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Demand Prediction

Does it differ to Demand Forecasting?

If you’re in the business of predicting the future, you might have heard the term Demand Prediction being thrown around. While there has been some debate around the difference between Demand Prediction and Demand Forecasting, we’re here to settle things. Demand Prediction is synonymous with Demand Forecasting, and refers to the suite of analysis tools that are used to predict the demand for products and services at particular points in the future.

What is Demand Prediction?

Now that we’ve cleared the air, let’s get into some of the basics of Demand Forecasting as a concept. In order to predict what the demand for a given product will be - whether you’re looking a week, a few months, or further down the line - there are some things you’ll need.

Firstly, you’ll need data. The most important source of data that you need is historical data, but you can also integrate promotions, competitor data and site traffic (otherwise known as quantitative data) or the results of market research or expert opinions (also called qualitative data).

How to Forecast Demand

So how do you go about forecasting demand? The process of creating a forecast can be broadly broken down into five steps:

  1. Setting the forecast objective

  2. Determine the time period to forecast for

  3. Choose the type of forecasting to use

  4. Collect data

  5. Train the model

To start with, you’ll want to set the objective of your forecast - i.e. why you’re even forecasting in the first place. Understanding why you’re forecasting informs your choice of a forecasting method since different objectives require differing levels of accuracy, time, and cost. For instance, a forecast that is used to decide whether to enter a certain market requires less accuracy than one used for budgeting or stock replenishment. Your own budget restrictions may narrow down method selection even more.

The time period you choose to forecast can also be influenced by the objective of your forecast and can affect which type of demand forecasting will be the most suitable. These can vary in complexity, ease of use, and cost, from simple statistical methods to AI and Machine Learning-driven models. When it comes to data, as mentioned above, there are two types which play a big role in method selection. To find out more about the different types of Demand Forecasting and the kinds of data they use, head here.

Finally, your method of choice can be tested (or trained in the case of Machine Learning models) and fine-tuned before it is implemented. Analysing the forecast results and testing the accuracy of the forecast involves retrospective modeling - where the model predicts demand for a period that you already have sales data for - and calculating the difference between predicted and real values. This can be done with techniques such as the Mean Absolute Percentage Error (or MAPE for short) or Mean Squared Error (MSE).

Final Thoughts

While there may be some debate or confusion over the name, one thing no one in the know denies is the importance and impact of demand forecasting. Demand directly influences everything from turnover and profit to risk assessments and capacity planning, so it’s crucial (now more than ever) to understand what this demand will look like going into the future. Whether you’re a start-up or a multinational corporation, Demand Forecasting (which is AKA Demand Prediction but we strongly suggest you drop that phrase from your lexicon) is a useful tool that can aid decision-making and facilitate growth across industries.

Now we’ve gone through the knowledge-drop, here’s the good news: Implementing Demand Forecasting doesn’t have to be confusing. Services such as Remi’s Demand Forecasting platform use Automated Machine Learning (Auto-ML) to test and choose the best algorithmic approach for you and your product range, and can even improve the accuracy of your current forecasting strategy.


Want to find out more about demand forecasting? Why not have a read through of our case studies, and find out how we’ve used demand forecasting to help increase stock availability and improve the accuracy of forecasts. Once you’ve had a look through those, why not check out our blog for the latest AI reads. Or, if you’re looking to implement Demand Forecasting for your business or improve your current forecasting methods, drop us a line here.


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