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工作中我们为了业务表现需要做出决策，然而，这些决策的依据常常是经验、直觉、甚至是压力下的慌乱反应，正确与否全拼运气。数字化让获取数据变得容易，然而，面对海量数据我们却常常无从下手，苦于得不出有效结论。基于数据的业务决策课程专为破除以上困境设计，旨在帮助学员建立数据导向思维和科学决策习惯从而在工作中提升业务表现。本课程兼具系统化、实操性、易掌握三个特点：

  • 系统化：从思维方式入手，为学员搭建起清晰的科学决策逻辑框架，并在合适的步骤中有机地插入数据分析工具，内容关联紧密。学员将掌握系统性的方法，而非相互割裂的单个工具。
  • 实操性：培训中贯彻了一个沉浸式的业务表现提升沙盘模拟，学员将把所学工具带入该场景实操练习，不仅能学会"是什么"和"为什么"，还能在培训现场掌握"怎么做"并取得可见的成果。
  • 易掌握：传统数据分析内容往往需要掌握编程语言或复杂的工具操作，而在本课程的数据分析部分，你只需要"说人话"就可以让AI帮你完成复杂的数据分析。


We need to make decisions for business performance at work. However, decisions are often based on experience, intuition, and even panic reactions under pressure. Whether they are correct or not is simply a matter of luck. Digitization makes it easier to obtain data, but we often struggle to draw valid conclusions when faced with massive amounts of data. This course is designed to overcome the above dilemmas. It helps students build a data analysis empowered decision-making mindset. The course is systematic and practical:

  • Systematic: Organically insert data analysis tools into a clear overall framework of decision-making. Students will master a systematic approach rather than discrete tools.
  • Practical: With a Business Performance Improvement simulation case-study embedded, students could actively practice the tools. Not only to learn the "what" and "why", but also master the " how" and achieve visible results onsite.
  • Easy to Master: Traditional data analysis content often requires coding or complex operating, but in the data analysis section of this course, you only need to speak "human language" to AI and get accurate data analysis result effortlessly.

Who Should Attend? / 适合人群

有业务表现提升和/或数据分析需求的企业各职能人群，包括但不限于研发、生产运营、销售、供应链、人士、行政、市场、卓越运营等。


Professionals in all industries and positions who are in need of business performance improvement and/or data analysis, including but not limited to R&D, production & operation, sales, SCM, HR, admin, marketing, OpEx, etc.

Training Details / 培训详情

Teaching Method / 授课方法


Lecture, Case Study, Group Discussion, Group Exercise, Simulation Game

课堂讲授、案例分析、分组讨论、小组练习，模拟游戏

在课程结束时，学员将：

  • 改变固有思维模式：打破"跟着感觉走"和"被数据推着走"的固有模式，建立基于数据分析的科学决策思维。
  • 掌握问题解决方法：掌握通过科学决策提升业务表现的方法。
  • 熟练数据分析工具：熟练使用假设检验、相关分析、回归分析等常用数据分析工具，并掌握背后原理。
  • 学会AI数据分析：掌握通过AI工具完成数据分析的方法。
  • 解决实际工作问题：通过模拟游戏，在培训现场实现某个真实运营场景的业务表现提升。


Training Outcome


Through this course, participants will be able to:

  • Change the Mindset: Get rid of their fixed patterns for problem solving and data analysis, while establishing a systematic decision-making mindset based on data analysis.
  • Master the Method: Master the approach to improve business performance through systematic decision-making.
  • Upgrade the Skillset: Be proficient in using data analysis tools such as hypothesis testing, correlation analysis, regression analysis, etc., and mastering the logic principles.
  • Master AI Data Analysis: Learn how to perform data analysis using AI tools.
  • Solve a Real Problem: Participants will improve the business performance of simulated operation case.

Outline / 培训大纲

Part One: Establish the Mindset



  • Interaction Case 1: Scientific Decision-making Mindset
  • Interaction Case 2: Correct Data-driven Mindset
  • Reflection: How to Build a Data analysis Empowered Decision-making Mindset

第一部分：建立思维方式



  • 互动案例1：科学的决策思维
  • 互动案例2：正确的数据思维
  • 思考：如何建立基于数据分析的科学决策思维

Part Two: Master the Approach



A Simulation Case Study for Business Performance Improvement (Initial Status)

第二部分：掌握方法工具



某业务表现提升模拟情景的背景介绍（初始状态）

Step 1: Understand the Problem Itself

  • Establish Problem Indicators

SMART Principles

  • Clarify the Essence of the Problem

From "Voice of the Customer" to "Critical Quality Characteristics"

  • Team Exercise 1: Practice Phase 1 Tools in the Simulation Case

第一步：明确问题本身

  • 设立问题指标

SMART原则

  • 明确问题本质

从"客户的声音"到"关键质量特性"

  • 团队练习1：第一阶段工具在模拟情景中的实操练习

Step 2: Assess the Current State

  • Scientific Data Collection

Data Collection Approach

Reliability of Data Collection

Statistical Sampling Basics

Sampling Strategy

  • Effectively Present Data Collection Result

Key Statistics

Key Performance Indicators

Visualization

  • Team Exercise 2: Practice Phase 2 Tools in the Simulation Case

第二步：通过数据衡量现状

  • 科学收集数据

定义数据收集方法

检验数据收集方法的可靠性

理解抽样原理

选择抽样方法

  • 有效呈现结果

关键统计量

流程表现指标

可视化

  • 团队练习2：第二阶段工具在模拟情景中的实操练习

Step 3: Find out the Root Causes through AI Empowered Data Analysis

  • Data Analysis Foundation
  • Hypothesis Test
  • Correlation
  • Regression
  • Above-Mentioned Data Analysis Using AI Tools
  • Team Exercise 3: Data Analysis Practice With The Simulation Case Data (Using AI Tools)

第三步：通过AI助力的数据分析工具识别根本原因

  • 数据分析基本原理
  • 假设检验
  • 相关分析
  • 回归分析
  • 以上数据分析在AI工具中的实现
  • 团队练习3：基于模拟情景中数据的定量分析工具实操练习（需使用AI工具）

Step 4: Make Scientific Decision

  • Generate Potential Solutions: Structured Brainstorming
  • Select the Optimal Solutions: Prioritization Tools
  • Team Exercise 4: Decision Making Practice based on the Simulation Case

第四步：做出科学决策

  • 生成潜在方案：结构化头脑风暴
  • 筛选最优方案：优先级排序工具
  • 团队练习4：基于模拟情景的科学决策练习

Summary and Recap 

总结与回顾 

Speakers

  • Nadya Liu (Founder of Linke Consulting)

    Nadya Liu

    Founder of Linke Consulting

    http://www.linke-consulting.cn/

    Nadya has been deeply engaged in the field of Operations Excellence after graduated from Penn State with master’s in industrial engineering. She is proficient in Lean Six Sigma Methodology and data analysis tools, and has rich project experience in quality management, process optimization and operation transformation. She is one of the only 20+ ASQ certified Master Black Belts in China, and the author of the “Trainer Development Handbook”.

    She used to work in multinational corporates, responsible for LSS in Asia Pacific region, then worked as a management consultant in a world-leading strategic consulting firm. Currently, Nadya is the master trainer of Linke consulting, providing training and coaching on Operations Excellence for corporate. She has developed 20+ training courses in areas of LSS, Data Analysis, Problem Solving, PM, Effective Communication, TTT, etc., delivered 700+ training sessions with 8000+ students trained from 40+ countries, and coached 600+ continuous improvement projects. She is spoken highly of by both corporate clients and individual students for her solid knowledge and experience, clear course logic, and the engaging training style.

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Tickets

Please use your own name and email when registering for a training session. Additional fees may apply if you fail to register under your own identity. 


A prepayment is required for all full-day training sessions. 

全天培训课程需要提前付款。

Member Ticket

Definition of a Member: You are personally registered as a member and hold a membership card.

Member Early Bird Price RMB 2,895
Member Price RMB 3,050
Member Company Employee Ticket
Early Bird Price RMB 2,895
Standard Price RMB 3,050
Non-Member Ticket
Standard Price RMB 3,650

Registration

  • For full-day sessions, AmCham Shanghai members are eligible to attend at "Member Rate"; Employees who work at AmCham Shanghai member companies are eligible to attend at "Employee Rate"; Non-members are eligible to attend at "Non-Member Rate".
  • Limited spaces are available, and attendance will be given on a first-come, first-served basis.
Cancellation Policy
  • This session requires confirmation of attendance in advance.
  • Cancellation: If you need to cancel your registration for a half-day session, please notify Cynthia Xia at (86 21) 6169 3016 or email cynthia.xia@amcham-shanghai.org no less than 48 hours in advance. If you need to cancel your registration for a full-day session, please notify Cynthia Xia at least two days prior to the session for a full refund. Cancellation made within two working days before the session will charge 20% of ticket fee.
  • To ensure that you have a seat, please come to the session on time. Open seats will be released to other guests 15 minutes after the session starts.

Venue

AmCham Shanghai Conference Center

黄浦区湖滨路168号无限极大厦27楼
27F, Infinitus Tower, No.168 Hubin Road, Huangpu District.
Shanghai, China

If you have any questions please contact Cynthia Xia

Contact Organizer

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