Corporate Procurement Analytics Training Course

1. Importance and benefits of procurement analytics:

1. Improved decision-making
2. Cost savings and efficiency gains
3. Risk mitigation
4. Strategic value creation

2. Role of analytics in procurement decision-making:

1. Data-driven insights
2. Identifying patterns and trends
3. Supplier performance evaluation
4. Strategic sourcing and negotiation

1. Basics of data analytics in the procurement context:

1. Definition and purpose of data analytics
2. Data analytics process and methodologies
3. Key concepts and terminology

2. Data types and sources in procurement analytics:

1. Internal data (e.g., purchase orders, invoices)
2. External data (e.g., market intelligence, pricing trends)
3. Supplier data (e.g., performance metrics, delivery data)

3. Data collection and validation processes:

1. Data collection methods and techniques
2. Ensuring data accuracy and integrity
3. Data quality assessment and validation

4. Data management best practices:

1. Data governance and security
2. Data storage and organization
3. Data cleansing and pre-processing

1. Setting up a procurement analytics program:

1. Defining program objectives and scope
2. Establishing a program governance structure
3. Identifying key stakeholders and roles

2. Defining program goals and objectives:

1. Aligning with organizational objectives
2. Establishing measurable targets
3. Identifying performance metrics and KPIs

3. Establishing metrics and key performance indicators (KPIs):

1. Selecting relevant metrics and KPIs
2. Setting performance targets
3. Establishing data collection mechanisms

4. Course governance and stakeholder engagement:

1. Roles and responsibilities
2. Communication and collaboration strategies
3. Monitoring and reporting mechanisms

1. Key performance indicators (KPIs) for procurement performance evaluation:

1. Cost savings and cost avoidance
2. Supplier performance metrics
3. On-time delivery
4. Contract Compliance

2. Supplier evaluation and scorecards:

1. Criteria for supplier evaluation
2. Developing supplier scorecards
3. Performance rating and feedback mechanisms

3. Performance measurement frameworks:

1. Balanced scorecard approach
2. Procurement-specific measurement frameworks
3. Linking Performance to organizational goals

4. Continuous improvement strategies:

1. Analyzing performance gaps
2. Identifying improvement opportunities
3. Implementing process enhancements

1. Overview of procurement analytics software tools:

1. Types of software available
2. Features and functionalities
3. Vendor landscape and market trends

2. Features and functionalities of popular software solutions:

1. Data visualization
2. Data cleansing and preprocessing
3. Statistical analysis capabilities
4. Reporting and dashboarding

3. Selecting the right software for specific organizational needs:

1. Assessing requirements and budget constraints
2. Evaluating software capabilities
3. Vendor selection and implementation considerations

4. Implementing and integrating procurement analytics software:

1. Project planning and execution
2. Data migration and system integration
3. User training and change management

1. Introduction to Excel for procurement analytics:

1. Excel as a data analysis tool
2. Key Excel functions and formulas
3. Data manipulation and sorting in Excel

2. Data cleaning and preparation in Excel:

1. Removing duplicates and errors
2. Formatting and standardizing data
3. Data transformation techniques

3. Basic data analysis techniques in Excel:

1. Pivot tables and charts
2. Filtering and sorting data
3. Using Excel functions for analysis

4. Creating dashboards and visualizations in Excel:

1. Building interactive dashboards
2. Visualizing data with charts and graphs
3. Adding slicers and filters for interactivity

1. Statistical techniques for procurement analytics:

1. Descriptive statistics
2. Inferential statistics
3. Correlation analysis

2. Regression analysis for supplier evaluation:

1. Simple linear regression
2. Multiple regression
3. Interpreting regression output

3. Correlation analysis for identifying relationships in procurement data:

1. Pearson correlation coefficient
2. Scatter plots and correlation matrices
3. Determining correlation strength and direction

4. Hypothesis testing and significance analysis:

1. Null and alternative hypotheses
2. Type I and Type II errors
3. T-tests and ANOVA for hypothesis testing

1. Overview of business analytics in procurement:

1. The strategic importance of business analytics
2. Linking business analytics to procurement goals
3. Value creation through analytics

2. Predictive analytics for demand forecasting:

1. Time series analysis
2. Forecasting techniques (e.g., moving averages, exponential smoothing)
3. Accuracy assessment and model selection

3. Prescriptive analytics for decision optimization:

1. Optimization models (e.g., linear programming)
2. Constraint management
3. Sensitivity analysis and scenario planning