CN113887771A - Service level optimization method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a service level optimization method, a service level optimization device, computer equipment and a storage medium. The method comprises the following steps: grouping the stock products according to the product characteristics of the stock products to obtain stock product groups; the service cost of each stock product in the stock product grouping is mined to obtain the total service cost of the stock product grouping; and based on the total service cost of each inventory product group, traversing and optimizing the service level of each inventory product group, and determining the optimal service level of each inventory product group. The method can reduce the cost.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a service level optimization method and apparatus, a computer device, and a storage medium.
Background
The service level is used for measuring the capacity of the stock point to meet the demand, and represents the probability of meeting the demand of the user, such as the probability of not meeting the stock shortage. The level of service directly affects the amount of safety stock deposited at each inventory point, and thus the cost of inventory management. In the conventional method, the service level is generally specified for the inventory product by an administrator according to ABC (Activity Based Classification, pareto analysis) analysis Based on management experience.
However, the current method of determining the service level based on experience is too extensive to effectively control the service cost generated by the pre-estimated service, thereby increasing the inventory management cost.
Disclosure of Invention
In view of the above, it is necessary to provide a service level optimization method, apparatus, computer device and storage medium capable of reducing inventory management cost.
A method of service level optimization, the method comprising:
grouping the inventory products according to the product characteristics of the inventory products to obtain inventory product groups;
mining the service cost of each inventory product in the inventory product grouping to obtain the total service cost of the inventory product grouping;
determining an optimal service level for each of the groupings of inventory products by traversing the optimization of the service level for each of the groupings of inventory products based on the total cost of service for each of the groupings of inventory products.
In one embodiment, the mining the service cost of each of the inventory products in the inventory product grouping to obtain the total service cost of the inventory product grouping includes:
deriving a unit cost and a demand order for each of the inventory products in the inventory product grouping, the unit cost including a unit safe inventory holding cost and a unit backorder cost;
carrying out demand statistics according to the demand orders of the inventory products to obtain the demand quantity, demand expectation and demand variance of the inventory products;
predicting the safe stock holding quantity and the stock shortage quantity of the stock products according to the demand quantity, the demand expectation and the demand variance;
and determining the goods holding cost of the inventory products according to the unit safety inventory goods holding cost and the safety inventory goods holding quantity, and determining the goods shortage cost of the inventory products according to the unit goods shortage cost and the goods shortage quantity to obtain the total service cost of the inventory product grouping.
In one embodiment, the performing demand statistics according to the demand orders of the inventory products to obtain a demand quantity, a demand quantity expectation and a demand quantity variance of the inventory products includes:
counting the required quantity of the stock products according to the required orders of the stock products, and sequencing the required quantity according to the required orders to obtain a required quantity time sequence;
calculating a mean and variance of the demand quantity sequence of the inventory product based on the demand quantity time sequence of the inventory product, resulting in a demand quantity expectation and a demand quantity variance of the inventory product.
In one embodiment, said determining an optimal service level for each of said groupings of inventory products by traversing optimization of the service level for each of said groupings of inventory products based on a total cost of service for each of said groupings of inventory products comprises:
generating constraint conditions according to a preset expected service level; the expected service level comprises an upper limit and a lower limit of the service level of each stock product and a service level target value of each stock product group;
determining an optimal service level for each of the groupings of inventory products with a goal of minimizing the total cost of service for each of the groupings of inventory products under the constraint of the constraint.
In one embodiment, said determining an optimal service level for each said group of inventory products with a goal of minimizing said total cost of service for each said group of inventory products under a constraint based on said constraint comprises:
solving the lowest total service cost of each inventory product group by using a least square planning algorithm under the constraint of the constraint condition;
and taking the service level corresponding to the lowest total service cost as the optimal service level of the inventory product group.
In one embodiment, the grouping the inventory products according to the product features of the inventory products to obtain an inventory product group includes:
determining the category of the stock product according to the product characteristics of the stock product;
and dividing the inventory products with the same belonged category into a group to obtain the inventory product group corresponding to the belonged category.
A service level optimization apparatus, the apparatus comprising:
the grouping module is used for grouping the inventory products according to the product characteristics of the inventory products to obtain inventory product groups;
the mining module is used for mining the service cost of each inventory product in the inventory product grouping to obtain the total service cost of the inventory product grouping;
and the optimization module is used for traversing and optimizing the service level of each inventory product group based on the total service cost of each inventory product group and determining the optimal service level of each inventory product group.
In one embodiment, the mining module is further configured to,
deriving a unit cost and a demand order for each of the inventory products in the inventory product grouping, the unit cost including a unit safe inventory holding cost and a unit backorder cost; carrying out demand statistics according to the demand orders of the inventory products to obtain the demand quantity, demand expectation and demand variance of the inventory products; predicting the safe stock holding quantity and the stock shortage quantity of the stock products according to the demand quantity, the demand expectation and the demand variance; and determining the goods holding cost of the inventory products according to the unit safety inventory goods holding cost and the safety inventory goods holding quantity, and determining the goods shortage cost of the inventory products according to the unit goods shortage cost and the goods shortage quantity to obtain the total service cost of the inventory product grouping.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the service level optimization method of any one of the above when the computer program is executed.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the service level optimization method of any of the preceding claims.
The service level optimization method, the service level optimization device, the computer equipment and the storage medium are used for grouping the inventory products according to the product characteristics of the inventory products to obtain the inventory product grouping; further, the service cost of each stock product in the stock product grouping is mined to obtain the total service cost of the stock product grouping; and based on the total service cost of each stock product group, traversing and optimizing the service level of each stock product group through a service cost-service level relation model, and outputting the optimal service level of each stock product group. The method realizes the optimization of the service level of the stock product by mining the service cost of the stock product, and can effectively control the service cost while optimizing the service level, thereby reducing the inventory management cost. Meanwhile, the inventory products are grouped through the product characteristics, the service level of each inventory product group is optimized, the method can be simultaneously applied to large batches of inventory products, and the optimization efficiency is improved.
Drawings
FIG. 1 is a diagram of an application environment for a method of service level optimization in one embodiment;
FIG. 2 is a flow diagram that illustrates a method for service level optimization in one embodiment;
FIG. 3 is a flowchart illustrating the step of mining the cost of service for each inventory product in the inventory product group to obtain the total cost of service for the inventory product group in one embodiment;
FIG. 4 is a block diagram of an apparatus for service level optimization in one embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application 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 present application and are not intended to limit the present application.
The service level optimization method provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. After the terminal 102 receives the service level optimization instruction of the user, the terminal 102 may implement the service level optimization method separately according to the service level optimization instruction. The terminal 102 may also send the service level optimization instruction to the communicating server 104, and the server 104 may implement the service level optimization method in response to the service level optimization instruction.
Taking the server 104 as an example, specifically, the server 104 groups each stock product according to the product characteristics of the stock product to obtain a stock product group; the server 104 excavates the service cost of each stock product in the stock product grouping to obtain the total service cost of the stock product grouping; the server 104 determines an optimal service level for each inventory product grouping by traversing the optimized service level for each inventory product grouping based on the total cost of service for each inventory product grouping. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a method for optimizing service level is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
and step 202, grouping the inventory products according to the product characteristics of the inventory products to obtain inventory product groups.
The stock product is a product actually stored in a warehouse, and the product is anything which is provided to the market as a commodity, used and consumed by people, and can meet certain requirements of people. Products typically include tangible items, intangible services, organizations, ideas, or combinations thereof. In this embodiment, the product specifically refers to a tangible article capable of being transported by logistics, and may be understood as SKU (stock keeping unit). The product characteristics refer to characteristic information for distinguishing products, including but not limited to product types, product brands, and the like, as long as the characteristic information can distinguish different inventory products. The inventory product grouping is a collection of inventory products resulting from the grouping of inventory products.
Specifically, after the server receives the service level optimization instruction, the server responds to the service level optimization instruction to acquire the product characteristics of each inventory product. Then, the server divides and groups the characteristics of the stock products based on the product characteristics of the stock products, and determines the stock product group to which each stock product belongs. That is, the stock products having the same product characteristics are grouped into one group, resulting in a stock product group consisting of stock products having the same product characteristics. For example, when the service level is mainly for different products, then all products belonging to the same type may be grouped together when the product type is taken as the product feature. Such as grouping all different brands of air conditioners into one group, grouping all different brands of napkins into one group, etc. When the service level is aimed at different customers, different stock products of the same brand can be classified into one type. Specifically, the product features may be configured according to the emphasis of the actual service level.
And step S204, the service cost of each stock product in the stock product grouping is mined to obtain the total service cost of the stock product grouping.
The service cost refers to the cost generated by each stock product when each stock product is managed, and the total service cost is the sum of the service levels of all the stock products grouped by the stock products.
Specifically, when the server receives the service level optimization instruction, unit costs of each stock product are mined and demand variables of each stock product are counted at the same time. The server determines the service cost of each stock according to each unit cost of each stock product and the demand variable, and then sums up the service costs of all the stock products to obtain the total service cost of the stock product group. Wherein, each unit cost of the stock product comprises unit safety stock keeping cost and unit stock shortage cost. The demand variables include the demand quantity and demand quantity expectation of the stock product in the lead period.
And step S206, traversing and optimizing the service level of each stock product group based on the total service cost of each stock product group, and determining the optimal service level of each stock product group.
Specifically, after the server excavates and obtains the total service cost of each inventory product group, the server outputs the service level corresponding to the lowest total service cost as the optimal service level of the inventory product group by aiming at minimizing the total service cost according to a function of the relationship between the service level and the total cost generated by the service.
In one embodiment, the total cost of service for the inventory product grouping is input into a service cost-service level relationship model, the service cost-service level relationship model is targeted to minimize the service cost, and the service level corresponding to the lowest total cost of service is output as the optimal service level for the inventory product grouping.
The service cost-service level relation model is a model constructed based on a functional relation between the service level and the total cost of service generated by the service, and is used for reflecting the relation between the service level and the total cost generated by the service. The cost of service-service level relationship model may be understood as a function of f (x), where x represents the service level and f (x) is the total cost of service at service level x. That is, the optimal service level in this embodiment refers to the service level with the lowest total cost generated by the service, and is the service level output by the service cost-service level relation model with the goal of minimizing the service cost. Wherein the total cost of service is the sum of the inventory cost of the safety stock and the stock out cost.
In addition, when the server traverses all the stock product groups by using the service cost-service level relation model and outputs the optimal service level corresponding to each stock product in all the stock product groups, the output result is sorted. And outputting the information to the user in a format of stock point-stock product-service level, wherein the stock point refers to the stock point where the stock product is positioned.
The service level optimization method groups the inventory products according to the product characteristics of the inventory products to obtain inventory product groups; further, the service cost of each stock product in the stock product grouping is mined to obtain the total service cost of the stock product grouping; and based on the total service cost of each stock product group, traversing and optimizing the service level of each stock product group through a service cost-service level relation model, and outputting the optimal service level of each stock product group. The method realizes the optimization of the service level of the stock product by mining the service cost of the stock product, and can effectively control the service cost while optimizing the service level, thereby reducing the inventory management cost. Meanwhile, the inventory products are grouped through the product characteristics, the service level of each inventory product group is optimized, the method can be simultaneously applied to large batches of inventory products, and the optimization efficiency is improved.
In one embodiment, as shown in fig. 3, step S204 includes:
step S302, unit cost and demand order of each stock product in the stock product grouping are derived, wherein the unit cost comprises unit safety stock keeping cost and unit stock shortage cost.
Wherein the unit cost is a pre-configured known cost value, including a unit safety stock stocking cost and a unit stock shortage cost. The unit cost refers to the cost of one inventory product, such as the safe inventory holding cost of one inventory product, the backorder cost of one product. The demand order is a voucher for reflecting the demand of the user for the product, and can reflect the consumption quantity of the product consumed.
Specifically, when the server mines the cost of service for the inventory product, the unit cost and demand order for the inventory product are first derived. The server may derive the unit cost of the inventory product directly from the configuration items. The export of the demand order can be directly obtaining the demand order uploaded by the user, or can be through communicating with the order management system, exporting the demand order from the order management system, and obtaining the demand order of each stock product. For example, when the order management system is deployed on another terminal or server, the server communicates with the terminal or server corresponding to the order management system through the network, and sends a request for obtaining a required order to the terminal or server corresponding to the order management system. And the terminal or the server corresponding to the order management system responds to the order acquisition request and transmits the demand order of the stock product to the server, so that the server obtains the demand order of the stock product. Alternatively, when the order management system is deployed with the server, the server may derive the demand orders for each inventory product directly from the local order management system.
Step S304, carrying out demand statistics according to the demand orders of the stock products to obtain the demand quantity, demand expectation and demand variance of the stock products.
The demand quantity is a statistical consumption quantity of the stock product, the demand expectation is a mean value according to the demand statistics of the stock product, and the demand variance is a variance value according to the fluctuation statistics of the demand.
Specifically, after the server obtains the demand order of the stock products, the server counts the stock product data on the demand order to obtain the demand quantity of each stock product on each demand order. For example, when the demand order is purchase order a, purchase order a includes purchased inventory product a and purchased inventory product B, and the purchase quantity of purchased inventory product a is 100 and the purchase quantity of purchased inventory product B is 50, then the server counts that the demand quantity of inventory product a is 100 and the demand quantity of inventory product B is 50. Then, the server determines the demand expectation and the demand variance according to the counted demand quantity.
In one embodiment, step S302 includes: counting the required quantity of the stock products according to the required orders of the stock products, and sequencing the required quantity according to the required orders to obtain a required quantity time sequence; and calculating the mean value and the variance of the demand quantity sequence of the stock products based on the demand quantity time sequence of the stock products to obtain the demand quantity expectation and the demand quantity variance of the stock products.
Specifically, the server sorts the required quantity by the required quantity on each required order based on the time of the required order after counting the required quantity, and obtains the required quantity time sequence of each inventory product. The daily consumption of each inventory product can be determined by the demand quantity time series. Then, the server calculates a mean value and a variance based on the obtained demand quantity time series of each stock product, thereby obtaining a demand quantity expectation and a demand quantity variance of each stock product. The calculation of the mean and the variance can be performed by adopting any existing mean calculation mode. For example, the average value is a sum of products of all the demand quantities in the demand quantity time series and probability probabilities corresponding to the demand quantities. The probability of the demand quantity is obtained by fitting historical demand data to judge which distribution the demand belongs to, and the probability density function of the probability is an expression of the distribution, such as normal distribution, negative binomial distribution, poisson distribution and the like. However, the requirement of normal distribution is not general, and the expression property of the function of normal distribution is good, so the probability density function of this embodiment is preferably normal distribution. After the demand expectation of the product is obtained, the variance of the product can be calculated according to the demand expectation of the product, and the demand variance is obtained.
And step S306, predicting the safe stock holding quantity and the stock shortage quantity of the stock products according to the demand quantity, the demand expectation and the demand variance.
Specifically, after the server obtains the demand quantity, the demand expectation and the demand variance, the server predicts and obtains the out-of-stock quantity of each stock product according to the demand quantity and the in-stock inventory of each stock product in the stock product grouping. And summing the backorder quantity of each product to obtain the backorder quantity of the logistics grouping. The out-of-stock quantity of each product is obtained by integrating the quantity of the stock products required to be greater than the quantity of the stock products in the warehouse, and calculating the expected value of the quantity of the stock products required to be greater than the quantity of the stock products in the warehouse. Meanwhile, the safe stock quantity of the stock products is obtained according to the demand variance of the stock products and the expected lead time prediction, and the sum of the safe stock quantities of all the stock products in the stock product grouping is the safe stock quantity of the stock product grouping. Wherein the in-stock inventory and expected lead time of the inventory product are preset known values.
Step S308, determining the goods holding cost of the inventory products according to the goods holding cost of the safety inventory and the goods holding quantity of the safety inventory, and determining the goods shortage cost of the inventory products according to the goods shortage cost and the goods shortage quantity to obtain the total service cost of the inventory product grouping.
Specifically, after the server predicts the stock quantity and the stock shortage quantity of the safety stock, the product operation is carried out on the obtained unit safety stock cost and the stock quantity of the safety stock to obtain the stock cost of the safety stock of the stock product. And similarly, multiplying the stock product unit stock shortage cost and the stock shortage quantity to obtain the stock shortage cost of the stock product. The product of the unit cost and the quantity can be understood as the cost of one stock product and all the quantities of the stock product, and the total cost of the stock product is obtained. And then summing the stock out cost and the safe stock keeping cost of each stock product in the stock product group to obtain the total service cost of the stock product group.
In the embodiment, the demand quantity, the demand expectation and the demand variance of the product are obtained through the actual demand order statistics of the inventory product, so that the accuracy of the demand statistics is ensured, and the accuracy of the cost prediction is further ensured. Further, since the service level is a level that can satisfy the customer's demand (no stock out) with a high probability, intuitively, the higher the service level is, the more stock is stored in the safe, the lower the stock out probability is, the lower the stock out cost is, but the higher the stock in the safe is, the higher the stock in the safe is. Otherwise, the stock shortage cost is increased, and the stock keeping cost of the safety stock is reduced. Therefore, the stock keeping cost and the stock shortage cost of each stock product are predicted through the quantity demand, the demand expectation and the demand variance obtained through statistics, the grouped total service cost is obtained through summation, accurate cost data are provided for subsequent optimization, the service level can be optimized by taking the cost related to the service level as a target, the cost control is realized, and the cost is reduced.
In one embodiment, step S206 includes: generating constraint conditions according to a preset expected service level; and determining the optimal service level of the inventory product grouping by taking the minimization of the total service cost of each inventory product grouping as a target under the constraint of the constraint condition.
The expected service level is a preset parameter for restricting the solved optimal service level, and comprises upper and lower limits of the service level of each stock product in the stock product group and a target value of the service level of each stock product group. The upper and lower service level limits include an upper service level limit for the inventory product and a lower service level limit for the inventory product.
Specifically, the optimal service level is solved under the constraint of the constraint condition, and the service level expectation of the user can be simultaneously met. That is, after the service generates the constraint condition according to the acquired desired service level, the server outputs the optimal service level of the stock product group with the aim of minimizing the total service cost of each stock product group by the service cost-service level relation model based on the generated constraint condition.
In one embodiment, the constraints are as follows:
where N is the total number of products in the inventory product grouping, i represents the ith inventory product in the inventory product grouping, lbiIs a preset upper limit, ub, of the service level of the ith inventory productiIs a preset lower limit, delta, of the service level of the ith inventory productiFor the service level of the ith inventory product, δiIs an independent variable, Di is demand expectation, LiTo expect lead time, targetiA service level target value for a preset group of inventory products.
In one embodiment, the cost of service-service level relationship model is as follows:
where TC is a total cost of service for the inventory product grouping, N is a total number of inventory products in the inventory product grouping, i represents an ith inventory product in the inventory product grouping, and HiFor the unit cost of goods in the ith inventory product per unit time,
representing a service level of δiThe inverse of the cumulative probability density function of the standard normal distribution, deltaiIs an independent variable, LiExpected lead time for said inventory product group, Vi demand variance, BiCost per unit out of stock for ith inventory product, Di is demand expectation, QiFor ordering volume, economic ordering volume can be adopted
Determining that x is the required quantity of the inventory product, f (x) is a probability density distribution function of x and conforms to normal distribution, siIs the inventory of the ith product in the warehouse. It will be appreciated that the first term on the right of the above equation calculates the cost of stock in the safety stock and the second term calculates the cost of stock out.
After solving the above integral, the service cost-service level relation model is as follows:
where TC is a total cost of service for the inventory product grouping, N is a total number of inventory products in the inventory product grouping, i represents an ith inventory product in the inventory product grouping, and HiFor the unit cost of goods in the ith inventory product per unit time,
representing a service level of δiThe inverse of the cumulative probability density function of the standard normal distribution, LiExpected lead time for said inventory product group, Vi demand variance, BiCost per unit out of stock for ith inventory product, Di is demand expectation, AiFor a known cost of ordering for the ith inventory product,
to represent
Logarithmic expression with natural base e as base, deltaiFor the service level of the ith inventory product, δiIs an independent variable.
In particular, the aim is to minimize the total cost of service, i.e. the service level δ when solving the MinTCi。
In one embodiment, the cost-of-service-level relationship model is a non-linear band-constrained objective function. Therefore, for the nonlinear band-constrained objective function optimization solution of the present embodiment, an optimal solution can be obtained by using a progressive least squares programming algorithm (slsrqp). That is, the implementation optimizes the gradual least square programming algorithm to solve the MinTC, and obtains the optimal service level in the time of the MinTC. Under the constraint of the constraint condition, solving the lowest total service cost of each inventory product group by using a least square planning algorithm; and taking the service level corresponding to the lowest total service cost as the optimal service level of the inventory product group.
In this embodiment, by generating a constraint condition that a group of service levels with the minimum total cost of service is obtained when the service level of each stock product is limited and the average service level of a group of stock products needs to reach the target service level, the service level with the minimum cost can be found when the actual condition is met, thereby reducing the cost. The method can meet the actual requirement on the service level and reduce the cost.
In one embodiment, step S202 includes: determining the category of the stock product according to the product characteristics of the stock product; and dividing the inventory products with the same category into a group to obtain the inventory product group corresponding to the category.
Specifically, after the server acquires the inventory product, the category to which the inventory product belongs is determined according to product characteristics, such as product type. Then, the server divides the inventory products belonging to the same category into a group to obtain inventory product groups corresponding to the category, and then performs service level optimization on the inventory products by taking the inventory product groups as a unit.
In the embodiment, the product features are grouped, so that the method can be simultaneously applied to large-batch inventory products, and the efficiency of service level optimization is improved.
It should be understood that although the various steps in the flow charts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order 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 some of the steps in fig. 2-3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 4, there is provided a service level optimizing device, including: a grouping module 402, a mining module 404, and an optimization module 406, wherein:
the grouping module 402 is configured to group inventory products according to product characteristics of the inventory products, so as to obtain an inventory product group.
And the mining module 404 is configured to mine the service cost of each inventory product in the inventory product group to obtain the total service cost of the inventory product group.
An optimization module 406 for determining an optimal service level for each inventory product grouping by traversing and optimizing the service level for each inventory product grouping based on the total cost of service for each inventory product grouping.
In one embodiment, the mining module 404 is further configured to derive a unit cost and a demand order for each inventory product in the inventory product grouping, the unit cost including a unit safe inventory cost of stock and a unit cost of stock out; carrying out demand statistics according to the demand order of each stock product to obtain the demand quantity, demand expectation and demand variance of each stock product; predicting the safe stock holding quantity and the stock shortage quantity of the stock products according to the demand quantity, the demand expectation and the demand variance; and determining the stock cost of the stock products according to the unit safety stock holding cost and the safety stock holding quantity, and determining the stock shortage cost of the stock products according to the unit stock shortage cost and the stock shortage quantity to obtain the total service cost of the stock product grouping.
In one embodiment, the mining module 404 is further configured to count the required quantity of the stock product according to the required order of the stock product, and sort the required quantity according to the required order to obtain a required quantity time sequence; and calculating the mean value and the variance of the demand quantity sequence of the stock products based on the demand quantity time sequence of the stock products to obtain the demand quantity expectation and the demand quantity variance of the stock products.
In one embodiment, the optimization module 406 is further configured to generate a constraint condition according to a preset expected service level; and determining the optimal service level of the inventory product grouping by taking the minimization of the total service cost of each inventory product grouping as a target under the constraint of the constraint condition.
In one embodiment, the optimization module 406 is further configured to solve the lowest total service cost of each inventory product group by using a least squares planning algorithm under the constraint of a constraint condition; and taking the service level corresponding to the lowest total service cost as the optimal service level of the inventory product group.
In one embodiment, the grouping module 402 is further configured to determine a category to which the inventory product belongs based on product characteristics of the inventory product; and dividing the inventory products with the same category into a group to obtain the inventory product group corresponding to the category.
For the specific definition of the service level optimization device, reference may be made to the above definition of the service level optimization method, which is not described herein again. The respective modules in the service level optimizing apparatus described above may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as unit cost, demand orders and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a service level optimization method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 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, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
grouping the stock products according to the product characteristics of the stock products to obtain stock product groups;
the service cost of each stock product in the stock product grouping is mined to obtain the total service cost of the stock product grouping;
and based on the total service cost of each inventory product group, traversing and optimizing the service level of each inventory product group, and determining the optimal service level of each inventory product group.
In one embodiment, the processor, when executing the computer program, further performs the steps of: deriving unit cost and demand order of each stock product in the stock product group, wherein the unit cost comprises unit safety stock keeping cost and unit stock shortage cost; carrying out demand statistics according to the demand order of each stock product to obtain the demand quantity, demand expectation and demand variance of each stock product; predicting the safe stock holding quantity and the stock shortage quantity of the stock products according to the demand quantity, the demand expectation and the demand variance; and determining the stock cost of the stock products according to the unit safety stock holding cost and the safety stock holding quantity, and determining the stock shortage cost of the stock products according to the unit stock shortage cost and the stock shortage quantity to obtain the total service cost of the stock product grouping.
In one embodiment, the processor, when executing the computer program, further performs the steps of: counting the required quantity of the stock products according to the required orders of the stock products, and sequencing the required quantity according to the required orders to obtain a required quantity time sequence; and calculating the mean value and the variance of the demand quantity sequence of the stock products based on the demand quantity time sequence of the stock products to obtain the demand quantity expectation and the demand quantity variance of the stock products.
In one embodiment, the processor, when executing the computer program, further performs the steps of: generating constraint conditions according to a preset expected service level; and determining the optimal service level of the inventory product grouping by taking the minimization of the total service cost of each inventory product grouping as a target under the constraint of the constraint condition.
In one embodiment, the processor, when executing the computer program, further performs the steps of: solving the lowest total service cost of each inventory product group by using a least square planning algorithm under the constraint of a constraint condition; and taking the service level corresponding to the lowest total service cost as the optimal service level of the inventory product group.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining the category of the stock product according to the product characteristics of the stock product; and dividing the inventory products with the same category into a group to obtain the inventory product group corresponding to the category.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
grouping the stock products according to the product characteristics of the stock products to obtain stock product groups;
the service cost of each stock product in the stock product grouping is mined to obtain the total service cost of the stock product grouping;
and based on the total service cost of each inventory product group, traversing and optimizing the service level of each inventory product group, and determining the optimal service level of each inventory product group.
In one embodiment, the computer program when executed by the processor further performs the steps of: deriving unit cost and demand order of each stock product in the stock product group, wherein the unit cost comprises unit safety stock keeping cost and unit stock shortage cost; carrying out demand statistics according to the demand order of each stock product to obtain the demand quantity, demand expectation and demand variance of each stock product; predicting the safe stock holding quantity and the stock shortage quantity of the stock products according to the demand quantity, the demand expectation and the demand variance; and determining the stock cost of the stock products according to the unit safety stock holding cost and the safety stock holding quantity, and determining the stock shortage cost of the stock products according to the unit stock shortage cost and the stock shortage quantity to obtain the total service cost of the stock product grouping.
In one embodiment, the computer program when executed by the processor further performs the steps of: counting the required quantity of the stock products according to the required orders of the stock products, and sequencing the required quantity according to the required orders to obtain a required quantity time sequence; and calculating the mean value and the variance of the demand quantity sequence of the stock products based on the demand quantity time sequence of the stock products to obtain the demand quantity expectation and the demand quantity variance of the stock products.
In one embodiment, the computer program when executed by the processor further performs the steps of: generating constraint conditions according to a preset expected service level; and determining the optimal service level of the inventory product grouping by taking the minimization of the total service cost of each inventory product grouping as a target under the constraint of the constraint condition.
In one embodiment, the computer program when executed by the processor further performs the steps of: solving the lowest total service cost of each inventory product group by using a least square planning algorithm under the constraint of a constraint condition; and taking the service level corresponding to the lowest total service cost as the optimal service level of the inventory product group.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining the category of the stock product according to the product characteristics of the stock product; and dividing the inventory products with the same category into a group to obtain the inventory product group corresponding to the category.
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 hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as 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 application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for service level optimization, the method comprising:
grouping the inventory products according to the product characteristics of the inventory products to obtain inventory product groups;
mining the service cost of each inventory product in the inventory product grouping to obtain the total service cost of the inventory product grouping;
determining an optimal service level for each of the groupings of inventory products by traversing the optimization of the service level for each of the groupings of inventory products based on the total cost of service for each of the groupings of inventory products.
2. The method of claim 1, wherein said mining a cost of service for each of said inventory products in said group of inventory products to obtain a total cost of service for said group of inventory products comprises:
deriving a unit cost and a demand order for each of the inventory products in the inventory product grouping, the unit cost including a unit safe inventory holding cost and a unit backorder cost;
carrying out demand statistics according to the demand orders of the inventory products to obtain the demand quantity, demand expectation and demand variance of the inventory products;
predicting the safe stock holding quantity and the stock shortage quantity of the stock products according to the demand quantity, the demand expectation and the demand variance;
and determining the goods holding cost of the inventory products according to the unit safety inventory goods holding cost and the safety inventory goods holding quantity, and determining the goods shortage cost of the inventory products according to the unit goods shortage cost and the goods shortage quantity to obtain the total service cost of the inventory product grouping.
3. The method of claim 2, wherein said conducting demand statistics based on said demand orders for each of said inventory products to derive demand quantities, demand expectations, and demand variances for each of said inventory products comprises:
counting the required quantity of the stock products according to the required orders of the stock products, and sequencing the required quantity according to the required orders to obtain a required quantity time sequence;
calculating a mean and variance of the demand quantity sequence of the inventory product based on the demand quantity time sequence of the inventory product, resulting in a demand quantity expectation and a demand quantity variance of the inventory product.
4. The method of claim 1, wherein determining the optimal service level for each of the groupings of inventory products by traversing the optimization of the service level for each of the groupings of inventory products based on the total cost of service for each of the groupings of inventory products comprises:
generating constraint conditions according to a preset expected service level; the expected service level comprises an upper limit and a lower limit of the service level of each stock product and a service level target value of each stock product group;
determining an optimal service level for each of the groupings of inventory products with a goal of minimizing the total cost of service for each of the groupings of inventory products under the constraint of the constraint.
5. The method of claim 4, wherein determining an optimal service level for each of the groupings of inventory products with a goal of minimizing the total cost of service for each of the groupings of inventory products under the constraint of the constraint comprises:
solving the lowest total service cost of each inventory product group by using a least square planning algorithm under the constraint of the constraint condition;
and taking the service level corresponding to the lowest total service cost as the optimal service level of the inventory product group.
6. The method of claim 1, wherein grouping each of the inventory products according to product characteristics of the inventory product, resulting in a group of inventory products, comprises:
determining the category of the stock product according to the product characteristics of the stock product;
and dividing the inventory products with the same belonged category into a group to obtain the inventory product group corresponding to the belonged category.
7. An apparatus for service level optimization, the apparatus comprising:
the grouping module is used for grouping the inventory products according to the product characteristics of the inventory products to obtain inventory product groups;
the mining module is used for mining the service cost of each inventory product in the inventory product grouping to obtain the total service cost of the inventory product grouping;
and the optimization module is used for traversing and optimizing the service level of each inventory product group based on the total service cost of each inventory product group and determining the optimal service level of each inventory product group.
8. The apparatus of claim 7, wherein the excavation module is further configured to,
deriving a unit cost and a demand order for each of the inventory products in the inventory product grouping, the unit cost including a unit safe inventory holding cost and a unit backorder cost; carrying out demand statistics according to the demand orders of the inventory products to obtain the demand quantity, demand expectation and demand variance of the inventory products; predicting the safe stock holding quantity and the stock shortage quantity of the stock products according to the demand quantity, the demand expectation and the demand variance; and determining the goods holding cost of the inventory products according to the unit safety inventory goods holding cost and the safety inventory goods holding quantity, and determining the goods shortage cost of the inventory products according to the unit goods shortage cost and the goods shortage quantity to obtain the total service cost of the inventory product grouping.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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