KR20210046571A - How to Create a Business Model for Managing Multiple Stores - Google Patents

Abstract

An objective of the present invention is to provide a method for creating a business model for management of multiple stores to improve the overall operational efficiency of each store by comparing the relative operational efficiency of each store, selecting a role model store, which is superior to each store, and creating an optimal business model for each store for managing multiple stores conducting the same business activities under the same business entity. The method comprises: a data input step; a weight calculation step; a resource utilization calculation step; a role model store selection step; and a business model creation step.

Description

How to Create a Business Model for Managing Multiple Stores}

The present invention relates to a method for creating a business model for managing multiple stores, and more particularly, managing multiple stores for creating an optimal business model for each store in order to manage a plurality of stores performing the same business activity under the same business entity. It is about how to create a business model.

In general, companies that sell products or provide services use various business methods to attract customers, and a franchise business method is widely used as one of these business methods.

In the franchise sales, the franchise head office promotes and markets the franchise trademark, and the franchise stores use the trademark of the franchise head office, and the franchise head office operates in a sales method that provides products or services developed and advertised.

Accordingly, even franchisees who lack marketing ability and experience in business operation can operate the business in a more stable manner by operating the product or service that has already been verified by the franchise headquarters in the manner proposed by the franchise headquarters, and franchise sales are currently the most active. It has established itself as one of the business methods.

However, despite the merits of franchise sales as described above, too many franchise companies have recently been flooded, and the franchise headquarters does not provide proper operation know-how to franchisees in many cases, resulting in a very high business failure rate of franchise franchisees.

Therefore, in franchise sales, a franchise sales method that can ultimately increase the operating profit of the franchise headquarters by lowering the business failure rate of the franchise store by actively managing or providing information to the franchise store. The situation is in need of development.

The prior art (Patent Document 1) relates to a server and system that supports franchise operation, and is a menu that is predicted to have high customer preference at each franchise based on customer evaluation information on the sales volume of each menu of all franchisees and satisfaction of each menu. A configuration recommending is disclosed.

However, the prior art only recommends menus that are predicted to have high customer preference, and does not present the target points to be aimed at by comparing the relative operational efficiency of each franchise store, so that the operational efficiency of each franchise store is improved. There is a problem that cannot be improved.

Republic of Korea Patent Publication No. 10-2125526 (Name of invention: affiliate store operation support server and system, announcement date: 2020.06.22)

Accordingly, the present invention is to solve the problems of the prior art as described above, and in order to manage a plurality of stores that perform the same business activities under the same business entity, the relative operational efficiency of each store is compared and a role model store that is superior to each store The purpose of this is to provide a method of creating a business model for managing multiple stores in order to improve the overall operational efficiency of each store by selecting and generating an optimal business model for each store.

The problems of the present invention are not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above-described problem, in the method for generating a business model for managing multiple stores according to the present invention, in a method for generating a business model for managing multiple stores of a management system for managing a plurality of stores, operation data and performance data for each store are An input data input step, a weight calculation step of calculating a weight for each operation data element and a weight for each performance data element for each store, and the sum of the performance data elements to which the weight for each performance data element is applied for each store is operated. Resource utilization calculation step of calculating resource utilization by dividing by the sum of operation data elements to which weights are applied for each data element, and operation data with the reference store while the resource utilization value among the stores is higher than the resource utilization value of the reference store The role model store selection step of selecting a store with the least sum of the difference values of the ratios for each element or the least difference value from the element having the highest priority among the operation data elements as the role model store of the reference store, and the role model store And a business model generation step of setting a resource utilization value as a target value of the reference store.

In addition, in the step of calculating the weight, the weight for each operation data element and the weight for each performance data element may be calculated so that the resource utilization value of each store is maximized.

According to the method for generating a business model for managing multiple stores of the present invention, one or more of the following effects are provided.

First, there is an advantage of being able to derive an optimal business model by selecting a role model store that each store should aim for by measuring and comparing the relative operational efficiency of each store in a company operating a plurality of stores.

Second, by creating an optimal business model for each store, there is an advantage of presenting the target points that each store should aim for, and providing specific improvement plans and operation strategies.

Third, there is an advantage of improving the overall operational efficiency of a plurality of stores by creating an optimal business model for each of a plurality of stores.

Fourth, there is an advantage in that it is possible to reasonably select a role model store for each store by calculating resource utilization by assigning a weight for each store according to the situation in which each store is located.

The effects of the present invention are not limited to the above-mentioned effects, and other effects that are not mentioned will be clearly understood by those skilled in the art from the description of the claims.

1 is a flowchart of a method for generating a business model for managing multiple stores according to an embodiment of the present invention.
2 is a diagram illustrating a resource utilization calculation step according to an embodiment of the present invention.
3 is a view for explaining a role model store selection step according to an embodiment of the present invention.

Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, and only these embodiments make the disclosure of the present invention complete, and are common knowledge in the technical field to which the present invention pertains. It is provided to completely inform the scope of the invention to those who have, and the invention is only defined by the scope of the claims. The same reference numerals refer to the same elements throughout the specification.

The terms used in this specification are used to describe specific embodiments, and are not intended to limit the present invention. As used herein, the singular form may include a plural form unless the context clearly indicates a different case. In addition, when a part of the present specification "includes" a certain component, it means that other components may be further included unless otherwise specified.

Prior to the description, the method for generating a business model for managing multiple stores according to the present invention relates to a method for generating a business model for increasing operational efficiency of a plurality of stores performing the same business activity under the same business entity, wherein the plurality of stores It is a franchise store operated by the franchise headquarters.

In addition, all steps of the method for generating a business model for managing multiple stores according to the present invention, which will be described later, are performed by a management system that manages multiple stores, and the management system may be implemented in a PC or other mobile device.

In addition, the management system may include a data input unit for receiving data of each store, a resource utilization calculation unit for calculating resource utilization, a role model store selection unit for selecting a role model store, and a business model generation unit for generating a business model, The resource utilization calculation unit may include a weight calculation unit.

Hereinafter, the present invention will be described with reference to the drawings in order to explain a method of generating a business model for managing multiple stores according to embodiments of the present invention.

1 is a flowchart of a method for generating a business model for managing multiple stores according to an embodiment of the present invention.

Referring to FIG. 1, a method of generating a business model for managing multiple stores according to an embodiment of the present invention includes a data input step (S10), a weight calculation step (S20), a resource utilization calculation step (S30), and a role model store selection step ( S40) and a business model generation step (S50).

First, in the data input step S10, the management system receives operation data and performance data for each store in order to evaluate the relative operational efficiency of the plurality of stores.

Operational data is data related to the operation of the store, and may include factors that affect the operation of the store, such as labor costs, rental fees, material costs, etc., expenditures for operation, or the area of the store, the number of competitors, and population density.

Performance data is data related to store performance, and may include consumer evaluation data made by consumers visiting the store, such as direct performance such as sales, employee friendliness, and store cleanliness.

The operation data and performance data can be directly uploaded by the owner of each store to the head office operation system, or by synchronizing data such as credit card sales stored in each store system by linking the head office operation system to each store system. have. In the case of consumer evaluation data, it may be collected from surveys provided on the web.

In the weight calculation step (S20), in order to calculate the resource utilization to be described later, the weight of each store's operation data element and the weight of each performance data element are calculated so that the value of the resource utilization of each store is maximized.

Since each element of operation data and performance data has different units and attributes, it is necessary to calculate resource utilization by assigning weights to each element in order to calculate resource utilization.

However, since each store has different conditions (location, rent, population density, etc.), giving all stores the same weight can be against equity.

Therefore, in managing multiple stores, weights are assigned to each store by reflecting the difference according to the situation in which each store is located to derive the best resource utilization value that each store can have, and reasonably select a role model store for each store. can do.

For example, store 1 and store 2 exist, and labor costs may be included as one of the store's operating data elements. At this time, in order to maximize the resource utilization value of each store in consideration of other factors, the labor cost weight of the store 1 is 0.1, and the labor cost weight of the store 2 is 0.14, and each store may have different weights. .

Specifically, it is assumed that the operational data elements include labor costs, material costs, and rental fees, and the performance data elements include sales, employee-friendly railroads, and store cleanliness. In the case of store 1 in Seoul, the rent is relatively higher than in the case of store 2 in the province, but the area may be small and the number of customers may be large. In this case, in the case of store 1, labor and rental costs are higher than in store 2, the material cost is high, sales are high, and the employee-friendly railroad or store cleanliness may be lower. In this case, if the weights for each element of store 1 and store 2 are the same, of course, the value of resource utilization is determined according to where the weight is increased. In other words, if the weight of sales is high, then, of course, store 1's resource utilization will be high, and if the weight of employee-friendliness is high, of course, store 2's resource utilization will be high. Therefore, it is desirable to set the weight according to the situation of each store.

Here, the weight is calculated through a machine learning algorithm, that is, an artificial intelligence algorithm so that the maximum value of resource utilization at each store can be obtained.

Specifically, as the weight for calculating the resource utilization of each store, the value at which the value of the resource utilization at each store is the maximum is selected, but the weight must be a reasonable value. In other words, the weight of employee friendliness or store cleanliness should not be zero just because a store has high sales. Accordingly, the weight is selected from the range of the existing weight through artificial intelligence, the value at which the resource utilization of the target store is maximized.

For example, after learning a dataset of weights previously used to calculate the resource utilization of a store through an artificial intelligence algorithm, the weight to calculate the resource utilization of the current target store is calculated.

In addition, as described above, since the weight must be realistically reflected, a real number of zero or more is used, and it is preferable to use the first or second decimal place of the weight obtained through the artificial intelligence algorithm.

In the resource utilization calculation step (S30), resource utilization is calculated by dividing the sum of the performance data elements to which the weight of each element is applied for each store by the sum of the operation data elements to which the weight of each element is applied. Resource utilization can be calculated as in Equation 1 below.

[Equation 1]

는 각 매장의 성과데이터 요소별 가중치, 상기

는 각 매장의 운영데이터 요소별 가중치, 상기 n은 성과데이터 요소 개수, 상기 m은 운영데이터 요소 개수를 나타낸다.In Equation 1, x is a performance data element of each store, y is an operation data element of each store, and

Is the weight of each store's performance data element

Denotes the weight of each store's operation data element, n denotes the number of performance data elements, and m denotes the number of operation data elements.

For example, when including sales, employee friendliness, and store cleanliness as performance data elements, x1 may be sales, x2 may be employee friendliness, and x3 may be store cleanliness, and w1, w2, w3 are the weights of each of x1, x2, and x3. Becomes. Here, since there are three performance data elements, n is 3. Since the units of each value of x1, x2, and x3 are different, a ratio to the reference value can be used to match the units. Alternatively, the employee-friendly railroad or store cleanliness can be converted into an amount and set as a whole.

2 is a diagram illustrating a resource utilization calculation step according to an embodiment of the present invention.

An example of calculating resource utilization will be described with reference to FIG. 2 as follows.

First of all, labor costs, material costs, rental fees, etc. are entered at each store or checked through a slip purchased at the franchise headquarters. In addition, sales can be checked in the same way, and employee friendliness and store cleanliness can be checked at the franchise headquarters through a separate app, internet, handwritten questionnaires, and interviews.

When data input is completed, the weight at which the resource utilization of each store is maximized is calculated based on the input data through an artificial intelligence algorithm as described above.

Employee friendliness and store cleanliness are converted into sales by taking into account the degree to which they have contributed to sales. Here, we use this figure as it contributes to sales in terms of 5 million won.

In this way, when calculated through Equation 1 above, the resource utilization value is the highest at store 1 at 3.01, and the lowest at store 4 at 1.48. On the other hand, the resource utilization of the standard store is 2.27, which is lower than that of stores 1 to 3 and 5. That is, the standard store selects a role model store from store 1 to store 3 and store 5.

The resource utilization is a parameter for measuring operational efficiency by quantifying how well each store utilizes operating elements. By calculating resource utilization, businesses that manage multiple stores can measure and compare the operational efficiency of each store.In order to increase the resource utilization of each store, stores with relatively higher resource utilization are selected as role model stores for each store. It is possible to suggest target points that each store should aim for.

3 is a view for explaining a role model store selection step according to an embodiment of the present invention.

In the role model store selection step (S40), stores having a resource utilization value higher than the resource utilization value of the reference store among a plurality of stores performing the same business activity under the same business entity are classified. Here, the reference store may be all of the stores in which a business model is to be generated among the plurality of stores.

Thereafter, among the classified stores, the store with the smallest sum of the difference between the ratio of operating data elements with the reference store or the least difference with the element with the highest priority among the operational data elements is selected as the role model store of the reference store. do.

Hereinafter, based on the value calculated with reference to FIG. 2 in the resource utilization calculation step (S30), a role model store selection step (S40) will be described with reference to FIG. 3.

Referring to FIG. 3, as described above in the resource utilization calculation step (S30), for example, when the operation data elements include material cost, labor cost, and rent, first, the reference store and the store 4 whose resource utilization value is lower than that of the reference store are described. By dividing the operation data elements of each excluded store into the operation data elements of the reference store, the ratio of the operation data elements of the remaining stores to the reference store can be obtained.

As shown in FIG. 3, if the average monthly material cost of store 1 is 3 million won and the average monthly material cost of the reference store is 2 million won, the ratio of the average monthly material cost to the reference store of store 1 is 1.5, and The difference in the ratio of the monthly average material cost is 0.5. As described above, for each store by operation data element, the difference value of the ratio of the operation data element to the reference store is calculated, and the sum of the difference value is calculated. Thereafter, among the stores, the store with the smallest sum of the difference between the ratio of the operating data element and the reference store may be selected as the role model store. Referring to FIG. 3, in this case, store 2 may be selected as a role model store of the reference store.

In addition, continuing to refer to FIG. 3, a store having the least difference value from the element having the highest priority among the operation data elements compared to the reference store may be selected as the role model store of the reference store. If the labor cost of a standard store needs to be improved more than the material cost or rent, the labor cost may be the top priority. Accordingly, in this case, by comparing only the difference value between the labor cost and the reference store, the store 5 having the smallest difference between the reference store and the labor cost may be selected as the role model store of the reference store.

In addition, by calculating the difference value from the standard store for each operating data element for each store, the store with the highest ranking in the order of the least difference can be selected as the role model store. When there are multiple branches, the role model store may be finally selected by comparing the operational data elements having the highest priority among them.

In the business model generation step (S50), the resource utilization value of the role model store is set as the target value of the reference store.

The goal that the base store can realistically achieve as close as possible by creating an optimal business model for each store by selecting a store with a higher resource utilization value than the reference store and the least difference in operation data elements from the reference store as the role model store of the reference store. Can be presented.

The business model analyzes the difference between the elements of the operation data and performance data of the reference store and the role model store so that the reference store can reach the resource utilization value of the role model store, and compares it with the role model store to compare the operation data and performance data of the reference store. In order to compensate for the relatively sluggish elements among the factors, it is possible to present the target points that the reference store should aim for, and provide specific improvement plans and operation strategies.

In addition, when the reference store reaches the resource utilization value of the role model store, the data input step (S10), the weight calculation step (S20), the resource utilization calculation step (S30), the role model store selection step (S40), and the business model generation step ( By repeating S50) to create a new business model, the resource utilization of the reference store can be increased and operational efficiency can be continuously improved through this.

By applying this process to all of a plurality of stores that perform the same business activities under the same business entity, the operational efficiency of each store can be maximized and the overall operational efficiency of multiple stores can be improved.

As described above, a preferred embodiment of the present invention has been illustrated and described with reference to the drawings, but the present invention is not limited to the specific embodiments described above, and the present invention is not departing from the gist of the present invention claimed in the claims. Various modifications can be made by those of ordinary skill in the art to which the invention pertains, and these modifications should not be individually understood from the technical spirit or prospect of the present invention.

Claims (2)

In a method for generating a business model for managing multiple stores of a management system for managing multiple stores,
A data input step of receiving operation data and performance data for each store;
A weight calculation step of calculating a weight for each operation data element and a weight for each performance data element for each store;
A resource utilization calculation step of calculating resource utilization by dividing the sum of the performance data elements to which the weight of each performance data element is applied for each store by the sum of the operation data elements to which the weight of each operation data element is applied;
Among the stores, the resource utilization value is higher than the resource utilization value of the reference store, and the sum of the difference values of the ratios of the operation data elements with the reference store is the least, or the operation data element has the highest priority. A role model store selection step of selecting a store with the smallest difference value as a role model store of the reference store;
A method of generating a business model for managing multiple stores, including a business model generation step of setting the resource utilization value of the role model store as a target value of the reference store. The method of claim 1,
In the step of calculating the weight, a method for generating a business model for managing multiple stores calculates a weight for each element of the operation data and a weight for each element of the performance data so that the resource utilization value of each store is maximized.

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