ORIGINAL COMPANY-CONTEXT PRACTICE
Compare terminal delays fairly
Original practice scenario inspired by TotalEnergies SE's public business context; this is not a reported interview question or a description of its internal systems. A synthetic logistics dataset shows that one energy terminal has longer average truck turnaround than another. Its trucks also arrive with different cargo types and appointment patterns. How would you investigate the difference?
Written for independent practice using company context; this is not a verified question from a company interview.
A REASONED APPROACH
Model answer.
Separate a descriptive difference from a causal explanation. Cargo mix, arrival congestion, and incomplete visits can change the observed average without identifying a worse terminal process.
Work through it
- Define start and end events, timezone rules, and how open visits are handled. Inspect duration distributions and timestamp quality before comparing means.
- Compare like-for-like cargo and appointment strata, then standardize to a common mix. Show sample sizes and uncertainty rather than only a single adjusted estimate.
- Examine time-of-day queues and process stages to locate where delay accumulates. If testing an operational change, choose a credible randomized or phased comparison.
Tradeoffs to discuss
- Adjustment improves comparability only for measured differences. Median and tail metrics answer different service questions and should be reported together.
Common pitfalls
- Dropping unfinished long visits biases results downward.
- An adjusted association alone does not establish that terminal choice caused the delay.