COVID-19, global supply chain disruptions, soaring raw material prices, and rapidly changing consumer patterns—over the past few years, businesses have faced an environment more unpredictable than ever before. According to Capgemini research, 72% of manufacturers have experienced inventory losses, a problem that has gone beyond simple costs to become critical for business survival.
Traditional demand forecasting methods have reached their limits. The era of relying on Excel spreadsheets and past experience is over. Now, AI-based demand forecasting solutions that can simultaneously consider hundreds of variables and respond instantly to real-time market changes have become essential.
In this article, we will explore how AI demand forecasting solutions can be utilized in various industries, and what specific methodologies and approaches are possible from a practical perspective. Our goal is to provide strategic insights that can be directly applied to real-world business scenarios, not just theoretical concepts, to help you innovate your business.
The key to AI demand forecasting in manufacturing is not a single model but a multi-layered forecasting system. You need to build an integrated system that independently models raw material supply, intermediate product output, and finished product demand, while also considering their interrelationships.
For industries with complex supply chains, such as semiconductors or automotive parts, it's crucial to analyze the production plans of Tier 1 and Tier 2 suppliers along with the new product launch schedules of key customers. An AI system can synthesize this multi-dimensional data to forecast demand 6-12 months in advance.
AI demand forecasting for manufacturing can be used as a tool to gain visibility into the entire supply chain, not just to predict sales volumes. It allows for the dynamic adjustment of production plans by monitoring real-time fluctuations in the prices of key raw materials, the availability of substitutes, logistics conditions, and geopolitical risks.
For example, you can preemptively predict the impact of external factors like changes in China's environmental policies or the US's tariff policies on raw material supply and establish alternative procurement plans. This signifies an evolution beyond simple demand forecasting to an integrated platform that supports risk management and strategic decision-making.
If a manufacturer manages hundreds or thousands of SKUs, it needs individual forecasting models tailored to each product's lifecycle and characteristics. New products should use models that have learned initial sales patterns of similar products, mature products should use models focused on seasonality and substitute effects, and products scheduled for discontinuation should use models focused on optimizing inventory clearance.
AI systems can automatically classify product characteristics and use a meta-learning approach to select the most suitable forecasting algorithm. This helps increase overall forecasting accuracy while reducing management complexity.
Modern retailers operate across various channels, including brick-and-mortar stores, online shops, mobile apps, and social commerce. Since each channel has different customer demographics and purchasing patterns, it is important to perform both channel-specific and integrated forecasting simultaneously.
An AI demand forecasting system can comprehensively consider customer movement patterns between channels, the effects of channel-specific promotions, and inventory movement optimization. For example, it can learn customer behavior patterns, like picking up out-of-stock online items at a physical store, to optimize overall inventory allocation.
A key differentiator for AI demand forecasting in the retail industry is its ability to reflect social media trends and the effects of influencer marketing in real-time. By analyzing the volume of social media mentions for a specific product or brand, influencer collaboration schedules, and the spread of viral content, you can preemptively forecast a surge in demand.
This is particularly effective for fashion, beauty, and lifestyle products. An AI system can detect changes in product demand related to hashtag trends, celebrity-worn items, and TikTok challenges 3-4 weeks in advance, and use this information to prepare inventory and develop marketing strategies.
Linking AI demand forecasting with dynamic pricing policies can maximize profitability. By analyzing a combination of the predicted demand curve, inventory levels, and competitor pricing trends, the system can suggest the optimal price point in real-time.
For seasonal products, it can automatically suggest strategies to secure profitability with higher prices at the beginning of the season and discounts for inventory clearance at the end. AI can also recommend strategic price adjustments in real-time to respond to competitor promotions or new product launches.
tIn the healthcare industry, AI demand forecasting requires a customized approach that reflects the characteristics of each medical department. Internal medicine must consider seasonal infectious diseases and chronic illness management patterns, surgery must consider the ratio of emergency to scheduled surgeries, and pediatrics must consider school schedules and the cycles of epidemic diseases.
AI systems can use various external data sources, such as weather data, epidemic outbreaks, local event schedules, and school reopening information, to forecast patient demand for each department. This allows for efficient planning of medical staff assignments, equipment usage, and hospital bed management.
In hospitals and pharmacies, managing the expiration dates of pharmaceuticals is crucial. An AI demand forecasting system can optimize inventory turnover by learning drug usage patterns, seasonality, and prescribing preferences of individual doctors.
For expensive specialty drugs or biological products requiring refrigeration, accurate demand forecasting can minimize disposal losses while ensuring patients receive their treatments without interruption. AI can also dynamically adjust safety stock levels to prepare for emergencies.
AI demand forecasting can also be used to develop new healthcare services. By analyzing regional disease patterns, population aging trends, and the increase in demand for health screenings, you can evaluate the market potential of new medical services or wellness programs.
For example, if the system detects a persistent increase in diabetes patients in a specific area, it can prompt the consideration of a specialized diabetes clinic or nutritional counseling service. This is a strategic approach that improves both the quality of medical services and profitability.
The key to AI demand forecasting in the construction industry is accurate prediction of raw material price fluctuations. The prices of major construction materials like steel, cement, and wood are influenced by a complex set of factors, including global economic conditions, policy changes in key producing countries, and international logistics costs.
An AI system can monitor real-time data on China's real estate policies, the US's infrastructure investment plans, Australia's iron ore production, and Brazil's raw material export policies to predict price changes 3-6 months in advance. This allows construction companies to proactively purchase or hedge before prices rise.
Large construction projects span several years, with the types and quantities of materials needed varying at each stage. An AI demand forecasting system can learn from the material usage patterns of similar past projects to accurately forecast the material requirements for the current project at each phase.
Crucially, it can adjust material delivery dates by considering how factors like weather conditions, labor availability, and the progress of nearby projects affect construction speed. This helps reduce storage and financing costs from excessive on-site inventory while preventing project delays.
By using AI demand forecasting, you can anticipate changes in regional construction demand. By comprehensively analyzing information such as population migration patterns, regional development plans, transportation infrastructure expansions, and the creation of industrial complexes, you can forecast regional construction demand for the next 2-3 years.
This provides critical insights for a construction company's expansion strategy. It enables data-driven strategic decisions on where to open new offices, what types of projects to focus on, and when to expand equipment and personnel.
The greatest value of AI demand forecasting in the food industry is its ability to quickly capture changing consumer trends. It can detect various trends early on through social media data and online search trends, such as increased interest in health, the vegan trend, preference for protein-fortified foods, and demand for eco-friendly packaging.
An AI system can learn the correlation between changes in keyword mentions (like "protein," "vegan," "gluten-free," and "organic") and actual product sales to evaluate the potential success of new product categories in advance. This helps increase the success rate of new product development investments and optimize market entry timing.
The food industry experiences significant demand fluctuations due to seasonality and various events. An AI demand forecasting system can accurately predict short-term demand surges by considering not only past data but also weather forecasts, holiday schedules, sports events, and cultural events.
For example, it can predict surges in demand for chicken and beer during the World Cup, increased sales of ice cream and beverages during heatwaves, or the demand for gift-packaged food before holidays like Chuseok, allowing for the adjustment of production and distribution volumes. This helps minimize missed opportunities and reduce disposal losses from overproduction.
Due to the nature of the food industry, managing expiration dates is a critical issue. An AI demand forecasting system can link each product's expiration date with its expected sales velocity to create an optimal production schedule and distribution plan.
For fresh or frozen foods, a complex optimization that considers temperature control costs and expiration dates is necessary. AI can comprehensively analyze inventory turnover, the effect of discounts, and disposal costs for each distribution channel to suggest a profit-maximizing inventory management strategy.
The ImpactiveAI Deepflow solution can bring these diverse industry-specific strategies to life. As a 5-year-old AI company specializing in demand and price forecasting, ImpactiveAI possesses 224 advanced machine learning and deep learning models and has filed 46 patents related to demand forecasting to date.
Deepflow's core strength lies in providing customized prediction models that reflect the unique characteristics of each industry. Deepflow Forecast specializes in product demand forecasting, utilizing transformer-based time series prediction models like I-transformer and TFT, as well as various other algorithms like GRU, TCN, and LSTM. It forecasts product sales or shipment volumes for the next 6-12 months and offers customized models for each SKU's sales/shipment patterns, along with inventory depletion date management by linking with base inventory.
Deepflow Material is specialized in raw material price forecasting, supporting purchasing decisions. Through AI-based analysis of various economic indicators and variables, it can be applied to a wide range of items, including minerals, agricultural and fishery products, and construction materials. It provides accurate price predictions by collecting and analyzing global market data in real-time.
Actual performance indicators prove its effectiveness. It has achieved up to 98.6% accuracy in raw material price forecasting, and its clients have experienced an average 33.4% reduction in inventory over/understocking. One client was able to reduce monthly inventory costs by up to 24.8 billion KRW, and work productivity improved by an average of 99%.
AI-based demand forecasting solutions are more than just tools for reducing inventory and cutting costs; they are becoming a strategic weapon for creating new business opportunities and securing a competitive advantage. Proactive decision-making based on accurate demand forecasting enables quick responses to market changes, enhances customer satisfaction, and fosters sustainable growth.
While each industry has its own unique characteristics and challenges, the essence of AI demand forecasting lies in integrating diverse data sources, incorporating industry-specific expertise into models, and continuously improving prediction accuracy through ongoing learning and refinement. In an era of accelerating change, AI-based demand forecasting is no longer an option—it is a necessity.
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