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Optimizing Inventory & Anticipating Maintenance
适用行业
- 航天
服务
- 数据科学服务
挑战
一家飞机制造商希望改善 15,000 架停产飞机的备件库存管理。客户需要无法采购的库存零件，因为它们从未被要求在飞机上进行维修或更换。这些零件需要按需生产，并且生产周期很长——通常长达六个月——导致航空公司停飞他们的飞机平均每天损失一百万。因此，制造商需要在问题被传达之前预测他们的哪些备件会出现故障，从而更好地进行库存规划、供应链决策，并全面降低运营成本。此外，该公司想知道在报告问题时是否在给定时间购买多个备件，由于交货时间长，是否应提前库存某个备件，以及是否可以减少数量服务工程师的工时通过更好地访问信息来回答客户有关库存零件的问题。由于备件管理员工和服务工程师之间的沟通有限，库存决策也变得困难，导致制造商出现大量“死”库存。目标是在不影响飞机生产质量、安全和寿命的情况下降低维护成本并提高客户满意度和保留率。
客户
未公开
关于客户
飞机制造商
解决方案
Saffron 被要求统一来自 15 个数据源的结构化和非结构化数据。首先，Saffron 摄取所有数据以创建所有数据的持久知识库；统一每个实体的所有来源（事件、零件、组件、系统、尾号、机械师、飞行员、日期、位置、航空公司等），代表每个实体的所有连接、相关性及其上下文。然后应用基于无模型相似性推理的 SaffronMemoryBase 来识别相似性模式，以回答“我们以前见过这个问题吗？如果是这样，我们对此做了什么？结果好坏在哪里？我可以利用过去的经验来解决这个问题吗？”使用无模型相似性分析，Saffron 快速识别需要维修或更换的零件，以预测库存中需要哪些零件。该平台通过分析飞机运营商先前与这些库存部件沟通问题的数据，确定了某个库存部件的活动增加的实例。软件组件： - SaffronMemoryBase
收集的数据
Inventory Cycle Count, Parts Type and Number , Per-Unit Maintenance Costs, Service Lifecycle, Supply Chain Optimization
运营影响
数量效益
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