DIY - Simple Exponential Smoothing with Excel

This article is an extract from my book Data Science for Supply Chain Forecast, available here.?

This article is following another article on the theoretical introduction to simple exponential smoothing. You can find it here. You can find a similar article on how to make the same model with Python here.

Do It Yourself - Simple Exponential Smoothing with Excel

In this article, you will implement, in excel, a simple exponential smoothing forecast model.

1. We start our data table by creating three columns: 

• Date on column A
• Demand on column B
• Forecast on column C

2. Next to this, let's add a cell with alpha (F1 in our example). Don't forget to indicate that this cell is alpha clearly. 

3. Once this is done, you can initialize the first forecast (cell C2) as the first demand (cell B2). We used dummy values here.

4. We can now populate our forecast; as of cell C3, we can use this formula:

C3=$F$1*B2+(1-$F$1)*C2

5. To continue our forecast until the end of the historical period, we can drag this formula until the end of the table.

6. All the future forecasts (i.e., the forecasts out of the historical period), will simply be equivalent to the very last forecast based on historical demand (as shown below). 

That's already it. You can find more advanced models on my blog here, or in my book here.

You can also implement the simple exponential smoothing in Python (here).

About the author

Nicolas Vandeput is a supply chain data scientist specialized in demand forecasting and inventory optimization. He founded his consultancy company SupChains in 2016 and co-founded SKU Science—a fast, simple, and affordable demand forecasting platform—in 2018. He enjoys discussing new quantitative models and how to apply them to business reality. Passionate about education, Nicolas is both an avid learner and enjoys teaching at universities: he has taught forecasting and inventory optimization to master students since 2014 in Brussels, Belgium. He published Data Science for Supply Chain Forecasting in 2018 and Inventory Optimization: Models and Simulations in 2020.