CN115965140A - Inventory optimal planning method, system, equipment and storage medium - Google Patents
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Abstract
The invention is suitable for the technical field of inventory planning, and provides an optimal inventory planning method, a system, equipment and a storage medium, wherein the method comprises the following steps: according to the cost of goods, the logistics cost and the keeping cost, a total cost optimization model of the single goods is established; according to the total cost optimization model of the single goods, establishing a Bayesian whole-goods cost optimization model by taking the minimum total cost as a target function; according to the Bayes whole-product cost optimal model, the inventory planning result is output, and the Bayes optimization is performed on the model by using an artificial intelligence algorithm, so that dynamic variable safety inventory management is realized, the inventory total cost and the inventory turnover period are reduced, cash flow is increased, the customer satisfaction is increased, and the enterprise competitiveness is improved.
Description
Technical Field
The invention belongs to the technical field of inventory planning, and particularly relates to an inventory optimal planning method, system, equipment and storage medium.
Background
As the economic development and the living standard of people are continuously improved, the automobile holding capacity of China is continuously increased, the demand of the public on automobiles is increased, the automobile market competition in the industry is fierce, and the reasonable spare part inventory management has important significance for enterprises in improving the core competitiveness, obtaining higher profits, brand influence and the like; however, the spare parts of the automobile are various and large in quantity, the requirements of the spare parts in different areas are easily affected by the factors such as the local automobile holding quantity, the driving environment, the season and the like, so that the requirements of the spare parts are greatly different, the requirements are strong in dynamics and volatility, the rules of the requirements are difficult to master, and the supply period is different in length due to the different factors such as the types of the spare parts and the supply sources, so that the inventory management of the spare parts is quite difficult. For example: the service cycle of spare parts is greatly shortened due to the fact that the vehicle model updating speed is high. Once a new vehicle model starts to be used, the demand of spare parts of the original vehicle model is reduced, and after long-term accumulation, dead stock is formed; after-sales departments respectively adopt subjective ordering strategies to complete replenishment under the condition that the regional distribution center operates independently, and the occurrence probability of stock overstock and shortage is increased. Because the areas can not be allocated by themselves, the unified coordination capability of the inventory between the areas is weaker, spare parts are overstocked, the fund is occupied, and meanwhile, the ordering lead period is forced to be prolonged to generate local shortage of goods; the central distribution center has small inventory space and mainly has a transfer function, and the poor inventory management of individual regional distribution centers is easy to cause burst, so that a large amount of spare parts are overstocked at a carrier, and the service level is greatly influenced because the spare parts cannot reach the regional distribution centers in time to cause shortage of goods. Changes in the production capacity of suppliers and the transportation capacity of carriers can cause fluctuation of lead time, and untimely supply is caused.
At present, most automobile spare parts realize static inventory management according to a mode of a central bin, a distribution center and a service station, and cannot realize dynamic adjustment of inventory according to actual requirements. The problems of insufficient inventory, overstock of inventory, turnover of inventory, long delivery period of customers and the like generally exist, the capital flow of enterprises is seriously influenced, the inventory cost is increased, and the customer satisfaction is reduced.
Disclosure of Invention
Embodiments of the present invention provide an inventory optimization planning method, system, device, and storage medium, which are intended to solve the problems in the prior art identified in the background art.
The embodiment of the invention is realized in such a way that an inventory optimal planning method comprises the following steps:
according to the cost of goods, the logistics cost and the keeping cost, a total cost optimization model of the single goods is established;
according to the total cost optimization model of the single goods, establishing a Bayesian whole-goods cost optimization model by taking the minimum total cost as a target function;
and outputting an inventory planning result according to the Bayesian whole product cost optimal model.
Another objective of an embodiment of the present invention is to provide an inventory optimization planning system, where the system includes:
the single piece total cost optimization model establishing module is used for establishing a single piece goods total cost optimization model according to the goods cost, the logistics cost and the keeping cost;
the system comprises a total cost optimal model establishing module, a Bayes total cost optimal model establishing module and a Bayes total cost optimal model establishing module, wherein a user establishes a Bayes total cost optimal model by taking the minimum total cost as a target function according to a total cost optimal model of a single cargo;
and the planning result output module is used for outputting an inventory planning result according to the Bayesian whole product cost optimal model.
It is a further object of embodiments of the invention to provide a computer arrangement comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to carry out the steps of the inventory optimization planning method.
It is a further object of embodiments of the invention to provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of the inventory optimization planning method.
The embodiment of the invention uses an artificial intelligence algorithm to carry out Bayesian optimization on the model, realizes dynamic variable safety inventory management, reduces the total inventory cost and the inventory turnover period, increases cash flow, increases customer satisfaction and improves enterprise competitiveness.
Drawings
Fig. 1 is a flowchart of an inventory optimization planning method according to an embodiment of the present invention;
FIG. 2 is a flowchart of establishing a total cost optimization model for a piece of goods according to an embodiment of the present invention;
FIG. 3 is a graph of total cost versus order size provided by an embodiment of the present invention;
FIG. 4 is a schematic view of a whole-commodity optimal model provided by an embodiment of the present invention;
fig. 5 is a block diagram of an optimal inventory planning system according to an embodiment of the present invention;
fig. 6 is a block diagram of an internal configuration of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
As shown in fig. 1, in an embodiment, a method for optimally planning inventory is provided, which specifically includes the following steps:
and S100, establishing a total cost optimization model of the single goods according to the goods cost, the logistics cost and the keeping cost.
In the embodiment of the invention, in order to improve profit and reduce the overall inventory cost, the overall cost of the single goods is optimized by considering the overall cost of the single goods, and an overall cost optimization model of the single goods can be established according to the goods cost, the logistics cost and the keeping cost.
And S200, establishing a Bayes full-product cost optimal model by taking the minimum total cost as an objective function according to the single-piece goods total cost optimal model.
And step S300, outputting an inventory planning result according to the Bayesian whole-product cost optimal model.
In the embodiment of the invention, the artificial intelligence algorithm is used for carrying out Bayesian optimization on the model, thereby realizing dynamic variable safety inventory management, reducing the total inventory cost and the inventory turnover period, increasing cash flow, increasing the customer satisfaction and improving the enterprise competitiveness.
In an embodiment, as shown in fig. 2, step S100 may specifically include the following steps:
and S101, defining the total cost of the single goods to be equal to the sum of the cost of the single goods, the cost of single logistics and the cost of single custody. I.e. total cost = cargo cost + logistics cost + custody fee.
Step S102, establishing an equation according to the total cost of the single goods:
wherein P is the cost of a single piece, C is the distribution cost, R is the annual demand, Q is the order batch, and F is the annual average keeping cost of the single piece; TC is the total cost.
Step S103, obtaining the optimal ordering batch by calculating the partial derivative of the ordering batch Q through the total cost TC:
where H is the storage cost and Q is the single piece optimal order batch.
In the embodiment of the invention, the derivation process is as follows:
the final optimal ordered single piece batch Q is:
accordingly, a graph of the relationship between the total cost and the order quantity can be obtained, as shown in fig. 3.
In one embodiment, step S200 may specifically include the following steps:
the objective function is defined as: MIN (z) = ∑ B i x i +D i (x i-t )+K i x i Wherein, B i For spare part cost, D i (X i-t ) Is a loading function; ki is a warehousing cost function.
The constraint conditions are set as follows: the single-piece inventory capacity and the total inventory capacity are both lower than a set threshold, and the satisfaction rate and the work order satisfaction degree are both higher than the set threshold. Namely:
single piece inventory capacity: p i x i ≤maxP i x i 。
Total reservoir capacity: sigma P i x i Less than or equal to the total storage capacity.
The satisfaction rate is as follows: sigma F i (x i )*W i (x i )>The overall satisfaction.
The work order satisfaction degree: sigma CLASS (x) j -k) p 。
For F i (x i ) The probability density function is obtained through a Bayes prediction result; w i (x i ) Is a weight function and W i (x i )＝μ i /∑μ j ，μ i Estimate the expectation for a single piece j Is the current full product demand; CLASS (C) j-k ) p Satisfaction of a set of orders consisting of j-k singletons.
The satisfaction rate is obtained by the product of the estimated probability density and the average value of the specific single piece mean ratio total inventory, and the work order represents the combination of multiple single pieces, namely, the single pieces are grouped to reach the preset work order satisfaction degree.
As shown in FIG. 4, the best-fit model visual chart x of the whole product 1 ～x i The different units are respectively shown, and the specific meanings of the probability distribution and the mean value among the different units are clearly shown in the figure.
As shown in fig. 5, in an embodiment, an inventory optimization planning system is provided, which specifically includes a single-piece total cost optimization model building module 100, a full cost optimization model building module 200, and a planning result output module 300.
The module 100 for establishing the overall cost optimization model of the single piece of goods is used for establishing the overall cost optimization model of the single piece of goods according to the goods cost, the logistics cost and the keeping cost.
The full-product cost optimal model establishing module 200 is used for establishing a Bayes full-product cost optimal model by taking the minimum total cost as a target function according to a single-piece goods total cost optimal model.
The planning result output module 300 is configured to output an inventory planning result according to the bayesian whole-product cost optimal model.
In the embodiment of the invention, the artificial intelligence algorithm is used for carrying out Bayesian optimization on the model, thereby realizing dynamic variable safety inventory management, reducing the total inventory cost and the inventory turnover period, increasing cash flow, increasing the customer satisfaction and improving the enterprise competitiveness.
FIG. 6 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device comprises a processor, a memory, a network interface, an input device and a display screen which are connected through a system bus. The memory comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement a method for optimal inventory planning. The internal memory may also have stored therein a computer program that, when executed by the processor, causes the processor to perform a method for inventory optimization. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the inventory optimization planning system provided herein may be implemented in the form of a computer program that is executable on a computer device such as that shown in fig. 6. The memory of the computer device may store various program modules constituting the inventory optimization planning system, such as the one-piece total cost optimization model building module 100, the whole cost optimization model building module 200, and the planning result output module 300 shown in fig. 5. The computer program of each program module causes the processor to perform the steps of the inventory optimization planning method of each embodiment of the present application described in the present specification.
For example, the computer device shown in fig. 6 may execute step S100 through the single-piece total cost optimization model building module 100 in the inventory optimization planning system shown in fig. 5. The computer device may perform step S200 through the whole cost optimal model building module 200. The computer device may perform step S300 through the planning result output module 300.
In one embodiment, a computer device is proposed, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
and S100, establishing a total cost optimization model of the single goods according to the goods cost, the logistics cost and the keeping cost.
And S200, establishing a Bayes full-product cost optimal model by taking the minimum total cost as an objective function according to the single-piece goods total cost optimal model.
And step S300, outputting an inventory planning result according to the Bayesian whole-product cost optimal model.
In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of:
and S100, establishing a total cost optimization model of the single goods according to the goods cost, the logistics cost and the keeping cost.
And S200, establishing a Bayes full-product cost optimal model by taking the minimum total cost as an objective function according to the single-piece goods total cost optimal model.
And step S300, outputting an inventory planning result according to the Bayesian whole-product cost optimal model.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. A method for inventory optimization planning, the method comprising:
according to the cost of goods, the logistics cost and the keeping cost, a total cost optimization model of the single goods is established;
according to the total cost optimization model of the single goods, establishing a Bayesian whole-goods cost optimization model by taking the minimum total cost as a target function;
and outputting an inventory planning result according to the Bayesian whole product cost optimal model.
2. The method according to claim 1, wherein the step of establishing a total cost optimization model of the single piece goods according to the goods cost, the logistics cost and the custody cost specifically comprises:
defining the total cost of the single goods to be equal to the sum of the cost of the single goods, the cost of the single logistics and the cost of the single custody;
establishing an equation according to the total cost of the single cargo:
wherein P is the cost of a single piece, C is the distribution cost, R is the annual demand, Q is the order batch, and F is the annual average keeping cost of the single piece; TC is the total cost;
obtaining the optimal ordering batch by solving the partial derivative of the ordering batch Q through the total cost TC:
wherein H is the storage cost, and Q is the single piece optimal order batch.
3. The method according to claim 1, wherein the step of establishing a bayesian total cost optimization model based on the total cost optimization model of the single piece goods with the minimum total cost as an objective function specifically comprises:
the objective function is defined as: MIN (z) = ∑ B i x i +D i (x i-t )+K i x i Wherein, B i Cost of spare parts D i (x i-t ) Is a loading function; k i Is a warehousing cost function;
the constraint conditions are set as follows: the single piece inventory capacity and the total inventory capacity are both lower than a set threshold, and the satisfaction rate and the work order satisfaction degree are both greater than the set threshold;
the satisfaction rate is obtained by the product of the estimated probability density and the average value of the specific single piece average ratio total inventory, and the work order represents the combination of multiple single pieces, namely, the single pieces are grouped to reach the preset work order satisfaction degree.
4. The method of claim 3, wherein the satisfaction rate is calculated by:
∑F i (x i )*W′ i (x i )
wherein, F i (x i ) As a function of probability density, W i (x i ) Is a weight function and W i (x i )＝μ i /∑μ j ，μ i Estimate expectation for a single piece j The demand of the full product at the current period;
the calculation formula of the work order satisfaction degree is as follows: sigma CLASS (x) j -k) p Wherein, CLAss (C) j-k ) p Satisfaction of a set of orders consisting of j-k singletons.
5. An inventory optimization planning system, the system comprising:
the single piece total cost optimization model establishing module is used for establishing a single piece goods total cost optimization model according to the goods cost, the logistics cost and the keeping cost;
the system comprises a total cost optimal model establishing module, a Bayes total cost optimal model establishing module and a Bayes total cost optimal model establishing module, wherein a user establishes a Bayes total cost optimal model by taking the minimum total cost as a target function according to a total cost optimal model of a single cargo;
and the planning result output module is used for outputting an inventory planning result according to the Bayesian whole product cost optimal model.
6. A computer arrangement comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, causes the processor to carry out the steps of the inventory optimization planning method according to any one of claims 1 to 4.
7. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of the inventory optimization planning method according to any one of claims 1 to 4.
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