The Modules

Introduction to Forecasting
This module introduces the true purpose of forecasting—not to predict the future with precision, but to prepare for it by understanding uncertainty and using insight to guide decisions. It explains why forecasts must be viewed in context rather than judged as right or wrong, and how embracing ranges, scenarios, and if-then thinking supports clearer, more confident planning. It also shows how effective forecasting strengthens problem-solving and strategic thinking, helping organisations make better decisions across budgets, operating models, and customer experience.

Driving Improvement The Planning Cycle
Learn the role planning plays in implementing your strategic objectives.  Understand the importance of your role and how you can help others.

• Understand how the strategic objective is implemented into a forecasting, scheduling and real-time plan.
• Learn about the importance of effective data checkpoints, change governance and hand-overs
• Play a part in driving continuous improvement and become the nerve-centre of the business

Predictability of Events
This module covers:

• Forecasting's role in preparing for future uncertainties, highlighting the importance of distinguishing between knowns and unknowns in event predictability.
• The emphasis on considering a broad spectrum of factors in forecasting, from workload and technology changes to external influences.
• The introduction of the VUCA framework and predictability matrix as tools to navigate future volatility and uncertainty, enhancing strategic readiness.

Forecastable Metrics
In many organisations forecasting is limited to volumes of contacts or work, but we can do so much more. Whether we are forecasters or analysts, applying these techniques to our other metrics can open up a wealth of opportunities for improvement.
Learn how using forecasting techniques can open up new opportunities in your organisation.
Hear how innovation awards finalists are putting this into practice.

Forecasting assumptions
This module explores why every forecast is built on assumptions, and how clarity about those assumptions is essential for accuracy, trust, and effective decision-making. It explains how good assumptions blend data-driven insight with real-world intelligence from across the business, and why making them visible helps teams spot issues before forecasts fail. It also highlights the growing importance of transparency—especially with complex AI models—and shows how shared, realistic, and well-communicated assumptions strengthen both forecasting and organisational confidence.

Forecast Timing and Granularity
This module explores how to match the timing of a forecast with the decisions it supports, showing why forecasts must be delivered early enough to act on but late enough to use the best available information. It explains how different decisions require different levels of detail—from high-level long-term projections for budgeting to precise interval forecasts for day-to-day operations. It also demonstrates how aligning timing and granularity creates clearer planning, stronger control, and more confident, proactive operational decision-making.

Types of Forecast
This module explores the four core forecasting approaches—no-data methods, time-series models, cause-and-effect modelling, and hybrid techniques—and explains the strengths and limitations of each. It shows how different measures behave in different ways, and why understanding the drivers behind a metric is essential to choosing the right model. It also highlights the value of combining methods, especially in complex real-world environments where trends, events, and uncertainty all interact to shape demand.

Forecasting With No Data
This module explores how to build forecasts when historical data is limited, unreliable, or no longer relevant—using structured expert insight, shared assumptions, and collaborative thinking. It introduces techniques such as the Delphi Method, to draw knowledge from across the organisation and turn judgement into a practical forecast. It also highlights how these approaches build trust, shared ownership, and preparedness, creating forecasts that are understood, valued, and resilient in uncertain and changing environments.

Time Series Models
This module introduces the core family of time series forecasting techniques, explaining how they use historical patterns—trends, seasonality, and randomness—to project future demand. It explores models ranging from naïve forecasts and moving averages to exponential smoothing, Holt-Winters, ARIMA, and decomposition, showing where each performs well and where they struggle. It also highlights the strengths of time series approaches—simplicity, transparency, and realistic use of past data—while emphasising the need to start simple, test rigorously, and add complexity only when it truly improves accuracy.

Cause and Effect (Explanatory) Models
This module explores how cause-and-effect forecasting links outcomes to the drivers that shape them, helping analysts understand why demand behaves the way it does rather than simply extending historical patterns. It explains key concepts such as regression, multiple drivers, lag effects, and data availability, showing how these models support meaningful “what-if” scenarios and more informed planning. It also highlights the strengths and limits of explanatory models, emphasising how they deepen business understanding and provide actionable levers for shaping the future—not just predicting it.

Machine Learning Methods
This module explores how machine learning enhances forecasting by identifying complex patterns and relationships that traditional models may miss. It introduces key techniques—including decision trees, random forests, gradient boosting, neural networks, LSTMs, Prophet, and feature engineering—explaining what they are, when they’re useful, and how they differ from classic approaches. It also highlights the importance of data quality, operational insight, and interpretability tools like SHAP to ensure machine learning models are meaningful, trusted, and effectively applied in real-world forecasting.

Hybrid and Ensemble Models
This module explores how hybrid and ensemble forecasting methods combine multiple modelling techniques to balance their strengths, reduce weaknesses, and deliver more stable, reliable results. It explains how hybrid models blend approaches within a single framework, while ensemble models merge separate forecasts—often outperforming any individual method by smoothing volatility and cancelling out errors. It also highlights the importance of interpretability, operational insight, and collaboration between analysts and data scientists to ensure these powerful models remain trusted, resilient, and decision-ready.

Scenario Analysis – What if and if then
This module explores how scenario analysis elevates forecasting from simple prediction to strategic preparation, showing how What If models help organisations understand potential events while If Then models work backwards from desired outcomes. It introduces the four stages of insight—from reporting the past to shaping the future—and explains how combining insight thinking with forecasting unlocks deeper, more actionable value. It also demonstrates how scenarios and playbooks enable adaptable decisions, proactive planning, and greater resilience in a world of uncertainty and change.

Forecasting Accuracy and Value
This module explores why forecasts are rarely accurate, showing how knowns, unknowns, randomness, scale, and time horizons all introduce unavoidable uncertainty into prediction. It explains why the value of forecasting lies not in precision but in preparing for a range of outcomes, using ranges, scenarios, and playbooks to help operations respond confidently to whatever happens. It also highlights how shifting from single-number forecasts to expectation ranges improves understanding, reduces blame, and encourages proactive planning across the organisation.

An Introduction to Accuracy and Confidence
Understand the key statistical considerations that will ensure your conclusions are well founded and can drive confidence. The separate Statistical Confidence box set is an opportunity to drive this further

Descriptive Statistics
In this module we be exploring the Descriptive Statistics tool in excel. We will cover:

• How to use descriptive statistics
• How to use the output to better understand your data
• Alternative ways of calculating
• How to Calculate Standard Deviation
• Standard deviation is an essential tool for statistical analysis. In this module, we will talk through how to calculate this manually

Descriptives Statistics Example - Using Averages
In this worked example we will be exploring the pitfalls to avoid when using averages. And when to use Mean, Median & Mode

Descriptive Statistics Example - Understanding our data
In this module, we are going to look at descriptive statistics in action and explore how we can use this to better understand our data.

Descriptive Statistics Example - Measuring Benefits
In this module, we are going to look at descriptive statistics in action and explore how we can use this to ensure we are measuring benefits correctly.

Descriptive Statistics Example – Calculating the Accuracy of your Forecast
In this module, we are going to look at a use of descriptive statistics that is essential to every organisation, and that is measuring the accuracy of a forecast. Or to put this another way, setting clear expectations around volatility.
 

The cost of this box set including accreditation is great value at £325 + VAT and free for students on our assisted learning pathway.

Complete the form below and we will be in touch to arrange payment if necessary.