82% of companies worldwide experience serious financial losses due to inaccurate sales volume forecasting processes. According to a 2023 report by global consulting firm McKinsey, every 10% increase in sales volume forecasting error is associated with an average 3.2% decrease in operating profit for companies.
In particular, in the manufacturing industry, it was found that the cost of excess inventory due to inaccurate forecasting amounts to 12-15% of annual sales.
Despite the importance of sales volume forecasting being emphasized like this, many companies still fail to build effective forecasting systems.
According to a recent survey by Forbes, 67% of global companies still use a spreadsheet-based, manual forecasting method, and the cost of lost opportunities is estimated to be in the hundreds of billions of dollars per year.
The fundamental cause of companies' sales volume forecasts failing begins with the phenomenon of data fragmentation. Sales data, inventory data, and marketing data within the company were managed in different systems, making it difficult to conduct integrated analysis.
In fact, according to Gartner's research, companies are using an average of six or more different data sources, and more than 40% of them are not properly integrated.
This difficulty in integrating data creates organizational challenges that go beyond technical issues. The format and quality of the data held by each department are different, and consistent data management is not possible due to the lack of a data governance system.
In particular, linking with external data, which is essential for predicting sales volume, is an even more difficult task. In a situation where various external variables such as market trends, consumer behavior patterns, and competitor information change in real time, it is a reality that these cannot be effectively reflected in the prediction model.
In addition, existing static predictive models are very poor at responding to rapid changes in the market or exceptional situations. Traditional time series analysis or statistical forecasting methods are based on the assumption that patterns in historical data will repeat similarly in the future.
However, in the modern business environment, this assumption is no longer valid. There are constantly unpredictable variables such as global crises like COVID-19, rapid changes in consumer preferences, and the emergence of new technologies.
To overcome these limitations, a fundamental review of existing forecasting methods is required. It is time to move beyond simply analyzing historical data and transition to real-time data processing and dynamic forecasting models using AI technology.
Modern sales volume forecasting models are evolving to a new level through the combination of data and AI technology. Moving away from the conventional simple statistical-based forecasting, hybrid models using machine learning and deep learning are at the center.
This hybrid approach combines the stability of time series analysis with the pattern recognition capabilities of machine learning to increase the accuracy of forecasting while also presenting forecasting uncertainty.
The core of the modern sales volume forecasting model lies in the integrated analysis of multidimensional data. Data from various sources, including internal sales data, customer behavior data, and market environment data, is collected and processed in real time.
In this process, the data lake architecture plays an important role. The data lake can store and process structured, semi-structured, and unstructured data, allowing for the integrated management of various types of data.
Of particular note is the automated feedback loop of the predictive model. A system is built to continuously monitor the difference between the predicted value and the actual value and automatically reflect this in the improvement of the model.
This self-learning capability enables the model to automatically adapt to changes in the market environment and improves the predictive accuracy over time.
In addition, the introduction of explainable AI (XAI) technology has made it possible to interpret the results of the prediction. This means that it is possible to present not only the prediction value, but also the cause and main influencing factors of the prediction.
This allows business decision-makers to use the forecast results more reliably.
Another feature of the modern sales volume forecasting model is the ability to make scenario-based forecasts. Forecast values for multiple scenarios can be derived simultaneously, assuming various business situations and market conditions. This is a great help for companies to develop various strategies for an uncertain future.
In modern sales volume prediction models, real-time data integration is the most critical element.
This means that the entire process, from data quality control to pre-processing, integration, and analysis, must be carried out in real time, rather than simply collecting data. This requires the establishment of a systematic data pipeline.
The data pipeline is largely composed of four stages: collection, pre-processing, integration, and analysis. In the collection stage, external data such as market trends, social media, and weather information are collected in real time, along with internal data such as point-of-sale (POS) information, ERP systems, and CRM data.
It is important to set the appropriate collection cycle and method for each data source.
The pre-processing stage ensures the quality of the collected data. Operations such as missing value handling, outlier detection, and data normalization are performed through an automated pipeline.
In particular, since real-time data may contain a lot of noise, it is essential to apply advanced filtering techniques. This is where the process of effectively separating signals from noise and extracting only meaningful data takes place.
In the integration phase, data with different formats and structures are converted and integrated into a consistent format. Data lakes and data warehouses are effectively used in this process.
For real-time data, a structure that can integrate and analyze data without delay using streaming processing technology must be in place.
Finally, in the analysis stage, real-time predictions are made based on the integrated data. The important thing here is that the predictive model has a structure that allows it to continuously learn and adapt to new data.
It is also important to efficiently manage the computational load that occurs during this process by using an online learning algorithm to ensure that the model is updated in real time.
This real-time data integration system not only greatly improves the accuracy of sales volume forecasts, but also enables immediate response to market changes. This is becoming a key factor in determining a company's competitiveness, especially in a rapidly changing market environment.
A sales volume forecasting model for the manufacturing industry requires an integrated approach that encompasses the entire supply chain. It needs a forecasting model that takes into account the lead time of the entire process from raw material procurement to production, distribution, and final sales, as well as the volatility of each stage.
In particular, a forecasting model for the manufacturing industry must simultaneously consider the constraints of both supply and demand.
On the demand side, the order patterns of the market are analyzed in detail. Different forecasting models are applied to each type of order, such as regular orders, project-based orders, and urgent orders, and forecasting cycles and methodologies are set according to the characteristics of each.
For regular orders, the time series analysis is based on the customer's production plan and inventory policy. Project-based orders take into account the progress of ongoing projects along with the pattern analysis of similar projects in the past.
On the supply side, production capacity and raw material supply and demand are reflected in the prediction model. In addition to internal production data such as facility utilization rate, production efficiency, and quality indicators, market trends and supplier conditions of major raw materials are monitored in real time.
In particular, for core raw materials, global supply chain risks are continuously assessed and reflected in the prediction model.
Another feature of the manufacturing-specific forecasting model is its hierarchical forecasting structure. Starting with the prediction of the finished product level, it is broken down step by step to the level of parts and raw materials.
In this process, the Bill of Materials (BOM) is used to accurately reflect the relationship between each layer and to derive optimized forecast values that take into account inventory policies and production schedules.
In addition, sales volume forecasts in the manufacturing industry are directly linked to production plans. The forecasting model has a circular structure in which the optimal production schedule is established based on the forecasting results, which are then used as input values for the forecasting model.
In this process, various production site variables such as production capacity constraints, facility maintenance schedules, and worker availability must be considered.
Forecasting sales in the retail industry requires a granular approach by product, store, and time of day. In particular, in the modern retail industry, the key to responding to the omnichannel environment is to comprehensively analyze online and offline sales data.
To do this, it is essential to design a forecasting model that takes into account the characteristics of each channel.
In the case of offline stores, the characteristics of each store become an important variable in the prediction model. Stores are clustered based on their location, size, characteristics of the surrounding commercial district, and the purchasing patterns of the main customer base, and an optimized prediction model is applied to each cluster.
In addition, micro factors such as the location of products in the store, the status of promotions, and the impact of competing stores are also reflected in the prediction model.
In online channels, digital behavioral data such as website traffic, search keyword trends, and shopping cart data are important predictors. Real-time user behavior analysis is used to detect immediate changes in demand and reflect them in inventory management.
In particular, algorithms that detect anomalies early are essential because sudden increases in demand can occur due to the nature of online shopping.
Another feature of the retail industry forecasting model is its differentiated approach based on the product life cycle.
For new products, the forecasting model is built using initial sales data and sales patterns of similar products. For mature products, the model is based on stable time series patterns, but takes into account promotional and seasonal effects. For products in the end-of-life stage, a special forecasting logic is applied to account for inventory depletion.
The influence of external factors such as weather, events, and promotions is also an important consideration in the retail forecasting model. In particular, for fresh food and seasonal products, the impact of these external factors on sales volume is very significant.
Therefore, a structure is needed that can quantify the impact of each factor and dynamically reflect it in the prediction model.
Finally, sales volume prediction in the retail industry is directly linked to inventory optimization. Based on the prediction results, the appropriate inventory level is determined, which is then used to determine the timing and quantity of orders.
In this process, various operational constraints, such as the storage capacity of the distribution center, delivery lead time, and minimum order unit, must be considered.
Service industry sales volume prediction requires an approach that takes into account the characteristics of intangible goods.
In consideration of these characteristics, the service industry sales volume prediction model is built as a dynamic forecasting system linked to real-time pricing policies.
In particular, it is important to analyze the patterns of reservation data, and to precisely analyze the characteristics and cancellation rates for each lead time from the time of reservation to the time of actual use. In addition, demand patterns vary greatly by season, day of the week, and time of day, so it is important to effectively reflect these temporal characteristics in the prediction model.
Performance evaluation of sales volume prediction models should consider a balance between technical accuracy and business performance. Technical evaluation indicators such as the mean absolute percentage error (MAPE) and the root mean square error (RMSE) are used, but these indicators alone are not sufficient to measure the practical value from a business perspective.
For effective performance evaluation, the output values of the predictive model must be directly linked to business KPIs. Actual business indicators such as inventory turnover, inventory holding costs, opportunity loss costs, and operational efficiency must be monitored along with the accuracy of the prediction.
For example, it is important to find the optimal balance by quantifying the inventory costs caused by over-prediction and the opportunity loss costs caused by under-prediction.
In addition, the performance of the predictive model should be evaluated by differentiating the prediction intervals. Different evaluation criteria should be applied to short-term predictions (1-4 weeks) and mid- to long-term predictions (1-12 months), and the acceptable error range should be set for each interval.
In this case, it is important to set realistic target values by considering the characteristics of each industry and the business environment.
Performance evaluation of predictive models should be conducted through a regular review process. Weekly, monthly, and quarterly reviews of the accuracy of predictions and business KPIs are conducted, and the causes of poor performance and improvement measures are identified.
Close collaboration between the business department and the data analysis team is essential in this process.
Recently, explainability of predictive models has emerged as an important evaluation factor. The basis for the derived forecast value and the main influencing factors must be clearly explained, which is a key factor in gaining the trust of business decision-makers.
The sales volume forecasting model should be a continuously evolving system, not a static system. Continuous learning and optimization of the model should be carried out at three levels: data level, algorithm level, and system level.
Data-level optimization is a process of continuously improving the quality and diversity of input data. This includes discovering new data sources and improving the quality of existing data.
In particular, timeliness of data is important, and the key is to optimize the collection cycle of real-time data and minimize the delay in the data processing process. In addition, the impact of data noise on the prediction results should be reduced by upgrading the outlier detection and processing logic.
Algorithmic-level optimization is a process of improving the model's learning method and prediction logic. By introducing an online learning method, the model is automatically trained whenever new data is obtained, and in this process, sophisticated validation logic is required to prevent overfitting.
In addition, it is also effective to combine the advantages of multiple models using ensemble techniques and dynamically adjust the weight of each model.
System-level optimization means improving the operating environment of the predictive model. By establishing an MLOps framework, the deployment, monitoring, and retraining of the model are carried out as an automated pipeline.
It is important to build a system that can detect and automatically respond to a decline in model performance at an early stage. To do this, you need features such as model drift monitoring, A/B test automation, and version management.
The computational efficiency of the model is also an important optimization target. In particular, when real-time forecasting is required, it is essential to reduce the model's weight and improve the inference speed. To this end, techniques such as model compression, quantization, and pruning are applied, and the model is optimized to run in an edge computing environment as needed.
Sales volume prediction models are evolving in a direction that is becoming more sophisticated and intelligent with the advancement of technology. In particular, the introduction of federated learning has made collaborative learning possible without the need for data sharing between companies.
This is an innovative method that can increase the performance of predictive models while protecting the data privacy of individual companies.
In addition, the evolution of artificial intelligence technology is accelerating the evolution into an autonomous decision-making system. Predictive models are evolving beyond simply predicting future sales volume to become comprehensive decision-making support systems that automatically determine optimal inventory levels and ordering times, and even suggest pricing policies.
The development of metaverse and digital twin technologies is expected to add a new dimension to sales volume forecasting. By simulating various scenarios in a virtual environment, insights gained from the simulations can be reflected in the actual forecasting model. This will be of great value, especially in situations with high levels of uncertainty, such as new product launches or entering new markets.
Environmental factors and sustainability will also be important considerations for future forecasting models. By reflecting environmental impacts such as carbon emissions, energy consumption, and waste generation in forecasting models, it will be possible to make decisions linked to ESG management.
Strengthening the organization's digital capabilities is essential for the successful establishment and operation of next-generation sales volume forecasting models. In addition to recruiting and training data scientists and ML engineers, improving the data literacy of frontline practitioners is also an important task.
In particular, the cultivation of hybrid talent with both business domain knowledge and data analysis capabilities will be key.
Changing the organizational culture is also an important preparation. A data-driven decision-making culture must be established and an organizational process must be established to actively utilize the prediction results. This requires the firm commitment and support of management and an enterprise-wide change management program.
Finally, a systematic knowledge management system for continuous learning and innovation must be established. A platform for accumulating and sharing the operational experience and know-how of prediction models must be established to lay the foundation for the continuous development of the organization's prediction capabilities.
The challenges of sales volume forecasting faced by modern companies can be summarized as the fragmentation of data, the lack of consideration of external variables, and the lack of real-time responsiveness. As discussed in this article, building a real-time data integration system, developing industry-specific predictive models, and continuously optimizing models are essential to solve these problems.
It is not easy for a company to solve these tasks on its own, so consider introducing a proven AI-based sales volume forecasting solution.
Deepflow solves companies' sales volume forecasting challenges by analyzing more than 50,000 external environmental variables, automatically collecting internal data through ERP integration, and providing customized forecasting models for each SKU.
In particular, you can experience not only tangible results such as a 49.1% reduction in inventory shortages and a 70.9% reduction in excess inventory, but also innovations in terms of work efficiency, such as reducing inventory management work from 15 days to 7 minutes per month.
If you are a company that is considering business innovation through advanced sales volume forecasting, why not start with Deepflow?
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