  ​There are three important functions which could be improved significantly from current practices to optimize labor utilization: wave planning, tasking, and labor allocation   

• Labor allocation – in warehousing, work varies between different functions, like picking, sorting, packing, and shipping, throughout the day. Labor assignment is done at beginning of the shift and then is seldom reassessed. Therefore, warehousing labor ends up working in a specific function for the entire shift or day. This leads to an imbalance of number of workers with respect tothe requirement in different functions atvarious times during a shift. It reduces labor utilization and throughput.

Solution

The solution lies in a software system with threereal time analytical algorithms for intelligent execution of warehousing functions.WMS will have to continuouslyfeedthese algorithms with liveoperational statistics like tasks assigned, tasks completed, capacity, item locations etc.

Wave optimization algorithm will find suitable orders to be sent to the floor based on available capacity, even distribution of picks across aisles etc. Afterwards, from these chosen groups of orders or wave, the second algorithm called task optimization will build optimal tasks. This algorithm will combine orders whose items are in close proximity. In doing so it will minimize pick path for pickers. Finally, dynamic labor allocationoptimizationalgorithmwill receive inputs based on activity in each function. Then this algorithm will make labor re-allocation decisions based on current assignments, labor skills, requirement in other functions etc.These three analytical algorithms, working in tandem, will make waving, tasking and picking more efficient. The result would be improved pick productivity, order cycle times and increased throughput.

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