Optimize inventory and service levels for raw materials
Optimize inventory and service levels for purchase parts
优化成品的库存和服务水平
Optimize energy companies’ inventory and service levels for in-transit industrial goods
BHC3™库存优化适用于高级AI /机器学习和优化技术,以帮助石油和天然气公司降低工业部件和设备库存水平,同时保持信心,以便他们有股票在他们需要的地方以及他们需要的地方。
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.)支持规划和自动库存管理的软件解决方案。但是,大多数MRP软件解决方案都没有设计通过从数据中不断学习来优化工业组件库存水平。
BHC3库存优化考虑了几个现实世界的不确定性,包括需求变异,供应商交货时间,供应商提供的零件的质量问题,以及生产线中断。该应用程序动态地和连续优化工业部件和设备的重新排序参数,并最大限度地减少每个工业部件或产品的库存持有和运输成本。
“What the teams found is ingestion is happening about 80% faster with about 1/10 the resources.”
"The value of the Baker Hughes C3.ai partnership comes from the fact that we're both experts in our own domains."
减少工业部件和设备的库存,并提高现金流,而不会影响部分可用性。优化工业组件的重新排序参数,如安全股票和安全时间,具有必要的置信水平。
通过改善对供应商绩效的理解,改善能源供应商管理和谈判。模拟组件订单参数变化对供应商性能KPI的影响。
Increase visibility into critical energy-sector uncertainties such as seasonality, uncertainty in arrivals, potential quality issues with suppliers, transportation bottlenecks and production-line disruptions.
提高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.
通过基于新数据的自动建议获得工业部件和设备库存分析师的生产力,并与运营系统的实时集成。始终如一地向供应商订单应用建议。
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库存优化聚合数据BHC3™ AI Suitefrom 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.
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