Method and device for determining procurement data, and computer-readable storage medium

Abstract

The present invention discloses a method for determining procurement data, a device thereof, and a computer-readable storage medium. The method includes: obtaining market-oriented data of a target product, wherein the market-oriented data is data that affects the sales quantity of the target product; using the market-oriented data to predict the estimated sales quantity of the target product; determining predetermined cost data corresponding to the estimated sales quantity; processing the predetermined cost data by a predetermined algorithm to optimize the estimated sales quantity and obtain target procurement data. The present invention solves the technical problem that the reasonable ratio of raw material procurement quantity and production capacity cannot be controlled during procurement in the related technology, resulting in high production costs.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for determining procurement data, a device thereof, and a computer-readable storage medium. Background Art

[0002] Investing in and building factories overseas is one of the important ways for manufacturing companies to go global. However, setting up production bases overseas involves many factors, such as remote geographical location, local differences, market demand, and storage costs. When purchasing overseas, companies need to be market-oriented and consider many factors. If they purchase more, the raw materials are cheap, but the production capacity is likely to be oversupplied, which will lead to additional storage costs. If they purchase less, the raw materials will be more expensive, the production cost will increase, and the market may be in short supply. Therefore, it is urgent to introduce a market-oriented optimization method to balance the relationship between these factors and achieve the goal of the optimal procurement plan.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a method for determining procurement data and an apparatus thereof, as well as a computer-readable storage medium, to at least solve the technical problem in the related technology that the reasonable ratio between the procurement quantity of raw materials and the production capacity cannot be controlled during procurement, resulting in high production costs.

[0005] According to one aspect of an embodiment of the present invention, there is provided a method for determining procurement data, comprising: obtaining market-oriented data of a target product, wherein the market-oriented data is data that affects the sales quantity of the target product; using the market-oriented data to predict an estimated sales quantity of the target product; determining predetermined cost data corresponding to the estimated sales quantity; and processing the predetermined cost data by a predetermined algorithm to optimize the estimated sales quantity to obtain target procurement data.

[0006] Optionally, the market-oriented data includes: current sales time, current sales location, historical sales quantity, historical selling price and sales strategy.

[0007] Optionally, the predicting the estimated sales quantity of the target product by using the market-oriented data includes: predicting the estimated sales quantity corresponding to the market-oriented data by using a first formula, wherein the first formula is: S j =T j +P j +Q j +R j +Sp j , S j represents the estimated sales quantity, T jrepresents the impact of the current sales time on the estimated sales quantity, P j represents the impact of the current sales location on the estimated sales quantity, Q j Represents the impact of the historical sales quantity on the estimated sales quantity, R j Indicates the impact of the historical selling price on the estimated sales quantity, Sp j represents the impact value of the sales strategy on the estimated sales quantity, j represents the type of the target product, and j is a positive integer.

[0008] Optionally, the predetermined cost data includes: purchasing cost data, transportation cost data and inventory management cost data.

[0009] Optionally, determining the purchase cost data corresponding to the estimated sales quantity includes: determining the purchase cost corresponding to the estimated sales quantity by using a second formula, wherein the second formula is: Mc represents the purchase cost, x i represents the purchase quantity of the i-th raw material at the first location, y i represents the purchase quantity of the i-th raw material at the second location, c i represents the purchase price of the i-th raw material at the first location, c bi represents the purchase price of the i-th raw material at the second location, n represents the number of types of the raw materials, and i represents the type of the raw material.

[0010] Optionally, the sum of the purchase quantity of the i-th raw material at the first location and the purchase quantity of the i-th raw material at the second location is not less than the total demand for the i-th raw material.

[0011] Optionally, determining the transportation cost data corresponding to the estimated sales quantity includes: determining the transportation cost data corresponding to the estimated sales quantity by a third formula, wherein the third formula is: Tc represents the transportation cost data, p con represents the unit price of the container required for transportation, w con represents the unit load of the container, y i Represents the purchase quantity of the i-th raw material at the second location.

[0012] Optionally, determining the inventory management cost data corresponding to the estimated sales quantity includes: determining the inventory management cost data corresponding to the estimated sales quantity by using a fourth formula, wherein the fourth formula is: Ic represents the inventory management cost, q irepresents the number of warehouses required for the i-th raw material, r i represents the storage price per unit of the i-th raw material per unit time, t 1 represents the inventory time of the raw material at the first predetermined location, t 2 Indicates the inventory time of the raw material at the second predetermined location.

[0013] Optionally, the predetermined algorithm is one of the following: simulated annealing algorithm, hill climbing algorithm, particle swarm algorithm and genetic algorithm.

[0014] According to another aspect of an embodiment of the present invention, a device for determining procurement data is also provided, including: an acquisition module, used to acquire market orientation data of a target product, wherein the market orientation data is data that affects the sales quantity of the target product; a prediction module, used to predict the estimated sales quantity of the target product using the market orientation data; a determination module, used to determine predetermined cost data corresponding to the estimated sales quantity; and an optimization module, used to process the predetermined cost data through a predetermined algorithm to optimize the estimated sales quantity to obtain target procurement data.

[0015] Optionally, the market-oriented data includes: current sales time, current sales location, historical sales quantity, historical selling price and sales strategy.

[0016] Optionally, the prediction module includes: an estimation unit, configured to predict the estimated sales quantity corresponding to the market-oriented data by using a first formula, wherein the first formula is: S j =T j +P j +O j +R j +Sp j , S j represents the estimated sales quantity, T j represents the impact of the current sales time on the estimated sales quantity, P j represents the impact value of the current sales location on the estimated sales quantity, O j Represents the impact of the historical sales quantity on the estimated sales quantity, R j Indicates the impact of the historical selling price on the estimated sales quantity, Sp j represents the impact value of the sales strategy on the estimated sales quantity, j represents the type of the target product, and j is a positive integer.

[0017] Optionally, the predetermined cost data includes: purchasing cost data, transportation cost data and inventory management cost data.

[0018] Optionally, the determining the purchase cost data corresponding to the estimated sales quantity includes: a first determining unit, configured to determine the purchase cost corresponding to the estimated sales quantity by using a second formula, wherein the second formula is: Mc represents the purchase cost, x i represents the purchase quantity of the i-th raw material at the first location, y i represents the purchase quantity of the i-th raw material at the second location, c j represents the purchase price of the i-th raw material at the first location, c bi represents the purchase price of the i-th raw material at the second location, n represents the number of types of the raw materials, and i represents the type of the raw material.

[0019] Optionally, the sum of the purchase quantity of the i-th raw material at the first location and the purchase quantity of the i-th raw material at the second location is not less than the total demand for the i-th raw material.

[0020] Optionally, the determining the transportation cost data corresponding to the estimated sales quantity includes: a second determining unit, configured to determine the transportation cost data corresponding to the estimated sales quantity by a third formula, wherein the third formula is: Tc represents the transportation cost data, p con represents the unit price of the container required for transportation, w con represents the unit load of the container, y i Represents the purchase quantity of the i-th raw material at the second location.

[0021] Optionally, the determining the inventory management cost data corresponding to the estimated sales quantity includes: a third determining unit, configured to determine the inventory management cost data corresponding to the estimated sales quantity by using a fourth formula, wherein the fourth formula is: Ic represents the inventory management cost, q i represents the number of warehouses required for the i-th raw material, r i represents the storage price per unit of the i-th raw material per unit time, t 1 represents the inventory time of the raw material at the first predetermined location, t 2 Indicates the inventory time of the raw material at the second predetermined location.

[0022] Optionally, the predetermined algorithm is one of the following: simulated annealing algorithm, hill climbing algorithm, particle swarm algorithm and genetic algorithm.

[0023] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for determining procurement data described above.

[0024] According to another aspect of an embodiment of the present invention, a processor is provided, wherein the processor is used to run a computer program, wherein the computer program executes any one of the above-mentioned methods for determining procurement data when running.

[0025] In an embodiment of the present invention, market-oriented data of a target product is obtained, wherein the market-oriented data is data that affects the sales quantity of the target product; the estimated sales quantity of the target product is predicted using the market-oriented data; the predetermined cost data corresponding to the estimated sales quantity is determined; the predetermined cost data is processed by a predetermined algorithm to optimize the estimated sales quantity, thereby obtaining target procurement data. The procurement data determination method of the embodiment of the present invention achieves the purpose of estimating the sales quantity and the predetermined cost data using the obtained market-oriented data of the target product and optimizing the estimated sales quantity using the sales quantity and the predetermined cost data, thereby achieving the technical effect of reducing production costs, and further solving the technical problem that the reasonable ratio of the raw material procurement volume and the production capacity cannot be controlled during procurement in the related technology, resulting in high production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0027] Figure 1 is a flowchart of a method for determining procurement data according to an embodiment of the present invention;

[0028] Figure 2 is a flow chart of a method for optimizing a procurement plan based on market demand according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of a device for determining procurement data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Example 1

[0033] According to an embodiment of the present invention, a method embodiment of a method for determining procurement data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] Figure 1 is a flowchart of a method for determining procurement data according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0035] Step S102, obtaining market orientation data of the target product, wherein the market orientation data is data that affects the sales volume of the target product.

[0036] Optionally, in the above steps, market-oriented data mainly refers to data that affects the sales volume of the target product, such as: the sales season, the sales location, the sales volume of the product in the same period of previous years, the price of the same-positioned products in previous years, and the local sales policy for the target product.

[0037] Step S104: predicting the estimated sales volume of the target product using the market orientation data.

[0038] Optionally, in the above steps, all market-oriented data can be combined and assigned different weights, and the sales volume for the year can be estimated through a certain algorithm.

[0039] Step S106, determining the predetermined cost data corresponding to the estimated sales quantity.

[0040] Optionally, in the above steps, the estimated sales quantity can be combined with various costs to calculate the total predetermined cost data.

[0041] Step S108, processing the predetermined cost data through a predetermined algorithm to optimize the estimated sales quantity and obtain target procurement data.

[0042] As can be seen from the above, in the embodiment of the present invention, first, the market-oriented data of the target product can be obtained, wherein the market-oriented data is data that affects the sales quantity of the target product; then the estimated sales quantity of the target product is predicted using the market-oriented data; then the predetermined cost data corresponding to the estimated sales quantity is determined; finally, the predetermined cost data is processed by a predetermined algorithm to optimize the estimated sales quantity and obtain the target procurement data. The method for determining procurement data of the embodiment of the present invention achieves the purpose of estimating the sales quantity and the predetermined cost data using the acquired market-oriented data of the target product and optimizing the estimated sales quantity using the sales quantity and the predetermined cost data, thereby achieving the technical effect of reducing production costs, and further solving the technical problem that the reasonable ratio of the raw material procurement volume and the production capacity cannot be controlled during procurement in the related technology, resulting in high production costs.

[0043] Due to insufficient demand for the same product at different times, such as the demand for air conditioners in summer is generally different from that in winter; people in different places may also have different demands for air conditioners, such as residents in the northern region have less demand for air conditioners than residents in the southern region; therefore, in an embodiment of the present invention, time and location are used as factors for predicting market sales.

[0044] In addition, the sales volume of the product in the current year can also be predicted by a simple mathematical analysis based on the sales volume in the same period of previous years. Generally speaking, the company will promote several products of different grades each year, and the sales volume of this year's products can be predicted by analogy with the sales volume of products with the same positioning in previous years. The impact of the company's promotional policy on product sales. Therefore, in the embodiment of the present invention, the sales volume of products in the same period of previous years, the prices of products with the same positioning in previous years, and the sales policy are also used as factors for predicting market sales.

[0045] As an optional embodiment, the market-oriented data includes: current sales time, current sales location, historical sales quantity, historical selling price and sales strategy.

[0046] In this embodiment, the data that influences the market orientation includes, but is not limited to: current sales time, current sales location, historical sales quantity, historical selling price, and sales strategy, etc.

[0047] Furthermore, for example, if the target sales product is a refrigeration air conditioner, the current sales time and season must be considered. If the season is winter and the perceived temperature is lower than 15°C, it will greatly affect the sales volume of the refrigeration air conditioner. At this time, the sales strategy needs to be adjusted to maximize the sales data.

[0048] For example, the historical sales volume of the target product last year was lower than the average expectation. The reason for this is that the market share was eroded by local overseas companies, while the market share did not increase significantly that year. In this case, the estimated sales volume needs to be reduced to avoid unnecessary losses caused by excessive inventory.

[0049] As an optional embodiment, using the market-oriented data to predict the estimated sales volume of the target product includes: predicting the estimated sales volume corresponding to the market-oriented data by using a first formula, wherein the first formula is: S j =T j +P j +O j +R j +Sp j , S j represents the estimated sales quantity, T j Indicates the impact of the current sales time on the estimated sales quantity, P j Indicates the impact of the current sales location on the estimated sales quantity, O i Represents the impact of historical sales quantity on estimated sales quantity, R i Indicates the impact of historical selling prices on estimated sales volume, Sp j It represents the impact of sales strategy on the estimated sales quantity, j represents the type of target product, and j is a positive integer.

[0050] It should be noted that in the above optional embodiments, many influencing factors work together to affect the estimated sales volume, and there may also be influencing effects between the influencing factors. For example, when the target sales product is a refrigeration air conditioner, and when the local temperature overseas drops, since the estimated sales volume may decrease, it is considered that the sales strategy is changed to discount the refrigeration air conditioner to promote sales. At this time, the current sales time indirectly affects the sales strategy parameter.

[0051] As an optional embodiment, the predetermined cost data includes: purchasing cost data, transportation cost data and inventory management cost data.

[0052] It should be pointed out that procurement cost data refers to the cost expenditure of enterprises in purchasing target sales products, which can be roughly divided into two ways: procurement from domestic factories and procurement from local factories. Among them, if procurement is from domestic factories, transportation costs will increase greatly, and if procurement is from local factories, warehousing costs will increase greatly.

[0053] As an optional embodiment, determining the purchase cost data corresponding to the estimated sales quantity includes: determining the purchase cost corresponding to the estimated sales quantity by using a second formula, wherein the second formula is: Mc represents the purchase cost, xi represents the purchase quantity of the i-th raw material at the first location, yi represents the purchase quantity of the i-th raw material at the second location, c i represents the purchase price of the i-th raw material at the first location, c bi represents the purchase price of the i-th raw material at the second location, n represents the number of types of raw materials, and i represents the type of raw material.

[0054] In the above optional embodiment, the purchase cost data is determined using the estimated sales quantity, and the purchase cost corresponding to the estimated sales quantity is determined by a second formula, wherein the second formula is obtained by adding the value obtained by multiplying the purchase quantity purchased domestically (i.e., the first location) by the purchase cost and the value obtained by multiplying the purchase quantity purchased locally (i.e., the second location) by the purchase cost.

[0055] As an optional embodiment, the sum of the purchase quantity of the i-th raw material at the first location and the purchase quantity of the i-th raw material at the second location is not less than the total demand for the i-th raw material.

[0056] That is, x i +y i ≥Q i , i=1,2...n.

[0057] Optionally, when there are j products that have a purchase demand for the i-th material, the formula can be used To represent, where Q i represents the total demand for the ith material of all products, Q ji It represents the demand of the j-th product for the i-th material.

[0058] It should be noted that, in the embodiment of the present invention, c i =W(Mi),c bi =W b (Mi). Among them, W(Mi) represents the functional relationship between the market estimated sales volume and the local procurement cost price of raw materials, W b (Mi) represents the functional relationship between market estimated sales volume and cross-border raw material procurement prices.

[0059] As an optional embodiment, determining the transportation cost data corresponding to the estimated sales quantity includes: determining the transportation cost data corresponding to the estimated sales quantity by a third formula, wherein the third formula is: Tc represents the transportation cost data, p con represents the unit price of the container required for transportation, w con represents the unit load of the container, y i Represents the purchase quantity of the i-th raw material at the second location.

[0060] It should be noted that, when using the estimated sales quantity to determine the transportation cost data, the required number of containers can be calculated by first dividing the purchase quantity in the local area (i.e., the second location) by the unit carrying capacity of the container, and then the required number of containers is multiplied by the unit price of the containers required for transportation to obtain the total transportation cost data.

[0061] As an optional embodiment, determining the inventory management cost data corresponding to the estimated sales quantity includes: determining the inventory management cost data corresponding to the estimated sales quantity by using a fourth formula, wherein the fourth formula is: Ic represents inventory management costs, q i represents the number of warehouses required for the i-th raw material, r i represents the storage price per unit of the i-th raw material per unit time, t 1 represents the inventory time of the raw material at the first predetermined location, t 2 Indicates the inventory time of the raw materials at the second predetermined location.

[0062] In this embodiment, the inventory management cost data is calculated using the estimated sales quantity by multiplying the number of warehouses required for the various raw materials by the corresponding storage unit prices of the various raw materials per unit time, and then multiplying the result by the sum of the inventory time of the raw materials at the first predetermined location and the inventory time of the raw materials at the second predetermined location to obtain the total inventory management fee.

[0063] As an optional embodiment, the predetermined algorithm is one of the following: simulated annealing algorithm, hill climbing algorithm, particle swarm algorithm and genetic algorithm.

[0064] It should be noted that the simulated annealing algorithm in the above embodiment is an algorithm that can probabilistically jump out of the local optimal solution and eventually obtain the global optimal solution; the hill climbing algorithm is a local optimization algorithm that uses a heuristic method to improve the depth-first search; the particle swarm algorithm is a random search algorithm based on group collaboration developed through the foraging behavior of bird flocks; the genetic algorithm is a computational model of the biological evolution process that simulates the natural selection and genetics mechanism of Darwin's theory of biological evolution.

[0065] Among them, in the embodiment of the present invention, the evaluation function can be established with the minimum procurement cost as the objective function: is the cost type, E refers to the cost type, E 1 =Mc,E 2 =Tc,E 3 =Ic. Taking market orientation as the main influencing factor, consider the minimum production cost under the condition of meeting market sales volume.

[0066] Figure 2 is a flow chart of a method for optimizing a procurement plan based on market demand according to an embodiment of the present invention. Figure 2 As shown, first consider the impact of various market factors on product sales, such as time, location, product sales in the same period of previous years, price and sales of products with the same positioning in previous years, sales policy and other factors; then consider the current five major factors for market orientation, such as time, the demand for the same product at different times is insufficient, such as the demand for air conditioners in summer is different from that in winter; location, people in different places may have different demands for air conditioners, such as residents in the northern region have less demand for air conditioners than residents in the south; product sales in the same period of previous years, we can predict this year's air conditioner sales through simple mathematical analysis based on sales in the same period of previous years; product prices with the same positioning in previous years, generally speaking, companies will promote several air conditioners of different grades each year, we can predict this year's product sales by analogy with the sales of products with the same positioning in previous years; sales policy, the impact of the company's promotion policy on product sales, then set the following variables to establish a model to predict market sales, and then establish a mathematical model, combined with market conditions, estimate market sales, and analyze procurement volume, transportation costs, and warehousing costs, and finally continuously iterate the algorithm to find the optimal procurement plan.

[0067] From the above, it can be seen that in the embodiment of the present invention, by taking market orientation as the main factor and logistics capacity, warehousing capacity and other factors as the auxiliary factors, comprehensive consideration is taken to minimize the procurement cost, and big data is used to analyze the supply mode of existing material parts, the market demand for products, and the comprehensive comparison of storage cost and procurement cost, so as to optimize the enterprise's procurement plan.

[0068] Example 2

[0069] According to another aspect of the embodiment of the present invention, a device for determining procurement data is also provided. Figure 3 is a schematic diagram of a device for determining purchase data according to an embodiment of the present invention. Figure 3 As shown, the purchase data determination device includes: an acquisition module 31, a prediction module 33, a determination module 35 and an optimization module 37. The purchase data determination device is described below.

[0070] The acquisition module 31 is used to acquire market orientation data of the target product, wherein the market orientation data is data that affects the sales volume of the target product.

[0071] The prediction module 33 is used to predict the estimated sales volume of the target product by using the market-oriented data.

[0072] The determination module 35 is used to determine the predetermined cost data corresponding to the estimated sales quantity.

[0073] The optimization module 37 is used to process the predetermined cost data through a predetermined algorithm to optimize the estimated sales quantity and obtain the target procurement data.

[0074] It should be noted that the acquisition module 31, prediction module 33, determination module 35 and optimization module 37 correspond to steps S102 to S108 in Example 1, and the examples and application scenarios implemented by the modules are the same as those of the corresponding steps, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the modules as part of the device can be executed in a computer system such as a set of computer executable instructions.

[0075] As can be seen from the above, in the embodiment of the present invention, firstly, the acquisition module 31 can be used to acquire the market-oriented data of the target product, wherein the market-oriented data is data that affects the sales quantity of the target product; then, the market-oriented data is used to predict the estimated sales quantity of the target product with the help of the prediction module 33; then, the predetermined cost data corresponding to the estimated sales quantity is determined by the determination module 35; finally, the predetermined cost data is processed by the optimization module 37 through a predetermined algorithm to optimize the estimated sales quantity and obtain the target procurement data. Through the procurement data determination device of the embodiment of the present invention, the purpose of estimating the sales quantity and the predetermined cost data using the acquired market-oriented data of the target product and optimizing the estimated sales quantity using the sales quantity and the predetermined cost data is achieved, thereby achieving the technical effect of reducing the production cost, and further solving the technical problem that the reasonable ratio of the raw material procurement quantity and the production capacity cannot be controlled during procurement in the related technology, resulting in high production costs.

[0076] Optionally, the market-oriented data includes: current sales time, current sales location, historical sales quantity, historical selling price and sales strategy.

[0077] Optionally, the prediction module includes: an estimation unit, configured to predict the estimated sales quantity corresponding to the market-oriented data by using a first formula, wherein the first formula is: S j =T j +P j +O j +R j +Sp j , Sj represents the estimated sales quantity, T j Indicates the impact of the current sales time on the estimated sales quantity, P j represents the impact of the current sales location on the estimated sales quantity, Q j Represents the impact of historical sales quantity on estimated sales quantity, R j Indicates the impact of historical selling prices on estimated sales volume, Sp i It represents the impact of sales strategy on the estimated sales quantity, j represents the type of target product, and j is a positive integer.

[0078] Optionally, the predetermined cost data includes: purchasing cost data, transportation cost data and inventory management cost data.

[0079] Optionally, determining the purchase cost data corresponding to the estimated sales quantity includes: a first determining unit, configured to determine the purchase cost corresponding to the estimated sales quantity by using a second formula, wherein the second formula is: Mc represents the purchase cost, xi represents the purchase quantity of the i-th raw material at the first location, yi represents the purchase quantity of the i-th raw material at the second location, c i represents the purchase price of the i-th raw material at the first location, c bi represents the purchase price of the i-th raw material at the second location, n represents the number of types of raw materials, and i represents the type of raw material.

[0080] Optionally, the sum of the purchase quantity of the i-th raw material at the first location and the purchase quantity of the i-th raw material at the second location is not less than the total demand for the i-th raw material.

[0081] Optionally, determining the transportation cost data corresponding to the estimated sales quantity includes: a second determining unit, configured to determine the transportation cost data corresponding to the estimated sales quantity by using a third formula, wherein the third formula is: Tc represents the transportation cost data, p con represents the unit price of the container required for transportation, w con represents the unit load of the container, y i Represents the purchase quantity of the i-th raw material at the second location.

[0082] Optionally, determining the inventory management cost data corresponding to the estimated sales quantity includes: a third determining unit, configured to determine the inventory management cost data corresponding to the estimated sales quantity by using a fourth formula, wherein the fourth formula is: Ic represents inventory management costs, q i represents the number of warehouses required for the i-th raw material, r irepresents the storage price per unit of the i-th raw material per unit time, t 1 represents the inventory time of the raw material at the first predetermined location, t 2 Indicates the inventory time of the raw materials at the second predetermined location.

[0083] Optionally, the predetermined algorithm is one of the following: simulated annealing algorithm, hill climbing algorithm, particle swarm algorithm and genetic algorithm.

[0084] Example 3

[0085] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein when the computer program is executed by a processor, the device where the computer-readable storage medium is located is controlled to execute any of the above-mentioned methods for determining procurement data.

[0086] Example 4

[0087] According to another aspect of an embodiment of the present invention, a processor is provided, and the processor is used to run a computer program, wherein the computer program executes any of the above-mentioned methods for determining procurement data when running.

[0088] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0089] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0090] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0091] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0092] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0094] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining procurement data, characterized in that: include: Acquire market orientation data of the target product, wherein the market orientation data is data that affects the sales volume of the target product, and the market orientation data includes: current sales time, current sales location, historical sales volume, historical selling price and sales strategy; forecasting the estimated sales volume of the target product using the market-oriented data; Determining predetermined cost data corresponding to the estimated sales quantity, wherein the predetermined cost data includes: purchasing cost data, transportation cost data and inventory management cost data; Processing the predetermined cost data by a predetermined algorithm to optimize the estimated sales quantity and obtain target procurement data; Wherein, the predicting the estimated sales volume of the target product by using the market-oriented data includes: Predicting the estimated sales quantity corresponding to the market-oriented data by a first formula, wherein the first formula is: S_j=T_j+P_j+O_j+R_j+Sp_j, S_j represents the estimated sales quantity, T_j represents the impact value of the current sales time on the estimated sales quantity, P_j represents the impact value of the current sales location on the estimated sales quantity, O_j represents the impact value of the historical sales quantity on the estimated sales quantity, R_j represents the impact value of the historical selling price on the estimated sales quantity, Sp_j represents the impact value of the sales strategy on the estimated sales quantity, j represents the type of the target product, and j is a positive integer; Wherein, the determining the purchase cost data corresponding to the estimated sales quantity includes: determining the purchase cost corresponding to the estimated sales quantity by a second formula, wherein the second formula is: Mc represents the purchase cost, x i represents the purchase quantity of the i-th raw material at the first location, y i represents the purchase quantity of the i-th raw material at the second location, c i represents the purchase price of the i-th raw material at the first location, c bi represents the purchase price of the i-th raw material at the second location, n represents the number of types of the raw materials, and i represents the type of the raw material; Wherein, the determining the transportation cost data corresponding to the estimated sales quantity includes: determining the transportation cost data corresponding to the estimated sales quantity by a third formula, wherein the third formula is: Tc represents the transportation cost data, p con represents the unit price of the container required for transportation, w con represents the unit load of the container, y i represents the purchase quantity of the i-th raw material at the second location; Wherein, determining the inventory management cost data corresponding to the estimated sales quantity includes: determining the inventory management cost data corresponding to the estimated sales quantity by a fourth formula, wherein the fourth formula is: Ic represents the inventory management cost, q i represents the number of warehouses required for the i-th raw material, r i represents the storage price of the i-th raw material per unit quantity per unit time, t represents the inventory time of the raw material at the first predetermined location, and t2 represents the inventory time of the raw material at the second predetermined location.

2. The method according to claim 1, characterized in that The sum of the purchase quantity of the i-th raw material at the first location and the purchase quantity of the i-th raw material at the second location is not less than the total demand for the i-th raw material.

3. The method according to claim 1 or 2, characterized in that: The predetermined algorithm is one of the following: simulated annealing algorithm, hill climbing algorithm, particle swarm algorithm and genetic algorithm.

4. A device for determining procurement data, characterized in that: include: An acquisition module, used to acquire market orientation data of a target product, wherein the market orientation data is data that affects the sales volume of the target product, and the market orientation data includes: current sales time, current sales location, historical sales volume, historical selling price and sales strategy; A forecasting module, used to forecast the estimated sales volume of the target product using the market orientation data; A determination module, configured to determine predetermined cost data corresponding to the estimated sales quantity, wherein the predetermined cost data includes: purchase cost data, transportation cost data, and inventory management cost data; An optimization module, used for processing the predetermined cost data through a predetermined algorithm to optimize the estimated sales quantity and obtain target procurement data; The prediction module includes: an estimation unit, which is used to predict the estimated sales volume corresponding to the market-oriented data by using a first formula, wherein the first formula is: S j =T j +P j +O j +R j +Sp j , S j represents the estimated sales quantity, T j represents the impact of the current sales time on the estimated sales quantity, P j represents the impact value of the current sales location on the estimated sales quantity, O j Represents the impact of the historical sales quantity on the estimated sales quantity, R j Indicates the impact of the historical selling price on the estimated sales quantity, Sp j represents the impact value of the sales strategy on the estimated sales quantity, j represents the type of the target product, and j is a positive integer; Wherein, the determining of the purchase cost data corresponding to the estimated sales quantity includes: a first determining unit, configured to determine the purchase cost corresponding to the estimated sales quantity by a second formula, wherein the second formula is: Mc represents the purchase cost, x i represents the purchase quantity of the i-th raw material at the first location, y i represents the purchase quantity of the i-th raw material at the second location, c i represents the purchase price of the i-th raw material at the first location, c bi represents the purchase price of the i-th raw material at the second location, n represents the number of types of the raw materials, and i represents the type of the raw material; Wherein, the determining of the transportation cost data corresponding to the estimated sales quantity includes: a second determining unit, configured to determine the transportation cost data corresponding to the estimated sales quantity by a third formula, wherein the third formula is: Tc represents the transportation cost data, p con represents the unit price of the container required for transportation, w con represents the unit load of the container, y i represents the purchase quantity of the i-th raw material at the second location; Wherein, the determining of the inventory management cost data corresponding to the estimated sales quantity includes: a third determining unit, configured to determine the inventory management cost data corresponding to the estimated sales quantity by a fourth formula, wherein the fourth formula is: Ic represents the inventory management cost, q i represents the number of warehouses required for the i-th raw material, r i represents the storage price of the i-th raw material per unit quantity per unit time, t1 represents the inventory time of the raw material at the first predetermined location, and t2 represents the inventory time of the raw material at the second predetermined location.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed by a processor, the device where the computer-readable storage medium is located is controlled to execute the method for determining procurement data described in any one of claims 1 to 3.

6. A processor, characterized in that: The processor is used to run a computer program, wherein the computer program executes the method for determining procurement data according to any one of claims 1 to 3 when running.

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