Procurement Analytics Corporate Training Course

1. Importance and benefits of procurement analytics:

• Improved decision-making
• Cost savings and efficiency gains
• Risk mitigation
• Strategic value creation

2. Role of analytics in procurement decision-making:

• Data-driven insights
• Identifying patterns and trends
• Supplier performance evaluation
• Strategic sourcing and negotiation

1. Basics of data analytics in the procurement context:

• Definition and purpose of data analytics
• Data analytics process and methodologies
• Key concepts and terminology

2. Data types and sources in procurement analytics:

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

3. Data collection and validation processes:

• Data collection methods and techniques
• Ensuring data accuracy and integrity
• Data quality assessment and validation

4. Data management best practices:

• Data governance and security
• Data storage and organization
• Data cleansing and pre-processing

1. Setting up a procurement analytics program:

• Defining program objectives and scope
• Establishing a program governance structure
• Identifying key stakeholders and roles

2. Defining program goals and objectives:

• Aligning with organizational objectives
• Establishing measurable targets
• Identifying performance metrics and KPIs

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

• Selecting relevant metrics and KPIs
• Setting performance targets
• Establishing data collection mechanisms

4. Program governance and stakeholder engagement:

• Roles and responsibilities
• Communication and collaboration strategies
• Monitoring and reporting mechanisms

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

• Cost savings and cost avoidance
• Supplier performance metrics
• On-time delivery
• Contract Compliance

2. Supplier evaluation and scorecards:

• Criteria for supplier evaluation
• Developing supplier scorecards
• Performance rating and feedback mechanisms

3. Performance measurement frameworks:

• Balanced scorecard approach
• Procurement-specific measurement frameworks
• Linking Performance to organizational goals

4. Continuous improvement strategies:

• Analyzing performance gaps
• Identifying improvement opportunities
• Implementing process enhancements

1. Overview of procurement analytics software tools:

• Types of software available
• Features and functionalities
• Vendor landscape and market trends

2. Features and functionalities of popular software solutions:

• Data visualization
• Data cleansing and preprocessing
• Statistical analysis capabilities
• Reporting and dashboarding

3. Selecting the right software for specific organizational needs:

• Assessing requirements and budget constraints
• Evaluating software capabilities
• Vendor selection and implementation considerations

4. Implementing and integrating procurement analytics software:

• Project planning and execution
• Data migration and system integration
• User training and change management

1. Introduction to Excel for procurement analytics:

• Excel as a data analysis tool
• Key Excel functions and formulas
• Data manipulation and sorting in Excel

2. Data cleaning and preparation in Excel:

• Removing duplicates and errors
• Formatting and standardizing data
• Data transformation techniques

3. Basic data analysis techniques in Excel:

• Pivot tables and charts
• Filtering and sorting data
• Using Excel functions for analysis

4. Creating dashboards and visualizations in Excel:

• Building interactive dashboards
• Visualizing data with charts and graphs
• Adding slicers and filters for interactivity

1. Statistical techniques for procurement analytics:

• Descriptive statistics
• Inferential statistics
• Correlation analysis

2. Regression analysis for supplier evaluation:

• Simple linear regression
• Multiple regression
• Interpreting regression output

3. Correlation analysis for identifying relationships in procurement data:

• Pearson correlation coefficient
• Scatter plots and correlation matrices
• Determining correlation strength and direction

4. Hypothesis testing and significance analysis:

• Null and alternative hypotheses
• Type I and Type II errors
• T-tests and ANOVA for hypothesis testing

1. Overview of business analytics in procurement:

• The strategic importance of business analytics
• Linking business analytics to procurement goals
• Value creation through analytics

2. Predictive analytics for demand forecasting:

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

3. Prescriptive analytics for decision optimization:

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