Introduction

Advanced Demand Planning in Manufacturing is a strategic, data-driven training program designed to equip professionals with cutting-edge capabilities in forecasting accuracy, supply chain optimization, and production planning excellence. In today’s volatile, uncertain, complex, and ambiguous (VUCA) manufacturing environment, organizations must leverage AI-driven demand forecasting, predictive analytics, S&OP integration, and real-time inventory optimization to stay competitive. Advanced Demand Planning in Manufacturing Training Course provides a deep dive into modern demand planning frameworks that align customer demand with manufacturing capacity, procurement cycles, and logistics efficiency.

The training emphasizes the transformation from traditional spreadsheet-based forecasting to intelligent, automated planning ecosystems powered by machine learning demand models, ERP integration, advanced statistical forecasting, and digital supply chain twins. Participants will gain hands-on expertise in reducing forecast error, improving service levels, minimizing stockouts, and optimizing working capital. By integrating global best practices such as Lean Manufacturing, Just-in-Time (JIT), Sales & Operations Planning (S&OP), and Integrated Business Planning (IBP), this course prepares professionals to lead high-performance demand planning functions in modern manufacturing enterprises.

Programme Curriculum

Advanced Demand Planning in Manufacturing Training Course

Introduction

Course Duration

10 days

Course Objectives

Master AI-powered demand forecasting techniques in manufacturing
Apply predictive analytics for supply chain optimization
Design robust Sales & Operations Planning (S&OP) frameworks
Implement Integrated Business Planning (IBP) systems
Reduce forecast error using machine learning models
Optimize inventory using multi-echelon inventory optimization (MEIO)
Enhance production alignment through demand-supply synchronization
Improve agility with real-time demand sensing tools
Apply statistical forecasting methods (ARIMA, exponential smoothing)
Integrate ERP systems with advanced planning systems (APS)
Strengthen decision-making using data-driven demand intelligence
Manage volatility with scenario planning and what-if analysis
Achieve cost reduction via lean demand planning strategies

Target Audience

Demand Planning Managers
Supply Chain Analysts
Production Planning Engineers
Inventory Control Managers
Procurement Specialists
Operations Managers
Manufacturing Executives
ERP / Supply Chain System Consultants

Course Modules

Module 1: Fundamentals of Advanced Demand Planning

Evolution of demand planning systems
Role in modern manufacturing ecosystems
Key KPIs: forecast accuracy, bias, MAPE
Traditional vs AI-driven forecasting
Demand planning maturity models
Case Study: Transition from Excel-based forecasting to SAP IBP in a FMCG company

Module 2: Statistical Forecasting Techniques

Moving averages and exponential smoothing
Seasonal decomposition methods
ARIMA modeling basics
Trend analysis techniques
Error measurement methods
Case Study: Seasonal demand forecasting for automotive spare parts

Module 3: AI & Machine Learning in Demand Planning

Supervised learning models for forecasting
Neural networks in demand prediction
Feature engineering for demand signals
Model training and validation
Automation of forecasting cycles
Case Study: AI-based demand forecasting in electronics manufacturing

Module 4: Sales & Operations Planning (S&OP)

S&OP process framework
Cross-functional alignment
Demand vs supply balancing
Executive decision-making dashboards
KPI governance
Case Study: S&OP implementation in a global beverage company

Module 5: Integrated Business Planning (IBP)

IBP vs traditional S&OP
Financial integration in planning
Scenario simulation
Strategic demand alignment
End-to-end visibility
Case Study: IBP transformation in a pharmaceutical manufacturer

Module 6: Demand Sensing & Real-Time Analytics

Short-term demand detection
POS and IoT data integration
Real-time dashboards
Signal vs noise analysis
Agile response systems
Case Study: Retail demand sensing in a consumer goods chain

Module 7: Inventory Optimization Strategies

Safety stock optimization
Multi-echelon inventory planning
Service level optimization
ABC/XYZ classification
Stockout prevention techniques
Case Study: Inventory reduction in an automotive OEM

Module 8: Supply Chain Risk & Volatility Management

Demand volatility analysis
Risk mitigation strategies
Scenario-based planning
Buffer strategies
Disruption management
Case Study: Managing demand disruption during global semiconductor shortage

Module 9: ERP & APS Integration

ERP data synchronization
Advanced Planning Systems (APS)
Master data management
System interoperability
Automation workflows
Case Study: SAP APO integration in industrial manufacturing

Module 10: Forecast Accuracy Improvement Techniques

Bias correction methods
Forecast reconciliation
Collaborative forecasting
Data cleansing techniques
Continuous improvement loops
Case Study: Reducing forecast error in textile manufacturing

Module 11: Demand Planning KPIs & Analytics

KPI framework design
Dashboard creation
Data visualization tools
Root cause analysis
Performance benchmarking
Case Study: KPI redesign in a global electronics firm

Module 12: Collaborative Planning Across Supply Chain

Supplier collaboration models
Demand sharing systems
CPFR (Collaborative Planning Forecasting Replenishment)
Cross-functional integration
Communication frameworks
Case Study: CPFR implementation in retail-manufacturing partnership

Module 13: Lean Demand Planning

Waste elimination in planning
Lean forecasting principles
Continuous improvement (Kaizen)
Value stream mapping
Agile planning systems
Case Study: Lean transformation in food manufacturing

Module 14: Scenario Planning & Simulation

What-if analysis
Monte Carlo simulation
Demand shock modeling
Capacity planning scenarios
Decision tree analysis
Case Study: Scenario planning in aerospace manufacturing

Module 15: Digital Transformation in Demand Planning

Industry 4.0 integration
Digital twins in supply chain
Cloud-based planning systems
Automation and RPA
Future of demand planning
Case Study: Digital twin implementation in smart factory ecosystem

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Forecasting models Inventory planning Production alignment Demand analytics

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000