Duration 5 days – 35 hrs
Overview
This comprehensive training course equips participants with the knowledge and practical skills to manage warehousing and logistics operations effectively using open-source technologies and Big Data analytics. Participants will learn how to streamline supply chain operations, monitor logistics performance, and apply data-driven decision-making using open-source tools such as Apache Hadoop, PostgreSQL, Python (Pandas, NumPy), Apache Superset, and more. The course emphasizes practical implementation, real-world case studies, and interactive hands-on labs for a holistic learning experience.
Objectives
- Understand core principles of warehousing and logistics management.
- Map and optimize warehouse processes (inbound, storage, outbound, inventory).
- Leverage Big Data analytics for supply chain optimization and forecasting.
- Use open-source tools (e.g., PostgreSQL, Apache Hadoop, Python, Superset) to extract, analyze, and visualize logistics data.
- Build dashboards and reports for real-time tracking of warehousing KPIs.
- Identify inefficiencies and improve warehouse layout using data insights.
- Make data-informed logistics decisions to reduce cost and improve delivery times.
Audience
- Warehouse and Logistics Managers
- Supply Chain Analysts and Coordinators
- Operations Managers
- IT Professionals in logistics roles
- Data Analysts interested in logistics applications
- SMEs looking to implement open-source solutions in supply chain
Pre- requisites
- Basic understanding of logistics or supply chain operations
- Familiarity with Excel and general data analysis concepts
- Prior programming or database experience is helpful but not required
- (Intro to Python/PostgreSQL modules will be included)
Course Content
Module 1: Introduction to Warehousing and Logistics
- Overview of modern warehousing functions
- Key logistics concepts and supply chain interdependencies
- Common challenges in warehousing and transportation
- Warehousing KPIs and performance measurement
Module 2: Fundamentals of Big Data Analytics
- What is Big Data?
- Open-source tools landscape (Apache, Python, PostgreSQL, Superset)
- Introduction to data lakes and distributed storage systems
- Use cases in logistics and supply chain
Module 3: Warehouse Process Mapping and Data Capture
- Inbound, Putaway, Picking, Packing, Outbound, Returns
- Data collection points in warehouse processes
- Introduction to barcoding, RFID, IoT devices
- Structuring warehouse data for analysis
Module 4: Data Handling with Open-Source Tools
- Installing and using PostgreSQL for warehouse data storage
- Importing and cleaning logistics data using Python (Pandas)
- Creating relational models and managing inventory tables
- Introduction to Apache Hadoop for large-scale logistics data
Module 5: Descriptive Analytics for Warehousing
- Analyzing inbound/outbound volumes, turnaround time, and dwell time
- SKU performance and ABC analysis
- Visualizing trends using Python + Apache Superset dashboards
- Inventory aging and stockout analytics
Module 6: Predictive Analytics in Logistics
- Forecasting demand using open-source time series libraries (Prophet, statsmodels)
- Lead time prediction and route performance analysis
- Inventory optimization using machine learning models
Module 7: Dashboarding and Visualization
- Building interactive dashboards using Apache Superset or Metabase
- Creating warehouse heatmaps and zone performance visualizations
- Setting up automated KPI reporting systems
Module 8: Open-Source Tools Integration & Real-World Use Cases
- Connecting PostgreSQL to Superset for live data visualization
- Case Study: Optimizing last-mile delivery using Python and data
- Open-source ERP/WMS systems overview (Odoo, ERPNext)
- Building an open-source analytics pipeline from warehouse to decision-maker
