Demand Forecasting & Analytics

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1. Data Aggregation & Cleaning
To ensure 1M+ user transactions provide accurate insights.
  • Historical Sales Integration: Pulling at least 12–24 months of sales data from WooCommerce and the Mobile App.
  • External Factor Tracking: Incorporating non-sales data such as Public HolidaysSeasonal Trends, and Marketing Campaign Calendars.
  • Anomaly Detection: Automatically filtering out "outlier" data (e.g., a one-time bulk B2B order) that could skew average consumer demand.
2. Predictive Forecasting Models
  • Time-Series Analysis: Using algorithms like Prophet (by Meta) or ARIMA to predict future sales based on past patterns.
  • Safety Stock Calculation: AI-driven formulas to determine the ideal "buffer" based on Demand Volatility (how much sales fluctuate).
  • Product Lifecycle Prediction: Identifying when a product is entering its "Obsolescence" phase to avoid over-ordering dead stock.
3. Real-Time Inventory Analytics
  • Inventory Turnover Ratio: Automated tracking of how many times inventory is sold and replaced over a period.
  • Sell-Through Rate (STR): Measuring the percentage of received inventory sold within a specific timeframe (e.g., monthly).
  • "Stock-out" Risk Analysis: Highlighting SKUs with high Sales Velocity that are predicted to run out before the next supplier delivery.
4. Actionable Business Intelligence
  • Executive Dashboards: High-level views of GMV (Gross Merchandise Volume)Profit Margins, and Warehouse Utilization.
  • What-If Scenarios: Tools to simulate the impact of a 20% discount on stock depletion levels.
  • Automated Reporting: Scheduled PDF/Excel reports sent to the procurement team with "Top 10 Reorder Recommendations."
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