Locus Robotics in Practice – Key Learnings

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1. Multibot Principle: Employees Work with the Fleet, Not Individual Bots

Locus uses what's called a multibot approach: Picking employees are not assigned to a specific robot, but work together with all AMRs in the vicinity. The robots each handle multiple orders simultaneously and guide operators step by step through their mission. This enables dynamic zone picking – scalable, collaborative and efficient even at high volumes.

2. Quick & Qualified: Why Rough Planning Is Often the Better First Step

Precise detailed planning takes time and data – but many companies first need to find out whether an AMR system is fundamentally suitable at all. That's exactly what the webinar focused on: With just a few inputs on floor space, productivity, operating hours and pick profile, a valid result could be modeled. This shortens decision-making processes and provides clarity on whether a deeper dive is worthwhile.

3. Flexibility Is Part of the System – Not Just the Software

A key argument for Locus: The system works without structural modifications and can therefore be adapted even when conditions change. Unlike stationary automation, no steel is installed, which simplifies planning and allows for later modifications. This applies to both layout adjustments and fluctuations in order volume or pick profile.

4. Performance Calculation Based on Realistic Parameters – Not Ideal Values

Productivity forecasts at Locus are not estimated, but modeled: Robot and employee cycle times, travel times, interactions with forklifts, navigation factors and peak scenarios all feed into the calculation. The evaluation is based on real process parameters – e.g. how many robots are deployed per mission, how travel paths are divided or how temporary employees change performance during peak periods.

5. The Business Case Hinges on Robot Fleet Size

In the case shown with a B2B/e-commerce mixed warehouse, productivity compared to the current state was doubled – while at the same time staffing requirements were reduced by around 50%. Planning showed a requirement of 24 robots for 10,000 pick positions per day. Particularly noteworthy: The ROI was realistically achievable in less than one year – partly because Locus works with a 100% rental model. Depending on volume and order distribution, temporary "peak bots" can also be added.

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