Notes by Louisa
Notes by Louisa
Notes by Louisa
  • Introduction
  • Chapter1 Python Cheatsheet
    • Reference, Deep Copy and Shallow Copy
    • Iterators
    • List Comprehensions
    • Numpy
    • Pandas
    • Data Visualization
    • DateTime
    • Python Good to knows
  • Chapter2 Java Cheatsheet
    • Fundamentals to Java
    • Interface, Abstract Class, Access Modifier, Exceptions
    • Linked List and Java List
    • Java Queue, Stack and Deque
    • Binary Tree
    • Heap in Java
    • Map/Set/Hash
    • OOD
  • Chapter3 Algorithm
    • Fundamental Knowledge
    • Binary Search
    • Basic Sorting
    • Advanced Sorting
    • Linked List
    • Recursion 1
    • HashTable
    • Queue
    • Sliding Window
    • Stack
    • Binary Tree
    • Binary Search Tree
    • Heap
    • String
    • Graph Search DFS1 (Back Tracking)
    • Recursion II and Memoization
    • Dynamic Programming
    • Complete Binary Tree, Segment Tree, Trie Tree
    • Graph Search BFS
    • Graph Search BFS 2
    • Graph Search DFS2
    • Problems from 'JianZhi Offer'
    • Problems Categorized
    • Bit Operations
  • Chapter4 Model
    • Linear Regression
    • Logistic Regression
    • Regularization and Feature Selection
    • Model Evaluation
    • Nonlinear Models
    • PCA
    • Unsupervised Learning
    • Gradient Descent and Gradient Boosting
    • XG Boost and Light GBD
    • Deep Learning
    • Tensorflow/Keras
    • RNN
  • Chapter5 Statistics and A/B Testing
    • Inference about independence
    • Probability, Sampling and Randomization with Python
    • A/B Testing
    • Stats Interview Review
    • Statistics Glossary
  • Chapter6 SQL
    • Student Scores Query
    • Order Query
    • Movie Rating Query
    • Social-Network Query
    • LeetCode SQL题目总结
    • Spark SQL
  • Chapter7 Big Data and Spark
    • Introduction to Pyspark
    • Data Cleaning with Apache Spark
    • Feature Engineering with Pyspark
    • Building Recommendation Engines with Pyspark
    • Building Data Engineering Pipelines in Python
    • Hadoop MapReduce
    • Big Data Related Paper
  • Chapter8 Code Walk-Throughs
    • Python
    • R
    • Shell
  • Chapter9 Special Topics
    • Anomaly Detection
    • E Commerce
    • Supply Chain
    • Social Network Analysis
    • NLP intro
    • Time Series
    • Challenge Prophet with LSTM models
  • Project: The Winning Recipes to an Oscar Award
  • Project: A Crime Analysis of the Last Decade NYC
  • Project: Predict User Type Based on Citibike Data
  • GeoSpark/GeoSparkVis for Geospatial Big Data
  • Single Scattering Albedo
  • Sea Ice Albedo Retrievals
  • Lidar Project
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  • 挑战
  • Data角度的Supply Chain
  • 正在解决这些问题的公司/平台
  • Retailers
  • Demand Forecasting
  • 资源
  1. Chapter9 Special Topics

Supply Chain

PreviousE CommerceNextSocial Network Analysis

Last updated 5 years ago

挑战

丝绸之路就是最早的supply chain，从中国运丝绸和茶叶，从欧洲运香料。而现在的supply chain复杂度更高。

现在Supply Chain的挑战：Faster, more flexible, more granular, and more efficient

  1. Rising Customer Expectations: - Real Time Tracking - Precise Inventory Management - Wide range of product availability - High availability, reliable lead times, transparency

  2. Fast Fulfillment and Large Volume of Orders - Fast-paced timeline of ordering (overnight shipping for holiday) - Quick growth of parcel and less-than-truckload shipment

  3. Uniqueness and Large Variation of Order - Special requirement of configuration, urgency and delivery locations - Ad hoc and real time planning (last minute change, change typo, etc.)

  4. Leader and Management Team requires 360 degree visibility - Leaders orchestrate demand and supply, information from different channels - End-to-End transparency throughout the supply chain

  5. Accelerating Scale, Scope and Depth of Data - Utilize such immense data sets to drive contextual intelligence - Enable more complex supplier networks that focus on knowledge sharing and collaboration instead of just completing transactions is critical

RFID: Radio Frequency Identification

Twitter feeds: sentiment 大众对产品的考虑，可以给出降价

Data角度的Supply Chain

正在解决这些问题的公司/平台

Oracle SCM, SAP APO (Advanced planning and optimization), JDA, IBM Watson supply chain, Kechie ERP, E2 Open, Deepsense.ai

Retailers

Demand forecasting, supply planning, A/B testing, root cause analysis, inventory optimization, price optimization, on-time delivery, customer survey etc.

Logistic Companies

Transportation/fuel optimization, lead-time prediction, on time performance

Consumer Goods Companies

Manufactoring

Pharmaceutical

Demand Forecasting

  1. Traditional Commercial Software: SPSS, SAS

  2. Commercial Machine Learning Platform: Google Cloud AutoML, DataRobot, Dataiku, H2O

  3. Build in-house solution from scratch

资源

Bullwhip Effects
Predictive Modeling and AnalyticsCoursera
Supply Chain Analytics EssentialsCoursera
供应链物流Coursera
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