Supply Chain Analytics Decoded: The Beginner'S Handbook

Analytics with Python , Chat GPT & Excel

What you'll learn

Become Supply Chain Data Scientist

From Novice to Pro: A Comprehensive Guide to Mastering Supply Chain Analytics

Navigating the Analytics Landscape: The Beginner's Roadmap to Supply Chain Optimization

Data-Driven Insights Made Easy

The Power of Data in Your Hands

Embarking on the Analytics Journey

A Beginner's Guide to Supply Chain Optimization

Data Driven Decision Making

Become Supply Chain Data Analyst

Become Supply Chain Data Engineer

Requirements

Basic Mathematics

Basic SCM

Basic Statistics

Description

"Automation and AI have the potential to disrupt certain job roles, but they also create new opportunities and transform industries."With the increasing demand for professionals skilled in supply chain analytics, learning this domain can open up various career opportunities. Supply chain analysts, data scientists, operations managers, and supply chain consultants are among the roles where expertise in supply chain analytics is highly valued.Learning Supply Chain Analytics can provide several benefits and advantages in today's business landscape. Here are some reasons why learning Supply Chain Analytics can be valuable:Optimization of Operations: Supply Chain Analytics enables you to analyze and optimize various aspects of the supply chain, such as inventory management, demand forecasting, production planning, and logistics.Improved Decision Making: Supply Chain Analytics provides insights into the supply chain processes, helping you make data-driven decisions. It enables you to identify trends, patterns, and potential risks in the supply chain, allowing you to take proactive measures to mitigate issues and make informed strategic choices.Enhanced Efficiency and Cost Savings: By applying analytics techniques to supply chain data, you can identify inefficiencies, bottlenecks, and areas for improvement. Demand Forecasting and Planning: Supply Chain Analytics helps in accurate demand forecasting and planning, allowing businesses to align their inventory levels, production capacities, and distribution networks with anticipated customer demand. Supply Chain Risk Management: Analytics can help identify and assess potential risks in the supply chain, such as disruptions in logistics, supplier delays, natural disasters, or geopolitical events. Enhanced Customer Service: Supply Chain Analytics enables businesses to gain insights into customer behavior, preferences, and buying patterns. Competitive Advantage: In today's competitive business landscape, leveraging analytics in the supply chain can provide a significant competitive advantage. Sustainability and CSR:  By analyzing supply chain data, businesses can identify opportunities to reduce environmental impact, optimize transportation routes, minimize waste, and support ethical sourcing practices.Future-Proofing: As businesses become more data-driven and rely on analytics for decision-making, understanding and applying analytics in the supply chain becomes crucial. Learning supply chain analytics equips you with skills that are in high demand and can future-proof your career in an increasingly data-centric business environment.Overall, learning Supply Chain Analytics empowers businesses and professionals to make informed decisions, optimize operations, drive efficiency, and gain a competitive edge in the complex and interconnected world of supply chains."Job cuts due to AI are a reminder of the need for continuous learning and upskilling to stay relevant in a rapidly evolving job market."

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Best Practices To Understand Course Content

Lecture 3 Course Navigation

Section 2: Supply Chain & Analytics

Lecture 4 Efficient Supply Chain

Lecture 5 Responsive Supply Chain

Lecture 6 Why Analytics in Supply Chain - Part 1

Lecture 7 Why Analytics in Supply Chain - Part 2

Lecture 8 Models

Lecture 9 Prescriptive , Predictive & Descriptive Analytics

Section 3: Rise of Python & Chat GPT

Lecture 10 Python Vs Excel & R

Lecture 11 Installing Python

Lecture 12 Python Libraries

Lecture 13 Getting Used to Jupyter Notebook

Lecture 14 Getting Started with Chat GPT

Section 4: Supply Chain & Statistics

Lecture 15 Functions

Lecture 16 Linear Function

Lecture 17 Polynomial Function

Lecture 18 Power Function

Lecture 19 Inverse Function

Lecture 20 Exponential Function

Lecture 21 Logarithmic Function

Lecture 22 Central Tendencies

Lecture 23 Spread & Dispersion

Section 5: Python Reloaded

Lecture 24 Warming Up with Python

Lecture 25 Data Structures & Lists

Lecture 26 How to Subset

Lecture 27 Dictionary

Lecture 28 Array

Lecture 29 Battle of File Reading & Beginning of Data Analysis

Section 6: Fun & Learn

Lecture 30 Surprise

Section 7: Descriptive Analytics - Visualization

Lecture 31 Plotting a Line Chart

Lecture 32 Plotting a Scatter Chart

Lecture 33 Plotting a Histogram

Section 8: Predictive Analytics - Building A Supply Chain Causal Model with Excel

Lecture 34 Causal Model Theory

Lecture 35 Building A Causal Model

Lecture 36 Chat GPT & Causal Model Output

Section 9: Predictive Analytics-Building A Supply Chain Causal Model with Python & Chatgpt

Lecture 37 Simple Linear Regression - Causal Model with Python

Supply Chain Manager,Consultants and Strategy Professionals,Supply Chain Analyst,Business Analysts and Data Scientists,Demand Planners and Forecasting Analysts,Inventory Planners and Managers,Logistics and Transportation Managers,Operations Managers