I worked with a Fortune 500 grocery retail outlet for predicting its sales demand.This forecasting ensured that the outlet stocked the right quantity of products, met customer expectations, and maximized profitability.

Historical Sales Data: Data on previous sales across interested product categories, times of year, and market conditions.

Market Data: Local economic conditions, population trends, and competitor activity.

Promotional Data: Information to evaluate impact of discounts, coupons, or marketing campaigns on sales.

Seasonality: Data about seasons, holidays - statutory and local that could affect sales

Customer Trends: Changing consumer preferences and buying behavior using RFM (Recency, Frequency and Monetary value) model

Data Cleaning: Transforming data convenient for modeling

Feature Engineering: Adding variables such as time features - day of week, month, day of month, etc., seasonality, weather, and promotions.

The project involved experimentation with multiple models to ensure good predictability along with interpretability to ensure reproducible results:

Statistical models: Models include ARIMA, SARIMA, and Exponential Smoothing.

Regression Models: Models that performed multi-step forecasting or recursive forecasting using gradient boost regression models

Standard and custom error functions were utilized to evaluate the performance of the models. Some include:

Mean absolute error

Root mean square error

Custom error function : Skew [ (actual-pred) / actual]

A detailed document outlining the projected sales demand for each product category over a specified period (e.g., daily, weekly, monthly).

Breakdown of expected demand during seasonal peaks (holidays, back-to-school, summer months).

Inventory Optimization Recommendations: Strategies to manage inventory based on the sales forecast, including stocking levels

Scenario Analysis: - Simulations of various “what-if” scenarios, such as sudden demand spikes, supply chain disruptions, or market changes, to prepare contingency plans..