As the digital transformation accelerates, many companies are considering introducing AI inventory management systems. However, many companies are struggling with the actual implementation process, and a significant number of projects are not achieving the expected results.
When looking at the main reasons why AI inventory management systems fail to be introduced in the manufacturing sector, many companies tend to perceive AI systems as simple IT solutions and focus only on the technical aspects.
However, AI inventory management is a project that requires enterprise-wide change management and must be accompanied by an overall innovation of organisational culture and work processes.
In addition, many companies rush to introduce AI systems without considering the quality and standardisation of data or preparing sufficient data, which often leads to failure.
In particular, in industries that require strict quality control, such as the pharmaceutical, bio, and chemical industries, the accuracy and consistency of data are even more important. Refining and standardising legacy data accumulated over many years is a task that requires considerable time and effort.
The Rand Corporation interviewed 65 data scientists and engineers from industry and academia. According to the report, more than 80% of AI projects are found to be unsuccessful. This is twice the failure rate of typical technology projects. The five reasons for failure were: ▲misunderstanding or miscommunication of problems to be solved by AI ▲lack of AI training data ▲focusing only on introducing technology ▲lack of data management and AI infrastructure ▲challenging problems that cannot be solved by AI.
'80% of companies that introduce AI fail... Manufacturing is ‘like finding a star in the sky’
Along with these problems, the most fatal cause of failure is the failure to select the right partner. An AI inventory management system must take into account the characteristics of each industry and the unique operating environment of each company, so it requires a deep understanding of the industry, not just technical skills.
However, many companies do not fully consider this industry expertise in the partner selection process or make decisions with a focus on short-term cost savings.
A systematic approach is required to build a successful AI inventory management system. This article will look at the key elements that practitioners must consider, divided into building a technical foundation and establishing a strategic operating system, and present specific evaluation criteria for selecting the right partner.
This will allow practitioners to identify potential risks in the process of introducing an AI inventory management system in advance and check practical guidelines for successful implementation.
The success of an AI inventory management system starts with the quality of the data. The accuracy and consistency of data collected on-site are key factors that determine the performance of an AI model. To do this, you must first carefully review your company's current data collection system.
Manual data entry is prone to errors and lacks real-time capabilities. Therefore, it is necessary to establish an automated data collection system using sensor networks and IoT devices.
In addition, a standardized process for data quality management is required. Clear standards for raw material code systems, quality data recording methods, and inventory inspection cycles must be established and applied company-wide.
In particular, the pharmaceutical and food manufacturing industries require a more stringent data management system because they need to precisely manage the expiration date, storage conditions, and quality data of raw materials.
In terms of technical infrastructure, a cloud-based data storage and processing system is essential. A system capable of real-time processing and analysis of large amounts of data must be built, and scalability for future data growth must also be considered.
At the same time, designing an interface for smooth integration with legacy systems is also an important task.
Since AI systems handle core operational data of companies, it is essential to build a robust security system. In particular, in the pharmaceutical industry, compliance with Good Manufacturing Practice (GMP) regulations is essential, and a system design that can ensure data integrity is required.
To this end, a comprehensive security policy must be established, including data encryption, access rights management, and audit tracking.
In addition, companies operating in the global market must consider both data protection regulations such as the GDPR and industry-specific regulations of each country.
Explainability of the decision-making process of the AI system is also an important requirement. The system must be able to clearly present the basis for making specific inventory decisions, which is essential for internal audits and external regulatory compliance.
The success of introducing an AI system depends on how clearly you define the problem you want to solve. You should set specific problems and improvement goals, not just a vague goal of ‘improving inventory management.’
For example, you should set measurable goals such as reducing the frequency of production stoppages due to insufficient inventory, reducing storage costs due to excess inventory, and improving demand forecast accuracy.
A multidimensional KPI system is required for performance measurement. It should include not only traditional indicators such as inventory turnover, inventory accuracy, and lead time, but also new indicators such as the accuracy of AI system predictions and the reliability of automated decision-making.
In addition, a system should be established to analyse how these indicators are linked to the actual financial performance of the company and to calculate the return on investment (ROI).
Modern inventory management should go beyond internal optimisation to consider the efficiency of the entire supply chain.
AI inventory management systems can achieve more accurate demand forecasting and inventory optimisation through real-time information sharing with suppliers, logistics companies, and customers. This requires a standardised data sharing protocol and security system.
In particular, in the chemical industry, where the supply and demand of raw materials is unstable and price volatility is high, it is very important to secure visibility across the supply chain. AI systems must be able to monitor inventory levels, production plans, and logistics at each stage of the supply chain in real time and detect potential risks in advance.
To do this, it is necessary to consider building a reliable data sharing platform using blockchain technology.
The biggest obstacle to introducing AI systems is not the technology, but the resistance of the organisation. In particular, middle managers may be concerned about the transfer of existing decision-making authority to AI systems.
Therefore, it is important to make it clear that AI systems are tools that complement and support human decision-making, not replace it.
A phased approach is required for effective change management. First, the effectiveness of the system should be verified through a pilot programme, and then the system should be expanded company-wide based on the results. At the same time, differentiated training programmes should be operated by job title and department to strengthen the digital capabilities of employees.
In particular, in-depth training should be provided to field managers so that they can understand the decision-making logic of the AI system and intervene appropriately if necessary.
Selecting a partner to build an AI inventory management system is an important decision that will determine the success of the project. To select the right partner, you need to carefully review several key evaluation criteria. The following are the main evaluation criteria to consider when selecting a partner.
A deep understanding of the industry is the foundation for a successful system implementation.
Each industry, such as pharmaceuticals, biotechnology, and chemicals, has its own characteristics and regulatory requirements. Therefore, you should make sure that your partner understands the inventory management characteristics of the industry and has the ability to comply with relevant regulations and quality standards.
It is advisable to review the actual implementation cases and performance of the reference company and, if possible, visit the site to directly check the status of system operation. In particular, implementation experience in a similar scale and environment is an important factor in increasing the likelihood of project success.
The technical capabilities of the partner company are a key factor in determining the quality of the system. The accuracy of the demand forecasting algorithm, the reliability of the optimal inventory level calculation model, and the performance of the anomaly detection system must be objectively evaluated.
Continuous investment in research and development and efforts to innovate technology are also important evaluation criteria. It is necessary to form an evaluation team including technical experts to verify the core technologies and algorithms of the partner company and to confirm the technology roadmap and future development plans.
A systematic implementation methodology and project management skills are essential for a smooth system implementation. You should review the detailed plan for each implementation stage, risk management measures, and quality management system presented by the partner.
You should also check whether the composition and division of roles of the project execution organisation are clear and whether the experience and expertise of the key personnel are sufficient. In particular, the leadership and communication skills of the project manager are important variables for the success of the project.
AI systems require ongoing management and improvement even after deployment. You should understand the scope and level of maintenance services provided by the partner.
In particular, you should check whether a response system is in place in the event of a system failure, whether a regular performance optimisation plan is in place, and whether a user training programme is in place. The specificity of the service level agreement (SLA) and the adequacy of the compensation system are also important review items.
Finally, the cost-effectiveness should be comprehensively analyzed. The total cost of ownership (TCO), including not only the initial deployment cost but also the license cost, maintenance cost, and infrastructure cost, should be calculated and compared with the expected effects.
When analysing the return on investment (ROI), both the quantitative and qualitative effects, such as inventory cost reduction, operational efficiency improvement, and customer service improvement, should be considered. It is also necessary to examine whether the initial investment size and payback period are in line with the company's financial plan.
Deepflow uses not only historical data but also various external data such as market environment, trends, diseases, and weather to predict future demand. With the introduction of the Deepflow system, companies can improve the efficiency of inventory management and productivity through AI-based demand forecasting and inventory management.
First, Deepflow prevents over-ordering through accurate demand forecasting and reduces losses caused by excess inventory. It also predicts the possibility of stock shortages, thereby minimising the occurrence of emergencies.
Deepflow's AI-based automatic ordering system can reduce the stress of ordering and inventory management for the person in charge. In particular, it boasts higher accuracy than ERP/Excel-based inventory management, and you can see an improvement in inventory management efficiency of 70-80% or more compared to the existing method.
In fact, the industry estimates that even a 10% improvement in forecasting accuracy can reduce inventory costs by more than 30%. Companies that have introduced Deepflow have seen inventory cost savings of more than 1.1 billion won per year, and have reduced the time it takes to process tasks that used to take more than 15 days to just seven minutes.
IMPACTIVE AI has deep expertise in the field of demand forecasting through selection and concentration. It boasts superior forecasting performance than advanced IT services by developing its own advanced AI forecasting model.
In 2025, Deepflow is establishing itself as a core solution that increases the efficiency of companies' inventory management and contributes to productivity improvement.
IMPACTIVE AI will support the successful implementation of AI inventory management systems for our clients based on our industry-specific expertise, extensive implementation experience, proven AI technology, and systematic project management capabilities.
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