Commercial Analyst Interview

Interview Question:

Can you describe a time when you used a mathematical model to influence a strategic decision?

Candidate's Answer:

I used a mathematical model when forecasting stock replenishment taking into account the stock lead time, we look at the following:
* The time it takes a specific SKU to arrive in the warehouse after placing the order.
* The number of orders and volume each SKU has on a day to day to help us to also understand the demand of the SKU.
* The current SOH is the last factor considered.

AI Evaluation:

Your answer does address the question about using a mathematical model to influence a strategic decision, specifically in the context of stock replenishment. Here's an evaluation of your response:

1. Clarity and Relevance: You clearly articulated how the model incorporates various factors such as lead time, order volume, and stock on hand (SOH). This is relevant to the role of a Commercial Analyst, as forecasting and inventory management are critical components of the role.

2. Detail and Depth: While you mention key elements of the mathematical model, your answer could benefit from more detail about how these components interact in the model. For example, explaining the specific mathematical techniques used (e.g., linear regression, time series analysis) or how the output of the model influenced a strategic decision would strengthen your response.

3. Impact: You have not mentioned the specific strategic decision that was influenced by your model. This is a crucial aspect; providing an example of the decision and its outcome would illustrate the impact of your analysis and add weight to your answer.

4. Improvement Suggestions:
- Add a brief explanation of how the model was implemented and its precision or success rate.
- Discuss a specific outcome or decision that was made based on the results of your model.
- Consider mentioning any collaboration with team members or stakeholders to enhance the narrative of your influence.

Overall, your answer is a good start but would benefit from additional context and specific examples that showcase your skills and the impact of your work.

Rating: 3.5/5.

Interview Question:

What experience do you have in developing statistical models for evaluating investment decisions?

Candidate's Answer:

An algorithm that decides the amount of quantities to purchase per SKU (Stock Keeping Unit) can indeed be considered an investment decision. This is because it involves allocating resources (capital) to inventory, which impacts the overall financial performance of a business. The goal is to optimize inventory levels to meet demand while minimizing costs, which is a key aspect of investment strategy in supply chain management.

AI Evaluation:

Your answer touches on a relevant aspect of investment decisions, particularly in the context of supply chain management. You correctly highlight that inventory management involves capital allocation and financial performance. However, your response could benefit from a bit more depth regarding the statistical modeling aspect specifically.

To enhance your answer, consider incorporating the following points:

1. Statistical Techniques: Mention specific statistical models or techniques you’ve used in your experience, such as time series analysis, regression models, or machine learning algorithms. This shows a deeper understanding of the tools available for developing models.

2. Data Sources: Identify the types of data utilized (e.g., historical sales data, market trends, seasonality) to emphasize how data informs your modeling approach.

3. Outcomes: Discuss the results achieved from using your model, such as improved forecast accuracy, reduced holding costs, or increased sales, to illustrate tangible impacts on investment decisions.

4. Collaboration: If applicable, reference collaboration with other departments (e.g., finance, marketing) to show how cross-functional work led to more informed investment strategies.

5. Decision-Making Context: Outline how the model influences not just inventory levels but broader investment strategies or resource allocation across various SKUs.

Considering these improvements, I would rate your current answer a 3.5/5. It addresses the core idea but lacks specific details on statistical modeling techniques and measurable outcomes. Incorporating these suggestions would enhance the completeness and relevance of your response.