CN113780612A - Method and device for optimizing purchasing data - Google Patents

Method and device for optimizing purchasing data Download PDF

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CN113780612A
CN113780612A CN202011462097.XA CN202011462097A CN113780612A CN 113780612 A CN113780612 A CN 113780612A CN 202011462097 A CN202011462097 A CN 202011462097A CN 113780612 A CN113780612 A CN 113780612A
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period
purchase
data
cost
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詹昌飞
陈磊
戚永志
申作军
郭旭波
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Beijing Jingdong Shangke Information Technology Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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Beijing Wodong Tianjun Information Technology Co Ltd
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Abstract

The invention discloses a method and a device for optimizing purchase data, and relates to the technical field of computers. One embodiment of the method comprises: acquiring relevant target attribute data in a purchase period by taking a preset time period for multiple purchases as the purchase period, and performing structured storage on the target attribute data through a preset processing model; respectively calling a preset cost calculation engine and a preset income calculation engine, and extracting corresponding data in the stored target attribute data to respectively calculate and obtain the purchasing cost and purchasing income of multiple times of purchasing in a purchasing period; and according to the purchasing cost and the purchasing income of multiple times of purchasing in the purchasing period, carrying out optimization solution on the preset purchasing income model so as to obtain and output the purchasing quantity of each time of purchasing in multiple times of purchasing. In this embodiment, the cost and income of the multiple purchases are calculated based on the purchase period in which the multiple purchases are performed, and the optimized purchase amount per purchase can be obtained in consideration of the mutual influence of the multiple purchases.

Description

Method and device for optimizing purchasing data
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for optimizing purchase data.
Background
The profit of a sales business depends on revenue and cost, where provider rebates have a large impact on the revenue component, and rebate-type purchases are one of the important scenarios for business daily decision-making. For rebate-type procurement, the method mainly comprises annual planning and daily procurement decision.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
1) the cost calculation is coarse in logic granularity, and the considered items are not comprehensive.
2) The current calculation logic is mainly used for calculating single purchasing, and the considered cost and income factors are only generated in single purchasing and can not realize optimization of purchasing quantity aiming at multiple times of purchasing.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for optimizing purchasing data, which can take mutual influence of multiple purchases into consideration and obtain an optimized purchasing amount per purchase.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method for optimizing procurement data, including:
taking a preset time period for multiple times of purchasing as a purchasing period, acquiring related target attribute data in the purchasing period, and performing structured storage on the target attribute data through a preset processing model;
respectively calling a preset cost calculation engine and a preset income calculation engine, and extracting corresponding data in the stored target attribute data to respectively calculate and obtain the purchasing cost and purchasing income of multiple times of purchasing in the purchasing period; and
and according to the purchasing cost and the purchasing income of multiple times of purchasing in the purchasing period, carrying out optimization solution on a preset purchasing income model so as to obtain and output the purchasing quantity of each time of purchasing in multiple times of purchasing.
Optionally, in the method for optimizing procurement data, the method includes:
calling the cost calculation engine, extracting corresponding data in the target attribute data, and calculating the dynamic inventory in the procurement period; and
and calculating the warehousing cost in the procurement period based on the dynamic inventory amount.
Optionally, in the method for optimizing procurement data, the method includes:
and calling the cost calculation engine, extracting corresponding data in the target attribute data, and calculating the cash flow cost of multiple times of purchase in the purchase period.
Optionally, in the method for optimizing procurement data, the method includes:
acquiring the target attribute data through an inventory system, a sales volume system, a rebate system and service input; and
and carrying out structured storage on the target attribute data through a preset processing model, and storing the target attribute data in a database.
Optionally, in the method for optimizing procurement data, the method includes:
and calling the income calculation engine to calculate the return profit and price difference profit of the suppliers who purchase for multiple times through corresponding data in the target attribute data.
Optionally, in the method for optimizing procurement data, the method includes:
optimizing and solving the purchasing income model through a constraint rule to obtain the purchasing quantity of each time of purchasing in multiple times of purchasing;
the constraint rules include at least one of the following constraints:
excess warehouse fee constraint;
an inventory equality constraint;
a maximum supplier rebate constraint;
an inventory minimum constraint;
constraint of consistent purchase amount; and
supplier rebate constraints.
Optionally, in the method for optimizing procurement data, the method includes:
outputting at least one of the following data while outputting the purchase amount of each of the plurality of purchases:
total purchase amount, maximum available rebate point, purchase spending period turnover, total earnings, sales earnings, supplier rebates, regular warehousing fees, overdue warehousing fees, capital occupation, and
a chart showing dynamic inventory, expected sales, and purchases.
According to another embodiment of the present invention, there is also provided a device for optimizing procurement data, including:
the data acquisition module is used for acquiring relevant target attribute data in a purchase period by taking a preset time period for multiple purchases as the purchase period, and performing structured storage on the target attribute data through a preset processing model;
the cost income calculation module calls a preset cost calculation engine and a preset income calculation engine respectively, extracts corresponding data in the stored target attribute data, and calculates and obtains the purchasing cost and the purchasing income of multiple times of purchasing in the purchasing period respectively; and
and the optimization solving module is used for carrying out optimization solving on a preset purchasing income model according to the purchasing cost and the purchasing income of multiple times of purchasing in the purchasing period so as to obtain and output the purchasing quantity of each time of purchasing in multiple times of purchasing.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided an electronic device for optimizing a purchasing method, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method as described above.
To achieve the above object, according to one aspect of the embodiments of the present invention, there is provided a computer readable medium on which a computer program is stored, characterized in that the program realizes the method as described above when executed by a processor.
One embodiment of the above invention has the following advantages or benefits: since the cost and income of the multiple purchases are calculated based on the purchase period for performing the multiple purchases, the mutual influence of the multiple purchases can be considered, so that the optimized purchase amount per purchase can be obtained
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main flow of a method for optimization of procurement data according to an embodiment of the invention;
FIG. 2 is a schematic diagram of data obtained by a method for optimizing procurement data according to an embodiment of the invention;
FIG. 3 is a chart showing inventory, sales, and procurement quantities output by the method for optimizing procurement data according to an embodiment of the invention;
FIG. 4 is a schematic diagram of the major modules of a procurement data optimization apparatus according to an embodiment of the invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a method for optimizing procurement data according to an embodiment of the present invention, and as shown in fig. 1, the method for optimizing procurement data according to an embodiment of the present invention includes:
and step S101, taking a preset time period for multiple times of purchasing as a purchasing period, acquiring related target attribute data in the purchasing period, and performing structured storage on the target attribute data through a preset processing model.
Here, the "procurement period" refers to a predetermined period of time (e.g., a quarter or a year) set according to a sales plan, during which a plurality of procurements are performed. The target attribute data includes, for example: expected sales in a purchasing period, rebate rules, time point of each purchasing in multiple purchasing, estimated average transaction price, stocking purchasing price, account period, initial inventory, historical purchased amount, warehousing charging standard, cash flow yield and the like. The target attribute data is data required for a specific calculation process, and only a part thereof is described here.
In an embodiment of the present invention, optionally, the target attribute data is obtained from an inventory system, a sales system, a rebate system, and a business input.
The inventory system stores an inventory of each product. The sales system is constituted by, for example, a distributed file management system HDFS, and for example, the total sales volume of a commodity a on the current day is x pieces by totaling the total sales volumes of 10 users by placing an order by 10 users on 1 month and 1 day of 2020. In the rebate system, data such as purchase period, cash flow rate of return, expected sales volume, rebate rule, purchase time, estimated average transaction price, stock purchase price, account period, initial stock, historical purchased amount, overdue turnover days, storage charging standard and the like are stored. For data in the rebate system, the structured data stored in the system needs to be analyzed and stored in the MYSQL database. The business inputs contain, for example, various constraint rules.
The target attribute data acquired by HDFS and MYSQL (inventory system, sales system, rebate system, business input) and the like are structurally stored by a preset processing model. The target attribute data stored in the database needs to be updated periodically to ensure the validity of the data.
FIG. 2 is a schematic diagram of data obtained by the method for optimizing procurement data according to the embodiment of the invention. As shown in FIG. 2, the data format and content obtained by the inventory system, sales system, rebate system, and business inputs is shown as an example.
And step S102, respectively calling a preset cost calculation engine and a preset income calculation engine, and extracting corresponding data in the stored target attribute data so as to respectively calculate and obtain the purchasing cost and purchasing income of multiple times of purchasing in a purchasing period.
In an embodiment of the invention, the cost calculation engine and revenue calculation engine are implemented by java.
Alternatively, in step S102, a cost calculation engine and a revenue calculation engine are invoked, target attribute data stored in the database is invoked, warehousing costs and cash flow costs for multiple purchases over a procurement period are calculated as procurement costs for the multiple purchases, and supplier return and price difference benefits for the multiple purchases over the procurement period are calculated as procurement revenue.
In an embodiment of the invention, warehousing costs and cash flow costs are calculated as procurement costs.
For the calculation of the storage cost, firstly, a cost calculation engine is called, and data such as the purchasing times in the purchasing period, the purchasing time of each purchasing, the initial stock and the expected sales volume in the purchasing period are called from a database to calculate the dynamic stock in the purchasing period.
For example, the dynamic inventory during the procurement period can be expressed as:
stockj＝stockj-1-salesj-1+repi，
wherein: stockjIndicating the inventory on day j within the procurement period; stockj-1Indicating the inventory on day j-1; salesj-1Represents sales on day j-1; repiIndicating the purchase amount of the ith purchase within the purchase period. As can be appreciated, stock0Is stored in the beginning of the period.
In the embodiment of the invention, the warehousing cost can be finely calculated by calculating the dynamic change of the inventory in the procurement period. Such as warehousing cost distribution routine warehousing fee and overdue warehousing fee, and obtaining warehousing charge standard store from the databasefee(target attribute data) and calculating a regular warehouse fee and an overdue warehouse fee according to the warehouse charge standard and the dynamic inventory amount.
For example, a conventional warehousing fee may be expressed as:
Figure BDA0002830188570000061
the overrun warehousing fee may be expressed as:
examt＝cs1*2*storefee-exstock*2*storefee*em1。
wherein: emjThe value is 0 or 1, so that the overdue storage fee of the commodity is guaranteed to be either charged or not charged; exstockIndicating a specified inventory threshold amount; cs is1Representing total inventory and emjThe product of (a) is linearized here, which facilitates subsequent solution. It will be appreciated that the out-of-date warehousing fee is a conventional warehousing fee that is 2 times greater than the specified inventory threshold.
According to the scheme, the influence of multiple times of purchasing on the inventory amount in one purchasing period can be considered when optimizing the purchasing data by calculating the dynamic inventory amount in the purchasing period. Since the warehousing cost is an important component of the procurement cost, the optimization of the procurement data can take into account the differences in cost caused by the influence of different procurements on each other over a plurality of procurements. Namely, the embodiment of the invention has more precise calculation of the warehousing cost, so that the influence of the warehousing cost can be better considered when the purchasing data is optimized.
In an embodiment of the invention, for cash flow cost calculation, the cost calculation engine obtains a purchase price and an account period (pay) from a databasedays) The cash flow cost is calculated from the purchase price and the account period.
For example, cash flow costs may be expressed as:
Figure BDA0002830188570000071
Figure BDA0002830188570000072
wherein repi represents the ith purchasing quantity in the purchasing period, L represents the duration of the purchasing period, and N represents the total purchasing times in the purchasing period. cashfeeRepresenting cash flow revenue, typically 12%. price represents stock purchase price.
In this way, more cost items can be considered when calculating the cost, and the cost is more finely calculated. According to the optimization method of the purchasing data, disclosed by the embodiment of the invention, when the cash flow cost is calculated, one purchasing period is also used as a time period for calculation, so that the influence of multiple times of purchasing in one purchasing period on the cash flow can be considered, and the cash flow cost is calculated more finely.
It should be noted that, in the optimization method of purchasing data according to the embodiment of the present invention, when the purchasing cost is calculated, only the regular warehousing cost and the overdue warehousing cost may be calculated, and the cash flow cost is not calculated. Thus, although the cost calculation is rough, the relatively optimized purchasing mode can be obtained, and the calculation amount can be reduced.
In the embodiment of the invention, for the calculation of the purchasing income, the income calculation engine is called to extract corresponding data in the target attribute data and calculate the return profit and the price difference profit of suppliers who purchase for multiple times in the purchasing period.
Specifically, the revenue calculation engine extracts the supplier rebate rule { qd ] in the target attribute datai；ddi}, stock purchase price of commodity, estimated transaction average price, and purchase price difference price of commoditygapAnd the data are processed to calculate supplier return profit and price difference profit in the purchase.
For example, a supplier rebate may be expressed as:
rbtamt＝spi*ddi*price-smi*ddi*qdi*price。
wherein: smjThe step of returning the profit is shown to which the purchase falls, the sum is 1, and the purchase only falls into one step of returning the profit. qdjIndicating the purchase amount, sp, of each node of the rebate rulejDenotes smjAnd qdjThe linearization process facilitates the model solution. ddjAnd expressing the rebate proportion of each node of the rebate rule. price represents stock purchase price. It will be appreciated that the purchase amount reaches a rebate stage, according to which the provider rebate is calculated.
According to the scheme, the supplier rebate corresponding to different purchasing quantities of each purchase in multiple purchases in one purchase period can be calculated, and the influence of different purchasing modes on the supplier rebate can be calculated. By optimizing the procurement data as revenue by the provider rebate derived in this manner, the optimum procurement amount per procurement can be truly determined.
For example, the price difference gain may be expressed as:
Figure BDA0002830188570000081
wherein repiIndicating the purchase amount, price, of the ith purchasegapIndicating the purchasing price difference of the commodity, and N is the total purchasing times in the purchasing period. It can be seen that the supplier rebate and purchase price differences are calculated by adding the supplier rebate and purchase price differences for multiple purchases over the purchase period.
It should be noted that the purchase price difference is that the supplier has to make a lot of purchasesA reduced price advantage may be given. For example, if the average estimated transaction price is 6 Yuan and the stock purchase price of mass purchase is 5 Yuan, the purchase price difference price isgapIn order to calculate the price difference profit, the estimated mean transaction price and the stock purchase price can be obtained from the database to calculate the price difference profit, wherein the estimated mean transaction price minus the stock purchase price is 1 yuan.
It is obvious to those skilled in the art that the historical purchased amount is needed when calculating the purchase price difference, and in this specification, a description of this part of the calculation process is omitted for the sake of brevity.
That is, in the purchasing process, the amount of purchase varies, and the price quoted by the supplier varies, thereby affecting the yield of different purchasing methods. According to the scheme, the price difference income of multiple times of purchasing in a purchasing period can be calculated, and the influence of the price difference income under different purchasing modes on the total purchasing income can be determined.
In addition, in the embodiment, only supplier return can be calculated as purchasing income, so that although the income is calculated roughly, a relatively optimized purchasing mode can be obtained, and the calculation amount can be reduced.
And step S103, according to the purchasing cost and purchasing income of multiple purchasing in the purchasing period, optimizing and solving the preset purchasing income model to obtain and output the purchasing quantity of each purchasing in the multiple purchasing.
Specifically, in the present embodiment, the purchase cost includes a warehousing fee (regular warehousing fee and overdue warehousing fee) and a cash flow cost, and the purchase income includes a supplier return interest and a price difference profit. And solving the purchasing income by subtracting the value of the purchasing cost from the purchasing income to obtain the purchasing quantity of each purchase in multiple purchases.
For example, the purchase revenue model may be expressed as:
MAX rbtamt+puramt-storeamt-examt-cashamt。
and according to the calculated return profit, price difference profit, conventional storage cost, overdue storage cost and cash flow cost of the supplier, carrying out optimization solution on the purchase profit model to obtain the purchase quantity of each purchase in multiple purchases, for example, outputting the obtained purchase quantity of each purchase in multiple purchases to a purchasing system.
According to the scheme, the purchasing income can be calculated by taking the purchasing period of multiple times of purchasing as a period, and compared with the prior art that the cost income is calculated by aiming at single purchasing, the optimizing method of the purchasing data provided by the embodiment of the invention can consider the mutual influence of multiple times of purchasing in one purchasing period and can really determine the optimal income of multiple times of purchasing.
Optionally, in step S203, the purchasing profit model is optimally solved through constraint rules to obtain the purchasing quantity of each purchasing in multiple purchases.
Optionally, the constraint rule comprises at least one of the following constraints:
1) and (4) constraint of overdue warehouse charge: cs is more than or equal to 00≤emi*exstock，em1*exstock≤cs1，
Figure BDA0002830188570000106
Wherein cs is0And cs1And taking a value of 0 or 1, and summing the value of the two values to be equal to 1, so as to ensure that the overdue storage fee is either charged or not. emiAnd the value is also 0 or 1, the sum of the two is equal to 1, the two is used for calculating the excess inventory, if the excess inventory exists, the value is 1, and if not, the value is 0. 2) Inventory equality constraint:
Figure BDA0002830188570000102
for ensuring that the over-time calculated cumulative inventory equals the regular cumulative inventory,
3) maximum supplier rebate constraint:
Figure BDA0002830188570000103
for limiting the maximum supplier rebate,
4) inventory minimum constraints:stockj≥0，j∈[1，L]；
the stock is guaranteed to be positive,
5) and (5) constraint of consistent purchase quantity:
Figure BDA0002830188570000104
ensuring that the purchase amount of the return interest of the supplier is consistent with the decision-making purchase amount, an
6) Supplier rebate constraints:
Figure BDA0002830188570000105
the guarantee supplier rebate will fall on only one step of rebate.
Optionally, other data obtained in the calculation process may also be output together for reference. For example, the following may be output together: total purchase amount, maximum available rebate point, purchase consumption period turnover, total income, sales income, supplier rebate, conventional storage fee, overdue storage fee and fund occupation.
As can be understood from the above description of the embodiments, these data outputted together do not need to be calculated separately or only need to be calculated simply, and therefore, the occupied computing resources are very small. By outputting these data together, the worker can be helped to intuitively understand the results of other aspects of the determined purchase amount. Compared with the prior art, the work of workers is further facilitated.
In addition, in the embodiment of the invention, the chart showing the inventory, the sales volume and the purchase volume can be output while the purchase volume is output, so that more references can be provided for the working personnel, and the working efficiency is improved.
As a comparative example, a method of determining the procurement amount in the prior art is exemplified.
Aiming at two scenes of rebate type purchasing, the currently adopted method mainly comprises the following steps:
1) planning at the beginning of the year: predicting the total purchasing quantity of the year according to the total purchasing quantity of the last year and the service growth expectation, and negotiating a rebate step of the purchasing quantity and a rebate proportion corresponding to the purchasing quantity rebate step with a supplier according to the predicted total purchasing quantity of the year, so as to strive for increased benefits for the supplier;
2) and (3) daily purchase decision making: calculating the difference between the total purchasing quantity and the purchasing quantity of the next step according to the previous total purchasing quantity, calculating the total profit of purchasing according to the difference quantity by combining the average goods holding cost of the classes, the cash flow cost of purchasing and paying and the return profit from purchasing to the next return step point, if the total profit is positive, purchasing, and if the purchasing quantity is negative, not performing excessive purchasing (stock stocking) operation, and only purchasing according to the normal demand.
It can be seen that in the prior art method, there are the following drawbacks:
for the planning in the beginning of the year, the rebate step and the rebate proportion of the purchase amount are determined according to the total purchase amount predicted in the last year, so that the cost cannot be optimal really, for example, whether the purchase exceeding the planned amount can be realized to achieve excellent income cannot be known, and the cost change caused by different purchase amounts of each purchase in batch purchase cannot be determined.
For daily purchasing decision, the cost item only considers the cost caused by single purchasing, and does not consider the change of the storage amount caused by multi-period purchasing and the change of the storage fee.
That is, in the prior art, the cost and the profit are calculated for a single purchase, and when a plurality of purchases are taken into consideration, the influence of each purchase and other purchases in the plurality of purchases cannot be considered. In addition, in the prior art, the cost is calculated too roughly, and the influence of different purchasing modes on the cost cannot be determined at all.
According to the embodiment of the invention, the purchasing quantity of each purchasing in the multiple purchasing is determined according to the purchasing cost and income of the multiple purchasing in the whole purchasing period, so that the cost and income of the multiple purchasing in the whole purchasing period can be comprehensively calculated, and the optimal purchasing mode is really determined.
In addition, according to the above-described embodiment of the present invention, it is possible to determine the dynamic stock amount in the procurement period and calculate the cost from the dynamic stock amount, so that the grasp of the cost is more refined.
In the following, a specific implementation process is shown for the optimization method of the procurement data of the invention for daily procurement decision.
Firstly, the following data are obtained through an inventory system, a sales volume system, a rebate system and service input, and are structurally stored in a database through a preset processing model:
and (3) during the procurement period: 3 months (2019.12.01-2020.02.29);
end-of-term inventory constraints: conventional turnover of around (+ -100%) 20 days;
expected sales volume: {12 months: 2000, month 1: 1000, 2 months: 800 };
and (3) a rebate rule: {0: 0.01, 1000: 0.02, 5000: 0.03, 10000: 0.04 }; (wherein, for example, "1000: 0.02" means that the rebate step is 2% when the purchase amount is 1000.)
And (3) purchasing time: the seeds are collected at the end of the year (12 months) for 4 times (every seven days);
estimating the average price of the finished deals: 6-membered;
stock purchase price: 5 yuan;
accounting period: 45 days;
initial stage inventory: 500 pieces;
historical purchased volume: 7500 pieces;
storage charging standard: 0.017 yuan/piece per day;
cash flow yield: 0.12/year
After the above target attribute data is acquired, the data is processed, specifically,
1) respectively calling a preset cost calculation engine and a preset income calculation engine, and calculating the dynamic inventory in the procurement period according to the data;
2) calculating a purchase cost and a purchase income, wherein the conventional storage fee and the overdue storage fee are calculated according to the dynamic inventory;
3) and according to the purchase cost and the purchase income obtained by the calculation, carrying out optimization solution on the preset purchase income model so as to obtain the purchase quantity [611, 733, 879 and 1057] of each purchase in multiple purchases and outputting the purchase quantity to the purchase system.
In addition, besides outputting the determined purchase amount, the following contents can be simultaneously output for the staff to refer to:
total purchase amount: 3280 pieces
The highest rebate point achievable is 4%.
Turnover of the purchase consumption period: day [24.7, 37.6, 14.4 ];
total yield: 3514.4 yuan;
sales revenue: 3280.1 yuan;
the supplier returns a profit: 2156.0 yuan;
conventional storage fee: 1673.7 yuan;
excess storage fee: 0-bit;
and (4) fund occupation: 248.0-membered;
that is, according to the present embodiment, the purchasing data is optimized for four purchases in 3 months (i.e., 12 months, 1 month, and 2 months) of one purchasing period, and the total profit of the four purchases is optimized by determining the purchasing amount of each purchase of the four purchases. Compared with the prior art that the purchasing income of single purchasing is calculated in four times, the optimizing method of the purchasing data can really realize the optimization of the purchasing income during multiple purchasing. Moreover, the optimization method of the purchase data, provided by the embodiment of the invention, can consider more cost items in the process of calculating the cost, and the cost is more finely calculated.
Fig. 3 is a graph showing inventory, sales, and purchase quantities output by the optimization method of purchase data according to an embodiment of the present invention. For example, as shown in fig. 3, according to the optimization method of purchase data according to the embodiment of the present invention, in addition to the above data, a chart showing the inventory, the sales volume, and the purchase volume is output, so that the staff can visually see the trend of the change of the inventory and the sales volume.
In the following, a specific implementation procedure is shown for the optimization method of procurement data of the present invention for the early years planning.
Firstly, the following data are obtained through an inventory system, a sales volume system, a rebate system and service input, and are structurally stored in a database through a preset processing model:
and (3) during the procurement period: 12 months (2020.01.01-2020.12.30);
end-of-term inventory constraints: the conventional turnover is about (+/-100 percent) in 20 days
Expected sales volume: {12: 2000,1: 1000,2: 800 };
and (3) a rebate rule: {0: 0.01, 1000: 0.02, 5000: 0.03, 10000: 0.04 };
and (3) purchasing time: beginning of each month;
estimating the average price of the finished deals: 6-membered;
stock purchase price: 5 yuan;
accounting period: 45 days;
initial stage inventory: 500 pieces;
historical purchased volume: 0 piece
And (3) in overrun turnover: 60 days
Storage charging standard: 0.017 yuan/piece per day;
cash flow yield: 0.12/year
According to the data, the same calculation steps as the daily purchasing decisions are carried out, so that the purchasing quantity of each purchase in the early year planning scene is obtained: [120, 139, 139, 198, 171, 438, 163, 162, 141, 170, 232, 443] and output to the procurement system.
Besides outputting the determined purchase amount, the following contents can be simultaneously output for the staff to refer to:
total purchase amount: 2519 pieces, total expected sales 2317 pieces; the return profit can not reach the high point of the last year;
turnover of the purchase consumption period: [15.2, 12.8, 16.2, 15.3, 16.3, 16.9, 14.3, 14.8, 12.8, 16.9, 12.7, 57.1] days
Total yield: 720.3 yuan
Sales revenue: 2519.9 yuan
The supplier returns a profit: 252.0 yuan
Conventional storage fee: 726.1 yuan
Excess storage fee: 0 yuan
That is, according to the embodiment of the present invention, in the early years planning scenario, the purchase amount of each purchase can be determined in detail in the purchase period, so that the cost of multiple purchases in the purchase period can be optimized. It can be determined whether the annual optimal procurement quantity can reach the rebate high point of the last year under the condition of optimal procurement cost, so that rebate rules can be better negotiated with suppliers. Moreover, the optimization method of the purchase data, which is related by the embodiment of the invention, can consider more cost items when calculating the cost, and the cost is more finely calculated.
For example, according to the optimization method of purchase data according to an embodiment of the present invention, in addition to the optimization method of purchase data described above, a chart showing inventory, sales, and purchase is output, so that a worker can visually see a corresponding trend of change in inventory and sales.
As shown in fig. 4, an embodiment of the present invention further provides an apparatus 400 for optimizing procurement data, including:
the data acquisition module 401 is configured to acquire relevant target attribute data in a purchase period by using a predetermined time period for multiple purchases as the purchase period, and perform structured storage on the target attribute data through a preset processing model;
a cost and income calculation module 402, which calls a preset cost calculation engine and a income calculation engine respectively, extracts corresponding data in the stored target attribute data, and calculates and obtains the purchasing cost and purchasing income of multiple purchases in a purchasing period; and
and the optimization solving module 403 is configured to perform optimization solving on the preset purchasing profit model according to the purchasing cost and the purchasing income of multiple purchases in the purchasing period, so as to obtain and output the purchasing quantity of each purchase in the multiple purchases.
Fig. 5 illustrates an exemplary system architecture 500 to which embodiments of the invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. The network 504 may include various connection types.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 501, 502, 503. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the optimization method of the procurement data provided by the embodiment of the present invention is generally performed by the server 505, and accordingly, the optimization device of the procurement data is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a data acquisition module, a cost-revenue calculation module, and an optimization solution module. The names of these modules do not in some cases constitute a limitation on the module itself, and for example, the data acquisition module may also be described as a "module that acquires target attribute data".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:
acquiring relevant target attribute data in a purchase period by taking a preset time period for multiple purchases as the purchase period, and performing structured storage on the target attribute data through a preset processing model;
respectively calling a preset cost calculation engine and a preset income calculation engine, and extracting corresponding data in the stored target attribute data to respectively calculate and obtain the purchasing cost and purchasing income of multiple times of purchasing in a purchasing period; and
and according to the purchasing cost and the purchasing income of multiple times of purchasing in the purchasing period, carrying out optimization solution on the preset purchasing income model so as to obtain the purchasing quantity of each time of purchasing in multiple times of purchasing.
According to the technical scheme of the embodiment of the invention, because the dynamic inventory in the purchasing period is calculated, the purchasing cost and the income are considered by taking the purchasing period of multiple times of purchasing as a period, the mutual influence of multiple times of purchasing in one purchasing period can be considered, the purchasing cost and the income of multiple times of purchasing are more finely managed, and the optimal purchasing mode is really determined.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A method for optimizing procurement data, comprising:
taking a preset time period for multiple times of purchasing as a purchasing period, acquiring related target attribute data in the purchasing period, and performing structured storage on the target attribute data through a preset processing model;
respectively calling a preset cost calculation engine and a preset income calculation engine, and extracting corresponding data in the stored target attribute data to respectively calculate and obtain the purchasing cost and purchasing income of multiple times of purchasing in the purchasing period; and
and according to the purchasing cost and the purchasing income of multiple times of purchasing in the purchasing period, carrying out optimization solution on a preset purchasing income model so as to obtain and output the purchasing quantity of each time of purchasing in multiple times of purchasing.
2. The method for optimizing procurement data of claim 1, comprising:
calling the cost calculation engine, extracting corresponding data in the target attribute data, and calculating the dynamic inventory in the procurement period; and
and calculating the warehousing cost in the procurement period based on the dynamic inventory amount.
3. The method for optimizing procurement data of claim 1 or 2, characterized by comprising:
and calling the cost calculation engine, extracting corresponding data in the target attribute data, and calculating the cash flow cost of multiple times of purchase in the purchase period.
4. The method for optimizing procurement data of claim 1 or 2, characterized by comprising:
acquiring the target attribute data through an inventory system, a sales volume system, a rebate system and service input; and
and carrying out structured storage on the target attribute data through a preset processing model, and storing the target attribute data in a database.
5. The method for optimizing procurement data of claim 1 or 2, characterized by comprising:
and calling the income calculation engine to calculate the return profit and price difference profit of the suppliers who purchase for multiple times through corresponding data in the target attribute data.
6. The method for optimizing procurement data of claim 1, comprising:
optimizing and solving the purchasing income model through a constraint rule to obtain the purchasing quantity of each time of purchasing in multiple times of purchasing;
the constraint rules include at least one of the following constraints:
excess warehouse fee constraint;
an inventory equality constraint;
a maximum supplier rebate constraint;
an inventory minimum constraint;
constraint of consistent purchase amount; and
supplier rebate constraints.
7. The method for optimizing procurement data of claim 1 or 2, characterized by comprising:
outputting at least one of the following data while outputting the purchase amount of each of the plurality of purchases:
total purchase amount, maximum available rebate point, purchase spending period turnover, total earnings, sales earnings, supplier rebates, regular warehousing fees, overdue warehousing fees, capital occupation, and
a chart showing dynamic inventory, expected sales, and purchases.
8. An apparatus for optimizing procurement data, comprising:
the data acquisition module is used for acquiring relevant target attribute data in a purchase period by taking a preset time period for multiple purchases as the purchase period, and performing structured storage on the target attribute data through a preset processing model;
the cost income calculation module calls a preset cost calculation engine and a preset income calculation engine respectively, extracts corresponding data in the stored target attribute data, and calculates and obtains the purchasing cost and the purchasing income of multiple times of purchasing in the purchasing period respectively; and
and the optimization solving module is used for carrying out optimization solving on a preset purchasing income model according to the purchasing cost and the purchasing income of multiple times of purchasing in the purchasing period so as to obtain and output the purchasing quantity of each time of purchasing in multiple times of purchasing.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
CN202011462097.XA 2020-12-10 2020-12-10 Method and device for optimizing purchasing data Pending CN113780612A (en)

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