Demand forecasting models have become a critical element determining the efficiency of modern business strategies and operations.

With recent advances in AI technology, demand forecasting models have made remarkable progress in terms of accuracy and applicability. Accurate demand forecasting directly impacts business cost reduction and customer satisfaction improvement through inventory optimization, production planning, and supply chain management efficiency.

In this article, we will systematically classify the major demand forecasting models used in 2025, compare and analyze each model's characteristics and application cases to help business decision-makers select the appropriate model for their companies.

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Evolution and Major Classifications of Demand Forecasting Models

Demand forecasting technology has evolved from simple statistical methodologies through machine learning to current deep neural networks and real-time adaptive AI systems. This progression is attributed to improvements in data processing capabilities and algorithm sophistication.

Current demand forecasting models can be broadly classified into three categories: 'Basic Statistical and Machine Learning Models', 'Advanced AI and Integrated Platforms', and 'Specialized and Adaptive Models'.

Basic statistical and machine learning models are relatively easy to implement and clear to interpret, while advanced AI and integrated platforms can capture complex patterns and integrate various data sources. Specialized and adaptive models provide solutions customized for specific industries or real-time changes.

Now, let's examine the characteristics and application cases of models in each category in detail.

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Basic Statistical and Machine Learning-Based Demand Forecasting Models

Regression Analysis-Based Models | Simple but Fundamental Techniques

Regression analysis-based demand forecasting models mathematically define relationships between historical sales data and various external variables to predict future demand. The core value of this model is its ability to clearly identify correlations between variables.

For example, pharmaceutical companies link specific medicine sales volumes with disease incidence data to predict future demand. Auto parts manufacturers predict component demand using regression models that connect economic leading indicators with production plans.

Regression models can be applied to data with simple patterns. They are suitable for short and medium-term forecasting, appropriate for predicting demand patterns of 3-6 months. The greatest advantages of this model are its ability to clearly identify how much each factor influences demand through coefficient analysis, and its speed.

Therefore, decision-makers can focus on important variables to develop strategies. However, there are limitations in modeling non-linear relationships or complex interactions, which may reduce accuracy in environments with high market volatility.

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Machine Learning Ensemble Models | Synergistic Effects of Various Algorithms

Machine learning ensemble models derive more accurate predictions by integrating results from multiple prediction algorithms. By combining powerful algorithms such as XGBoost and LightGBM, they mutually complement the weaknesses of individual algorithms.

An ensemble is like asking several experts for opinions simultaneously and synthesizing those opinions to reach a conclusion. In other words, it's a method of running multiple models in parallel and combining the results for final prediction. The biggest advantage of this approach is the ability to process various data sources comprehensively.

In practice, global fashion brand Zara quickly responds to trend changes by analyzing POS, ERP, and social media data with ensemble models.

A cascade is a special form of ensemble that runs models in sequence. Similar to an interview process where you must pass the first interview to proceed to the next, a subsequent model is only executed if sufficient confidence is obtained from the previous model.

If it's an easy problem, a cascade can solve it more quickly, but for difficult problems, more computational resources may be required as multiple models need to be processed. Ensembles have the advantage of higher accuracy than using a single model since they utilize predictions from various models. Additionally, ensembles are often easy to create, simple to maintain, and cost-effective to train.

Machine learning ensembles are particularly suitable for mid-sized companies with large amounts of data but limited access to specialized data scientists. They can be implemented with relatively few computing resources and are easily integrated with existing systems.

However, due to the complexity of ensembles, interpreting results may not be intuitive, requiring additional effort to verify model reliability and ensure explainability.

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Advanced AI and Integrated Platform Demand Forecasting Models

Deep Learning and Transformer Models: Key to Discovering Complex Patterns

Deep learning-based demand forecasting models possess powerful capabilities to capture complex non-linear patterns in large volumes of data.

They effectively model long-term dependencies and seasonality in time series data using various neural network architectures such as LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), and transformers. The biggest differentiator of these DNN models is their ability to process unstructured data.

They can extract demand influence factors from unstructured data like social media text, product images, and customer reviews, enabling proactive detection of market trend changes. Similarly, Impactive AI's DeepFlow can derive conditions for potentially successful new products based on consumer preference data for new product features and market environment data at launch time.

DNN models also have self-optimization capabilities through the backpropagation algorithm, improving prediction accuracy through continuous learning.

Despite these advantages, they require substantial computing resources for model training and have "black box" characteristics that make result interpretation difficult, limiting their use in regulated industries where clear decision-making rationales are required.

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Hybrid AI Platforms: One-Stop Prediction Solutions

Hybrid AI platforms are comprehensive solutions that integrate various prediction models and analytical tools, encompassing the entire process from data collection to prediction, visualization, and decision support.

Major platforms like IBM Watson, AWS Forecast, and C3 AI fall into this category. IBM Watson specializes in complex demand simulations for the semiconductor industry based on more than 6 million rule engines, while AWS Forecast supports real-time data streaming through integration with over 30 services. C3 AI features hierarchical forecasting capabilities between products, customers, and locations.

A common characteristic of these platforms is their ability to integrate multiple data sources. They typically analyze various data according to weightings (sales history, weather data, social media trends, competitor information, etc.). They also support decision-making for various market situations by simulating more than 200 scenarios.

Impactive AI's DeepFlow provides LLM-based consumer preference prediction functionality. Impactive AI's DeepFlow uses generative AI-based agents to create preference surveys for key features of new products and automatically generates response data by performing responses.

It conducts sophisticated quantitative analysis based on data and allows you to see results at a glance through advanced reporting functions. It has received positive evaluations for actively improving the pain points of market research processes that have previously been unable to thoroughly address the diversity of all consumers and receive prompt feedback.

While hybrid AI platforms are primarily used by large corporations due to high implementation complexity and cost, recently MLaaS (Machine Learning as a Service) models have been spreading, allowing medium and small businesses to selectively utilize necessary functions. When selecting a platform, ease of integration with existing systems, scalability, and industry-specific features should be prioritized.

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Specialized and Adaptive Demand Forecasting Models

Real-Time Adaptive Models: Immediate Response to Changing Markets

Real-time adaptive demand forecasting models are designed to respond agilely to rapid changes in the market environment. These models collect real-time data from IoT sensors, mobile apps, and POS systems, utilizing distributed processing architecture that connects edge computing with the cloud. The entire process from data collection to prediction adjustment is automated, allowing forecasts to be updated within 15 minutes after market fluctuations occur.

The biggest advantage of real-time adaptive models is their ability to respond to unexpected market changes (such as pandemics or rapid consumer trend shifts).

However, these models have high initial deployment costs, potential data security and privacy issues, and risks of overfitting due to noise in real-time data. Therefore, they are most suitable for industries with high market volatility where real-time response is important, such as fashion, electronics, and food.

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Domain-Specific Models: Industry-Tailored Approaches

Domain-specific demand forecasting models provide optimized prediction solutions that reflect the unique characteristics of specific industries or business areas. They are based on general models but include industry-specific variables and relationships in modeling.

For example, in the medical industry, models combining RNN and LSTM consider special variables such as disease incidence rates, number of surgeries, and pharmaceutical distribution periods. In the energy industry, CNN and transformer models integrate analysis of satellite imagery, weather radar, and power plant operation rate data.

The technological evolution direction of domain-specific models lies in the integration of quantum computing and generative AI. Examples include solving O(n³) complexity problems using 1000-qubit processors or linking with GPT-4 based scenario planning tools. This enables simulations and predictions at previously impossible levels.

These domain-specific models are optimized for industry characteristics, providing high prediction accuracy and direct aid to decision-making.

However, they require both expert knowledge of the domain and data science capabilities during development, and have limited expandability to other industries. Therefore, they are most suitable for industries with high levels of expertise and regulation, such as medical, energy, and finance.

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Guide to Selecting the Optimal Demand Forecasting Model by Business Situation

To select an appropriate demand forecasting model, companies need to consider various factors including company size, data characteristics, prediction cycles, and industry characteristics. For small and medium businesses processing less than 10PB of data annually and requiring real-time predictions, a hybrid model combining LSTM and transformers is effective. For industries like manufacturing that need predictions centered on time series data, ensemble models combining ARIMA and Prophet are suitable.

Key questions to consider when selecting a model include:

• Is the explainability of prediction results important?
• Is real-time adjustment capability necessary?
• Is unstructured data processing essential?
• What are the current technical capabilities and infrastructure?

An appropriate model can be selected based on answers to these questions.

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Conclusion

In 2025, AI-based demand forecasting models have become essential business intelligence supporting strategic decision-making beyond simple prediction tools. The six major models examined in this article have different strengths and application situations, making it important to select an appropriate model according to the company's characteristics and requirements.

Notably, with the spread of cloud-based services (MLaaS), even small and medium-sized businesses can utilize advanced prediction technologies. Most major platforms offer free trials, allowing various models to be tested before actual implementation.

In the future, demand forecasting models are expected to develop in directions strengthening multimodal data integration and autonomous learning capabilities. Companies should monitor these technological developments and continuously evolve their demand forecasting strategies suitable for their business models and data environments. Accurate demand forecasting is no longer an option but an essential element for business success.

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