Improving production yield through standardized KPI reporting（通过标准化KPI报告提升生产良率）¶
Industry Sector: Industrials
Business Function: Production
Foundry enabled an automotive equipment manufacturer to produce more good parts with less raw materials, through a combination of improving productivity, efficiency, or plan comparison.
Challenge¶
Previously, there was no standardized tracking of the Material Yield. KPIs weren't used by all manufacturing plants consistently and could only be done infrequently and on an ad-hoc basis due to data-scale limitations and bandwidth constraints. The process of reporting was tedious and required a lot of manual steps to handle data came from multiple different data sources.
Solution¶
The goal is therefore to generalize best practices of best-in-class plants when it comes to KPI tracking and automate it to reflect daily improvements. First, a set of KPI’s are defined across all plants and standardized tooling is developed to reduce the requirements of each plant. Bringing the different KPIs into one consolidated dataset required an integration of multiple different sources. In order to get data on daily basis, while retaining granularity and thus accuracy, billions of rows of data needed to be ingested, a scale previously impossible to work with in legacy tools. The daily updates enable users to act faster and generate gains in a systematic way.
The plants' operational users connect on a weekly basis to the application and prioritize the different efforts that can be made to improve the Material Yield based on estimated potential savings. Users send "action items" to engineers and operational users who are then in charge of making gains through productivity and efficiency levers.
The Central Business Stakeholders track the efforts and gains generated and benchmark plants to incentivize them based on objective results.
Implement a similar use case¶
This use case implements the following Pattern. Follow the link below to read more about a particular Pattern and learn how it is implemented within Foundry.
- Investigation and cohorting (used for 3 other use cases)
Want more information on this use case? Looking to implement something similar? Get started with Palantir. ↗
中文翻译¶
通过标准化KPI报告提升生产良率¶
行业领域：工业
业务职能：生产
Foundry帮助一家汽车设备制造商通过提升生产力、效率或计划对比，以更少的原材料生产出更多优质零件。
挑战¶
此前，材料良率（Material Yield）缺乏标准化追踪。各制造工厂并未统一使用KPI，且受限于数据规模与带宽限制，只能以临时方式不定期进行统计。报告流程繁琐，需要大量人工步骤来处理来自多个不同数据源的数据。
解决方案¶
因此，目标是将标杆工厂在KPI追踪方面的最佳实践进行推广，并实现自动化以反映每日改进成果。首先，在所有工厂中定义一套KPI，并开发标准化工具以降低各工厂的负担。将不同KPI整合至统一数据集需要集成多个不同数据源。为获取每日数据并保持粒度与准确性，需摄入数十亿行数据——这一规模在传统工具中此前无法实现。每日更新使用户能够更快采取行动，并以系统化方式产生收益。
各工厂运营用户每周登录应用，根据预估潜在节约额，优先安排可提升材料良率的不同工作。用户向工程师及运营人员发送"行动项"，由后者通过生产力和效率杠杆实现收益。
中央业务利益相关方追踪各项努力与收益，并对工厂进行对标排名，依据客观结果激励各工厂。
实施类似用例¶
本用例实现了以下模式。点击下方链接可了解更多关于特定模式的内容及其在Foundry中的实现方式。
- 调查与分组（Investigation and cohorting）（另用于3个其他用例）
想获取更多关于本用例的信息？希望实施类似方案？立即联系Palantir。 ↗