Imagine that you manage a large e-commerce platform. Your warehouses are full, but your goods are not rotating as they should. At the same time, some key products are in short supply, leading you to lose customers to your competitors. Sound familiar? That's the challenge most e-commerce companies face, where the key problem is effectively forecasting demand and managing inventory in the retail industry to ensure adequate merchandise availability and customer satisfaction.
Today's e-commerce is not just about sales, it is also a race against time, costs and rising customer expectations. Excess inventory generates storage costs, and lack of inventory generates revenue and reputation losses.
In this article you will learn how to effectively forecast demand, optimize inventory management using modern technologies that allow you to win in this dynamic market.
Why is accurate demand forecasting crucial?
Accurate demand forecasting is the foundation of effective e-commerce management. Accurate forecasts allow you to adjust the scale of production to actual demand, which leads to minimized costs and improved product quality. The more accurately you can predict what, when and in what quantities your customers will buy, the more effectively you can control costs and improve your bottom line.
Consequences of not making accurate forecasts:
- Warehousing costs: Excess products are associated with storage fees and the risk of out-of-date goods.
- Revenue losses: Lack of key products in stock results in loss of customers.
- Non-rotating products: Backlogged goods block space and freeze capital.
- Poor purchasing decisions: Without proper analytics, purchases are often based on intuition, leading to losses.
The result is not only a financial burden, but also a lack of flexibility in responding to changing customer needs.
What data is worth analyzing? Analysis of market trends
To effectively forecast demand based on historical data, it is essential to collect and analyze key data. Here's what to pay attention to:
- Dane sprzedażowe: Analiza historii sprzedaży, uwzględniając sezonowość, pomaga przewidzieć przyszłe trendy. Ważne jest, aby dane zbierać i analizować z rozróżnieniem na wszystkie rynki i kanały sprzedaży, aby móc właściwie oceniać dynamikę zmian.
- Trendy rynkowe: Informacje o zachowaniach konsumentów i zmianach w ich preferencjach wpływające na cykl życia produktu pozwalają nie tylko lepiej oceniać przyszłe zapotrzebowanie, ale też pozwalają na lepsze dostosowanie oferty
- Informacje o zwrotach: Zrozumienie, które produkty są najczęściej zwracane i dlaczego, pomaga zrozumieć wpływ na stany magazynowe, jendocześnie pozwalając wprowadzać usprawnienia, pozwalające uniknąć niechcianych kosztów
Integrating data into a single source of truth
Collecting data from various sources (e.g., sales, inventory, returns) and centralizing it in a data warehouse enables comprehensive analysis. Data standardization, e.g. unification of product identifiers between platforms (Allegro, Amazon, own store), ensures consistency and improves the efficiency of decision-making processes.
Modern quantitative methods of demand forecasting
Predictive technologies are revolutionizing inventory management, and quantitative demand forecasting methods, using mathematical and statistical methods such as the moving average, are playing a key role in accurately predicting market needs. Here are the most important methods:
- Analysis of seasonality and cyclical patterns: Provides an understanding of how sales change during specific periods of the year. Is an essential component of exploratory data analysis prior to building machine learning models
- Predictive models
- ARIMA: A simple statistical model for time series prediction that works well for low-complexity problems.
- Neural networks: Effective for processing large and complex data sets. Models dedicated to time series prediction, such as the Temporal Fusion Transformer, can effectively solve problems involving multiple variables.
- XGBoost: A popular model in machine learning, derived from the decision tree family. Although it is not natively adapted for time series analysis, with proper preparation and data labeling it achieves very high performance in this type of task as well. In addition, it performs well even with a limited amount of available data. Thanks to its lower complexity compared to neural networks, its implementation is much cheaper and more affordable.
- BI tools (Tableau, Power BI): Enable visualization and consumption of data to facilitate quick decisions.i.
Compared to traditional methods (e.g. Excel, intuition), advanced tools such as Machine Learning are more precise, allow you to act faster and save time by automating the process.
Strategies for effective inventory management
Forecasting is only half the battle - implementing appropriate inventory management strategies is equally important.
An automated order recommendation process can be based on accurate demand predictions, knowledge of current inventory levels, lead time and established safety stock levels. Demand forecasting can predict future product demand, while current inventory and lead time can determine when replenishment is needed. The introduction of safety stock protects against unforeseen fluctuations in demand or delivery delays. Based on this data, the system can generate automatic recommendations, indicating the optimal moment for placing an order and the suggested number of items, which minimizes the risk of stock shortages while optimizing warehousing costs.
Forecasting challenges and how to overcome them
Demand forecasting and inventory management is a process fraught with many challenges, including the need to take into account the existing process for effective demand forecasting:
- Seasonality and changes in trends: Cyclical fluctuations introduce volatility that requires consideration of periodic patterns and appropriate data preparation for predictive models.
- Multiplicity of markets and sales channels: The large number of data sources requires carefully designed information flow processes, which are crucial for accurate predictions at the level of each active market. This can effectively ensure the availability of goods at each location.
- Merchandise availability gaps in historical data: Gaps in sales data due to commodity unavailability make it impossible to accurately determine historical demand. This makes data preparation difficult and requires filling in gaps by modeling missing values.
- Lack of data completeness and standardization: The scattering of information across different systems and the lack of integration in a central repository significantly hinders predictive analysis. Centralizing information by building a data warehouse not only improves the quality of demand forecasting and reduces implementation costs, but also provides flexibility to further expand data solutions as needs grow.
An example of applying modern strategies
Our client, a mid-sized e-commerce platform selling casual and sportswear, was struggling with excess seasonal inventory and insufficient stocking of bestsellers. The problem was a lack of accurate demand forecasting and manual inventory management. Analyzing the value of sales over specific time periods was key to better demand forecasting. Data was centralized through integration in the BigQuery data warehouse and the XGboost predictive model was implemented. This provided the company with a complete picture of its business and the ability to dynamically manage orders.
Results of implementation
- Reduced inventory losses: In the first six months, the company reduced the value of backlogged seasonal inventory by 40%, reducing the need to sell out at reduced margins.
- Improved availability of key products: More accurate forecasting increased the availability of bestsellers by 25%, which directly impacted revenue growth.
- Increased operational efficiency: Automating ordering processes and dynamically setting reorder points, allowed our client's team to focus on strategic tasks instead of crisis management.
Summary
Demand forecasting and effective inventory management are a necessity today, not a luxury. With advanced analytical tools, predictive models and well-integrated data, companies can operate accurately, reduce costs and increase revenues.
If your company is struggling with inventory challenges, contact Alterdata. We'll help you achieve new standards of efficiency using our expertise and experience.
