Original practice scenario: A hypothetical energy logistics report combines terminal receipts, transfers and dispatches. Some rows are in liters and others in mass units. How would you design a defensible reconciliation?
Read model answer +
Select a measurement basis and preserve the conversion inputs. Adding values labeled as quantity without compatible units can produce a plausible-looking total that has no physical meaning.
Work through it
Identify each movement and its direction, measurement unit, timestamp and product; deduplicate source events and separate corrections from new movements.
Use an approved conversion reference at the appropriate product and measurement conditions, retaining its version and original reported values.
Reconcile opening stock plus receipts minus dispatches and authorized adjustments against closing stock, returning unexplained differences and unavailable conversions separately.
Tradeoffs to discuss
Standardizing units simplifies comparison but introduces conversion assumptions; retaining multiple physical measures helps reviewers understand discrepancies.
Common pitfalls
Do not assume one fixed density across products and operating conditions.
A missing movement or conversion reference is not zero volume, and unmatched transfers require their own investigation population.
Original practice scenario: A hypothetical distribution planner orders fuel several days ahead. A model beats a baseline on next-day forecasts but performs poorly on ordering decisions. Explain the mismatch.
Read model answer +
Evaluate at the decision horizon and available-information cutoff. A model can be accurate one day ahead while providing little value when replenishment commitments must be made earlier.
Work through it
Define location-product demand, order lead time and decision timestamps; reconstruct only the information available when each historical order would have been placed.
Backtest rolling origins at the actual horizons, comparing seasonal-naive and simple calendar baselines with the candidate under identical training cutoffs.
Report horizon-specific error and uncertainty alongside stockout and surplus simulations using stated assumptions about delivery limits and inventory.
Tradeoffs to discuss
Optimizing forecast error is simpler than evaluating inventory decisions, but operational value also depends on capacity and asymmetric shortage costs.
Common pitfalls
Do not use revised historical demand that was unknown at the order time without marking the experiment retrospective.
Separate lost sales from observed demand when stockouts censor what customers could purchase.
