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 energy companies’ inventory and service levels for in-transit industrial 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 industrial parts and equipment inventory levels, while maintaining confidence that they will have stock when and where they need it.
Oil and gas companies often carry excess industrial component inventory to address uncertainties in operations or demand. This often manifests as excess inventory of industrial parts and equipment to prevent unplanned downtime. Many downstream manufacturers also carry large inventories of industrial components 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 industrial component 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 for industrial parts and equipment and minimizes inventory holding and shipping costs for each industrial part or product.
Features
Real-time recommendations
Real-time monitoring
Optimization by confidence level
Summary view for energy operators
Individual parts performance view
Benchmark industrial parts
“What-if” scenario planning
Optimization with real-time data
Scalability to millions of parts
Demo
Testimonials
Scott Parent
VP, Enterprise Engineering & Technology
“What the teams found is ingestion is happening about 80% faster with about 1/10 the resources.”
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."
Baker Hughes Inventory Optimization
Benefits
Reduce
Reduce inventory holding costs for industrial parts and equipment and improve cash flow without compromising part availability. Optimize reorder parameters for industrial components such as safety stock and safety time with necessary confidence levels.
Improve
Improve energy supplier management and negotiations through improved understanding of supplier performance. Simulate effects of changes in component order parameters on supplier performance KPIs.
Increase
Increase visibility into critical energy-sector uncertainties such as seasonality, uncertainty in arrivals, potential quality issues with suppliers, transportation bottlenecks and production-line disruptions.
Enhance
Enhance organizational efficiency of industrial operations through a common view across various departments (e.g., material management, supplier management, logistics management), leading to optimized inventory of industrial parts and equipment that is aligned with organizational goals.
Gain
Gain productivity of industrial part and equipment 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 of industrial component inventory 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 industrial component inventory is maintained.
BHC3 Inventory Optimization factors in several real-world uncertainties including variability in demand, supplier delivery times, quality issues with industrial parts delivered by suppliers, and production line disruptions. The application uses machine learning to analyze variability, dynamically and continually optimize reorder parameters, and minimize industrial part and equipment inventory holding and shipping costs for each item.
Proven results in weeks, not years
