Warehouse periodic replenishment method, device and equipment and storage medium

Abstract

The invention provides a warehouse periodic replenishment method, device and equipment and a storage medium, and the method comprises the steps: obtaining the demand and inventory data of a warehouse, the ordering data between the warehouse and a supplier, and the supply data of the supplier, according to the demanded quantity and inventory data of the warehouse, the ordering data between the warehouse and the supplier and the supply data of the supplier, constructing a replenishment funding model under constraint conditions; and solving the replenishment funding model by taking the minimum difference value between the end-of-period inventory and the target end-of-period inventory in the whole planned period as an operation funding target to obtain replenishment quantities in different time periods. Based on the demand prediction and the target inventory level, the minimum holding cost of the additional inventory can be optimized under the condition of considering the supplier capacity distribution, the minimum order number and the minimum package number, so that the complete replenishment time and the corresponding replenishment amount in the plan period are given, and the replenishment efficiency is improved under the condition that the demand is not reduced. And the extra inventory holding cost is effectively reduced.

Description

Technical Field

[0001] The present application relates to the technical field of logistics supply chain management, and in particular to a warehouse periodic replenishment method, device, equipment and storage medium. Background Art

[0002] Inventory management is a critical link in supply chain and logistics management. Efficient inventory management can ensure that customer needs are met while minimizing inventory holding costs and out-of-stock risks. In order to better manage inventory, many companies have introduced inventory targets to balance inventory costs and service levels. When formulating replenishment plans, companies must meet specific inventory level targets.

[0003] Traditional inventory management methods often rely on simple ordering rules, such as fixed-cycle ordering and fixed-batch ordering. In the prior art, the cycle inventory replenishment method (also known as the (OUL, T) method) can perform periodic inventory of warehouses, with a cycle length of T. If the inventory level is lower than the target inventory level each time, replenishment is carried out so that the inventory level after replenishment reaches the target inventory level.

[0004] However, in actual operations, companies also need to consider multiple complex factors in the supply chain, such as supplier capacity, minimum order quantity, minimum packaging unit quantity, etc. These factors make it more difficult to achieve specific inventory level targets, thus affecting the balance between inventory costs and service levels. Summary of the invention

[0005] In view of the problem that multiple complex factors in the supply chain make it more difficult to achieve inventory level targets, the present application aims to provide a warehouse cycle replenishment method, device, equipment and storage medium, which can try to achieve the inventory level targets set by the enterprise while taking into account the supplier's production capacity allocation, minimum order quantity and minimum packaging unit quantity.

[0006] In a first aspect, an embodiment of the present application provides a warehouse periodic replenishment method, the method comprising:

[0007] Obtain the warehouse's demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data. The warehouse's demand is the forecasted demand for each planning unit and each time unit during the planning period. The warehouse's inventory data includes the target ending inventory and the beginning inventory of this period. The ordering data between the warehouse and the supplier includes the minimum order quantity and lead time. The supply data includes the capacity calendar data and the minimum packaging quantity.

[0008] Construct a replenishment planning model under constraints based on warehouse demand and inventory data, order data between warehouse and supplier, and supplier supply data;

[0009] Taking minimizing the difference between the ending inventory and the target ending inventory during the entire planning period as the operational goal, the replenishment logistics model is solved to obtain the replenishment quantities in different time periods.

[0010] In a possible embodiment, the replenishment planning model under constraints is constructed based on the warehouse demand and inventory data, the order data between the warehouse and the supplier, and the supplier's supply data, including:

[0011] Determine business needs based on warehouse demand and inventory data, order data between warehouse and supplier, and supplier supply data;

[0012] Build a replenishment logistics model under constraints based on business needs.

[0013] In a possible embodiment, the cargo replenishment planning model constructed under constraints based on business needs includes:

[0014] Determine the parameters, sets and auxiliary variables in the replenishment planning model according to business needs;

[0015] Determine a constraint relationship according to the constraint condition;

[0016] The cargo replenishment planning model is constructed according to the parameters, sets, auxiliary variables and the constraint relationship in the cargo replenishment planning model.

[0017] In a possible embodiment, the constraint condition includes the following requirements:

[0018] 1) The inflow and outflow of each planning unit in each time unit are balanced;

[0019] 2) The replenishment quantity for each planning unit and each time unit must meet the minimum order quantity and minimum packaging quantity;

[0020] 3) The demand for each planning unit and each time unit cannot exceed the current capacity of the supplier's capacity calendar data;

[0021] 4) The total amount of demand satisfied during the entire planning period is equal to the total demand during the entire planning period;

[0022] 5) The ending inventory at each time unit needs to meet the target inventory requirements.

[0023] In a possible embodiment, the constraint condition further includes:

[0024] 6) in the case of delayed satisfaction of demand, determine the amount of demand delay;

[0025] Accordingly, the operational objectives also include: minimizing the penalty for demand delay.

[0026] In a possible embodiment, the constraint condition further includes:

[0027] 7) The ending inventory has a certain amount of shortfall or excess relative to the target inventory requirement;

[0028] Accordingly, the operational objectives also include: minimizing the difference between the end-of-period inventory days and the target end-of-period inventory days during the entire planning period.

[0029] In a possible embodiment, solving the operations research model to obtain replenishment quantities in different time periods includes:

[0030] Converting the operations research model into a programming language;

[0031] Importing the programming language into an operations research optimization solver;

[0032] The optimal solution is calculated according to the operational objectives as the replenishment quantity for different time periods.

[0033] In a second aspect, an embodiment of the present application further provides a warehouse periodic replenishment device, comprising:

[0034] An acquisition module is used to acquire the demand and inventory data of the warehouse, the order data between the warehouse and the supplier, and the supply data of the supplier. The demand of the warehouse is the predicted demand in the planning period for each planning unit and each time unit. The inventory data of the warehouse includes the target ending inventory and the beginning inventory of this period. The order data between the warehouse and the supplier includes the minimum order quantity and the lead time. The supply data includes the capacity calendar data and the minimum packaging quantity.

[0035] A model building module is used to build a replenishment planning model under constraints based on the warehouse's demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data;

[0036] The solution module is used to solve the replenishment logistics model with the minimization of the difference between the end-of-period inventory and the target end-of-period inventory during the entire planning period as the logistics goal, and obtain the replenishment quantity in different time periods.

[0037] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor;

[0038] The memory stores computer-executable instructions;

[0039] The processor executes the computer-executable instructions stored in the memory to implement the method in any possible implementation manner of the first aspect.

[0040] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method in any possible implementation manner of the above-mentioned first aspect.

[0041] The warehouse cycle replenishment method, device, equipment and storage medium provided in the present application, wherein the warehouse cycle replenishment method: first obtains the warehouse demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data; then builds a replenishment planning model under constraints based on the warehouse demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data; finally, the replenishment planning model is solved with the minimization of the difference between the end-of-period inventory and the target end-of-period inventory during the entire planning period as the operational goal, and the replenishment planning model is obtained to obtain the replenishment quantity for different time periods. The present application provides a complete automated replenishment strategy based on demand forecasts and target inventory levels in supply chain scenarios, which can optimize the minimum holding cost of additional inventory during the planning period while considering the supplier's capacity allocation, the minimum order quantity and the minimum packaging quantity, thereby providing a complete replenishment time and corresponding replenishment quantity during the planning period, and effectively reducing the holding cost of additional inventory without reducing demand satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] Figure 1 A schematic diagram of an application scenario of a warehouse cycle replenishment system for this application;

[0044] Figure 2 A flow chart of a warehouse cycle replenishment method provided for one embodiment of the present application;

[0045] Figure 3 A flow chart of a warehouse cycle replenishment method provided for another embodiment of the present application;

[0046] Figure 4 A schematic diagram of the structure of a warehouse periodic replenishment device provided in one embodiment of the present application;

[0047] Figure 5 A hardware structure diagram of an electronic device provided for one embodiment of the present application.

[0048] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0049] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0050] In the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. It should be noted that words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in this application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way. In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more.

[0051] It should be noted that the "at..." in the embodiment of the present application can be the instant when a certain situation occurs, or it can be a period of time after a certain situation occurs, and the embodiment of the present application does not specifically limit this. In addition, the replenishment method provided in the embodiment of the present application is only an example, and the replenishment method can also include more or less content.

[0052] In order to clearly describe the technical solutions of the embodiments of the present application, some terms and technologies involved in the embodiments of the present application are briefly introduced below:

[0053] SKU: Stock Keeping Unit, which refers to the smallest divisible management unit in inventory management. It is a unique identifier used to identify and track inventory, usually associated with specific product attributes (such as color, size, style, etc.).

[0054] Target inventory: The ideal inventory level set by the enterprise for the warehouse in order to meet the expected demand and operational needs. It is an inventory quantity target determined based on a comprehensive consideration of market demand, supply cycle, cost and other factors.

[0055] Safety stock: additional warehouse inventory held to cope with uncertainty in demand or supply. Its purpose is to reduce the risk of out-of-stocks and ensure business continuity, so that a certain level of customer demand can be met even in unexpected situations such as sudden increases in demand or delays in supply.

[0056] Beginning inventory: The amount of inventory a company's warehouse has at the beginning of a specific time period (such as an accounting period, inventory counting cycle, etc.).

[0057] Ending Inventory: The amount of inventory remaining in the warehouse at the end of the above specified time period. It is calculated by adding the purchase (or production) quantity during the period to the beginning inventory, and then subtracting the shipment (or sales, use) quantity during the period.

[0058] Inventory level: refers to the inventory quantity of a certain commodity that a warehouse has at a specific point in time.

[0059] MOQ: Minimum Order Quantity. Suppliers usually set a minimum quantity that a business must order.

[0060] MPQ: Minimum Packing Quantity, which means the minimum packing quantity. It indicates the quantity contained in the smallest packing unit of the supplier's goods.

[0061] Lead Time (LT): The time between when a warehouse places an order with a supplier and when it receives the goods or services.

[0062] Planning period: During this period, the company will forecast and plan the inflow (such as purchasing, production) and outflow (such as sales, use) of warehouse inventory to determine the appropriate inventory level, replenishment time and replenishment quantity, so as to control inventory costs and ensure service levels.

[0063] Inventory management is a critical link in supply chain and logistics management. Efficient inventory management can ensure that customer needs are met while minimizing inventory holding costs and out-of-stock risks. In order to better manage inventory, many companies have introduced inventory targets to balance inventory costs and service levels. When formulating replenishment plans, companies must meet specific inventory level targets.

[0064] Traditional inventory management methods often rely on simple ordering rules, such as fixed-cycle ordering and fixed-batch ordering. Take the periodic inventory replenishment method (also known as the (OUL, T) method) as an example. This is a warehouse that conducts periodic inventory with a cycle length of T. The time unit "period" here can be days, weeks, months, etc. If the inventory level is lower than the target inventory level each time, replenishment is carried out so that the inventory level after replenishment reaches the target inventory level.

[0065] Table 1 lists several indicators that enterprises usually observe when using the cycle inventory replenishment method to carry out replenishment planning. These indicators include demand, target ending inventory, beginning inventory, ending inventory, replenishment quantity, arrival in this period, minimum order quantity (MOQ), lead time, and inventory deviation. It can be seen that this is an example of an enterprise with a planning period of 5 weeks, a minimum order quantity of 70, and a lead time of 1 week.

[0066] Table 1

[0067] Week 1 Week 2 Week 3 Week 4 Week 5 Demand 10 10 10 10 10 Target ending inventory 80 80 80 80 80 Beginning inventory 20 10 80 140 130 Ending inventory 10 80 140 130 120 Replenishment 80 70 0 0 0 Current arrival 80 70 0 0 MOQ 70 Lead time 1 Inventory deviation -70 0 60 50 40

[0068] Table 1 shows the status of various indicators under the periodic inventory replenishment strategy, where the target inventory level is 80, the inventory cycle is 1 week, and the lead time is 1 week.

[0069] The beginning inventory at the beginning of the first week is 20, and the ending inventory at the end of the first week is 10. Every time the inventory is counted, the inventory and future demand are observed to decide whether to replenish. If the end-of-period inventory of this period can meet the demand of the next week, and the end-of-period inventory after meeting the demand is greater than the target end-of-period inventory of next week, then no replenishment will be made this week. Otherwise, the gap between the end-of-period inventory of this period and the target end-of-period inventory of next week will be considered, and the relationship between this gap and MOQ will be examined, and the larger value of the two will be taken as the replenishment quantity.

[0070] The replenishment quantity is as follows:

[0071] When the target ending inventory for the next week is ≤ the ending inventory of this period - the demand for the next period, the replenishment quantity is 0.

[0072] When the target ending inventory of the next week > the ending inventory of this period - the demand of the next period, the replenishment quantity meets:

[0073] Replenishment quantity = max{inventory at the end of the next period - (inventory at the end of this period - demand for this period), MOQ}.

[0074] For example, in the first week, the demand for the second week is 10, and the ending inventory of the first week is 10. After meeting the demand of the second week, only 0 is left. Therefore, there is a gap of 80 between the remaining quantity 0 and the target ending inventory of 80 for the second week. Since the minimum order quantity is 70, which is less than the gap of 80, an order for replenishment of 80 is placed from the supplier in the first week. Since the lead time is 1 week, the replenishment ordered this week will arrive next week to meet the demand of the next week. As shown in Table 1, the replenishment of 80 in the first week will arrive in the second week and meet the demand of the second week.

[0075] In the second week, the demand for the second week is 10. With the inventory of 10 and the arrival of replenishment quantity 80 at the end of the previous cycle, the ending inventory of the second week is 80. After meeting the demand of 10 in the second week, there is still 70 left in the ending inventory. Therefore, there is a gap of 10 between the remaining quantity 70 and the target ending inventory of 80 for the third week. Since the minimum order quantity is 70, which is greater than the gap of 10, an order for replenishment quantity 70 is placed from the supplier in the second week, and the replenishment quantity 70 can arrive in the third week.

[0076] In the third week, the demand is 10. With the inventory of 80 and the replenishment of 70 arriving at the end of the previous cycle, the ending inventory of the third week is 140. After meeting the demand of 10 in the second week, there is still 130 left. Therefore, the remaining quantity of 130 can meet the target ending inventory of 80 for the third week. There is no need to replenish from the supplier in the third week, and so on.

[0077] From the above analysis, it can be seen that if the enterprise uses the inventory level as a direct reference for inventory cost accounting, then it is relatively simple and short-sighted when only considering the minimum order quantity, which is not flexible and cannot fully consider the inventory level, and will also cause a holding burden on the warehouse. For example, in Table 1, although the target end-of-period inventory level was reached in the second week, in the following weeks, due to the slow consumption of demand, there will be an additional holding cost of 60+50+40=150 inventory units in the third, fourth and fifth weeks.

[0078] In actual operations, companies also need to consider multiple complex factors in the supply chain, such as supplier capacity, minimum order quantity, minimum packaging unit quantity, etc. These factors make it more difficult to achieve specific inventory level targets, thus affecting the balance between inventory costs and service levels.

[0079] In order to solve the above problems, the embodiments of the present application provide a warehouse periodic replenishment method, device, equipment and storage medium. The warehouse periodic replenishment method provides a complete automated replenishment strategy based on demand forecast and target inventory level in the supply chain scenario. It can optimize the minimum holding cost of additional inventory during the planning period while considering the supplier's production capacity allocation, minimum order quantity and minimum packaging unit quantity, thereby providing a complete replenishment time and corresponding replenishment quantity during the planning period, thereby effectively reducing the additional inventory holding cost without reducing demand satisfaction.

[0080] Figure 1 This is a schematic diagram of an application scenario of a warehouse cycle replenishment system for this application. In this application scenario, there is a first terminal 101 located at a supplier and a second terminal 102 located at an enterprise, wherein the supplier uses raw materials to produce goods with different production capacities (sku1, sku2, ...), and the production capacity of each commodity in different time periods is obtained through the sku production capacity calendar data, wherein the inventory data of the enterprise warehouse can be obtained from the target inventory calendar and the procurement in transit calendar, the demand can be obtained from the warehouse demand calendar, the ordering data includes the minimum order quantity and lead time negotiated between the enterprise and the supplier, and the supply data includes the minimum package quantity that the enterprise needs to order a certain sku commodity from the supplier.

[0081] The second terminal 102 can obtain a replenishment strategy based on inventory data, demand, order data, supply data and operational goals. The replenishment strategy includes purchasing different quantities of SKU products from suppliers to the warehouse in different time units during the planning period, and then the second terminal 102 can purchase SKU products according to the replenishment strategy.

[0082] The first terminal 101 and the second terminal 102 are electronic devices used by suppliers and enterprises respectively, and the electronic devices may be personal computers, mobile phones, tablet computers, notebooks, vehicle-mounted terminals, etc. In addition, clients related to warehouse cycle replenishment may be installed on the first terminal 101 and the second terminal 102, and the clients may be software (e.g., APP, browser, etc.), or web pages, applets, etc. The target object may use the above-mentioned clients related to warehouse cycle replenishment through the first terminal 101 and the second terminal 102 to perform supply and replenishment operations.

[0083] There is no restriction on the first terminal 101 and the second terminal 102, that is, there can be multiple suppliers and multiple purchasing companies. However, in order to simplify the technical solution of the present application, only one first terminal 101 and one second terminal 102 are considered.

[0084] It should be noted that the replenishment method in the embodiment of the present application can be executed separately by the first terminal 101 or the second terminal 102, or can be executed jointly by them.

[0085] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

[0086] The following describes the replenishment method provided by the exemplary embodiment of the present application in combination with the above-mentioned application scenarios and with reference to the accompanying drawings. It should be noted that the above-mentioned application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the implementation methods of the present application are not subject to any limitations in this regard.

[0087] Figure 2 A flow chart of a warehouse cycle replenishment method provided by an embodiment of the present application. Figure 2 As shown, the warehouse cycle replenishment method provided by this embodiment is applied to Figure 1 The second terminal 102 shown may include the following steps:

[0088] S210: Obtain the warehouse's demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data. The warehouse's demand is the forecasted demand for each planning unit and each time unit during the planning period. The warehouse's inventory data includes the target ending inventory and the beginning inventory of this period. The ordering data between the warehouse and the supplier includes the minimum order quantity and lead time. The supply data includes the capacity calendar data and the minimum packaging quantity.

[0089] In the embodiment of the present application, the demand of the warehouse has a replenishment time granularity consistent with the planning period, including but not limited to days, weeks, months, etc., and each planning unit under each time unit has a separate forecast demand within the planning period. As shown in Table 2:

[0090] Table 2

[0091] sku demand DS sku1 80 2024-08-12 sku1 80 2024-08-19 sku2 60 2024-08-12 sku2 60 2024-08-19

[0092] In the above table, the meaning of each column is as follows:

[0093] DS: Inventory and replenishment time point within the planning period, which is also the planning time point. In this embodiment, the planning time unit is a week, so the data in the DS column in Table 2 is the Monday of each natural week, and the Monday of that week is the beginning of the period, and the Sunday of that week is the end of the period.

[0094] sku: Inventory holding unit, which is also the basic planning unit for all data specified in this application.

[0095] Demand: Demand reflects the consumption of the time unit to which each planning unit belongs at each planning time point.

[0096] The warehouse inventory data includes the target ending inventory and the current period beginning inventory. The target ending inventory is the ideal inventory level set to meet the demand and operational needs of the next week. The beginning inventory is the inventory quantity that the enterprise warehouse has at the beginning of each week.

[0097] In the embodiment of the present application, the minimum order quantity data is signed by the enterprise and the supplier according to the production cooperation contract, reflecting the business demand for replenishment orders for each planning unit during the planning period; the minimum packaging quantity indicates the quantity contained in the smallest packaging unit of the supplier's goods sent to the warehouse. As shown in Table 3:

[0098] Table 3

[0099] sku MOQ MPQ sku1 70 10 sku2 60 10

[0100] In Table 3, the meaning of each column is as follows:

[0101] sku: Inventory holding unit, which is also the basic planning unit for all data specified in this application.

[0102] MOQ: minimum order quantity, MPQ: minimum package quantity. MOQ and MPQ data generally do not change over time, at least they are consistent for each planning unit during the planning period. In this embodiment, the minimum order quantity of sku1 is 70, and the minimum package quantity is 10, which means that the allowed replenishment quantity is MOQ+n·MPQ, n∈N * , that is, the replenishment quantity can only be [70, 80, 90, ...]. Similarly, the replenishment quantity allowed for sku2 can only be [60, 70, 80, 90, ...].

[0103] In the embodiment of the present application, the capacity calendar data reflects the maximum quantity available for production of each planning unit during the planning period, which is generally signed by the enterprise and the supplier according to the production cooperation contract, and reflects the business constraints of replenishment orders for each planning unit during the planning period. As shown in Table 4:

[0104] Table 4

[0105]

[0106] In Table 4, the meaning of each column is as follows:

[0107] DS: Inventory and replenishment time point within the planning period, which is also the planning time point. In this embodiment, the planning time unit is a week, so the data in the DS column in Table 2 is the Monday of each natural week, and the Monday of that week is the beginning of the period, and the Sunday of that week is the end of the period.

[0108] sku: Inventory holding unit, which is also the basic planning unit for all data specified in this application.

[0109] supplier: This role negotiates with the company's planners to specify parameters such as MOQ and LT for different replenishment plans, agree on capacity calendar data, etc.

[0110] week_capacity: Weekly capacity, which is the maximum number of replenishment orders allowed for each planning unit at each planning time point. In this embodiment, when sku1 is replenished on 2024-08-12, the maximum number of replenishments allowed is 100, and when sku2 is replenished on 2024-08-12, the maximum number of replenishments allowed is 80.

[0111] S220: Construct a replenishment planning model under constraints based on the warehouse's demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data.

[0112] In the embodiment of the present application, data for establishing a cargo replenishment planning model is obtained through step S210. The purpose of this step is to construct a cargo replenishment planning model with constraints based on the data obtained in S210.

[0113] In a specific embodiment, a replenishment planning model under constraints is constructed based on the demand and inventory data of the warehouse, the order data between the warehouse and the supplier, and the supply data of the supplier, which may include:

[0114] S221: Determine business needs based on the warehouse's demand and inventory data, the order data between the warehouse and the supplier, and the supplier's supply data.

[0115] In the embodiment of the present application, the following business requirements can be obtained based on the demand and inventory data of the warehouse, the order data between the warehouse and the supplier, and the supply data of the supplier:

[0116] participate Figure 1 As shown, the supplier is responsible for the production process according to the sku capacity calendar number, and the raw materials used for production have unlimited inventory by default. After the enterprise places an order to replenish the sku, the product sku arrives at the enterprise's production warehouse after a lead time.

[0117] S222: Construct a replenishment logistics model under constraints based on business needs.

[0118] In the embodiment of the present application, the constraint conditions are determined by demand, inventory data, order data, supply data, etc.

[0119] In a specific embodiment, building a replenishment model under constraints based on business needs may include the following steps:

[0120] S222a: Determine the parameters, sets and auxiliary variables in the replenishment logistics model based on business needs.

[0121] First consider parameters and collections, as follows:

[0122] T: set of planning periods, t∈T, t end At the end of the planning period;

[0123] T balance : A set of planning periods that considers inventory days equilibrium;

[0124] I: sku collection;

[0125] A collection of SKUs that share the same capacity r;

[0126] I MPQ : Enable the sku set for MPQ constraints;

[0127] R: production resource set;

[0128] DMD i,t : the demand for sku i at time t, t∈T;

[0129] OHS i，t : The initial inventory or purchase arrival quantity of sku i at time t, t∈T;

[0130] TI i,t : The target inventory quantity of sku i at time t, t∈T;

[0131] TID i,t : The target inventory days of sku i at time t, t∈T;

[0132] RC r,t : The upper limit of resource r that can be used at time t, t∈T;

[0133] LT i，t : Order lead time for sku i,

[0134] PBT i，t : Capacity utilization lead time of sku i,

[0135] MOQ i : minimum order quantity for sku i,

[0136] MPQ i : minimum packing quantity of sku i,

[0137] Whether the target stock of sku i allows shortage;

[0138] Whether the target inventory of sku i is allowed to be exceeded;

[0139] IF_DELAY i :Whether the demand for sku i allows delayed satisfaction.

[0140] Then consider the auxiliary variables, as follows:

[0141] Non-negative continuous variable, ending inventory of sku i at time t,

[0142] Non-negative continuous variable, the amount of demand for sku i at time t′ satisfied at time t,

[0143] Non-negative continuous variable, the supply order quantity of sku i at time t,

[0144] Non-negative continuous variable, the target inventory unmet quantity of sku i at time t, T;

[0145] Non-negative continuous variable, the amount by which sku i exceeds the target inventory at time t,

[0146] Non-negative continuous variable, skui is the actual number of days of inventory balance at time t,

[0147] Non-negative continuous variable, skui is the number of days of unmet target inventory at time t,

[0148] Non-negative continuous variable, the proportion of the actual balance of sku i at time t that meets the demand at time t′,

[0149] 0-1 integer variable, whether the actual balance of sku i at time t meets the demand at time t′,

[0150] Non-negative continuous variable, the number of days of excess target inventory for sku i at time t,

[0151] Non-negative continuous variable, skui is the amount of delayed satisfaction of demand at time t,

[0152] Non-negative continuous variable, the inventory equilibrium days of the capacity group corresponding to resource r at time t,

[0153] A non-negative integer variable, the multiple of the supply of sku i at time t and the minimum order quantity,

[0154]

[0155] 0-1 integer variable, sku i adds linear constraints when balancing inventory days at time t,

[0156] 0-1 integer variable used to linearize when adding the logical constraint of whether the ending inventory of sku i at time t meets the rolling demand.

[0157] S222b: Determine a constraint relationship according to the constraint conditions. The constraint conditions in this embodiment are as follows:

[0158] 1) The inflow and outflow of each planning unit in each time unit are balanced;

[0159] The constraint condition needs to be satisfied: inventory at the end of the previous period + arrival of this period = inventory at the end of this period + demand of this period. The constraint relationship is as follows:

[0160]

[0161] 2) The replenishment quantity for each planning unit and each time unit must meet the minimum order quantity and minimum packaging quantity;

[0162] Among them, for each sku, the replenishment quantity must meet the minimum order quantity, and the constraint relationship is as follows:

[0163]

[0164] For each sku, the replenishment quantity must meet the minimum packaging quantity, that is, the replenishment quantity must be an integer multiple of the MPQ. The constraint relationship is as follows:

[0165]

[0166] 3) The demand for each planning unit and each time unit cannot exceed the current capacity of the supplier's capacity calendar data;

[0167] This constraint requires that when an sku is ordered, the replenishment quantity cannot exceed the upper limit of the current production capacity. The constraint relationship is as follows:

[0168]

[0169] 4) The total amount of demand satisfied during the entire planning period is equal to the total demand during the entire planning period;

[0170] For each sku and each time unit, the constraint relationship is as follows:

[0171]

[0172] 5) The ending inventory at each time unit needs to meet the target inventory requirements.

[0173] If the ending inventory has a certain gap or excess relative to the target inventory requirement, the constraint relationship under this constraint condition is as follows:

[0174]

[0175] S222c: Construct a cargo replenishment planning model based on the parameters, sets, auxiliary variables and constraint relationships in the cargo replenishment planning model.

[0176] In the embodiment of the present application, after determining the parameters, sets, auxiliary variables and constraint expressions in the cargo replenishment planning model, the cargo replenishment planning model can be expressed by formula, which is conducive to the subsequent solution of the optimal replenishment method.

[0177] S230: Taking the difference between the ending inventory during the entire planning period and the target ending inventory as the operation target, the replenishment operation model is solved to obtain the replenishment quantity in different time periods.

[0178] In the embodiment of the present application, the replenishment rhythm and replenishment quantity given by the inventory strategy are allowed to enable the inventory level to reach a certain deviation range of the inventory target, such as 30%. Then the operation goal of this embodiment gives priority to minimizing the difference between the end-of-period inventory and the target end-of-period inventory during the entire planning period. The specific relationship is as follows:

[0179]

[0180] Based on the above logistics objectives, the replenishment logistics model is solved.

[0181] In the above embodiment, the present application takes the minimum holding cost of additional inventory during the planning period as the operational goal while considering the supplier's capacity allocation, minimum order quantity and minimum packaging quantity, thereby providing a complete replenishment time and corresponding replenishment quantity during the planning period.

[0182] Figure 3 A flow chart of a warehouse cycle replenishment method provided by another embodiment of the present application. Figure 3 As shown, the warehouse cycle replenishment method provided by this embodiment is Figure 2 The embodiment shown is improved and may specifically include the following steps:

[0183] S310: Obtain the warehouse's demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data. The warehouse's demand is the forecasted demand for each planning unit and each time unit during the planning period. The warehouse's inventory data includes the target ending inventory and the beginning inventory of this period. The ordering data between the warehouse and the supplier includes the minimum order quantity and lead time. The supply data includes the capacity calendar data and the minimum packaging quantity.

[0184] In the embodiment of the present application, the specific process of this step can refer to the description of step S210 and will not be repeated here.

[0185] S320: Construct a replenishment logistics model under constraints based on the warehouse's demand and inventory data, the order data between the warehouse and the supplier, and the supplier's supply data.

[0186] In the embodiment of the present application, data for establishing a cargo replenishment planning model is obtained through step S310. The purpose of this step is to construct a cargo replenishment planning model with constraints based on the data obtained in S310.

[0187] In a specific embodiment, a replenishment planning model under constraints is constructed based on the demand and inventory data of the warehouse, the order data between the warehouse and the supplier, and the supply data of the supplier, which may include:

[0188] S321: Determine business needs based on the warehouse's demand and inventory data, the order data between the warehouse and the supplier, and the supplier's supply data.

[0189] In the embodiment of the present application, the following business requirements can be obtained based on the demand and inventory data of the warehouse, the order data between the warehouse and the supplier, and the supply data of the supplier:

[0190] participate Figure 1As shown, the supplier is responsible for the production process according to the sku capacity calendar number, and the raw materials used for production have unlimited inventory by default. After the enterprise places an order to replenish the sku, the product sku arrives at the enterprise's production warehouse after a lead time.

[0191] S322: Construct a replenishment logistics model under constraints based on business needs.

[0192] In the embodiment of the present application, the constraint conditions are determined by demand, inventory data, order data, supply data, etc.

[0193] In a specific embodiment, building a replenishment model under constraints based on business needs may include the following steps:

[0194] S322a: Determine the parameters, sets and auxiliary variables in the replenishment logistics model according to business needs.

[0195] For this step, please refer to the description of step S222a, which will not be repeated here.

[0196] S322b: Determine the constraint relationship according to the constraint condition

[0197] In the embodiment of the present application, in addition to the five constraints described in step S222b, the following constraints may also be included:

[0198] 6) In the case of delayed satisfaction of demand, determine the amount of demand delay.

[0199] The calculation formula of the delay under this constraint is as follows:

[0200]

[0201] 7) When the actual ending inventory of sku is greater than the accumulated demand, it is considered to be able to meet the demand

[0202] The constraint relation of this constraint condition is:

[0203]

[0204]

[0205] 8) Rolling satisfaction of demand: if the demand is not met in the previous period, it will definitely not be met in the current period; if the demand is met in the next period, it will definitely be met in the current period

[0206] The constraint relation of this constraint condition is:

[0207]

[0208] The proportion of all satisfaction in the current period is 1, and the constraint relationship is:

[0209]

[0210] 9) When the target inventory cannot be met, the missing and excess values ​​of the inventory days are calculated. The constraint relationship of this constraint is:

[0211]

[0212] S322c: Construct a cargo replenishment planning model based on the parameters, sets, auxiliary variables and constraint relationships in the cargo replenishment planning model.

[0213] In the embodiment of the present application, after determining the parameters, sets, auxiliary variables and constraint expressions in the cargo replenishment planning model, the cargo replenishment planning model can be expressed by formula, which is conducive to the subsequent solution of the optimal replenishment method.

[0214] S330: Taking minimizing the difference between the ending inventory and the target ending inventory during the entire planning period as the operations research goal, convert the operations research model into a programming language.

[0215] In the embodiment of the present application, when the constraint condition 6) is added in step S320, the present embodiment correspondingly adds a new operation goal, that is, minimization of the delay amount is required, and the specific relationship is as follows:

[0216]

[0217] Accordingly, in addition to taking the minimum difference between the end-of-period inventory and the target end-of-period inventory during the entire planning period as the operational goal, this embodiment can also take the minimization of the difference between the end-of-period inventory days during the entire planning period and the target end-of-period inventory days as the operational goal under the constraint condition 9). The specific relationship is as follows:

[0218]

[0219] In this embodiment, the parameters, sets, auxiliary variables and constraint expressions in the cargo replenishment planning model are converted into a specific programming language, such as Python.

[0220] S340: Import the programming language into the operations research optimization solver.

[0221] In this embodiment, the operations research optimization solver is a software tool or algorithm library specially designed to solve various optimization problems in operations research, which can use efficient algorithms and numerical techniques to search the feasible solution space and find a solution that satisfies the constraints and optimizes the objective function. Common operations research solvers include CPLEX, Gurobi, SCIP, etc.

[0222] S350: Calculate the optimal solution as the replenishment quantity in different time periods according to the operation research goal.

[0223] Taking more operations research objectives into consideration, the optimal solution is solved with the help of operations research optimization solver, which is the replenishment strategy that includes replenishment quantities in different time periods.

[0224] In the above embodiment, by adding new constraints and logistics objectives, the replenishment logistics model can be solved to obtain the replenishment quantities for different time periods that meet more conditions.

[0225] Figure 4 This is a schematic diagram of the structure of a warehouse periodic replenishment device provided by an embodiment of the present application. The device may be in the form of software and / or hardware. Figure 4 As shown, the inventory transfer device provided by this embodiment includes: an acquisition module 401, a model building module 402 and a solution module 403.

[0226] The acquisition module 401 is used to acquire the demand and inventory data of the warehouse, the order data between the warehouse and the supplier, and the supply data of the supplier. The demand of the warehouse is the predicted demand in the planning period for each planning unit and each time unit. The inventory data of the warehouse includes the target ending inventory and the beginning inventory of this period. The order data between the warehouse and the supplier includes the minimum order quantity and the lead time. The supply data includes the capacity calendar data and the minimum packaging quantity.

[0227] A model building module 402, for building a replenishment model under constraints based on the warehouse's demand and inventory data, the order data between the warehouse and the supplier, and the supplier's supply data;

[0228] The solving module 403 is used to solve the operations research model with minimizing the difference between the end-of-period inventory and the target end-of-period inventory during the entire planning period as the operations research goal, and obtain the replenishment quantity in different time periods.

[0229] In a possible embodiment, the model building module 402 is specifically used to determine business needs based on warehouse demand and inventory data, order data between the warehouse and the supplier, and supplier supply data; the model builds a replenishment planning model under constraints based on the business needs.

[0230] In a possible embodiment, the model building module 402 is specifically used to determine business needs based on the warehouse's demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data; determine the parameters, sets and auxiliary variables in the replenishment planning model based on the business needs; determine the constraint relationship based on the constraint conditions; and build the replenishment planning model based on the parameters, sets, auxiliary variables and constraint relationship in the replenishment planning model.

[0231] In a possible embodiment, the constraints include the following requirements:

[0232] 1) The inflow and outflow of each planning unit in each time unit are balanced;

[0233] 2) The replenishment quantity for each planning unit and each time unit must meet the minimum order quantity and minimum packaging quantity;

[0234] 3) The demand for each planning unit and each time unit cannot exceed the current capacity of the supplier's capacity calendar data;

[0235] 4) The total amount of demand satisfied during the entire planning period is equal to the total demand during the entire planning period;

[0236] 5) The ending inventory at each time unit needs to meet the target inventory requirements.

[0237] In a possible embodiment, the constraint condition further includes: determining the amount of demand delay when the demand is met with delay; accordingly, the operational goal further includes minimizing the penalty for the amount of demand delay.

[0238] In a possible embodiment, the constraint condition also includes: the end-of-period inventory is required to have a certain gap or excess relative to the target inventory; accordingly, the operational goal also includes: minimizing the difference between the end-of-period inventory days and the target end-of-period inventory days during the entire planning period.

[0239] In a possible embodiment, the solution module 403 is further used to: convert the operations research model into a programming language; import the programming language into an operations research optimization solver; and calculate the optimal solution as the replenishment quantity for different time periods according to the operations research goal.

[0240] The warehouse periodic replenishment device provided in the embodiment of the present application has the same implementation principle and technical effects as those of the aforementioned replenishment method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned replenishment method embodiment.

[0241] Figure 5 A hardware structure diagram of an electronic device provided in an embodiment of the present application. This embodiment provides an electronic device, comprising: at least one processor 501, and a memory 502 in communication with the at least one processor 501; the memory 502 stores computer-executable instructions; the processor 501 executes the computer-executable instructions stored in the memory 502 to implement the replenishment method described in any of the above embodiments.

[0242] Figure 5 The electronic device shown also includes a communication interface 503 and a communication bus 504, wherein the processor 501, the memory 502 and the communication interface 503 are connected to each other via the communication bus 504. The communication bus 504 can be divided into an address bus, a data bus, a control bus, etc. Figure 5 In the figure, only one thick line is used to represent the communication bus 504, but it does not mean that there is only one communication bus 504 or one type of communication bus 504. The processor 501 may also be called a controller, and there is no limitation on the name.

[0243] In the embodiment of the present application, the memory 502 stores instructions that can be executed by at least one processor 501. The at least one processor 501 can execute the replenishment method discussed above by executing the instructions stored in the memory 502. The processor 501 can implement Figure 4 The functions of each module in the device shown.

[0244] Among them, the processor 501 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 502 and calling data stored in the memory 502, the various functions of the device and processing data, the device can be monitored as a whole.

[0245] In one possible design, the processor 501 may include one or more processing units, and the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 501. In some embodiments, the processor 501 and the memory 502 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0246] The processor 501 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the replenishment method disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0247] The memory 502 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 502 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 502 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 502 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0248] By programming the processor 501, the code corresponding to the replenishment method described in the above embodiment can be fixed into the chip, so that the chip can execute the replenishment method when running. Figure 2 Steps of the replenishment method of the embodiment shown. How to design and program the processor 501 is a technique known to those skilled in the art and will not be described in detail here.

[0249] Based on the same inventive concept, the embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the replenishment method described in any of the above embodiments. Therefore, they will not be described in detail here. In addition, the description of the beneficial effects of the same method will not be described in detail. For technical details not disclosed in the computer storage medium embodiment involved in the present invention, please refer to the description of the method embodiment of the present invention.

[0250] In some possible implementations, various aspects of the replenishment method provided in the present application may also be implemented in the form of a program product, which includes program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of the replenishment method according to various exemplary embodiments of the present application described above in this specification.

[0251] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0252] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0253] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0254] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0255] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A warehouse periodic replenishment method, characterized in that: The method comprises: Obtain the warehouse's demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data. The warehouse's demand is the forecasted demand for each planning unit and each time unit during the planning period. The warehouse's inventory data includes the target ending inventory and the beginning inventory of this period. The ordering data between the warehouse and the supplier includes the minimum order quantity and lead time. The supply data includes the capacity calendar data and the minimum packaging quantity. Construct a replenishment planning model under constraints based on warehouse demand and inventory data, order data between warehouse and supplier, and supplier supply data; Taking minimizing the difference between the ending inventory and the target ending inventory during the entire planning period as the operational goal, the replenishment logistics model is solved to obtain the replenishment quantities in different time periods.

2. The method according to claim 1, characterized in that The replenishment planning model constructed under constraints based on the warehouse demand and inventory data, the order data between the warehouse and the supplier, and the supplier's supply data includes: Determine business needs based on warehouse demand and inventory data, order data between warehouse and supplier, and supplier supply data; Build a replenishment logistics model under constraints based on business needs.

3. The method according to claim 2, characterized in that The cargo replenishment planning model constructed under constraints based on business needs includes: Determine the parameters, sets and auxiliary variables in the replenishment planning model according to business needs; Determine a constraint relationship according to the constraint condition; The cargo replenishment planning model is constructed according to the parameters, sets, auxiliary variables and the constraint relationship in the cargo replenishment planning model.

4. The method according to claim 1, characterized in that The constraints include the following requirements: 1) The inflow and outflow of each planning unit in each time unit are balanced; 2) The replenishment quantity for each planning unit and each time unit must meet the minimum order quantity and minimum packaging quantity; 3) The demand for each planning unit and each time unit cannot exceed the current capacity of the supplier's capacity calendar data; 4) The total amount of demand satisfied during the entire planning period is equal to the total demand during the entire planning period; 5) The ending inventory at each time unit needs to meet the target inventory requirements.

5. The method according to claim 4, characterized in that The constraint conditions also include: determining the amount of demand delay in the event that the demand is met with delay; Accordingly, the operational objectives also include: minimizing the penalty for demand delay.

6. The method according to claim 4, characterized in that The constraints also include: The ending inventory is required to have a certain amount of shortfall or excess relative to the target inventory; Accordingly, the operational objectives also include: minimizing the difference between the end-of-period inventory days and the target end-of-period inventory days during the entire planning period.

7. The method according to claim 1, characterized in that The minimization of the difference between the end-of-period inventory and the target end-of-period inventory during the entire planning period is taken as the operational goal, and the replenishment logistics model is solved to obtain the replenishment quantities in different time periods, including: Converting the operational research model and operational research goal into a programming language; Importing the programming language into an operations research optimization solver; The optimal solution is calculated according to the operational objectives as the replenishment quantity for different time periods.

8. A warehouse periodic replenishment device, characterized in that: include: An acquisition module is used to acquire the demand and inventory data of the warehouse, the order data between the warehouse and the supplier, and the supply data of the supplier. The demand of the warehouse is the predicted demand in the planning period for each planning unit and each time unit. The inventory data of the warehouse includes the target ending inventory and the beginning inventory of this period. The order data between the warehouse and the supplier includes the minimum order quantity and the lead time. The supply data includes the capacity calendar data and the minimum packaging quantity. A model building module is used to build a replenishment planning model under constraints based on the warehouse's demand and inventory data, the ordering data between the warehouse and the supplier, and the supplier's supply data; The solution module is used to solve the replenishment logistics model with the minimization of the difference between the end-of-period inventory and the target end-of-period inventory during the entire planning period as the logistics goal, and obtain the replenishment quantity in different time periods.

9. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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