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Demand Planner Course
More than 2 million students worldwide

Demand Planner Course

4

Master the full demand planning process — from data collection and statistical forecasting to S&OP integration and inventory optimisation. This course gives supply chain professionals the technical skills and strategic frameworks needed to drive smarter business decisions. Build the expertise that companies are actively hiring for.

Dedika for businesses

What you will learn:

You will learn how to collect, cleanse, and structure demand data from internal and external sources. You will apply statistical models — including exponential smoothing, regression, and machine learning methods — to generate reliable forecasts. You will integrate qualitative inputs and causal factors to enrich your baseline predictions. You will segment product portfolios using ABC-XYZ analysis and manage items across their full lifecycle. You will embed demand plans into the S&OP process, align them with financial budgets, and communicate results to executive stakeholders. You will also calculate safety stock, set replenishment parameters, and measure inventory performance against planning targets.

How you study in practice Demand Planner Course

How you practise Demand Planner Course

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Course content

8 Chapters • 39 LessonsDuration between 4 and 360 hours (you decide)

Chapter 1See details

Foundations of Demand Planning

  • Lesson 1 • What Demand Planning Is

    Defines demand planning and distinguishes it from sales forecasting and inventory management. Establishes the conceptual baseline for the entire course.

  • Lesson 2 • The Demand Planning Process Cycle

    Outlines the end-to-end planning cycle from data collection to plan publication. Shows how each step feeds the next, setting up process chapters ahead.

  • Lesson 3 • Core Terminology and Metrics

    Introduces standard vocabulary and performance indicators used throughout the profession. Provides the shared language needed to interpret data and communicate results.

  • Lesson 4 • Key Stakeholders and Organisational Context

    Maps the internal and external stakeholders who influence and consume demand plans. Clarifies cross-functional dependencies critical to later collaboration topics.

Chapter 2See details

Data Collection and Management

  • Lesson 1 • Data Cleansing and Outlier Management

    Teaches methods to detect and correct errors, anomalies, and missing values in historical data. Ensures the statistical models introduced later operate on trustworthy inputs.

  • Lesson 2 • Internal Data Sources

    Covers transactional sales history, order data, and ERP system outputs as primary inputs. Connects data quality directly to forecast reliability established in Chapter 1.

  • Lesson 3 • External Data Sources

    Examines market data, syndicated research, and economic indicators that enrich internal history. Broadens the data picture needed for market-driven forecasting.

  • Lesson 4 • Building a Demand Data Hierarchy

    Explains product, customer, and geographic hierarchies used to aggregate and disaggregate demand. Prepares learners for multi-level forecasting covered in later chapters.

  • Lesson 5 • Data Governance and Storage

    Establishes policies for data ownership, version control, and retention in planning systems. Supports consistent, auditable forecasting processes across planning cycles.

Chapter 3See details

Statistical Forecasting Methods

  • Lesson 1 • Forecast Accuracy Measurement

    Applies the accuracy metrics from Chapter 1 to evaluate and compare statistical model outputs. Establishes a feedback loop that drives continuous model improvement.

  • Lesson 2 • Regression-Based Forecasting

    Introduces simple and multiple linear regression to model demand as a function of causal variables. Bridges statistical methods to causal modelling covered in the next chapter.

  • Lesson 3 • Demand Pattern Recognition

    Teaches identification of trend, seasonality, cyclicality, and randomness in time-series data. Pattern recognition determines which statistical model to apply in subsequent sections.

  • Lesson 4 • Trend and Seasonality Models

    Applies Holt's and Holt-Winters' methods to data with trend and seasonal components. Extends smoothing skills to handle the most common real-world demand patterns.

  • Lesson 5 • Moving Average and Smoothing Methods

    Covers simple moving averages, weighted moving averages, and exponential smoothing models. These foundational methods underpin more advanced techniques introduced later.

Chapter 4See details

Causal and Qualitative Forecasting

  • Lesson 1 • New Product Forecasting

    Addresses the challenge of forecasting items with no sales history using analogues and market research. Prepares planners for product launches and portfolio expansion scenarios.

  • Lesson 2 • Causal Factor Identification

    Identifies internal and external variables that drive demand shifts, such as pricing and promotions. Builds on regression skills from Chapter 3 to select meaningful causal inputs.

  • Lesson 3 • Promotional Demand Modelling

    Quantifies the incremental demand generated by trade promotions and marketing campaigns. Teaches lift calculation and baseline separation essential for accurate event forecasting.

  • Lesson 4 • Combining Statistical and Judgemental Forecasts

    Teaches weighted combination and override protocols that blend model outputs with human insight. Produces a single best-estimate forecast ready for the consensus process.

  • Lesson 5 • Qualitative Forecasting Techniques

    Covers structured expert judgment methods including Delphi, market surveys, and sales force input. Provides tools for situations where data is sparse or market conditions are novel.

Chapter 5See details

Demand Segmentation and Portfolio Management

  • Lesson 1 • Portfolio Rationalisation Decisions

    Provides criteria and processes for adding, modifying, or discontinuing SKUs based on demand data. Ensures the portfolio remains manageable and aligned with business strategy.

  • Lesson 2 • Slow-Moving and Intermittent Demand

    Applies specialised models such as Croston's method to items with sporadic demand patterns. Addresses a common portfolio challenge not covered by standard smoothing methods.

  • Lesson 3 • XYZ Analysis and Demand Variability

    Segments items by demand variability to match forecasting methods to predictability levels. Complements ABC analysis to form a two-dimensional planning matrix.

  • Lesson 4 • ABC Analysis and Volume Segmentation

    Ranks products by revenue or volume contribution to prioritise planning resources. Directly applies the data hierarchy built in Chapter 2 to portfolio-level decisions.

  • Lesson 5 • Product Lifecycle Stage Planning

    Adapts forecasting and inventory strategies to introduction, growth, maturity, and decline stages. Connects new product forecasting from Chapter 4 to ongoing lifecycle management.

Chapter 6See details

Sales and Operations Planning Integration

  • Lesson 1 • Demand Planning KPIs and Governance

    Establishes performance metrics, review cadences, and accountability structures for the planning function. Ensures continuous improvement through structured measurement and governance.

  • Lesson 2 • Linking Demand to Financial Plans

    Translates volume forecasts into revenue and margin projections aligned with financial budgets. Connects demand planning to the financial planning cycle for integrated business management.

  • Lesson 3 • S&OP Process Architecture

    Maps the five-step S&OP cycle and the demand planner's role at each stage. Provides the process context needed to position demand plans within executive decision-making.

  • Lesson 4 • Consensus Forecasting Process

    Teaches the structured process of reconciling statistical forecasts with commercial and operational inputs. Produces a single agreed-upon number that drives supply and financial planning.

  • Lesson 5 • Demand Plan Communication and Reporting

    Covers how to present demand plans, variances, and risks to diverse stakeholder audiences. Builds the communication skills needed to drive action from planning outputs.

Chapter 7See details

Inventory Optimisation and Safety Stock

  • Lesson 1 • Inventory Performance Measurement

    Tracks inventory health using turns, days of supply, and obsolescence metrics tied to demand plans. Closes the loop between planning accuracy and inventory cost outcomes.

  • Lesson 2 • Reorder Point and Replenishment Parameters

    Calculates reorder points and order quantities that align with demand plan outputs. Connects planning outputs to procurement and warehouse execution systems.

  • Lesson 3 • Safety Stock Calculation Methods

    Covers statistical safety stock formulas using demand variability and lead time uncertainty. Translates forecast accuracy data directly into actionable inventory buffers.

  • Lesson 4 • Multi-Echelon Inventory Considerations

    Introduces inventory optimisation across distribution networks with multiple stocking locations. Prepares planners for complex supply chain structures common in global operations.

  • Lesson 5 • Inventory Fundamentals for Demand Planners

    Reviews inventory types, holding costs, and the relationship between forecast error and stock levels. Grounds inventory decisions in the accuracy metrics mastered in earlier chapters.

Chapter 8See details

Advanced Forecasting and Strategic Demand Management

  • Lesson 1 • Demand Sensing and Short-Cycle Planning

    Uses high-frequency signals such as point-of-sale and order data to update near-term forecasts daily. Reduces latency between market signals and supply response for competitive advantage.

  • Lesson 2 • Demand Shaping Strategies

    Covers pricing, promotion, and product mix levers used to actively influence customer demand. Shifts the planner's role from passive forecaster to active demand manager.

  • Lesson 3 • Demand Planning Maturity and Roadmap

    Assesses organisational planning maturity and designs a capability improvement roadmap. Enables planners to lead transformation initiatives and benchmark against industry best practices.

  • Lesson 4 • Scenario Planning and Risk Management

    Builds probabilistic and scenario-based forecasts to quantify demand uncertainty and plan contingencies. Equips planners to advise leadership on risk exposure and mitigation options.

  • Lesson 5 • Machine Learning in Demand Forecasting

    Introduces gradient boosting, neural networks, and ensemble methods applied to demand prediction. Extends statistical foundations from Chapter 3 into modern algorithmic approaches.

Certification

Your valid completion certificate

This course is for you:

  • Supply chain analyst: ready to specialise and move into a dedicated planning role.

  • Operations coordinator: managing inventory decisions without a formal forecasting framework.

  • Logistics professional: seeking broader strategic impact beyond day-to-day execution tasks.

  • Recent business or engineering graduate: building practical skills for a supply chain career.

  • Sales or finance professional: wanting to understand how demand plans affect their function.

  • Career changer: transitioning into supply chain from a data-adjacent or analytical background.

What our students say

Your lessons are perfect. I purchased the one-year package and finally have the opportunity to follow various topics of interest without needing to change platforms... I'm grateful for everything you do, I've already recommended you to other people...
Giulio Carlo
Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can change chapters and skip content I don't need.
Mariana Ferres
Mariana FerresPhotography Student
I like the content and the way videos are presented and transcribed, which speeds up the process!
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Luciana AlvarengaNail Design Student
The platform is fast, simple to use. The diversity of content and complementary videos really help with learning.
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André FelipePrompt Engineering Student

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