Forecasting using average vs ML

In demand forecasting, a key challenge is the accuracy of predictions. Traditionally, many companies have used simple methods such as calculating averages of historical sales data. While these methods may be easy to implement and understand, they are increasingly giving way to more advanced machine learning techniques that offer much better results. In today’s coffee, we’ll take a look at why machine learning (ML) is superior to traditional average methods in demand forecasting.
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Traditional Forecasting Methods: Averages

Traditional forecasting methods, such as the arithmetic average, involve calculating the average of historical sales data. For example, to predict sales for the next month, you can calculate the average of sales from several previous months. While this is a simple and quick way, it has its limitations:

1. Does not take volatility into account: The average does not take into account seasonality, trends or unusual patterns in the data.
2. Lacks adaptability: Does not respond to changes in real-time data.
3. Ignores complex relationships: Fails to grasp complex relationships between various factors affecting demand.

Machine Learning: A Modern Approach to Forecasting

Machine learning, especially algorithms such as neural networks and regression models, offer a much more sophisticated approach to demand forecasting. Here’s why they are superior:

1. Consideration of multiple variables:
   1. Complex relationships: ML algorithms can analyze many variables simultaneously and identify complex relationships between them. For example, the model can take into account factors such as competitor prices, promotions, seasonality, market trends and many others.
   1. Automatic feature selection: ML models can automatically select the most important features that affect demand, improving forecast accuracy.
2. Adaptation to change:
   1. Learning from current data: ML models can be regularly updated with new data, allowing them to adapt to changing market conditions and demand.
   1. Predicting trends: Models can identify and predict trends based on historical data to better prepare for future changes.
3. Higher accuracy:
   1. Error reduction: With advanced algorithms, ML models can minimize prediction errors.
   1. Predicting anomalies: ML models are able to catch anomalies and unusual patterns in the data, which is impossible with simple averages.
4. Personalization of forecasts:
   1. Market segmentation: ML models can produce forecasts for different market segments, allowing for a more personalized approach to inventory management and marketing strategies.
   1. Individual buying patterns: Models can analyze customers’ individual buying patterns to better understand their needs and better forecast demand at the micro level.

Summary

Machine learning yields significantly better results in demand forecasting than traditional methods based on averages. With the ability to account for multiple variables, adapt to change, higher accuracy and personalized forecasting, ML algorithms offer more sophisticated and accurate tools for predicting future sales trends. In a dynamic and competitive market environment, the use of machine learning is becoming an indispensable component of effective demand management.

DATURE ENTERPRISE software uses artificial intelligence and machine learning in the process of demand forecasting and inventory optimization. This makes it possible to generate much more accurate forecasts and to make optimal decisions on the stocking .

The system provides methods for forecasting seasonal demand and demand influenced by calendar days. Inventory management methods allow for both pre-season inventory building approaches, dynamic safety stock control and JIT.

TheDATURE application ENTERPRISE can also use expertise in the demand forecasting process. Authorized users can enter expert forecasts and adjust statistical forecasts with them. The process is fully auditable in terms of who changed the forecast when and how. This makes it possible to track the accuracy of both statistical and expert forecasts. As a result, the organization learns how to forecast more accurately and improve process efficiency.