ADAPT

Dynamic Pricing

Real-time price optimization for logistics services based on capacity, demand, and market conditions to maximize profitability under volatile costs.

Understanding the Problem

Logistics providers face razor-thin margins (15-35% average gross margin) under constant pressure from fuel volatility, labor cost increases, and competitive pricing. Traditional static rate cards fail to reflect current costs, while manual quoting processes lack margin visibility and delay competitive responses. Dynamic pricing enables real-time adjustment of rates based on capacity utilization, demand forecasts, competitor pricing, and market conditions to protect margins while remaining competitive.

THE CHALLENGE

What Makes it Hard

Dynamic pricing requires real-time revenue optimization across complex transportation networks with discrete pricing decisions, capacity constraints, customer segmentation, and multi-modal service options while balancing profitability against customer expectations.

WHO FACES IT

Logistics service providers managing freight and transportation pricingThird-party logistics companies optimizing contract vs. spot ratesAir cargo operators balancing high-value time-sensitive shipmentsTrucking companies managing LTL and FTL pricing strategiesE-commerce platforms optimizing delivery pricing and capacity

Market volatility from fuel price fluctuations and sustained inflation directly impacts costs

Data challenges including accuracy, freshness, integration, and real-time processing at scale

Service complexity across air cargo, rail intermodal, trucking, and last-mile with unique constraints

BUSINESS IMPACT

ML-powered dynamic pricing increases adjusted gross margin by up to 10%, improves forecast accuracy by 13%, targeting the 50% of freight in spot markets.

Margin Improvement

Up to 10%

Adjusted Gross Margin^[1]

ML-powered dynamic pricing increases adjusted gross margin of logistics service providers by up to 10%.

Spot Market Volume

Up to 50%

Of Freight Market^[2]

The spot market represents up to 50% of the overall freight market, creating massive opportunity for dynamic pricing optimization.

Forecast Accuracy

13%

Improvement^[3]

AWS teams built ML models that improved forecasting accuracy by 13% for logistics customers implementing dynamic pricing.

How We Solve It

Implement revenue management optimization using MILP for discrete pricing decisions and capacity allocation, combined with machine learning (reinforcement learning, Bayesian models) to learn demand functions. Deploy real-time pricing engines with sub-second response times, integrating market data feeds, capacity utilization, and cost tracking to automatically generate competitive, profitable quotes.

Heterogeneous
Hybrid Compute

What We Bring

Mixed-integer linear programming for contract vs. spot pricing decisions

Reinforcement learning for adaptive pricing under market dynamics

Bayesian pricing models for demand curve learning and uncertainty quantification

Real-time pricing engines with high-throughput data processing (Kafka, Flink)

FUTURE POSSIBILITIES

The
Quantum Horizon

QAOA and quantum annealing map naturally to discrete pricing decisions (price level selection, contract acceptance) formulated as QUBO problems. D-Wave case study demonstrates price optimization on AWS Braket, with production deployments at NTT DOCOMO and Ford Otosan.

Exploratory Work

Near-term opportunity (1-3 years) for hybrid quantum-classical approaches to large-scale pricing optimization across logistics networks. As quantum hardware reaches 10,000+ qubits (2-5 years), expect demonstration of quantum advantage for specific pricing problem classes with complex constraints.