Supply chain inventory optimization method and device, storage medium and electronic equipment

Abstract

The invention provides a supply chain inventory optimization method and device, a storage medium and electronic equipment, and the method comprises the steps: generating an optimization target setting interface, obtaining an operation level, an optimization target and a constraint condition inputted by a user through the optimization target setting interface, constructing a target function of an accessory library according to the operation level and the optimization target, and carrying out the optimization of the accessory library. Then, according to constraint conditions, solving the target function to obtain a target value of the accessory library and a target stock of each part; the target value includes a target inventory value and / or a target service level. It can be seen that according to the scheme, by generating the optimization target setting interface, a user only needs to input the operation level, the optimization target and the constraint condition, the target function can be automatically constructed, the target function is solved according to the constraint condition, the target value of the accessory library and the target inventory of each part are obtained, all parameters do not need to be manually adjusted, and the user experience is improved. Therefore, the calculation efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of supply chain technology, and in particular to a supply chain inventory optimization method and device, a storage medium, and an electronic device. Background Art

[0002] After-sales parts planning is the core process of spare parts supply chain management. It requires maximizing customer satisfaction and ensuring smooth ordering of suppliers' supplies. At the same time, it requires controlling the headquarters' inventory funds to provide higher service levels with lower inventory.

[0003] The complexity of the parts business comes from its own characteristics. Accessories have a longer life cycle than complete vehicles, and the number of accessory varieties is huge. At the same time, the demand for accessories is very unstable and difficult to predict. At present, the number of accessory categories is close to 110,000, and the number of active accessories (in demand within the past 6 months) is also between 30,000 and 40,000. There is also data on the accessories replacement chain. At present, various parameters are generally adjusted manually to make the parts inventory close to the target inventory. However, due to the huge number of parts varieties and the instability of demand, the efficiency is low and the accuracy is poor. Summary of the invention

[0004] The present application provides a supply chain inventory optimization method and device, a storage medium and an electronic device, the purpose of which is to solve the problems of low efficiency and poor accuracy existing in the prior art.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] A supply chain inventory optimization method, comprising:

[0007] In response to the optimization instruction, generating an optimization target setting interface;

[0008] Obtaining the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface; the operation level is used to describe the objects involved in the operation;

[0009] Constructing an objective function of the accessory library according to the operation level and the optimization goal;

[0010] According to the constraint conditions, the objective function is solved to obtain the target value of the parts library and the target inventory of each part; the target value includes the target inventory value and / or the target service level.

[0011] In the above method, optionally, constructing the objective function of the accessory library according to the operation level and the optimization target includes:

[0012] If the optimization target only includes inventory value, when the operation level is the first operation level, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, and the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the optimization objective function of the parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category;

[0013] When the operation level is the second operation level, the first inventory coefficient and the second inventory coefficient of each part are calculated based on the historical inventory data and sales order data of each part, and the optimization objective function of the parts library is constructed based on the first inventory value and the second inventory value of each part.

[0014] In the above method, optionally, constructing the objective function of the accessory library according to the operation level and the optimization target includes:

[0015] If the optimization target only includes the service level, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, the monthly average demand share ratio of each comprehensive parameter category is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each comprehensive parameter category;

[0016] When the operation level is the second operation level, the average monthly demand share of each part is calculated based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, and the objective function of the parts library is constructed based on the average monthly demand share of each part.

[0017] In the above method, optionally, constructing the objective function of the accessory library according to the operation level and the optimization target includes:

[0018] If the optimization target is the inventory value and service level, when the operation level is the first operation level, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories based on the historical inventory data and sales order data of each part included in the parts library within the preset time period, and the first inventory coefficient, the second inventory coefficient and the average monthly demand share ratio of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the objective function of the parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category and the average monthly demand share ratio of each comprehensive parameter category;

[0019] When the operation level is the second operation level, the first inventory coefficient, the second inventory coefficient and the average monthly demand share of each part are calculated based on the historical inventory data and sales order data of each part, and the objective function of the spare parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each part, as well as the average monthly demand share of each part.

[0020] Optionally, the above method includes determining the comprehensive parameter category to which each part belongs from the preset comprehensive parameter categories based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, including:

[0021] According to the historical inventory data and sales order data of each part included in the spare parts library within a preset time, the parameter value of the fluctuation parameter, the parameter value of the frequency parameter, the parameter value of the amount parameter and the parameter value of the flow parameter of each part are determined;

[0022] For each part, the parameter value of the first process parameter of the part is determined based on the parameter value of the fluctuation parameter and the parameter value of the frequency parameter of the part, the parameter value of the second process parameter of the part is determined based on the parameter value of the amount parameter and the parameter value of the flow parameter of the part, and the comprehensive parameter category to which the part belongs is determined from preset comprehensive parameter categories based on the parameter value of the first process parameter and the parameter value of the second process parameter.

[0023] The above method, optionally, after solving the objective function according to the constraint conditions to obtain the target value of the parts library and the target inventory of each part, further comprises:

[0024] Based on the target value of the parts library and the target inventory of each part, a report template is called to generate an optimization report for the parts library.

[0025] A supply chain inventory optimization device, comprising:

[0026] A generating unit, used to generate an optimization target setting interface in response to the optimization instruction;

[0027] An acquisition unit, used to acquire the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface; the operation level is used to describe the objects involved in the operation;

[0028] A construction unit, used for constructing an objective function of a parts library according to the operation level and the optimization target;

[0029] A solving unit is used to solve the objective function according to the constraint conditions to obtain the target value of the parts library and the target inventory of each part; the target value includes the target inventory value and / or the target service level.

[0030] In the above device, optionally, the construction unit is specifically used for:

[0031] If the optimization target only includes inventory value, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the accessories library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, and the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the optimization objective function of the accessories library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category; when the operation level is the second operation level, the first inventory coefficient and the second inventory coefficient of each part are calculated based on the historical inventory data and sales order data of each part, and the optimization objective function of the accessories library is constructed based on the first inventory value and the second inventory value of each part;

[0032] Or, if the optimization target only includes the service level, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, the monthly average demand share ratio of each comprehensive parameter category is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each comprehensive parameter category; when the operation level is the second operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the monthly average demand share ratio of each part is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each part;

[0033] Or, if the optimization target is inventory value and service level, then when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the accessories library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, and the first inventory coefficient, the second inventory coefficient and the average monthly demand share ratio of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the objective function of the accessories library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category and the average monthly demand share ratio of each comprehensive parameter category; when the operation level is the second operation level, based on the historical inventory data and sales order data of each part, the first inventory coefficient, the second inventory coefficient and the average monthly demand share ratio of each part are calculated, and the objective function of the accessories library is constructed based on the first inventory coefficient and the second inventory coefficient of each part and the average monthly demand share ratio of each part.

[0034] In the above device, optionally, the construction unit is specifically used to determine the comprehensive parameter classification to which each part belongs from the preset comprehensive parameter classification based on the historical inventory data and sales order data of each part included in the parts library within a preset time period:

[0035] According to the historical inventory data and sales order data of each part included in the spare parts library within a preset time, the parameter value of the fluctuation parameter, the parameter value of the frequency parameter, the parameter value of the amount parameter and the parameter value of the flow parameter of each part are determined;

[0036] For each part, the parameter value of the first process parameter of the part is determined based on the parameter value of the fluctuation parameter and the parameter value of the frequency parameter of the part, the parameter value of the second process parameter of the part is determined based on the parameter value of the amount parameter and the parameter value of the flow parameter of the part, and the comprehensive parameter category to which the part belongs is determined from preset comprehensive parameter categories based on the parameter value of the first process parameter and the parameter value of the second process parameter.

[0037] The above device may optionally further include:

[0038] The calling unit is used to call the report template according to the target value of the parts library and the target inventory of each part to generate an optimization report for the parts library.

[0039] A storage medium stores an instruction set, wherein the instruction set, when executed by a processor, implements the supply chain inventory optimization method as described above.

[0040] An electronic device, comprising:

[0041] A memory for storing at least one set of instructions;

[0042] The processor is used to execute the instruction set stored in the memory, and implement the supply chain inventory optimization method as described above by executing the instruction set.

[0043] Compared with the prior art, this application has the following advantages:

[0044] The present application provides a supply chain inventory optimization method and device, storage medium and electronic device, which generates an optimization target setting interface, obtains the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface, then constructs the objective function of the parts library based on the operation level and optimization target, and then solves the objective function based on the constraint conditions to obtain the target value of the parts library and the target inventory of each part; the target value includes the target inventory value and / or the target service level. It can be seen that the present application solution generates an optimization target setting interface, and the user only needs to input the operation level, optimization target and constraint conditions to automatically construct the objective function, and solve the objective function based on the constraint conditions to obtain the target value of the parts library and the target inventory of each part, without manually adjusting each parameter, thereby improving calculation efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0046] Figure 1 An architectural diagram of a supply chain inventory optimization method provided in this application;

[0047] Figure 2 An example diagram of a supply chain inventory optimization method provided in this application;

[0048] Figure 3 Another example diagram of a supply chain inventory optimization method provided in this application;

[0049] Figure 4 A method flow chart of a supply chain inventory optimization method provided in this application;

[0050] Figure 5 Another example diagram of a supply chain inventory optimization method provided in this application;

[0051] Figure 6 Another method flow chart of a supply chain inventory optimization method provided in this application;

[0052] Figure 7 Another method flow chart of a supply chain inventory optimization method provided in this application;

[0053] Figure 8 Another example diagram of a supply chain inventory optimization method provided in this application;

[0054] Fig. 9 Another example diagram of a supply chain inventory optimization method provided in this application;

[0055] Fig.10 Another example diagram of a supply chain inventory optimization method provided in this application;

[0056] Fig.11 Another example diagram of a supply chain inventory optimization method provided in this application;

[0057] Fig.12 Another example diagram of a supply chain inventory optimization method provided in this application;

[0058] Fig.13 Another example diagram of a supply chain inventory optimization method provided in this application;

[0059] Fig.14 Another example diagram of a supply chain inventory optimization method provided in this application;

[0060] Fig.15 A schematic diagram of the structure of a supply chain inventory optimization device provided in this application;

[0061] Fig.16 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0064] It should be noted that the concepts such as "first" and "second" mentioned in the present application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0065] It should be noted that the modifications of "one" and "plurality" mentioned in the disclosure of this application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0066] The present application can be used in many general or special computing device environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or equipment, etc.

[0067] In this embodiment, for ease of understanding, the relevant terms involved in this application are explained as follows:

[0068] The flow parameter is used to illustrate the liquidity of the average monthly demand share of parts, where the average monthly demand share = total sales order quantity / N / minimum number of packages, where N represents the preset time period and can be set according to demand.

[0069] When the average monthly demand share of a part is ≥ the first demand threshold, the parameter value of the flow parameter of the part is F, that is, fast flow, Fast;

[0070] When the average monthly demand share of a part is less than the second demand threshold, the parameter value of the flow parameter of the part is S, i.e., slow flow, Slow;

[0071] When the average monthly demand share of a part is less than the first demand threshold and greater than or equal to the second demand threshold, the parameter value of the flow parameter of the part is M, which means medium flow, Middle;

[0072] Among them, the first demand threshold is greater than the second demand threshold. The first demand threshold and the second demand threshold can be set according to demand. Preferably, the first demand threshold can be 100 and the second demand threshold can be 30.

[0073] Fluctuation parameter, used to describe the demand volatility of parts, volatility = (maximum monthly demand share - minimum monthly demand share) / average monthly demand share;

[0074] When the fluctuation rate of a part is ≤ the first fluctuation threshold, the parameter value of the fluctuation parameter of the part is 1, which means low volatility;

[0075] When the fluctuation rate of a part is less than the second fluctuation threshold and greater than the first fluctuation threshold, the parameter value of the fluctuation parameter of the part is 2, which is medium fluctuation;

[0076] When the volatility of a part ≥ the second volatility threshold, the parameter value of the volatility parameter of the part is 3;

[0077] Among them, the first volatility threshold is less than the second volatility threshold. Preferably, the first volatility threshold is 1 and the second volatility threshold is 2, that is, high volatility.

[0078] The frequency parameter is used to describe the monthly sales frequency of the part. The monthly sales frequency = the total number of sales order orders / N;

[0079] Sort the parts in descending order according to the monthly sales frequency;

[0080] For the parts ranked in the top K, the parameter value of the frequency parameter of the part is H, that is, the order frequency is high frequency;

[0081] For the parts ranked between I and K, the parameter value of the frequency parameter of the part is M, that is, the order frequency is medium frequency;

[0082] For the remaining parts, the parameter value of the frequency parameter of the part is L, that is, the order frequency is low frequency.

[0083] Among them, K < I. Preferably, K is 10% and I is 25%.

[0084] The amount parameter is used to describe the value of the part;

[0085] Sort the monthly average demand amounts from large to small and count according to the 80 / 20 rule;

[0086] Accumulate the monthly average amounts of the parts in descending order;

[0087] When the accumulated result is not greater than the preset first amount threshold, determine the parameter value of the amount parameter of the part corresponding to the accumulated result as A, that is, high value;

[0088] When the accumulated result is greater than the preset first amount threshold and less than the preset second amount threshold, determine the parameter value of the amount parameter of the parts excluding the high-value parts among the parts corresponding to the accumulated result as B, that is, medium value;

[0089] Determine the parameter value of the amount parameter of the remaining parts as C, that is, low value.

[0090] Among them, the preset second amount threshold is greater than the preset first amount threshold. The preset first amount threshold and the preset second amount threshold are both determined according to the total monthly average demand amount. Preferably, the preset first amount threshold can be 80% of the total monthly average demand amount, and the preset second amount threshold can be 95% of the total monthly average demand amount.

[0091] The first process parameter is determined according to the fluctuation parameter and the frequency parameter. The parameter value of the first process parameter can be 1, 2, or 3. The relationship between the first process parameter, the fluctuation parameter, and the frequency parameter is shown in Table 1:

[0092] Fluctuation Parameters Frequency parameters First process parameter 1 L 1 1 M 1 1 H 1 2 L 2 2 M 2 2 H 1 3 L 3 3 M 2 3 H 1

[0093] Table 1

[0094] The second process parameter is determined according to the amount parameter and the flow parameter. The parameter value of the second process parameter can be A, B, or C. The relationship between the second process parameter, the amount parameter, and the flow parameter is shown in Table 2:

[0095] Amount parameter Flow parameters Second process parameter A F A A M A A S A B F A B M B B S B C F A C M B C S C

[0096] Table 2

[0097] The comprehensive parameter classification includes A1, A2, A3, B1, B2, B3, C1, C2, and C3. The comprehensive parameter classification is determined based on the first process parameter and the second process parameter. The relationship between the comprehensive parameter classification and the first process parameter and the second process parameter is shown in Table 3:

[0098] First process parameter Second process parameter Comprehensive parameter classification 1 A A1 2 A A2 3 A A3 1 B B1 2 B B2 3 B B3 1 C C1 2 C C2 3 C C3

[0099] Table 3

[0100] Delivery cycle coefficient = average delivery cycle / 30;

[0101] The ordering cycle system is determined by the second process parameter. When the parameter value of the second process parameter is A, the ordering cycle coefficient is equal to 0.25; when the parameter value of the second process parameter is B, the ordering cycle coefficient is 0.5; when the parameter value of the second process parameter is C, the ordering cycle coefficient is 1.

[0102] The scenario of inventory optimization is: set the target supply rate of a group of SKUs to minimize the overall inventory value or maximize the overall service level, and the target supply rates of different SKU categories need to meet certain size order and upper and lower limits. Among them, whether it is the calculation of inventory value or the calculation of overall service level, or the size order that the target supply rates of different SKU categories need to meet, they are all linear and suitable for solving as linear programming.

[0103] The architecture diagram of a supply chain inventory optimization method provided in an embodiment of the present application is as follows: Figure 1As shown in the figure, it includes two parts. One part is the outer parameter processing module, the core of which is the Flask API. It defines decision variables in a multi-process form through components such as pandas, numpy, and cvxpy, sets the objective function and constraints, and finally constructs a linear programming problem and inputs it into the optimization solution module. The other part is the inner optimization solution module, the core of which is to call the CBC solver through cvxpy, input the standardized linear programming problem, and obtain the solution result.

[0104] Parameter processing module such as Figure 2 As shown, it is responsible for receiving various parameters passed in by the user (including custom parameters and basic data, that is, receiving the upper and lower limits or size relationships of custom decision variables, as well as constraints, and the operation level of the optimization target), and calculating the A1-C3 classification, organizing the objective function and constraints, so as to realize the conversion into the format required by the inner optimization solution module, and outputting the standard problem (linear programming problem consisting of objective function and constraints). It also receives the solution results of the optimization addition module and converts the solution results.

[0105] Optimization solution module such as Figure 3 As shown, it is responsible for solving the organized objective function and constraints, and transmitting the solution results to the outer parameter processing module, which converts the optimal solution into an easy-to-understand optimization report and conveys it to the user. The specific conversion process refers to organizing the satisfaction rate obtained by the solution and the inventory value and service level calculated in the middle into a table form and storing it in the database. The user interface can be directly queried.

[0106] The present application embodiment provides a supply chain inventory optimization method, which can be applied to a target application program. The method flow chart of the method is as follows: Figure 4 As shown, specifically including:

[0107] S401. In response to an optimization instruction, generate an optimization target setting interface.

[0108] In response to the optimization instruction, generating an optimization target setting interface;

[0109] Get the operation level, optimization target and constraint conditions entered by the user through the optimization target setting interface.

[0110] In this embodiment, in response to the optimization instruction, an optimization target setting interface is generated; the optimization target setting interface includes at least three items to be set, namely, operation level, optimization target and constraint conditions. Among them, the operation level items to be set include at least two operation levels, namely, the first operation level and the second operation level. The first operation level is the ABC category, which means that the object of the operation is each type of parts. It should be noted that all parts of the same type have the same satisfaction rate. The second operation level is SKU, which means that the object of the operation is each part, and each part can have a different satisfaction rate.

[0111] The optimization objectives to be set include "inventory value", "service level" and "inventory value & service level", and the constraints to be set include multiple options, including but not limited to Option 1, Option 2, Option 3, Option 4, Option 5, Option 6, Option 7 and Option 8.

[0112] Option 1: Do not open the satisfaction rate;

[0113] Option 2: Open the satisfaction rate of all comprehensive parameter categories;

[0114] Option 3: Open A1's satisfaction rate;

[0115] Option 4: Open the satisfaction rates of A1 and B1;

[0116] Option 5: Open the satisfaction rates of A1 and A2;

[0117] Option 6: Open the satisfaction rates of A1, B1 and A2;

[0118] Option 7: Open the satisfaction rates of A1, A2 and A3;

[0119] Option 8: Open the satisfaction rates of A1, B1 and C1.

[0120] For example, see Figure 5 , Figure 5 The optimization target setting interface is shown, and the items to be set include constraints, optimization targets, operation levels, planning scope and copyright number, A1 satisfaction rate, A2 satisfaction rate, A3 satisfaction rate, B1 satisfaction rate, B2 satisfaction rate, B3 satisfaction rate, C1 satisfaction rate, C2 satisfaction rate, C3 satisfaction rate, service level upper limit, service level lower limit, inventory value upper limit and inventory value lower limit. Among them, whether the A1 satisfaction rate, A2 satisfaction rate, A3 satisfaction rate, B1 satisfaction rate, B2 satisfaction rate, B3 satisfaction rate, C1 satisfaction rate, C2 satisfaction rate and C3 satisfaction rate are allowed to be set, that is, whether they are open, is associated with the constraint items to be set, and whether the service level upper limit, service level lower limit, inventory value upper limit and inventory value lower limit are allowed to be set is associated with the optimization target items to be set.

[0121] It should be noted that the satisfaction rate of each comprehensive parameter classification, the upper and lower limits of the service level, and the upper and lower limits of the inventory value are all constraints to be input.

[0122] The optimization instruction may be an instruction generated by a user by triggering a related function key in the target application.

[0123] S402: Obtain the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface.

[0124] In this embodiment, the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface are obtained, that is, the case selection, optimization target and operation level selected by the user through the optimization target setting interface are obtained, and according to the case selection and optimization target selected by the user, the to-be-set items of satisfaction rate, service level and inventory value corresponding to the case selection and optimization target are determined, the setting state of the determined items to be set is changed to the allowed setting state, and the constraint conditions input by the user for each item to be set whose setting state is the allowed setting state are received, thereby achieving the acquisition of the operation level, optimization target and constraint conditions input by the user.

[0125] The operation level is used to describe the objects involved in the operation.

[0126] S403: Construct an objective function of the parts library according to the operation level and optimization target.

[0127] In this embodiment, the objective function of the parts library is constructed based on the calculation and optimization objectives.

[0128] Optimization objectives include inventory value and / or service level.

[0129] See also Figure 6 ,According to the operation level and optimization target, the process of constructing the objective function of the accessories library includes the following steps:

[0130] S601. Determine whether the optimization target only includes inventory value. If so, execute S602. If not, execute S603.

[0131] In this embodiment, it is determined whether the optimization target only includes the inventory value. If the optimization target only includes the inventory value, step S602 is executed. If the optimization target does not include the inventory value, or does not only include the inventory value, step S603 is executed.

[0132] S602, determine whether the operation level is the first operation level, if so, execute S604, if not, execute S605.

[0133] In this embodiment, if the optimization target only includes inventory value, it is further determined whether the operation level is the first operation level, that is, whether the operation level is ABC classification. If so, execute step S604. If not, it means that the operation level is the second operation level, that is, SKU, then execute step S605.

[0134] S603: Determine whether the optimization target only includes the service level. If so, execute S606; if not, execute S607.

[0135] In this embodiment, if the optimization target does not include inventory value, or includes not only inventory value, it is further determined whether the optimization target only includes service level. If so, step S606 is executed. If not, it indicates that the optimization target includes inventory value and service level.

[0136] S604. Based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, determine the comprehensive parameter category to which each part belongs from the preset comprehensive parameter categories, calculate the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category based on the historical inventory data, sales order data of each part and the comprehensive parameter category to which it belongs, and construct the optimization objective function of the parts library based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category.

[0137] In this embodiment, if the optimization target only includes the inventory value, and the operation level is the first operation level, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories according to the historical inventory data and sales order data of each part included in the parts library within the preset time period. Specifically, the historical inventory data and sales order data within the preset time period are determined from the pre-stored historical inventory data and sales order data of each part included in the parts library, and the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories according to the historical inventory data and sales order data of each part included in the parts library within the preset time period.

[0138] It should be noted that the preset time period is a manually set time period and can be modified according to needs. Preferably, the preset time period can be 24 weeks.

[0139] In this embodiment, the preset comprehensive parameter categories include A1, A2, A3, B1, B2, B3, C1, C2, and C3.

[0140] See also Figure 7 , according to the historical inventory data and sales order data of each part included in the parts library within a preset time period, the process of determining the comprehensive parameter classification to which each part belongs from the preset comprehensive parameter classification specifically includes the following steps:

[0141] S701. Determine the parameter value of the fluctuation parameter, the parameter value of the frequency parameter, the parameter value of the amount parameter and the parameter value of the flow parameter of each part based on the historical inventory data and sales order data of each part included in the parts library within a preset time.

[0142] In this embodiment, the parameter value of the fluctuation parameter, the parameter value of the frequency parameter, the parameter value of the amount parameter and the parameter value of the flow parameter of each part are determined based on the historical inventory data and sales order data of each part included in the parts library within a preset time.

[0143] Specifically, for each part, the average monthly demand share, the maximum monthly demand share and the minimum monthly demand share are calculated based on the historical inventory data and sales order data of the part within a preset time period; based on the average monthly demand share, the maximum demand share and the minimum monthly demand share, the volatility of the part is calculated; if the volatility of the part is ≤ the first fluctuation threshold, the parameter value of the fluctuation parameter of the part is 1; if the volatility of the part is < the second fluctuation threshold, and > the first fluctuation threshold, the parameter value of the fluctuation parameter of the part is 2; if the volatility of the part is ≥ the second fluctuation threshold, the parameter value of the fluctuation parameter of the part is 3.

[0144] Among them, the average monthly demand share = total sales order quantity / N / minimum packaging number.

[0145] Among them, the maximum monthly demand share is the monthly demand share with the largest value among all monthly demand shares in the preset time period, and the minimum monthly demand share is the monthly demand share with the smallest value among all monthly demand shares in the preset time period.

[0146] For each part, based on the historical inventory data and sales order data of the part within a preset time period, the total number of sales orders for the part is counted, and based on the total number of sales orders, the monthly sales frequency of the part is calculated.

[0147] According to the monthly sales frequency of each part, the monthly sales frequency of each part is sorted, and based on the sorting result, the parameter value of the frequency parameter of each part is determined.

[0148] According to the sales order data of each part included in the spare parts library within the preset time, the average monthly demand amount of each part is determined, and the average monthly demand amount of each part is sorted according to the average monthly demand amount of each part. Based on the sorting result, the parameter value of the amount parameter of each part is determined.

[0149] For each part, when the average monthly demand share of the part is ≥ the first demand threshold, the parameter value of the flow parameter of the part is F; when the average monthly demand share of the part is < the second demand threshold, the parameter value of the flow parameter of the part is S; when the average monthly demand share of the part is < the first demand threshold and ≥ the second demand threshold, the parameter value of the flow parameter of the part is M.

[0150] S702. For each part, determine the parameter value of the first process parameter of the part based on the parameter value of the fluctuation parameter and the parameter value of the frequency parameter of the part; determine the parameter value of the second process parameter of the part based on the parameter value of the amount parameter and the parameter value of the flow parameter of the part; and determine the comprehensive parameter category to which the part belongs from the preset comprehensive parameter categories based on the parameter value of the first process parameter and the parameter value of the second process parameter.

[0151] In this embodiment, for each part, according to the parameter value of the fluctuation parameter and the frequency parameter of the part, the parameter value of the first process parameter of the part is determined through the relationship between the first process parameter and the fluctuation parameter and the frequency parameter as shown in Table 1.

[0152] In this embodiment, for each part, according to the parameter value of the amount parameter and the flow parameter of the part, the parameter value of the second process parameter of the part is determined through the relationship between the second process parameter, the amount parameter and the flow parameter as shown in Table 2.

[0153] For each part, according to the parameter value of the first process parameter and the parameter value of the second process parameter of the part, the comprehensive parameter classification to which the part belongs is determined through the relationship between the comprehensive parameter classification and the first process parameter and the second process parameter as shown in Table 3.

[0154] In this embodiment, after determining the comprehensive parameter classification to which each part belongs, the first inventory coefficient and the second inventory coefficient of each comprehensive parameter classification are calculated based on the historical inventory data, sales order data and the comprehensive parameter classification to which each part belongs.

[0155] Among them, the known business logic is: target inventory value = target inventory (quantity) * cost = average monthly demand (quantity) * ordering parameters * cost. Ordering parameters = ordering cycle coefficient + arrival cycle coefficient + safety stock coefficient. Safety stock coefficient = arrival cycle safety factor + demand safety stock coefficient. Arrival cycle safety factor = (the maximum value of the actual arrival time of the last ten times * target supply rate - average arrival time) / average arrival time. Demand safety stock coefficient = (the maximum value of the demand quantity in the last N months * target supply rate - average monthly demand share) / average monthly demand share. Therefore, based on the above business logic, the first inventory coefficient and the second inventory coefficient of each comprehensive parameter classification can be calculated.

[0156] According to the first inventory coefficient and the second inventory coefficient of each comprehensive parameter classification, an optimization objective function of the accessories library is constructed. Specifically, according to the first inventory coefficient and the second inventory coefficient of each comprehensive parameter classification, an overall inventory value function of the accessories library is constructed, and based on the overall inventory value, an objective function of the accessories library is constructed.

[0157] In this embodiment, based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter classification, the overall inventory value function of the accessories library is constructed as follows:

[0158]

[0159] Among them, C represents the overall inventory value, k i represents the first inventory coefficient of the i-th type of parts, b i represents the second inventory coefficient of the i-th type of parts, x i Represents the satisfaction rate of the i-th type of parts.

[0160] Among them, k i x i +b i Represents the inventory value of the i-th type of parts.

[0161] In this embodiment, if the optimization target is the inventory value, the objective function is used to find the minimum inventory value. Therefore, based on the overall inventory value, the objective function for constructing the parts library is:

[0162] It should be noted that when the optimization objective only includes inventory value, the constructed objective function is to find the minimum value of the overall inventory value function.

[0163] In this embodiment, the process of determining the comprehensive parameter classification to which each part belongs from the preset comprehensive parameter classification based on the historical inventory data and sales order data of each part included in the parts library within a preset time period is illustrated as follows:

[0164] See also Figure 8 , set the strategies of amount parameters, frequency parameters and flow parameters in advance through inventory strategy and ABC analysis interface.

[0165] Optionally, you can directly create a formal version, which does not change frequently, or you can create a simulation version, such as setting the value of the configuration item to something else, such as reducing the parameter setting, in order to compare the final inventory optimization solution results under the formal version and the simulation version.

[0166] According to the set inventory strategy, trigger the following Fig. 9 The interface shown above obtains historical inventory data and sales order data within a preset time period (that is, 24 weeks), including but not limited to Fig. 9 The standard price, minimum packaging quantity, average monthly demand quantity (that is, average monthly demand share), average monthly demand amount and frequency in the preset time period (that is, 24 weeks) are used to obtain the following inventory strategies based on the historical inventory data and sales order data. Fig.10Each part fluctuation parameter, amount parameter, frequency parameter, flow parameter, process parameter 1 (first process parameter), process parameter 2 (second process parameter) and comprehensive ABC (that is, comprehensive parameter classification) are shown.

[0167] S605. Calculate the first inventory coefficient and the second inventory coefficient of each part based on the historical inventory data and sales order data of each part, and construct an optimization objective function of the parts library based on the first inventory value and the second inventory value of each part.

[0168] In this embodiment, if the optimization target only includes inventory value and the operation level is the second operation level, the first inventory coefficient and the second inventory coefficient of each part are calculated based on the historical inventory data and sales order data of each part.

[0169] According to the first inventory value and the second inventory value of each part, the optimization objective function of the accessories library is constructed. Specifically, according to the first inventory value and the second inventory value of each part, the overall inventory value of the accessories library is constructed. According to the overall inventory value of the accessories library, the objective function of the accessories library is constructed.

[0170] The objective function of the constructed accessories library is as follows:

[0171]

[0172] Among them, C represents the overall inventory value, p i represents the first inventory coefficient of the i-th part, r i represents the second inventory coefficient of the i-th part, n represents the number of parts included in the accessories library, and y i represents the satisfaction rate of the i-th part.

[0173] Among them, p i y i +r i Represents the inventory value of the i-th part.

[0174] It should be noted that when the optimization objective only includes inventory value, the constructed objective function is to find the minimum value of the overall inventory value function.

[0175] S606, determine whether the operation level is the first operation level, if so, execute S608, if not, execute S609.

[0176] In this embodiment, if the optimization target only includes the service level, it is further determined whether the operation level is the first operation level. If the operation level is the first operation level, step S608 is executed; if the operation level is the second operation level, step S609 is executed.

[0177] S607 , determine whether the operation level is the first operation level, if so, execute S610 , if not, execute S611 .

[0178] In this embodiment, if the optimization target includes inventory value and service level, it is further determined whether the operation level is the first operation level. If the operation level is the first operation level, step S609 is executed.

[0179] S608. Based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, determine the comprehensive parameter category to which each part belongs from the preset comprehensive parameter categories, calculate the average monthly demand share of each comprehensive parameter category, and construct the objective function of the parts library based on the average monthly demand share of each comprehensive parameter category.

[0180] In this embodiment, if the optimization target only includes the service level and the operation level is the first operation level, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories according to the historical inventory data and sales order data of each part included in the parts library within the preset time period. The specific implementation process is described in step S604 and will not be repeated here.

[0181] In the present embodiment, the monthly average demand share ratio of each comprehensive parameter classification is calculated. Specifically, the monthly average demand share of each part is calculated based on the historical inventory data and sales order data of each part included in the accessories library within a preset time period. The monthly average demand share of each comprehensive parameter classification is calculated based on the monthly average demand share of each part and the comprehensive parameter classification to which each part belongs. The monthly average demand share ratio of each comprehensive parameter classification is calculated based on the monthly average demand share of each comprehensive parameter classification; wherein, the monthly average demand share ratio of each comprehensive parameter classification is the ratio of the monthly average demand share of each comprehensive parameter classification to the sum of the monthly average demand shares of all comprehensive parameter classifications.

[0182] In this embodiment, the objective function of the parts library is constructed based on the average monthly demand share of each comprehensive classification. Specifically, the service level function of the parts library is constructed based on the average monthly demand share of each comprehensive parameter classification, and the objective function of the parts library is constructed based on the service level function.

[0183] It should be noted that when the optimization objective only includes the service level, the objective function constructed is to find the maximum value of the service level function.

[0184] S609. Calculate the average monthly demand share of each part based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, and construct an objective function of the parts library based on the average monthly demand share of each part.

[0185] In this embodiment, if the optimization target only includes the service level and the operation level is the second operation level, the average monthly demand share of each part is calculated based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, and then the objective function of the parts library is constructed based on the average monthly demand share of each part.

[0186] Among them, according to the average monthly demand share of each part, the objective function of the spare parts library is constructed, specifically including: according to the average monthly demand share of each part, the service level function of the spare parts library is constructed, and based on the service level function, the objective function of the spare parts library is constructed.

[0187] The constructed objective function is:

[0188]

[0189] Among them, Z represents the service level of the parts warehouse, c i represents the average monthly demand ratio of the i-th part, n represents the number of parts included in the parts library, and y i represents the satisfaction rate of the i-th part.

[0190] It should be noted that when the optimization objective only includes the service level, the objective function constructed is to find the maximum value of the service level function.

[0191] S610. Based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, determine the comprehensive parameter category to which each part belongs from the preset comprehensive parameter categories; calculate the first inventory coefficient, the second inventory coefficient and the average monthly demand share of each comprehensive parameter category based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs; and construct the objective function of the parts library based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category and the average monthly demand share of each comprehensive parameter category.

[0192] In this embodiment, if the optimization objectives include service level and inventory value, and the operation level is the first operation level, then based on the historical inventory data and sales order data of each part included in the accessories library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories. The specific implementation process is described in step S604 and will not be repeated here.

[0193] In this embodiment, after determining the comprehensive parameter category to which each part belongs, the first inventory coefficient, the second inventory coefficient and the average monthly demand share of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs; among which, the specific implementation process of calculating the average monthly demand share of each comprehensive parameter category is described in step S608 and will not be repeated here.

[0194] In this embodiment, the objective function of the parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter classification, and the average monthly demand share of each comprehensive parameter classification.

[0195] S611. Based on the historical inventory data and sales order data of each part, calculate the first inventory coefficient, the second inventory coefficient and the average monthly demand share of each part. Based on the first inventory coefficient and the second inventory coefficient of each part, and the average monthly demand share of each part, construct the objective function of the spare parts library.

[0196] In this embodiment, if the optimization objectives include service level and inventory value, and the operation level is the second operation level, the first inventory coefficient, the second inventory coefficient and the average monthly demand share of each part are calculated, so as to construct the objective function of the spare parts library based on the first inventory coefficient and the second inventory coefficient of each part, as well as the average monthly demand share of each part.

[0197] S404. Solve the objective function according to the constraint conditions to obtain the target value of the parts library and the target inventory of each part; the target value includes the target inventory value and / or the target service level.

[0198] In this embodiment, the objective function is solved according to the constraint conditions to obtain the target value of the parts library and the target inventory of each part, wherein the target value includes the target inventory value and / or the target service level.

[0199] It should be noted that the target inventory value is the target inventory amount, and the target service level is the target supply rate.

[0200] In this embodiment, the objective function is solved according to the constraints to obtain the target value of the parts library and the target inventory of each part. The process specifically includes: according to the constraints, using the optimization solution algorithm to solve the objective function to obtain the target value of the parts library and the target inventory of each part.

[0201] Specifically, a linear programming problem is constructed according to the constraints and the objective function, and the linear programming problem is solved using the optimization algorithm to obtain the target value of the parts library and the target inventory of each part.

[0202] Among them, if the optimization target only includes inventory value, the objective function is solved according to the constraints to obtain the target inventory value of the parts warehouse and the target inventory of each part; if the optimization target only includes service level, the objective function is solved according to the constraints to obtain the target service level of the parts warehouse and the target inventory of each part; if the optimization target includes inventory value and service level, the objective function is solved according to the constraints to obtain the target inventory value of the parts warehouse, the target service level, and the target inventory of each part.

[0203] Exemplary optimization solution methods include, but are not limited to, gradient descent method or simplex method.

[0204] It should be noted that the objective function and constraint conditions in this embodiment are linear.

[0205] The present application provides a supply chain inventory optimization method, which generates an optimization target setting interface, obtains the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface, then constructs the objective function of the parts library based on the operation level and optimization target, and then solves the objective function based on the constraint conditions to obtain the target value of the parts library and the target inventory of each part; the target value includes the target inventory value and / or the target service level. It can be seen that the present application solution generates an optimization target setting interface, and the user only needs to input the operation level, optimization target and constraint conditions to automatically construct the objective function, and solves the objective function based on the constraint conditions to obtain the target value of the parts library and the target inventory of each part, without manually adjusting each parameter, thereby improving calculation efficiency and accuracy.

[0206] In the supply chain inventory optimization method provided in the embodiment of the present application, after step S404, the following steps may also be included:

[0207] Based on the target value of the parts library and the target inventory of each part, the report template is called to generate an optimization report for the parts library.

[0208] In this embodiment, based on the target value of the accessories library and the target inventory of each part, the report template is called to generate an optimization report for the accessories library. Specifically, the target value of the accessories library and the target inventory of each part are written into the report template to obtain an optimization report for the accessories library.

[0209] Exemplarily, the report template may be in the form of a table.

[0210] Optionally, the intermediate satisfaction rate can also be written into the report template.

[0211] In this embodiment, after the optimization report is generated, the optimization report can also be linked to the target library recommendation interface, and a data bar of the optimization report is generated on the target library recommendation interface, and the optimization report can be queried and displayed through the export button corresponding to the data bar. The data bar of the optimization report includes the target number, plan range, version number, operation level, optimization target, case selection, service level upper limit, service level lower limit, inventory level upper limit, inventory level lower limit and optimization result (including solution or no solution) corresponding to the optimization report.

[0212] For example, the target library recommendation interface is as follows: Fig.11 As shown, it can be Fig.11 The export button in the query is as follows Fig.12 The optimization report shown.

[0213] The supply chain inventory optimization method provided in the embodiment of the present application generates an optimization report to facilitate subsequent inquiries by users.

[0214] See also Fig.13 The specific implementation process of the supply chain inventory optimization method provided in the above embodiment of the present application is illustrated as follows:

[0215] Get the basic data of user-configured custom parameters and settings:

[0216] Custom parameter configuration includes strategies for configuring volatility parameters, flow parameters, amount parameters, and frequency parameters, as well as open conditions and constraints for satisfaction rates. Because A1 is a top part with a high amount share of 80%, and is also a fast-flowing, low-volatility part, these stable parts have the highest satisfaction rate. So the recommended rules are: A1>B1>C1, A2>B2>C2, A3>B3>C3, A1>A2>A3, B1>B2>B3, C1>C2>C3.

[0217] Specifically, custom parameter configuration refers to the upper and lower limits or size relationship of custom decision variables, which are defined by front-end input. For example, if the satisfaction rate of the "A1" category is limited to no less than 95%, enter 0.95 in the front-end A1 input box.

[0218] Among them, the basic data obtained from user input includes operation level, optimization target, satisfaction rate of each comprehensive parameter classification, upper and lower limits of inventory value and upper and lower limits of service level.

[0219] In this embodiment, the target interface is optimized to obtain constraint conditions, optimize the target and the operation level.

[0220] Optimization goal:

[0221] Inventory value - find the minimum inventory value, with the constraints being the upper and lower limits of the service level and the satisfaction rates of A1 to C3.

[0222] Service level - find the maximum value of the overall target supply rate, with the constraints being the upper and lower limits of inventory value and the satisfaction rates of A1 to C3.

[0223] Inventory Value & Service Level - Find the balance point between the minimum inventory value and the maximum overall target supply rate. The constraints are the upper and lower limits of the service level and the upper and lower limits of the inventory value, and the A1~C3 satisfaction rates.

[0224] Operation level: SKU or ABC category. It refers to whether the operation object is each part (SKU) or each type of part (ABC category).

[0225] Generate the objective function and constraints:

[0226] From a business perspective, good service means that the number of parts required by customers is met as much as possible, and the key indicator is sales volume. If all parts are 100% satisfied, then a huge inventory and site will need to be prepared in advance, and the total inventory amount and operating costs will be very high. However, since the demand for each part is unstable, it is not appropriate to buy too much or too early. Therefore, the ABC parameter statistical model obtained through demand analysis follows the principle that A1 parts with fast flow, high frequency, low volatility, and high amount can be set with a high satisfaction rate, and the satisfaction rate can be reduced in turn through other parts to achieve a slight decrease in the overall service level. For example, if the upper and lower limits of the input service level drop from 100% to 98% to 97%, the total inventory amount will drop significantly, and the overall warehousing operation costs will drop accordingly. The passenger car business is based on this goal and runs weekly to solve.

[0227] On the contrary, when the goal is clear, for example, the total inventory amount cannot be higher than the predetermined amount, then by entering the upper and lower limits of the inventory value, the reasonable value of the service level obtained may be in the range of only 80% to 85%.

[0228] If the optimization target only includes the inventory value, when the operation level is the first operation level, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories based on the historical inventory data and sales order data of each part included in the accessories library within the preset time period, and the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the optimization objective function of the accessories library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category; when the operation level is the second operation level, the first inventory coefficient and the second inventory coefficient of each part are calculated based on the historical inventory data and sales order data of each part, and the optimization objective function of the accessories library is constructed based on the first inventory value and the second inventory value of each part;

[0229] If the optimization target only includes the service level, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the parts library within the preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, the monthly average demand share ratio of each comprehensive parameter category is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each comprehensive parameter category. When the operation level is the second operation level, based on the historical inventory data and sales order data of each part included in the parts library within the preset time period, the monthly average demand share ratio of each part is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each part.

[0230] If the optimization objectives are inventory value and service level, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the accessories library within the preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, and the first inventory coefficient, the second inventory coefficient and the average monthly demand share ratio of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs. According to the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category, as well as the average monthly demand share ratio of each comprehensive parameter category, the objective function of the accessories library is constructed; when the operation level is the second operation level, based on the historical inventory data and sales order data of each part, the first inventory coefficient, the second inventory coefficient and the average monthly demand share ratio of each part are calculated, and the objective function of the accessories library is constructed based on the first inventory coefficient and the second inventory coefficient of each part, as well as the average monthly demand share ratio of each part.

[0231] For example, if the optimization target is inventory value and the operation level is ABC classification, the supply rate of A1 and B1 is set to be no less than 80%, the supply rate of each comprehensive parameter classification part = demand share x satisfaction rate, and the satisfaction rate of each classification of A1, B1, C1...C3 is set as the decision variable, corresponding to x 1 、x 2 ……x 9 .

[0232] Generate the objective function:

[0233] According to the following business logic, the first library inventory coefficient k of a single category can be determined i and the second inventory coefficient b i .

[0234] Inventory value = standard inventory * cost = average monthly demand * ordering parameters * cost;

[0235] Ordering parameters = ordering cycle coefficient + delivery cycle coefficient + safety stock coefficient;

[0236] Safety stock factor = delivery cycle safety factor + demand safety stock factor;

[0237] Delivery cycle safety factor = (maximum of the last ten actual delivery times * target supply rate - average delivery time) / average delivery time;

[0238] Demand safety stock coefficient = (maximum demand quantity in the last N months * target supply rate - average monthly demand share) / average monthly demand share.

[0239] Therefore, the overall inventory value C can be expressed as:

[0240]

[0241] The objective function is to minimize the inventory value, that is:

[0242]

[0243] Generate constraints:

[0244] Because the supply rate of A1 and B1 categories is not less than 80%, and the overall service level is not less than 90%, that is, the supply rate of each category is not less than 90% after weighted aggregation according to the average monthly demand share, and the supply rate of each category satisfies the preset relationship, thus generating the following constraints:

[0245]

[0246] It should be noted that the business categories A1, B1, C1, A2, B2, C2, A3, B3, C3 correspond to x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ,x 7 ,x 8 ,x 9 ABC classification is a qualitative analysis based on comprehensive consideration of four dimensions: amount parameter, frequency parameter, volatility parameter, and liquidity parameter.

[0247] When the optimization objective is "inventory value", the upper and lower limits of the service level must be entered, and the constraint condition is that one option must be selected from the 8 options.

[0248] The 8 options are:

[0249] Option 1: A1, B1, C1, A2, B2, C2, A3, B3, C3 are all closed, cannot be defined by users, and have no front and back constraints.

[0250] Option 2: A1, B1, C1, A2, B2, C2, A3, B3, C3 are all user-defined, with no preceding or following constraints.

[0251] Option 3: If only A1 is open, the following rules must be met: A1>B1>C1, A2>B2>C2, A3>B3>C3, A1>A2>A3, B1>B2>B3, C1>C2>C3. For example, if A1 is set to 1, it means that A1 parts must be 100% satisfied. If it is set to 0.99, it means that the total A1 parts must reach a 99% satisfaction rate.

[0252] Option 4: Only open A1 and B1, and ensure the rules: B1>C1, A2>B2>C2, A3>B3>C3, A1>A2>A3, B1>B2>B3, C1>C2>C3.

[0253] Option 5: Only open A1 and A2, and ensure the rules: A1>B1>C1, A2>B2>C2, A3>B3>C3, A2>A3, B1>B2>B3, C1>C2>C3.

[0254] Option 6: Only open A1, A2, B1, and ensure the rules: B1>C1, A2>B2>C2, A3>B3>C3, A2>A3, B1>B2>B3, C1>C2>C3.

[0255] Option 7: Only open A1, A2, A3, and ensure the rules: A1>B1>C1, A2>B2>C2, A3>B3>C3, B1>B2>B3, C1>C2>C3.

[0256] Option 8: Only open A1, B1, C1, and ensure the rules: A1>A2>A3, A2>B2>C2, A3>B3>C3, B1>B2>B3, C1>C2>C3.

[0257] Optimization solution:

[0258] Use the gradient descent algorithm and the simplex method to solve the linear programming problem consisting of the objective function and constraints to obtain the solution, that is, the target value of the accessories library and the target inventory of each part.

[0259] In the process of optimization and solving, if there is a solution, an easy-to-read optimal solution report is generated based on the solution result and fed back to the user. If there is no solution, the reason for the lack of solution is generated and fed back to the user.

[0260] See also Fig.14 For ease of understanding, the supply chain inventory optimization method provided in this application is further illustrated as follows:

[0261] Take the two decision variables x and y as an example. Assuming that x and y are the satisfaction rate decision variables of two categories (categories refer to two of the nine categories from A1 to C3), the triangle area in the middle is the satisfaction rate value range limited by the constraint conditions, and the upper and lower lines can be seen as the trend of inventory value or service level changing with the satisfaction rate. Points ABC are all desirable points that meet the constraint conditions, and the satisfaction rate combination represented by point B can maximize the objective function, and the satisfaction rate combination represented by point C can minimize the objective function. Points B1, D, and D1 are all points that do not meet the constraint conditions.

[0262] If you want to maximize the objective function, you need to move to the upper right along the gradient direction, that is, the direction in which the function value increases, until you reach the vertex of the constraint area, point B, and obtain the maximum value of the objective function.

[0263] On the contrary, if you want to minimize the objective function, you need to move to the lower left along the direction of the inverse gradient, that is, the direction in which the function value decreases, until you reach the vertex of the constraint area, that is, point C, and obtain the minimum value of the objective function.

[0264] If the optimization goal is inventory value & service level, and the focus is on inventory value, the smaller the objective function, the better. Then the best value point is C, and the corresponding x and y coordinates are the best satisfaction rate settings for the two categories (it should be noted that the satisfaction rate will not be negative in real situations, this is just an example).

[0265] It should be noted that although the operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order. In certain circumstances, multitasking and parallel processing may be advantageous.

[0266] It should be understood that the various steps described in the method embodiments disclosed in the present application can be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0267] and Figure 1 Corresponding to the method described above, the embodiment of the present application also provides a supply chain inventory optimization device for Figure 1 The specific implementation of the method is shown in the following diagram: Fig.15 As shown, specifically including:

[0268] The generating unit 1501 is used to generate an optimization target setting interface in response to the optimization instruction;

[0269] The acquisition unit 1502 is used to acquire the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface; the operation level is used to describe the objects involved in the operation;

[0270] A construction unit 1503 is used to construct an objective function of a parts library according to the operation level and the optimization target;

[0271] The solving unit 1504 is used to solve the objective function according to the constraint conditions to obtain the target value of the parts library and the target inventory of each part; the target value includes the target inventory value and / or the target service level.

[0272] The supply chain inventory optimization device provided in the embodiment of the present application generates an optimization target setting interface, obtains the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface, then constructs the objective function of the parts library based on the operation level and optimization target, and then solves the objective function based on the constraint conditions to obtain the target value of the parts library and the target inventory of each part; the target value includes the target inventory value and / or the target service level. It can be seen that the present application solution generates an optimization target setting interface, and the user only needs to input the operation level, optimization target and constraint conditions to automatically construct the objective function, and solve the objective function based on the constraint conditions to obtain the target value of the parts library and the target inventory of each part, without manually adjusting each parameter, thereby improving calculation efficiency and accuracy.

[0273] In one embodiment of the present application, based on the above solution, the construction unit 1503 is specifically used to:

[0274] If the optimization target only includes inventory value, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the accessories library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, and the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the optimization objective function of the accessories library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category; when the operation level is the second operation level, the first inventory coefficient and the second inventory coefficient of each part are calculated based on the historical inventory data and sales order data of each part, and the optimization objective function of the accessories library is constructed based on the first inventory value and the second inventory value of each part;

[0275] Or, if the optimization target only includes the service level, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, the monthly average demand share ratio of each comprehensive parameter category is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each comprehensive parameter category; when the operation level is the second operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the monthly average demand share ratio of each part is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each part;

[0276] Or, if the optimization target is inventory value and service level, then when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the accessories library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, and the first inventory coefficient, the second inventory coefficient and the average monthly demand share ratio of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the objective function of the accessories library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category and the average monthly demand share ratio of each comprehensive parameter category; when the operation level is the second operation level, based on the historical inventory data and sales order data of each part, the first inventory coefficient, the second inventory coefficient and the average monthly demand share ratio of each part are calculated, and the objective function of the accessories library is constructed based on the first inventory coefficient and the second inventory coefficient of each part and the average monthly demand share ratio of each part.

[0277] In one embodiment of the present application, based on the above-mentioned solution, the construction unit 1603 determines the comprehensive parameter classification to which each part belongs from the preset comprehensive parameter classification according to the historical inventory data and sales order data of each part included in the parts library within a preset time period, specifically for:

[0278] According to the historical inventory data and sales order data of each part included in the spare parts library within a preset time, the parameter value of the fluctuation parameter, the parameter value of the frequency parameter, the parameter value of the amount parameter and the parameter value of the flow parameter of each part are determined;

[0279] For each part, the parameter value of the first process parameter of the part is determined based on the parameter value of the fluctuation parameter and the parameter value of the frequency parameter of the part, the parameter value of the second process parameter of the part is determined based on the parameter value of the amount parameter and the parameter value of the flow parameter of the part, and the comprehensive parameter category to which the part belongs is determined from preset comprehensive parameter categories based on the parameter value of the first process parameter and the parameter value of the second process parameter.

[0280] In one embodiment of the present application, based on the above solution, it can also be configured as follows:

[0281] The calling unit is used to call the report template according to the target value of the parts library and the target inventory of each part to generate an optimization report for the parts library.

[0282] An embodiment of the present application further provides a storage medium storing an instruction set, wherein when the instruction set is executed, the supply chain inventory optimization method disclosed in any of the above embodiments is executed.

[0283] The present application also provides an electronic device, a schematic diagram of which is shown in FIG. Fig.16 As shown, it specifically includes a memory 1601 for storing at least one set of instruction sets; a processor 1602 for executing the instruction set stored in the memory, and implementing the supply chain inventory optimization method disclosed in any of the above embodiments by executing the instruction set.

[0284] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

[0285] Although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope disclosed in the present application. Certain features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0286] The above description is only a preferred embodiment disclosed in this application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the disclosure of this application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the disclosure of this application (but not limited to) to form a technical solution.

Claims

1. A supply chain inventory optimization method, characterized in that: include: In response to the optimization instruction, generating an optimization target setting interface; Obtaining the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface; the operation level is used to describe the objects involved in the operation; Constructing an objective function of the accessory library according to the operation level and the optimization goal; Solving the objective function according to the constraint conditions to obtain the target value of the parts library and the target inventory of each part; The target values ​​include a target inventory value and / or a target service level.

2. The method according to claim 1, characterized in that The objective function of the accessory library is constructed according to the operation level and the optimization target, including: If the optimization target only includes inventory value, when the operation level is the first operation level, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, and the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the optimization objective function of the parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category; When the operation level is the second operation level, the first inventory coefficient and the second inventory coefficient of each part are calculated based on the historical inventory data and sales order data of each part, and the optimization objective function of the parts library is constructed based on the first inventory value and the second inventory value of each part.

3. The method according to claim 1, characterized in that The objective function of the accessory library is constructed according to the operation level and the optimization target, including: If the optimization target only includes the service level, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, the monthly average demand share ratio of each comprehensive parameter category is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each comprehensive parameter category; When the operation level is the second operation level, the average monthly demand share of each part is calculated based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, and the objective function of the parts library is constructed based on the average monthly demand share of each part.

4. The method according to claim 1, characterized in that: The objective function of the accessory library is constructed according to the operation level and the optimization target, including: If the optimization target is the inventory value and service level, when the operation level is the first operation level, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories based on the historical inventory data and sales order data of each part included in the parts library within the preset time period, and the first inventory coefficient, the second inventory coefficient and the average monthly demand share ratio of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the objective function of the parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category and the average monthly demand share ratio of each comprehensive parameter category; When the operation level is the second operation level, the first inventory coefficient, the second inventory coefficient and the average monthly demand share of each part are calculated based on the historical inventory data and sales order data of each part, and the objective function of the spare parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each part, as well as the average monthly demand share of each part.

5. The method according to any one of claims 2 to 4, characterized in that: The method of determining the comprehensive parameter classification to which each part belongs from the preset comprehensive parameter classification based on the historical inventory data and sales order data of each part included in the parts library within the preset time period includes: According to the historical inventory data and sales order data of each part included in the spare parts library within a preset time, the parameter value of the fluctuation parameter, the parameter value of the frequency parameter, the parameter value of the amount parameter and the parameter value of the flow parameter of each part are determined; For each part, the parameter value of the first process parameter of the part is determined based on the parameter value of the fluctuation parameter and the parameter value of the frequency parameter of the part, the parameter value of the second process parameter of the part is determined based on the parameter value of the amount parameter and the parameter value of the flow parameter of the part, and the comprehensive parameter category to which the part belongs is determined from preset comprehensive parameter categories based on the parameter value of the first process parameter and the parameter value of the second process parameter.

6. The method according to claim 1, characterized in that After solving the objective function according to the constraint conditions to obtain the target value of the parts library and the target inventory of each part, the method further includes: Based on the target value of the parts library and the target inventory of each part, a report template is called to generate an optimization report for the parts library.

7. A supply chain inventory optimization device, characterized in that: include: A generating unit, used to generate an optimization target setting interface in response to the optimization instruction; An acquisition unit, used to acquire the operation level, optimization target and constraint conditions input by the user through the optimization target setting interface; the operation level is used to describe the objects involved in the operation; A construction unit, used for constructing an objective function of a parts library according to the operation level and the optimization target; A solving unit, used for solving the objective function according to the constraint conditions to obtain a target value of the parts library and a target inventory of each part; The target values ​​include a target inventory value and / or a target service level.

8. The device according to claim 7, characterized in that The construction unit is specifically used for: If the optimization target only includes inventory value, when the operation level is the first operation level, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, and the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category are calculated based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, and the optimization objective function of the parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category; When the operation level is the second operation level, the first inventory coefficient and the second inventory coefficient of each part are calculated according to the historical inventory data and sales order data of each part, and the optimization objective function of the parts library is constructed according to the first inventory value and the second inventory value of each part; Or, if the optimization target only includes the service level, when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, the monthly average demand share ratio of each comprehensive parameter category is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each comprehensive parameter category; when the operation level is the second operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the monthly average demand share ratio of each part is calculated, and the objective function of the parts library is constructed based on the monthly average demand share ratio of each part; Or, if the optimization target is inventory value and service level, then when the operation level is the first operation level, based on the historical inventory data and sales order data of each part included in the parts library within a preset time period, the comprehensive parameter category to which each part belongs is determined from the preset comprehensive parameter categories, and based on the historical inventory data, sales order data and the comprehensive parameter category to which each part belongs, the first inventory coefficient, the second inventory coefficient and the average monthly demand share of each comprehensive parameter category are calculated, and based on the first inventory coefficient and the second inventory coefficient of each comprehensive parameter category and the average monthly demand share of each comprehensive parameter category, the objective function of the parts library is constructed; When the operation level is the second operation level, the first inventory coefficient, the second inventory coefficient and the average monthly demand share of each part are calculated based on the historical inventory data and sales order data of each part, and the objective function of the spare parts library is constructed based on the first inventory coefficient and the second inventory coefficient of each part, as well as the average monthly demand share of each part.

9. The device according to claim 8, characterized in that The construction unit is specifically used to determine the comprehensive parameter classification to which each part belongs from the preset comprehensive parameter classification based on the historical inventory data and sales order data of each part included in the parts library within a preset time period: According to the historical inventory data and sales order data of each part included in the spare parts library within a preset time, the parameter value of the fluctuation parameter, the parameter value of the frequency parameter, the parameter value of the amount parameter and the parameter value of the flow parameter of each part are determined; For each part, the parameter value of the first process parameter of the part is determined based on the parameter value of the fluctuation parameter and the parameter value of the frequency parameter of the part, the parameter value of the second process parameter of the part is determined based on the parameter value of the amount parameter and the parameter value of the flow parameter of the part, and the comprehensive parameter category to which the part belongs is determined from preset comprehensive parameter categories based on the parameter value of the first process parameter and the parameter value of the second process parameter.

10. The device according to claim 7, characterized in that Also includes: The calling unit is used to call the report template according to the target value of the parts library and the target inventory of each part to generate an optimization report for the parts library.

11. A storage medium, characterized in that: The storage medium stores an instruction set, wherein the instruction set, when executed by a processor, implements the supply chain inventory optimization method according to any one of claims 1 to 6.

12. An electronic device, characterized in that: include: A memory for storing at least one set of instructions; A processor is used to execute an instruction set stored in the memory, and implement the supply chain inventory optimization method as described in any one of claims 1 to 6 by executing the instruction set.

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