Introduction
In the dynamic world of e-commerce, effective demand forecasting and inventory management are key to the success of any ecommerce business. Proper planning allows managing inventory at the right level across multiple warehouses and warehouse shelves, which translates into customer satisfaction and optimization of costs related to storage, stock management, and order fulfillment.
Companies operating in electronic commerce face challenges such as changing sales trends, increasing customer expectations for faster delivery, and the need for efficient organization of production, warehouse operations, and supply chain processes. Excess inventory represents unnecessary costs, while shortages lead to lost transactions and weakened market position.
In this article, we will present how modern technologies and systems, such as WMS (Warehouse Management Systems) and enterprise resource planning (ERP) software, can optimize inventory management and demand forecasting, saving time and financial resources and increasing the efficiency of the entire fulfillment process in an online store.
Why accurate demand forecasting is crucial?
Accurate demand forecasting is the foundation of effective e-commerce management and stock management. Precise forecasts allow adjusting production scale and inventory needs to actual customer demand, leading to cost minimization and product quality improvement. The better you can predict what, when, and in what quantities your customers buy products online, the more effectively you can control costs and improve cash flow. Data analysis plays a key role in demand forecasting, providing the basis for assessing market trends, seasonality, and inventory accounting.
Consequences of lacking precise forecasts:
- Storage costs: Excess products involve storage fees and the risk of product expiration, which affect inventory control and cash flow.
- Revenue losses: Lack of key products in stock results in losing customers to competitors, whether they shop on e commerce websites or brick and mortar stores.
- Non-moving products: Items lingering in warehouses block space and freeze capital, reducing the company's ability to fulfill orders efficiently.
- Poor purchasing decisions: Without proper analytics and resource allocation, purchases often rely on intuition, leading to losses.
The result is not only financial burden but also a lack of flexibility in responding to changing customer preferences and market conditions.
What data shoud be analyzed? Market Trend Analysis
To effectively forecast demand based on historical data, it is essential to collect and analyze key inventory data. Here is what to pay attention to:
- Sales data: Analyzing sales history, including seasonality and days sales, helps predict future trends. It is important to collect and analyze data separately for all markets, sales channels, and online platforms to properly assess change dynamics.
- Market trends: Information about consumer behavior and changes in their preferences affecting the product lifecycle allows not only better assessment of future demand but also better offer adjustment. It is worth analyzing data from various business areas, including social commerce platforms and m commerce, to get a fuller market picture and more effectively identify development opportunities.
- Return information: Understanding which products are most frequently returned and why helps grasp the impact on inventory levels while enabling improvements to avoid unwanted costs.
Data Integration into a Single Source of Truth
Collecting data from various sources (e.g., sales, inventory levels, returns) and centralizing it in a data warehouse enables comprehensive analysis. Standardizing data, e.g., unifying product identifiers across platforms (Allegro, Amazon, own store), ensures consistency and improves decision-making efficiency. Analysis of user and potential customer data allows better understanding of their needs and adjusting the offer to market expectations. It is also important to consider the time users need to make purchase decisions, enabling the design of more effective marketing and sales strategies.
Key performance indicators in e-commerce
In the e-commerce industry, monitoring key performance indicators (KPIs) is the basis for effective management and making sound business decisions. The most important indicators include: average order value, number of orders in a given period, order fulfillment time, customer acquisition cost, and total e commerce sales value. Analyzing this data allows not only assessing the effectiveness of marketing and sales activities but also quickly reacting to changing sales trends.
Increasingly important are also qualitative data, such as customer opinions and reviews collected via social media, which provide valuable information about their needs and expectations. Thanks to integrating this data with analytical systems, companies can better segment audiences, personalize offers, and plan marketing activities more effectively. As a result, monitoring key performance indicators in e-commerce translates into increased customer satisfaction, cost optimization, and higher total sales value.
Nowoczesne ilościowe metody prognozowania popytu
Predictive analytics and other technologies are revolutionizing inventory management, and quantitative demand forecasting methods, which use mathematical and statistical techniques such as regression analysis and moving averages, play a key role in accurately predicting market needs. Here are the most important methods:
- Seasonality and cyclical pattern analysis: Allows understanding how sales change during specific times of the year. It is an essential part of exploratory data collection and analysis, preceding the construction of machine learning models.
- Predictive models:
- ARIMA: A simple statistical model used for time series forecasting, effective for problems with low complexity.
- Neural networks: Effective in processing large and complex datasets. Models dedicated to time series forecasting, such as the Temporal Fusion Transformer, efficiently handle problems involving multiple variables.
- XGBoost: A popular machine learning model derived from decision tree families. Although not natively designed for time series analysis, with proper data preparation and labeling, it achieves high accuracy in such tasks. Additionally, it performs well even with limited data availability. Due to its lower complexity compared to neural networks, its implementation is significantly cheaper and more accessible.
- BI tools (Tableau, Power BI): Enable data visualization and consumption, facilitating quick decision-making. Increasingly, artificial intelligence-based solutions are also used in demand forecasting processes, automating and optimizing forecasting and inventory planning.
Systems analyze historical data and then pass forecasting results to other business decision support tools, such as ERP systems or order management platforms.
Compared to traditional methods (e.g., Excel, intuition), advanced tools like machine learning are more precise, allow faster operation, and save time through process automation.
Effective inventory management strategies
Forecasting is only half the success – implementing appropriate inventory management methods and strategies is equally important.
An automated order recommendation process can be based on precise demand predictions, knowledge of current inventory levels, lead times, and established safety stock levels. Demand planning allows anticipating future product needs, while current inventory and lead times enable determining when replenishment is necessary. Introducing safety stock protects against unforeseen demand fluctuations or delivery delays. Additionally, constant monitoring of raw materials and finished goods in the warehouse is crucial to ensure production continuity and ongoing customer service. Based on this data, the inventory system can generate automatic recommendations indicating the optimal time to place an order and the suggested quantity, minimizing the risk of stockouts while optimizing storage costs.
Challenges associated with forecasting and how to overcome them
Demand forecasting and inventory management are processes fraught with many challenges, including the need to consider the historical course of the process for effective demand prediction:
- Seasonality and trend changes: Cyclical fluctuations introduce variability, requiring consideration of periodic patterns and appropriate data preparation for predictive models.
- Multiple markets and sales channels: A large number of data sources require carefully designed information flow processes, which are key to precise predictions at each active market level. This ensures effective product availability in every location, whether through brick and mortar stores or e commerce websites.
- Data gaps due to product unavailability in historical sales: Missing sales data caused by product unavailability prevent accurate determination of historical demand. This complicates data preparation and requires filling gaps through missing value modeling.
- Data incompleteness and lack of standardization: Information scattered across various systems and lack of integration into a central repository significantly hinder predictive analysis. Centralizing information through building data warehouses not only improves demand forecasting quality and reduces implementation costs but also ensures flexibility for further data solution expansion as needs grow.
Example of applying modern strategies
Our client, a medium-sized e-commerce platform selling casual and sportswear, struggled with excess seasonal inventory and insufficient stock of bestsellers. The problem was a lack of precise demand forecasting and manual inventory management. Analyzing sales values over specific time periods was key to better demand prediction. Data was centralized through integration in a BigQuery data warehouse, and an XGBoost predictive model was implemented. Thanks to this, the company gained a full view of operations and the ability to dynamically manage orders.
Implementation results
- Reduction of warehouse losses: Within the first six months, the company reduced the value of excess seasonal inventory by 40%, limiting the need for clearance sales with reduced margins.
- Better availability of key products: More precise forecasting increased bestseller availability by 25%, directly impacting revenue growth.
- Precise demand forecasting enabled more effective acquisition of new customers through better offer matching to their expectations and purchase behavior analysis.
- Increased operational efficiency: Automation of ordering processes and dynamic setting of reorder points allowed our client’s team to focus on strategic tasks instead of crisis management.
The future of e-commerce
The future of the e-commerce sector looks extremely dynamic and innovative. With the development of modern technologies such as artificial intelligence and process automation, companies in the electronic commerce sector gain new opportunities to optimize activities and build competitive advantage. Artificial intelligence supports not only demand forecasting and inventory management but also offer personalization, customer behavior analysis, and service automation – for example, through chatbots that provide quick answers to questions and assist in order fulfillment.
In the coming years, particular attention should be paid to growing customer expectations regarding service quality, delivery speed, and transparency of purchasing processes. Companies that effectively implement modern technologies and continuously analyze their customers’ needs will gain customer loyalty and strengthen their market position. The future of e-commerce is not only technological development but above all a focus on the customer and continuous improvement of the online shopping experience.
Summary
Demand forecasting and effective inventory management are today a necessity, not a luxury. Thanks to advanced analytical tools, predictive models, and well-integrated data, companies can operate precisely, reduce costs, and increase revenues.
If your company faces challenges related to inventory, contact us at Alterdata. We will help you achieve new efficiency standards using our knowledge and experience.
