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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

How to Become a Supply Chain Analyst in 2023?

Nitika Sharma
Nitika Sharma Last Updated : 25 Sep, 2023
4 min read

Introduction

In a world where laptops travel thousands of miles before reaching your doorstep and your favorite restaurant’s secret ingredient arrives from afar, the global supply chain orchestrates this intricate dance of goods and services. The supply chain analysts emerge as the unsung heroes, navigating these challenges to ensure products reach consumers seamlessly. With a 28% job growth rate, aspiring supply chain analysts have a golden opportunity to shape this vital industry. This article sheds light on their pivotal role and offers a roadmap to embark on a rewarding career in supply chain analysis.

Table of contents

What is a Supply Chain Analyst? 

A Supply Chain Analyst is a professional responsible for optimizing and managing various aspects of the supply chain, ensuring the efficient flow of products or services from manufacturers to end consumers. Their role involves analyzing data, monitoring logistics, and making strategic decisions to enhance the supply chain’s performance. They play a critical role in minimizing costs, maximizing productivity, and addressing potential disruptions to ensure products reach their destination smoothly and on time. These professionals are essential in industries like manufacturing, retail, logistics, and more, where they use their analytical skills to drive operational excellence and contribute to overall business success.

Roles and Responsibilities of Supply Chain Analyst 

  • Oversee large-scale projects spanning multiple countries.
  • Train and lead supply chain teams to optimize operations.
  • Proactively assess and strategize for potential risks.
  • Establish procurement, packaging, and delivery standards.
  • Boost revenue through research, networking, and relationship building.
  • Ensure adherence to company policies and procedures.
  • Improve efficiency and reduce inventory costs.
  • Interact with vendors and supply chain personnel to resolve issues.
  • Maintain positive relationships with partners.
  • Report on supply chain performance metrics.
  • Negotiate cost-effective deals for the organization.
  • Monitor key performance indicators of supply chain professionals.
  • Analyze and optimize procedures for cost savings.
  • Initiate and manage large international purchases.
  • Collaborate with various organizational departments to streamline supply chain processes.

Also Read: How to Become a Data Analyst With No Experience?

A Day In The Life of a Supply Chain Analyst

Source: SCM Talent Group 

Skills and Education

Education

A bachelor’s degree in Mathematics, Logistics, Business Analytics, or related fields is essential for starting a career in supply chain analysis. Prior experience in roles like supply chain analyst or logistics coordinator, preferably gained through internships, is highly desirable.

Technical Skills

  • Proficiency in MS Office.
  • Familiarity with ERP systems like SAP, supply chain processes, and relational databases.
  • Strong problem-solving abilities.
  • Multitasking capability.
  • Effective time management.
  • Adherence to business ethics.
  • Critical thinking skills.
  • Attention to detail.
  • Capacity to independently execute complex operations.
  • Experience with SQL analysis tools.
  • Data analysis skills, utilizing various software and tools to identify cost-effective supply chain paths.
  • Proficiency in mathematics for discussions with supply chain stakeholders and applications in logistics, automation, optimization, predictive maintenance, machine learning, and inventory management.
  • Quantitative and analytical skills for processing and interpreting extensive datasets to make data-driven decisions.
  • Project management skills, including leadership abilities, multitasking, and overseeing project operations.
Skills Needed for Supply Chain Analyst

Non-Technical Skills

  • Logical thinking for unbiased and efficient decision-making, evaluating proposed modifications, and finding alternatives.
  • Interpersonal skills, including active listening, empathy, and relationship building to collaborate with authorities, stakeholders, and consumers.
  • Effective communication skills for presenting ideas, suggestions, and improvements logically and clearly.
  • Strong organizational skills to efficiently multitask, produce better results, and manage workloads.
  • Teamwork abilities for handling diverse teams, training supply chain teams, and collaborating with different departments while interacting with individuals professionally, regardless of personal relationships or ideological differences.

Salary in India

Supply Chain Analysts in India typically earn an average annual salary ranging from INR 2.5 lakhs to a maximum of INR 12 lakhs. The average annual salary for this role is approximately INR 5.2 lakhs. With increasing experience, salaries can be expected to rise. On a monthly basis, the average take-home salary for Supply Chain Analysts in India falls within the range of INR 39,472 to INR 40,645.

Salary in USA

According to data from the US Bureau of Labor Statistics, individuals working in logistics, including supply chain analysts, earn a median annual salary of $77,030. This field is expected to experience rapid growth, with a projected 28 percent increase in job opportunities between 2021 and 2031, significantly outpacing the average job growth rate across all industries, which stands at 8 percent.

Supply chain analysts working in various sectors reported different median salaries, with the federal government offering the highest median salary of $88,710. Additionally, those employed in management of companies and enterprises, manufacturing, professional, scientific, and technical services, as well as wholesale trade, earned competitive median salaries ranging from $63,930 to $78,660.

As the realm of e-commerce continues its expansion, the outlook for careers related to supply chain management remains exceptionally promising.

IndustryMedian Salary
Federal government$88,710
Management of companies and enterprises$78,660
Manufacturing$77,740
Professional, scientific, and technical services$76,840
Wholesale trade$63,930
Souece: US BLS

Job Roles

Supply Chain Analysts, also known as Supply Chain Data Analysts, focus on enhancing the efficiency of current supply chain operations or working on new projects with the same objective. They play a vital role in performance analysis, data evaluation, problem identification, and the development of effective strategies. Various job titles associated with this roles include:

  • Commodity Specialist
  • Data Analyst
  • Materials Analyst
  • Director of Supply Chain Management
  • Purchase Manager
  • Director of Operations
  • Warehouse Manager
  • Demand Planning 

Conclusion 

The role of a supply chain analyst is undeniably crucial in today’s dynamic business landscape. These professionals are at the forefront of optimizing supply chain operations, ensuring timely deliveries, and effectively mitigating challenges that arise from global logistics.

If you’re aspiring to become a supply chain analyst, consider enrolling in our Blackbelt Plus Program. This comprehensive course will equip you with the knowledge and practical expertise needed to excel in this field. Explore the course now!

Frequently Asked Questions 

Q1. Is supply chain analyst a hard job?

A. Supply chain analyst can be challenging due to the need for analytical, technical, and problem-solving skills, but it’s rewarding.

Q2. Is the Certified supply chain analyst worth it?

A. Yes, the Certified Supply Chain Analyst program is worth it as it provides valuable skills and enhances career prospects.

Q3. How long does it take to become a supply chain analyst?

A. Becoming a supply chain analyst typically requires a bachelor’s degree and relevant experience, taking several years.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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