CN111340421A - Purchasing method - Google Patents
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Abstract
The embodiment of the application discloses a purchasing method, which comprises the following steps: after a first purchasing period begins, acquiring first historical sales data and first real-time inventory data of commodities; calculating a first predicted sales volume for the goods for the first purchase period based on the first historical sales data; calculating a first upper limit of the inventory level of the commodity in a second purchasing period according to the first predicted sales amount, wherein the second purchasing period is a later time period of the first purchasing period; judging whether the first real-time inventory data reaches a first inventory level upper limit, and if not, bringing the commodities into a purchasing plan; and purchasing the commodity according to the purchasing plan. The method realizes the digital and intelligent management of the purchasing link, thereby realizing that the inventory level can meet the daily sales demand, not occupying too much capital, simultaneously liberating manpower and improving the working efficiency.
Description
Technical Field
The application relates to the technical field of warehouse logistics, in particular to a purchasing method.
Background
In recent years, the transaction scale of cross-border e-commerce keeps growing rapidly, and the penetration rate in import and export trade is rising year by year. As a key link of the supply chain, purchasing management and inventory management have an important influence on the continuous operation capacity of the cross-border e-commerce enterprises. Lower inventory may reduce customer order fulfillment rates, resulting in product out-of-stock and lost sales opportunities; higher inventory will increase capital occupancy of the enterprise and reduce capital turnover, thus requiring a balance between high inventory and low inventory. Reasonable procurement can ensure reasonable inventory, so that the design of a scientific, reasonable and strong-operability procurement method is of great importance to enterprises.
Disclosure of Invention
The embodiment of the application provides a purchasing method, and the problems that the stock level is difficult to meet the sales requirement and excessive funds are not occupied at the same time are solved.
In view of the above, the present application provides a method for purchasing, the method comprising:
after a first purchasing period begins, acquiring first historical sales data and first real-time inventory data of commodities;
calculating a first predicted sales volume for the commodity for a first purchase period based on the first historical sales data;
calculating a first upper limit of inventory level of the commodity in a second purchasing period according to the first predicted sales amount, wherein the second purchasing period is a later time period of the first purchasing period;
judging whether the first real-time inventory data reaches the upper limit of the first inventory level, and if not, bringing the commodity into a purchasing plan;
and purchasing the commodity according to the purchasing plan.
Preferably, the purchasing of the commodity according to the purchasing plan specifically includes:
calculating the purchasing quantity of the commodities in the purchasing plan;
purchasing the commodity according to the purchasing amount;
the procurement amount is the difference between the first inventory level cap and the first real-time inventory data.
Preferably, the method further comprises the following steps:
acquiring second historical sales data and second real-time inventory data of the commodities in a previous production period from the first purchasing period;
calculating a second predicted sales volume for the commodity for a first purchase period based on the second historical sales data;
calculating a second upper limit of inventory level of the goods in a second purchasing period according to the second predicted sales amount;
judging whether the second real-time inventory data reaches the second inventory level upper limit, and if not, bringing the commodity into a demand plan;
sending the demand plan to a supplier.
Preferably, the sending the demand plan to the supplier specifically includes:
calculating the demand of the commodity;
sending a demand plan containing the demand amount to a supplier;
the demand is a difference between the second upper inventory level and the second real-time inventory data.
Preferably, before purchasing the commodity according to the purchasing plan, the method further includes:
and sending the purchasing plan to the supplier, negotiating the purchasing quantity with the supplier, adjusting the purchasing quantity according to a negotiation result, finally determining the purchasing quantity of each commodity, and updating the purchasing plan according to the adjusted purchasing quantity.
Preferably, the calculating a first predicted sales volume of the commodity for a first purchase period according to the first historical sales data specifically includes:
calculating the actual daily average sales volume of the year-on-year period according to the first historical sales data;
calculating the ratio of the sum of the daily average sales in a plurality of time periods before the first purchasing period and the corresponding time period with the same ratio as a first correction coefficient;
and calculating a first predicted sales amount of the commodity in the first purchasing period according to the first correction coefficient, the actual daily average sales amount and the number of days in the first purchasing period.
Preferably, after calculating the actual daily average sales volume of the year-on-year period according to the first historical sales data, the method further comprises:
judging whether the first purchasing period is a promotion period or not, and if so, setting a second correction coefficient according to promotion budget and a sales plan;
and calculating a first predicted sales amount of the commodity in the first purchasing period according to the second correction coefficient, the actual daily average sales amount and the number of days in the first purchasing period.
Preferably, the method further comprises the following steps:
calculating a first lower inventory level limit of the commodity in a second purchasing period according to the first predicted sales amount;
if the real-time inventory in the second purchasing period is lower than the first inventory level lower limit, the commodity is brought into a replenishment plan;
and purchasing the commodity according to the replenishment plan.
Preferably, the purchasing the commodity according to the replenishment plan specifically includes:
calculating the replenishment quantity of the commodity, and purchasing the commodity according to a replenishment plan containing the replenishment quantity;
the replenishment quantity is the product of the predicted daily average sales quantity in the third purchasing period and the number of days from the time point of replenishment purchasing to the beginning of the third purchasing period, and the third purchasing period is the later time period of the second purchasing period.
Preferably, after the obtaining the first historical sales data and the first real-time inventory data of the goods, the method further includes:
and preprocessing the first historical sales data and the first real-time inventory data, wherein the preprocessing comprises data association, data cleaning, and complement of missing data or smooth processing of abnormal data.
According to the technical scheme, the embodiment of the application has the following advantages:
in an embodiment of the present application, a purchasing method is provided, including: after a first purchasing period begins, acquiring first historical sales data and first real-time inventory data of commodities; calculating a first predicted sales volume for the goods for the first purchase period based on the first historical sales data; calculating a first upper limit of the inventory level of the commodity in a second purchasing period according to the first predicted sales amount, wherein the second purchasing period is a later time period of the first purchasing period; judging whether the first real-time inventory data reaches a first inventory level upper limit, and if not, bringing the commodities into a purchasing plan; and purchasing the commodity according to the purchasing plan. The method realizes the digital and intelligent management of the purchasing link, thereby realizing that the inventory level can meet the daily sales demand, not occupying too much capital, simultaneously liberating manpower and improving the working efficiency.
Drawings
FIG. 1 is a diagram of a procurement system architecture in an embodiment of the application;
FIG. 2 is a flow chart of a method of purchasing in one embodiment of the present application;
FIG. 3 is a flow chart of a method of purchasing in another embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a system architecture diagram of a purchasing system in an embodiment of the present application, and fig. 1 includes a purchasing execution algorithm 11, a goods database 12, a sales database 13, real-time inventory data 14, a demand planning report 15, a purchasing planning report 16, and a temporary replenishment plan 17.
The purchasing execution algorithm 11 is a package of the purchasing method, and is a core part of the whole purchasing execution system, and is used for calculating the type and quantity of goods required by each purchasing. The purchasing method adopted by the system takes a Min-Max inventory control model as a prototype, implements a regular ordering strategy, namely after a fixed purchasing period is set for a commodity, a purchasing execution algorithm 11 regularly calls dynamic data in a commodity database 12, a sales database 13 and real-time inventory data 14 to execute purchasing operation, and outputs a demand plan report 15 and a purchasing plan report 16; the procurement execution algorithm 11 has a real-time early warning function, that is, whenever the procurement execution algorithm 11 detects that the real-time inventory of a certain commodity in the real-time inventory data 14 is lower than an early warning value, the procurement early warning is automatically performed, and a temporary replenishment plan 17 is output.
The commodity database 12, the sales database 13, and the real-time inventory data 14 are connected to the purchase execution algorithm 11, and store commodity information, sales data, and inventory data, respectively, as data input sources for the purchase execution algorithm 11. The main maintenance contents of the commodity database 12 include commodity category codes, commodity names, commodity codes, country of origin, delivery specifications, interval periods between demand plans and purchase plans (production periods of suppliers), purchase lead periods, purchase periods and target stock shortage rates; the sales database 13 mainly maintains contents including order numbers, commodity names, commodity codes, sales volumes, whether to promote days, promotion strength, and the like. When making a demand plan/purchase plan, the purchase execution algorithm 11 may call the same/ring ratio historical data in the sales database 13, calculate the predicted sales volume and the safety inventory of each commodity, and further obtain the upper stock level limit and the lower stock level limit of each commodity.
The real-time inventory data 14 is used to maintain inventory information of goods, and the main fields include trade names, goods codes, real inventory, waiting for shelving, actual in-transit inventory, virtual in-transit inventory, real frozen inventory, virtual frozen inventory, defective goods, expired goods, and the like. According to the dynamic data in the real-time inventory data 14, the procurement execution algorithm 11 can calculate the real-time available inventory of each commodity, and then according to the Min-Max inventory control model principle, the difference between the upper limit of the inventory level of the commodity and the real-time available inventory is the demand/procurement quantity of each demand plan/procurement plan. In order to realize the real-time early warning function, the purchase execution algorithm 11 can dynamically monitor the real-time available inventory of each commodity, and when the real-time available inventory is lower than the lower limit of the inventory level of the commodity, a temporary replenishment prompt is sent.
The demand plan report 15, the procurement plan report 16, and the provisional replenishment plan 17 are output contents of the procurement execution algorithm 11 at different times. The content of the demand plan report 15 is the type and quantity of the goods required by the demand plan, and the report provides a certain reference for the supplier to arrange the production plan; the contents of the purchasing plan report 16 are the types and the quantities of the commodities required by the purchasing plan, finally, a formal purchasing order is formed, the two reports are output periodically, and a clear front-back corresponding relation is formed on a time axis. When the system sends a replenishment early warning prompt but the time for making the purchase plan is not reached, the purchase execution algorithm 11 outputs a temporary replenishment plan 17, the contents include temporary replenishment commodities and temporary replenishment quantity, and the emergency demand of the commodities is met, so the output time is indefinite.
For easy understanding, please refer to fig. 2, fig. 2 is a flowchart of a method of the purchasing method in the embodiment of the present application, which specifically includes:
101, after a first procurement cycle begins, first historical sales data and first real-time inventory data for the goods are acquired.
According to the commodity purchasing characteristics of the cross-border e-commerce industry, relevant parameters are set, such as a demand plan and purchasing plan issuing interval period (a supplier production period), a purchasing lead period, a purchasing period, a target stock shortage rate and the like. It should be noted that to purchase the commodities in the first purchasing period I, first historical sales data of the commodities needs to be obtained, inventory checking is performed on all the commodities, and a first real-time inventory I of each commodity is obtainedi. The calculation formula of the real-time inventory is as follows: the real-time inventory is real inventory, waiting for shelving, virtual in-transit inventory, real freezing inventory, virtual freezing inventory, defective goods and overdue goods.
102, calculating a first predicted sales volume for said commodity for a first purchase period based on said first historical sales data.
It will be appreciated that based on the first historical sales data, a first predicted sales volume for each item during the first purchase period i can be calculated.
And 103, calculating a first upper limit of the inventory level of the commodity in the second purchasing period according to the first predicted sales amount.
It will be appreciated that after the first predicted sales amount is calculated, a first inventory level upper limit Max for a second procurement period i +1 for a period subsequent to the first procurement period i may be calculated based on the first predicted sales amounti+1. It is to be understood that the required inventory for the second procurement period i +1 can be inferred from the calculated first forecasted sales volume, and that due to uncertainty in the forecasts, the skilled person can set the float ranges, i.e. the upper and lower inventory levels, in combination with experience or other influencing factors, for the inventory quantities that are forecasted.
And 104, judging whether the first real-time inventory data reaches the upper limit of the first inventory level, and if not, bringing the commodity into a purchasing plan.
In order to ensure sufficient inventory of goods, the first real-time inventory data I is judged when the purchasing plan is madeiWhether the first inventory level upper limit Max is reachedi+1If not, the goods are included in the purchasing plan.
And 105, purchasing the commodity according to the purchasing plan.
After the purchasing plan is made, the commodity is purchased according to the purchasing plan.
According to the purchasing method provided by the embodiment of the application, the first historical sales data and the first real-time inventory data of the commodities are obtained, the first predicted sales amount of the first purchasing period and the first inventory level upper limit of the second purchasing period are calculated, whether the commodities need to be purchased or not is determined according to the judgment that whether the first real-time inventory data reaches the first inventory level upper limit or not, digital and intelligent management of a purchasing link is achieved, and therefore the purpose that the inventory level can meet daily sales requirements and cannot occupy too much funds is achieved, labor is liberated, and working efficiency is improved.
Referring to fig. 2, the procurement method proposed by the present application is further described below on the basis of the above embodiment.
In step 105, purchasing the commodity according to the purchasing plan specifically includes:
1051, calculating the purchase amount of the goods in the purchase plan;
1052, purchasing the commodity according to the purchasing amount, wherein the purchasing amount is the difference between the first upper inventory level limit and the first real-time inventory data.
Further, the method also comprises the following steps:
and 201, acquiring second historical sales data and second real-time inventory data of the commodities in a production period before the first purchasing period.
It should be noted that, in order to ensure that the supplier can produce the commodities according to the demand and avoid the situations of selling and stocking, a prediction can be made on the demand of the commodities before purchasing. In order to ensure that the supplier can supply goods on time, the second historical sales data and the second real-time inventory data of the commodities are obtained in the previous production period from the first purchasing period i. It is understood that the second historical sales data is historical sales data when making a demand plan, that is, the second historical sales data is updated to the first historical sales data after a production period. Similarly, the second real-time inventory data is updated to the first real-time inventory data after a production cycle.
202, calculating a second predicted sales volume for said commodity for the first purchase period based on said second historical sales data.
And 203, calculating a second upper limit of the inventory level of the goods in the second purchasing period according to the second predicted sales amount.
And 204, judging whether the second real-time inventory data reaches the second inventory level upper limit, and if not, bringing the commodity into a demand plan.
205, sending the demand plan to the supplier.
After the demand plan is issued, the beginning of the first purchasing period is reached through a production period of a supplier.
Specifically, step 205 includes:
2051, calculating the demand of the commodity.
2052, sending a demand plan including the demand amount to the supplier, the demand amount being a difference between the second upper inventory level limit and the second real-time inventory data.
Further, since there may be discrepancy between the purchasing plan and the demand plan after a production cycle, in order to ensure the balance of rights and interests between the supplier and the purchasing party, step 105, before purchasing the goods according to the purchasing plan, further includes:
and sending the purchasing plan to the supplier, negotiating the purchasing quantity with the supplier, adjusting the purchasing quantity according to a negotiation result, finally determining the purchasing quantity of each commodity, and updating the purchasing plan according to the adjusted purchasing quantity.
Further, the present application provides a sales predicting method, taking calculating a first predicted sales as an example, and step 102, calculating the first predicted sales of the commodity for the first purchase period according to the first historical sales data specifically includes:
1021, calculating the actual daily average sales volume of the period of the same proportion according to the first historical sales data.
When predicting sales volume of the ith period (first purchasing period) of the year, firstly, a year period of the ith period of the year is positioned, and the actual daily average sales volume D of the year period is calculated by using the first historical sales datai’。
1022, calculating the ratio of the sum of the daily average sales in a plurality of time intervals before the first purchasing period and the corresponding time interval with the same ratio as the first correction coefficient.
When there is no special case of promotion or the like, the variation in the amount of sales is approximately linear. Calculating the ratio of the sum of the daily average sales in a plurality of time periods before the first purchasing period and the corresponding time period with the same ratio as the first correction coefficient theta1. I.e. theta1＝(Di-1+Di-2…+Di-n)/(Di-1’+Di-2’+…+Di-n’)。
1023, calculating a first predicted sales volume of said commodity in a first purchasing period according to said first correction factor, said actual daily average sales volume and said number of days in said first purchasing period.
That is, the first predicted sales Di ═ θ1g Di', where g is the number of days of the first procurement period.
Preferably, when there is a special situation such as promotion, and the reference meaning of the predicted sales amount predicted only according to the historical sales data is not great, at step 1021, after calculating the actual daily average sales amount in the comparable time period according to the first historical sales data, the method further includes:
and 1024, judging whether the first purchasing period is a promotion period, and if so, setting a second correction coefficient according to the promotion budget and the sales plan.
If the ith time slot of the year is a promotion time slot, considering that the sales volume of the commodity is influenced by the strength of promotion activities and has large fluctuation, the correction coefficient theta is set according to promotion budget, sales plan and the like2Therefore, the actual sales volume of the commodities in the period of year comparison is adjusted, and the predicted sales volume of the ith period of the year is obtained.
1025, calculating a first predicted sales of the commodity in the first purchasing period according to the second correction coefficient, the actual daily average sales and the days of the first purchasing period.
When the ith annual period is a sales promotion day, the average daily predicted sales Di is θ2g Di’。
Further, in order to ensure sufficient inventory, inventory checking needs to be carried out on the commodities every day, real-time inventory is calculated, and when the inventory is possibly insufficient, replenishment needs to be carried out, so that the situation that selling is delayed for delivery is avoided. The purchasing method provided by the application further comprises a temporary replenishment method, and the method specifically comprises the following steps:
after step 102, further comprising:
301, calculating a first lower inventory level limit for said commodity for a second purchase period based on said first forecasted sales amount.
302, if the real-time inventory in the second procurement period is below the first inventory level lower limit, then the good is included in a replenishment plan.
303, purchasing the commodity according to the replenishment plan.
and calculating the replenishment quantity of the commodity, and purchasing the commodity according to a replenishment plan containing the replenishment quantity, wherein the replenishment quantity is the product of the predicted average daily sales quantity in a third purchasing period and the number of days from the completion of the second purchasing period to the replenishment purchasing time point, and the third purchasing period is the later stage of the second purchasing period.
It should be noted that, whether the goods need to be restocked depends on whether the real-time inventory in the current stage is lower than the lower limit of the inventory level in the current stage, and since the calculation conditions of the upper limit and the lower limit of the inventory level in the second purchasing period are described above, taking the second purchasing period as an example here, purchasing is actually a continuous and uninterrupted process, and a restocking plan can be used in each stage.
The replenishment quantity in the second purchasing period is the product of the predicted average daily sales in the third purchasing period and the number of days from the start of the third purchasing period at the replenishment purchasing time point, namely the replenishment quantity is Di+2× d, wherein d is the number of days between the replenishment time point and the start of the third procurement period.
Further, after obtaining the historical sales data and the real-time inventory data of the goods, the method further comprises the following steps:
and preprocessing the historical sales data and the real-time inventory data, wherein the preprocessing comprises data association, data cleaning, and complement of missing data or smooth processing of abnormal data.
The historical sales data and the real-time inventory data comprise first historical sales data, first real-time inventory data, second historical sales data and second real-time inventory data.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A method of purchasing, comprising:
after a first purchasing period begins, acquiring first historical sales data and first real-time inventory data of commodities;
calculating a first predicted sales volume for the commodity for a first purchase period based on the first historical sales data;
calculating a first upper limit of inventory level of the commodity in a second purchasing period according to the first predicted sales amount, wherein the second purchasing period is a later time period of the first purchasing period;
judging whether the first real-time inventory data reaches the upper limit of the first inventory level, and if not, bringing the commodity into a purchasing plan;
and purchasing the commodity according to the purchasing plan.
2. The procurement method of claim 1 characterized by, the procurement of goods according to the procurement plan is specifically:
calculating the purchasing quantity of the commodities in the purchasing plan;
purchasing the commodity according to the purchasing amount;
the procurement amount is the difference between the first inventory level cap and the first real-time inventory data.
3. The procurement method of claim 1 characterized by further comprising:
acquiring second historical sales data and second real-time inventory data of the commodities in a previous production period from the first purchasing period;
calculating a second predicted sales volume for the commodity for a first purchase period based on the second historical sales data;
calculating a second upper limit of inventory level of the goods in a second purchasing period according to the second predicted sales amount;
judging whether the second real-time inventory data reaches the second inventory level upper limit, and if not, bringing the commodity into a demand plan;
sending the demand plan to a supplier.
4. The procurement method of claim 3 characterized by, the sending the demand plan to a supplier is specifically:
calculating the demand of the commodity;
sending a demand plan containing the demand amount to a supplier;
the demand is a difference between the second upper inventory level and the second real-time inventory data.
5. The procurement method of claim 3 characterized by, prior to the procurement of items according to the procurement plan, further comprising:
and sending the purchasing plan to the supplier, negotiating the purchasing quantity with the supplier, adjusting the purchasing quantity according to a negotiation result, finally determining the purchasing quantity of each commodity, and updating the purchasing plan according to the adjusted purchasing quantity.
6. The procurement method of claim 1 characterized by, the calculating a first predicted sales volume for the commodity for a first procurement period based on the first historical sales data is specifically:
calculating the actual daily average sales volume of the year-on-year period according to the first historical sales data;
calculating the ratio of the sum of the daily average sales in a plurality of time periods before the first purchasing period and the corresponding time period with the same ratio as a first correction coefficient;
and calculating a first predicted sales amount of the commodity in the first purchasing period according to the first correction coefficient, the actual daily average sales amount and the number of days in the first purchasing period.
7. The procurement method of claim 6 wherein, after calculating the actual daily average sales over a period of parity based on the first historical sales data, further comprising:
judging whether the first purchasing period is a promotion period or not, and if so, setting a second correction coefficient according to promotion budget and a sales plan;
and calculating a first predicted sales amount of the commodity in the first purchasing period according to the second correction coefficient, the actual daily average sales amount and the number of days in the first purchasing period.
8. The procurement method of claim 1 characterized by further comprising:
calculating a first lower inventory level limit of the commodity in a second purchasing period according to the first predicted sales amount;
if the real-time inventory in the second purchasing period is lower than the first inventory level lower limit, the commodity is brought into a replenishment plan;
and purchasing the commodity according to the replenishment plan.
9. The procurement method of claim 8, wherein the procurement of the commodity according to the replenishment plan is specifically:
calculating the replenishment quantity of the commodity, and purchasing the commodity according to a replenishment plan containing the replenishment quantity;
the replenishment quantity is the product of the predicted daily average sales quantity in the third purchasing period and the number of days from the time point of replenishment purchasing to the beginning of the third purchasing period, and the third purchasing period is the later time period of the second purchasing period.
10. The procurement method of claim 1 characterized by, after obtaining the first historical sales data and the first real-time inventory data for the good, further comprising:
and preprocessing the first historical sales data and the first real-time inventory data, wherein the preprocessing comprises data association, data cleaning, and complement of missing data or smooth processing of abnormal data.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111915261A (en) * | 2020-08-10 | 2020-11-10 | 辉岭加拿大有限公司 | Medical laboratory consumable fixed number management algorithm |
| CN112036795A (en) * | 2020-08-31 | 2020-12-04 | 平安医疗健康管理股份有限公司 | Method, device, equipment and storage medium for determining medicine purchasing plan |
| CN112465427A (en) * | 2020-11-26 | 2021-03-09 | 上海领健信息技术有限公司 | Delivery plan generation method, system and terminal based on purchase demand |
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| CN114298625A (en) * | 2021-12-21 | 2022-04-08 | 中武(福建)跨境电子商务有限责任公司 | Intelligent cross-border marine procurement analysis system based on big data |
| CN114757703A (en) * | 2022-04-14 | 2022-07-15 | 荃豆数字科技有限公司 | Method and device for replenishing traditional Chinese medicine commodities, electronic equipment and storage medium |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007140785A (en) * | 2005-11-16 | 2007-06-07 | Nomura Research Institute Ltd | Inventory management system |
| CN102968670A (en) * | 2012-10-23 | 2013-03-13 | 北京京东世纪贸易有限公司 | Method and device for predicting data |
| US20170068973A1 (en) * | 2012-12-03 | 2017-03-09 | Dimitri Sinkel | System and method for inventory management |
| CN106971249A (en) * | 2017-05-05 | 2017-07-21 | 北京挖玖电子商务有限公司 | A kind of Method for Sales Forecast and replenishing method |
| CN109740793A (en) * | 2018-11-29 | 2019-05-10 | 杭州览众数据科技有限公司 | A kind of inventory optimization method based on the distribution of probability demand |
| CN110570154A (en) * | 2019-09-18 | 2019-12-13 | 深圳市昂捷信息技术股份有限公司 | retail enterprise replenishment method |
| CN110807596A (en) * | 2019-11-06 | 2020-02-18 | 广东卓志供应链科技有限公司 | Cross-border e-commerce consignment warehouse storage management system and method |
-
2020
- 2020-02-19 CN CN202010103164.2A patent/CN111340421A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007140785A (en) * | 2005-11-16 | 2007-06-07 | Nomura Research Institute Ltd | Inventory management system |
| CN102968670A (en) * | 2012-10-23 | 2013-03-13 | 北京京东世纪贸易有限公司 | Method and device for predicting data |
| US20170068973A1 (en) * | 2012-12-03 | 2017-03-09 | Dimitri Sinkel | System and method for inventory management |
| CN106971249A (en) * | 2017-05-05 | 2017-07-21 | 北京挖玖电子商务有限公司 | A kind of Method for Sales Forecast and replenishing method |
| CN109740793A (en) * | 2018-11-29 | 2019-05-10 | 杭州览众数据科技有限公司 | A kind of inventory optimization method based on the distribution of probability demand |
| CN110570154A (en) * | 2019-09-18 | 2019-12-13 | 深圳市昂捷信息技术股份有限公司 | retail enterprise replenishment method |
| CN110807596A (en) * | 2019-11-06 | 2020-02-18 | 广东卓志供应链科技有限公司 | Cross-border e-commerce consignment warehouse storage management system and method |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111915261B (en) * | 2020-08-10 | 2023-07-18 | 辉岭加拿大有限公司 | Medical laboratory consumable constant number management algorithm |
| CN111915261A (en) * | 2020-08-10 | 2020-11-10 | 辉岭加拿大有限公司 | Medical laboratory consumable fixed number management algorithm |
| CN112036795A (en) * | 2020-08-31 | 2020-12-04 | 平安医疗健康管理股份有限公司 | Method, device, equipment and storage medium for determining medicine purchasing plan |
| CN112561562A (en) * | 2020-11-13 | 2021-03-26 | 广汽蔚来新能源汽车科技有限公司 | Order issuing method and device, computer equipment and storage medium |
| CN112465427A (en) * | 2020-11-26 | 2021-03-09 | 上海领健信息技术有限公司 | Delivery plan generation method, system and terminal based on purchase demand |
| CN112734320A (en) * | 2020-11-26 | 2021-04-30 | 上海领健信息技术有限公司 | Purchasing task plan generation method, system and terminal |
| CN113780611A (en) * | 2020-12-10 | 2021-12-10 | 北京沃东天骏信息技术有限公司 | Inventory management method and device |
| CN113780612A (en) * | 2020-12-10 | 2021-12-10 | 北京沃东天骏信息技术有限公司 | Method and device for optimizing purchasing data |
| CN112598358A (en) * | 2020-12-30 | 2021-04-02 | 杭州拓深科技有限公司 | Intelligent aid decision purchasing method |
| CN112819404A (en) * | 2021-01-13 | 2021-05-18 | 中国联合网络通信集团有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN113191814A (en) * | 2021-05-14 | 2021-07-30 | 扬州互江船舶科技有限公司 | Method and system for automatically inquiring price and purchasing |
| CN113487386A (en) * | 2021-07-20 | 2021-10-08 | 唯品会(珠海)电子商务有限公司 | Purchasing data generation method, device, equipment and computer readable medium |
| CN114298625A (en) * | 2021-12-21 | 2022-04-08 | 中武(福建)跨境电子商务有限责任公司 | Intelligent cross-border marine procurement analysis system based on big data |
| CN114757703A (en) * | 2022-04-14 | 2022-07-15 | 荃豆数字科技有限公司 | Method and device for replenishing traditional Chinese medicine commodities, electronic equipment and storage medium |
| CN116777571A (en) * | 2023-08-22 | 2023-09-19 | 山东创恩信息科技股份有限公司 | Merchant recommendation and purchase method and system based on big data and Internet platform |
| CN116777571B (en) * | 2023-08-22 | 2023-11-10 | 山东创恩信息科技股份有限公司 | Merchant recommendation and purchase method and system based on big data and Internet platform |
| CN117273599A (en) * | 2023-08-22 | 2023-12-22 | 昆仑数智科技有限责任公司 | Method and system for predicting commodity demand and determining commodity replenishment quantity |
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