The precision of AI in demand forecasting: lessons from a Proof of Concept study

In our initial tests, we worked with a synthetically generated data set that allowed us to simulate different market conditions and scenarios.

The AI model we developed achieved an outstanding 98% accuracy rate in our predictions. This demonstrates exceptional pattern recognition capabilities that hold great promise for future real-world applications.

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As shown in the chart, our model accurately tracks demand patterns, whether they are sudden spikes or seasonal changes. The synthetic data allowed us to test the robustness of the system by simulating different market situations.

Compared to various forecasting and averaging methods, our AI-based solution delivers a significant improvement in forecast accuracy. The MAPE (Mean Absolute Percentage Error) value of 1.07% indicates outstanding accuracy, especially when compared to the 12-19% error rates of traditional methods.

We have developed a complex multi-layer ensemble architecture that combines the advantages of gradient boosting and deep learning methods. It is based on three approaches: XGBoost and LightGBM as gradient boosting implementations and a neural network implemented in TensorFlow. A metamodel layer tunes these for optimal predictions.

To validate the robustness of the model, we worked with three years of daily data per product (close to 1,000 observations per product). We repeated the experiments, simulating different market environments such as rising or falling trend markets. During feature engineering, we paid particular attention to capturing temporal correlations, including rolling statistics across different time windows and cyclical temporal features. This extensive testing ensured that the model performed reliably in different economic contexts. The hyperparameters of the gradient boosting models were fine-tuned using Bayesian optimisation, which allowed the model to automatically adapt to the characteristics of the problem at hand. The entire pipeline was written in Python, taking advantage of the scikit-learn and TensorFlow ecosystems for pre-processing and validation.

One of the most significant potential benefits of AI-based demand forecasting is the reduction of food waste. With accurate forecasting, companies can significantly reduce excess inventory while ensuring adequate supply to meet customer demand. This is not only financially beneficial but also has a significant environmental impact.

In the area of stock optimisation, our system can automatically forecast expected stock shortages or surpluses. A predictive approach allows proactive intervention before problems occur. This is particularly valuable in industries where inventory management is critical to operations.

In marketing and sales, forecasting can help optimise promotional campaigns. The system can predict the likely impact of different promotions, allowing the marketing team to plan the timing and intensity of campaigns more accurately. This can lead to increased revenue as well as more efficient use of marketing spend.

The model has been successful in identifying the impact of various external factors on demand. The distribution of forecast errors is concentrated around ±2%.

Building on the success of the Proof of Concept, the next phase will continue with real data. As we continue to develop the model, we plan to integrate several external data sources that can help maintain the predictive capabilities of the model in the complexity of the real-world environment. Identifying and incorporating the various influencing factors into the model will be key to its application in the real environment.

Implementing real-time data processing will allow for immediate response to changing market conditions. By developing a user-friendly dashboard, we aim to make forecasts and analyses easy to interpret and use for business decision-makers.

While there is a lot of attention today on generic AI solutions, our project proves that specific machine learning models tailored to a particular business problem can achieve outstanding efficiency. Demand forecasting is a complex task where domain knowledge and a deeper understanding of the specific business environment are key. A purpose-built model can take into account local characteristics, seasonal patterns and unique business rules to deliver more accurate forecasts than a generic solution. This approach enables businesses to reduce operational costs, optimise inventory management and improve customer satisfaction, all tailored to your specific business context.

The results of our project have confirmed that modern machine-learning techniques can be effectively applied to demand forecasting. Although the outstanding accuracy achieved on synthetic data is likely to be modified in real-world settings, the developed model provides a promising basis for further improvements. The next phase will focus on addressing the challenges of real data and business environments. We aim to create a system that will demonstrate the value of AI-based demand forecasting solutions in practice.