In a competitive market, demand forecasting is essential for optimizing the supply chain and making strategic decisions. With the evolution of machine learning (ML), these forecasts have become more accurate and accessible, helping companies to improve efficiency and maximize profits – or, if you prefer, we can develop software for you!
So, we will present the strategies that really work in demand forecasting using machine learning, emphasizing technical aspects and best practices.
Data collection and preparation
That’s why, in order to forecast demand, it’s essential to collect historical sales data, stock information and marketing data. In adittion to prices, seasonality and even external factors, if necessary, such as economic conditions and the weather. Data cleaning and preparation involves:
- Manipulation of missing data: Use methods such as interpolation or imputation techniques to manipulate missing data.
- Outlier Detection and Treatment: Identifying anomalies that may distort the model and treating them.
- Normalization and Standardization: Ensure that the data is on the same scale to improve the efficiency of the model.
Choosing the Machine Learning Model
Selecting the right ML model depends on the nature of the data and the specific problem. Some popular techniques include:
- Regression Models: linear regression with simple and interpretable data, but limited in capturing complexities;
- Time Series Models: effective for data with trend and seasonality; such as ARIMA and SARIMA;
- Tree-Based Models: Powerful for capturing non-linear interactions; such as LightGBM and XGBoost;
- Neural Networks: Suitable for complex time series
Feature Engineering
The creation of relevant features (variables) can significantly improve the accuracy of the model being developed. The techniques for their construction involve:
- Lags and Time Windows: Create variables that represent past values (lags) or moving averages to capture trends and seasonality.
- External Variables: Incorporate macroeconomic variables, weather data, promotional events, etc., as external variables.
- Interactions between Variables: Create new features that represent relevant interactions between existing variables.
Model Training and Validation
Here, you will divide the data into groups for training and validation, to see how the model behaves. Thus, techniques such as k-fold cross-validation help you get a better idea of the model’s accuracy. Some metrics for accuracy include:
- MAE (Mean Absolute Error), RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error).
Adjusting Hyperparameters
Adjusting your hyperparameters can greatly improve the model’s performance. Methods such as grid search and random search help to find the best combinations. More recently, techniques such as Bayesian Optimization are used to optimize hyperparameters more efficiently.
Implementation and Monitoring
Once validation, the model is put to real use. So, monitor and update the model continually to ensure its accuracy. Changes in demand, new products, or changes in the market must be reflected in the training data and in the model’s adjustment.
Demand forecasting with machine learning isn’t a one-size-fits-all solution, but when executed effectively, it offers a substantial competitive edge. Sp, the secret to success is in the quality of the steps described here. Remember, we’re here to support you on your journey!Some metrics for accuracy include Some metrics for accuracy include
