Stock-Out Patterns

Requirements Clarification & Assessment

1. Dataset Structure:

   • Understand the dataset structure, including data types and any missing values.
   • Confirm the meaning of 'Stock status' and how it is encoded (e.g., 1 for in stock, 0 for out of stock).

2. Objective Clarification:

   • Clearly define the metrics needed:
     • Completely free of stock shortages: Companies with no stock-outs across all countries and products.
     • Experienced a shortage in a single country: Companies with stock-outs limited to one country.
     • Faced shortages in multiple countries simultaneously: Companies experiencing stock-outs in two or more countries at the same time.

3. Data Granularity:

   • Determine if the data is aggregated by day, week, or another time unit.
   • Assess if the dataset covers all necessary countries and companies to provide meaningful insights.

4. Business Context:

   • Understand the business context and how stock-out patterns impact financial performance, customer satisfaction, and supply chain efficiency.

5. Hypothesis Generation:

   • Formulate potential business-related hypotheses based on the derived metrics.
   • Consider external factors such as economic conditions, geopolitical events, and seasonal variations that could influence stock-out patterns.