Model cost per delivery, attempt cascade, zones, own vs 3PL mix and returns. Find per-zone break-even and the optimal split. AI, 3 scenarios. Free.
Advanced simulator
Which zones should I price up or drop?
Find which zones stop being profitable, where to raise prices or stop serving, and whether your own fleet or a 3PL fits better.
Volume
Total daily orders and monthly operating days.
Delivery zones
The three zones sum to 100%. Each has its own density, distance, service time and success rate.
Σ 0%
UrbanDowntown / CBD, high density.
SuburbanMedium residential areas.
RuralLow density, long distances.
Own fleet
Vehicles, drivers and direct costs of the in-house operation.
3PL / outsourced
Share of volume handled by an external carrier. Their fees may include success, failed attempt and return.
Revenue, returns and SLA
Revenue per delivery, real returns, fixed overhead and SLA-miss penalties.
Saved configurations
Fill in your data to see the report
This simulator only generates a diagnosis, charts and recommendations when it has your real business values. Fill the editor above and the report will appear automatically.
Required data
Orders per day
Operating days per month
Own vehicles
Driver cost per hour
Vehicle cost per day
Fuel price
Revenue per delivery
Fixed monthly ops cost
Want to explore it first?
Load a realistic case to see how the report looks. You can edit any field afterwards.
Internal editorial estimate based on industry best practices.
How it works
1. Declare volume and zones
Orders/day, operating days and the urban/suburban/rural mix with its own distance, service time and FTR.
2. Own fleet and 3PL
Own vehicles with costs, and the 3PL mix with its fees per success, failed attempt and return.
3. Revenue, returns and SLA
Revenue per delivery, return rate, fixed overhead and SLA penalty. The simulator crosses everything and compares 3 scenarios.
Frequently asked questions
1How is this simulator different from the Delivery Routes one?
Delivery Routes solves fleet + density with Daganzo (where to operate and with how many vehicles). This simulator focuses on per-attempt and per-zone economics, and on the own-vs-3PL decision. Use them together: first size the operation with Routes, then decide the operating model with Last-Mile.
2How does FTR affect real cost per delivery?
Cost per successful delivery is amortized over attempts. With 90% FTR you need 1.11 attempts per success; with 70% FTR that jumps to 1.43 — 29% more direct cost. If the failure also costs money (driver + fuel without revenue), real impact is higher. That is why serious operators invest in notifications, windows and PUDO before adding fleet.
3When is own fleet better than 3PL?
Rough rule: own is cheaper when density is high and volume fills the shift. 3PL is cheaper when density is low (rural), volume is irregular, or the mix requires wide geographic coverage. The simulator shows per-zone margin on both channels — if your own loses money in urban, something is off (low utilization or inflated costs).
4What do I do if my rural zone is negative?
Three routes: (1) repricing — charge an explicit rural surcharge; (2) consolidate — deliveries every 2-3 days instead of daily; (3) outsource to a rural-specialized 3PL. What does NOT work: raising the general service price to 'cover' rural — you kill your competitiveness in urban, where you actually make money.
5How do returns fit in the model?
We model two different things: failed attempts (could not deliver, retry) and real returns (you delivered but the customer sent it back). Each has its own cost. A return rate >10% is typical for apparel and can double your real cost per successful delivery — that is why many ecommerce operations never reach positive margin until they attack that number.
1What percentage of shipping cost is last-mile delivery?
Between 41% and 53% of total shipping cost per Capgemini and CSCMP 2024. In dense urban (Manhattan, Chicago Loop, London Zone 1) it can exceed 60%. It's the most expensive segment from low stop density (20-50 on B2C vs 200+ consolidated), tight time windows, failed attempts, returns and rising velocity expectations.
2At what volume does an in-house fleet make sense?
Typically >200-300 daily deliveries concentrated in dense zones with stop radius <10 miles. Below that threshold, fixed cost of vehicles, drivers and dispatch doesn't amortize vs marginal cost of a 3PL (which variabilizes with volume). For operations with strong seasonal demand, hybrid almost always wins: in-house fleet for constant base + 3PL and crowdsource for peaks.
3What is a good first-time delivery success rate (FTDR)?
88-94% in healthy urban; below 85% signals a problem. Main levers: 3-6h time windows (instead of 8-12h), in-transit notifications (SMS 30-60 min out), authorized-neighbor fallback, PIN/OTP at the door, and PUDO as alternative. A failed attempt costs 1.5x-3x the original shipment, so lifting FTDR 5 points typically reduces total cost 10-15%.
4What is PUDO?
Pick-Up / Drop-Off points — network of points where customers pick up or return parcels: Amazon Lockers, UPS Access Point, FedEx OnSite, OXXO in Mexico, Parcelly in UK. Key advantage: density collapses cost per drop (50-200 deliveries at one point vs 50 at 50 doors). Cost per package 30-60% lower than home delivery. US penetration 15-25%; LatAm 8-14%; Europe 30-45%.
5What are dark stores?
Mini urban warehouses not open to the public, 2-5 km from final customer, built for fast fulfillment (10-30 min). Getir, Jokr, and in LatAm Rappi Turbo, Merqueo model. Economically viable in high density (>400 daily orders per dark store) and core urban. Enable same-day and same-hour delivery with competitive cost per drop through ultra-concentrated geography.
6Does crowdsourced delivery work for e-commerce?
Yes for peaks and variable demand, not as backbone. Platforms (Uber Direct, DoorDash Drive, Amazon Flex, Roadie) give immediate capacity elasticity but carry variable quality and high turnover. Successful model is hybrid: backbone with in-house fleet or stable 3PL + crowdsource for specific window peaks (season, daily peaks, unplanned same-day). Growing regulatory risk in US, LatAm and Europe (gig-worker classification, rider reform).
7How do I reduce return rate in e-commerce?
Four measured-impact levers: (1) interactive sizing with 3D models or augmented reality (−15-25% fashion); (2) locker/PUDO return instead of logistics pickup (avoids 40% of carrier returns); (3) rich product description with video and reviews (closes expectation-reality gap); (4) fit prediction based on customer history (−10-18% size-driven returns). Benchmark fashion return rate 25-40%; electronics 8-12%; general e-commerce 15-20%.
What you'll see, what it prevents, and where you shouldn't trust it
Every simulator on Simúlalo ships with the same editorial structure: two hypothetical worked examples with numbers, the errors it helps you avoid, the model's declared limitations, and a visible financial disclaimer. The review is signed and dated.
Hypothetical case·Case A
An e-commerce that finds out 'same-day' loses $42 per delivery
An e-commerce offers 'same-day' in CDMX at $89/delivery cost (dedicated rider, higher FTR, hub investment). Customer charge is $69 plus product. The simulator decomposes: logistics revenue $69, cost $89 = loss $20. After adding returns (4.5% on same-day vs 2.1% on standard) and re-delivery costs, the real loss is $42/delivery. Volume: 720 same-day deliveries/mo = $30,240/mo loss. The decision: charge $109 for same-day or limit availability to high-density postal codes where cost falls to $58.
Illustrative figures. Does not represent a real company or an investment recommendation.
Hypothetical case·Case B
A marketplace that postpones expansion where break-even requires 14 deliveries/day
A marketplace evaluates opening its own operation in a secondary city of 380,000 people. Initial study projects 2,400 orders/mo by month 6. The simulator, with hub fixed cost ($120,000/mo), 6 riders, 5% returns, and 48h SLA, computes break-even at 14 deliveries/day per rider — and the projection is 13. The decision: postpone the proprietary expansion until organic volume exceeds 3,000 orders/mo; meanwhile operate with a local 3PL with smaller margin but controlled risk.
Illustrative figures. Does not represent a real company or an investment recommendation.
Common mistakes it helps you avoid
Things a team or decision-maker might assume that this simulator forces you to verify before committing.
Calculating last-mile cost on successful delivery only, without adding the failures: if FTR is 88%, the remaining 12% returns, retries, or refunds, and that multiplies cost.
Subsidizing 'free shipping' without measuring the margin impact: free shipping lifts conversion, but if unit cost doesn't amortize with higher AOV (average order value), the business breaks.
Mistaking SLA for capability: an aggressive SLA (same-day) is a commercial commitment, not an operational capacity — if your operation can't sustain it, ratings drop and the loss compounds.
Treating all zones as equal: unit cost in central zones can be 3-5x lower than periphery. Profitability per zone defines the coverage strategy.
Model limitations
What the simulator does not do, and where you need a professional or a specialized tool.
Does not optimize routes. For dynamic routing use OR-tools, OptimoRoute, OnFleet, or similar. This simulator models aggregate cost from declared densities and SLAs.
Does not include specific operational frictions: load caps per motorcycle, hour restrictions in pedestrian zones, hospital delivery windows.
3PL costs are market reference values. For a real decision, request quotes by zone and volume.
Break-even calculation assumes you can scale fleet linearly. In practice, hiring and training riders takes 30-60 days.
When NOT to use this simulator
If you're going to define the logistics proposal for fundraising or a business case for enterprise investment, this simulator is a pre-analysis tool. You'll need real per-zone quotes, historical FTR data, channel cannibalization analysis, and volume projections backed by paid marketing. Use it to prepare the conversation with the VP of Operations or COO; not to present to the investor.
Financial notice
Results are illustrative estimates and do not constitute financial, tax, accounting, or legal advice. Use the results as a reference point and validate important decisions with a certified professional.
Editorial review
Reviewed by the Simúlalo editorial team
This simulator was reviewed by the people listed below before being published. The review covers the declared formula, the model's assumptions, the explicit limitations, and the absence of unsupported financial claims.
They are part of the Simúlalo editorial team, focused on building financial tools that are clear, educational, and easy to interpret.
Last updated: ·We update this page when the methodology, sources used, or simulator structure change.
This tool uses standard financial formulas and user-supplied data. To explain concepts like rates, credit, risk, or cash flow we consult public and official sources (Banxico, SAT, CONDUSEF, CNBV, Banco de España, IFRS, BIS, among others). Simúlalo is not affiliated with, sponsored by, or endorsed by these institutions.