Most supply chains generate enormous amounts of data and act on almost none of it, relying instead on instinct and habit while dashboards sit unopened in the background. Supply Chain Analytics exists to close that gap, turning raw operational data into decisions that actually improve cost, service, and reliability rather than simply documenting what already happened. The Supply Chain Analytics and Performance Management Training Course, delivered by the British Academy for Training and Development, is built for supply chain professionals responsible for making data a genuine part of daily decision-making, not a report generated after the fact.

This course treats analytics and performance management as connected disciplines built around action, not reporting for its own sake. Participants learn how to apply business intelligence tools to surface patterns hidden in operational data, how to define and track KPIs that reflect what actually matters to the business, and how to build dashboards that make performance visible to the people who can act on it. The course also covers predictive analytics techniques for anticipating disruption and demand shifts before they occur, disciplined performance measurement practices that hold teams accountable to meaningful standards, and how to build a culture of data-driven decisions that replaces instinct with evidence. Participants leave with a practical analytics framework they can apply directly to their own supply chain operations.

By the end of this course, participants will be able to:

• Apply supply chain analytics principles to improve cost, service, and reliability
• Use business intelligence tools to identify patterns in operational data
• Define and track KPIs that reflect meaningful supply chain performance
• Build dashboards that make performance visible to decision-makers
• Apply predictive analytics techniques to anticipate disruption and demand shifts
• Implement performance measurement practices that drive accountability
• Build a data-driven decision-making culture across supply chain teams
• Translate analytics findings into concrete operational improvements

Target Group

• Supply chain analysts and performance management professionals
• Supply chain and logistics managers responsible for KPI tracking
• Business intelligence professionals supporting supply chain functions
• Operations managers seeking to strengthen data-driven decision-making
• Planning and forecasting professionals working with predictive analytics
• Professionals preparing for leadership roles in supply chain analytics

• Foundations of Supply Chain Analytics
  • Core principles connecting analytics to real operational decision-making
  • Distinguishing actionable analytics from data collected but never used
• Business Intelligence for Supply Chain
  • Applying business intelligence tools to surface patterns in operational data
  • Structuring data sources to support reliable, timely analysis
• Defining and Tracking KPIs
  • Selecting KPIs that reflect meaningful supply chain performance
  • Avoiding vanity metrics that look good but don't drive decisions
• Building Effective Dashboards
  • Designing dashboards that make performance visible to decision-makers
  • Prioritizing clarity and actionability over data volume
• Predictive Analytics for Supply Chain
  • Applying predictive analytics to anticipate disruption and demand shifts
  • Using predictive models to support proactive, not reactive, decisions
• Performance Measurement Practices
  • Implementing performance measurement systems that drive accountability
  • Aligning individual and team performance with supply chain objectives
• Building a Data-Driven Culture
  • Encouraging data-driven decisions across supply chain teams and leadership
  • Overcoming resistance to replacing instinct with evidence-based decisions
• Turning Analytics Into Action
  • Translating analytics findings into concrete operational improvements
  • Closing the loop between insight generation and implemented change
• Sustaining Analytics Maturity Long-Term
  • Building ongoing analytics capability rather than one-off reporting projects
  • Reviewing analytics practices regularly as data and tools evolve