Excel Data Analysis for Supply Chain: Forecasting

Course details

This course provides a comprehensive introduction to using Excel for supply chain forecasting, covering fundamental concepts, forecasting methods, error analysis, and more. Explore the essentials of time series analysis, moving averages, regression techniques, and seasonality, along with practical applications like bias correction and compound annual growth rate calculations. By combining theoretical knowledge with hands-on Excel demonstrations, instructor Eddie Davila equips you with the skills you need to create accurate and actionable forecasts. Additionally, gain insights into interpreting forecast errors and aligning methodologies with your unique business needs. This course is an ideal fit for supply chain professionals looking to enhance decision-making and operational planning.

Learning objectives
Understand the fundamentals of supply chain forecasting, including key methods and their applications.
Analyze forecasting data using Excel tools like time series, moving averages, and regression models to identify trends and patterns.
Evaluate the accuracy and biases in forecasts using error metrics such as MAD, MAPE, and RMSE, and apply techniques to mitigate bias.
Create seasonal and trend-based forecasts using Excel functions like CAGR, Ratio to Moving Average, and TREND.
Apply advanced forecasting techniques, including multiple regression models, to generate precise and actionable insights for supply chain management.

Concepts

Introduction

Using Excel for supply chain forecasting
What you need to know
Using exercise files

Forecasting Fundamentals

The role of forecasting in supply chain management
Preparing to forecast
Quantitative vs. qualitative forecasting
Key types of forecasting methods

Fundamentals of Using Excel in Time Series Forecasting

Time series explained
Define level and trend with Excel
Using Excel to define seasonality
Introduce challenge
Challenge solution

Understanding Forecasting Errors

Measuring forecast errors
Mean absolute deviation (MAD)
Mean absolute percentage error (MAPE)
Root mean squared error (RMSE)
Introduce challenge
Challenge solution

Bias in Forecasting

What is forecast bias
Calculating bias
Determining bias significance
Introduce challenge
Challenge solution

Moving Average Forecasting

Moving averages and smoothing
Simple and weighted moving average
Exponential moving average
Introduce challenge
Challenge solution

Trendlines and Simple Linear Regression

Simple linear regression fundamentals
Simple linear regression
Standard error of the regression
Introduce challenge
Challenge solution

Compound Annual Growth Rate (CAGR)

Linear vs. exponential trends
Exponential curves
Calculating compound annual growth rate (CAGR)
Introduce challenge
Challenge solution

Seasonality and Ratio to Moving Average Method

Seasonal index
Centered moving average
Periodic index
Seasonal index
Seasonal trendline formula
Seasonal forecast
Introduce challenge
Challenge solution

Multiple Regression Forecasting

What is multiple regression
Preparing data for multiple regression in Excel
Running a multiple regression in Excel
Interpreting multiple regression output
Developing a multi-variable equation to develop a forecast using regression output

Conclusion

Continue exploring supply chain forecasting concepts