Online/ e-LMS
Mentor Based
Advanced
3 Week
About
AI for Supply Chain Management: Optimizing Logistics with Artificial Intelligence is a 10-week program designed for M.Tech and M.Sc students, as well as professionals in BFSI, IT services, and consulting. The course delves into AI applications such as forecasting, inventory management, and transportation logistics, and teaches participants to develop and implement AI models to optimize supply chains.
Aim
The course aims to equip participants with the skills to integrate AI into supply chain management, enhancing efficiency, reducing costs, and improving decision-making processes across logistics operations.
Program Objectives
- Comprehensive AI Application Skills: Develop skills to apply AI in various aspects of supply chain management.
- Strategic Implementation Knowledge: Learn to strategically integrate AI tools to optimize logistics operations.
- Ethical AI Deployment: Understand the ethical implications of deploying AI in supply chains.
Program Structure
-
Module 1: Introduction to AI in Supply Chain Management
Section 1.1: Overview of Supply Chain and AI
- Subsection 1.1.1: Understanding the Supply Chain
- Key components: Procurement, manufacturing, warehousing, transportation, and distribution.
- Challenges in traditional supply chains: Delays, inefficiencies, and lack of real-time visibility.
- Subsection 1.1.2: Role of AI in Supply Chain Management
- How AI enhances efficiency, reduces costs, and improves decision-making.
- Applications: Demand forecasting, inventory optimization, route planning.
Section 1.2: Tools and Technologies in AI for Supply Chain
- Subsection 1.2.1: AI Algorithms for Supply Chain
- Machine Learning (ML): Prediction and classification models.
- Deep Learning (DL): Handling complex data like images and videos.
- Reinforcement Learning (RL): Optimizing logistics and routing.
- Subsection 1.2.2: AI-Powered Supply Chain Platforms
- Tools: SAP Leonardo, Oracle SCM Cloud, and Microsoft Dynamics.
- Open-source frameworks: TensorFlow, PyTorch, and Scikit-Learn.
Module 2: Data Management in Supply Chain
Section 2.1: Understanding Supply Chain Data
- Subsection 2.1.1: Types of Data in Supply Chain
- Structured data: Inventory levels, sales orders, delivery times.
- Unstructured data: Supplier communication, customer feedback.
- External data: Weather patterns, market trends, and geopolitical events.
- Subsection 2.1.2: Data Challenges in Supply Chain
- Data silos across different departments.
- Inaccuracies in real-time data collection and integration.
Section 2.2: Data Preparation and Integration
- Subsection 2.2.1: Cleaning and Preprocessing Supply Chain Data
- Handling missing or inconsistent data.
- Techniques: Normalization, encoding, and feature engineering.
- Subsection 2.2.2: Integrating Data Across Systems
- Combining data from ERP, CRM, and TMS systems.
- Using APIs and ETL pipelines for seamless integration.
Section 2.3: Real-Time Data Processing with AI
- Subsection 2.3.1: IoT for Real-Time Data Collection
- Role of IoT sensors in tracking inventory, vehicles, and assets.
- Examples: RFID tags, GPS tracking.
- Subsection 2.3.2: AI in Data Stream Analysis
- Analyzing real-time data for immediate insights.
- Applications: Live tracking and dynamic decision-making.
Module 3: AI Applications in Supply Chain Optimization
Section 3.1: Demand Forecasting with AI
- Subsection 3.1.1: Traditional vs AI-Driven Demand Forecasting
- Limitations of traditional forecasting methods.
- How AI improves accuracy using historical and external data.
- Subsection 3.1.2: Machine Learning Models for Forecasting
- Regression models for sales prediction.
- Time-series analysis for seasonal trends.
Section 3.2: Inventory Management
- Subsection 3.2.1: AI for Inventory Optimization
- Reducing overstock and stockouts with AI.
- Tools: Economic Order Quantity (EOQ) models enhanced with AI.
- Subsection 3.2.2: Automated Replenishment Systems
- Using AI to automate restocking decisions.
- Integration with warehouse management systems.
Section 3.3: Route Optimization and Logistics
- Subsection 3.3.1: AI for Route Planning
- Minimizing delivery times and fuel costs.
- Tools: AI-powered GPS and dynamic routing algorithms.
- Subsection 3.3.2: Reinforcement Learning for Logistics
- Using RL to optimize complex logistics networks.
- Example: RL for last-mile delivery optimization.
Section 3.4: Supplier Relationship Management
- Subsection 3.4.1: Evaluating Suppliers with AI
- Using AI to assess supplier performance based on KPIs.
- Example: Delivery reliability, quality scores.
- Subsection 3.4.2: Predictive Risk Management
- Identifying and mitigating supply chain risks using AI models.
Module 4: AI in Supply Chain Monitoring and Control
Section 4.1: Real-Time Monitoring Systems
- Subsection 4.1.1: AI for Warehouse Monitoring
- Automating inventory counts with computer vision.
- Real-time alerts for anomalies in warehouse operations.
- Subsection 4.1.2: AI in Fleet Management
- Tracking vehicle performance and predicting maintenance needs.
- Tools: Telematics systems integrated with AI.
Section 4.2: Predictive Maintenance with AI
- Subsection 4.2.1: Role of AI in Equipment Maintenance
- Preventing equipment breakdowns with predictive models.
- Examples: Monitoring machinery vibrations and temperature.
- Subsection 4.2.2: Using IoT and AI Together
- IoT sensors collect data; AI predicts failures.
- Benefits: Reduced downtime and maintenance costs.
Module 5: Ethical and Security Considerations
Section 5.1: Data Privacy in Supply Chains
- Subsection 5.1.1: Ensuring GDPR and CCPA Compliance
- Safeguarding customer and partner data.
- Subsection 5.1.2: Ethical Use of AI in Supply Chain
- Avoiding biases in AI-driven decision-making.
- Building transparency in AI models.
Section 5.2: Cybersecurity in AI-Powered Supply Chains
- Subsection 5.2.1: Protecting AI Models from Cyber Threats
- Securing AI algorithms from adversarial attacks.
- Subsection 5.2.2: Blockchain for Secure Supply Chains
- Enhancing transparency and traceability with blockchain.
- Subsection 1.1.1: Understanding the Supply Chain
Participant’s Eligibility
- M.Tech, M.Sc students in Computer Science, IT, and related fields.
- Professionals in BFSI, IT services, consulting, and KPO interested in applying AI in supply chain management.
Program Outcomes
- Enhanced Supply Chain Efficiency: Mastery of AI applications that streamline operations and reduce costs.
- Improved Decision-Making: Ability to leverage AI for better forecasting, inventory management, and logistical planning.
- Innovative Problem Solving: Skills to implement cutting-edge solutions to modernize and transform supply chains.
Fee Structure
Fee: INR 8,499 USD 112
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
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Key Takeaways
Program Deliverables
- Access to e-LMS
- Real Time Project for Dissertation
- Project Guidance
- Paper Publication Opportunity
- Self Assessment
- Final Examination
- e-Certification
- e-Marksheet
Placement Assistance
- Career Services: Workshops on resume preparation and job interviews, tailored for the logistics and AI fields.
- Networking Opportunities: Webinars and live sessions with industry leaders to provide insights and facilitate professional connections.
- Corporate Engagement: Partnerships with leading companies to align course content with industry demands and enhance employment opportunities.
Future Career Prospects
- AI Supply Chain Analyst: Develop and optimize supply chain strategies using AI.
- Logistics Innovation Manager: Drive innovations in logistics operations with AI applications.
- Supply Chain Data Scientist: Analyze data and improve supply chain decisions.
- AI Implementation Specialist: Oversee the deployment of AI technologies in supply chain environments.
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