Pharmaceutical AI is providing the answer to inventory management, which has been the industry's biggest headache. Out-of-stock products caused by failure to properly predict demand lead to missed opportunities for patient treatment, and excessive inventory directly leads to disposal costs due to expiration.

In the midst of this dilemma, Hanmi Science's recent case showed the possibility of change. The company reduced inventory costs by 55.1% and the out-of-stock rate by 22.6% by introducing a demand forecasting system using AI technology.

What is particularly noteworthy is that the company analyzed data on 224 diseases and predicted the number of patients with an accuracy of 96.5%.

This is a level that cannot be achieved with conventional statistical methods or predictions based on experience. AI's ability to analyse more than 6 million pieces of data in real time and clearly explain the results is opening up new horizons for the pharmaceutical industry.

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Pharmaceutical AI Current Status and Challenges

Market Reality and Limitations of Existing Methods

The pharmaceutical industry is more aware than ever of the need for accurate demand forecasting. Due to the nature of drugs with a shelf life, a shortage of stock can lead to a loss of treatment opportunities for patients, while excess stock leads directly to huge disposal costs.

In particular, demand for over-the-counter drugs is more difficult to predict because numerous variables, such as seasonality, disease incidence, and competitor trends, affect demand.

The existing demand forecasting method relied heavily on historical data and the experience of the person in charge, but it was virtually impossible to identify the correlation between numerous disease data and drugs based on human intuition alone.

Moreover, when an unpredictable situation such as COVID-19 occurs, the limitations of existing experience-based predictions become clear. When you consider the complexity of the pharmaceutical supply chain, changes in the regulatory environment, and market volatility due to new drug launches, the need for a more sophisticated forecasting system becomes clear.

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Innovative solutions offered by AI

AI in the pharmaceutical industry is attracting attention as a key technology that will change the industry landscape.

The case of Hanmi Science shows the potential of such changes. The AI-based demand forecasting system analysed a huge amount of data of over 6 million cases in real time and predicted sales volume for the next six months with an accuracy of 80.1%.

The sales volume of these pharmaceuticals follows disease trends, so we made a prediction in advance, and it showed an accuracy of 96.5% in predicting the number of patients for 224 diseases. This is the result of the AI's sophisticated analysis of the complex correlation between various variables such as weather, seasonality, disease occurrence patterns, and prescription trends.

Furthermore, by providing explainable artificial intelligence (XAI), it is now possible to clearly identify the causes of changes in sales volume for each drug.

For example, it can explain specifically whether the increase in demand for a particular drug is due to seasonal factors, out-of-stock products from competitors, or changes in new prescription patterns. These insights have enabled decision-makers to make more strategic decisions.

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Practical effects of AI in the pharmaceutical industry

Significant improvement in operational efficiency

Hanmi Science's AI adoption case shows the practical changes that technological innovation can bring to the pharmaceutical industry. As a result of applying AI demand forecasting to more than 60 OTC drugs, the rate of stock shortages and out-of-stock products decreased by 22.6%, and the rate of excess inventory decreased by 32.5%.

This has led to an impressive 55.1% reduction in monthly inventory costs.

What is particularly significant is that the time spent on routine tasks, such as sales volume forecasting and order volume calculation, has been reduced by 80%. Tasks that previously required personnel to review numerous Excel files and calculate manually have been automated through AI.

In addition, the management of important expiration dates has become more sophisticated due to the nature of pharmaceuticals, enabling the minimisation of disposal costs. This shows that AI in the pharmaceutical industry can go beyond simple cost savings and revolutionise the way companies operate.

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Intelligent pharmaceutical distribution realised with Deepflow

Deepflow Forecast is an AI solution that is specialized for the pharmaceutical industry and fundamentally solves the limitations of existing systems. It achieved a high prediction accuracy of 80.1% by analyzing more than 6 million cases, and the patient number prediction data in particular showed an amazing accuracy of 96.5%.

This is because advanced machine learning-based predictive algorithms have accurately identified the correlations between complex variables such as disease occurrence patterns, prescription trends, and seasonality.

In particular, explainable AI (XAI) technology has enabled decision-makers to clearly understand the basis of the prediction results.

For example, if the demand for a particular drug is expected to increase, the major variables and their respective influences can be identified in concrete figures. This has enabled pharmaceutical companies to make more scientific and strategic decisions.

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AI in the pharmaceutical industry: Future prospects and preparation strategies

The spread of AI technology that drives innovation

Pharmaceutical AI is now changing the paradigm of the industry beyond simple demand forecasting. The introduction of Hanmi Science's demand forecasting system is just the beginning of the use of AI. Currently, the pharmaceutical industry is making more innovative changes using AI.

Daewoong Pharmaceutical has established an AI new drug team and has revolutionised the new drug development process through virtual exploration and molecular dynamics simulation. JW Pharmaceutical has opened up new possibilities through the development of AI-based anticancer drugs, and BasenBio has begun using the ‘DEEPCT’ platform to analyse AI even genomic data.

In particular, Chaperone has opened up new horizons in the development of new drugs by increasing the accuracy of its AI cytotoxicity algorithm to 92%.

These domestic innovations are already in line with global trends.

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Key success factors for the introduction of AI

A phased and strategic approach is essential for successful AI adoption. Hanmi Science first set a clear goal of demand forecasting and produced verifiable results for more than 60 general medicines.

This approach is also consistent with the strategy demonstrated by Pharos iBio through its AI platform Chemiverse. It is advisable to start with areas where immediate effects can be seen and gradually expand the scope of application.

The case of Astellas Pharma shows how important it is to optimise decision-making through big data analysis and simulation. In particular, the quality and quantity of data determine the performance of AI, so a systematic data collection and management system must be established first.

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Organisational innovation and future preparation

The introduction of AI requires more than just technological change; it requires an innovation in organizational culture. As in the case of GSK, AI changes the very way companies work. As decision-making becomes data-driven, the capabilities of the organization's members must evolve accordingly.

In particular, understanding and acceptance of the field is essential for the effective application of AI system predictions and suggestions to the workplace. As Hanmi Science has shown, the improved work efficiency (80% time reduction) brought about by the introduction of AI provides employees with the opportunity to focus on more valuable tasks.

To successfully respond to these changes, continuous education and change management programmes must be implemented in parallel.

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AI leading the pharmaceutical industry to a new leap

The innovative results achieved by Deepflow Forecast have opened up new possibilities for the pharmaceutical industry. With a demand forecast accuracy of 80.1% and inventory cost savings of 55.1%, this is just the beginning of the changes that AI will bring.

Many pharmaceutical companies are already using AI in a variety of areas, from new drug development to clinical optimisation and production management, and are achieving remarkable results.

The introduction of AI in the pharmaceutical industry is no longer an option, but a necessity. In the face of growing market uncertainty and intensifying competition, AI will become a key factor in determining a company's competitiveness.

In particular, given the special nature of pharmaceuticals, accurate demand forecasting and efficient inventory management are directly linked to a company's profitability as well as to patients' treatment opportunities.

Deepflow Forcast is at the centre of this change, leading the digital transformation of the pharmaceutical industry.

With the ability to analyse more than 6 million cases of data in real time, operate 224 disease prediction models, and provide clear explanations of the results of the predictions, Deepflow's technology will become the new standard in the pharmaceutical industry. Now, we are opening up a new future for the pharmaceutical industry with AI.