Working Simulator · ML Solution Design
ML / Predictive Model
Delivery Risk Simulator
Logistics operators absorb significant cost from failed delivery attempts — nobody home, wrong addresses, access issues. Delivery Risk Simulator is a concept build that scores at-risk deliveries before dispatch so a team can intervene early. The interactive demo below is a simulator — illustrative scoring, not a trained model.
0–100
Risk score, per delivery
6
Risk feature categories scored
GBM
Gradient-boosting approach
Demo
Interactive simulator — try it
The Problem
Every failed delivery costs twice.
Regional delivery networks processing 800–1,200 deliveries per day typically see 10–15% first-attempt failure rates — a driver visit, a failed attempt, a redelivery, and an unhappy customer.
Each failed attempt costs an operator in fuel, driver time, and redelivery logistics — estimated at roughly $8–12 per failed stop. On an illustrative 30,000-delivery month at a 14% first-attempt failure rate — about 4,200 failed attempts — gross cost lands on the order of $34,000–$50,000 per month. Only a share of those failures is preventable; at an illustrative 25% preventability, the avoidable portion is roughly $8,000–$13,000. All figures here are illustrative ranges, not measured client results.
Most operations teams have no predictive signal — failures only become visible after they happen. There's no way to prioritize or intervene before dispatch.
Historical delivery data usually exists but sits siloed in legacy systems — never analyzed, never used to inform routing or scheduling decisions.
What a Production Version Would Do
What a production version would do
Would score every upcoming delivery for failure risk before dispatch.
Would learn from six categories of historical and contextual delivery data.
Would flag the highest-risk stops so a team can intervene proactively.
Would surface a daily prioritized brief instead of finding out about failures after the fact.
What the Model Looks At
What the model scores
Delivery Risk Simulator uses a gradient-boosting approach that would learn from an operator’s own historical delivery outcomes. It scores each delivery before dispatch across six feature categories:
Feature 01
Address History
Prior delivery success rate at the exact address and surrounding area — expected to be informative where enough prior outcomes exist.
Feature 02
Time Window
Requested delivery window vs. historical success rates by time-of-day and day-of-week for that zone.
Feature 03
Customer History
The recipient’s own delivery success rate, where enough prior orders exist. New recipients fall back to zone, window, and access signals.
Feature 04
Package Type
Signature-required and oversized packages would be evaluated as candidate risk features.
Feature 05
Zone Density
Delivery zone characteristics — apartment buildings, gated communities, and commercial addresses each have distinct patterns.
Feature 06
Weather Signal
Adverse weather correlates with both driver delays and recipient unavailability — integrated via weather API.
Evaluation Approach
How we’d prove it works
This is a concept, so there are no performance numbers to report — quoting precision or AUC for a model that hasn’t been trained on a real operator’s data would be theater. What matters is the method, and the method is where most predictive-ML projects quietly fail:
Time-aware split, never random. Train on earlier months and test on later deliveries the model has never seen — a random split can mix earlier and later operating conditions and give an over-optimistic estimate.
Beat real baselines. A risk score only earns its place if it outperforms simple rules at the same intervention capacity — flag every signature-required package, flag the historically worst zones, or a plain logistic regression.
Precision–recall and calibration, not just AUC. Delivery failure is imbalanced, so the honest measures are the precision–recall curve, a confusion matrix at the operating threshold, and whether a 0.8 score really means about 80% observed failure.
Cold-start tested separately. Because address and recipient history are features, performance is reported twice — for repeat addresses, and for addresses the model has never seen.
The threshold is a business decision. It’s set against the cost of a missed failure, the cost of an unnecessary intervention, and how many stops the team can actually work each morning — not by “optimizing precision” in the abstract.
How It Works
Target operating workflow
Nightly batch scoring
scheduler → extract_deliveries() → score_batch()
Each evening, the next day's manifest would be extracted from the operations system and scored. Every delivery would get a risk score from 0–100.
At-risk deliveries flagged
flag_high_risk() → rank_by_avoidable_cost()
Deliveries above a tuned risk threshold would be flagged. The threshold would be set against intervention capacity — how many stops the team can actually work — not a fixed cutoff.
Operations team notified
send_daily_brief(flagged_deliveries)
A morning brief would rank flagged deliveries by expected avoidable cost — not just probability — so a capacity-limited team works the highest-value stops first. Each would carry its top contributing factors; the team could contact recipients, reschedule, or assign experienced drivers.
Outcomes feed back into the model
log_outcome() → evaluate_challenger() → promote_if_better()
Every outcome would be logged. Instead of retraining blindly on a schedule, a challenger model would be tested against the current one on future deliveries and promoted only if it actually wins — with the prior version kept for rollback.
Tech Stack
Technology
What’s running in the interactive simulator today, and the stack a production build would use.
Implemented — interactive simulator
Frontend
HTML · CSS · JS
Scoring
Client-side rules
Hosting
Vercel
Proposed — production ML stack
Modeling
scikit-learn
Language
Python
API
FastAPI
Database
PostgreSQL
Scheduling
Airflow
Infra
AWS
What It Demonstrates
What this demonstrates
Predictive, not reactive. Scores delivery risk before dispatch so a team can intervene on at-risk stops early.
Sound ML design. A gradient-boosting approach with time-aware evaluation, real baselines, and a capacity-aware threshold — not a black box.
No packaged-prediction SaaS dependency. Designed to run inside an operator’s existing cloud and data stack — no per-seat ML platform subscription.
Improves under guardrails. A new model ships only when it beats the current one on future data — retraining is gated, not automatic.
Knows its limits. On unfamiliar deliveries — new zones, missing history — it abstains and falls back rather than emit a falsely precise score.
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