C3.ai Inventory Optimization

Optimize inventory and service levels for raw materials

C3.ai Inventory Optimization

Optimize inventory and service levels for purchase parts

C3.ai Inventory Optimization

Optimize inventory and service levels for finished goods

C3.ai Inventory Optimization

Optimize inventory and service levels for in-transit goods

Reduce Inventory Costs, Free Up Working Capital, and Reduce Stock-Out Risks

C3.ai Inventory Optimization™ applies advanced AI/machine learning and optimization techniques to help manufacturers reduce inventory levels, while maintaining confidence that they will have stock when, and where, they need it.

Manufacturers often allow customers to configure hundreds of individual options, leading to products that could have thousands of permutations. Since the final configuration of a product is often not known until close to submission of the order, manufacturing companies need to have significant excess inventory on hand to be able to fulfill their orders on time. Over the years, manufacturing companies have deployed Material Requirements Planning (MRP) software solutions that support planning and automated inventory management. However, most MRP software solutions were not designed to optimize inventory levels by continuously learning from data.

C3.ai Inventory Optimization solves this problem by considering several real-world uncertainties including variability in demand, supplier delivery times, quality issues with parts delivered by suppliers, and production-line disruptions. The application dynamically and continuously optimizes reorder parameters and minimizes inventory holding and shipping costs for each part.

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Real-time recommendations

Real-time recommendations

Get real-time recommendations to optimize reorder parameters by part and by location and keep them updated as new data is available.
Real-time monitoring

Real-time monitoring

View inventory metrics in real time to anticipate issues with inventory levels and get notified when certain KPIs exceed thresholds.
Optimization by confidence level

Optimization by confidence level

Specify the maximum acceptable risk of stock-out for any part to optimize recommendations.
Summary view for operators

Summary view for operators

View inventory savings to date, actual and optimized inventory by location, and prioritized lists of high-opportunity parts, leading to faster value realization.
Detailed view of individual parts performance in the supply chain

Individual parts performance view

View details of individual parts and compare a range of KPIs such as actual vs. optimal inventory and actual vs. recommended reorder parameters.
Benchmark parts performance

Benchmark parts

Compare and benchmark different parts or suppliers over time using a range of KPIs such as OTIF, defect rate, and average cost.
What-if scenario planning

“What-if” scenario planning

Define scenarios and understand potential business implications of changing reorder parameters before committing the changes to the system.
Live optimization

Optimization with real-time data

Dynamically optimize reorder parameters as new data is received and bi-directionally connect to source systems to update reorder parameters.
Scalability to millions of parts

Scalability to millions of parts

Individually optimize inventory levels of millions of parts at different production locations across a manufacturer’s global footprint.



Jim Hannan Thumbnail
Jim Hannan

Jim Hannan

EVP, CEO Enterprises

“There’s no question that C3.ai has capabilities we haven’t seen before, with something that gives us a platform to drive enterprise AI.”

Scott Fedor thumbnail
Scott Fedor

Scott Fedor

Digital Transformation Leader, Global Supply Chain

"The value of the Baker Hughes C3.ai partnership comes from the fact that we're both experts in our own domains."

Jeff Ranch
Jeff Ranch

Jeff Ranch

Supply Chain Digitization Manager

“What’s impressive is that we have the ability to go from monthly to weekly to daily data, approximately two years’ worth of data, on everything from inventory, product availability, OTIF, and service in about six seconds.”

Henry Chang
Henry Chang

Henry Chang

Vice President, 3M Connect

“We’ve been engaged with the C3.ai team to figure out better processes and make user-friendly applications that enable our data scientists to draw insights and conclusions to drive outcomes.”



Reduce inventory holding costs and improve cash flow without compromising part availability. Optimize reorder parameters such as safety stock and safety time with necessary confidence levels.


Improve supplier management and negotiations through improved understanding of supplier performance. Simulate effects of changes in order parameters on supplier performance KPIs.


Increase visibility into critical uncertainties such as seasonality, uncertainty in arrivals, potential quality issues with suppliers, transportation bottlenecks, and production-line disruptions.


Enhance organizational efficiency through a common view across various departments (e.g., material management, supplier management, logistics management), leading to optimized inventory aligned with organizational goals.


Gain productivity of inventory analysts through automated recommendations based on new data and live integration with operational systems. Consistently apply recommendations to supplier orders.


Minimize total landed costs that include standard and expedited shipping costs, as a result of reduced inventory in the supply chain.

Data Sources

C3.ai Inventory Optimization aggregates data from different disparate source systems including production orders (actuals and planned), product configurations, bills of material, inventory movements (e.g., arrivals from suppliers, consumption in a production line, intra- and inter-facility shipments), historical settings of reorder parameters, lead time and shipping costs from suppliers, and part-level costs for each location where inventory is maintained.

C3.ai Inventory Optimization factors in several real-world uncertainties including variability in demand, supplier delivery times, quality issues with parts delivered by suppliers, and production line disruptions. The application uses machine learning to analyze variability, dynamically and continually optimize reorder parameters, and minimize inventory holding and shipping costs for each part.

Model-driven architecture for C3.ai Inventory Optimization

Proven results in weeks, not years

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