Technical practice
How would you use statistics to test whether a supply chain pattern is meaningful?
What to coverSuggested preparation: Explain how you would define a business question, examine variation, and distinguish a credible signal from noise. Discuss assumptions and uncertainty before turning the result into a decision.
How would you choose a time series forecasting approach for supply chain data?
What to coverSuggested preparation: Discuss trend, seasonality, data history, and the decision the forecast will support. Explain why you would compare an approach with a simple baseline rather than assume a more complex model is better.
How would you evaluate a supply chain forecast before using it?
What to coverSuggested preparation: Describe time-ordered validation and an error measure that reflects the business decision. Explain how you would check whether performance varies across periods or parts of the supply chain.
What would you investigate if forecast accuracy suddenly deteriorated?
What to coverSuggested preparation: Check data quality, changes in demand patterns, and whether the model's assumptions still hold. Describe how you would separate a data issue from a modeling issue before recommending a change.
How would you frame a supply chain problem as an optimization problem?
What to coverSuggested preparation: Identify the decision variables, objective, and operational constraints using a clearly labeled hypothetical example. Explain how you would check that the proposed solution is feasible and useful to the client.
How would forecast uncertainty affect an optimization recommendation?
What to coverSuggested preparation: Explain why an optimal solution based on a single forecast may be fragile. Discuss testing alternative scenarios and communicating the trade-off between performance and robustness.
What checks would you make before analyzing supply chain data from multiple sources?
What to coverSuggested preparation: Cover definitions, time periods, missing values, and consistency between sources. Explain how unresolved data limitations would qualify the insights you report.
When would predictive modeling add value beyond reporting historical supply chain results?
What to coverSuggested preparation: Start with the decision to be supported, then describe what a prediction would add and how you would evaluate its usefulness. Contrast that with a simpler report when prediction would not change the action.
How would you turn an analytical result into a useful client insight?
What to coverSuggested preparation: Connect the finding to a supply chain decision, quantify its relevance where the data permits, and state uncertainty plainly. Distinguish what the analysis demonstrates from what it merely suggests.
What would make a supply chain analytics asset reusable?
What to coverSuggested preparation: The posting mentions contributing to asset development, so consider clear inputs, documented assumptions, and repeatable checks. Explain the trade-off between standardization and adapting an analysis to a particular client problem.
Behavioural practice
Tell me about a time you clarified an ambiguous analytics problem.
What to coverSuggested preparation: Use a real situation, the questions you asked to define the decision, and the analysis you chose to pursue. End with the result and what changed because the problem was better defined.
Describe a time you communicated a statistical finding to someone making a business decision.
What to coverSuggested preparation: Explain the audience's decision, how you presented the finding and its limitations, and the response. Keep your own actions and the observed result distinct.
Tell me about a time you collaborated on a reusable analytical deliverable.
What to coverSuggested preparation: If you have a relevant example, describe the shared goal, your contribution, and how you incorporated others' input. State what was delivered and how its usefulness was assessed, without implying this employer uses a particular asset process.
Describe a time data challenged your initial analytical approach.
What to coverSuggested preparation: Set out the original assumption, the evidence that called it into question, and the action you took. Explain the result, including any remaining uncertainty.