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AR

Procurement and Supply Chain Management

Using Predictive Analytics to Enhance Supply Chain Performance

7 units

Please select a city/session before registration.

About this program

Supply chains produce vast volumes of data, and organizations that utilize predictive analytics gain a strategic advantage. Predictive models enable the forecasting of demand changes, mitigation of risks, inventory optimization, and enhancement of overall operational performance.
This Predictive Data Analytics for Supply Chain Performance Training Course equips participants with advanced analytical techniques, such as forecasting methods, machine learning applications, and scenario-based simulations. Attendees will learn to convert data into actionable insights that promote efficiency, robustness, and profitability.
Through interactive workshops, case analyses, and practical simulations, participants will apply predictive analytics tools to boost supply chain responsiveness and strategic decision-making.

Course benefits

  • Forecast demand and supply variations using predictive analytics.
  • Optimize inventory management and allocation of resources.
  • Enhance decision-making through data-driven insights.
  • Minimize risks by anticipating disruptions and bottlenecks.
  • Improve supply chain transparency and resilience.

Key outcomes

  • Grasp predictive analytics principles relevant to supply chains.
  • Implement forecasting models for effective demand and supply planning.
  • Utilize machine learning techniques to generate predictive insights.
  • Apply scenario simulations to support risk management and resilience strategies.
  • Integrate predictive analytics into overall supply chain planning.
  • Create dashboards and visual tools for supporting decisions.
  • Formulate a strategic plan for deploying predictive analytics solutions.

Who should attend

  • Managers in supply chain and logistics.
  • Data analysts and business intelligence professionals.
  • Procurement and operations management personnel.
  • Executives leading digital transformation in supply chains.

Course outline

1

Unit 1: Overview of Predictive Analytics in Supply Chain Management

  • The significance of predictive analytics within contemporary supply chains.
  • Distinguishing between descriptive, diagnostic, and predictive analytics approaches.
  • Advantages and obstacles associated with predictive analytics implementations.
  • Illustrative success stories involving predictive analytics.
2

Unit 2: Techniques for Forecasting Demand and Supply

  • Core principles of forecasting methodologies.
  • Application of time series analysis and regression techniques.
  • Leveraging past data to forecast future patterns.
  • Interactive session: simulation of demand forecasting.
3

Unit 3: Utilizing Machine Learning to Enhance Supply Chain Efficiency

  • Implementation of machine learning algorithms for predictive purposes.
  • Examples include supplier risk assessment, lead time fluctuation, and inventory management optimization.
  • Data prerequisites and preprocessing steps for machine learning models.
  • Ethical aspects in AI-powered supply chain systems.
4

Unit 4: Analytics for Risk Assessment and Supply Chain Resilience

  • Detection of risks via predictive modeling techniques.
  • Scenario analysis for managing disruptions and delays.
  • Modeling strategies to strengthen supply chain resilience.
  • Case analysis: mitigation of risks using predictive approaches.
5

Unit 5: Integrating Predictive Analytics with Inventory and Resource Management

  • Connecting predictive analytics to inventory control processes.
  • Minimizing surplus inventory and avoiding stockouts.
  • Enhancing resource distribution through data-driven insights.
  • Practical workshop: developing predictive inventory models.
6

Unit 6: Creating Dashboards and Visualization Solutions

  • Crafting dashboards focused on predictive key performance indicators.
  • Visualization of real-time data to support decision-making.
  • Incorporating predictive analytics into ERP and SCM systems.
  • Hands-on project: constructing a performance monitoring dashboard.
7

Unit 7: Advancements and Future Directions in Predictive Supply Chain Analytics

  • New developments involving AI, IoT, and big data technologies.
  • The role of predictive analytics in circular economy and sustainable supply chains.
  • Expanding predictive analytics capabilities across international operations.
  • Strategic plan for ongoing enhancement.