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Оптимизация производственной программы малых и средних машиностроительных предприятий с учётом потенциала заказчика

Authors: Ershova, I.V.; Klyuev, A.V.;

Оптимизация производственной программы малых и средних машиностроительных предприятий с учётом потенциала заказчика

Abstract

Ершова Ирина Вадимовна, доктор экономических наук, профессор кафедры «Организации машиностроительного производства», Уральский федеральный университет имени первого Президента России Б.Н. Ельцина, Екатеринбург, Россия, i.v.ershova@urfu.ru Клюев Андрей Васильевич, старший преподаватель кафедры «Организации машиностроительного производства», Уральский федеральный университет имени первого Президента России Б.Н. Ельцина, Екатеринбург, Россия, a.v.klyuev@gmail.com Irina V. Ershova, Doctor of Sciences (Economics), Professor of the Department of Machine-Building Production Organization, Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russia, i.v.ershova@urfu.ru Andrey V. Klyuev, senior lecturer at the Department of Machine-Building Production Organization, Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russia, a.v.klyuev@gmail.com Цель исследования состоит в разработке методического подхода формирования и корректировки производственной программы для малых и средних предприятий машиностроительной отрасли. Выбор объекта исследования объясняется развитием среднего и малого бизнеса, спецификой рынка и заказчиков, ограничениями использования существующего методического инструментария, новыми возможностями цифровых технологий. Авторы предлагают выделять перспективных заказчиков на основе вводимого показателя «коэффициент потенциала заказчика». На первом этапе коэффициент потенциала рассчитывается на основании статистики объема заказов и определяет рост объемов продаж по клиенту в последующие периоды. На втором этапе находится регрессионная зависимость между расчетным коэффициентом и открытыми данными внешней отчетности по клиентам. Это позволяет рассчитывать коэффициент для любого нового заказчика. Вводимый показатель используется в целевой функции оптимизационной модели производственной программы. В статье представлен алгоритм расчетов и иллюстрации применения метода для машиностроительного предприятия среднего и малого бизнеса. Результаты апробации показали достоверность предлагаемого подхода. The purpose of the study is to develop a methodological approach to the formation and adjustment of the production program for small and medium-sized enterprises of the machine-building industry. The choice of the object of study is explained by the development of medium and small-sized businesses, the specifics of the market and clients, the limitations to the use of the existing methodological tools, and by the new opportunities of digital technologies. The authors propose to identify prospective clients based on the introduced indicator of customer potential coefficient. At the first stage, the potential coefficient is calculated based on the statistics of the number of orders and is used to determine the growth of the number of sales on the client in subsequent periods. At the second stage, the regressional dependence between the calculated coefficient and the open data of external reporting on the client is observed. This makes it possible to calculate the coefficient for any new client. The entered indicator is used in the objective function of the optimization model of the production program.

Keywords

перспективный клиент, производственная программа, prospective client, малый и средний бизнес, production program, small and medium-sized business, customer potential, УДК 338.33

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green