Foundations of Advanced Data Analytics in Procurement

•       The evolution of data analytics in supply chain and procurement.
•       Types of procurement data.
•       Big data and its relevance to procurement decision-making.
•       Key concepts: descriptive, diagnostic, predictive, and prescriptive analytics.
•       Challenges and opportunities in applying analytics to procurement.
•       Data governance and quality in procurement analytics.

Data Collection, Cleaning, and Integration

•       Sources of procurement and supplier data.
•       Data cleaning techniques to ensure accuracy and consistency.
•       Data integration across ERP, SRM, and external systems.
•       Handling missing data and outliers in procurement datasets.
•       Data normalization and standardization methods.
•       Master data management for procurement analytics.
•       Tools and technologies for procurement data management.

Advanced Descriptive Analytics for Procurement

•       Spend analysis: techniques and tools.
•       Price variance and cost breakdown analysis.
•       Supplier performance analytics.
•       Purchase order and invoice analytics.
•       Contract compliance analytics.
•       Visualization of descriptive procurement data.

Predictive Analytics for Demand and Price Forecasting

•       Principles of predictive analytics in procurement.
•       Forecasting demand using historical procurement data.
•       Predicting price trends and market movements.
•       Regression analysis for procurement forecasting.
•       Time series analysis for procurement planning.
•       Identifying leading indicators in supplier markets.
•       Predictive tools and platforms in procurement analytics.

Risk Management and Supplier Analytics

•       Supplier risk profiling and scoring models.
•       Using analytics to monitor supplier financial health.
•       Geo-political and market risk analytics.
•       Predictive analytics for supply disruption risks.
•       Fraud detection using procurement analytics.
•       Multi-tier supplier risk visibility through analytics.

Advanced Cost Modelling and Should-Cost Analysis

•       Building should-cost models with data analytics.
•       Cost drivers identification and analysis.
•       Parametric and regression cost models.
•       Benchmarking costs using external data.
•       Scenario modelling for cost optimization.
•       Total cost of ownership (TCO) analytics.
•       Dynamic cost models for negotiation preparation.

Sustainability and Ethical Sourcing Analytics

•       Data analytics for tracking sustainability KPIs.
•       Supplier sustainability performance dashboards.
•       Lifecycle analysis through data models.
•       Monitoring carbon footprint in procurement.
•       Using data to assess ethical sourcing compliance.
•       Sustainable procurement risk analytics.
•       Reporting on ESG metrics.

Dashboard Design and Data Visualization for Procurement

•       Principles of effective data visualization.
•       Designing procurement dashboards in BI tools.
•       Key procurement KPIs to visualize.
•       Interactive charts and drill-down analysis.
•       Real-time data visualization for procurement decisions.
•       Custom reporting for internal and external stakeholders.

Data-Driven Negotiation and Supplier Collaboration

•       Using data analytics to prepare for supplier negotiations.
•       Identifying negotiation levers through data insights.
•       Data-enabled supplier segmentation and strategies.
•       Collaborative analytics for joint value creation.
•       Performance-based contracting with analytics support.
•       Data transparency in supplier relationships.
•       Tools that support analytics-driven collaboration.

Developing Procurement Analytics Strategy

•       Developing a procurement analytics roadmap.
•       Integrating AI and machine learning in procurement analytics.
•       The role of blockchain in procurement data transparency.
•       Cloud platforms and data lakes for procurement analytics.
•       Building procurement analytics capability within the team.