Why Accurate Forecasting is Becoming a Key Factor in Business Efficiency
Businesses are increasingly relying on data, and the accuracy of forecasts is becoming a competitive advantage. Errors in demand assessment lead to excess purchases, frozen working capital, overdue deliveries, logistical losses, and untimely sales. Today, these risks can be reduced with the help of modern machine learning models that analyze dozens of factors simultaneously and predict changes in demand with high accuracy. Below we'll break down how neural networks help companies of all sizes—from retail to logistics—adapt procurement strategies and plan periods of declining sales in advance.
Intelligent Forecasting Models: How They Work and Why They Are More Accurate Than Traditional Approaches
Traditional forecasting methods—moving averages or simple linear models—do not take into account the complex relationships between seasonality, customer behavior, competitor prices, and external factors. **Neural networks**, on the other hand, are able to learn from large datasets and identify nonlinear dependencies that cannot be seen manually. Recurrent architectures, transformers, and hybrid ensembles combining different types of models are particularly effective.
To predict sales and demand peaks, neural networks use a variety of input data: marketing activity, weather, price dynamics, assortment changes, sales history, macroeconomic indicators, and user behavior patterns. In practice, models form **distribution forecasts** rather than a single number—this allows for making management decisions in conditions of uncertainty.
Key Business Tasks Solved by AI-Based Forecasting
Modern forecasting is not just about building sales charts. It's a set of management mechanisms that neural networks simultaneously optimize:
- Procurement Optimization. AI calculates how much goods are really needed to reduce excess inventory and avoid shortages.
- Sales Planning. Models predict periods of declining demand and help choose the right moment to launch promotions.
In companies with a wide range of product lines, these tools form the basis for decision-making: from planograms to strategic procurement.
Examples of neural network applications in demand forecasting
To better demonstrate the capabilities of the technology, we will provide examples of AI usage in the industry:
- Retail: forecasting seasonal categories. Supermarket chains use transformers to determine demand for garden goods, beverages and festive categories, reducing dead stock by 18–25%.
- Pharmaceuticals: predicting drug needs. Models take into account the epidemiological situation, regional differences and logistics — this reduces the likelihood of shortages during peak periods.
- E-commerce: personalized sale recommendations. Neural networks calculate the probability of purchase at different discount levels, which increases conversion without aggressive discounting.
- Manufacturing: raw material planning. Algorithms optimize raw material purchase volumes, shift allocation and capacity utilization, minimizing overspending.
Real-world cases of neural network applications in demand and sales forecasting
Case #1: Pura Esencia (FMCG) — increased forecast accuracy and reduced manual analytics
→ Spanish brand Pura Esencia implemented an AI demand forecasting system to move away from manual calculations and errors in planning. The neural network solution takes into account seasonality, promotions, weather and regional features, forming short-term and long-term forecasts. The result is a 25% increase in forecast accuracy in the first week, 20% in the first month, a reduction in deviations to 1%, and a 40% reduction in analysts' workload. This allowed the company to plan purchases and sales more accurately, reduce excess inventory and logistical losses.
Case #2: Toridoll Holdings & Fujitsu - AI demand forecasting for the Marugame Udon restaurant chain
→ Toridoll Holdings Group, which manages the Marugame Udon restaurant chain, implemented Fujitsu ODMA Demand Forecasting to forecast the number of visitors and sales for each restaurant. The AI model predicts guest flow by day and hour, which allows automating staff and product order scheduling, optimizing cooking volumes and energy consumption. According to Fujitsu, the system helped reduce losses from write-offs, reduce the workload on managers, improve energy management and link demand forecasts to sustainability goals and carbon footprint reduction for more than 800 restaurants in the network.
These examples show that the main effect is a reduction in uncertainty. The more dynamic the market, the more important accurate forecasting.
How to integrate forecasting models into business processes?
A neural network is not a separate tool, but a component of the decision-making chain. To work effectively, it requires integration into procurement, warehouse accounting, sales and financial planning. The implementation process typically includes:
- Collecting and cleaning historical data;
- Creating time series and categorization;
- Choosing a model architecture: LSTM, TCN, transformers, ensembles;
- Построение системы переобучения и мониторинга качества;
- Встраивание прогнозов в цикл закупок, планирования и переговоров с поставщиками.
На этом этапе компаниям важно оценить масштаб будущей разработки и ожидаемый эффект. Чтобы сделать процесс прозрачнее, можно заранее рассчитать примерный бюджет проекта — это помогает понять, какой будет архитектура и насколько сложной окажется интеграция.
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Так внедрение начинается не с разработки, а с корректного планирования, которое снижает риски и повышает эффективность работы модели в будущем.
Почему нейросети особенно эффективны в прогнозировании распродаж?
Промо и скидочные кампании — одни из самых ресурсоёмких процессов. Ошибка приводит либо к потерям маржи, либо к дефициту и упущенной выручке. Нейросети учитывают кросс-эластичность товаров, поведение покупателей, реакцию конкурентов, историю промо, климатические факторы и локальные особенности спроса. Это позволяет формировать реалистичные прогнозы поведения клиентов при разных уровнях цен.
Также ИИ помогает определять момент начала замедления спроса, что позволяет запускать промо заранее, а не в момент, когда показатели уже упали. Такой подход делает управление распродажами гибким и предсказуемым.
📌FAQ: частые вопросы касательно прогнозирования спроса и распродаж с помощью ИИ
Вопрос: Какие данные нужны для обучения модели прогнозирования спроса?
Ответ: Обычно используются данные о продажах, остатках, промо-активностях, сезонности, ценах, региональных особенностях и маркетинговых воздействиях. Чем богаче датасет, тем стабильнее прогноз.
Question: Are neural networks suitable for products with low turnover?
Answer: Yes. Models with boosting, knowledge transfer, or category aggregation are used for rare sales, which increases accuracy.
Question: How much more accurate is AI compared to traditional models?
Answer: In dynamic industries, the accuracy increase reaches 15–40%, especially for a wide range of products.
Question: Can one model be used for all product categories?
Answer: Usually no. Each product group has its own demand patterns, so segmentation or multi-modal ensembles are used.
Question: Do we need to change procurement processes before implementing AI?
Answer: No. Companies start with pilot categories and gradually expand integration.
Question: Is it worth developing a custom model if there are ready-made SaaS solutions?
Answer: SaaS is suitable for standard scenarios, but for unique business processes, a custom model provides more accuracy and flexibility.