In keeping with the digital era, inventory management solutions are now going beyond simple inventory management to become a key driver of business innovation. In particular, domestic mid-sized and large companies are accelerating their transition to AI-based inventory management systems to overcome the limitations of traditional inventory management systems.
According to Salesforce's Manufacturing Trends Report, 73% of domestic manufacturing companies have introduced AI or are in the testing phase. However, this is less than the global average of 80%.
Accordingly, the government has announced a plan to increase the AI utilization rate in the distribution industry from the current less than 3% to 30% within three years. This is expected to reduce inventory costs by 20% and delivery times by 10%.
The background to this increase is the growing uncertainty in the global supply chain and the diversification of consumer demand patterns. Companies are realizing that it is no longer possible to effectively respond to changes in the market with inventory management that relies on past experience or simple statistics.
Companies that have currently introduced AI-based inventory management systems have a wide variety of on-site voices. While some companies have experienced dramatic cost savings and efficiency improvements, others have faced lower-than-expected effects and new problems.
In this article, we will analyze the practical advantages and disadvantages of AI-based inventory management systems in depth based on the real-life experiences of practitioners. We hope that this will help decision-makers and practitioners alike to have a balanced view of AI-based inventory management systems.
Limitations of traditional inventory management and changes brought about by AI-based inventory management solutions
Innovation in demand forecasting and financial performance
The biggest problem with traditional inventory management systems is the inaccuracy of demand forecasts. Most companies use simple statistical models based on historical sales data and market trends to predict demand.
This method has been somewhat effective in a stable market environment, but it has shown significant limitations in the rapidly changing market environment of recent years.
In particular, for trend-sensitive items such as fashion, food, and electronics, the financial losses due to forecasting errors are significant. Inaccurate demand forecasts result in direct losses such as deterioration of cash liquidity due to excess inventory, deterioration of profitability due to discount sales, and increased emergency logistics costs.
This leads to long-term losses in the form of customer churn and a decline in brand value.
AI-based inventory management systems dramatically improve these problems. Machine learning algorithms provide more accurate predictions by analyzing various external variables such as weather, social issues, competitor activities, and social media trends in real time.
In particular, the system's self-learning ability improves the accuracy of predictions over time, continuously improving the operational efficiency and profitability of the company.
Real-time visibility of global supply chains
The increasing complexity of global supply chains is making the limitations of traditional inventory management systems more apparent. In particular, the biggest problem is the inability to manage lead time variability that occurs in the process of connecting multiple suppliers and logistics hubs.
Traditional systems cannot respond in real time to external factors that are difficult to predict, such as exchange rate fluctuations, changes in international affairs, and natural disasters. As a result, companies often face the situation of having to hold excessive amounts of unnecessary safety stock or bear high emergency transportation costs.
AI-based inventory management systems address these issues by providing real-time visibility across the entire supply chain.
In particular, the proactive detection capabilities of AI systems present a new paradigm for supply chain management. By identifying potential delays or problems in advance and suggesting optimal countermeasures, the system minimizes risk and maximizes operational efficiency.
In the current environment of increased uncertainty in the global supply chain, these capabilities are becoming a core competitive advantage for companies.
Real-time inventory status and integrated data management
One of the core problems with traditional inventory management systems is the difficulty of real-time inventory status. Many companies still rely on daily or weekly inventory audits, which can lead to significant discrepancies between the actual inventory status and the figures in the system.
The root cause of these problems is the fragmentation of data. Since stores, distribution centers, and inventory in transit are managed in different systems, it is difficult to get an integrated view of inventory status.
This is a particularly serious problem for companies pursuing an omnichannel strategy that encompasses both online and offline channels.
AI-based inventory management systems solve this problem of data disconnections. Through real-time data collection and integrated analysis, you can get an overview of the entire inventory status at a glance, and the automated inventory survey system minimizes human error.
This not only improves inventory accuracy, but also greatly contributes to improving the efficiency of inventory movement between channels and the quality of customer service.
The pros and cons of inventory management solutions as told by practitioners
The practical effects of AI-based inventory management as experienced on the front lines
The biggest advantage of AI-based inventory management systems is the automation of simple, repetitive tasks. The automation of tasks such as calculating daily order volumes, inventory audits, and safety stock level adjustments, which were previously done manually, has greatly reduced the workload of practitioners.
This has enabled practitioners to focus on more strategic decision-making and exception management.
It is also very helpful that the system monitors inventory levels in real time 24 hours a day and detects any anomalies. In the past, we were only able to react to inventory shortages or excesses after the fact, but now we can take a proactive approach to addressing issues before they arise.
Practitioners say that this preventive management approach has greatly reduced work stress.
Another important change is that it has enabled data-based, objective decision-making. In the past, the responsibility for making decisions was unclear and decision-making was inconsistent because it relied on the experience and intuition of the person in charge, but now, decisions can be made based on clear data.
Nevertheless, practitioners are not entirely welcoming the introduction of AI solutions. To understand this, it is necessary to look at the advantages and disadvantages of AI inventory management solutions as experienced in the field.
The problem of opacity in the AI decision-making process
The biggest problem with AI systems is the opacity of the decision-making process. Practitioners complain that it is difficult to clearly understand the reasons and basis for the system's decision-making.
This opacity is particularly problematic when the system's predictions or recommendations differ significantly from the practitioner's experience.
Even if the system determines that the order quantity or inventory level is not appropriate, the process of challenging or requesting a correction is very complicated.
As a result, many practitioners are forced to follow the system's decisions or, conversely, make the extreme choice of ignoring the system and relying on their own experience.
In addition, it is very difficult to identify the cause of a system performance degradation or error. This delays problem solving and increases distrust in the system.
Limitations in responding to exceptional situations and lack of system flexibility
AI systems perform well in standardized situations, but they still show limitations in exceptional situations. In unstructured situations such as sudden market changes, special events, and natural disasters, the system often fails to respond appropriately.
In such situations, manually adjusting the system is very complex and time-consuming. For example, in order to reflect a special promotion in the system, several stages of approval and configuration changes are required, and the time delay that occurs during this process is a great burden for practitioners.
In particular, the rigidity of the system is more pronounced in complex situations involving multiple departments or bases. In such situations, practitioners will bypass the system to handle their work, creating a vicious cycle that undermines the accuracy of the data and the reliability of the system.
Difficulty in reflecting the on-site situation and burden of data entry
It is a very difficult task to accurately reflect the complex and subtle situations on-site in the system. There are many factors that are difficult to quantify, such as the state of the store display, the physical storage conditions of the products, and the status of personnel operations, and it is a real challenge to input and reflect this information in the system.
Moreover, this data entry work significantly increases the workload of the practitioners. When all the data required by the system is entered accurately, it is often difficult to focus on the original work. This burden is especially greater during busy peak seasons or special event periods.
Another major challenge is verifying the accuracy of the input data. It is difficult to maintain consistency in data input by multiple people, and the process of finding and correcting input errors is also complex.
Why Deepflow, an AI-based demand forecasting and inventory management solution, is the solution of choice
Transparent decision-making with explainable AI
Deepflow solves the 'black box' problem, which has been the biggest challenge in AI-based inventory management, in an innovative way. By quantitatively analyzing and presenting the process of deriving the prediction results, practitioners can clearly understand and trust the decision-making process of AI.
In particular, by quantifying the influence of environmental factors that cause changes in predicted sales, practitioners no longer have to blindly follow AI's judgment.
Deepflow's explainable AI (XAI) technology goes beyond simply explaining the results and actively supports practitioners' decision-making process.
For example, if the demand for a particular product is expected to increase, the system presents the main factors of the forecast in order of their influence and quantifies the exact contribution of each factor to the forecast.
This allows practitioners to verify the validity of the forecast based on their experience and expertise and make appropriate adjustments if necessary, rather than simply accepting the AI's forecast.
Prediction accuracy achieved by multiple advanced prediction models
Deepflow's AI Stacking Ensemble Predictive Model has dramatically improved the ability to respond to exceptional situations, which was the limitation of existing AI solutions.
IMPACTIVE AI has independently developed advanced predictive models, and currently has about 224 models in operation. This system maximizes the advantages of each model and complements its shortcomings, thereby providing stable predictive performance even in the event of rapid changes in the market or unpredictable special situations.
In particular, Deepflow automatically collects and analyzes 1,700 macroeconomic data, 6 million trend data, and 100 industrial data. This allows for a comprehensive consideration of various market variables, unlike existing systems that mainly relied on past sales data and simple statistics.
For example, in the steel industry, more sophisticated forecasts are now possible by reflecting global commodity price trends, changes in international trade policies, and trends in major demand industries in real time.
User convenience realized by flexible system design focused on the field
Deepflow has solved the problem of field applicability, which was another limitation of existing AI solutions, through flexible system design that reflects the practical needs of field workers.
Deepflow's unique strength lies in the automation of data input and management. It automatically collects basic operational data through seamless integration with the ERP system, and external data is also automatically updated through API integration.
This has dramatically reduced the data input work that was the biggest burden for practitioners. In particular, the customized data collection system, which takes into account the characteristics of each industry, enables the site situation to be more accurately reflected.
In addition, Deepflow provides a complex data analysis process as an intuitive visualization tool, making it easy for non-AI experts to understand and use. By automating data analysis and predictive model operation through a no-coding solution, practitioners can effectively use the system without the need for complex technical knowledge.
Continuous system improvement through an expert collaboration system
Deepflow is promoting continuous improvement of the system by establishing a close collaboration system among the sales team, research team, and development team. Each team contributes to system improvement based on their different expertise, and this multifaceted approach helps to more accurately identify and effectively address the needs of the field.
In particular, the AI Research Team is constantly researching the latest technology trends and applying them to the system, thereby constantly improving the accuracy of predictions and system performance. The development team implements the results of this research into stable and easy-to-use features, while the sales team collects feedback from the field to suggest the direction of system improvement.
In addition, Deepflow is increasing the understanding of the system among practitioners through regular user training and technical sessions. These trainings go beyond simply teaching how to use the system, helping practitioners use the system more effectively by increasing their understanding of the basic concepts and applications of AI technology.
Future prospects and development direction of inventory management solutions
AI-based inventory management solutions are going beyond simple task automation and fundamentally changing the decision-making paradigm of companies. In particular, the development of real-time data analysis and predictive technologies is enabling companies to manage their inventories in a more scientific and strategic way.
The AI-based inventory management market is expected to show high growth rates over the next five years. The key driver of this growth is the acceleration of digital transformation across industries.
In particular, AI-based inventory management is expected to become an essential competitive factor in the manufacturing and distribution industries. In fact, leading companies are already achieving breakthrough results through AI-based inventory management, which shows that the changes brought about by AI technology have now become a reality.
In this changing market, Deepflow has focused on solving the problems that practitioners actually experience.
Deepflow's unique value proposition is to ensure transparency in decision-making through explainable AI, improve prediction accuracy through an ensemble of 224 predictive models, and design flexible, field-oriented systems.
In particular, the automatic collection and analysis of vast amounts of external data and customized solutions that take into account industry-specific characteristics enable companies to effectively respond to the rapidly changing market environment.
Now, the introduction of AI-based inventory management systems is becoming a necessity for companies, not an option. At the center of this, Deepflow is listening to the voices of the field and constantly evolving to solve practical problems and create value.
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