Description

This comprehensive training program on Advanced Analytics for Supply Chain Optimization provides participants with the knowledge and skills needed to leverage cutting-edge analytical techniques for enhancing supply chain efficiency and performance.

Participants will delve into advanced statistical and machine learning models tailored for supply chain applications, gaining a deep understanding of predictive analytics, demand forecasting, inventory optimization, and network design. The course will cover practical tools and methodologies, empowering participants to extract actionable insights from large datasets, implement data-driven decision-making strategies, and optimize end-to-end supply chain processes.

Through hands-on exercises and real-world case studies, participants will master advanced analytics techniques, allowing them to address complex challenges such as demand variability, lead time uncertainty, and inventory management. This training equips professionals with the expertise to drive innovation, reduce costs, and improve overall supply chain resilience in today’s dynamic business environment.

TABLE OF CONTENT

1 . Introduction

1.1 Background
1.2 Purpose of the Document
1.3 Scope and Objectives
1.4 Key Definitions

2 . Overview of Advanced Analytics for Supply Chain

2.1 Definition and Concept
2.2 Importance in Modern Business
2.3 Key Components of Supply Chain Analytics

3 . Data Collection in Supply Chain Analytics

3.1 Types of Data Sources
3.2 Data Collection Methods
3.3 Challenges in Data Collection

4 . Data Processing and Cleaning

4.1 Data Pre-processing Steps
4.2 Cleaning and Validation Techniques
4.3 Tools and Technologies in Advanced Analytics for Supply Chain

5 . Key Metrics and Performance Indicators

5.1 Inventory Turnover
5.2 Order Fulfillment Cycle Time
5.3 Supplier Performance Metrics
5.4 Demand Forecast Accuracy

6 . Data Analysis Techniques

6.1 Descriptive Analytics
6.2 Predictive Analytics
6.3 Prescriptive Analytics
6.4 Case Studiesin Advanced Analytics for Supply Chain

7 . Technological Tools for Supply Chain Analytics

7.1 Business Intelligence (BI) Tools
7.2 Advanced Analytics Platforms
7.3 Artificial Intelligence (AI) and Machine Learning (ML) Applications

8 . Integration with ERP Systems

8.1 Benefits of ERP Integration
8.2 Challenges and Solutions in Advanced Analytics for Supply Chain

9 . Real-world Applications and Case Studies

9.1 Case Study 1: Optimizing Inventory Management
9.2 Case Study 2: Enhancing Demand Forecasting

10 . Challenges and Solutions in Supply Chain Analytics

10.1 Data Security and Privacy
10.2 Integration Challenges
10.3 Overcoming Resistance to Change

Please Visit PowerBI Official Site: || Locus Academy ha s more than a decade experience in delivering the training/staffing on Advanced Analytics for Supply Chain Optimization for corporates across the globe. The participants for the training/staffing on Supply Chain Optimization are extremely satisfied and are able to implement the learnings in their on going projects.

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