Certificate in Supply Chain Analytics

This module establishes the core principles of supply chain analytics and introduces Python for fundamental data manipulation. You’ll understand key supply chain concepts, widely used KPIs, and learn Python basics including data types, variables, operators, control flow, and essential data structures like lists and tuples.

• Overview of Supply Chain Analytics and Key KPIs
• The Supply Chain Analytics Process
• Types of Supply Chain Data
• Introduction to Python: Installation and setup
• Basic Python data types, variables, and operators
• Control flow: Conditional statements
• Data structures: Lists and tuples

Building on the previous module, this section delves deeper into Python’s capabilities for supply chain analysis and optimization. You’ll explore advanced data structures (dictionaries, sets), master loops and functions for efficient data processing, and be introduced to NumPy and Pandas for data analysis, including creating and manipulating DataFrames. You’ll also learn to implement basic network optimization and warehouse allocation models in Python.

• Advanced Python data structures: Dictionaries and sets
• Looping constructs and functions in Python
• Introduction to NumPy and Pandas DataFrames
• Building basic Network Optimization Models in Python
• Applying Linear Programming for warehouse allocation using Python

This module introduces Microsoft Power BI for data visualization and business intelligence in the context of supply chain. You’ll get familiar with Power BI Desktop, connect to supply chain data sources, and learn the principles of effective data visualization. You’ll create key supply chain visualizations and build basic dashboards to monitor essential KPIs, including transportation and inventory metrics.

• Introduction to Power BI Desktop and connecting to data
• Principles of Effective Data Visualization in Supply Chain
• Creating visualizations for transportation performance metrics
• Visualizing inventory trends and KPIs
• Building basic dashboards for supply chain KPIs

In this module, you’ll leverage Power BI for more advanced supply chain analysis and reporting. You’ll learn to create geospatial visualizations for network optimization and supply chain routes. You’ll also master building supplier scorecards and dashboards for supplier profiling and performance monitoring, as well as creating end-to-end visibility dashboards with point-in-time KPIs.

• Creating geospatial visualizations for supply chain routes
• Building Power BI dashboards for supplier profiling and scorecards
• Developing dashboards for end-to-end supply chain visibility
• Implementing “point-in-time” visibility in Power BI
• Importing and visualizing network optimization results in Power BI

This module establishes the core principles of supply chain analytics and its strategic importance in today’s data-driven business environment. You’ll gain a comprehensive understanding of the supply chain analytics process, the various types of data used, and how analytics drives effective decision-making in logistics and operations. We will also introduce key supply chain concepts and widely used KPIs that form the basis for subsequent analytical applications.

• Overview of Supply Chain Analytics and its Scope
• The Supply Chain Analytics Process and Data-Driven Decisions
• Types of Supply Chain Data: Transactional, Master, Sensor Data, etc.
• Understanding Key Supply Chain Concepts (e.g., Lead Time, Inventory Management)
• The Role of Analytics in Different Supply Chain Functions (e.g., Procurement, Logistics, Manufacturing)
• Introduction to Widely Used Supply Chain KPIs
• Ethical Considerations and Data Privacy in Supply Chain Analytics

This module introduces the critical aspects of designing an efficient and resilient supply chain network. You’ll learn about the key elements of Supply Chain Network Design (SCND), including supplier selection, inventory management strategies, transportation planning, and capacity planning. We will also cover the key objectives of SCND, focusing on optimizing the network components such as suppliers, warehouses, and customers, and understanding the flows between these nodes.

• Key Elements of SCND: Supplier Selection, Inventory Management, Transportation Planning, Capacity Planning etc.
• Key objectives of SNDC: Cost optimization, service level improvement, risk mitigation
• Network Components: Suppliers, warehouses, and customers
• Understanding flows between nodes in the supply chain network
• Introduction to decision variables in network design (e.g., which warehouses to use)
• Overview of constraints in network design (e.g., capacity, demand, and costs)

Building upon the principles of network design, this module delves into the application of optimization techniques to improve supply chain efficiency. You’ll learn the fundamentals of building network optimization models and gain practical experience implementing these models in Python. We will also cover how to import optimization results into Power BI for visualization and analysis.

• Building Network Optimization Models in Python
• Understanding objective functions and constraints in optimization
• Using Python libraries for linear programming
• Importing network optimization results into Power BI
• Analyzing optimization outputs for decision-making

This module introduces you to the Shortest Path Problem and the Vehicle Routing Problem (VRP), fundamental challenges in logistics optimization. You’ll gain practical experience solving these problems using OR-Tools, a powerful optimization library. The module also covers techniques for identifying transport bottlenecks and visualizing transport performance metrics.

• Shortest path problem and its applications in logistics
• Vehicle Routing Problem (VRP) and its variations
• Solving routing problems with OR-Tools in Python
• Identifying transport bottlenecks using data analysis
• Visualizing transport performance metrics in Power BI

This module introduces the concept of supplier scorecards as a crucial tool for evaluating and managing supplier performance. You’ll learn how to define relevant parameters for supplier selection and build weighted scorecards. You’ll also gain hands-on experience creating Power BI dashboards for supplier profiling and understanding supplier risk scoring methodologies.

• Weighted scorecard for supplier selection
• Defining parameters for supplier selection
• Building Power BI dashboards for supplier profiling
• Understanding Supplier Risk Scoring methodologies
• Applying Power BI for supplier performance monitoring

This module explores the application of Machine Learning (ML) techniques in supplier management and procurement. You’ll learn how to build ML models for supplier risk scoring based on performance and compliance data. We will also cover price elasticity modeling to optimize purchasing strategies.

• ML models for supplier risk scoring based on performance and compliance
• Feature engineering for supplier risk prediction
• Price elasticity modeling to optimize purchasing strategies
• Evaluating and deploying ML models for supplier management