I am going to attempt to pull back the curtain and unveil the magic behind the most common algorithms used in predictive analytics for business forecasting, and demonstrate what exactly goes on behind the scenes in the world of AI.

Here I focus on the top methods and algorithms that enable the execution of applications for demand planning and business forecasting. The following are the preferred Machine Learning and Predictive Analytics models of Demand Planners and Data Scientists (in reverse order):

7) Artificial Neural Networks

6) Decision Trees

5) Logistic Regression

4) Naïve Bayes

3) Linear Regression

2) Smoothing and Averaging Time Series Models

1) Simple Ratio Models

7) Artificial Neural Networks: (ANN) are a class of pattern matching techniques inspired by the structure of biological neural networks. ANNs combine logistic regressions into a neural network. ANNs less complicated than they may first appear  – ANNs are a collection of logistic regressions, so if you understand logistic regression, you can easily understand ANNs. Developing the final prediction is generally done by training the model and calibrating all of the “weights” for each neuron and repeating two key steps: forward propagation and back propagation.

6) Decision Trees: The concept and algorithms underpinning decision trees are relatively simple compared to other models. The general purpose of decision trees is to create a training model that can be used to predict the class or value of target variables. Decision trees build classification or regression models in the form of an upside-down tree structure.

5) Logistic Regression: These help demystify neural networks and help answer probability type questions. While it is listed as a type of regression model, it is less a calculation and more an iterative process. This can be used for anything from predicting failures to identifying if the object in a picture is a cat or not, for example.

4) Naïve Bayes: Naïve Bayes are probabilistic, which means that they calculate the probability of each class for a given set of features, and then output the class with the highest observed probability in the data set. In simple terms, based on a bunch of x’s, we’re looking at the odds of Y being y. It can be used for natural language processing, to classification, to simple prediction.

3) Linear Regression: Regression is a simple cause-and-effect modeling that investigates the relationship between dependent (target) and independent variables (predictor). Regression models come in all types and applications. One of the most common is a simple linear regression. The first step in finding a linear regression equation is to determine if there is a relationship between the two variables. This is often still a judgment call for many Demand Planners.

2) Smoothing & Averaging Time Series Models: With these models, one needs only the data of a series to be forecasted. They are simple and widely used by companies that own historical data. Here we assume that the training data set of sales history contains a trend and seasonality, and we extrapolate these patterns forward.

1) Simple Ratio Models: These express the relationship between two or more quantities.  Ratio models are utilized for a range of day-to-day purposes: understanding a seasonality index, calculating the velocity of sales and market penetration, or disaggregating a family level forecast, just to scratch the surface.  This easy-to-calculate statistic is used in various ways to guide decision making and drive forecasts.

What we find is that sometimes the simplest methods provide us a good forecast and are the best use of our time. Fancy techniques are great, but our overriding goal is to select the model that fits our business purposes and the resources available to us. We need to evaluate the model properly to ensure that it can do what we need it to. The most sophisticated techniques and most advanced technologies accomplish little if nobody understands the results. To complete the process we must step back, sometimes simplify, and communicate our analysis effectively. Or just tell them it was AI and leave them in awe  of your magical skills…


Don’t forget to join myself and a host of predictive analytics, demand planning, and forecasting leaders at IBF’s Virtual Business Predictive Analytics, Forecasting & Planning Conference from April 20-22, 2021. At just $499 for this insight-packed 2-day event, it’s an extremely cost-effective way to evolve your skills for the future of demand planning and forecasting.


To add the above-mentioned models to your bag of tricks, get your hands on Eric Wilson’s new book Predictive Analytics For Business ForecastingIt is a must-have for the demand planner, forecaster or data scientist looking to employ advanced analytics for improved forecast accuracy and business insight. Get your copy.