Program Overview

Principles of Demand Forecasting

Bespoke course for demand and supply planners and inventory managers

About the Course

Our bespoke course teaches demand planning using the client's software. The course is delivered over two days at the client's premises, using real-life examples based on the client's data.

Key Features

• Forecasting fundamentals: Addressing real-life problems, not just software operation.
• Practical problem-solving: Understanding which parameters are appropriate for different problems and how models can improve operations.
• Practicalities of forecasting: The course is not taught in a vacuum; it acknowledges potential practical problems and their solutions.
• Forecasting and beyond: The course explains how forecasting translates into demand planning and inventory management.

Who is the Course For?

• Demand planners: Experts and novices will learn the principles of demand forecasting and how to put them into practice.
• Supply planners: The principles explained will also be useful for supply planners.
• Inventory managers: To make adequate decisions about stock levels and inventory policies, understanding where demand forecasts come from is essential.

Course Content

The topics for the course will be selected from the list below, depending on the company's needs and preferences.

Forecasting Principles

• What to do and what not to do in forecasting
• Evaluating forecasting accuracy via error measures
• Uncertainty, prediction intervals, and their evaluation
• How to inform decisions based on forecasting

Time Series Components

• Introduction to time series taxonomy
• Basics of time series analysis
• Classical decomposition
• With examples in MS Excel

Simple Forecasting Methods

• Basic forecasting benchmarks, such as Naďve and Average
• Simple Moving Average
• Weighted Average

Exponential Smoothing

• Simple (aka Single) Exponential Smoothing
• Holt's method (aka Double or Linear Exponential Smoothing)
• Winter's method (or Seasonal Exponential Smoothing)
• Holt-Winter's method (Triple Exponential Smoothing)
• Damped trend methods

Intermittent Demand Forecasting

• Challenges of Intermittent Demand
• SBC classification and its modifications
• Croston's method
• Syntetos-Boylan Approximation (SBA)
• Teunter-Syntetos-Babai method (TSB)

Linear Regression

• Simple linear regression
• Multiple linear regression
• Regression diagnostics
• Transformation of variables
• Variables selection
• Using regression in promotional modelling

Advanced Modelling Approaches

• ETS (Error-Trend-Seasonality model)
• ETS with explanatory variables
• ARIMA
• Multiple frequencies
• Combination of forecasts
• Hierarchical forecasting: cross-sectional and temporal hierarchies
• Judgement and organisational aspects of forecasting

Learning Outcomes

By doing this course, participants will be able to:

• Understand how forecasting models work
• Understand what parameters of models mean
• Analyse time series structure
• Identify time series components
• Produce point forecasts and prediction intervals for any time series
• Evaluate the accuracy of different forecasting methods
• Make relevant managerial decisions based on the point and interval forecasts

Meet Your Tutors

• Professor Robert Fildes
• Dr. Ivan Svetunkov
• Dr. Anna-Lena Sachs
• Dr. Anna Sroginis
• Dr. Sven Crone

Testimonials

Previous attendees have praised the course for its clarity and practical application.

Course Prerequisites

Basic understanding of MS Excel. The rest will be covered in the course.

• No prior knowledge of forecasting is required.
• No mathematics or statistics background is necessary.
• No need to be an MS Excel expert.

Questions and Answers

How Long Does the Course Last?

Two days.

Where Will It Be Delivered?

On the client's premises, or an alternative location if agreed upon.

What is the Language of the Course?

The course will be delivered in English. Supplementary materials are also in English. Other languages may be considered for bespoke courses on similar topics, subject to discussion with the tutors.