Analytics for Operations & Supply Chain Management Course

This program aims to provide participants with a comprehensive foundation in supply chain operations and analytical techniques that enhance decision-making. The course focuses on developing skills in demand forecasting, inventory management, logistics optimization, and supplier performance analysis. Learners will gain exposure to modern data-driven tools and methodologies that help streamline supply chain processes, reduce operational costs, and improve overall efficiency across the supply network.

Category: Analytics

Description

1. Learning Outcomes

Understand key concepts in supply chain design and operations.
Analyze demand, inventory, and logistics data for actionable insights.
Build forecasting and optimization models using analytical tools.
Apply problem-solving skills to improve supply chain performance.
Use tools like Python, Excel, or supply chain analytics software.
Develop a simple analytics project addressing supply chain challenges.

2. Program Highlights & Key Features

Real-world case studies from procurement, logistics, and retail.
Hands-on training with common supply chain analytics tools.
Project-based learning supported by a practical capstone project.
Expert guidance grounded in industry best practices.
Coverage of current trends like digital supply chains and automation.

Module 1 — Introduction to Operations, Supply Chain & Analytics
Module 2: Overview of Supply Chain & Operations Processes
Module 3: Decision-Making in Operations & Supply Chain
Module 4: Introduction to Analytics & Decision Science
Module 5: Data Foundations for Operations & SCM Analytics
Module 6: Industry Challenges in Operations & Supply Chain
Module 7: Analytics for Demand Forecasting & Planning
Module 8: Analytics for Inventory & Capacity Management
Module 9: Analytics for Procurement & Supplier Management
Module 10: Analytics for Operations & Logistics
Module 11: AI & Machine Learning in Operations & SCM
Module 12: Digital & Real-Time Analytics
Module 13: Integrated Analytics-Driven Decision Making
Module 14: Capstone Project

Introduction to Operations, Supply Chain & Analytics

Meaning and Scope of Operations Management
Meaning and Scope of Supply Chain Management
Relationship Between Operations, Supply Chain, and Business Performance
Evolution from Traditional to Analytics-Driven Supply Chains
Role of Analytics in Modern Operations and SCM
Conclusion and QnA

Overview of Supply Chain & Operations Processes

Supply Chain Process View
Supply Chain Macro Processes
Operations Process Model
End-to-End Supply Chain Flow
Objectives of Operations and SCM
Key Performance Measures
Conclusion and QnA

Decision-Making in Operations & Supply Chain

Strategic, Tactical, and Operational Decisions
Phases of Supply Chain Decisions
Supply Chain Planning Decisions
Operational-Level Decisions
Supply Chain Surplus and Value Creation
Conclusion and QnA

Introduction to Analytics & Decision Science

Meaning of Analytics
Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
Role of Decision Science in Business
Quantitative Models in Operations and SCM
Conclusion and QnA

Data Foundations for Operations & SCM Analytics

Role of Data in Operations and Supply Chain
ERP, Production, Inventory, and Logistics Data Sources
Data Quality and Preparation
Key KPIs for Operations and SCM
Conclusion and QnA

Industry Challenges in Operations & Supply Chain

Demand Uncertainty and Forecasting Errors
Inventory Imbalance
Capacity Planning Issues
Supplier and Logistics Challenges
Bullwhip Effect and Lack of Visibility
Conclusion and QnA

Analytics for Demand Forecasting & Planning

Demand Pattern Analysis
Time-Series Forecasting Basics
Forecast Accuracy Measurement
Using Analytics to Improve Planning
Conclusion and QnA

Analytics for Inventory & Capacity Management

Inventory Analytics and KPIs
Safety Stock and Reorder Point Analytics
Capacity Utilization Analysis
Cost and Service Level Trade-Offs
Conclusion and QnA

Analytics for Procurement & Supplier Management

Supplier Evaluation and Scorecards
Supplier Risk Analytics
Spend and Cost Analysis
Data-Driven Sourcing Decisions
Conclusion and QnA

Analytics for Operations & Logistics

Process Performance Analytics
Bottleneck Identification
Cycle Time and Throughput Analysis
Logistics Performance Dashboards
Conclusion and QnA

AI & Machine Learning in Operations & SCM

Introduction to Artificial Intelligence and Machine Learning
ML Applications in Forecasting and Planning
Predicting Delays, Risks, and Failures
Benefits and Limitations of AI

Digital & Real-Time Analytics

Digital Operations and Supply Chains
IoT and Real-Time Data
Strategic, Tactical, and Operational Dashboards
Alerts and Anomaly Detection
Digital Twins

Integrated Analytics-Driven Decision Making

End-to-End Analytics View
Scenario Planning and What-If Analysis
Risk and Resilience Analytics
Sustainable Operations Using Analytics
Conclusion and QnA

Capstone Project

Demand Forecasting Project
Inventory Optimization Project
Supplier Risk Analysis Project
Operations or Logistics Analytics Dashboard
Project Presentation and Review

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