Machine Learning in Demand Forecasting

Demand forecasting is crucial in decision making across the supply chain. Knowing how many products a company expects to sell dictates manufacturing decisions, logistical choices, and of course inventory distribution across retailers and distribution centers. As such an important factor driving the choices a company makes, having reliable and accurate demand forecasts and planning techniques is indispensable. Traditionally, data-driven statistical models have proved useful in providing companies with an idea of the type of demand they can expect for their products or services. However, traditional data-based models are unable to account for each of the wide variety of factors that impact actual demand. They can be built to analyze previous datasets and produce predicted outcomes based on recognized patterns, but are incapable of processing all the variables that coalesce to create a product’s true market. Perhaps as important is the matter of speed and efficiency, and the lengthy processing time required when new data is inputted, limiting companies’ abilities to quickly respond to market changes, let alone anticipate them.

Fortunately for companies the world over, AI and machine learning technologies are making breakthroughs in demand forecasting. Machine learning models make up for the deficiencies of previous models, allowing businesses around the world to process more up to date and varied datasets, that can learn on the fly to generate more accurate and reliable demand forecasts.

Greater Data Compatibility and Capabilities

The crux of machine learning’s strength in demand forecasting is its ability to process significantly more types of datasets than existing statistical models. These datasets come from many different sources, each of which can be used to create a more realistic and detailed picture of the factors shaping a product’s market. Traditional models process data covering factors such as historical product sales, easily quantifiable information most companies and retailers already have readily on hand. Machine learning models are able to incorporate a much more expansive range of data sources, some of which can help get to the “why” of product sales, which in turn allows for more accurate forecasting. These factors include seasonal trends, weather data, larger economic factors, social media trends, and more. The greater size and complexity of these datasets enable a more detailed analysis to take place. Moreover, having more analyzable factors creates a more comprehensive understanding of the aforementioned “why”, which gets to the “learning” element of machine learning.

Machine “Learning”

The utility of being able to process a wider range of datasets is revealed in the improvements machine learning models exhibit as they process more and more data. Machine learning models “learn” as they go, identifying patterns and trends and updating their analysis accordingly. Traditional statistical models also pick up on patterns, but over a much longer period of time and in a much more limited scope. Machine learning models, once fed enough data, can recognize patterns as they emerge, and more importantly, automatically adjust their forecasts in response to this new information. The self-adjustment element of machine learning models is what truly sets them apart, allowing for near-guaranteed accuracy improvements as the model further develops. This allows for dynamic information input and analysis, with these models capable of not only recognizing but adapting to new trends, making it so that companies can have the most up-to-date and accurate forecasts possible. These models learn from their own mistakes, auto-analyze discrepancies between their predictions and actual outcomes, and by having more nuanced and diverse data sources, can even identify which of the factors analyzed may have led to said discrepancies. This ability to identify underlying elements improves accuracy and can help provide companies with a competitive edge. Manual input and analysis from a supervisor is always welcome, but oftentimes unnecessary as the machine learning models can identify their own errors and adjust accordingly faster than almost any person could. This automation element frees up time for employees to focus on other tasks, and also crucially improves the speed of forecasts.

Speed and Efficiency

One of the most apparent advantages of machine learning-based demand forecasting models is their impressive speed. Machine learning demand forecasting systems can not only process much more varied and complex datasets than their predecessors, they can also process them significantly faster. New data can also be added continuously to machine learning systems, allowing for, in essence, real-time analysis of new information. This ties in with the unprecedented scalability of machine learning models, and their capacity to process far more data than traditional systems. This is also what makes machine learning models dynamic, as they can continuously receive and process new data, analyze trends as they unfold, and make adjustments in immediate response to these developments. In our globally connected world, in which a change in one element can disrupt thousands of others, volatility is to be expected. By having a dynamic machine learning-based forecasting model, you can have greater confidence in the predicted demand, and know that you’ll be able to make any necessary adjustments as soon as possible.

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