BHC3 Inventory Optimization

Optimize inventory and service levels for raw materials

BHC3 Inventory Optimization

Optimize inventory and service levels for purchase parts

BHC3 Inventory Optimization

Optimize inventory and service levels for finished goods

BHC3 Inventory Optimization

Optimize inventory and service levels for in-transit goods

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

BHC3 Inventory Optimization™ applies advanced AI/machine learning and optimization techniques to help oil and gas companies reduce inventory levels, while maintaining confidence that they will have stock when, and where, they need it.

Oil and gas companies often carry excess inventory to address uncertainties in operations or demand. This often manifests as excess inventory of parts and equipment to prevent unplanned downtime. Many downstream manufacturers also carry large inventories to prevent stock-outs or to offer better lead times and flexibility to customers. Over the years, 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.

BHC3 Inventory Optimization considers 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 or product.

Features

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 bidirectionally 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 company’s global footprint.

Demo

Testimonials

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.”

Benefits

Reduce

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

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

Increase

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

Enhance

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

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

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

BHC3 Inventory Optimization Data Sources

BHC3 Inventory Optimization aggregates data in the BHC3 AI Suite 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.

BHC3 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 BHC3 Inventory Optimization

Proven results in weeks, not years

timeline
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