Deployment Scenarios

What does an autonomous forklift deployment actually look like? Below are illustrative deployment patterns drawn from typical industry profiles — fleet composition, integration scope, timeline, and the order-of-magnitude outcomes that operators in each vertical commonly target.

About these scenarios. The profiles below describe typical patterns for the named industry archetypes — they are not customer testimonials or specific case studies. Quantified outcome ranges are based on industry research and our own deployment experience but will vary substantially by site. Request site-specific modelling for a quote and outcome projection tailored to your operation.
Scenario 1

Major Western Sydney 3PL Distribution Centre

Industry: Third-party logistics | Site: 60,000-90,000m² Eastern Creek / Erskine Park / Marsden Park | Shifts: 3

Operating Profile

  • 1,500-3,000 pallet movements per shift
  • 3-6 customer brands sharing the facility
  • Container-deep dewanning + cross-dock + buffer storage flow
  • Mature WMS (Manhattan, SAP EWM, or Korber)
  • 40-60 dock doors active across split shifts

Typical Fleet Composition

Typical Outcome Range

  • Direct labour displacement: 12-20 FTE across 3 shifts
  • Throughput increase: 15-30% same-footprint
  • Pallet damage: 30-50% reduction
  • Pedestrian incident risk: substantial reduction
  • Payback: 18-30 months on labour cost alone

Implementation Timeline

  • Week 1: Site survey + WMS API discovery
  • Week 2-3: System design + fleet sizing
  • Week 4-7: Truck procurement (in parallel)
  • Week 8-10: Floor mapping + commissioning
  • Week 11-12: Operator training + go-live
Scenario 2

Mid-Tier Frozen Food Distribution DC

Industry: Frozen food / cold chain | Operating temperature: -22°C to -25°C | Shifts: 3

Operating Profile

  • 800-1,500 pallet movements per shift
  • Ice cream, frozen meals, frozen meat mix
  • Manual operators rotating every 45-90 min into warm-up
  • Effective productive time per operator: ~5 hours per 8-hour shift
  • 15-25% understaffed permanently due to recruitment difficulty

Typical Fleet Composition

Typical Outcome Range

  • Effective operating hours per truck: 22+ per day (eliminates rotation)
  • Operator wage premium recovered: 18-30%
  • Recruitment + training cost reduction: very high
  • Throughput per refrigerated m²: 25-40% improvement
  • Payback: 14-22 months (fastest-returning vertical)

Implementation Timeline

  • Week 1: Cold-zone site survey + thermal mapping
  • Week 2-4: System design (frozen-spec engineering)
  • Week 5-9: Frozen-spec truck procurement
  • Week 10-12: Cold-zone mapping + commissioning
  • Week 13: Training + go-live
Scenario 3

Wine Logistics During Vintage Season

Industry: Wine logistics | Region: Barossa Valley / McLaren Vale / Hunter Valley | Pattern: Seasonal surge

Operating Profile

  • Year-round baseline: ~400 pallet movements per shift
  • Vintage surge (Mar-May): 4-6× baseline
  • Bottle-pallet handling demands gentle acceleration
  • Cellar-to-pack-house-to-dispatch flow with temperature zones
  • Export documentation requires batch-level traceability

Typical Fleet Composition

Typical Outcome Range

  • Vintage seasonal labour need: eliminated
  • Bottle breakage: 60-80% reduction vs operator average
  • Export documentation accuracy: substantial improvement
  • After-hours pack-house labour cost: removed
  • Payback: 24-36 months (seasonal pattern slows ROI vs 24/7 operation)

Implementation Timeline

  • Best deployed during quiet season (Jun-Feb)
  • Week 1-2: Cellar + pack-house mapping
  • Week 3-5: System design (multi-zone)
  • Week 6-9: Truck procurement
  • Week 10-12: Mapping + commissioning + WMS integration
  • Week 13-14: Pre-vintage validation + go-live
Scenario 4

Automotive Manufacturer JIT Line-Feeding

Industry: Automotive components / heavy manufacturing | Pattern: Takt-time-driven line replenishment | Shifts: 2-3

Operating Profile

  • Production line cycles every 60-180 seconds
  • JIT material delivery to 8-15 line-side drop points
  • eKanban replenishment from buffer warehouse
  • Mixed-model production (multiple variants on same line)
  • Manual milk-runs currently lose 2-4 deliveries per shift to scheduling slip

Typical Fleet Composition

Typical Outcome Range

  • Line stoppages from material outage: substantial reduction
  • Schedule adherence: 99%+ vs 80-92% manual
  • Tugger train operator cost: eliminated
  • Schedule volatility absorption: high (software-defined route changes)
  • Payback: 18-24 months on tug operator + line stoppage cost

Implementation Timeline

  • Week 1: Line survey + takt-time analysis
  • Week 2-3: Route design + MES integration scope
  • Week 4-7: Truck procurement
  • Week 8-10: Mapping + commissioning
  • Week 11-12: Mixed-model variant validation + go-live
Scenario 5

Mining Supply Spares Warehouse

Industry: Mining supply / resource sector | Region: Pilbara / Bowen Basin / Goldfields | Shifts: 2-3

Operating Profile

  • High-value spares (single SKU values $5k-$2M)
  • Heterogeneous size range: from O-rings to 4T grinding mill liners
  • FIFO mine-site labour competition makes operator recruitment hard
  • Heat (40-45°C summer) and dust (red iron-ore) ingress
  • 99.5% on-shelf availability KPIs typical

Typical Fleet Composition

Typical Outcome Range

  • FIFO labour avoidance: full
  • Cycle counting accuracy improvement: 99%+ from typical 95-97%
  • Critical-spare picking priority: software-driven, no roster lag
  • Heat-related operator productivity loss: eliminated
  • Payback: 24-36 months (longer for high-value, lower-volume warehouses)

Implementation Timeline

  • Week 1-2: Site survey (factor in remote-region access)
  • Week 3-5: Heat/dust spec engineering review
  • Week 6-10: Truck procurement (longer lead with site-specific spec)
  • Week 11-13: Field commissioning + remote-diagnostic setup
  • Week 14-15: Training + go-live
Scenario 6

SME Manufacturer Pilot Deployment

Industry: Mid-sized manufacturing or 3PL | Pattern: Single-truck pilot | Shifts: 1-2

Operating Profile

  • 200-500 pallet movements per shift
  • Single-task focus (e.g., dock-to-stage replenishment)
  • Pre-existing manual fleet retained alongside
  • 3-12 month pilot before broader rollout decision
  • Defined exit criteria for fleet expansion or pilot conclusion

Typical Fleet Composition

Typical Outcome Range

  • Operational confidence built within 8-16 weeks of operation
  • Throughput on the piloted task: matches or exceeds manual
  • Pilot-to-production conversion: ~80% of well-scoped pilots
  • Lessons learned roll into multi-truck Phase 2 deployment
  • Payback (single truck): 24-48 months — lower than full-fleet ROI

Implementation Timeline

  • Week 1: Pilot scope definition + KPI agreement
  • Week 2-3: Site survey + integration design
  • Week 4-6: Truck procurement
  • Week 7-8: Mapping + commissioning
  • Week 9-10: Operator training + pilot go-live
  • Months 4-12: Operation + monthly KPI review + Phase 2 decision

Want Modelling for Your Site?

Each of the scenarios above represents a class of deployment, not a specific customer. Your site will have its own throughput profile, building constraints, and integration requirements that change the fleet composition and outcomes. Site-specific modelling gives you accurate fleet sizing, capex, 5-year TCO, and payback period before you commit.

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