Abstract on the topic of graduation work

Procurement planning at a non-manufacturing commercial enterprise is advisable in cases where the products sold have seasonal demand and a narrow trade focus. At the same time, the activities of the enterprise should be conducted for at least three years - this allows you to establish the preferences of buyers and the seasonality of the products sold – demand in a certain time period. Procurement planning also depends on many factors that are usually not taken into account in the information accounting at the enterprise. Examples of such factors in the field of selling medical products can be the specifics of storage and transportation of medical products (for example: temperature regime), as well as situations associated with the peculiarities of working with contractors. Basically, existing software products specializing in procurement planning do not take these factors into account or require expensive customization for a specific area of the business of a commercial enterprise.

The emerging need for the constant formation of procurement plans leads to the need to automate the planning process taking into account the preferences of buyers and the seasonality of the products sold. The system under consideration calculates this coefficient of seasonality of goods using the modified ABC / XYZ analysis method. But today, management decisions at enterprises cannot be based on discrete data. Economic concepts are best defined in numerical intervals and take into account, for example, periods of growth or decline in demand for products, for this, the apparatus of fuzzy logic is used. It allows you to customize variables for any operating conditions of the enterprise, as well as the specifics of its work, and takes into account force majeure when planning purchases. The implementation of the procurement planning module at a non-manufacturing commercial enterprise using a fuzzy logic apparatus should be integrated into the software environment where financial and accounting records are maintained, in this case, it is 1C. Enterprise v8.2. The main module of the system should, guided by the user's actions (take into account the scenario, the planning period, the amount and delivery time, etc.), make decisions (take into account the current period, the conditions of transportation of goods and their seasonality) and provide the necessary information in a familiar interface.

Since the most common software for information and accounting in this area is the database system 1C. Company – then it is important to create a decision support system for it on the purchase and transportation of medical goods. The use of fuzzy logic, which serves as the basis for the implementation of fuzzy control methods, will make it possible to more naturally describe the nature of human thinking and the course of his reasoning than traditional formal logical systems, which will provide greater accuracy in forecasting.

The master's work is devoted to the urgent scientific task of developing a model for planning the procurement of medical products, built on the principles of fuzzy logic. The model has been introduced into the 1C. Enterprise system, improved earlier in the bachelor's work, and tuned to the specifics of the functioning of this organization.

The purpose of creating the developed model is to minimize the time for decision-making in the field of logistics activities in the field of procurement of a commercial enterprise, by providing several options for a procurement plan based on the results of the enterprise's work for previous periods and the enterprise's financial resources. It should be noted that this model is focused on the operation of an enterprise, planning their purchases as needed, thereby reducing the cost of storing goods in the warehouse.

To achieve the goal, you must:

1. Explore the subject area;
2. Develop a fuzzy model of the procurement process for a commercial enterprise of medical goods;
3. Implement the developed fuzzy model into the procurement planning system;
4. Check the software implementation of the procurement planning system.

This model was developed for a company specializing in the sale of medical products. Medical products (products) are medical products made of glass, polymer, rubber, textile and other materials, reagent kits and control materials for them, other consumables and products, mainly of single use, which do not require maintenance during use. This group of products occupies about 20 percent of the total market for medical devices. Legally, these goods are accounted for by the classifier code, but for convenience at the enterprise in question, the products are grouped as follows:

1. disinfectants;
2. laboratory supplies;
3. media / agars, sera;
4. diagnostic reagent kits;
5. various chemical substances;
6. equipment.

ВAll medical products have a shelf life (shelf life) indicated by the manufacturer in the certificate for a medical device, conditionally these periods can be divided into: less than a month, less than three months, not more than six months, not more than a year, an unlimited amount of time, etc. the expiration date is also stored by the production series of the goods. When purchasing short-term storage goods, you should take into account orders from buyers, focus on demand, and the duration of transportation of products. When purchasing goods with a long shelf life (more than a year, a year), it is necessary to take into account the cost of their storage, therefore, it is also desirable to focus on orders of contractors.

Seasonality of goods is a coefficient that characterizes the frequency of orders for a particular product. The user chooses a temporary variable, this information depends on the specifics of sales. Using the reporting of previous periods (preferably more than three years of work), it is convenient to display the quarterly demand for goods, but taking into account additional information, the monthly forecast of orders can also be obtained as accurate. Information about the frequency of purchases is most easily derived from invoices, in this way you can calculate a purchase plan for a specific consumer. The demand and seasonality of goods is often produced by specialized planning software products that take into account fluctuations in the market or the experience of the enterprise.

From an economic point of view, fuzzy logic allows us to form a full range of possible scenarios. In procurement planning, a situation often arises when it is impossible to characterize the demand for products: the need for ten thousand units is a lot or a little in a given period. Indeed, certain goods, especially in the field of medicine, have different critical points at different times of the year (in cold periods – the need for diagnostic materials related to ARVI and other diseases increases, in warm times – sales statistics for these goods fall). Setting the value of a fuzzy variable in words, without using numbers, is more natural for a person. Every day, when planning purchases, decisions are made on the basis of linguistic information such as: to deliver the goods very quickly, the demand for the goods is too small, the products are too high at cost, etc. Therefore, it is advisable to use such fuzzy intervals, the boundaries of which would be calculated by the program for the planning period, based on the numerical indicators of trade in past periods.

The basis for the operation of a fuzzy logical inference is a rule base containing fuzzy statements in the form If – then and membership functions for the corresponding linguistic terms. The result of fuzzy inference is a clear value of the variable based on the defined clear values. Fuzzy inference algorithms differ mainly in the type of rules used, logical operations and a kind of defuzzification method. Fuzzy inference models for Mamdani, Sugeno, Larsen, Tsukamoto have been developed. The most common method of inference in fuzzy systems is the Mamdani mechanism. It uses minimax composition of fuzzy sets.

This mechanism includes the following sequence of actions.

1. creating a database of rules;
2. Fuzzification of input variables;
3. Aggregation of sub-conditions;
4. Activating sub-conclusions;
5. accumulating conclusions;
6. Defuzzification of output variables.

Since economic, financial and social systems are very complex and are the result of actions and counteractions of different people, it is very difficult (if not impossible) to create a complete mathematical model, taking into account all possible actions and counteractions. It is almost impossible to approximate in detail a model based on such traditional parameters as utility maximization or profit maximization. In systems of such complexity, it is natural and most effective to use models that directly imitate the behavior of society and the economy. And this is exactly what the methodology of the apparatus of fuzzy logic can offer

The purchase of goods is represented by the following sequence:

1. Determination and formation of the need for: composition, volume and cost of the purchase;
2. Comparative analysis of suppliers and evaluation of the offered goods;
3. Formation of orders, conclusion of transactions and execution of contracts for the purchase and supply of goods.

The procurement process and the influence of external factors on it can be represented by the Black box model shown in Figure 4.1.

1. И1 – sales statistics for the previous period;
2. И2 – is a general indicator of the optimal purchase values of goods;
3. И3 – transportation costs;
4. И4 – the cost of paperwork (for example, the services of a customs broker);
5. И5 – costs for storage in a warehouse;
6. И6 – enterprise budget for the purchase of goods;
7. О1 – covering the supply capacity for various contractors;
8. О2 – developed procurement plans.

Figure 4.1– Black box model

Based on the factor И1, the program that maintains financial records at the enterprise, by analyzing the sales of the previous period, determines the number of goods required for purchase. Calculations can be performed using XYZ / ABC analysis, fuzzy logic model or simpler analysis methods.

Factors И3, И4, И5 – together determine the amount of side costs when buying a product. И2 is a monetary indicator of the purchase price of a product, which, in combination with incidental costs, determines the optimal product in the pricing policy (with the lowest purchase value and the price of accompanying costs for delivery, registration, etc.). Those. from all manufacturers, the one with the price of the goods and the associated costs is lower (if there are several of them, the one with whom the company has worked more often) is selected and so on with each product. However, there are unforeseen human factors in working with suppliers, therefore, a coefficient of trust has been introduced to each (which can be turned off at the request of the user).

Most often, an enterprise has a situation with a lack of budget for the purchase of goods, therefore, factor И6 is introduced into the model, which allows you to limit the list of purchased goods. Output indicator O1 – represents the coefficient required in case of shortage budget and inability to cover demand (it is associated with the peculiarities of working with deferred payments) – it shows how the developed procurement plan covers the needs of a particular buyer. This coefficient will allow the user to determine himself which of the developed procurement plans is most relevant for the enterprise at the moment (it depends on the terms of contracts with buyers, reporting periods, etc.). Figure 4.2 shows a generalized block diagram of a procurement planning system.

Figure 4.2 – Generalized block diagram of the procurement planning system

This model is universal, since the apparatus of fuzzy logic allows you to adjust the variables for any conditions and take into account force majeure circumstances. Fuzzy variables in this model can be considered: purchase volumes, depending on sales of previous periods, optimal prices of goods and associated costs, depending on suppliers, as well as coverage ratio. In addition, the choice of the optimal purchasing plan, where purchase prices and incidental costs are combined, is also determined by a fuzzy model. An example of a force majeure circumstance can be an increase in the factor I5 in the case of purchasing a consolidated cargo with a delay of some delayed goods (due to an increase in the shelf life of other goods).

The software package used to account for the movement of goods in the considered commercial enterprise is 1C. Enterprise v8.2. 1C: Enterprise is a development environment designed to automate accounting and management accounting (including payroll and personnel management), economic and organizational activities of the enterprise. Consists of a configuration (a set of accounting objects, forms and algorithms) and a technological platform. The platform allows you to modify the logic of the applied solution (configuration) in accordance with the needs of the user. A technological platform is a software shell over a database. The client part of the platform operates in Microsoft Windows, Linux and Mac OS X. The development environment allows you to reconstruct and supplement basic configurations, as well as develop the necessary software packages and individual processing from scratch.

The procurement planning system contains eighteen window forms, taking into account the settings windows. Procurement planning is available from several interfaces at once: general, administrative and procurement planning menu.

Figure 5.1 shows the interface of the planning system. The user is given the opportunity to choose a calculation strategy (it is recommended to use the sales volume "), as well as to define the start and end dates of the period for which the purchase plan is created. It is recommended to choose the period for the entire period of the enterprise's operation.

Figure 5.1 – Planning Assistant Interface, Quantity Strategy Tab

On the Selections tab, you can select fields (see Fig. 5.2) with the value of the counterparty (if the procurement plan is made for a specific client), the type of comparison and the value of the selection of the result data.

Figure 5.2 – Selecting additional scheduling options, Tackles tab

When you click the Execute button, variations of the procurement plan for the selected period will appear, the plans are not posted and are stored in a temporary table. The number of plans is equal to the number of possible purchase scenarios if a limit on the purchase amount is introduced. If there is no limit, the program will provide one purchase plan for the selected period (see Fig. 5.3).

Figure 5.3 –The result of the algorithm, proposed procurement plans

The program speeds up the planning process by offering automatically generated procurement plans – both internal and external, thereby minimizing the time spent on user decision making. For convenience, the information is provided in the form of reports, by which you can control the analysis of the demand for certain nomenclature units and the work of the program. The interface does not differ from the interface of the development environment and contains many tips for the user.

At the time of writing this essay, the master's work is in the debugging stage and is not fully completed. Estimated completion date: May 2021. Materials on the topic can be obtained from the author or his manager after that date.

1. Гаврилов, Д.А. Управление производством на базе стандарта MRPII 2е изд. / Д.А. Гаврилов – СПб. : Санкт-Петербург, 2008. – 416 с.

2. Дашков, Л.П. Коммерция и технология торговли. / Л.П. Дашков, В.К. Памбухчиянц – М. : Издательство Дис, 1999. – 210 с.

3. Глухов, В.В. Финансовый менеджмент. Учебное пособие. / В.В. Глухов, Ю.М. Бахрамов. – СПб. : Питер, 1995. – 345 с.

4. Удовика Ю.Д. Анализ существующих систем планирования ресурсов и их методов работы для коммерческого предприятия / Ю.Д. Удовика, И.А. Тарасова, В.И. Елисеев // Сборник материалов студенческой секции IX Международной научно-технической конференции Информатика, управляющие системы, математическое и компьютерное моделирование. 2018 г., Донецк, ДонНТУ – С. 447-449.

5. Удовика Ю.Д. Разработка системы планирования товаров медицинского назначения на коммерческом предприятии / Ю.Д. Удовика, И.А. Тарасова // Сборник материалов студенческой секции IX Международной научно-технической конференции Информатика, управляющие системы, математическое и компьютерное моделирование. – 2019 г., Донецк, ДонНТУ – С. 482-485.

6. Удовика Ю.Д. Разработка системы планирования закупок товаров медицинского назначения на коммерческом предприятии / Ю.Д. Удовика, И.А. Тарасова // Сборник статей II Всероссийской научно-практической конференции Вызовы цифровой экономики: итоги и новые тренды, ФГБОУ ВО Брянский государственный инженерно-технологический университет, Брянск. – 2019 г. – С. 594-599.

7. Удовика Ю.Д. Система планирования закупок изделий медицинского назначения / Ю.Д. Удовика, И.А. Тарасова // Сборник материалов Сборник материалов IV международной научно-практической конференции БИЗНЕС-ИНЖИНИРИНГ СЛОЖНЫХ СИСТЕМ: МОДЕЛИ,ТЕХНОЛОГИИ, ИННОВАЦИИ, Донецк-Екатеринбург. – 2019 г. – С. 250-254.

8. Лебедева М.Е. Нечеткая логика в экономике – формирование нового направления / М.Е. Лебедева // Идеи и идеалы. – 2019. – С. 197–212.

9. Александрова И.А Моделирование инновационных процессов в отраслях экономики на основе принципов нечеткой логики / И.А. Александрова, А.М. Губернаторов // Современные проблемы науки и образования. – 2015. – 10 с.

10. Недосекин А.О. Применение теории нечетких множеств к задачам управления финансами / А.О. Недосекин // Аудит и финансовый анализ. – 2000 – 5с.

11. Радченко, М.Г. 1C: Предприятие 8.2. Практическое пособие разработчика. Примеры и типовые приемы М.Г. / М.Г. Радченко, Е.Ю. Хрусталева. – Москва : Изд. центр ЕАОИ, 2009. – 847 с.

12. Дуброва, Т.А. Статистические методы прогнозирования в экономике. / Т.А. Дуброва, М.Ю. Архипова. / М. : Изд. центр ЕАОИ, 2008. – 346 с.

13. Бондарев, А.В., 1С:Документооборот. 250 вопросов и ответов. / А.В. Бондарев, Н.Ю. Бурцева. / Москва : ООО 1С.Предприятие, 2018. – 334 с.

14. Алешкина, Е.В. Практикум по 1С: Предприятие 8. / Е.В Алешкина, Г.М. Крылов. – Москва : Изд. центр Профессионал, 2017. – 132 c.

15. Хрусталева, Е.Ю. Расширения конфигураций. Адаптация прикладных решений с сохранением поддержки. / Е.Ю. Хрусталева. – М. : Изд. Центр 1С-Паблишинг, 2017. – 288 c.

16. Антонов, А.В. Системный анализ: Учебник для вузов / А.В. Антонов. – М. : Высш. шк., 2008. – 454 c.

17. Анфилатов, В.С. Системный анализ в управлении / В.С. Анфилатов. – М. : Финансы и статистика, 2009. – 368 c.

18. Пирогов, В.Ю. Информационные системы и базы данных: организация и проектирование: Учебное пособие / В.Ю. Пирогов. – СПб. : БХВ-Петербург, 2009. – 528c.

19. Герасимова, В.Д. Анализ и диагностика финансово-хозяйственной деятельности промышленного предприятия / В.Д. Герасимова. – М. : КНОРУС, 2011. – 356с.

20. Арсенова, Е.В. Экономика фирмы: схемы, определения, показатели / Е.В. Арсенова, О.Г. Крюкова. – М.: Магистр, ИНФРА-М, 2014. – 271 с.