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

• Understand the role of data and analytics in modern supply chain leadership
• Shift from intuition‑based to evidence‑based decision making
• Distinguish between descriptive, diagnostic, predictive, and prescriptive analytics
• Identify the right KPIs and insights for strategic and operational decisions
• Interpret dashboards and analytics outputs critically
• Use data to balance cost, service, risk, and resilience
• Improve cross‑functional alignment through shared data and insights
• Establish a data-driven decision culture within supply chain organizations

This course is designed for:

• Supply Chain Directors and Senior Managers
• Operations and Logistics Leaders
• Planning, Inventory, and Distribution Managers
• Procurement and Category Management Leaders
• Manufacturing and Operations Excellence Leaders
• Digital Transformation and Analytics Sponsors
• Business Unit Heads overseeing supply chain performance
• High‑potential managers preparing for senior leadership roles

No technical, statistical, or data science background is required.

The course uses a leadership‑focused, highly interactive approach, including:

• Executive‑level briefings and frameworks
• Real‑world supply chain case studies
• Scenario‑based decision simulations
• Dashboard and KPI interpretation exercises
• Group discussions and peer learning
• Hands‑on workshops using simplified data
• Decision labs focused on trade‑offs and outcomes

Emphasis is on decision quality, judgment, and action, not analytics theory.

Day 1 – Foundations of Data‑Driven Supply Chain Leadership

Topics:

• Why data‑driven leadership matters in today’s supply chains
• Common decision‑making traps:
• Bias
• Gut feeling
• Overconfidence
• Analysis paralysis
• Evolution of supply chain decision making:
• Experience‑based → Data‑supported → Insight‑driven
• Types of analytics and how leaders should use them:
• Descriptive
• Diagnostic
• Predictive
• Prescriptive
• Understanding data quality, limitations, and uncertainty
• Asking the right questions of data and analytics teams
• Aligning supply chain decisions with business strategy

Leadership Exercise:

• Evaluate recent supply chain decisions: intuition vs data

Day 2 – Turning Supply Chain Data into Actionable Insights

Topics:

• Key data domains in supply chains:
• Demand
• Inventory
• Capacity
• Suppliers
• Logistics

• Selecting the right KPIs for leaders (not operational overload)
• Leading vs lagging indicators in supply chain performance
• Interpreting dashboards and reports:
• What to trust
• What to challenge
• Using data to manage trade‑offs:
• Cost vs service
• Efficiency vs resilience
• Short‑term vs long‑term
• Scenario analysis and “what‑if” thinking
• Using predictive insights without over‑reliance

Case Study:

• Data‑driven response to a supply disruption

Workshop:

• Redesign an executive supply chain dashboard
  
  

Day 3 – Strategic Decisions, Governance & Culture

Topics:

• Data‑driven decision making in:
• Network design
• Inventory strategy
• Sourcing and supplier risk
• Make vs buy decisions
• Decision rights and governance:
• Who decides?
• Based on which data?
• Balancing human judgment and analytics
• Managing uncertainty and imperfect data
• Communicating data‑based decisions to stakeholders
• Building a data‑driven culture:
• Leadership behaviors
• Incentives and KPIs
• Cross‑functional alignment
• Roadmap for becoming a data‑driven supply chain organization

Final Leadership Exercise:

• Decision Lab:
  Participants analyze a complex supply chain scenario and make a data‑driven executive decision, including:
• Key insights used
• Risks considered
• Trade‑offs accepted

Actions decided

CDGA attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration.