A volatile landscape fuelled by social media, geo-political changes, and innovation demands more than Traditional forecasting methods. How does Machine Learning help in this case?

In a fast-paced business landscape, evolving consumer choices pose one challenge for businesses: Demand Volatility. Blame geopolitical changes, social media influence, fierce competition amongst enterprises, and sometimes a global pandemic. Traditional Forecasting mechanisms cannot always give accurate results based solely on historical data. Moreover, what about a variety of data sets and multiple points of consideration that directly impact the dynamics of consumer demands? Thankfully, we have AI and Machine Learning (ML) to our rescue, revolutionizing Demand Forecasting. 

In this article, we dive into the realms of Machine Learning Demand Forecasting and gauge how it surpasses traditional forecasting methods to offer deep insight into the future purchase predictions of a thriving consumer base. 

What exactly is Demand Forecasting and what are the Traditional forecasting methods?

Demand forecasting is the process of predicting customer needs for a product or service in the future. It helps make adjustments to inventory, or rather inventory decisions, and an informed supply to meet consumer needs. 

Traditional forecasting, or Statistical forecasting, encompasses methods like linear regression, simple exponential smoothing, ARIMA, ARIMAX, and more. These methods offer a high level of transparency but are only based on historical data and apply to a perfect scheme of situations that are not necessarily prone to disruptions. Do we completely discard the traditional methods? We will analyze this later in this article after we uncover Machine Learning’s capabilities in forecasting. 

How does Machine Learning revolutionize Demand Forecasting?

Machine Learning, on the contrary, works on multiple data sources, including many variables that would affect consumer demand. It does not just depend on historical data of purchase behavior gathered over, let’s say, the past two years and considers current factors and drives a high degree of predictive analysis. 

Machine Learning models are built on data-driven predictions that consider internal and external factors influencing a product or service’s demands. Some of the data sources that Machine Learning utilizes are marketing polls, macroeconomic indicators, weather forecasts, local events, social media influence, competitors’ activity, and historical data. It is safe to categorize these data sources as structured data like past purchase orders, customer POS information, inventory, and sales transactions, and unstructured data like social media, marketing campaigns, reviews, and more.

ML forecasting models use complex mathematical algorithms and understand complicated relationships in datasets while adapting to volatile conditions. Some popular ML forecasting models include Artificial neural networks, Classification and regression trees (CART), Generalized regression neural networks, and Gaussian processes.

While traditional forecasting models mostly use linear regression methods, Machine learning models use a combination of linear and non-linear methods, to arrive at a prediction. The result is a high level of accuracy of forecasts and minimum loss function. It has been observed that the error metrics like Mean absolute percentage error, Root mean square error, or Weighted root mean square errors are significantly lesser in an ML model than in a Statistical model. 

Having said that, ML works best for predictive analysis with volatile demand patterns and short-to-mid-term forecasting while launching new products or services and dynamic business environments. For example, the leading dairy brand Granarolo achieved 85 to 95% accuracy in Forecast by integrating machine learning with its existing systems. 

How To Maximize the Benefits of ML in Demand Forecasting?

To maximize ML’s offerings, enterprises must choose one compatible with their existing ERP or Inventory management system for a smooth operation. Businesses must know the data sources from which the solution would pull information as it gives accurate results only with the help of a large and high-quality dataset. Organizations must conduct extensive training programs for their staff to seamlessly use ML solutions. Businesses can also choose to buy ERP or WMS with an in-built model or build a custom model that requires ample investment. Finally, the ML solution needs to be tested thoroughly to see if the level of accuracy in predictions is acceptable. Otherwise, a brand would never have the correct inventory to suffice its consumer needs due to incorrect predictions by an ML model. 

How are Traditional Forecasting models still relevant?

Though ML models offer a holistic approach to forecasting, we cannot completely discard Traditional Forecasting models. Statistical forecasting models offer a high transparency level and are perfect for mid to long-term planning. They are apt for products or services that have survived the storm of demand volatility and would never run out of choice.


While Statistical Forecasting methods have been used for ages, the rising demand for models that predict masked market trends and navigate volatility has led to ML-driven Demand forecasting. As with every complex AI-based model with high computational prowess, Machine Learning has its requisites to perform optimally, like a smooth integration with existing systems, investment, and resource training. When integrated and utilized well, it can help businesses forecast accurately, and promote operational efficiency and cost reduction along the supply chain. ML’s offerings, coupled with human intervention, can aid in strategic decision-making for better growth and revenue. 


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