In the rapidly changing global market environment, manufacturers are facing the need for more sophisticated inventory management than ever before. Traditional inventory management methods have fundamental limitations, including inaccuracy in demand forecasting, limitations in real-time response, and inefficiency due to human error.

In particular, in the current manufacturing environment where the system of small-lot production of many varieties is expanding and responding to customers' immediate demands has become essential, the inventory management method that relies on past experience and intuition is no longer able to guarantee competitiveness.

In this changing environment, the introduction of AI technology is becoming a necessity, not an option.

AI-based inventory management systems can accurately predict demand by analyzing vast amounts of data in real time, automatically calculate optimal inventory levels, and proactively detect risk factors across the supply chain.

This is becoming a core competitive advantage that is directly linked to a company's profitability, going beyond simple work efficiency.

Meanwhile, the manufacturing industry's digital transformation has become an unstoppable trend. In particular, the digital transformation in the inventory management area creates multi-layered value: reducing operating costs, improving customer satisfaction, and responding quickly to market changes.

Leading manufacturing companies are already creating tangible results through inventory management innovation using AI technology, which is becoming an important benchmark for latecomers.

In this article, we will take a closer look at how companies have created concrete business value through the introduction of AI inventory management in manufacturing companies. This will help you clearly see the practical effects and investment value that the introduction of an AI-based inventory management system can bring.

‍

‍

Key technologies for AI inventory management

Demand forecasting algorithms

‍

The demand forecasting algorithm presents an innovative approach that goes beyond the limitations of conventional statistical methodologies.

Advanced deep learning models, such as Long Short-Term Memory (LSTM) networks, capture complex patterns inherent in time series data, enabling sophisticated predictions that take into account both short-term volatility and long-term trends.

A key feature observed in industrial settings is that the algorithm dynamically learns the influence of external variables.

The accuracy of the prediction is continuously improving through an integrated analysis of changes in consumer preferences through social media data, seasonal effects through weather data, and even information on competitors' promotions.

‍

What is machine learning-based inventory optimization?

‍

The core of machine learning-based inventory optimization technology is to derive a balance across the complex supply chain through a multi-dimensional optimization algorithm. In particular, with the introduction of reinforcement learning technology, the system learns the optimal inventory management policy on its own through continuous trial and error.

The biggest benefit of this on-site observation is that it enables decision-making that takes into account dozens of variables at the same time, including the variability of lead times for different products, the reliability of suppliers, seasonal fluctuations in storage costs, and the availability of transportation.

This makes it possible to achieve the conflicting goals of reducing inventory holding costs and improving product availability at the same time.

‍

How real-time inventory tracking technology works

Real-time inventory tracking technology combines edge computing and AI vision technology to provide a new level of accuracy and efficiency.

The combination of the latest RFID technology and computer vision systems enables real-time tracking in milliseconds, and AI processing on edge devices enables immediate decision-making without the load on the central server.

An innovative application that is attracting attention in the industry is integration with digital twin technology. All movements of physical inventory are mapped in real time in the digital space, allowing the flow of inventory to be visually monitored and simulated.

This can lead to improved warehouse space utilization and reduced picking time.

‍

Predictive maintenance system

Predictive maintenance systems are becoming a key element in ensuring the stability of the entire supply chain, going beyond monitoring the health of production facilities.

The current state of the art is to analyze multi-sensor data such as vibration, noise, and power consumption patterns to detect abnormalities in facilities at an early stage, as well as to predict the life cycle of parts through big data analysis.

A particularly noteworthy trend in the industry is that these systems are organically linked to inventory management.

The standard operating procedure is to automatically optimize the inventory level of parts required for equipment maintenance and proactively adjust inventory operation plans when potential problems are detected.

The advanced technologies that have been developed so far are creating synergies that go beyond the sum of their simple functions. For example, through the cyclic feedback of real-time data, each component is constantly learning and developing, which can constantly increase the level of intelligence of the entire system.

Currently, in industrial sites, such AI systems are becoming a key means of achieving two goals at the same time: reducing inventory management costs and improving customer service levels.

‍

‍

AI Inventory Management & Demand Forecasting Solution, Deepflow Success Story

Digital Transformation of a Global Flavor Manufacturer

In the flavor industry, which is characterized by small-lot production of many varieties, accurate inventory management is a key element of corporate competitiveness. Seoul Flavor, which handles more than 1,500 flavors, has achieved remarkable results through digital transformation of its inventory management.

The existing manual inventory management was reaching its limits in many ways. The inventory and purchasing management that the person in charge manually performed using Excel took an average of 15 days per month, which resulted in an annual inefficiency of 180 work hours.

Even more serious was the decrease in data accuracy due to manual work.

However, after introducing Deepflow, an AI inventory management solution, the working time was dramatically reduced to seven minutes per month, and the prediction accuracy was improved by 70-80%. This led to a qualitative improvement in strategic decision-making, beyond simply streamlining work.

What is particularly noteworthy is that Deepflow can derive the optimal inventory level by comprehensively considering the characteristics of each flavor, shelf life, and substitutability.

‍

Innovative inventory optimization for remote control manufacturers

The 'whiplash effect' that commonly occurs in the electronics manufacturing industry causes inefficiencies throughout the supply chain. A remote control manufacturer found it difficult to solve this problem with its existing ERP system alone.

In particular, excessive inventory costs due to inaccurate demand forecasts were a major factor in hindering corporate profitability.

Deepflow was able to offer a breakthrough solution to these problems. Inventory shortages were reduced by 49%, and inventory excesses by 70%, which led to a reduction in inventory costs of 1.1 billion won per month.

In particular, the system can derive the optimal inventory management strategy by comprehensively considering the volatility of lead times of parts suppliers, the seasonality of market demand, and even new product launch plans.

‍

Strengthening the Trend Responsiveness of Vietnamese Fashion Companies

The rapidly changing trends and short product life cycles in the fashion industry add to the complexity of inventory management. A Vietnamese fashion company was facing a serious problem of excess inventory in 70% of its products.

The introduction of an AI inventory management system dramatically improved the accuracy of demand forecasting, which enabled the company to systematically reduce excess inventory by 10-20% each month.

In addition, through Deepflow, we confirmed the possibility of increasing the success rate of new product planning from 35% to over 70% by introducing the new product prediction module. This system enables more accurate demand forecasting by integrating analysis of social media trends, competitor product monitoring, and weather data.

‍

‍

Analysis of the effects of introducing AI inventory management

In today's business environment, accurate demand forecasting and optimized inventory management are directly linked to a company's profitability. When the ROI of Deepflow is analyzed from a ROI perspective, the investment value becomes clearer.

The most notable effect is the dramatic improvement in demand forecasting accuracy. Deepflow integrates and analyzes not only internal ERP data, but also a vast amount of external data, including 1,700 macroeconomic indicators, 6 million market trend data, and 100 industry-specific variables.

More than 200 advanced AI predictive models process this big data in real time to provide customized demand forecasts for each SKU. The 70-80% improvement in forecast accuracy, as seen in real-life cases, is the result of this advanced technology.

Even more notable in terms of profitability is the direct cost-saving effect of inventory optimization.

As shown in the example of the remote control manufacturer, the company realized a monthly inventory cost reduction of 1.1 billion won. Deepflow creates tangible financial results by simultaneously solving both inventory shortages (49% reduction) and excesses (70% reduction).

This goes beyond simple cost reduction and leads to increased corporate value through efficient use of working capital.

The effects in terms of operational efficiency are also noteworthy. As seen in the case of a flavor company that manages more than 1,500 SKUs, the reduction of inventory management work from 15 days per month to 7 minutes is an example of maximizing the efficiency of human resources management.

The time and human resources gained through this can be reallocated to more strategic tasks.

One particularly notable achievement is the success in new product planning. As in the case of the Vietnamese fashion company, the AI system has the potential to improve the new product planning success rate from 35% to over 70%.

This demonstrates the strategic value of AI systems, which can support product portfolio strategy and decision-making on entering new markets, beyond simple inventory management.

Deepflow is constantly improving its prediction accuracy through continuous learning, and is also strengthening its ability to adapt to changes in the market environment. The function of quantitatively analyzing the influence of external environmental factors and providing them supports the strategic decision-making of management and practitioners based on data, thereby contributing to the long-term strengthening of corporate competitiveness.

The introduction of deep flow is evaluated as a core investment that supports strategic decision-making and long-term growth of a company, beyond simple task automation or cost reduction. Especially in the modern business environment where uncertainty is increasing, the value of accurate data-based predictions and optimized decision-making is expected to grow even more.

‍

‍

Future Outlook of Artificial Intelligence in Manufacturing

Evolution of AI inventory management

AI inventory management technology is expected to become more intelligent and sophisticated through continuous innovation. Beyond the current achievements, future AI inventory management will lead to more innovative changes across the entire value chain of companies.

‍

Hyper-personalized prediction system

The most notable direction of technological development is the implementation of a hyper-personalized forecasting system. Beyond the current SKU-level forecasting, it will be possible to forecast micro-demand at the individual customer and store level.

The advancement of deep learning algorithms and the improvement of computing power will enable the processing of millions of variables in real time, which will take forecasting accuracy to the next level.

‍

Innovation through the convergence of new technologies

The advancement of 5G and IoT sensors will further enhance real-time visibility across the supply chain, while the combination with blockchain technology will dramatically improve data reliability and traceability.

In addition, the advancement of XR (Extended Reality) technology is expected to bring revolutionary changes to warehouse management and inventory picking.

‍

Advancement of autonomous decision-making systems

Experts explained that the advancement of reinforcement learning algorithms will enable AI systems to autonomously make optimal decisions in more complex situations. As a result, they expect that in the near future, not only will simple inventory level optimization be achieved, but also the autonomous adjustment and optimization of the entire supply chain.

‍

Use of digital twins and metaverse

The combination of digital twin technology and AI will enable the detection of risks in advance and the establishment of response strategies through simulations in virtual spaces.

The advancement of metaverse technology will revolutionize the way supply chain stakeholders collaborate, and the practical application of quantum computing will enable optimization at a level that is difficult to imagine at present.

‍

‍

IMPACTIVE AI's future strategy as a leader in AI inventory management technology

IMPACTIVE AI will be at the forefront of this technological advancement, developing more innovative solutions through continuous research and development.

In particular, we will continue to invest in the development of industry-specific AI models and advanced predictive algorithms, and strive to provide the best value to our customers by proactively embracing global technology trends.

AI inventory management in the future will go beyond being a simple tool for streamlining work and will become a key driver of digital transformation for companies. In the future business environment, where uncertainty is increasing, advanced AI technology will become an essential element for the survival and growth of companies.

We will continue to innovate to prepare for this future and achieve sustainable growth with our customers.

‍