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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Leveraging Machine Learning for Efficiency in Supply Chain Management

G
Guest Blog Last Updated : 30 Jul, 2020
7 min read

Machine Learning For Supply Chain management

Business Case

Machine Learning and Supply Chain Management : Sample Historical Data exported from ERP to train the machine learning algorithm

Machine Learning in Supply Chain Management

 

I will explain each line of the code and then put the full code at the end of the article for reference. As mentioned earlier, I have oversimplified a few things here for us to understand the core concept, and in future series, I will build more real-life complex examples on it.

  1. Step 1

    First, we will import all the python modules which we will be using in our machine learning program. I will not go in detail about these libraries and modules here in this article, and you can find more details about these modules online.

    import pandas as pd
from sklearn.tree import DecisionTreeRegressor # Import Decision Tree Algorithm
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR #import for support vector regressor

    Pandas is a software library written in Python for data manipulation and analysis. It is very easy to read and write data in excel, CSV, and other file formats using Pandas, and work with time-series data.

    Scikit-learn is a free machine learning library for python. We will be predicting the defect percentage in the future supplier delivery using Decision Tree Learning and Support Vector Machine. We will import specifically these two modules from Scikit Learn for the same.

  2. Step 2

    Then, we will read the past data points in an excel file and save it to pandas dataframe SourceData. Similarly, we will read the data points of the delivery for which we want the algorithm to predict the delivery defect percentage.

    SourceData=pd.read_excel("Supplier Past Performance.xlsx") # Load the training data into Pandas DataFrameTestdata=pd.read_excel("Defect Predict.xlsx") # Load the test data

  3. Step 3

    SourceData_train_independent= SourceData.drop(["Defect Percent"], axis=1) # Drop depedent variable from training datasetSourceData_train_dependent=SourceData["Defect Percent"].copy() 
# New dataframe with only independent variable value for training dataset

  4. Step 4

    sc_X = StandardScaler()

X_train=sc_X.fit_transform(SourceData_train_independent.values) 
# scale the independent training datasetvariablesX_test=sc_X.transform(Testdata.values) # scale the independent test datasety_train=SourceData_train_dependent # scaling is not required for dependent variable

  5. Step 5

    svm_reg = SVR(kernel="linear", C=1)
svm_reg.fit(X_train, y_train) # fit and train the modelpredictions = svm_reg.predict(X_test)

print("Defect percent prediction by Support Vector model for the order value of 95827 GBP with 851 pallets sent 55 days before delivery data is " ,round(predictions[0],2) , "%")

    Machine Learning and Supply Chain Management : Defect Percentage

  6. Step 6

    In the same way, we will feed the training dataset (independent and dependent variable value) to the Decision Tree model.

    tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train, y_train) # fit and train the modeldecision_predictions = tree_reg.predict(X_test) # Predict the value of dependent variableprint("Defect percent prediction by Decision Tree model for the order value of 95827 GBP with 851 pallets sent 55 days before delivery data is " ,round(decision_predictions[0],2) , "%")

    Image for post

""" Step 1 - Import the required modules"""

import pandas as pd
from sklearn.tree import DecisionTreeRegressor 
from sklearn.preprocessing import StandardScaler 
from sklearn.svm import SVR

""" Step 2 - Read the data source"""
SourceData=pd.read_excel("Supplier Past Performance.xlsx") # Load the training data into Pandas DataFrame
Testdata=pd.read_excel("Defect Predict.xlsx") # Load the test data 

""" Step 3  - Declare the independent and dependent train data from the sample"""
SourceData_train_independent= SourceData.drop(["Defect Percent"], axis=1) # Drop depedent variable from training dataset
SourceData_train_dependent=SourceData["Defect Percent"].copy() #  New dataframe with only independent variable value for training dataset

""" Step 4  - Scale the independent test and train data"""
sc_X = StandardScaler()
X_train=sc_X.fit_transform(SourceData_train_independent.values) # scale the independent variables
y_train=SourceData_train_dependent # scaling is not required for dependent variable
X_test=sc_X.transform(Testdata.values)

""" Step 5  - Fit the test data in maching learning model - Support Vector Regressor"""
svm_reg = SVR(kernel="linear", C=1)
svm_reg.fit(X_train, y_train) # fit and train the modelpredictions = svm_reg.predict(X_test)

print("Defect percent prediction by Support Vector model for the order value of 95827 GBP with 851 pallets sent 55 days before delivery data is " ,round(predictions[0],2) , "%")

""" Step 6 - Fit the test data in maching learning model - Decision Tree Model"""
tree_reg = DecisionTreeRegressor()
tree_reg.fit(X_train, y_train) # fit and train the modeldecision_predictions = tree_reg.predict(X_test) # Predict the value of dependent variableprint("Defect percent prediction by Decision Tree model for the order value of 95827 GBP with 851 pallets sent 55 days before delivery data is " ,round(decision_predictions[0],2) , "%")

About the Author

Kaushik Choudhury – Senior Solution Design Architect

Kaushik is a senior solution design architect in one of the big four consulting firms’ Supply Chain capability. He is an experienced business transformation consultant and has delivered a wide range of supply chain programs for leading brands in the UK and continental Europe. Kaushik was involved in designing and delivering solutions in Supply Chain business transformation, business re-engineering, a complete end to end technology roadmap implementation projects covering multiple geographical footprints. You can reach him on twitter @think_kc

 

supply chain analytics
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Responses From Readers

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Dinesh Chauhan
Dinesh Chauhan

Hi, Thanks for the blog & its interesting problem to look into. Could you provide train/test files so that we can try it on out machine. Thanks & Regards

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