Optimized Inventory Management in Retail: A Comparative Analysis of Direct vs. Channel-Specific Sales Volume Forecasting for Perishable Goods to Enhance ROI

Project Details


Fall 2023


Julián Celedón, Didier Fernando Salazar Estrada, Fifi Ding, Grégoire Serex




Presentation recording

Business Challenge
Retail suppliers struggle to balance inventory levels for perishable goods, needing precise sales forecasts to avoid overstocking or stock-outs.

Data Analysis Approach
● Data Source: 714-day sales data from major retail stores, provided by Nuqleous.
● Forecasting Methods:
○ Direct Forecasting of total weekly sales.
○ Channel-Specific Aggregation across different sales channels.

Key Forecasting Models
Employed models like Naive, Seasonal Naive, ETS, TSLM, and ARIMA, tailored to specific data segments.

Core Findings
● Cost Efficiency: Channel-specific aggregation methods generally proved more cost-effective than
direct forecasting.
● Performance Note: Similar results for both methods when using regression, Naive, or Seasonal
Naive models.
● Exception Case: SKU 1538336 showcased better performance in the General Level during

● Data Duration: Minimum two-year data span for robust forecasting.
● Benchmarking: Essential to compare new methods against existing standards.
Channel-specific forecasting tends to yield better cost outcomes, except when certain models like regression or Naive are used, underscoring its importance in effective inventory management and ROI enhancement.

Application Area: