CN116645033A - ERP inventory optimization analysis method and system based on big data - Google Patents
ERP inventory optimization analysis method and system based on big data Download PDFInfo
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Abstract
The application discloses an ERP inventory optimization analysis method and system based on big data, in particular to the technical field of inventory optimization, which are used for solving the problems that the existing inventory optimization is not accurate enough, and inventory accumulation or insufficient inventory is easy to cause; the system comprises a data processing module, a data acquisition module, an early warning prompt module and a threshold value adjusting module, wherein the data acquisition module, the early warning prompt module and the threshold value adjusting module are in communication connection with the data processing module; the comparison of the inventory condition evaluation coefficient with the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient can help enterprises to master inventory conditions in time and take corresponding measures according to different conditions; the first threshold value of the inventory condition assessment coefficient and the second threshold value of the inventory condition assessment coefficient are secondarily adjusted in time intervals, so that the accuracy and timeliness of inventory management can be improved, and the demand condition and the inventory condition of the current market can be reflected more accurately according to the seasonal, periodic and other regular changes of product sales.
Description
Technical Field
The application relates to the technical field of ERP inventory optimization, in particular to an ERP inventory optimization analysis method and system based on big data.
Background
ERP is an abbreviation of enterprise resource planning (Enterprise Resource Planning), is integrated management software, and can help enterprises to realize comprehensive and efficient management and utilization of various resources (including materials, funds, equipment and the like) and improve the operating efficiency and management level of the enterprises.
In the rapid development stage of the manufacturing industry in China, the economic globalization trend is further enhanced, and higher requirements are provided for the operation and development of the manufacturing industry. In order to effectively cope with the changeable market environment, the research on inventory management of manufacturing enterprises has important significance; inventory refers to products that an enterprise stores to meet production, business, or personal consumption needs; inventory management is an important function in enterprise management; greatly affects the turnover of funds for the enterprise and plays an important role in meeting customer service requirements. Whether inventory management is reasonable or not directly influences whether the goods are backlogged or are in short supply.
In the aspect of current inventory optimization, the management and optimization of the enterprise on the inventory are mostly based on the experience and approximate market trend of the enterprise, so that the inventory is optimized according to the experience, the inventory is optimized insufficiently accurately, the situation of inventory accumulation or insufficient inventory is easily caused, and the benefit of the enterprise is affected.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present application provide a method and a system for optimizing and analyzing ERP inventory based on big data, so as to solve the problems set forth in the background art.
In order to achieve the above purpose, the present application provides the following technical solutions:
an ERP inventory optimization analysis method based on big data comprises the following steps: step S1: acquiring raw material supply information, inventory information and sales information; calculating an inventory condition assessment coefficient according to the raw material supply information, the inventory information and the sales information;
step S2: setting a first threshold value of an inventory condition evaluation coefficient, setting a second threshold value of the inventory condition evaluation coefficient, and carrying out early warning on the inventory condition by comparing the inventory condition evaluation coefficient with the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient;
step S3: acquiring the current sales cycle and the historical sales information, and calculating a historical sales trend value according to the historical sales information;
step S4: and calculating a reference threshold, calculating a secondary adjustment reference threshold according to the historical sales trend value, and calculating a secondary adjustment inventory condition assessment coefficient first threshold and a secondary adjustment inventory condition assessment coefficient second threshold through the secondary adjustment reference threshold.
In a preferred embodiment, in step S1, the raw material supply information includes a delivery timing rate and a goods qualification rate; the inventory information includes inventory quantity and inventory age ratio; sales information includes sales quantity, sales increase rate, customer quantity increase rate;
calculating an inventory condition assessment coefficient by normalizing the delivery timing rate, the goods qualification rate, the inventory quantity, the inventory age ratio, the sales quantity increase rate and the customer quantity increase rate, wherein the expression is as follows:
wherein Do, gr, iq, pa, ns, sr, nr is the delivery timing rate, the goods qualification rate, the stock quantity, the stock age ratio, the sales quantity increase rate, and the customer quantity increase rate, respectively; alpha 1 、α 2 、α 3 、α 4 、α 5 、α 6 、α 7 Preset proportionality coefficients of delivery timing rate, goods qualification rate, stock quantity, stock age ratio, sales quantity increasing rate and customer quantity increasing rate respectively, and alpha 6 ＞α 5 ＞α 3 ＞α 7 ＞α 1 ＞α 2 ＞α 4 ＞0。
In a preferred embodiment, in step S2, a first threshold value of the inventory condition assessment coefficient is set, a second threshold value of the inventory condition assessment coefficient is set, wherein the first threshold value of the inventory condition assessment coefficient is smaller than the second threshold value of the inventory condition assessment coefficient, and the first threshold value of the inventory condition assessment coefficient and the second threshold value of the inventory condition assessment coefficient are respectively marked as S a And S is b ；
When the inventory condition assessment coefficient is smaller than a first threshold value of the inventory condition assessment coefficient, the system generates an inventory tension early warning signal;
when the inventory condition evaluation coefficient is greater than or equal to a first threshold value of the inventory condition evaluation coefficient and the inventory condition evaluation coefficient is less than or equal to a second threshold value of the inventory condition evaluation coefficient, the system generates an inventory normal signal;
when the inventory condition assessment coefficient is greater than the inventory condition assessment coefficient second threshold, the system generates an inventory excess warning signal.
In a preferred embodiment, the current sales cycle is obtained; acquiring historical sales information, wherein the historical sales information comprises the number of historical sales in a time interval, the historical sales time interval, the historical total sales number and the historical sales period;
the time of the current sales period is equal to that of the historical sales period, and the current sales period is equally divided into n current sales time intervals; correspondingly, the historical sales period is equally divided into n historical sales time intervals, and the n current sales time intervals and the n historical sales time intervals are in one-to-one correspondence on a time sequence; wherein n is a positive integer;
calculating a historical sales trend value of a historical sales time interval corresponding to the current sales time interval, wherein the expression is as follows:
wherein Z is a historical sales trend value, hi is the historical sales number in the time interval, and Hv is the historical total sales number.
In a preferred embodiment, in step S4, the first threshold value of the inventory condition assessment coefficient and the second threshold value of the inventory condition assessment coefficient for the current sales time interval are secondarily adjusted according to the historical sales trend value, specifically as follows:
calculating a reference threshold;
performing secondary adjustment on the reference threshold according to the historical sales trend value, calculating a secondary adjustment reference threshold, and performing secondary adjustment on the reference thresholdThe expression of (2) is:
wherein, C is a secondary adjustment reference threshold, sc is a reference threshold, and delta is an adjustment coefficient;
calculating a first threshold value of a secondary adjustment inventory condition evaluation coefficient and a second threshold value of the secondary adjustment inventory condition evaluation coefficient, wherein the first threshold value is specifically:
secondarily adjusting the first threshold value of the inventory condition assessment coefficient:
secondarily adjusting the inventory condition assessment coefficient second threshold value:
wherein A is a first threshold value of the secondary adjustment inventory condition evaluation coefficient, and B is a second threshold value of the secondary adjustment inventory condition evaluation coefficient.
In a preferred embodiment, the ERP inventory optimization analysis system based on big data comprises a data processing module, a data acquisition module, an early warning prompt module and a threshold adjustment module, wherein the data acquisition module, the early warning prompt module and the threshold adjustment module are in communication connection with the data processing module;
the data acquisition module acquires raw material supply information, inventory information and sales information, sends the raw material supply information, the inventory information and the sales information to the data processing module, calculates an inventory condition evaluation coefficient by the data processing module, acquires a current sales period and historical sales information, and sends the historical sales information to the data processing module to calculate a historical sales trend value;
the data processing module is used for calculating a reference threshold value according to the historical sales trend value and the reference threshold value calculated by the data processing module;
the early warning prompt module receives the inventory condition assessment coefficient calculated by the data processing module, sets a first threshold value of the inventory condition assessment coefficient and a second threshold value of the inventory condition assessment coefficient, and performs early warning on the inventory condition by comparing the inventory condition assessment coefficient with the first threshold value of the inventory condition assessment coefficient and the second threshold value of the inventory condition assessment coefficient;
the threshold value adjusting module receives the historical sales trend value and the reference threshold value, calculates a secondary adjustment reference threshold value through the data processing module, and calculates a secondary adjustment inventory condition assessment coefficient first threshold value and a secondary adjustment inventory condition assessment coefficient second threshold value according to the secondary adjustment reference threshold value.
The ERP inventory optimization analysis method and the ERP inventory optimization analysis system based on big data have the technical effects and advantages that:
1. by comparing the inventory condition evaluation coefficient with the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient, enterprises can be helped to grasp inventory conditions in time, and corresponding measures are taken according to different conditions, so that stock backlog and stock stagnation are reduced, and stock management cost is reduced.
2. The first threshold value of the inventory condition assessment coefficient and the second threshold value of the inventory condition assessment coefficient are secondarily adjusted in time intervals, so that the accuracy and timeliness of inventory management can be improved, the demand condition and the inventory condition of the current market are reflected more accurately according to the seasonal, periodic and other regular changes of product sales, the enterprise is helped to conduct inventory management and planning more accurately, the conditions of shortage of inventory and surplus inventory are avoided, and the operation efficiency and the profitability of the enterprise are improved.
Drawings
FIG. 1 is a schematic diagram of an ERP inventory optimization analysis method based on big data;
fig. 2 is a schematic structural diagram of an ERP inventory optimization analysis system based on big data in the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
FIG. 1 shows a schematic diagram of an ERP inventory optimization analysis method based on big data, and the ERP inventory optimization analysis method based on big data comprises the following steps:
step S1: acquiring raw material supply information, inventory information and sales information; an inventory condition assessment coefficient is calculated based on the raw material supply information, the inventory information, and the sales information.
Step S2: setting a first threshold value of the inventory condition evaluation coefficient, setting a second threshold value of the inventory condition evaluation coefficient, and carrying out early warning on the inventory condition by comparing the inventory condition evaluation coefficient with the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient.
Step S3: and acquiring the current sales cycle and the historical sales information, and calculating a historical sales trend value according to the historical sales information.
Step S4: and calculating a reference threshold, calculating a secondary adjustment reference threshold according to the historical sales trend value, and calculating a secondary adjustment inventory condition assessment coefficient first threshold and a secondary adjustment inventory condition assessment coefficient second threshold through the secondary adjustment reference threshold.
In step S1, raw material supply information, inventory information, and sales information are acquired.
The raw material supply information comprises a delivery timing rate and a goods qualification rate, reflects whether the quality of raw materials and delivery time meet expectations, and for inventory management, if the raw material supply is not timely or the goods quality is not qualified, production lines can be stopped, normal operation of production and sales is affected, and inventory optimization is affected; raw material supply refers to the supply of raw materials required for the production of the enterprise products, the raw materials being supplied by suppliers.
The inventory information includes inventory quantity and inventory age ratio; the inventory information reflects the current inventory condition of the enterprise, and if the inventory quantity and the inventory age in the inventory information are high, the current inventory of the enterprise is more and the enterprise is in a diapause.
Sales information includes sales quantity, sales increase rate, customer quantity increase rate; the sales information reflects sales conditions of the enterprise, and if sales quantity and sales increase rate in the sales information are low, the sales of the enterprise are poor, and at the moment, the inventory may become a dead product or an old product, so that the inventory cost of the enterprise is increased.
The detailed descriptions of the delivery timing rate, the stock quantity of the goods qualification rate, the stock age ratio, the sales quantity, the sales increase rate and the customer quantity increase rate are as follows:
delivery timing rate: refers to the rate at which the provider completes the delivery in accordance with the contracted time. If the delivery timing rate is low, the inventory is too low, and the normal operation of the inventory is affected; however, too high a delivery timing rate may also result in too high an inventory level.
Cargo qualification rate: the product qualification rate refers to the proportion of products produced by enterprises meeting the specified standards, and is usually expressed in percentage. The calculation method comprises the following steps: quantity of acceptable product/(quantity of total product x 100%). The qualification rate of the goods can reflect the level and the management capability of the production quality of enterprises, and the high qualification rate indicates that the production quality of the enterprises is stable, and the stock quantity is higher than the stock quantity with lower qualification rate.
Inventory quantity: inventory quantity refers to the quantity of inventory of products in which the enterprise is located, and excessive or insufficient inventory affects the operation and benefits of the enterprise.
Warehouse age ratio: the inventory age ratio refers to the proportion of the total inventory of products with the inventory storage time exceeding a certain period, and the period of the inventory storage time exceeding the certain period is formulated according to actual conditions and is not repeated here; a high inventory age ratio may mean that inventory is heavily stacked, with an excess of inventory.
Sales amount: sales quantity refers to the quantity of products actually sold by the enterprise, and a high sales quantity generally means that the enterprise has good sales performance and the greater the demand for inventory.
Sales growth rate: sales growth rate refers to the increase in sales of an enterprise, typically expressed in percent; the calculation method comprises the following steps: (current sales-upper sales)/(upper sales x 100%). A high sales growth rate indicates good sales performance for the enterprise, as well as a greater demand for inventory.
The rate of increase of the number of clients refers to the rate of increase of the number of clients of the enterprise, typically expressed in percent; the customer quantity growth rate can reflect the market development capability and customer loyalty of the enterprise; for inventory management, a decrease in the rate of increase in customer quantity may mean a decrease in market competitiveness, a slow inventory turnover, and an increase in inventory.
All the parameters can be obtained and calculated through the financial statement, inventory management system, market research and other approaches of the enterprise.
Notably, the calculation of sales, sales growth rate, and customer growth rate are all calculated at the same time.
Calculating an inventory condition assessment coefficient by normalizing the delivery timing rate, the goods qualification rate, the inventory quantity, the inventory age ratio, the sales quantity increase rate and the customer quantity increase rate, wherein the expression is as follows:
wherein Do, gr, iq, pa, ns, sr, nr is the delivery timing rate, the goods qualification rate, the stock quantity, the stock age ratio, the sales quantity increase rate, and the customer quantity increase rate, respectively; alpha 1 、α 2 、α 3 、α 4 、α 5 、α 6 、α 7 Preset proportionality coefficients of delivery timing rate, goods qualification rate, stock quantity, stock age ratio, sales quantity increasing rate and customer quantity increasing rate respectively, and alpha 6 ＞α 5 ＞α 3 ＞α 7 ＞α 1 ＞α 2 ＞α 4 ＞0。
The above factors can be comprehensively considered by calculating the inventory condition assessment coefficient so as to assess the inventory condition of the enterprise; the method is beneficial to enterprises to know the stock condition in time, take measures to adjust the stock in time, avoid the situation of stock backlog or over-low stock, and improve the benefit and profit margin of the stock; meanwhile, the inventory condition assessment coefficient can also help enterprises to optimize inventory management, improve the delivery timing rate and the goods qualification rate, and reduce the inventory age ratio.
In step S2, a first threshold value of the inventory condition evaluation coefficient is set, a second threshold value of the inventory condition evaluation coefficient is set, wherein the first threshold value of the inventory condition evaluation coefficient is smaller than the second threshold value of the inventory condition evaluation coefficient, and the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient are respectively marked as S a And S is b 。
When the inventory condition assessment coefficient is smaller than a first threshold value of the inventory condition assessment coefficient, the system generates an inventory tension early warning signal; at this time, the stock quantity cannot meet the market demand, and the order is delayed or cancelled, so that the customer satisfaction degree and reputation are affected; resulting in reduced sales revenue and thus affecting the profitability of the enterprise; missing market opportunities, affecting the market competitiveness of the enterprise; the enterprises take measures, specifically:
optimizing a supply chain: the communication with suppliers is enhanced, the delivery time rate and the goods qualification rate are improved, the timely supply of raw materials is ensured, and the condition of supply chain breakage is avoided.
The production efficiency is improved: the inventory is increased by increasing production efficiency, for example, optimizing production plans, enhancing production facility maintenance.
When the inventory condition evaluation coefficient is greater than or equal to a first threshold value of the inventory condition evaluation coefficient and the inventory condition evaluation coefficient is less than or equal to a second threshold value of the inventory condition evaluation coefficient, the system generates an inventory normal signal; the enterprise does not need to take measures.
When the inventory condition evaluation coefficient is larger than a second threshold value of the inventory condition evaluation coefficient, the system generates an inventory surplus early warning signal; at this time, it is shown that the stock quantity exceeds the market demand, and the enterprise needs to take measures to solve the problem of excessive stock, specifically:
reducing throughput: production is stopped or reduced to avoid further increases in inventory.
Promotion activity: through sales promotion means such as price reduction, binding sales, gifts and the like, consumers are stimulated to purchase products, and the excessive pressure of inventory is relieved.
Inventory cleaning: the stock is rapidly digested by means of warehouse cleaning, donation or destruction of expired or diapause products, etc.
Improving the product quality: product quality is improved, market demand is improved, and excessive inventory risk is reduced.
Strengthen market research: the market research is enhanced, the market demand change is known, the production plan and the inventory management strategy are adjusted in time, and the problem of excessive inventory is avoided.
By comparing the inventory condition evaluation coefficient with the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient, enterprises can be helped to grasp inventory conditions in time, and corresponding measures are taken according to different conditions, so that stock backlog and stock stagnation are reduced, and stock management cost is reduced. Customer satisfaction and reputation are improved.
In step S3, if the relationship between the inventory amount and the market is evaluated based on the inventory condition evaluation coefficient in step S2 alone, the influence of the sales at different times is easily ignored, and the influence of the sales at different times is different depending on the influence of the sales at different times.
Acquiring a current sales period; the method comprises the steps of obtaining historical sales information, wherein the historical sales information comprises historical sales quantity in a time interval, historical sales time interval, historical total sales quantity and historical sales period.
The historical total sales number is the total sales number of the historical sales period, and the historical sales number in the time interval is the sales number of a certain historical sales time interval;
the time of the current sales period is equal to that of the historical sales period, and the current sales period is equally divided into n current sales time intervals; correspondingly, the historical sales period is equally divided into n historical sales time intervals, and the n current sales time intervals and the n historical sales time intervals are in one-to-one correspondence on a time sequence; wherein n is a positive integer.
Calculating a historical sales trend value of a historical sales time interval corresponding to the current sales time interval, wherein the expression is as follows:
wherein Z is a historical sales trend value, hi is the historical sales number in the time interval, and Hv is the historical total sales number.
In step S4, performing secondary adjustment on the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient in a certain current sales time interval, so as to better improve the accuracy and timeliness of inventory management; according to the historical sales trend value, the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient of the current sales time interval are secondarily adjusted, and the method specifically comprises the following steps:
a reference threshold is calculated, the reference threshold being an average of the first threshold of the inventory condition assessment coefficient and the second threshold of the inventory condition assessment coefficient.
Performing secondary adjustment on the reference threshold according to the historical sales trend value, and calculating a secondary adjustment reference threshold, wherein the expression of the secondary adjustment reference threshold is as follows:
wherein, C is a secondary adjustment reference threshold, sc is a reference threshold, and delta is an adjustment coefficient.
According to the secondary adjustment reference threshold, calculating a secondary adjustment inventory condition evaluation coefficient first threshold and a secondary adjustment inventory condition evaluation coefficient second threshold, specifically:
secondarily adjusting the first threshold value of the inventory condition assessment coefficient:
secondarily adjusting the inventory condition assessment coefficient second threshold value:
wherein A is a first threshold value of the secondary adjustment inventory condition evaluation coefficient, and B is a second threshold value of the secondary adjustment inventory condition evaluation coefficient.
According to the historical sales information, the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient are secondarily adjusted in a time interval to obtain the first threshold value of the secondarily adjusted inventory condition evaluation coefficient and the second threshold value of the secondarily adjusted inventory condition evaluation coefficient, so that the accuracy and timeliness of inventory management can be improved, the demand condition and the inventory condition of the current market can be reflected more accurately, and the historical sales information can reflect seasonal, periodic and other regular changes of product sales; the method is beneficial to the enterprise to more accurately manage and plan the inventory, avoid the conditions of shortage of the inventory and excess inventory, and improve the operation efficiency and the profit capability of the enterprise.
Example 2
The difference between the embodiment 2 and the embodiment 1 of the present application is that the present embodiment describes an ERP inventory optimization analysis system based on big data.
Fig. 2 shows a schematic structural diagram of an ERP inventory optimization analysis system based on big data, and the ERP inventory optimization analysis system based on big data includes a data processing module, and a data acquisition module, an early warning prompt module and a threshold adjustment module which are communicatively connected with the data processing module.
The data acquisition module acquires raw material supply information, inventory information and sales information, sends the raw material supply information, the inventory information and the sales information to the data processing module, calculates an inventory condition evaluation coefficient by the data processing module, acquires a current sales period and historical sales information, and sends the historical sales information to the data processing module to calculate a historical sales trend value;
the data processing module is used for calculating a reference threshold value according to the historical sales trend value and the reference threshold value calculated by the data processing module.
The early warning prompt module receives the inventory condition assessment coefficient calculated by the data processing module, sets a first threshold value of the inventory condition assessment coefficient and a second threshold value of the inventory condition assessment coefficient, and performs early warning on the inventory condition by comparing the inventory condition assessment coefficient with the first threshold value of the inventory condition assessment coefficient and the second threshold value of the inventory condition assessment coefficient.
The threshold value adjusting module receives the historical sales trend value and the reference threshold value, calculates a secondary adjustment reference threshold value through the data processing module, and calculates a secondary adjustment inventory condition assessment coefficient first threshold value and a secondary adjustment inventory condition assessment coefficient second threshold value according to the secondary adjustment reference threshold value.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform 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 (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (6)
1. The ERP inventory optimization analysis method based on the big data is characterized by comprising the following steps of:
step S1: acquiring raw material supply information, inventory information and sales information; calculating an inventory condition assessment coefficient according to the raw material supply information, the inventory information and the sales information;
step S2: setting a first threshold value of an inventory condition evaluation coefficient, setting a second threshold value of the inventory condition evaluation coefficient, and carrying out early warning on the inventory condition by comparing the inventory condition evaluation coefficient with the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient;
step S3: acquiring the current sales cycle and the historical sales information, and calculating a historical sales trend value according to the historical sales information;
step S4: and calculating a reference threshold, calculating a secondary adjustment reference threshold according to the historical sales trend value, and calculating a secondary adjustment inventory condition assessment coefficient first threshold and a secondary adjustment inventory condition assessment coefficient second threshold through the secondary adjustment reference threshold.
2. The ERP inventory optimization analysis method based on big data according to claim 1, wherein: in step S1, the raw material supply information includes a delivery timing rate and a goods qualification rate; the inventory information includes inventory quantity and inventory age ratio; sales information includes sales quantity, sales increase rate, customer quantity increase rate;
calculating an inventory condition assessment coefficient by normalizing the delivery timing rate, the goods qualification rate, the inventory quantity, the inventory age ratio, the sales quantity increase rate and the customer quantity increase rate, wherein the expression is as follows:
wherein Do, gr, iq, pa, ns, sr, nr is the delivery timing rate, the goods qualification rate, the stock quantity, the stock age ratio, the sales quantity increase rate, and the customer quantity increase rate, respectively; alpha 1 、α 2 、α 3 、α 4 、α 5 、α 6 、α 7 Preset proportionality coefficients of delivery timing rate, goods qualification rate, stock quantity, stock age ratio, sales quantity increasing rate and customer quantity increasing rate respectively, and alpha 6 ＞α 5 ＞α 3 ＞α 7 ＞α 1 ＞α 2 ＞α 4 ＞0。
3. The ERP inventory optimization analysis method based on big data according to claim 2, which comprises the following steps ofIs characterized in that: in step S2, a first threshold value of the inventory condition evaluation coefficient is set, a second threshold value of the inventory condition evaluation coefficient is set, wherein the first threshold value of the inventory condition evaluation coefficient is smaller than the second threshold value of the inventory condition evaluation coefficient, and the first threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient are respectively marked as S a And S is b ；
When the inventory condition assessment coefficient is smaller than a first threshold value of the inventory condition assessment coefficient, the system generates an inventory tension early warning signal;
when the inventory condition evaluation coefficient is greater than or equal to a first threshold value of the inventory condition evaluation coefficient and the inventory condition evaluation coefficient is less than or equal to a second threshold value of the inventory condition evaluation coefficient, the system generates an inventory normal signal;
when the inventory condition assessment coefficient is greater than the inventory condition assessment coefficient second threshold, the system generates an inventory excess warning signal.
4. A method for optimizing analysis of ERP inventory based on big data according to claim 3, wherein: acquiring a current sales period; acquiring historical sales information, wherein the historical sales information comprises the number of historical sales in a time interval, the historical sales time interval, the historical total sales number and the historical sales period;
the time of the current sales period is equal to that of the historical sales period, and the current sales period is equally divided into n current sales time intervals; correspondingly, the historical sales period is equally divided into n historical sales time intervals, and the n current sales time intervals and the n historical sales time intervals are in one-to-one correspondence on a time sequence; wherein n is a positive integer;
calculating a historical sales trend value of a historical sales time interval corresponding to the current sales time interval, wherein the expression is as follows:
wherein Z is a historical sales trend value, hi is the historical sales number in the time interval, and Hv is the historical total sales number.
5. The ERP inventory optimization analysis method based on big data according to claim 4, wherein: in step S4, the second threshold value of the inventory condition evaluation coefficient and the second threshold value of the inventory condition evaluation coefficient in the current sales time interval are adjusted according to the historical sales trend value, specifically as follows:
calculating a reference threshold;
performing secondary adjustment on the reference threshold according to the historical sales trend value, and calculating a secondary adjustment reference threshold, wherein the expression of the secondary adjustment reference threshold is as follows:
wherein, C is a secondary adjustment reference threshold, sc is a reference threshold, and delta is an adjustment coefficient;
calculating a first threshold value of a secondary adjustment inventory condition evaluation coefficient and a second threshold value of the secondary adjustment inventory condition evaluation coefficient, wherein the first threshold value is specifically:
secondarily adjusting the first threshold value of the inventory condition assessment coefficient:
secondarily adjusting the inventory condition assessment coefficient second threshold value:
wherein A is a first threshold value of the secondary adjustment inventory condition evaluation coefficient, and B is a second threshold value of the secondary adjustment inventory condition evaluation coefficient.
6. An ERP inventory optimization analysis system based on big data, which is used for realizing the ERP inventory optimization analysis method based on big data according to any one of claims 1 to 5, and is characterized in that: the system comprises a data processing module, a data acquisition module, an early warning prompt module and a threshold value adjusting module, wherein the data acquisition module, the early warning prompt module and the threshold value adjusting module are in communication connection with the data processing module;
the data acquisition module acquires raw material supply information, inventory information and sales information, sends the raw material supply information, the inventory information and the sales information to the data processing module, calculates an inventory condition evaluation coefficient by the data processing module, acquires a current sales period and historical sales information, and sends the historical sales information to the data processing module to calculate a historical sales trend value;
the data processing module is used for calculating a reference threshold value according to the historical sales trend value and the reference threshold value calculated by the data processing module;
the early warning prompt module receives the inventory condition assessment coefficient calculated by the data processing module, sets a first threshold value of the inventory condition assessment coefficient and a second threshold value of the inventory condition assessment coefficient, and performs early warning on the inventory condition by comparing the inventory condition assessment coefficient with the first threshold value of the inventory condition assessment coefficient and the second threshold value of the inventory condition assessment coefficient;
the threshold value adjusting module receives the historical sales trend value and the reference threshold value, calculates a secondary adjustment reference threshold value through the data processing module, and calculates a secondary adjustment inventory condition assessment coefficient first threshold value and a secondary adjustment inventory condition assessment coefficient second threshold value according to the secondary adjustment reference threshold value.
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