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Course Outline

Introduction to S&OP

  • Defining Sales and Operations Planning
  • Primary advantages and obstacles of S&OP
  • The S&OP cycle and its integration with supply chain planning

Demand Forecasting Techniques

  • Statistical forecasting approaches (moving averages, exponential smoothing)
  • The role of machine learning and AI in demand forecasting
  • Addressing seasonality, trends, and demand variability

Data Analytics for S&OP

  • Identifying data sources and analyzing demand signals
  • Leveraging Excel and Power BI for demand planning
  • Developing data-driven forecasting models

Optimizing Forecast Accuracy

  • Recognizing forecast errors and bias
  • Methods to elevate forecast precision
  • Best practices for harmonizing cross-functional teams

Inventory and Supply Planning

  • Balancing inventory levels against demand changes
  • Refining replenishment strategies
  • Managing the distinction between perishable and non-perishable products

Cross-Functional Collaboration

  • Aligning sales, operations, and finance within S&OP
  • Fostering consensus in forecasting and planning
  • Effective communication strategies for S&OP meetings

Technology and Tools for S&OP

  • Overview of contemporary S&OP software solutions
  • Deploying digital dashboards for real-time decision-making
  • Case studies highlighting successful S&OP implementations

Continuous Improvement in S&OP

  • Monitoring and refining the S&OP process
  • Tracking key performance indicators (KPIs)
  • Strategies for achieving 95% forecast accuracy

Summary and Next Steps

Requirements

  • Familiarity with supply chain management principles
  • Practical experience with demand planning and forecasting methodologies
  • Competency in using Excel or supply chain analytics software

Target Audience

  • Demand planners
  • Supply chain specialists
  • Operations managers
 14 Hours

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