AI Retail 150 employees

Retail Company B - 30% Inventory Loss Reduction with AI Demand Forecasting

Achieved 30% inventory loss reduction through AI-powered demand forecasting system

Challenge

Retail Company B, a fresh food store chain, faced challenges with demand forecasting accuracy.

  • Large demand fluctuations due to seasons and weather
  • High disposal losses from excess inventory
  • Sales opportunity losses from stockouts
  • Ordering dependent on veteran staff intuition

Solution

KIX Consulting built a demand forecasting system utilizing machine learning.

System Architecture

  1. Data Collection Infrastructure

    • 3 years of historical sales data
    • Weather data (Japan Meteorological Agency API integration)
    • Event and campaign information
    • Competitor store opening information

  2. Forecasting Model

    • Time series forecasting with LightGBM
    • Product category-specific forecasting models
    • Store-specific correction coefficients

  3. Order Support System

    • Recommended order quantities based on forecasts
    • Real-time inventory visualization
    • Alert functionality (anomaly detection)

Technologies Adopted

  • Machine Learning: Amazon SageMaker, LightGBM
  • Data Platform: Amazon S3, AWS Glue, Amazon Athena
  • Application: AWS Lambda, Amazon API Gateway
  • Visualization: Amazon QuickSight

Results

  • 30% Inventory Loss Reduction: Significant reduction in disposal losses
  • 20% Sales Opportunity Loss Reduction: Reduced opportunity losses from stockouts
  • 50% Ordering Workload Reduction: Improved efficiency through automation
  • 85% Forecast Accuracy: Significant improvement from traditional intuition-based ordering (60% accuracy)

Customer Testimonial

“AI-powered demand forecasting achieved accuracy beyond veteran staff intuition. Disposal losses decreased, and profit margins improved significantly.”

— Sales Division Manager

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