CN112598358A - Intelligent aid decision purchasing method - Google Patents
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- CN112598358A CN112598358A CN202011615167.0A CN202011615167A CN112598358A CN 112598358 A CN112598358 A CN 112598358A CN 202011615167 A CN202011615167 A CN 202011615167A CN 112598358 A CN112598358 A CN 112598358A
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Abstract
The invention relates to an intelligent aid decision-making purchasing method, which comprises the steps of establishing a material basic table related to purchased materials, configuring an inventory standard based on the material basic table, acquiring historical purchasing data of a current unit, and constructing an inventory removing model; after the user initiates a purchasing demand, an auxiliary purchasing decision is given based on the stock removal model. The invention provides an intelligent purchasing auxiliary decision suggestion based on the comparison of real-time storage data, ideal commodity inventory setting, commodity due and scrapped data and historical synchronization data, reduces the requirement of artificial experience level for judging the purchasing quantity of goods and materials, reduces the statistical time of related data, ensures that the purchasing quantity is more accurate, and reduces the inventory backlog on the basis of ensuring the requirement.
Description
Technical Field
The present invention relates to data processing systems or methods particularly suited for administrative, commercial, financial, management, supervisory or forecasting purposes; the technical field of processing systems or methods specifically adapted for administrative, business, financial, management, supervisory or forecasting purposes, not otherwise provided for, and in particular to an intelligent aid decision-making procurement method.
Background
Purchasing means that an individual or an entity acquires a product or a service from a supply market under a certain condition as a resource of the individual or the entity, and is an operation activity for meeting the needs of the individual or ensuring normal development of production and operation activities.
While purchasing, the inventory will change. The inventory is the goods actually stored in the warehouse, and includes production inventory for ensuring uninterrupted supply of materials consumed by enterprises and utilities, and distribution inventory which is the raw material or finished product inventory of the manufacturing enterprises, the inventory of production departments and the inventory of all levels of material departments.
In the prior art, in order to confirm a material purchase order of a next step, a user often decides a conclusion after acquiring inventory information according to human experience or a historical digestion list, and the above method has the following disadvantages:
(1) the personnel responsible for purchasing must be required to be experienced and have high cost;
(2) a large amount of statistics needs to be carried out firstly, and more time is spent on information arrangement and analysis;
(3) the problem that the purchasing quantity is too large or insufficient due to incomplete consideration, and the purchasing cost or the stock overstock is further improved is solved;
(4) the material data continuously changes, the statistical data is not necessarily accurate, and careless mistakes can still exist even if experience and analysis are careful.
Disclosure of Invention
The invention solves the problems in the prior art and provides an optimized intelligent aid decision purchasing method.
The technical scheme adopted by the invention is that an intelligent aid decision purchasing method comprises the following steps:
step 1: establishing a material basic table, wherein the material basic table is associated with the purchased materials;
step 2: configuring an inventory standard based on the material basic table;
and step 3: acquiring historical purchasing data of a current unit, and constructing a stock removing model;
and 4, step 4: and (4) initiating a purchasing demand by a user, and giving an auxiliary purchasing decision based on the stock removal model.
Preferably, in step 1, the material basis table associates information of purchased materials, where the information includes material identification information and purchase association information; the purchase associated information comprises the production time, the quality guarantee time and the loss rate alpha of the material.
Preferably, the purchase correlation information further includes a correlation coefficient γ of the loss rate and the time.
Preferably, the material identification information includes a name, a specification, and an identification code of the material.
Preferably, in step 3, if the historical purchase data of the current unit is empty, matching with other units closest to the current unit based on the unit belonging field and the operation range, otherwise, obtaining the historical purchase data of the current unit; the historical purchasing data comprises material identification information, purchasing correlation information, the total amount of any material and corresponding inventory removing data of the material.
Preferably, the stock removal model is:
for any material, acquiring data of purchasing and consumed in each purchasing period, wherein the data is related to time and is marked in time units to obtain a data line L1 of total material amount based on time and a data line L2 of single material consumption;
the total amount of the materials is S = Si-1- Si-1α γ -Q, wherein Si-1The amount of the materials remained after the last consumption, and Q is the materials reaching the shelf life;
if the L2 and the L1 are crossed, shortening the current time unit and acquiring the stock removing model again, otherwise, acquiring the stock P, and P = PL1-PL2，PL1And PL2The inventory P was analyzed for values corresponding to the ends of L1 and L2, respectively, to optimize the destocking model.
Preferably, if the stock P is less than or equal to the threshold value, the current purchasing scheme of the current material is reserved;
if the stock P is larger than the threshold value, judging whether the quality guarantee time is smaller than the purchasing period, if so, keeping the current purchasing scheme of the current material, otherwise, taking 1/N of the current purchasing period as a new purchasing period, and taking the total purchased material amount S' = S/N (1+ alpha gamma), wherein N is larger than 0.
Preferably, with preset time as a feedback period, acquiring data of purchasing and consumed in each purchasing period of the optimized stock-removing model of any material, wherein the data is related to time and is marked in time units to obtain a time-based total material amount data line L1 and a material single-consumption data line L2; if the stock P is less than or equal to the threshold and less than the stock value of the stock removing model before optimization, the current purchasing scheme of the current materials is reserved, otherwise, the stock removing model is modified into the stock removing model before optimization.
Preferably, in the step 4, the purchase demand is initiated by searching the material basic table and corresponding to the material; if the current materials do not exist, carrying out fuzzy retrieval, otherwise, giving out an auxiliary purchasing decision based on the stock removal model, and carrying out purchasing; and if the fuzzy search does not have corresponding materials, constructing new materials and presetting a material basic table.
Preferably, the fuzzy retrieval comprises splitting the names of the supplies and/or retrieving the categories of the supplies.
The invention provides an optimized intelligent aid decision purchasing method, which comprises the steps of establishing a material basic table related to purchased materials, configuring an inventory standard based on the material basic table, acquiring historical purchasing data of a current unit, and constructing an inventory removing model; after the user initiates a purchasing demand, an auxiliary purchasing decision is given based on the stock removal model.
The invention provides an intelligent purchasing auxiliary decision suggestion based on the comparison of real-time storage data, ideal commodity inventory setting, commodity due and scrapped data and historical synchronization data, reduces the requirement of artificial experience level for judging the purchasing quantity of goods and materials, reduces the statistical time of related data, ensures that the purchasing quantity is more accurate, and reduces the inventory backlog on the basis of ensuring the requirement.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention is described in further detail with reference to the following examples, but the scope of the present invention is not limited thereto.
The invention relates to an intelligent aid decision purchasing method, which comprises the following steps:
step 1: establishing a material basic table, wherein the material basic table is associated with the purchased materials;
in the step 1, the material basic table is associated with the information of the purchased materials, and the information comprises material identification information and purchase associated information; the purchase associated information comprises the production time, the quality guarantee time and the loss rate alpha of the material.
The purchase correlation information also comprises a correlation coefficient gamma of the loss rate and the time.
The material identification information comprises the name, specification and identification code of the material.
In the invention, perfect various material input standards are established, all purchased material data are electronically filed, and the expiration time, the warranty time and other data of the material are known, so that more accurate purchasing suggestions can be conveniently provided subsequently.
In the invention, the material basic table is constructed, and for a brand-new user constructing the purchasing auxiliary requirement, all the purchasing information in the later period should correspond to the information in the material basic table, including but not limited to the name, specification and identification code of the material; for a user who already has partial purchase data, the early-stage material information and the later-stage material information are gradually synchronized and unified through subsequent fuzzy retrieval and matching.
In the invention, the purchase associated information comprises the production time, the quality guarantee time and the loss rate alpha of the material, the production time and the quality guarantee time are mainly used for controlling the consumption period of the material, and the loss rate is closely related to the data of the material which should relax the allowance on the theoretical purchase value; further, the loss rate is fitted with a correlation coefficient gamma of the loss rate and time, generally, the correlation coefficient gamma increases exponentially, but may also increase linearly on different materials, and the judgment and the fitting should be performed based on different materials.
In the present invention, both α and γ are generally greater than 0.
Step 2: configuring an inventory standard based on the material basic table;
in the invention, different inventory standard configurations are established according to different requirements.
In the present invention, in particular, the inventory criteria include limit requirements for shelf life, and also should include acceptable inventory margins after a single purchase.
And step 3: acquiring historical purchasing data of a current unit, and constructing a stock removing model;
in the step 3, if the historical purchasing data of the current unit is empty, matching other units closest to the current unit based on the area to which the unit belongs and the operation range, otherwise, acquiring the historical purchasing data of the current unit; the historical purchasing data comprises material identification information, purchasing correlation information, the total amount of any material and corresponding inventory removing data of the material.
The stock removal model is as follows:
for any material, acquiring data of purchasing and consumed in each purchasing period, wherein the data is related to time and is marked in time units to obtain a data line L1 of total material amount based on time and a data line L2 of single material consumption;
the total amount of the materials is S = Si-1- Si-1α γ -Q, wherein Si-1The amount of the materials remained after the last consumption, and Q is the materials reaching the shelf life;
if the L2 and the L1 are crossed, shortening the current time unit and acquiring the stock removing model again, otherwise, acquiring the stock P, and P = PL1-PL2，PL1And PL2The inventory P was analyzed for values corresponding to the ends of L1 and L2, respectively, to optimize the destocking model.
If the stock P is less than or equal to the threshold value, the current purchasing scheme of the current materials is reserved;
if the stock P is larger than the threshold value, judging whether the quality guarantee time is smaller than the purchasing period, if so, keeping the current purchasing scheme of the current material, otherwise, taking 1/N of the current purchasing period as a new purchasing period, and taking the total purchased material amount S' = S/N (1+ alpha gamma), wherein N is larger than 0.
Taking preset time as a feedback period, acquiring data of purchasing and consumed in each purchasing period of the optimized stock-removing model of any material, wherein the data is related to time and is marked in time units to obtain a data line L1 of total amount of the material based on time and a data line L2 of single consumption of the material; if the stock P is less than or equal to the threshold and less than the stock value of the stock removing model before optimization, the current purchasing scheme of the current materials is reserved, otherwise, the stock removing model is modified into the stock removing model before optimization.
In the invention, if the historical purchasing data of the current unit is empty, the cold start condition is avoided based on the field of the unit and the matching of the operation range with other units which are closest.
In the invention, for a unit with historical purchasing data, material identification information, purchasing correlation information, the total amount of any material and corresponding stock removal data of the total amount of any material are acquired, and a stock removal model is constructed based on the information.
In the invention, for any material, the data of purchasing and consumed in each purchasing period, such as 1 year, is obtained to obtain a data line L1 of total material amount based on time and a data line L2 of single material consumption, wherein the total material amount S = Si-1- Si-1α γ -Q, which is the total amount of material removed from the amount of material left after the last consumption and the amount of material that reaches the shelf life; here, i is obviously 1 or more, S0I.e. representing the initial material.
In the invention, if L2 and L1 are crossed to indicate that the definition of the purchasing period is not accurate, the current time unit is shortened, the stock removing model is obtained again, otherwise, stock P is obtained and analyzed;
if the stock P is less than or equal to the threshold value, the current scheme is feasible, and the current purchasing scheme of the current material is reserved unless optimization is needed;
if the inventory P is greater than the threshold, several situations are also included:
when the quality guarantee time is less than the purchasing period, the purchasing specificity of the fresh product is determined, and the current purchasing scheme of the current material can be reserved;
and when the quality guarantee time is more than or equal to the purchasing period, taking 1/N of the current purchasing period as a new purchasing period, wherein the total purchased material amount S' = S/N (1+ alpha gamma), namely the average value of the initial purchased material amount after the loss amount is reserved.
In the invention, a feedback period is set, which is generally an integral multiple of a purchase period, the optimized stock removal model of any material is backtracked, and optimization is carried out based on actual requirements.
And 4, step 4: and (4) initiating a purchasing demand by a user, and giving an auxiliary purchasing decision based on the stock removal model.
In the step 4, the purchase demand is initiated by searching the material basic table and corresponding to the material; if the current materials do not exist, carrying out fuzzy retrieval, otherwise, giving out an auxiliary purchasing decision based on the stock removal model, and carrying out purchasing; and if the fuzzy search does not have corresponding materials, constructing new materials and presetting a material basic table.
The fuzzy retrieval comprises splitting the names of the materials and/or retrieving the types of the materials.
In the present invention, the fuzzy search is a content that is easily understood by those skilled in the art, for example, the search can be performed based on "empty" and "open", and the correspondence of the common names of the materials can also be performed through the internet and the big data.
In the invention, the fuzzy search content should be recorded in the material basic table, corresponding to the remark of the material, or directly replaced.
In the invention, in the actual operation, after selecting the inventory standard and the time to be purchased according to which the purchase is expected to be carried out, the materials required by the standard are automatically compared with the actual inventory materials at the time point (the overdue materials and the like are eliminated), and the primary suggested content is given; furthermore, according to the comparison of historical contemporaneous material loss conditions, the intelligent floating adjustment on the quantity is carried out by combining the given suggestion basis, so that the inventory is more appropriate to the actual requirement.
Claims (10)
1. An intelligent aid decision purchasing method is characterized in that: the method comprises the following steps:
step 1: establishing a material basic table, wherein the material basic table is associated with the purchased materials;
step 2: configuring an inventory standard based on the material basic table;
and step 3: acquiring historical purchasing data of a current unit, and constructing a stock removing model;
and 4, step 4: and (4) initiating a purchasing demand by a user, and giving an auxiliary purchasing decision based on the stock removal model.
2. The intelligent aid decision purchasing method according to claim 1, characterized in that: in the step 1, the material basic table is associated with the information of the purchased materials, and the information comprises material identification information and purchase associated information; the purchase associated information comprises the production time, the quality guarantee time and the loss rate alpha of the material.
3. The intelligent aid decision purchasing method according to claim 2, characterized in that: the purchase correlation information also comprises a correlation coefficient gamma of the loss rate and the time.
4. The intelligent aid decision purchasing method according to claim 2, characterized in that: the material identification information comprises the name, specification and identification code of the material.
5. The intelligent aid decision purchasing method according to claim 2, characterized in that: in the step 3, if the historical purchasing data of the current unit is empty, matching other units closest to the current unit based on the area to which the unit belongs and the operation range, otherwise, acquiring the historical purchasing data of the current unit; the historical purchasing data comprises material identification information, purchasing correlation information, the total amount of any material and corresponding inventory removing data of the material.
6. The intelligent aid decision purchasing method according to claim 5, characterized in that: the stock removal model is as follows:
for any material, acquiring data of purchasing and consumed in each purchasing period, wherein the data is related to time and is marked in time units to obtain a data line L1 of total material amount based on time and a data line L2 of single material consumption;
the total amount of the materials is S = Si-1- Si-1α γ -Q, wherein Si-1The amount of the materials remained after the last consumption, and Q is the materials reaching the shelf life;
if the L2 and the L1 are crossed, shortening the current time unit and acquiring the stock removing model again, otherwise, acquiring the stock P, and P = PL1-PL2，PL1And PL2The inventory P was analyzed for values corresponding to the ends of L1 and L2, respectively, to optimize the destocking model.
7. The intelligent aid decision purchasing method according to claim 6, characterized in that: if the stock P is less than or equal to the threshold value, the current purchasing scheme of the current materials is reserved;
if the stock P is larger than the threshold value, judging whether the quality guarantee time is smaller than the purchasing period, if so, keeping the current purchasing scheme of the current material, otherwise, taking 1/N of the current purchasing period as a new purchasing period, and taking the total purchased material amount S' = S/N (1+ alpha gamma), wherein N is larger than 0.
8. The intelligent aid decision purchasing method according to claim 6, characterized in that: taking preset time as a feedback period, acquiring data of purchasing and consumed in each purchasing period of the optimized stock-removing model of any material, wherein the data is related to time and is marked in time units to obtain a data line L1 of total amount of the material based on time and a data line L2 of single consumption of the material; if the stock P is less than or equal to the threshold and less than the stock value of the stock removing model before optimization, the current purchasing scheme of the current materials is reserved, otherwise, the stock removing model is modified into the stock removing model before optimization.
9. The intelligent aid decision purchasing method according to claim 1, characterized in that: in the step 4, the purchase demand is initiated by searching the material basic table and corresponding to the material; if the current materials do not exist, carrying out fuzzy retrieval, otherwise, giving out an auxiliary purchasing decision based on the stock removal model, and carrying out purchasing; and if the fuzzy search does not have corresponding materials, constructing new materials and presetting a material basic table.
10. The intelligent aid decision purchasing method according to claim 9, characterized in that: the fuzzy retrieval comprises splitting the names of the materials and/or retrieving the types of the materials.
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| CN114881545A (en) * | 2022-07-07 | 2022-08-09 | 国网浙江省电力有限公司 | Automatic material matching method and device based on cooperative interconnection |
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