RA: Data Science and Supply chain analytics.(A-Z with R)

Udemy

Learn fundamentals of data science for supply chain analytics, inventory, and revenue management using R, big data, machine learning.

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₹ 2799

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                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Introduction

Why I chose R for this course ?
Why we should Learn Coding.
Curriclum
Supply chain Visualization
Cost and Service Dynamics.
Service level and Product Characteristics
Customer and Supplier Characteristics
Supply chain Views
The Financial Flow
Why is supply chain Complicated

Supply chain Data!

Introduction
Types Of Data in supply chain
Data From suppliers
Data From Production
Data From Stocks
Data From Sales & Customers
Why we Need to learn Data Science?
Analytics Types

Installation and Overview of R

Welcome to the World of R!
What is R statistical Language.
How to Install R
How To install Rstudio
A walk through Tutorial
setup your Project!
install Packages!
Summary

R programming Fundmentals.

Introduction
Different Data Structures and types in R
Do arithmetic Calculations in R and write vectors
Creating a list
Importing Data in R and basic Exploration functions
Selecting Data in dataframe.
If Else function
Conditions
Function with conditions
for_loops
applying a function inside for loop
For loop on a data_frame
applying the function on a data frame
Assignment
Assignment section 4 answer Part 1
Assignment Section 4 Answer Part 2
Summary

Supply Chain Statistical Analysis

Intro
Calculating Measures of Centrality and spread 1
Calculating Measures of Centrality and spread 2
Putting the Measures together
Correlations
Correlation Thresholds
Calculating Correlations
Detecting outliers
Outliers in R
Intro to Linear Regression
Linear Regression
Intro to Distributions.
Distributions importance in supply chain
Chi-Square tests
Distributions in Excel
Distributions Chi-Square test
Cover for 90% of the Normal Distribution
Assignment Distributions in Excel
Assignment answer: Bike Demand Chi-square test
Distributions in R
Assignment
Assignment answer
Summary

Data Cleaning and Manipulation.

Intro
Intro To Dplyr
Investigate with Dplyr
Unique Invoices
Average Invoice Value Per Country
Average Number of items within an Invoice.
Joining
Changing date time to date
Pivot Wider
Pivot Longer
Separate and paste
putting it all together
Assignment: New York airlines
Assignment question 1 answer
Assignment question 2 & 3 answer
Assignment Question 4,5 and 6
Assignment question 7
Summary

Working with Dates in R.

Intro.
Motivation for working with dates
Parsing dates with R.
Make inference from dates in R
Woking with Lubridate
Modeling inter-arrival time of Customers 1
modeling inter arrival time of customers 2
Assignment
Assignment answer question1 to 4
Assignment answer question 5 and 6
Assignment Last Question
Summary

Visualize with ggplot2 and Plotly.

Introduction
line plots
scatter plots
Barplots
Distribution Plots
Boxplots
Histograms
Histograms 2
Assignment.
Assignment Solution question 1 and 2
Assignment solution_part2
Summary

Suppliers and Products Segmentation.

Intro
Intro to segmentation
Why we need segmentation in our supply chain?
Multi- Criteria segmentation.
Transforming the data for excel
ABC_Analysis_in Excel
Assignment Explanation
ABC_analysis in R
Multi-Criteria ABC analysis
Multi-Criteria ABC analysis_store level
Assignment
assignment answer part 1
assignment answer part2
Supplier Segmentation.
Supplier Segmentation 2
Krajic in R
Visualizing Krajic
Summary

Forecasting Basics.

Why We Need Forecasts
Qualitative and Quantitative Forecasting
Optimistic and Pessimistic Forecasting
Preparing the Data for regression
Changing the format of posixct to date.
Multiple linear regression in Excel Revised
Part 2
Assignment Explanation
fitting forecast with Regression_in_R
Forecasting with linear regression in R
Assignment.
Assignment solution part 1
Assignment solution part 2
Summary

Time-Series Forecasting

Introduction To Time series forecasting
Converting data to time-series
Weekly and Daily time_series
Analyze the time series
Getting time components and measure strength of trend and seasonality
Dissecting components inside R
Measuring strength of trend and seasonality.
Exponential Smoothing
Arima and it's components
Accuracy Measures for forecasting
Determine Arima Orders
Training and testing
Dynamic Harmonic regression
Measuring accuracy Of new model
Improving ARIMA: Grid search with SARIMA
Error handling while Grid search
Battle of the Arima(s)
Assignment
Assignment Answer Part 1
Assignment answer Part 2
Summary

Forecasting Aggregation

Intro
Hierarchal and Grouping
Aggregation approaches
Preparing the data for aggregation
Hierarchal Structuring
Aggregate forecasting
Testing and Accuracy for aggregation
Comparison between Middle out, Bottom Up and Top Down
Assignment
Assignment answer 1
Assignment answer 2
Summary

Products segmentation for Demand Planning.

Intro
Product's Classifications for forecasting.
Checking for holidays
SKU grouping by date
Customizing a holiday count Function
For-looping the holiday function
Calculating Average demand intervals and CV squared.
Visualizing the classification
Assignment
Assignment solution part1
Assignment Solution Part 2
Assignment solution part 3
Summary

Supply chain Simulations

Intro
Waiting Line and Queue theory.
Example Demonstration
Waiting line in excel
Simulation assignment
Waiting lines in R
Making 400 Simulations At once.
Waiting Line in call centre
Defining the Right K
Simulation With Capacity Constraints
Assignment
Assignment solution
Sequential services in one system
Many Services
Multiple Service simulations in R
Conclusion
Assignment
Assignment Solution
Summary

Inventory Basics

Intro
Why we need inventory?
Inventory Strategies
Inventory Types and EOQ
Total Logistics Cost and total relevant cost
Economic Order Quantity With Excel
EOQ with quantity discounts
Eoq Sensitivity
EOQ with inventorize
T practical in R
EOQ with Lead Time
Practical Example inside R
Assignment
Assignment Solution
Summary
Summary 2

Inventory with Uncertainty

Intro
Uncertainty and Variability in supply chain
Demand Lead-time and Sigma DL
Calculating average daily demand
Method 1 for safety stock calculation.
Method 2 for safety Stock Calculation
Preparing SKUs for safety stock calculations.
Calculating average demand and sd
Setting Cycle service level in R
Calculating re-order point with inventorize
Calculating re-order point with lead time variability
Lead Time Variability in Excel
lead Time Variability with Inventorize.
Wrap for Indeterministic Inventory
Assignment
Assignment solution
Summary

Inventory Simulations

Introduction to Inventory Policies
Min Q Demonstration
Min Q policy
Min,Q example in excel
Periodic Review Demonstration.
Periodic Review Policy
Excel Example for periodic review.
Min Max Demonstration.
Min Max Policy
Min Max Example in Excel
Base Stock Demonstration.
Base Stock Policies
Base stock Policy_Example
Assignment
Simulating (s,Q) Policy
Increasing the Quantity of S,Q Policy
Visualizing the simulation
Poisson Min-Max Simulation
Visualizing variations of one policy!
Visualizing all policies.
Metrics Comparison
Assignment.
Assignment Solution
Summary

Seasonal Inventory

Introduction
Seasonal Products
Point of maximum Profit
How much I will sell ?
Data Table
Critical Ratio
Critical Ratio in Excel
What's Actually happening.
Critical Ratio in R with inventorize
Expected Profit with Inventorize
Preparing the data for optimum Quantity Calculation
Creating a margin of error
Conclusion
Assignment
Assignment solution
Summary

Consumer Behavior and Pricing

Revenue Management
Pricing History
Why is Pricing important?
Customer's perception of Price.
Pricing Mechanisms
Commodities
Price response Function
Price Response Function Motivation In R.
Assignment
Assignment Solution
Elasticity Intro
Elasticity
Linear Elasticity with Inventorize
Practical Example: Preparing the data for modeling
Modeling all retail SKUs at once.
Optimum Price for All Skus
Optimum Pricing validation
Assignment
Assignment solution
Summary

Optimizing the Price for a single product.

intro
Intro to logistic regression
Logit modeling with inventorize
Assignment
Assignment Solution

Multi-Product Optimization

Intro
Competing Products.
The Co-relation Among products
Multi-Variate Regression fitting.
Relation_among_products
ANOVA
Updating the model
Assignment
Comparing between the two models
Prediction with Multivariate regression
multinomial choice models
Optimizing competing prices with Inventorize
Results of Optimization
Applying the Algoritm on 40000 Observations
Assignment
Summary

Markdowns and Time-based Discounts

Intro
Markdowns
Why We do Markdowns
Customers Segments to Markdowns
Problem Formulation
Markdowns for Multiple periods.
Setting Up Solver
Solver with salvage Value
Markdowns with forecasting
Sensitivity analysis
Markdowns for one period
Assignment

Customer Segmentations.

RFM Analysis
Segmentation of customers based on RFM Analysis.
Preparing the data.
Recency
Joining KPIs together.
Visualization
Ranking
Grouping into tiles.
3D scatterplots
Assignment

Machine Learning

Intro To machine learning
Decision tree demo
Kmeans
Overfitting
Kmeans in R
Total sum of squares
Silhouette
Interactive three dimensional scatter plot.
Assignment
Supervised learning : Linear regression.
Supervised Learning : Decision Trees and random forest.
Comparing Models
Classification data orientation
Exploring the data
Correlation matrix
Splitting
Training and Testing
Control the fitting.
Logistic Regression Classification.
Probabilities of the logistic Regression
Confusion Matrix
ROC
Decision tree model
Assignment
Conclusion
Summary

Association Rules

Introduction
Intro to Market basket analysis
Top 10 Products
Reading Transactions.
Summary of Transactions
Apriori
Top 10 rules
Subsetting
Assignment

Bonus Webinar! Noble Prog

Introduction To Webinar
Webinar

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RA: Data Science and Supply chain analytics.(A-Z with R)

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