A warehouse internal logistics case study examining RaaS versus direct ownership — throughput modelling, fleet economics, and capital allocation under utilisation uncertainty.
RaaS vs. OwnershipUtilisation EconomicsProgrammable Infrastructure
01 / 10 — System Definition
Defining the Operational Unit
Internal logistics is the movement layer between storage, production, and dispatch. In a mid-size facility, this layer runs at high frequency across four categories — managed through human-operated forklifts with no system-level coordination.
Internal logistics is a high-frequency, zero-margin-for-error operational layer. Throughput constraints here directly cap order fulfilment capacity — it is the binding constraint on warehouse output.
02 / 10 — Baseline Model
System Throughput Under Human Ops
Throughput Derivation
Parameter
Input
Value
Fleet size
Given
10 operators
Output per operator / hr
Observed avg
~40 mov
Gross system throughput
10 × 40
400 mov/hr
Effective throughput (−15% idle)
400 × 0.85
~340 mov/hr
Operational throughput ceiling
400 mov/hr
Annual Cost Structure
Cost Item
Formula
Amount
Operator monthly salary
Market rate
₹25,000
10-operator monthly payroll
₹25,000 × 10
₹2,50,000
Annual payroll
₹2,50,000 × 12
₹30,00,000
Daily output (2 shifts × 8 hrs)
400 × 16
6,400 mov/day
Unit cost per movement
₹4.69 / mov
400 mov/hr is a structural ceiling. Congestion rises nonlinearly past 10 operators — additional headcount degrades per-operator output without increasing system throughput.
Throughput Ceiling
400
mov / hr · hard limit
Annual Payroll
₹30L
10 operators · 12 months
Unit Cost / Movement
₹4.69
baseline human ops
03 / 10 — AMR Fleet Model
AMR Fleet Throughput Model
Per-Robot Throughput Derivation
Parameter
Formula
Value
Avg movement distance
Warehouse est.
~60 m
Robot speed
Spec
1.5 m/s
Transit time per trip (1 way)
60 ÷ 1.5
40 sec
Round trip + load/unload
40×2 + 40 buffer
~120 sec
Movements per robot / hr
3600 ÷ 120 × eff.
~80 mov/hr
Fleet Throughput Calculation
Item
Formula
Result
Throughput per robot
Derived above
80 mov/hr
Fleet size
Given
10 robots
Fleet throughput / hr
80 × 10
800 mov/hr
Daily operating window
Spec
20 hrs/day
Daily fleet output
16,000 mov/day
Fleet throughput: 800 mov/hr — 2× the human baseline. Daily output of 16,000 movements covers the full demand envelope with 100% operational headroom for surge capacity.
R01
R02
R03
R04
R05
R06
R07
R08
R09
R10
FLEET OUTPUT
800 mov/hr
Daily Fleet Output
16,000
movements / day · 20 hr window
04 / 10 — RaaS Model
RaaS: Procuring Throughput, Not Hardware
RaaS Cost Build-up
Line Item
Formula
Amount
Per-robot monthly fee
Subscription rate
₹90,000
Fleet size
Given
10 robots
Monthly fleet cost
₹90,000 × 10
₹9,00,000
Annual fleet cost
₹9,00,000 × 12
₹1,08,00,000
Cost per movement
₹1.08 Cr ÷ (8000 × 365)
₹0.37 / mov
RaaS vs. Human Cost Comparison
Model
Annual Cost
Cost / Movement
Human forklift ops
₹30,00,000
₹4.69
RaaS subscription
₹1,08,00,000
₹0.37
Cost efficiency gain
92% lower / mov
RaaS eliminates the ₹1.8 Cr capex decision entirely — automation capacity becomes a ₹1.08 Cr annual operating line. Fleet scaling is a contract amendment, not a capital allocation event.
CUSTOMER RECEIVES
Automation Capacity
SUBSCRIPTION CONTRACT
₹90,000 / robot / month
MAINTENANCE + SUPPORT
SLA-backed · Provider-managed
FLEET MANAGEMENT
Route Optimisation · WMS Integration
HARDWARE LAYER
10 × AMR Robots
ANNUAL FLEET COMMITMENT
₹1.08 Cr / yr
05 / 10 — Output Comparison
2× Throughput. Same Footprint.
Human Fleet 10 operators
400 MOV/HR
400/hr
AMR Fleet 10 robots (RaaS)
800 MOV/HR
800/hr
Metric
Human Fleet
AMR Fleet (RaaS)
Δ Delta
Units in fleet
10 operators
10 robots
—
Output per unit / hr
~40 mov
~80 mov
+100%
System throughput / hr
400 mov
800 mov
+400 mov
Daily operating hours
16 hrs (2 shifts)
20 hrs
+4 hrs
Daily movements output
6,400 mov
16,000 mov
+9,600 mov
Demand coverage (8,000/day)
80% capacity used
50% capacity used
+100% headroom
Continuity
20 hr
continuous daily operation
Routing
Zero Aisle Congestion
demand-driven scheduling
Headroom
+100%
surge capacity available
06 / 10 — Ownership Model
Direct Ownership: Capex Model
Capex Build-up
Item
Formula
Amount
Unit robot cost
Market price
₹18,00,000
Fleet size
Given
10 units
Total capex outlay
₹18,00,000 × 10
₹1,80,00,000
Useful life (SLM)
Assumed
5 years
Annual depreciation
₹1,80,00,000 ÷ 5
₹36,00,000
Monthly depreciation
₹36,00,000 ÷ 12
₹3,00,000 / mo
Depreciation Schedule (SLM)
Year
Depreciation
Cum. Charged
Book Value (EOY)
Year 1
₹36,00,000
₹36,00,000
₹1,44,00,000
Year 2
₹36,00,000
₹72,00,000
₹1,08,00,000
Year 3
₹36,00,000
₹1,08,00,000
₹72,00,000
Year 4
₹36,00,000
₹1,44,00,000
₹36,00,000
Year 5
₹36,00,000
₹1,80,00,000
₹0 — fully depreciated
Straight-line depreciation: ₹36L / yr over 5 years. The full capex is charged to P&L as a non-cash expense — creating a deferred tax benefit analysed in the next section.
DEPRECIATION TIMELINE
CAPEX OUTLAY ₹1,80,00,000
Yr 1
₹36L
BV: ₹144L
Yr 2
₹36L
BV: ₹108L
Yr 3
₹36L
BV: ₹72L
Yr 4
₹36L
BV: ₹36L
Yr 5
₹36L
BV: ₹0
TOTAL DEPRECIATION (5 YRS)
₹1,80,00,000
07 / 10 — Tax Analysis
Depreciation as a Capital Tool
Straight-line depreciation creates a non-cash P&L charge that reduces taxable income without a cash outflow — improving ownership IRR relative to RaaS.
Scenario A — No Robot
EBIT (pre-depreciation)₹80,00,000
Less: Depreciation₹0
Taxable Income₹80,00,000
Tax @ 25%₹20,00,000
Net Profit (post-tax)₹60,00,000
Scenario B — Owns Robot
EBIT (pre-depreciation)₹80,00,000
Less: Depreciation (non-cash)−₹36,00,000
Taxable Income₹44,00,000
Tax @ 25%₹11,00,000
Net Profit (post-tax)₹69,00,000
Item
Formula
Value
Annual tax shield
₹36L × 25%
₹9,00,000 / yr
Cumulative shield (5 yrs)
₹9L × 5
₹45,00,000
Gross capex outlay
Given
₹1,80,00,000
Effective net capex (post-shield)
₹1.80 Cr − ₹0.45 Cr
₹1,35,00,000
Annual Tax Shield
₹9L
per year · non-cash
5-Year Cumulative Shield
₹45L
reduces effective capex
Effective Net Capex
₹1.35 Cr
post tax shield
08 / 10 — Capital Allocation Framework
The Real Decision: Capital Structure Under Utilisation Uncertainty
This is not a technology decision. It is a capital structure decision — the choice between variable opex flexibility and the long-run cost efficiency of owned, fully-utilised assets.
Decision Factor
RaaS (Opex)
Ownership (Capex)
Upfront capital required
₹0 — fully variable
₹1.80 Cr at acquisition
Annual cash outflow
₹1.08 Cr / yr fixed
₹0 post-purchase
5-yr total outflow
₹5.40 Cr
₹1.35 Cr (post tax shield)
Balance sheet treatment
Off-balance · opex line
Fixed asset · on-balance
Tech obsolescence risk
Nil — provider's risk
Stranded asset exposure
Fleet scale flexibility
Contract amendment
Capital re-decision
Preferred utilisation scenario
Uncertain / seasonal
Predictable · >80%
Ownership advantage threshold
>80% utilisation over 3+ year stable horizon
Utilisation Certainty →
Low Util + Asset-Light
Neither
High Capex Appetite
Ownership
Uncertain Utilisation
RaaS ✓
High Util + Asset-Heavy
Ownership ✓
Low Capex AppetiteCapex Appetite →
09 / 10 — Model Summary
Three-Scenario Comparison
Metric
Human Ops
RaaS
Ownership
Upfront capex
₹0
₹0
₹1.80 Cr
Annual recurring cost
₹30L payroll
₹1.08 Cr
₹0 post-purchase
5-yr total cost
₹1.50 Cr
₹5.40 Cr
₹1.35 Cr eff.
System throughput / hr
400 mov
800 mov
800 mov
Unit cost per movement
₹4.69
₹0.37
₹0.23 (5-yr avg)
Optimal scenario
Low volume, stable
Uncertain demand
High utilisation (>80%)
Capital allocation · not tech selection · Utilisation drives the decision · Fleet software is the multiplier
10 / 10 — Systems Conclusion
Warehouse robotics is not about automation.
It is about converting internal logistics into programmable infrastructure — a schedulable, measurable, capital-allocatable system layer that responds to demand signals rather than shift structures.