Advanced Demand Planning and Forecasting Training Course – Dar es Salaam, 15–26 Nov 2027

Demand Planning and Forecasting is an advanced 2-week training course for demand planners, supply planners and S&OP analysts, built around a portfolio forecast model and demand planning process that sets each segment's method, the accuracy measures and the demand review. Wider product ranges, promotion-driven sales and tighter working capital leave no room for one spreadsheet forecast. Jupiter Prime Training designed this Demand Planning and Forecasting course for planners who own the consensus number.

Why Demand Planning and Forecasting Matters Now

Three groups now lean harder on the forecast. Finance sets working capital and revenue outlooks from it, so persistent bias hits cash and margin. Supply and procurement commit capacity and materials from it, so every error becomes excess stock or a missed order. Commercial teams want promotions, launches and customer intelligence reflected, not overwritten. Organisations that segment demand, measure bias openly and run a demand review earn trust in one number; others end up with sales, finance and supply each keeping their own. The course draws on sales and operations planning (S&OP) practice and the demand management content of the Association for Supply Chain Management (ASCM) body of knowledge.

Who Gains Most from Demand Planning and Forecasting

• Planners who build the statistical baseline and maintain forecast models by product family
• Analysts who prepare the demand review pack and track accuracy and bias
• Supply planners who convert the consensus forecast into replenishment and production requirements
• Commercial planners who add promotion, launch and customer inputs to the baseline
• Category analysts who segment portfolios and set forecasting policies by segment

A planning cell from one organisation, combining a demand planner, a supply planner and the S&OP analyst who runs the review, gains most when attending together and agreeing one set of accuracy measures.

Demand Planning and Forecasting Capabilities

Each outcome swaps an ad hoc forecast habit for a method that can be explained, measured and defended in the demand review.

• Segment a product portfolio by volume and variability to assign a forecasting method to each group — portfolio planners
• Build time series models with level, trend and seasonality, and test them on hold-out periods — demand planners
• Specify causal models for price, promotion and event effects — commercial partners
• Calculate MAPE, weighted error, bias and forecast value added at the right aggregation level — S&OP analysts
• Design a collaborative forecasting routine with key customers and channels — account teams
• Run a demand review that resolves gaps and hands a consensus number to supply — S&OP leadership

Key Demand Planning and Forecasting Terms

• Statistical baseline — the forecast produced from historical demand by a model, before commercial adjustments are added.
• Exponential smoothing — a time series method that weights recent observations more heavily, with variants for trend and seasonality.
• Forecast bias — a persistent tendency to forecast above or below actual demand, measured as the signed average error.
• Mean absolute percentage error (MAPE) — the average size of forecast errors as a percentage of actual demand, ignoring direction.
• Forecast value added (FVA) — the change in accuracy each step or adjustment contributes compared with a simpler forecast.
• Demand review — the monthly S&OP meeting that agrees the unconstrained consensus forecast and its assumptions.

Demand Planning and Forecasting Day by Day

Day 1: The Role of the Forecast in the Business

• Demand Planning Process Map — Inputs, Outputs and Decision Points
• Forecast Horizons and Aggregation Levels — Matching Decisions to Granularity
• Demand History Cleansing — Stock-Outs, Outliers and One-Off Orders
• Unconstrained Versus Constrained Demand — Separating Need From Supply
• Forecasting Maturity Assessment — Current Practice Baseline

Day 2: Demand Segmentation

• ABC-XYZ Classification — Volume and Variability Groupings
• Lifecycle Stage Tagging — New, Growth, Mature and Declining Items
• Intermittent Demand Identification — Sparse and Lumpy Patterns
• Forecasting Policy by Segment — Method, Review Frequency and Effort
• Hierarchy Design — Product, Customer and Location Levels

Day 3: Time Series Methods

• Moving Averages and Naive Forecasts — Benchmark Models
• Simple and Holt Exponential Smoothing — Level and Trend
• Holt-Winters Seasonal Models — Additive and Multiplicative Seasonality
• Croston Method — Forecasting Intermittent Items
• Hold-Out Testing and Parameter Tuning — Model Selection Discipline

Day 4: Causal and Regression Models

• Driver Identification — Price, Promotion, Weather and Economic Indicators
• Multiple Regression for Demand — Coefficients and Significance
• Promotion Uplift Modelling — Baseline Versus Incremental Volume
• Event and Holiday Effects — Calendar Variables
• Causal Model Validation — Overfitting and Multicollinearity Checks

Day 5: Forecast Accuracy and Bias

• Error Metrics Compared — MAPE, Weighted MAPE and MAE
• Bias Measurement and Tracking Signals — Persistent Over or Under Forecasting
• Forecast Value Added Analysis — Which Adjustments Help or Hurt
• Accuracy Targets by Segment — Realistic Expectations
• Accuracy Dashboard Design — Lag, Level and Exception Views

Day 6: Judgement and Commercial Inputs

• Structured Market Intelligence Capture — Assumption Logs
• Override Rules and Thresholds — When Planners Adjust the Baseline
• Cognitive Biases in Forecasting — Anchoring and Optimism
• New Product Forecasting — Analogues and Diffusion Curves
• Phase-In and Phase-Out Planning — Supersession and Run-Out

Day 7: Collaborative Forecasting

• Collaborative Planning, Forecasting and Replenishment — Shared Customer Forecasts
• Customer Order Pattern Analysis — Channel Inventory Effects
• Key Account Forecast Agreements — Data, Cadence and Exceptions
• Sales Input Templates — Account-Level Upside and Risk
• Consensus Reconciliation — Top-Down and Bottom-Up Views

Day 8: The Demand Review in S&OP

• Demand Review Agenda — Gaps, Assumptions and Decisions
• Demand Review Pack Design — Charts That Prompt Decisions
• Volume to Value Translation — Units Into Revenue Outlook
• Scenario Ranges — Upside, Base and Downside Demand
• Hand-Off to Supply Review — Consensus Forecast Sign-Off

Day 9: Running the Planning Process

• Monthly Planning Calendar — Cut-Offs and Responsibilities
• Forecasting Roles and Decision Rights — Who Owns the Number
• Planning System Configuration Checks — Model Settings and Hierarchies
• Forecast Performance Reviews — Root Cause of Large Misses
• Continuous Improvement Log — Model and Process Changes

Day 10: Practising the Portfolio Forecast Model

• Segmentation Exercise — Applying ABC-XYZ and Lifecycle Tags
• Model Build Case — Applying Time Series and Causal Methods
• Accuracy Review Simulation — Applying Bias and Value Added Metrics
• Demand Review Rehearsal — Applying Agenda, Pack and Scenarios
• Portfolio Forecast Model and Demand Planning Process — Segments, Methods and Review

Applying Demand Planning and Forecasting After the Course

• Classify the current portfolio with ABC-XYZ and lifecycle tags, then assign a forecasting method and review frequency to each segment.
• Run a forecast value added analysis on the last quarter's adjustments and present which overrides improved or worsened accuracy at the next demand review.

Demand Planning and Forecasting Questions (FAQ)

What is demand planning and forecasting, and why does it matter now?

It is the process of turning demand history, statistical models and commercial insight into one agreed forecast. It matters now because finance, supply and commercial teams depend on that number, and bias or inconsistency quickly becomes excess stock, lost sales or unreliable outlooks.

Which roles gain most from demand planning and forecasting training?

Demand planners, supply planners, S&OP analysts and commercial planners gain most. They choose forecasting methods, measure accuracy and bias, add market intelligence and prepare the demand review that hands a consensus forecast to supply.

How does demand planning and forecasting training differ from an AI forecasting course?

An AI forecasting course centres on machine learning platforms and data pipelines. This course builds the planner's own method: segmentation, exponential smoothing, regression, accuracy and bias metrics, judgement controls and the demand review, which any forecasting tool must then support.

What do you bring back from demand planning and forecasting training?

You bring back a portfolio forecast model and process with segment-level methods, tested model settings, accuracy and bias measures, override rules, a collaborative forecasting routine and a demand review agenda and pack.

Demand Planning and Forecasting: The Way Forward

The portfolio forecast model and demand planning process first informs which segments move to statistical forecasting and which need planner judgement. Demand planners own it, S&OP analysts report from it and supply planners take its consensus number. After the first few cycles, bias by segment and the value added by overrides deserve a fresh look to confirm methods still suit each part of the portfolio.