forklift-productivity-from-sixty-three-to-seventy-five
title: "Forklift productivity from 63% to 75%" description: "Stopwatch studies cannot keep up with every shift. Documented RTLS reporting lifted forklift productivity from 63% to 75%, cut two trucks, and freed four FTEs when pull removed search noise." slug: "wiki/forklift-productivity-from-sixty-three-to-seventy-five" type: wiki format: html section: "logistics-optimization" sectionTitle: "Logistics Optimization" topic: "impact" topicTitle: "Impact" readTime: "6 min read" order: 5
An SCM engineer still books a temporary worker with a stopwatch for four days whenever anyone asks how productive the fleet is. By the time the spreadsheet lands, the next shift pattern has already changed. Daily effectivity across all shifts at once was never on the table.
What manual effectivity actually costs
Before live tracking, many plants have no transport history database for forklifts, no route trails, and no OEE-style measure for movers. Effectivity calculation depends on a part-time stopwatch study. That work cannot run every day across every shift. Average effectivity for fifteen forklifts in one documented Tier-2 hall sat at 63% under that method.
Industry practice is moving the same question onto continuous location and dispatch records because empty travel and idle loops hide inside “busy” utilization. twinzo answers with logistics RTLS under internal logistics optimization, and with claimable jobs under material order automation when radio search still inflates the busy picture.
What the RTLS report changed on a 30,000 m² Tier-2 site
A documented 30,000 m² (about 323,000 ft²) automotive Tier-2 plant invested €81,000 in logistics optimization with RTLS. Daily reports included spaghetti diagrams for all forklifts with downtimes and tracking for the last 24 hours. Efficiency data could export to spreadsheets. Productivity rose from 63% to 75%. The fleet fell from 15 to 13 forklifts. Four FTEs were freed. First-year OPEX saving was €179,000.
That impact is the hiring and lease decision, not a prettier map. Loaded versus empty share for the same class of proof sits under loaded vs empty travel you can prove. Path overview is under real travel paths on the floor.
Why pull keeps the productivity number honest
On a documented Tier-2 hall of the same size band, AOS (commonly known as FGS) cut operator-to-driver phone calls by about 90% and eliminated over 95% of forklift patrolling. When drivers stop circling between radio jobs, utilization stops counting search as productive motion.
Customers who run both layers get a cleaner productivity story: RTLS measures the path, AOS removes the coordination waste that used to pad the clock. How the request becomes a claimable work order is under how a floor request becomes a work order.
What the daily record feeds next
Every closed trail adds to the spatially oriented dataset. Correlation and causality analytics can later ask which empty corridors keep preceding a starve, not only whether yesterday’s productivity cleared 75%. Continuous improvement owns that question as much as logistics.
How teams decide the stopwatch era is over
1. Ask whether effectivity can be computed tomorrow morning for every shift - If the answer needs a temp and four days, you are still in the before state.
2. Separate productive loaded time from empty repositioning on one peak day - Medium-precision location around 1–3 m (3–10 ft) is enough for corridor-scale splits.
3. Measure call and patrol share if utilization looks high while lines wait - Pull may be the missing half of the productivity gain.
4. Attribute euros to the module that earned them - Keep the €179,000 OPEX story on the RTLS case above. Capability depth sits on the features overview. Rollout shape is under pricing.
Get in touch if you want to replace the next stopwatch study with a daily fleet picture on your own hall model.