when-buying-more-forklifts-is-the-wrong-first-answer
title: "When buying more forklifts is the wrong first answer" description: "A hall that feels short on trucks often has empty travel and radio dispatch hiding free capacity. Documented RTLS and pull cases cut fleet and drivers before anyone ordered another machine." slug: "wiki/when-buying-more-forklifts-is-the-wrong-first-answer" type: wiki format: html section: "logistics-optimization" sectionTitle: "Logistics Optimization" topic: "impact" topicTitle: "Impact" readTime: "6 min read" order: 3
Delivery delays stack up. Utilization looks high. The purchase request for three more forklifts is already on the desk. Until someone measures empty loops and how jobs actually get claimed, that buy only adds more trucks to the same blind spots.
What the buy request is really answering
In many halls the only visible lever for late drops and stressed drivers is fleet size. Planners see trucks rolling and hear radio traffic. They do not see how much of that motion is empty travel between verbal instructions. When fleet management rests on gut feel, another lease looks safer than arguing about paths nobody recorded.
Industry practice says the same: plants that cannot measure fleet performance share tend to oversize the fleet, because busy-looking trucks still fail to protect the line. Pull-style forklift guidance and live location both exist to change that math before capital is committed. The twinzo stack puts claimable work beside live positions under internal logistics optimization and material order automation.
What one Tier-2 plant measured when paths replaced guesswork
A documented 30,000 m² (about 323,000 ft²) automotive Tier-2 plant ran 36 forklifts at roughly 80% utilization with 108 drivers across shifts. There was no live vehicle view. Route waste was invisible. Delivery delays were planned around from memory. The option on the table was more equipment.
After the logistics RTLS layer, the fleet fell from 36 to 20 forklifts (−44%) and drivers from 108 to 60 (−44%). Live 3D locations and route analytics exposed shift-by-shift discrepancies. Delivery delays were eliminated through data-driven routing. Investment was €120,000. First-year OPEX saving was €960,000. Path evidence for that class of decision sits under real travel paths on the floor.
Why pull strengthens the same fleet cut
On another documented 30,000 m² (about 323,000 ft²) Tier-2 hall, the coordination layer moved from push phone traffic to pull AOS (commonly known as FGS). Forklift patrolling dropped by more than 95%. Drivers stopped circulating for the next shout. That is the empty search that usually forces plants to rent more trucks when volume rises.
Most customers run both layers together. RTLS proves wastefull routes. AOS turns the next need into a work order a driver can claim. Coordination impact alone is under phone calls still coordinate most internal moves. The combined story is fewer vehicles because search and guesswork left the shift, not because someone drove faster.
What the stored record adds after the cut
Once positions and closed jobs share one hall, the plant keeps a spatially oriented dataset. Later correlation and causality analytics can ask which empty corridors keep preceding a starve, not only whether the fleet is smaller this month. Continuous improvement and management read that record as much as the logistics desk that signed the lease cancellation.
How teams decide before they buy another truck
1. Put one peak half-shift on spaghetti and empty share - If empty travel is high, rentals are the wrong first answer.
2. Count operator-to-driver calls on the same loop - High call volume usually means capacity is spent on search, not delivery.
3. Pilot pull on one high-traffic loop before a hall-wide cutover - Prove claim and trail quality under material order automation.
4. Compare the lease ask to documented first-year OPEX - The Tier-2 RTLS case above returned €960,000 against €120,000 invested.
Get in touch if you want to walk whether your next forklift buy is answering a capacity gap or a visibility gap.