Use of cluster analysis for the classification of perfumes and cosmetics, which are pharmacy assortment products


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

Introduction. Structuring the wide list of the positions of perfumes and cosmetics, which are represented on the pharmaceutical market, greatly simplifies work with this category of pharmacy goods at all stages of their distribution. Today's approaches do not fully take into account the specificity of this product group, which are mainly based on the demographic characteristics of a consumer, and also divide the assortment in accordance with the finished product output forms. Objective: to determine the possibilities of a cluster analysis in the classification of perfumes and cosmetics as pharmacy assortment products. Material and methods. For formation of a database, the investigators used the electronic price lists of national and regional firms supplying pharmacy products as of July 2018. The information array was formed according to the parameters characterizing the consumer properties of perfume and cosmetics. Statistical data were processed by the cluster analysis procedure using the one-way communication and Euclidean distance method, as well as the Statistica 10.0 software package. Results. Assessment of a dendrogram with the given consumer characteristics could identify and describe 2 cluster groups with the Euclidean distance of 1.57 in the segment of perfumes and cosmetics. The greatest contribution to the formation of clusters was made by the qualitative characteristics of a product (the availability of confirmed data on its biological activity; the need/absence of a doctor's recommendation; the need/lack of a pharmacist's consultation during vacations). Using indicators of descriptive statistics of the formed clusters, the investigators provide a rationale for the prospects for further investigations in terms of supplementing the list of approaches in the classification of perfumes and cosmetics. Conclusion. The use of cluster methods for the analysis of perfumes and cosmetics can identify patterns and justify possible criteria for the classification of pharmacy products.

Full Text

Restricted Access

About the authors

Anastasia I. Fitisova

St. Petersburg State Chemical-Pharmaceutical University

Email: anastasia.fitisova@pharminnotech.com
postgraduate of the Department of Management and Economics Pharmacy

Igor A. Narkevich

St. Petersburg State Chemical-Pharmaceutical University

Email: igor.narkevich@pharminnotech.com
rector, Head of the Department of Management and Economics Pharmacy

Oxana D. Nemyatykh

St. Petersburg State Chemical-Pharmaceutical University

Email: oksana.nemyatyh@pharminnotech.com
Deputy Head of the Department of Management and Economics Pharmacy

Sergei Z. Umarov

St. Petersburg State Chemical-Pharmaceutical University

Email: sergei.umarov@pharminnotech.com
Head of the Department of medical and pharmaceutical commodity research

References

  1. Фармацевтический рынок России. Итоги 2017 года: аналитический отчет. М.: ЗАО Группа «ДСМ», 2018; 110.
  2. Немятых О.Д., Фитисова А.И. Оценка ключевых аспектов национального фармацевтического рынка в рамках сегмента аптечной косметики. Научные ведомости БелГУ, 2017; 1: 123-8.
  3. ГОСТ 31678-2012. Продукция парфюмерная жидкая. Общие технические условия. [Электронный ресурс]. Режим доступа: http://www.consultant.ru.
  4. ГОСТ 31696-2012. Продукция косметическая гигиеническая моющая. Общие технические условия. Режим доступа: http://www.consultant.ru.
  5. ГОСТ 31698-2013. Продукция косметическая порошкообразная и компактная. Общие технические условия. Режим доступа: http://www.consultant.ru.
  6. ГОСТ 32853-2014. Продукция парфюмерная твердая и сухая. Общие технические условия. Режим доступа: http:// www.consultant.ru.
  7. Григорьев С.Г., Иванов В.В., Мизерене Р.В. и др. Многомерные математико-статистические модели классификации в медицине. СПб., 2005; 142.
  8. Лобутева А.В., Захарова О.В., Кривошеев С.А., Лобутева Л.А., Гребнева Д.Е. Кластерный анализ затрат на фармакотерапию больных катарактой в специализированных стационарах. Вестник ВГУ. Серия: Химия. Биология. Фармация, 2015; 3: 116-9.
  9. Гудилина Н.А., Иванова Э.С., Сибиряков А.В. и др. Использование кластерного анализа при разработке подходов по выбору и назначению схем лечения ВИЧ-инфицированных пациентов. Бюллетень сибирской медицины, 2017; 16 (3): 52-60. https://doi.org/10.20538/1682-0363-2017-3-52-60.
  10. Плохих И.В. Оптимизация обеспечения населения парафармацевтическими товарами на региональном уровне (на примере сегмента средств гигиены полости рта и зубов). Автореф. дис.. канд. фарм. наук. М., 2016; 23.
  11. Зубов Н.Н., Кувакин В.И. Методы статистического анализа данных в медицине и фармации. СПб.: 2017; 216.

Supplementary files

Supplementary Files
Action
1. JATS XML

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies