1. Prediction versus explanation
parcel_id identifies a record; dispatch_date controls temporal splitting. distance_km, load_ratio and zone are permitted predictors. actual_hours and late are targets; delivered_at and post_delivery_rating are later information and excluded. A prediction estimates delay risk; a vehicle-allocation effect cannot be established from this synthetic observational exercise.Independent extension: change one relevant input in a separate copy. Predict the result first, rerun the checks and explain any difference. Keep the supplied source unchanged.

2. Supervised and unsupervised learning
Hours is supervised regression; late/not late is supervised classification. Similar-route grouping is unsupervised and needs an explicit similarity definition. Last month’s observed delay summary is descriptive analysis and may need no ML at all. Labels are required for the supervised examples, with timing and coverage checks.Independent extension: change one relevant input in a separate copy. Predict the result first, rerun the checks and explain any difference. Keep the supplied source unchanged.

3. Deciding whether ML is appropriate
Proceed with the local learning experiment because timing, labels and evaluation are explicit. Pause real deployment pending representative operational data, action-impact evidence, error-cost agreement, review procedures and monitoring. A train-only mean/majority baseline is the minimum comparator; a simple operational rule is an additional business comparator.Independent extension: change one relevant input in a separate copy. Predict the result first, rerun the checks and explain any difference. Keep the supplied source unchanged.