Labor is the single largest operating cost in most DCs, and travel often accounts for half of all labor time, especially in order picking. Lucas travel optimization uses advanced mathematical models and artificial intelligence (AI) to reduce travel 30-70%. Customers have more than doubled picking productivity and cut labor hours in half without making any changes to their warehouse layout or adding expensive automation or robotics.
We use order, inventory, and location information from WMS and other systems and Jennifer™ – the brain, voice and orchestration engine of our system – applies real-time optimization algorithms to create optimal work assignments. Jennifer™ then uses a virtual model of your facility to define an optimal pick sequence or travel path.
Lucas uses order, inventory, and location information from WMS and other systems and Jennifer™ – the brain, voice and orchestration engine of the Lucas system – applies real-time optimization algorithms to create optimal work assignments. Jennifer™ then uses a virtual model of your facility to define an optimal pick sequence or travel path.
Jennifer™ prioritizes all available work assignments based on rules that you can configure and adjust throughout the day or week.
Rather than following a simple first-in-first-out batching method, Jennifer™ uses mathematical models to create optimized batches of pick lines and assigns them to pickers on-demand, as they request work. In contrast to typical WMS batching rules, the Lucas models weigh a variety of factors, including:
1) Order priority
2) Product locations
3) Product dimensions
4) Starting and ending points
5) Potential travel path
6) User permissions
After batches are created, Lucas applies multiple algorithms to determine an optimized path for the user to take through the warehouse to complete their work. The algorithms consider aisle directions (one-way aisles, for example), base item designations, and other factors to determine the most efficient pick path.
While Jennifer™ orchestrates and optimizes within picking zones, a DC can add robots for conveyance between pick zones or other staging locations. This can further reduce worker travel to and from fixed drop off or induction points. Likewise, Jennifer™ can be used to optimize the travel of robots for full pallet moves, both in inbound and outbound operations. Learn more →
We helped Baptist Health, a leading healthcare provider, double picking productivity.
“The previous average picking rate in the LUM area was 50-56 lines per hour (LPH). Today it is 100-110. That is a 100% improvement, and in bulk picking, the number is about 20 percent.”
-Dale Adamson, AVP of Logistics and Distribution
We helped Ace Endico, a leading regional food supplier, increase selector productivity by 28%.
“With Lucas, we have improved the productivity and quality of life for selectors.”
-Murray Hertzeberg, President
Interested in seeing how our AI-based travel optimization tool works and what it looks like? Watch our 10 minute recorded demo.