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33влияние на все стороны общественной жизни которых в настоящее время невозможно оценить до конца.
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УДК 004.891.2
doi:10.18720/SPBPU/2/id21 -353
Кульков Игнат Александрович1,
канд. экон. наук, PhD
БИЗНЕС-ПРОЦЕССЫ В ОТРАСЛИ: РОЛЬ ИСКУССТВЕННОГО
ИНТЕЛЛЕКТА
1 Финляндия, Турку, Академия Або, ignat.kulkov@abo.fi
Аннотация. Академические круги и промышленность рассматривают искусственный интеллект (ИИ) как способ прогресса в фармацевтической промышленности. Однако способ и роль ИИ в изменении компании недостаточно изучены. Цель статьи - определить, как именно ИИ влияет на бизнес-процессы фармацевтической отрасли. Наша выборка включает пять малых, пять средних и пять крупных фармацевтических компаний, где мы исследуем роль ИИ в их изменениях. Мы изучаем ключевые и дополнительные бизнес-процессы отрасли и то, как ИИ их трансформирует или адаптирует. Небольшие компании используют ИИ в основном для изменения процессов НИОКР, анализа и отчетности, а также человеческих ресурсов. Крупные компании в основном сосредоточены на продажах, маркетинге, производстве и планировании. Средние компании используют ИИ в зависимости от своей специализации.
Ключевые слова: фармацевтическая промышленность, бизнес-процессы, искусственный интеллект, трансформация отрасли.
Ignat A. Kul'kov\
Candidate of Economic Sciences, PhD
BUSINESS PROCESSES IN THE PHARMA INDUSTRY: THE ROLE OF ARTIFICIAL INTELLIGENCE
!Abo Akademi University, Turku, Finland, ignat.kulkov@abo.fi
Abstract. Academia and industry consider artificial intelligence (AI) as a way to progress in the pharmaceutical industry. However, the way and role of AI in company change has not been sufficiently studied. The purpose of the article is to determine exactly how AI affects the business processes present in the pharmaceutical industry. Our sample includes five small, five medium-sized, and five large pharmaceutical companies where we investigate the role of AI on their change. We study the key and supplementary business processes of the industry and how AI transforms or adapts them. Small companies use AI mainly for changes in R&D processes, analysis and reporting, and human recourses. Large companies are mainly concentrated on sales, marketing, production, and planning. Medium companies use AI based on their specialization.
Keywords: pharmaceutical industry, business processes, artificial intelligence, industry transformation.
Introduction
The pharmaceutical industry today has very long and costly product development cycles, and the role of insurance companies, patients and hospitals is increasing. On average, the development of one drug takes 10-15 years and costs about $ 2-3 billion. The drug development process begins with the analysis of millions of molecules, continues with highly costly clinical trials with low success rates. Despite careful preparation, no more than 15 % of clinical trials are successful. The entire pharmaceutical industry, made up of many small, medium, and large companies, is looking for opportunities to reduce drug development costs while adhering to the strict regulations that are inherent in the pharmaceutical industry.
Currently, the industry generates a large amount of data that is in demand from a business point of view, for example, sensors, electronic medical records and other sources. An individual approach to a patient in the pharmaceutical industry becomes possible due to the fact that manufacturing companies know much more about their patient than before. At the same time, IT capabilities are growing. Machine learning algorithms as a part of AI make it possible to use the features of AI to aid in drug development. Machine learning methods include the processing of text and audio messages with the subsequent analysis of the received data. These opportunities have created the precondi-
tions for the growth of a large number of new companies that use the collected data to carve their niche in the market.
According to some researchers, the pharmaceutical field has already been able to develop all available drugs, while the development of new drugs requires significantly larger financial and time investments compared to current ones. Pharmaceutical companies suffer from difficult-to-predict R&D results, outdated business models, and weak value creation. Therefore, AI contributes to reducing the time and cost of drug development. Our motivation in the current study is to explore the increased capabilities of AI to solve the complex problems of the pharmaceutical industry. We continue our research on the role of new technologies in healthcare [1] and medical technology business [2] (Kulkov, Barner-Rasmussen et al., 2020). We have already been able to demonstrate that the existing business models of companies developing new solutions are based on a combination of several areas of knowledge [3] and do not always resonate with the industry. Nevertheless, most of the healthcare industry participants speak favorably about the possibility of using new technologies in their work. Researchers say that the main benefit of AI for healthcare and pharmaceuticals is time and cost savings. However, the amount of research that demonstrates exactly how AI is cutting costs in the pharmaceutical field is small (for example, [4]). Therefore, we want to investigate exactly how AI affects the business processes of the pharmaceutical industry.
In order to answer the research question, we studied the business processes of 15 pharmaceutical companies (five small, five medium-sized, and five large companies). The analysis of the results obtained showed that the business processes of pharmaceutical companies change depending on their size. For example, small companies are more focused on business process changes related to R&D, data management, analytics and reporting, and human resources. In turn, large companies focus on changes related to sales, marketing, manufacturing, logistics, and warehousing. Medium-sized companies choose their area depending on their specialization and fall between large and medium-sized companies.
Summary of Results
Companies operating in the pharmaceutical industry are using the power of AI to alter their business processes based on the size of the company. For example, small companies use AI in key business processes, which primarily include R&D, while other key business processes remain largely untouched. Supplementary business processes related to R&D are also undergoing changes. Such supplementary business processes include data processing, forecasting results, work with personnel and others. The business models for small companies that use AI are quite diverse, and they use these opportunities to provide their services to medium and large companies. Large companies use the power of AI for production management, marketing, sales, and forecast-
ing. Even a small increase in efficiency in each of these business processes entails huge sums that are inherent in the pharmaceutical industry. Some large companies are leveraging the power of AI in highly specialized areas that are not very common among other companies. The use of AI to predict clinical trials is in high demand in the industry. This stage is a very costly event, the probability of failure is extremely high. The digitization of data previously obtained by large companies is driving demand among small and medium-sized companies using AI. The business processes of medium-sized companies are in the middle between large and small companies and vary depending on their specialization. For this type of company, AI could be used in R&D and clinical research planning. It is worth noting the supplementary business processes that are also changing for medium-sized companies, namely human resource management, working with collected data, analytics, and forecasting. The main priorities of medium-sized companies are difficult to identify, as they are often individual. On the one hand, small companies tend to work with large companies because they see them as a source of data and funding. On the other hand, small companies are wary of a takeover. It should be additionally noted that all procedures performed by AI in the pharmaceutical industry are necessarily rechecked with the use of humans.
However, the number of successful integrations of AI solutions among large pharmaceutical companies is small. Most pharmaceutical companies cannot generate millions of rows of data to fully exploit the power of AI in this area. It can be assumed that the use of internal and external collected data from open sources may be sufficient for the full use of the capabilities of AI. The most promising areas for the application of AI in the pharmaceutical industry include molecular analysis, forecasting, sales, marketing, and warehousing. Only large pharma companies are able to generate the necessary data for the full use of AI. In turn, small and medium-sized companies are actively using the capabilities of AI in their business. However, companies often doubt that their competitors are actually using AI in their work. Some of the interviewees say that companies can use advanced automated systems as pseudo-AI services. This is due to the growing interest from industry and users in new technologies. Nevertheless, small and medium-sized companies are actively using the capabilities of AI to create a new or adapt an existing business, while large companies still show a restrained interest in using new technologies, including AI to change their business processes.
At the moment, we can say that solutions based on AI for changing business processes do not meet the company's expectations, with the exception of those related to R&D. The benefits of AI-based software over current advanced automation solutions are not fully clear to the industry. AI can assist
a person in this industry. The complete transfer of business processes under the authority of AI in the pharmaceutical industry is currently impossible. References
1. Kulkov I., Berggren B., Hellström M., Wikström K. Navigating uncharted waters: Designing business models for virtual and augmented reality companies in the medical industry. // Journal of Engineering and Technology Management. - 2021. - Vol. 59, 101614. DOI: https://doi.org/ 10.1016/j.jengtecman.2021.101614
2. Kulkov I., Barner-Rasmussen W., Ivanova-Gongne M., Tsvetkova A., Hellström M., Wikström K. Innovations in veterinary markets: opinion leaders' social capital. // Journal of Business & Industrial Marketing. - 2021. - Vol. 36. No. 13. - Pp. 1-14. DOI: https://doi.org/10.1108/JBIM-02-2020-0098
3. Kulkov I., Hellström M., Wikström K. Struggling with conservatism: entrepreneurships' challenges in business model design. // International Journal of Value Chain Management. - 2021. - Vol. 12(1). - Pp. 45-61. DOI: https://doi.org/10.1504/IJVCM.2021.112844
4. Kulkov I. The role of artificial intelligence in business transformation: A case of pharmaceutical companies. // Technology in Society. - 2021. - Vol. 66, 101629. DOI: https://doi .org/10.1016/j .techsoc.2021.101629.
УДК 330+004
doi:10.18720/SPBPU/2/id21-354
Хубаев Георгий Николаевич1,
профессор кафедры Информационных систем и прикладной информатики, д-р экон. наук. профессор
КОМПЬЮТЕРНЫЕ СЕТИ: ЭКОНОМИЧЕСКИЙ АСПЕКТ ОБЕСПЕЧЕНИЯ БЕЗОПАСНОСТИ
1 Россия, Ростов-на-Дону, Ростовский государственный экономический университет (РИНХ), gkhubaev@mail.ru
Аннотация. Предложены оригинальные методы и инструментальные средства, обладающие рядом преимуществ и позволяющие: выделять ранжированные перечни основных угроз безопасности компьютерных сетей и количественно оценивать величину ущерба от взлома защиты компьютерной сети; оценивать затраты времени, трудовых и финансовых ресурсов на осуществление каждой операции процесса, нарушающего работоспособность компьютерной сети.
Ключевые слова, компьютерная сеть, оригинальный метод, инструментальные средства, угрозы безопасности, величина ущерба, затраты ресурсов.
Georgy N. Khubaev1, Professor, Department of Information systems and applied
Doctor of Economics, Professor
COMPUTER NETWORKS: THE ECONOMIC ASPECT OF SECURITY
1 Rostov State Economic University (RINH), Rostov-on-Don, Russia,
gkhubaev@mail.ru
