实例探究 > GE Tackles the Industrial Internet

GE Tackles the Industrial Internet

公司规模
1,000+
地区
  • America
国家
  • United States
产品
  • Flight Quest I
  • Flight Quest II
  • Hospital Quest
技术栈
  • Big Data Analytics
  • Predictive Analytics
  • Real Time Analytics
实施规模
  • Enterprise-wide Deployment
影响指标
  • Cost Savings
  • Customer Satisfaction
  • Productivity Improvements
技术
  • 分析与建模 - 大数据分析
  • 分析与建模 - 预测分析
  • 分析与建模 - 实时分析
适用行业
  • 医疗保健和医院
  • 运输
适用功能
  • 物流运输
  • 质量保证
用例
  • 过程控制与优化
  • 实时定位系统 (RTLS)
服务
  • 数据科学服务
  • 系统集成
关于客户
General Electric (GE) is a multinational conglomerate headquartered in Boston, Massachusetts. Founded in 1892, GE operates in various sectors including aviation, healthcare, power, renewable energy, digital industry, additive manufacturing, and venture capital and finance. With a workforce of over 200,000 employees, GE is a leader in industrial technology and innovation. The company has been at the forefront of leveraging advanced technologies to solve complex industrial challenges. Since 2012, GE has pursued a multi-year initiative to leverage the “Industrial Internet” of smart machines and advanced analytics. This initiative aims to integrate machine learning, big data, and IoT to optimize industrial operations and drive efficiency.
挑战
Flight dynamics change quickly. From weather to gate conflicts, efficiently adapting to changing flight conditions can save millions of dollars in annual fuel costs, as well as reducing carbon emissions. Flight Quest tackled this real-time big data analysis challenge. In Flight Quest I, participants were given multi-source flight and weather data and asked to predict precise runway and gate arrival times for domestic flights in the United States. The winners produced a 40% accuracy improvement over industry standards—equivalent to saving 5 minutes at the gate per flight (an annual savings of $6.2 million for a mid-sized airline). Flight Quest II was even more challenging: Participants optimized flights in real time. The second phase included significantly more complex weather data—rain, wind, barometric pressure, ice, and more—as well as crew and passenger counts, airport traffic, and no-fly-zones. The winning solution was evaluated in a flight simulator and found to be a 12% efficiency and cost improvement over real flights.
解决方案
GE engaged Kaggle to organize and run three competitions, offering prizes totaling $600,000 to solve high-impact business problems from flight efficiency to hospital operations. In Flight Quest I, participants were given multi-source flight and weather data and asked to predict precise runway and gate arrival times for domestic flights in the United States. The winners produced a 40% accuracy improvement over industry standards. Flight Quest II was even more challenging: Participants optimized flights in real time. The second phase included significantly more complex weather data—rain, wind, barometric pressure, ice, and more—as well as crew and passenger counts, airport traffic, and no-fly-zones. The winning solution was evaluated in a flight simulator and found to be a 12% efficiency and cost improvement over real flights. For Hospital Quest, participants developed healthcare solutions that were evaluated by experts for quality, impact, and ease of implementation. The winning team implemented a referral management system to simplify hospital discharge workflows and greatly improve the quality of post-acute care.
运营影响
  • The winning solutions from Flight Quest I and II demonstrated significant improvements in flight efficiency and cost savings.
  • The competitions fostered innovation and collaboration among data scientists and industry experts.
  • The Hospital Quest competition led to the development of a referral management system that improved hospital discharge workflows and post-acute care quality.
  • GE's initiative showcased the potential of leveraging big data and advanced analytics to solve complex industrial challenges.
  • The competitions highlighted the importance of real-time data analysis and predictive analytics in optimizing operations.
数量效益
  • 40% accuracy improvement in predicting runway and gate arrival times.
  • 5 minutes saved at the gate per flight, equivalent to an annual savings of $6.2 million for a mid-sized airline.
  • 12% efficiency and cost improvement over real flights.

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