CASE STUDY — LOGISTIC & MANUFACTURING
Route Pricing Optimization
Enhance profitability by predicting optimal pricing for offered routes
Sector:
Logistic & Manufacturing
Focus:
Route Pricing Optimization
BUSINESS CHALLENGE
Predicting optimal pricing for competitive route bids
Logistics companies work with a predefined set of routes for which they bid on pricing in competitive scenarios. Determining the ideal price to win a route while ensuring profitability is a challenge, especially given the variability in market conditions and customer expectations. Need for an AI-solution to predict the final price at which a route will likely be awarded.
SOLUTION IMPLEMENTED
A regression-based AI system predicting winning route prices
Regression-based AI system that analyzes historical pricing data for offered routes, including whether the bids were won or lost. The solution predicts the likely winning price for each route and suggests optimal pricing strategies to maximize profitability while improving the chances of securing the route.
TECHNOLOGY
Built to combine, analyze, and predict optimal route prices
DATA PLATFORM
Azure SQL Database
Combines historical route pricing data, operational costs, and other relevant data sources into a single view.
SERVING
Power BI
Visualizes regression results and historical trends, giving teams clear insight into pricing patterns.
AI
Azure Machine Learning
A regression-based machine learning model trained on historical route offers, accepted prices, and market conditions to predict the likely winning price.
Results & ROI
What changed once the model went live
EFFICIENCY
Reduced time spent on manual price analysis by automating the process with AI-driven insights.
PROFITABILITY
Enhanced profit margins by identifying pricing sweet spots that balance competitiveness and operational costs.
ACCURACY
Improved price prediction accuracy, leading to a 20% increase in winning bid rates for strategic routes.
