AI for Demand Forecasting: Benefits, Use Cases, and Implementation Tips

Key takeaways:

• Using traditional demand forecasting methods isn’t enough to account for all market fluctuations and get precise predictions.
• Artificial intelligence (AI) can help you accurately analyze demand and adjust your business strategy while considering forecast outcomes.
• Ready AI solutions are good for performing general demand forecasting tasks but are less agile when it comes to meeting industry-specific requirements.

Unpredictable external factors, shifts in customer behavior, and seasonal fluctuations trigger continual changes in demand for products and services. To maintain the right inventory levels and plan production effectively, businesses need to know exactly what demand to expect for their goods and services.

Using AI capabilities for demand analysis can assist organizations with promptly responding to market fluctuations and adjusting current strategies to meet customer expectations. However, finding the right AI solution is challenging, mostly because general-purpose products aren’t agile enough to fit unique business needs. At the same time, custom AI solutions for demand forecasting require extra effort to build and maintain. 

In this article, we overview the business benefits of AI for demand forecasting and the pros and cons of custom and off-the-shelf AI solutions. We also reveal what the main challenges of implementing AI for demand forecasting are.

If you are a technical or project leader considering implementing AI for demand forecasting, this article will help you discover what to consider before launching the development process. You’ll also find out whether you should use a ready AI solution or develop a custom one based on your project needs.

How AI changes demand forecasting

Demand forecasting helps businesses predict future demand for their products or services, optimize their production, and improve supply chain efficiency.

Traditional demand forecasting methods are based on historical data, statistical techniques, and expert opinions. However, non-AI solutions for demand forecasting usually can’t account for sudden market shifts, evolving consumer behavior, and unforeseen events. Therefore, conventional demand forecasting may result in inaccurate predictions and be ineffective for dynamically changing markets.

Unlike traditional methods, AI-powered demand forecasting solutions can process extensive volumes of data, identify non-trivial patterns, and update forecasts on the fly to account for changes. Using AI in demand forecasting helps businesses make prompt and timely decisions so they can satisfy customers’ exact needs for products and services. 

For example, to predict which ingredients and products will be in higher demand at specific locations, Starbucks analyzes sales, inventory, weather, customer trends, and events using its own Deep Brew AI platform. Tesla also has their own AI-powered demand forecasting system. It analyzes real-time sales data, customer reservations, and market trends to dynamically adjust manufacturing schedules, avoiding both overproduction and underproduction.

Let’s explore the differences between AI and traditional demand forecasting methods in detail, considering the following criteria:

Table 1. Comparing AI and traditional demand forecasting methods

Criterion	AI demand forecasting	Traditional demand forecasting
Data analysis	Uses machine learning (ML) algorithms for processing large datasets	Requires manual data analysis
Data variability	Identifies and considers complex patterns like market trends, external factors, and seasonality	Can’t account for market trends, external influences, or customer behavior
Forecasting period	Allows for short-term and long-term forecasting	Allows for short-term forecasting only
Adaptability	Promptly adapts to market changes and new data	Requires manual reevaluation to adapt to new data and market changes
Risk forecasting	Forecasts potential risks	Requires engaging an analyst to interpret data and predict risk
Speed	Processes data in real time, enabling rapid decision-making	Is time-intensive, requiring days or weeks to gather data and produce forecasts
Cost-efficiency	Requires high initial investments but lower operating costs	Requires low initial investments but higher operating costs
Market fit	Suits dynamic and complex markets	Fits stable and low-variability markets

What makes AI demand forecasting so effective?

AI demand forecasting solutions collect data from various sources like past sales, market trends, and social media. The collected data undergoes preprocessing, such as cleaning and structuring. Data cleaning removes duplicates and inconsistencies, raising data quality, which is critical for accurate analysis. The structured data is then fed to the model. The key difference is that AI-powered solutions can identify complex patterns and relationships that traditional demand forecasting methods would miss.

Leveraging AI in demand forecasting gives businesses certain advantages, which we explore in the next section.

Business benefits of AI-powered demand forecasting

Accurate and timely insights on future demand benefit businesses, enabling them to operate effectively and avoid potential revenue loss. Specific benefits include:  

• Lower cost of production and fewer inventory management errors. A precise demand forecast lets you adjust a manufacturing process to avoid unnecessary overproduction and warehouse overloading, reduce inventory levels, and adhere to sustainable production principles.
• Better risk management. AI demand forecasting allows for real-time insights into market trends and potential fluctuations, helping you prepare for economic shifts and seasonal changes and reducing the risk of revenue loss.
• Optimized pricing strategies. Forecasting demand fluctuations in real time allows you to adjust your pricing strategy during periods of peak and low demand, while predicting future market trends helps to optimize sales.
• Time savings. By automating demand analysis, processing vast datasets promptly, and reducing forecasting errors, you can spend more time analyzing outputs and adjusting your current strategies.
• Enhanced decision-making. AI demand forecasting allows you to make decisions based on the real-time market situation and tendencies.
• Improved customer satisfaction. Accurate demand predictions lead to thorough planning, letting you provide your customers with what they need without delays. This will allow you to increase customer loyalty and attract new customers.

These benefits have led to the rise of AI-driven demand forecasting in different industries. Let’s take a closer look at how organizations from various fields can use it.

AI demand forecasting use cases in different industries

Although AI-driven solutions generally aim to predict demand for products and services, every industry has its particular use cases. Let’s explore different cases of using AI for demand forecasting across industries:

Companies operating in the energy sector use AI to predict periods of peak electricity demand. This allows them to balance the load of electricity systems, ensuring stable production and preventing blackouts. Having a clear view of electricity demand patterns is also essential for planning infrastructure upgrades.

Automotive companies use AI to forecast demand for vehicle models, specific features, spare parts, and post-sale maintenance. Such forecasts help businesses thoroughly plan production schedules, tailor production processes to customers’ needs, and ensure sufficient inventory levels.

Leveraging AI in healthcare improves demand analysis, from patient flow and beds, outpatient services, and elective surgeries to drugs, blood products, and medical supplies. Thus, healthcare facilities can enhance resource management and patient care, avoiding overloading during peak or emergency periods.

In the financial sector, AI helps FinTech companies predict demand for loans, credit cards, insurance policies, and other financial products. This allows them to adapt their product offerings and adjust their business strategies. As a result, it helps them retain customers and remain competitive.

By analyzing seasonal trends, external factors, and customer behavior with AI, retailers can forecast future buying patterns. Accurate analysis allows them to prevent potential stockouts and overstocking, increase sales, and reduce waste.

In manufacturing, AI solutions help forecast demand for finished products and raw materials, including seasonal demand fluctuations, by analyzing historical order data and suppliers’ performance. This lets manufacturers reduce downtime, ensure consistent quality standards, and optimize staffing during peak periods.

In the travel industry, AI analyzes weather patterns, economic conditions, and social trends to forecast demand for destinations and tourist attractions, including demand spikes during major events or holiday seasons. The analysis results can be used in planning infrastructure development, arranging successful promotional campaigns, and managing resources effectively during peak periods.

However, to capture and reflect the industry’s specifics and factors influencing demand patterns, it’s crucial to choose an appropriate demand forecasting model or approach. In the next section, we look at the main demand forecasting models.

3 key AI models for demand forecasting

The choice of demand forecasting model impacts the solution’s accuracy, efficiency, and adaptability. To choose a model, you should thoroughly evaluate data complexity, forecasting goals, and industry-specific requirements.

There are a variety of AI models designed for different tasks and data types, but three models are commonly used for demand forecasting:

• Time series models analyze and forecast data that varies over time. They allow you to identify patterns such as trends, seasonality, and cycles, which are essential for predicting future demand. Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing State Space (ETS) are well-known time series models. ARIMA is suitable for short-term predictions. It converts non-stationary data into stationary data, enabling reliable forecasting even with data volatility. In turn, ETS decomposes time series into error, trend, and seasonal components and is powerful for seasonal time series analysis.
• Regression-based models assume a linear relationship between the dependent variable and predictors, making them suitable for datasets with relatively straightforward patterns. These models can be either simple or complex. Simple models predict future values using one independent variable, while complex models use multiple independent variables to capture complicated patterns.
• Neural networks and deep learning models handle complex data patterns and long-term dependencies, capturing nonlinear relationships between variables that traditional statistical models struggle with. For example, these models are used for forecasting energy demand based on weather patterns, time of day, and historical usage data. Deep learning models allow for efficiently processing massive datasets, making them ideal for big data applications.

You can also apply ensemble forecasting. This approach combines multiple models and uses a broader knowledge base to enhance predictive accuracy, allowing for better adaptation to sudden market changes. This approach helps you effectively capture complex seasonal patterns and trends by integrating models specialized in different aspects. Moreover, it lets you reduce reliance on any single model and minimize the risk of poor performance if one model fails.

Off-the-shelf vs custom AI solutions for demand forecasting

Which should you choose?

Off-the-shelf AI solutions for demand forecasting rely on models that are usually trained on general datasets and aim to cover general use cases. So, if you need to analyze common patterns and simple processes, a ready AI-powered demand forecasting solution might work just fine. 

Custom AI solutions are trained on unique datasets and can be tuned to meet complex forecasting requirements. If you operate in volatile markets with different external factors or need to forecast tailored products or services, a custom AI solution can cope with these non-trivial tasks.

Let’s take a closer look at the key pros and cons of both types of solutions:

Table 2. Comparison of off-the-shelf and custom AI solutions for demand forecasting

Type of AI-powered demand forecasting solution	Pros	Cons
Off-the-shelf	– Ready for use without the need for preliminary training – Minimal setup and configuration – Cost-effective – Proven effectiveness – Easier to maintain	– Less adaptable; can solve general problems but is not optimized for highly specialized tasks – Risk of vendor lock-in – Delays in requested updates – Risk of security vulnerabilities – Limited scalability – Problems integrating with third-party products
Custom	– Trained on proprietary data tailored to particular industries and individual business needs – Possibility to retrain, modify, and expand a solution with new data or depending on new business needs – Fully scaled – Data is fully controlled – Protected with advanced security measures – Seamless integration with third-party products	– Higher costs for implementation – Takes time to be developed – Requires lots of quality and pre-processed data – Requires continuous refinement and maintenance – Requires technical experts and infrastructure preparation

If you choose to build a custom solution, it’s important to thoroughly prepare for development to avoid possible challenges related to the specifics of AI technology. In the next section, we discuss what you need to consider before moving to implementation and provide you with helpful tips from Apriorit specialists.

Building a custom AI solution for demand forecasting

What to consider?

To get an effective and reliable AI solution for demand forecasting, take into account the following aspects:

1. Data quality

Collecting and preparing high-quality data can be a significant challenge. Thus, you need to arrange comprehensive data collection and its preprocessing to ensure that datasets are accurate, relevant, and error-free. Poor quality data can lead to inaccurate or biased forecasts and can impact your AI solution’s performance. 

Apriorit tips: First, gather data from reliable internal and external sources to ensure comprehensiveness, then clean and structure the data to remove errors, inconsistencies, and noise. It’s also crucial to implement mechanisms to feed real-time data into your AI model to update datasets and ensure that forecasts reflect the current market situation. Moreover, applying different metrics to track data accuracy, completeness, and consistency will allow you to discover areas for improvement.

2. Data quantity

Having more quality data allows AI models to learn from a wider range of patterns and variables, improving accuracy and adaptability to market changes. 

Apriorit tips: To get a sufficient quantity of data, you need to use internal data (like historical sales data), add external data via APIs, or cooperate with your partners to leverage their data in your datasets. It’s also possible to use data augmentation techniques, synthetic data generation, and automated data collection through generative AI.

3. Regulatory compliance

While implementing your AI solution, it’s vital to adhere to principles of responsible AI, industry standards, and laws and regulations such as the EU AI Act and the GDPR to ensure data privacy and avoid legal consequences.

Apriorit tips: To be sure that your AI solution meets all compliance requirements, you can create and leverage corresponding checklists starting from the discovery phase of your project.

4. Continuous refinement

As external factors regularly affect demand, AI models must adapt to provide accurate analysis results.

Apriorit tips: Regularly update and refine your AI model with new data to identify actual trends and customer behavior patterns. It’s also crucial to monitor the AI model to evaluate its accuracy and detect anomalies.

5. Integration with existing systems

AI solutions require high computational capacity and speed to efficiently process large datasets. Outdated infrastructure might not be compatible with modern AI systems. 

Apriorit tips: You need to thoroughly assess current business processes and IT infrastructure to identify the most effective integration points. Then, plan for phased integration, ensuring that the designed solutions fit seamlessly with existing processes. 

6. AI solution scalability and performance

It’s important to ensure that your AI-powered demand forecasting solution is capable of handling increasing data volumes without a decrease in processing speed or output accuracy.

Apriorit tips: Integrating your AI solution with cloud services will ensure its automatic scaling depending on your needs. In addition, preprocessing techniques like normalization let you maintain data quality and processing speed as data volumes grow.

7. AI solution security

Protecting your AI solution and data from unauthorized access is paramount. Since the risk of cyber threats increases daily, neglecting robust security measures while implementing your AI-powered software or integrating third-party solutions may lead to data breaches, operational downtime, or fines for non-compliance.

Apriorit tips: When identifying core cybersecurity and data privacy requirements, your product should meet not only cybersecurity standards relevant for your industry and region but also AI-specific recommendations. You can start with the NIST AI Risk Management Framework. It’s also important to conduct regular security audits to detect vulnerabilities and perform security updates to guarantee your solution’s reliability.

Working with professionals in AI and ML technologies can help you overcome development challenges and avoid potential pitfalls. Find out how Apriorit experts can help you build an AI solution that fully aligns with your business needs.

How Apriorit can help

Apriorit experts provide AI development services for different industries, from healthcare to energy. If you’re thinking of creating a custom AI-based solution for demand forecasting, we can assist you with:

• Building an efficient and secure AI-powered solution following the principles of a secure SDLC
• Ensuring that the developed feature or product meets regulatory requirements
• Selecting demand forecasting models and approaches corresponding to your specific business needs
• Preparing datasets and training ML algorithms
• Integrating the delivered demand forecasting solution with your existing infrastructure
• Conducting security audits and performing comprehensive testing of your AI solution 
• Providing continuous maintenance and support of your AI solution

Our AI and ML development team will help you at every stage of the SDLC, from preparing project estimates to refining your AI model after release.

Conclusion

Businesses from various industries choose AI-powered demand forecasting for high-speed data processing and precise forecasts that account for the slightest market fluctuations or changes in customer behavior.

While off-the-shelf AI solutions are cost-effective and easier to maintain, they are also less adaptable to specialized tasks. A custom AI solution can be trained and modified according to specific business needs but requires extensive preparation and expert assistance with its development, training, fine-tuning, and maintenance.

Implementing an AI solution for demand forecasting requires niche knowledge and experience, so employing a reliable vendor is crucial for your project’s success. Apriorit specialists in AI and ML will gladly assist you in building a custom AI solution fully tailored to your unique business goals.