Welcome to SC0x Supply Chain Analytics!

Intro to SCM and Analytics Basics

Learning Objectives

Unit 1.1: Contents and Timing

1.1.1: Introduction to Logistics and Supply Chain Management

QQ 1.1.1

1.1.2: What is a Supply Chain?

QQ 1.1.2

1.1.3: Logistics versus Supply Chain Management

QQ 1.1.3

1.1.4: Supply Chain Management Perspectives

QQ 1.1.4

1.1.5: Systems Perspective of Supply Chains

QQ 1.1.5

1.1.6: What are the major challenges?

QQ 1.1.6

1.1.7: Why do we care?

Unit 1.2: Contents and Timing

1.2.1: Analytics Basics

QQ 1.2.1

1.2.2: Mathematical Functions

QQ 1.2.2

1.2.3: Quadratic Functions

QQ 1.2.3

1.2.4: Quadratic Functions in Practice

QQ 1.2.4

1.2.5: Other Common Functional Forms

QQ 1.2.5 - Part A

QQ 1.2.5 - Part B

1.2.6: Logarithms

QQ 1.2.6 - Part A

QQ 1.2.6 - Part B

1.2.7: Multivariate Functions

QQ 1.2.7

1.2.8: Properties of Functions

QQ 1.2.8

1.2.9: Key Points from Lesson

Unit 1.3: Contents and Timing

1.3.1: Introduction to Data Management

1.3.2: Example - TinyCo

1.3.3: Cleaning Data

1.3.4: Querying Data in Spreadsheets

QQ 1.3.4

1.3.5: Data Management Challenges

Intro to Probability

Learning Objectives

Unit 2.1: Contents and Timing

2.1.1: Managing Uncertainty I

QQ 2.1.1

2.1.2: Basic Probability Laws 1 & 2

QQ 2.1.2

2.1.3: Basic Probability Laws 3 & 4

QQ 2.1.3

2.1.4: Key Points

PP 2.1.1

Unit 2.2: Contents and Timing

2.2.1: Characterizing Uncertainty

QQ 2.2.1

2.2.2: Discrete Probability Distributions

QQ 2.2.2

2.2.3: Poisson Distribution

QQ 2.2.3

2.2.4: Key Points

PP 2.2.1 - Summary Statistics

PP 2.2.2 - Discrete Distributions

Unit 2.3: Contents and Timing

2.3.1: Managing Uncertainty II

QQ 2.3.1 Data

QQ 2.3.1

2.3.2: Continuous Distributions

QQ 2.3.2

2.3.3: Uniform Distribution

QQ 2.3.3

2.3.4: Normal Distribution

QQ 2.3.4

2.3.5: Triangle Distribution

QQ 2.3.5

2.3.6: Key Points

PP 2.3.1 - Continuous Distributions

PP 2.3.2 - KiaraDT

PP 2.3.3 - Lovely Kitchen

Statistics I

Learning Objectives

Unit 3.1 Contents & Timing

3.1.1: Introduction to Statistical Testing

QQ 3.1.1

3.1.2: Central Limit Theorem

QQ 3.1.2

3.1.3: Normal Distribution I

QQ 3.1.3

3.1.4: Normal Distribution II

QQ 3.1.4

3.1.5: Applying Central Limit Theorem

QQ 3.1.5

PP1 - Chiquita Republic

Unit 3.2 Contents & Timing

3.2.1: Sampling

QQ 3.2.1

3.2.2: Confidence Intervals I

QQ 3.2.2

3.2.3: MoonDoe I

QQ 3.2.3

3.2.4: Confidence Intervals II

QQ 3.2.4

3.2.5: Confidence Intervals III

QQ 3.2.5

PP1 - Prediction Intervals

Unit 3.3 Contents & Timing

3.3.1: Hypothesis Testing Basics

QQ 3.3.1

3.3.2: Hypothesis Testing Example

3.3.3: P-Value

QQ 3.3.2

3.3.4: Hypothesis Testing - More Formally

QQ 3.3.3

3.3.5: Hypothesis Testing Examples

QQ 3.3.4

3.3.6: Chi-Squared Tests

QQ 3.3.5

3.3.7: Chi-Squared Test Example II

PP1 - Crimson Laboratory

PP2 - Lily Ambulance Services

PP3 - Chi-Square Test

Unit 3.4 Contents & Timing

3.4.1: Working with Multiple Random Variables

QQ 3.4.1 Data

QQ 3.4.1

3.4.2: Covariance and Correlation

QQ 3.4.2 Data

QQ 3.4.2

3.4.3: Linear Function of Random Variables

QQ 3.4.3

3.4.4: Sum of Random Variables

QQ 3.4.4

PP1 - Great Powder

Statistics II

Unit 3.5 Contents & Timing

3.5.1: Introduction to Regression

3.5.2: Variable Types

3.5.3: Linear Regression Basics

3.5.4: Initial Model Validation

3.5.5: Model 1

3.5.6: Model 2

3.5.7: Linear Transformation of Variables

3.5.8: Modeling Categorical Variables

3.5.9: Additional Model Validation

3.5.10: Re-Sampling

3.5.11: Key Points and Practical Tips

Optimization I

Optimization II

Learning Objectives

Unit 4.1 Contents & Timing

4.1.1: Classical Optimization - Unconstrained Optimization

4.1.2: Classical Optimization

4.1.3: Finding the Instantaneous Slope

4.1.4: Solving the iWidget Problem

4.1.5: Gears Unlimited

4.1.6: Example - boxy.com

4.1.7: Key Points from Lesson

Unit 4.2 Contents & Timing

4.2.1: Constrained Optimization

4.2.2: Problem Formulation

4.2.3: Graphical Representation

4.2.4: Interpreting Results and Sensitivity Analysis

4.2.5: Formal Notation

4.2.6: Anomalies

4.2.7: Key Points from Lesson

Algorithms, Approximations, and Simulation I

Algorithms, Approximations, and Simulations II

Learning Objectives

Unit 5.1 Contents & Timing

5.1.1: Introduction to Algorithms

5.1.2: Shortest Path Algorithm

5.1.3: Dijkstra's Algorithm

5.1.4: Traveling Salesman Problem

5.1.5: 2-Opt Heuristic

5.1.6: Vehicle Routing Problem

5.1.7: Sweep Heuristic

5.1.8: Clark-Wright Savings Algorithm

5.1.9: VRP with MILP

5.1.10: Key Points from Lesson

Unit 5.2 Contents & Timing

5.2.1: Approximation Methods

5.2.2: Quick Estimation

5.2.3: One to Many Distribution

Introduction to Spreadsheets

Spreadsheet Layout

Identifying Cell Values

Spreadsheet Functions

SUM function

Commonly Used Functions

Simple single parameters functions

Common multiple parameters functions

Advanced Functions

SUMPRODUCT Function

IF Function

Working with Data Files

Part 1: Median Sales

Part 2: Coefficient of Variation

Part 3: Total Cost

Part 4: Counting Big Sellers

Solutions for sample spreadsheets

Using Google Colab

Variables and Types

Creating and Overwriting Variables

Types

Printing

Printing the Output of Operations

Strings

String Formatting

Operators

Strings and Mathematical Operators

Common Mathematical Operations

Data Structures

Concatenating Lists

Lists

Dictionary

Data Structure Functions

Looping and Iteration

For Loop

While Loops

List Comprehension

Functions

Functions

Pip, Importing, and Help

Methods and Classes

Contents

Video 1: Basics

Video 2: Special Cases

Contents

Recitation 1E: Excel | Data Analysis in Excel

Recitation 2E: Excel | Regression in Excel Part 1

Recitation 3E: Excel | Regression in Excel Part 2

Contents

Question 1 - Prediction and Confidence Intervals

Solution to Question 1 - Prediction and Confidence Intervals

Question 2 - Two-tailed Hypothesis Test

Solution to Question 2 - Two-tailed Hypothesis Test

Question 3 - Right-tailed Hypothesis Test

Solution to Question 3 - Right-tailed Hypothesis Test