Excel and Power BI for Demand Planning & Forecasting

Understand the fundamentals of demand planning and its role in business operations.

This lesson explains how demand planning supports forecasting, decision-making, and overall supply chain efficiency.

Learn about the key stakeholders involved in demand planning and how different functions collaborate in the forecasting process.

This includes roles across supply chain, sales, finance, and operations.

Understand the roles and responsibilities of individuals involved in the demand planning process.

This lesson explains how accountability is structured and how different teams contribute to forecast accuracy

Learn what a forecast is and why it is essential for business planning.

This lesson explains how forecasts are used to support decisions related to inventory, production, and financial planning.

Understand the difference between macro and micro forecasting approaches.

Learn when to use each level of forecasting and how they support different types of business decisions.

Get an overview of the forecasting techniques covered in this module.

You’ll learn how different methods such as moving averages, time series, and regression are used in demand planning, and how to select the right approach for different scenarios.

Learn the fundamentals of moving average forecasting and how forecasts are calculated.

This lesson also introduces the concept of forecast responsiveness vs stability, and how the choice of averaging period impacts forecast behavior.

Learn how to build a moving average forecast in Excel using a step-by-step approach.

Learn how to create dynamic ranges in Excel to make your moving average calculations flexible and scalable.

Learn how to use the INDEX function in Excel to create dynamic ranges for moving average forecasts.

This approach helps make your forecasting model more flexible and automatically adaptable to new data.

Understand the key limitations of moving average forecasting and when this method may not be suitable.

Learn the fundamentals of time series forecasting and how it differs from simple methods like moving averages.

This lesson introduces key concepts such as trend and seasonality, which are essential for building more accurate forecasts.

Learn how to implement time series forecasting in Excel using built-in functions

Learn the fundamentals of linear regression and how it differs from time series forecasting.

This lesson explains how external factors such as price or promotions can be used to improve forecast accuracy.

Learn how to implement simple linear regression in Excel using a numerical independent variable.

This lesson shows how to model relationships between variables and use them for demand forecasting in Microsoft Excel.

Learn how to apply linear regression in Excel when the independent variable is categorical.

This lesson shows how to handle scenarios such as promotions or events and incorporate them into demand forecasts.

Learn when linear regression is an appropriate forecasting method and how to evaluate its effectiveness using R-squared.

This lesson helps you assess how well your model explains the relationship between variables.

Build a scalable approach to calculate R-squared in Excel using LINEST and INDEX functions.

This method helps you assess regression model performance across different products efficiently

Learn the fundamentals of multiple linear regression and how it extends simple regression by using multiple variables.

This lesson uses a practical example to show how combining different drivers can improve forecast accuracy.

Create a multiple linear regression model in Excel by combining multiple input variables.

This approach helps improve forecast accuracy by capturing the impact of different business drivers

Understand the key limitations of linear regression and when it may not be suitable for forecasting.

This lesson highlights scenarios where regression models may produce unreliable results.

Learn simple rule-based forecasting approaches such as extrapolation and growth-based methods.

These techniques are useful for quick forecasting in situations with limited data or low business impact.

Review the key forecasting techniques covered in this module, including their use cases and limitations.

This lesson helps reinforce your understanding before moving on to the next section.

Understand the concept of forecast segmentation and why different products require different forecasting approaches.

This lesson explains how segmentation helps improve forecast accuracy and efficiency.

Learn what forecast granularity means and how the level of detail impacts forecasting accuracy and usability.

This includes decisions around forecasting by product, customer, and time

Understand the concept of forecast horizon and how far into the future forecasts should be created.

This lesson explains how business needs and product characteristics influence forecasting horizons

Learn how forecast governance ensures consistency and accountability in the forecasting process.

This lesson covers how organizations structure forecasting cycles, reviews, and approvals.

Understand the full forecasting model before building it. We’ll walk through the structure, logic flow, and final output so you know exactly what each component is solving and how everything connects end-to-end

Design the layout of the model and use the UNIQUE function to dynamically extract SKU lists. This ensures your model updates automatically as new data is added—no manual maintenance.

Learn how to use the FILTER function to create flexible, dynamic data extraction. This will become a core building block for making your forecasting model responsive to user selections

Use Name Manager to create reusable named ranges and formulas. This makes your model easier to read, maintain, and scale—especially as complexity increases.

Use XLOOKUP to link datasets efficiently and accurately. We’ll focus on practical usage within the forecasting model, including handling missing values and improving reliability.

Build your own reusable Excel functions using LAMBDA. This allows you to simplify complex logic and reduce repetition across the model.

Apply logical functions to segment SKUs and assign appropriate forecasting approaches. This is where business logic starts driving the model.

Create structured mapping tables that allow users to control key inputs such as forecasting methods and assumptions—without touching formulas.

Use Data Validation to enable controlled user input. This ensures consistency and prevents errors when selecting forecasting techniques.

To build a regression model, we will need price and promotion forecast. In this lesson we set up the wireframe for price and promotion forecast data

Using the index function - we will lookup the values for price and promotion forecast that will ultimately be used in calculating regression metrics using LINEST function

In this lesson we bring everything together - using LINEST function, price and promo forecast, we calculate regression metrics and ultimately forecast volumes based on those regression metrics.

Improve calculation speed and efficiency by restructuring formulas, reducing volatility, and applying best practices for large Excel models.

In this video, we will parameterize the growth percentage input for simple growth forecasting technique

Use EDATE to automatically extend or adjust the forecast horizon. This ensures your model adapts seamlessly across forecast cycles.

Introduce validation checks to catch errors early. Learn how to design a model that protects itself from incorrect inputs and data issues.

Build checks to confirm all required mappings are in place. Prevent missing configurations that could break your forecasting logic.

Identify and prevent duplicate records in mapping tables to maintain data integrity and avoid incorrect forecasts.

Ensure all required price and promotion inputs are populated before running forecasts. This avoids incomplete or misleading results.

Validate that price and promotion forecasts exist for the entire forecast horizon, not just partial periods.

Simulate future forecast cycles to test how the model behaves with new data. Ensure it remains stable, dynamic, and scalable.

Understand why human judgment is critical in forecasting. Learn when and how overrides should be applied beyond statistical outputs.

Integrate override logic into the model so users can adjust forecasts while maintaining structure, transparency, and control.

Learn how to extend the model for larger datasets, multiple dimensions, and real business scenarios without breaking performance or usability.

Get introduced to Power BI and its role in the analytics workflow. Understand how Power BI connects data, transforms it, and turns it into interactive dashboards—setting the foundation for everything we’ll build next.