AI solutions
for Logistics & Shipping

Quantum-enhanced generative AI has the potential to transform optimization strategies for supply chain design, routing, scheduling, and disruption management. With increased efficiency, even a 5-10% overall reduction in supply chain costs would amount to $18 billion to $35 billion annually, according to McKinsey.

Featured Use Case

Delivery Routing and Scheduling

Challenge

Approach

Routing and scheduling problems are widespread throughout the shipping and logistics industry. They ask: given a limited set of resources and time frame, what is the most efficient plan to execute the delivery of goods from their source to their destination? It may seem simple, but the problem quickly becomes impractical for classical computers to solve as the number of destinations increases. While the industry has developed heuristic algorithms that provide satisfactory solutions, there is still room for improvement. Doing so would save millions in fuel costs and people hours, increase revenue, and reduce harmful emissions.

Use Cases

Applications for Logistics and Shipping

Supply Chain Design

Inventory Management

Transportation

Supply Chain Design

Inventory Management

Transportation

Key Challenges

Selecting the best suppliers, manufacturers, and vendors to satisfy multiple objectives
Locating facilities to satisfy market demand while minimizing setup and variable costs
Reducing logistical costs for product recalls
Minimizing waste and recouping costs through reuse and recycling

Zapata Solutions

Supply Chain Optimization

Optimize the selection of suppliers, distributors, and vendors for product quality, costs, delivery times, and demand coverage using generator-enhanced optimization (GEO).

Facility Location Optimization

Optimize the location of facilities in the supply chain for capacitated and uncapacitated systems using quantum-enhanced prescriptive analytics.

Reverse Logistics Optimization

Optimize logistics networks for recycling, reuse, disposal, and product recalls using quantum or quantum-inspired machine learning techniques.

Key Challenges

Minimizing inventory costs
Preventing lost sales on out-of-stock products
Setting the optimal stock allocation for multiple customer segments
Preventing excess costs and waste associated with the bullwhip effect
Increasing efficiency in warehouse operations

Zapata Solutions

Warehouse Optimization

Optimize the design of warehouses as well as order-picking scheduling and routing to increase efficiency using generator-enhanced optimization (GEO).

Market Demand Forecasting

Model market demand for replenishment to reduce inventory stock-outs and support production scheduling using quantum-enhanced machine learning techniques.

Bullwhip Effect Mitigation

Predict spikes in demand that could trigger a bullwhip using quantum-enhanced predictive analytics.

Inventory Policy Optimization

Design inventory policies optimized for minimizing costs across multiple product families using GEO.

Key Challenges

Minimizing costs and delivery times in distribution networks
Mitigating the impact of disruption from irregular operations
Optimizing workforce scheduling to minimize labor costs while still satisfying market demand

Zapata Solutions

Workforce Scheduling Optimization

Optimize workforce scheduling to balance workloads for multi-commodity teams using quantum or quantum-inspired techniques.

Delivery Routing Optimization

Optimize vehicle routing for delivery operations to reduce costs and shorten delivery times using quantum-enhanced prescriptive analytics.

Disruption Management

Predict and mitigate the effects of irregular operations (IROPs), such as extreme weather, using quantum-enhanced machine learning.

Fuel & Energy Optimization

Optimize vehicle control to minimize fuel consumption using generator-enhanced optimization (GEO).

Use Case Timeline (est.)

Featured Case Study

Top 5 Food & Beverage Company

An international beverage distributor routes drinks to over 700,000 vending machines in a territory worth $5.5B in revenue per year. Optimizing this distribution network would increase revenue, decrease fuel costs, and shrink the company’s carbon footprint.

Zapata used Orquestra® to upgrade the company’s classical delivery operations to support faster, more efficient daily routing computation. This enabled the company to begin operations ~45 minutes earlier each day, saving time and money in operations.

Read Full Case Study

Orquestra^® Benefits for Logistics and Shipping

Orchestration

Leverage the heterogenous compute resources best suited for your tasks, without getting locked into any one hardware platform. Deploy across hybrid backends at enterprise scale.

Data Management & Velocity

Store, retrieve, and analyze large datasets. Streamline data management from ingestion to export to accelerate data velocity.

Open Framework

Keep what works now. Integrate existing and future solutions with a framework optimized for extensibility, interoperability, and innovation.

Workflow Development and Deployment

One unified platform to go from research to development to deployment with extensible, scalable, modular workflows.

AI solutions for Logistics & Shipping

Delivery Routing and Scheduling

Challenge

Approach

Applications for Logistics and Shipping

Use Case Timeline (est.)

Top 5 Food & Beverage Company

Orquestra® Benefits for Logistics and Shipping

Request a briefing with our experts.

AI solutions
for Logistics & Shipping

Orquestra^® Benefits for Logistics and Shipping