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BOSCH DATA ANALYTICS

分散数据与业务洞察Fragmented data and business insight

我在 Bosch 如何使用 Excel、Power BI 与 Dashboard,支持采购、物流和运营场景中的数据分析与业务指标追踪。How I used Excel, Power BI, and dashboards at Bosch to support data analysis and business metric tracking across procurement, logistics, and operations contexts.

COMPANY博世(中国)投资有限公司Bosch (China) Investment Co., Ltd.
ROLE数据分析实习生Data Analytics Intern
PERIOD2026.05—2026.09
数据分析Data AnalyticsExcelExcelPower BIPower BIDashboardDashboard业务运营Business OperationsAI 辅助工作流AI-assisted Workflow

数据使用场景Data use context

采购、物流与运营数据来自不同流程和文件。只有统一数据结构、指标口径和展示方式,团队才能更直观地观察业务变化并理解指标含义。Procurement, logistics, and operations data can come from different workflows and files. A shared structure, metric definition, and display approach help teams observe change and understand what the indicators mean.

THE WORKING QUESTION

团队需要看到什么变化?指标会如何被理解?What change does the team need to see? How will the metric be understood?

数据分析与判断Data analysis and judgment

我将分散在不同文件和流程中的采购、物流与运营信息,整理成更容易追踪、解释和沟通的指标。I organized procurement, logistics, and operations information from different files and workflows into metrics that were easier to track, explain, and discuss.

01理解业务需求Understand the business need业务理解Business understanding
02整理与清洗数据Organize and clean data数据处理Data preparation
03梳理指标和统计逻辑Define metrics and logic结构化思考Structured thinking
04使用 Power BI 完成可视化Visualize with Power BI数据表达Data communication
05解释变化并支持团队理解Explain change and support understanding跨团队沟通Cross-functional communication

Dashboard 的价值不是展示更多图表,而是帮助使用者更快发现变化、理解指标,并明确下一步需要关注的问题。A dashboard is not valuable because it shows more charts. It is valuable when it helps people find change, understand metrics, and clarify what to look at next.

AI 辅助整理AI-assisted organization

我将 AI 用于信息整理、分析思路拆解和流程辅助,但业务口径、数据准确性与最终判断仍需要人工验证。I used AI for information organization, analysis-idea breakdown, and workflow support. Business definitions, data accuracy, and final judgment still required human verification.

01辅助整理信息Support information organization
02辅助拆解数据处理思路Break down data-processing ideas
03辅助检查公式或逻辑Check formulas or logic
04辅助总结数据变化Summarize data movement
05人工校验数据与结论Manually verify data and conclusions

AI-ASSISTED PROTOTYPING

业务需求与原型Business needs and prototypes

在 Bosch 的实际业务环境中,我开始尝试使用 Codex 等 AI Coding 工具,将工作中观察到的数据获取、信息整理与业务判断需求,快速转化为可以运行和测试的产品原型。In Bosch's business context, I began using AI Coding tools such as Codex to turn needs I observed around data access, information organization, and business judgment into prototypes that could run and be tested.

01识别业务问题Identify the business problem
02拆解用户与业务需求Break down user and business needs
03定义数据、功能和信息结构Define data, features, and information structure
04使用 AI Coding 搭建原型Use AI Coding to build a prototype
05测试数据与交互Test data and interactions
06根据问题持续迭代Iterate based on what the tests reveal

INDEPENDENT PROJECT

从业务观察到独立产品实践From business observation to an independent product practice

对采购、供应链数据和业务信息获取方式的观察,促使我进一步探索如何通过 AI 辅助开发构建可验证的业务工具,并形成了 Semiconductor Price Tracker 独立项目。Observing procurement, supply-chain data, and how business information is accessed led me to explore how AI-assisted development could shape a testable business tool, which became the Semiconductor Price Tracker independent project.

源于实习期间对业务场景的观察Inspired by observing business contexts during the internship

Semiconductor Price Tracker 是 Independent Project,不是 Bosch 官方项目;不使用 Bosch 内部数据、文件、代码或截图。基于公开数据。Semiconductor Price Tracker is an Independent Project, not an official Bosch project. It does not use Bosch internal data, files, code, or screenshots. Based on public data.

查看项目详情View case study

AI 产品与运营AI product and operations

DATA ANALYTICS → OPERATIONS

Bosch 实际工作What I did at BoschAI 产品/运营能力Transferable capability
01理解数据使用场景理解数据使用场景用户与业务需求理解Understand user and business needs
02梳理字段和指标口径梳理字段和指标口径需求结构化与产品定义Structured requirements and product definition
03设计 Dashboard 信息层级设计 Dashboard 信息层级信息架构与产品表达Information architecture and product expression
04观察指标变化观察指标变化数据驱动运营Data-driven operations
05与相关团队确认需求与相关团队确认需求跨团队协作Cross-functional collaboration

AI-ASSISTED PROTOTYPING → PRODUCT

实际经历What I practiced可迁移能力Transferable capability
01从数据工作中观察业务问题从数据工作中观察业务问题机会识别与用户洞察Opportunity identification and user insight
02将工作需求拆成功能和流程将工作需求拆成功能和流程产品需求分析Product requirements analysis
03使用 AI Coding 构建原型使用 AI Coding 构建原型AI-native 执行能力AI-native execution
04测试数据和交互结果测试数据和交互结果产品验证与质量意识Product validation and quality awareness
05根据问题继续调整根据问题继续调整产品迭代能力Product iteration

NEXT CONVERSATION

我也在寻找下一个真实的业务问题Looking for the next real business problem

查看我的项目View my projects 联系我Get in touch