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The Supply Chain Analytics Syllabus focuses on analyzing supply chain data to improve forecasting, inventory management, logistics, procurement, and operational efficiency. It covers data analysis techniques, KPI tracking, demand planning, reporting dashboards, and performance optimization across supply chain processes. This training explains how analytics helps organizations make smarter, data-driven supply chain decisions. It is important because efficient supply chain management directly impacts cost, customer satisfaction, and business performance. It is ideal for supply chain professionals, analysts, operations managers, procurement teams, and business intelligence professionals working in logistics and operations.
Duration of Training : 40 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Support-Focused • Implementation
Focus: Data-Driven Decisions | Forecasting | Reporting
Curriculum Designed by Experts
Topics:
End-to-end supply chain overview
Demand → Planning → Procurement → Inventory → Distribution
Key stakeholders
Subtopics:
Push vs Pull models
Make-to-Order vs Make-to-Stock
Supply chain KPIs
🧪 Assignment:
Map supply chain flow of a retail company
Topics:
What is data-driven decision-making
Types of data (structured/unstructured)
Decision frameworks
Subtopics:
Descriptive vs predictive vs prescriptive analytics
Data quality & governance
KPI-based decisions
💼 Scenario:
Choosing supplier based on performance data
Topics:
Data sources in supply chain
Data cleaning
Subtopics:
ERP data extraction
Data transformation
Handling missing values
🧪 Tools:
Microsoft Excel
SQL
Assignment:
Clean raw sales dataset
Topics:
Exploratory Data Analysis (EDA)
Dashboard creation
Subtopics:
Pivot tables
Charts & graphs
Trend analysis
Tools:
Power BI
Tableau
Project:
Build supply chain dashboard
Topics:
Forecasting basics
Time series analysis
Subtopics:
Moving averages
Exponential smoothing
Seasonality & trends
Advanced:
Regression models
ML-based forecasting
🧪 Tools:
Python
Assignment:
Forecast product demand
Topics:
Inventory models
Subtopics:
EOQ (Economic Order Quantity)
Safety stock
Reorder point
💼 Scenario:
Reduce stockouts and overstock
Topics:
Supplier performance metrics
Subtopics:
Lead time analysis
Cost optimization
Vendor scorecards
Assignment:
Analyze supplier data
Topics:
Production planning
Subtopics:
Capacity utilization
Bottleneck analysis
Scenario:
Optimize production schedule
Topics:
KPI design
Subtopics:
OTIF (On-Time In-Full)
Fill rate
Inventory turnover
Project:
Build KPI dashboard
Topics:
Role of ERP in supply chain
Tools:
SAP
Oracle ERP
Scenario:
Extract and analyze ERP data
🔹 Project 1: Demand Forecasting Model
Build forecast using historical data
🔹 Project 2: Inventory Optimization
EOQ + safety stock calculation
🔹 Project 3: End-to-End Supply Chain Dashboard
Data → Analysis → Visualization
Forecasting seasonal demand
Managing stock shortages
Supplier selection
Reducing logistics cost
Improving delivery
performance
Data cleaning & analysis
Forecasting model creation
Dashboard building
KPI tracking
Data-driven decision-making
Forecasting techniques
Reporting & visualization
Supply chain analytics
Supply Chain Analyst
Demand Planner
Operations Analyst
Business Analyst
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