Warehouse Analytics

This course is designed to provide students with a thorough understanding of warehouse analytics and its applications in warehouse management. Students will learn how to collect, preprocess, analyze, and visualize data to optimize warehouse operations, improve inventory management, and enhance customer satisfaction. The course will cover key concepts and methods in data analytics, statistical modeling, machine learning, and visualization. Additionally, students will explore the latest trends and emerging technologies in warehouse analytics.

Course Outline:

1. Introduction to Warehouse Analytics

• Definition of Warehouse Analytics
• Importance of Warehouse Analytics
• Key Concepts in Warehouse Analytics
• Applications of Warehouse Analytics

1. Data Collection and Management for Warehouse Analytics

• Types of Data in Warehouse Analytics
• Data Collection Methods
• Data Preprocessing and Cleansing
• Data Storage and Retrieval

1. Statistical Methods for Warehouse Analytics

• Descriptive Statistics and Exploratory Data Analysis
• Regression Analysis and Forecasting
• Time Series Analysis
• Machine Learning Techniques for Warehouse Analytics

1. Analytics for Warehouse Operations

• Inventory Management Analytics
• Order Fulfillment Analytics
• Resource Optimization Analytics
• Quality Control Analytics

1. Applications of Warehouse Analytics

• Cost Optimization
• Customer Satisfaction
• Supply Chain Visibility
• Demand Forecasting

1. Emerging Technologies in Warehouse Analytics

• Internet of Things (IoT) and Smart Warehouses
• Artificial Intelligence (AI) in Warehouse Analytics
• Blockchain in Warehouse Management
• Predictive Analytics in Warehouse Management

Prerequisites:

• Basic understanding of statistics and data analytics
• Familiarity with Excel and/or R programming language

Recommended Textbook:

• “Warehouse Management: A Complete Guide to Improving Efficiency and Minimizing Costs in the Modern Warehouse” by Gwynne Richards and David G. Twomey.

Assessment:

• Midterm Exam (30%)
• Final Exam (40%)
• Group Project (30%)

Note: The course syllabus and assessment methods are subject to change based on instructor’s discretion.