Machine Learning
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Project Overview:
Apex Retail Group, a leading retail chain in Canada, approached Midela Solutions to help leverage their vast sales and customer data to improve demand forecasting, reduce inventory waste, and personalize customer experiences. Their manual forecasting methods were leading to stockouts, overstocking, and missed sales opportunities.
Our Solution:
Midela Solutions developed a custom machine learning-powered predictive analytics engine, designed to:
Data Consolidation & Cleansing: Aggregated and cleaned sales, inventory, customer loyalty, and seasonal data from multiple sources into a centralized data warehouse.
Model Development: Built machine learning models using Python (scikit-learn, XGBoost) to forecast product demand at SKU, store, and regional levels.
Feature Engineering: Integrated external factors such as holidays, weather patterns, and promotional campaigns to improve forecast accuracy.
Automated Reporting Dashboards: Created real-time dashboards using Power BI to visualize demand forecasts, inventory alerts, and sales trends for decision-makers.
Model Deployment: Deployed the machine learning models on AWS SageMaker for scalable, automated predictions, integrated directly into Apex’s ERP system.
Continuous Model Improvement: Set up automated retraining pipelines to refresh models with new data and improve accuracy over time.
Results & Impact:
Increased forecast accuracy by 25% compared to their manual baseline
Reduced inventory holding costs by 18% in the first 6 months
Reduced stockouts by 22% across key product categories
Enabled personalized promotions based on customer purchase patterns, boosting loyalty program engagement by 15%
Empowered executives with real-time, actionable insights
Client Feedback:
“Midela Solutions turned our raw data into a strategic asset. Their machine learning solution has given us a level of demand forecasting and customer insight we never thought possible.” – Alicia Morgan, Chief Data Officer, Apex Retail Group