Automobile aftermarket full-link intelligent replenishment method and system and medium

Abstract

The invention discloses an automobile aftermarket full-link intelligent replenishment method and system and a medium, and belongs to the technical field of automobile aftermarket full-link sales. The method comprises the steps of obtaining a demand subject; aiming at each demand main body, carrying out data mining and conversion by utilizing a big data calculation center through a plan collaboration platform, and respectively constructing a central warehouse intelligent replenishment model, a front warehouse intelligent replenishment model and a customer intelligent replenishment model in cooperation with each level of network supply strategy; receiving a replenishment calculation result of the planned collaboration platform by using the planned replenishment platform, converting the replenishment calculation result into an actual replenishment strategy, and finally generating a replenishment execution list; and performing replenishment execution according to the replenishment execution list. The replenishment system has the characteristics of standardization, automation and integration, the types and the number of automatic replenishment are calculated through the model, the replenishment model and the replenishment system serve as software and hardware respectively, automatic calculation and execution of replenishment are achieved, linkage of all links of a supply chain is achieved, and reasonable and stable replenishment is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of full-link sales in the automotive aftermarket, and specifically relates to a full-link intelligent replenishment method, system and medium for the automotive aftermarket. Background Art

[0002] The automotive aftermarket refers to the various services provided during the use of a car after it is sold. It covers all the services that consumers need after buying a car. The automotive aftermarket can be roughly divided into seven industries: auto insurance industry; auto finance industry; auto IT industry; auto maintenance industry (auto boutiques, supplies, beauty, quick repair and modification); auto repair and accessories industry; auto culture and auto sports industry; used car and car rental industry.

[0003] In the prior art, for example, a Chinese invention patent with application number 202210597937.6 discloses an intelligent replenishment platform for automotive aftermarket stores: including a planning collaboration platform and a planned replenishment platform; the planning collaboration platform is responsible for the management of replenishment demand plans on the business side, including sales plans, store opening plans, maintenance and management of historical sales and sales forecasting algorithms, solving the access input and unified aggregation of different businesses; specifically including a big data computing center, a sales forecasting center and a planning task center.

[0004] The above-mentioned intelligent replenishment platform for automotive aftermarket stores is only based on the management of the replenishment demand plan and replenishment strategy of the store business. Since the store's operating capacity and logistics capacity are limited by actual resources, its replenishment demand plan and replenishment strategy are relatively simple. If the replenishment demand plan and replenishment strategy are applied to the full-link intelligent replenishment in the automotive aftermarket, the stable operation of the replenishment platform cannot be guaranteed. There is a lack of certain replenishment linkage strategies and full-link replenishment linkage mechanisms for various replenishment scenarios in the entire link.

[0005] In the automotive aftermarket, the stability of the supply chain is a prerequisite for business development. The existing intelligent replenishment technology mainly focuses on separate warehousing management for stores, warehouses, etc., and lacks full-link intelligent replenishment for the automotive aftermarket. Integrating non-standard data such as the large variety of auto parts industry products, discrete demand, and basic product information, full-link intelligent replenishment is an effective way to ensure the continuity and stability of spare parts supply. Summary of the invention

[0006] In response to the problems existing in the background technology, the present invention provides a full-link intelligent replenishment method, system and medium for the automotive aftermarket, which integrates the replenishment scenarios of various entities in the supply chain, aims to solve the problem of demand satisfaction in each link of the supply chain, effectively improve supply efficiency, and enable inventory management.

[0007] Technical solution: In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0008] A full-link intelligent replenishment method for the automotive aftermarket, comprising the following steps:

[0009] S1: Get the demand subject;

[0010] S2: For each demand entity, through the planning collaboration platform, the big data computing center is used for data mining and transformation, and in conjunction with the network supply strategies at all levels, the central warehouse intelligent replenishment model, the forward warehouse intelligent replenishment model and the customer intelligent replenishment model are respectively constructed;

[0011] S3: Use the planned replenishment platform to receive the replenishment calculation results of the planning collaboration platform, convert them into actual replenishment strategies, and finally generate replenishment execution orders;

[0012] S4: Carry out replenishment according to the replenishment execution order.

[0013] As a priority, the demand entities in S1 include forward warehouses, wholesale businesses, self-operated flagship stores, self-operated franchisees and car maintenance shops.

[0014] As a preferred option, the specific content of the central warehouse intelligent replenishment model in S2 is:

[0015] The central warehouse intelligent replenishment model: converts the central warehouse demand into the actual reasonable replenishment quantity, thereby triggering the mobilization plan and procurement plan to achieve intelligent replenishment of the central warehouse;

[0016] The calculation formula for the central warehouse replenishment quantity is:

[0017]

[0018] Among them, T represents the current time; VLT represents the replenishment lead time, which is the number of days it takes for the supplier to deliver the replenishment to the warehouse; BP represents the replenishment cycle, which is the number of days from the current time to the next available order time; BUFFER represents the safety stock buffer period, which is the minimum number of safety days used to cope with fluctuations in VLT and sales plans;

[0019] Safety stock represents the safety stock margin set by regional planning to manually avoid stockouts.

[0020] As a preferred embodiment, the specific content of the forward warehouse intelligent replenishment model in S2 is:

[0021] The forward warehouse intelligent replenishment model: through the forward warehouse intelligent replenishment model, the width and depth of the forward warehouse demand are transformed, and the forward warehouse demand is converted into an actual reasonable replenishment quantity, thereby mobilizing the replenishment plan to achieve intelligent replenishment of the forward warehouse and dynamic optimization of inventory;

[0022] Build a forward warehouse intelligent replenishment model based on safety inventory, sales forecast, and supplier delivery time, including:

[0023] Stocking list model: by verifying category brand planning, product status and sales characteristics;

[0024] Category brand planning includes region + category, region + brand and region + SKU;

[0025] Product status includes sales status, purchase status, new and old products, and left and right pairing;

[0026] Sales features include:

[0027] Stocking list selection logic: Determine the reasonable sales time range X for each category brand based on the proportion. If the brand in this category is available in the past X days, it will be included in the stocking list;

[0028] Stocking list exclusion logic: Because the stocking list selection logic circles brands of categories that have sales but low sales into the stocking list, a new exclusion logic is added to exclude brands of categories that are in the bottom 10% of sales;

[0029] Stocking list correction logic: re-enter products with low sales volume but high sales frequency, seasonal products, hot-selling season lists, and special new products to ensure the width of stocking.

[0030] As a preferred model, the safety stock model: after the SKU enters the stocking list, the safety stock is calculated. The safety stock is the minimum inventory that can meet the daily sales demand from the current time to the next arrival date;

[0031] Safety stock = MAX(N×X / 7, number of SKU single order transactions);

[0032] Among them, X is the second weekly sales value of the top product in the historical period, N is the number of days of SKU safety inventory, and SKU is the minimum inventory unit;

[0033] Safety stock correction model: Based on the top sales percentage, the safety stock is the inventory that can meet N days of turnover;

[0034] Promotional order exclusion logic: Use the normal distribution principle to identify multi-dimensional promotional orders and correct them during replenishment;

[0035] Replenishment implementation scenario: Calculate the actual replenishment quantity according to the configured replenishment node and submission method.

[0036] As a preferred embodiment, the specific content of the customer intelligent replenishment model in S2 is:

[0037] The customer intelligent replenishment model: the product pool is circled through the intelligent product selection model, and then converted into the actual reasonable replenishment quantity for the customer through the safety inventory model, and the customer's own needs are combined to realize the customer's intelligent replenishment;

[0038] Intelligent product selection model: Starting from the vehicle model adaptation relationship, combining brand-level strategic product planning, regional characteristics, and sales characteristics to circle out the customer stock selection pool;

[0039] Safety stock model: further combine category and regional characteristics in the product selection pool to perform width screening and depth calculation.

[0040] An automotive aftermarket full-link intelligent replenishment system, using any of the above-mentioned automotive aftermarket full-link intelligent replenishment methods, including an integrated intelligent replenishment platform, the integrated intelligent replenishment platform including a demand subject module, a planning collaboration platform, a planned replenishment platform, a replenishment execution module and network supply strategies at all levels;

[0041] The demand subject module includes forward warehouses, wholesale business, self-operated flagship stores, self-operated franchisees and car maintenance shops;

[0042] The planning collaboration platform: receives demand entities, conducts data mining and transformation through the big data computing center, generates an intelligent replenishment model, and provides a computing basis for replenishment implementation;

[0043] Planned replenishment platform: receives the replenishment calculation results from the planning collaboration platform, converts them into actual replenishment strategies, and finally generates replenishment execution orders;

[0044] The replenishment execution module: the replenishment execution order triggers the replenishment, which is carried out by the supplier fulfilling the central warehouse replenishment document, the central warehouse fulfilling the forward warehouse replenishment document, and the forward warehouse fulfilling the customer replenishment document;

[0045] The network supply strategies at all levels described above: consider both supply and demand sides and product characteristics, evaluate the width and depth of replenishment, and ensure that supply at all levels achieves a dynamic balance between demand satisfaction and reasonable inventory.

[0046] Preferably, the planned replenishment platform includes six sub-platform modules: a replenishment business center, a replenishment execution center, a replenishment sourcing center, a replenishment order collection center, a replenishment strategy center, and a replenishment monitoring center;

[0047] Replenishment business center: configure the basic data required for replenishment;

[0048] Replenishment Execution Center: Calculates actual replenishment quantity based on demand and configured replenishment strategy;

[0049] Replenishment sourcing center: After the replenishment execution center calculates the actual replenishment quantity, it searches for suppliers;

[0050] Replenishment order collection center: collects orders according to the configured order collection strategy and generates a plan order;

[0051] Replenishment Strategy Center: All strategies of the replenishment platform are uniformly deployed for different scenarios, different plans, and different replenishment tasks;

[0052] Replenishment Monitoring Center: monitors abnormal alarms of the replenishment platform and provides monitoring capabilities to ensure the stable operation of the replenishment platform.

[0053] As a preferred option, a full-link replenishment linkage strategy is adopted: through demand aggregation and algorithm dependence, replenishment effect inspection, and commodity ABC label support, the interaction and linkage of the replenishment link can be ensured to ensure the effectiveness of replenishment;

[0054] Demand aggregation, association and linkage of replenishment model algorithms to minimize redundant replenishment;

[0055] Product ABC labeling: Label products according to transaction characteristics, realize quantitative stratification of best-selling and long-tail products, and guide differentiated tiered stocking;

[0056] Replenishment effect test: Evaluate the replenishment model through data indicators, including general adoption rate and perfect adoption rate, and reversely optimize the model.

[0057] A computer-readable storage medium for storing a program, which, when executed, implements the steps of the full-link intelligent replenishment method for the automotive aftermarket as described in any of the above items.

[0058] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0059] (1) The automotive aftermarket full-link intelligent replenishment system of the present invention is standardized, integrating non-standard data such as the large variety of auto parts industry products, discrete demand, and basic product information, using a model to calculate the types and quantities of automatic replenishment, building a trusted replenishment data foundation within the system, and providing traceable data reference for actual replenishment strategy evaluation and execution, thereby achieving a closed-loop operation from data analysis, replenishment strategy, and effect evaluation.

[0060] (2) The automotive aftermarket full-link intelligent replenishment system of the present invention is automated, with the replenishment model and replenishment system acting as software and hardware respectively, supporting automated replenishment calculation and execution, continuously improving supply efficiency and intelligent supply weight, fully matching actual inventory with sales, out-of-stock and replenishment, and achieving a dynamic balance between replenishment and inventory management within the system, thereby providing support for achieving a precise supply chain.

[0061] (3) The full-link intelligent replenishment system for the automotive aftermarket of the present invention is integrated, realizing the linkage of various links in the supply chain and ensuring reasonable and stable replenishment. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the full-link intelligent replenishment of the present invention for solving the shortage problem in each link and improving the demand satisfaction rate;

[0063] Figure 2It is a technical link diagram of the full-link intelligent replenishment of the present invention;

[0064] Figure 3 It is a schematic diagram of the execution flow of the central warehouse intelligent replenishment of the present invention;

[0065] Figure 4 It is a schematic diagram of a replenishment cycle scenario of the central warehouse intelligent replenishment of the present invention;

[0066] Figure 5 It is a technical flow diagram of the central warehouse intelligent replenishment of the present invention;

[0067] Figure 6 It is a schematic diagram of a stock list model for intelligent replenishment of a forward warehouse of the present invention;

[0068] Figure 7 It is a schematic diagram of a safety inventory model for intelligent replenishment of a forward warehouse of the present invention;

[0069] Figure 8 It is a schematic diagram of the promotion factor model of the forward warehouse intelligent replenishment of the present invention;

[0070] Fig. 9 It is a schematic diagram of a replenishment implementation scenario of the forward warehouse intelligent replenishment of the present invention;

[0071] Fig.10 It is a schematic diagram of the intelligent product selection model for intelligent customer replenishment of the present invention;

[0072] Fig.11 It is a schematic diagram of the collection of replenishment linkage demand of the full-link intelligent replenishment of the present invention;

[0073] Fig.12 It is a schematic diagram of the replenishment linkage SKU stratification of the full-link intelligent replenishment of the present invention. DETAILED DESCRIPTION

[0074] The present invention is further illustrated below in conjunction with specific examples. The examples are implemented based on the technical solutions of the present invention. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0075] The full-link intelligent replenishment method for the automotive aftermarket provided in this embodiment includes the following steps:

[0076] like Figure 2 shown.

[0077] S1: Get the demand subject;

[0078] The demand entities (operating system) include forward warehouses (FDC), wholesale businesses, self-operated flagship stores, self-operated franchisees and car maintenance shops.

[0079] S2: For each demand entity, through the planning collaboration platform, the big data computing center is used for data mining and transformation, and in conjunction with the network supply strategies at all levels, the central warehouse intelligent replenishment model, the forward warehouse intelligent replenishment model and the customer intelligent replenishment model are respectively constructed;

[0080] like Figure 3-4 As shown in the figure, the specific content of the central warehouse (RDC) intelligent replenishment model is:

[0081] Business scenario: Replenishment of central warehouses requires purchasing staff to invest a lot of energy in tracking and implementation. It is expected that by building an RDC intelligent replenishment system to assist purchasing decisions, improve order making efficiency, maximize the satisfaction of forward warehouse ordering needs, and control inventory turnover within a certain range to achieve supply balance.

[0082] Execution process: The central warehouse intelligent replenishment model is used to convert the central warehouse demand into the actual reasonable replenishment quantity, thereby triggering the mobilization plan and procurement plan to achieve intelligent replenishment of the central warehouse.

[0083] Model algorithm: Build a central warehouse intelligent replenishment model based on safety stock, sales forecast, supplier delivery time, supply network, and product-warehouse relationship.

[0084] The calculation formula for the central warehouse replenishment quantity is:

[0085]

[0086] Among them, T represents the current time; VLT represents the replenishment lead time, which is the number of days it takes for the supplier to deliver the goods to the warehouse after replenishment; BP represents the replenishment cycle, which is the number of days from the current time to the next available order time; BUFFER represents the safety stock buffer period, which is the minimum number of safety days used to cope with fluctuations in VLT and sales plans.

[0087] Safety stock refers to the safety stock margin set by regional planners to avoid stockouts; safety stock = average daily sales × (safety stock days + supply days).

[0088] Safety stock dynamics: Based on product characteristics and sales volume characteristics, the replenishment plan is output based on parameter configuration of different business scenarios, matching the RDC replenishment quantity formula to provide differentiated safety stock settings.

[0089] Examples of dynamic implementation scenarios: best-selling brands tend to set higher safety inventory days, while slow-selling brands tend to set lower safety inventory days to cope with market fluctuations, ensure timely supply, and avoid out-of-stock situations.

[0090] Table 1 Central warehouse intelligent replenishment_replenishment implementation scenario

[0091]

[0092]

[0093] Among them, MPQ (minimum package quantity), setting the recommended order quantity for each product based on the minimum package quantity is one of the replenishment methods.

[0094] MOQ is the minimum order quantity. Setting a recommended order quantity for each product is one of the replenishment methods.

[0095] MOQ / MPQ can have different correction values ​​for different regions to adapt to the cost and efficiency requirements of intelligent replenishment. For example, a lower MOQ / MPQ is conducive to collaborating with suppliers to reduce inventory costs and reduce capital occupation.

[0096] like Figure 5 The figure shows the technical process of intelligent replenishment in the central warehouse: first, record the relevant information to trigger replenishment, determine whether the day is a replenishment day, and if so, read the replenishment cycle / lead time / buffer period, update the safety stock operation, and update the replenishment demand; otherwise, update the replenishment demand, output the actual replenishment quantity, and then update the sales volume of the day, record the relevant information at the end, update the remaining inventory, and repeat the above operations every day.

[0097] The specific contents of the forward warehouse intelligent replenishment model are as follows:

[0098] Business scenario: Ensure that the inventory in the forward warehouse is reasonable and there is no shortage of goods for sales.

[0099] Execution process: Through the forward warehouse intelligent replenishment model, the width and depth of the forward warehouse demand are transformed, and the forward warehouse demand is converted into the actual reasonable replenishment quantity, and then the replenishment plan is mobilized to realize the forward warehouse intelligent replenishment and dynamic inventory optimization;

[0100] Model algorithm: A forward warehouse intelligent replenishment model based on safety inventory, sales forecast, and supplier delivery time.

[0101] The calculation formula for the forward warehouse replenishment quantity is:

[0102] FDC replenishment quantity = safety stock - current available stock - internal in-transit stock;

[0103] Safety stock = MAX (safety stock days × average daily sales, SKU single order transaction number);

[0104] like Figure 6 As shown, the stocking list model: that is, the stocking width range, is calculated by verifying factors such as category brand planning, product status, and sales characteristics.

[0105] Category brand planning includes region + category, region + brand and region + SKU (minimum stock keeping unit).

[0106] Product status includes sales status, purchase status, new and old products, and left and right pairing.

[0107] Sales features include:

[0108] Stocking list selection logic: Determine the reasonable sales time range X for each category brand based on the proportion. When the brand in this category is available in the past X days, it will be included in the stocking list.

[0109] Stocking list exclusion logic: Because the selection logic will circle category brands that have sales but low sales into the stocking list, a new exclusion logic is added to exclude category brands that are in the bottom 10% of sales.

[0110] Stocking list correction logic: re-enter products with low sales volume but high sales frequency, seasonal products, hot-selling season lists, special new products, etc. to ensure the width of stocking.

[0111] Safety stock model: that is, the depth of stocking. Safety stock is calculated after the SKU enters the stocking list. Safety stock is the minimum inventory set to meet daily sales demand between the current time and the next arrival date (not the order date).

[0112] Safety stock = MAX(N×X / 7, number of SKU single order transactions).

[0113] Safety stock correction model, such as based on the top sales volume, the safety stock is the inventory that meets N days of turnover.

[0114] Specific contents of the safety stock correction model:

[0115] An example of a specific correction scenario is to correct the safety stock by grading according to sales volume:

[0116] For the top 20% of SKUs, the safety stock is N=n. 1 Inventory with daily turnover;

[0117] For the top 35% of SKUs, the safety stock is N=n. 2 Inventory with daily turnover;

[0118] For the top 50% of SKUs, the safety stock is N=n. 3 Inventory with daily turnover;

[0119] For the top 70% of SKUs, the safety stock is N=n. 4 Inventory with daily turnover;

[0120] For the top 80% of SKUs, the safety stock is N=n. 5 Inventory with daily turnover;

[0121] For the top 85% of SKUs, the safety stock is N=n. 6 Inventory with daily turnover;

[0122] For the remaining SKUs, the safety stock is the inventory that meets N = n days of turnover, thus obtaining the safety stock.

[0123] like Figure 7 As shown, this is how to calculate the stocking depth (FDC safety stock):

[0124] First, we introduce the promotional order exclusion logic (with the help of normal distribution theory): among the valid sales documents with successful transactions, we exclude promotional orders from two perspectives: abnormal orders in the SKU dimension and abnormal orders in the monthly dimension.

[0125] Secondly, monthly and weekly sales forecasts are made considering different categories and replenishment levels to obtain the average daily sales forecast value X of the minimum replenishment unit.

[0126] Finally, we verify again and use the seasonal product verification logic (using the basic information of the category to identify whether it is a seasonal product) to identify seasonal products such as detergents, antifreeze, and refrigerants. Combined with historical sales and business personnel, we confirm the minimum replenishment unit safety inventory days N.

[0127] like Figure 8 As shown, the promotional order exclusion logic: use the normal distribution principle to identify multi-dimensional promotional orders and correct them during replenishment.

[0128] First, all order data are divided into 360-day order data (SKU dimension promotion orders) and 180-day order data (monthly dimension promotion orders); from the 180-day order data, find out the orders outside σ=2.0, and there is at least 1 order left every month.

[0129] Judge whether the number of orders in the 360-day order data is less than N. If so, compare the order data with the corresponding filter value of the category. If the result is otherwise, find out the orders outside σ=1.8, and then compare the order data with the corresponding filter value of the category. If the result of the comparison between the order data and the corresponding filter value of the category is less than, no filtering is performed. Otherwise, the result is greater than, then it is aggregated with the result of the promotional order in the monthly dimension to obtain the aggregated promotional order.

[0130] like Fig. 9 As shown in the figure, the replenishment implementation scenario: according to the configuration replenishment node, submission method, etc., the actual replenishment quantity is calculated, specifically:

[0131] 1. At the forecast time point: replenishment quantity = max (weekly forecast, safety stock) - in stock - in transit;

[0132] Get the weekly sales data of the minimum replenishment unit in the historical time period. The weekly sales data needs to take into account that different categories have different time periods.

[0133] 2. Non-forecast time point: Replenishment quantity = safety stock - in stock - in transit

[0134] like Figure 10-11 As shown in the figure, the specific contents of the customer intelligent replenishment model are:

[0135] Business scenario: When stocking up to customers such as car maintenance shops and repair shops, we need to not only focus on the timeliness of meeting the needs of car owners, but also consider the limited inventory carrying capacity of customers themselves. Therefore, on the basis of improving demand satisfaction, the selection and depth of SKUs are more focused and precise.

[0136] Execution process: The product selection pool is circled through the customer selection model, and then converted into the customer's actual reasonable replenishment quantity through the customer replenishment model. Combined with the customer's own needs, intelligent customer replenishment is achieved.

[0137] Model algorithm: Customer intelligent replenishment model based on safety stock, sales forecast, and supplier delivery time.

[0138] Customer intelligent replenishment model:

[0139] The formula for calculating customer replenishment quantity is:

[0140] Customer replenishment quantity = safety stock - current available stock - internal stock in transit;

[0141] Safety stock = MIN(MAX(round up (safety stock days × average daily sales), single vehicle usage), safety stock days × MOQ).

[0142] Smart product selection model: For repair shops and car maintenance shops that have direct contact with car owners, it is crucial to select a small number of "most needed" products as spot goods. The product selection here should not only consider historical sales, but also cover the models entering the store as much as possible to provide more sales space. Therefore, taking the model adaptation relationship as the starting point, combined with the brand-level strategic product planning (GBB, distinguishing between Good, Better, and Best), regional characteristics, sales characteristics, etc., the customer stock selection pool is circled.

[0143] like Fig.10 As shown, including the following:

[0144] S21: Vehicle SKU pool, including:

[0145] 1. Vehicle VIN with a record of entering the store;

[0146] 2. Analyze vehicle models and mark vehicle prices;

[0147] 3. Adapt to all product codes.

[0148] S22: Category brand SKU pool, including:

[0149] 1. Region + category + car price determine the brand and priority;

[0150] 2. Determine the unique SKU based on the category details, including the recommended priority and product selection rules for the category brand level strategy;

[0151] An example of a strategy recommendation priority scenario: For the oil filter category, select Brand A, Brand B, Brand C, Brand D in order of priority; for the brake pad category, select Brand E, Brand A, Brand F, and so on in order of priority.

[0152] Table 2 Customer Intelligent Replenishment_Category Brand Priority Strategy

[0153]

[0154]

[0155] Example of product selection rule scenario: After the strategy priority recommendation, refine the product selection rules and further select the product range, such as giving priority to new coded products, adding left and right pairing, filtering products with abnormal status, etc.

[0156] 3. Filter the vehicle model SKU pool according to 1 and 2.

[0157] S23: Precision stocking pool, including:

[0158] 1. The VIN numbers of SKUs in the same category are arranged in descending order;

[0159] 2. Circle the SKUs that meet the target VIN coverage ratio;

[0160] 3. Level 3 stocking matches target VIN coverage.

[0161] S24: Intelligent product selection pool, including:

[0162] 1. Prepare a stock list carefully;

[0163] 2. General stocking list, using stocking logic.

[0164] S25: Prepare the stock.

[0165] Safety stock model: further combine category and regional characteristics in the product selection pool to perform width screening and depth calculation.

[0166] Breadth screening: Screen the target category brands according to the category brand strategy, see Table 3; determine whether to enter the stocking range based on whether the sales coverage reaches the threshold (such as 70%), see Table 4.

[0167] In-depth calculation: safety stock = MIN (MAX (round up (safety stock days × average daily sales), single vehicle usage), safety stock days × MOQ), see Table 5.

[0168] Table 3 Customer intelligent replenishment_Model step 1_Product brand level labeling

[0169]

[0170]

[0171] Table 4 Customer intelligent replenishment_Model step 2_Stocking width (whether to stock) calculation

[0172]

[0173] Table 5 Customer Intelligent Replenishment_Model Step 3_Safety Stock Calculation

[0174]

[0175] S3: The planned replenishment platform receives the replenishment calculation results from the planning collaboration platform, converts them into actual replenishment strategies, and finally generates a replenishment execution order;

[0176] S4: Carry out replenishment according to the replenishment execution order.

[0177] The full-link intelligent replenishment system for the automotive aftermarket provided in this embodiment includes an integrated intelligent replenishment platform, a central warehouse intelligent replenishment model, a forward warehouse intelligent replenishment model and a customer intelligent replenishment model.

[0178] The full-link intelligent replenishment system for the automotive aftermarket takes into account the replenishment scenarios of each supply chain entity in an integrated manner, aiming to solve the problem of demand satisfaction in each link of the supply chain, effectively improve supply efficiency, and enable inventory management.

[0179] like Figure 1 As shown in the figure, the system defines three intelligent replenishment models and algorithms and an integrated intelligent replenishment platform to solve the core supply problems respectively, as follows:

[0180] Central warehouse intelligent replenishment model and algorithm: Solve the central warehouse replenishment problem, what to replenish? How much to replenish? When to replenish?

[0181] Intelligent replenishment model and algorithm for forward warehouse: Solve the replenishment problem of forward warehouse, what to replenish? How much to replenish? When to replenish?

[0182] Customer intelligent replenishment model and algorithm: Solve customer replenishment problems, what to replenish? How much to replenish? When to replenish?

[0183] Integrated intelligent replenishment platform: Solve the replenishment link linkage problem, how to rely on each other and respond quickly?

[0184] like Figure 2 As shown, the integrated intelligent replenishment platform includes a demand subject module, a planning collaboration platform, a planned replenishment platform, a replenishment execution module, and network supply strategies at all levels.

[0185] Demand subject module: including forward warehouses, wholesale business, self-operated flagship stores, self-operated franchisees and car maintenance shops.

[0186] Planning collaboration platform: Receives the needs of business entities, including supply needs of supply networks at all levels and sales forecasts of business entities at all levels; conducts data mining and transformation through the big data computing center to generate intelligent replenishment models, providing a computing basis for replenishment implementation.

[0187] Planned replenishment platform: receives the replenishment calculation results from the planning collaboration platform, converts them into actual replenishment strategies, and finally generates replenishment execution orders. Specifically, it includes six sub-platform modules: replenishment business center, replenishment execution center, replenishment sourcing center, replenishment order collection center, replenishment strategy center, and replenishment monitoring center.

[0188] Among them, the replenishment business center configures the basic data required for replenishment, such as the supply network, supply cycle, product-warehouse relationship, etc.

[0189] The replenishment execution center calculates the actual replenishment quantity based on demand and the configured replenishment strategy.

[0190] The replenishment sourcing center will seek suppliers after the replenishment execution center calculates the actual replenishment quantity.

[0191] The replenishment collection center collects orders according to the configured collection strategy and generates a planned order.

[0192] The replenishment strategy center uniformly deploys all strategies of the replenishment platform for different scenarios, different plans, and different replenishment tasks.

[0193] The replenishment monitoring center monitors abnormal alarms of the replenishment platform and provides monitoring capabilities to ensure the stable operation of the replenishment platform.

[0194] Replenishment execution module: includes external procurement execution and internal procurement execution; external procurement execution includes purchase order number, and internal procurement execution includes replenishment order number and withdrawal order number.

[0195] Network supply strategies at all levels: including the supplier’s first fulfillment point and the first warehouse supply point.

[0196] This embodiment adopts a full-link replenishment linkage mechanism, the specific contents are as follows:

[0197] Linkage strategy: Ensure the effectiveness of replenishment by supporting the interaction and linkage of the replenishment chain through demand aggregation and algorithm dependence, replenishment effect inspection, and product ABC labeling.

[0198] Demand aggregation, association and linkage of replenishment model algorithms minimize redundant replenishment.

[0199] like Fig.11 As shown, in the demand pool of a certain repair shop, the demands for automatic replenishment of customers, automatic replenishment of forward warehouses and automatic replenishment of central warehouses are aggregated: the customer (repair shop) demands fed back by online flagship stores and car maintenance APPs are collected through a certain smart store management system, and customer (repair shop) demands are obtained through other channels, and these demands are aggregated to the central warehouse, forward warehouse and customers as a reference for automatic replenishment to meet the demands at the corresponding level; the forward warehouse demands are collected, and these demands are aggregated to the central warehouse and forward warehouse as a reference for automatic replenishment to meet the demands at the corresponding level; the central warehouse demands are collected, and these demands are aggregated to the central warehouse and forward warehouse as a reference for automatic replenishment to meet the demands at the corresponding level.

[0200] Product ABC labeling: Label products according to transaction characteristics, achieve quantitative stratification of best-selling and long-tail products, and guide differentiated tiered stocking.

[0201] like Fig.12 As shown in the figure, based on transaction frequency, number of transaction customers, sales amount, sales volume and seasonal characteristics, SKU stratification is carried out, and A: hot-selling products with better sales flow conversion than average, B: products with higher sales flow conversion than average and generally on the shelf for more than one year, C: products with lower sales flow conversion than average and generally on the shelf for more than one year, and D: products with lower sales flow conversion than average and long-tail attributes. And hierarchical stocking is carried out for customer stocking, forward warehouse stocking and central warehouse stocking.

[0202] SKUs are tiered based on a calculated composite score:

[0203] Comprehensive score = sales frequency score*0.4+sales quantity score*0.3+sales amount*0.3;

[0204] Scoring and stratification labels:

[0205] A-TOP products: 90-100, referring to the essential stock for customers / forward warehouses / central warehouses that sell products every day;

[0206] B-Intermediate products: 60-90, indicating that the forward warehouse / central warehouse with good sales volume should prepare appropriate amount of goods;

[0207] B- Slow-moving products: 60-90, indicating that the central warehouse with poor sales should prepare a proper amount of stock;

[0208] C-Long-tail products: 0~60, indicating that long-tail products need to be prepared in the central warehouse.

[0209] Among them, customer inventory includes A: hot-selling products with better sales flow conversion than the average; forward warehouse inventory includes A: hot-selling products with better sales flow conversion than the average and B: products with higher sales flow conversion than the average and generally on the shelves for more than one year; central warehouse inventory includes A: hot-selling products with better sales flow conversion than the average, B: products with higher sales flow conversion than the average and generally on the shelves for more than one year, C: products with lower sales flow conversion than the average and generally on the shelves for more than one year and D: long-tail products with poor sales flow conversion than the average.

[0210] Replenishment effect verification: Evaluate the replenishment model through data indicators, such as general adoption rate and perfect adoption rate, smart replenishment coverage rate and replenishment order fulfillment rate, and reverse model tuning.

[0211] General adoption rate and perfect adoption rate: Assuming the number of smart replenishment order lines is x, the number of confirmed document lines is y, and the number of confirmed document lines with the same order quantity as the confirmed line number is z, then the general adoption rate is: y / x×100%, and the perfect adoption rate is: z / x×100%. It can be understood that general adoption refers to adopting what to replenish, and perfect adoption refers to adopting what to replenish and how much to replenish.

[0212] Smart replenishment coverage rate: Assuming the number of FDCs is x (effective forward warehouses), the number of stores enabled for smart replenishment is y, and the number of stores that have submitted weekly smart replenishment orders is z, then the weekly activity is z, the weekly activity rate is z / y*100%, and the smart replenishment coverage rate is y / x*100%.

[0213] Replenishment order fulfillment rate: Assuming the order quantity of the smart replenishment order is x and the confirmed quantity of the smart replenishment order is y, the replenishment order fulfillment rate is: z / x*100%.

[0214] A computer-readable storage medium is used to store a program, which, when executed, implements the steps of the above-mentioned full-link intelligent replenishment method for the automotive aftermarket.

[0215] The full-link intelligent replenishment system for the automotive aftermarket of the present invention is standardized, integrating the non-standard data such as the large variety of commodities, discrete demand, and basic commodity information in the auto parts industry, and using the model to calculate the types and quantities of automatic replenishment. Automation, the replenishment model and the replenishment system act as software and hardware respectively to realize the automatic calculation and execution of replenishment. Integration realizes the linkage of various links in the supply chain to ensure reasonable and stable replenishment.

[0216] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A full-link intelligent replenishment method for the automotive aftermarket, characterized by: The following steps are involved: S1: Get the demand subject; S2: For each demand entity, through the planning collaboration platform, the big data computing center is used for data mining and transformation, and in conjunction with the network supply strategies at all levels, the central warehouse intelligent replenishment model, the forward warehouse intelligent replenishment model and the customer intelligent replenishment model are respectively constructed; S3: Use the planned replenishment platform to receive the replenishment calculation results of the planning collaboration platform, convert them into actual replenishment strategies, and finally generate replenishment execution orders; S4: Carry out replenishment according to the replenishment execution order.

2. The full-link intelligent replenishment method for the automotive aftermarket according to claim 1 is characterized by: The main demand entities in S1 include forward warehouses, wholesale businesses, self-operated flagship stores, self-operated franchisees and car maintenance shops.

3. The full-link intelligent replenishment method for the automotive aftermarket according to claim 1 is characterized in that: The specific content of the central warehouse intelligent replenishment model in S2 is: The central warehouse intelligent replenishment model: converts the central warehouse demand into the actual reasonable replenishment quantity, thereby triggering the mobilization plan and procurement plan to achieve intelligent replenishment of the central warehouse; The calculation formula for the central warehouse replenishment quantity is: Among them, T represents the current time; VLT represents the replenishment lead time, which is the number of days it takes for the supplier to deliver the replenishment to the warehouse; BP represents the replenishment cycle, which is the number of days from the current time to the next available order time; BUFFER represents the safety stock buffer period, which is the minimum number of safety days used to cope with fluctuations in VLT and sales plans; Safety stock represents the safety stock margin set by regional planning to manually avoid stockouts.

4. The full-link intelligent replenishment method for the automotive aftermarket according to claim 1 is characterized in that: The specific content of the forward warehouse intelligent replenishment model in S2 is: The forward warehouse intelligent replenishment model: through the forward warehouse intelligent replenishment model, the width and depth of the forward warehouse demand are transformed, and the forward warehouse demand is converted into an actual reasonable replenishment quantity, thereby mobilizing the replenishment plan to achieve intelligent replenishment of the forward warehouse and dynamic optimization of inventory; Build a forward warehouse intelligent replenishment model based on safety inventory, sales forecast, and supplier delivery time, including: Stocking list model: by verifying category brand planning, product status and sales characteristics; Category brand planning includes region + category, region + brand and region + SKU; Product status includes sales status, purchase status, new and old products, and left and right pairing; Sales features include: Stocking list selection logic: Determine the reasonable sales time range X for each category brand based on the proportion. If the brand in this category is available in the past X days, it will be included in the stocking list; Stocking list exclusion logic: Because the stocking list selection logic circles brands of categories that have sales but low sales into the stocking list, a new exclusion logic is added to exclude brands of categories that are in the bottom 10% of sales; Stocking list correction logic: re-enter products with low sales volume but high sales frequency, seasonal products, hot-selling season lists, and special new products to ensure the width of stocking.

5. The full-link intelligent replenishment method for the automotive aftermarket according to claim 4 is characterized in that: Safety stock model: After the SKU enters the stocking list, the safety stock is calculated. The safety stock is the minimum inventory that can meet the daily sales demand between the current time and the next arrival date; Safety stock = MAX(N×X / 7, number of SKU single order transactions); Among them, X is the second weekly sales value of the top product in the historical period, N is the number of days of SKU safety inventory, and SKU is the minimum inventory unit; Safety stock correction model: Based on the top sales percentage, the safety stock is the inventory that can meet N days of turnover; Promotional order exclusion logic: Use the normal distribution principle to identify multi-dimensional promotional orders and correct them during replenishment; Replenishment implementation scenario: Calculate the actual replenishment quantity according to the configured replenishment node and submission method.

6. The full-link intelligent replenishment method for the automotive aftermarket according to claim 1 is characterized in that: The specific content of the customer intelligent replenishment model in S2 is: The customer intelligent replenishment model: the product pool is circled through the intelligent product selection model, and then converted into the actual reasonable replenishment quantity for the customer through the safety inventory model, and the customer's own needs are combined to realize the customer's intelligent replenishment; Intelligent product selection model: Starting from the vehicle model adaptation relationship, combining brand-level strategic product planning, regional characteristics, and sales characteristics to circle out the customer stock selection pool; Safety stock model: further combine category and regional characteristics in the product selection pool to perform width screening and depth calculation.

7. An automotive aftermarket full-link intelligent replenishment system, using the automotive aftermarket full-link intelligent replenishment method according to any one of claims 1 to 6, characterized in that: It includes an integrated intelligent replenishment platform, which includes a demand subject module, a planning collaboration platform, a planned replenishment platform, a replenishment execution module and network supply strategies at all levels; The demand subject module includes forward warehouses, wholesale business, self-operated flagship stores, self-operated franchisees and car maintenance shops; The planning collaboration platform: receives demand entities, conducts data mining and transformation through the big data computing center, generates an intelligent replenishment model, and provides a computing basis for replenishment implementation; Planned replenishment platform: receives the replenishment calculation results from the planning collaboration platform, converts them into actual replenishment strategies, and finally generates replenishment execution orders; The replenishment execution module: the replenishment execution order triggers the replenishment, which is carried out by the supplier fulfilling the central warehouse replenishment document, the central warehouse fulfilling the forward warehouse replenishment document, and the forward warehouse fulfilling the customer replenishment document; The network supply strategies at all levels described above: consider both supply and demand sides and product characteristics, evaluate the width and depth of replenishment, and ensure that supply at all levels achieves a dynamic balance between demand satisfaction and reasonable inventory.

8. The full-link intelligent replenishment system for the automotive aftermarket according to claim 7 is characterized by: The planned replenishment platform includes six sub-platform modules: replenishment business center, replenishment execution center, replenishment sourcing center, replenishment order collection center, replenishment strategy center and replenishment monitoring center; Replenishment business center: configure the basic data required for replenishment; Replenishment Execution Center: Calculates actual replenishment quantity based on demand and configured replenishment strategy; Replenishment sourcing center: After the replenishment execution center calculates the actual replenishment quantity, it searches for suppliers; Replenishment order collection center: collects orders according to the configured order collection strategy and generates a plan order; Replenishment Strategy Center: All strategies of the replenishment platform are uniformly deployed for different scenarios, different plans, and different replenishment tasks; Replenishment Monitoring Center: monitors abnormal alarms of the replenishment platform and provides monitoring capabilities to ensure the stable operation of the replenishment platform.

9. The full-link intelligent replenishment system for the automotive aftermarket according to claim 8 is characterized by: Adopt a full-link replenishment linkage strategy: through demand aggregation and algorithm dependence, replenishment effect inspection, and commodity ABC label support, the interaction and linkage of the replenishment link ensures the effectiveness of replenishment; Demand aggregation, association and linkage of replenishment model algorithms to minimize redundant replenishment; Product ABC labeling: Label products according to transaction characteristics, realize quantitative stratification of best-selling and long-tail products, and guide differentiated tiered stocking; Replenishment effect test: Evaluate the replenishment model through data indicators, including general adoption rate and perfect adoption rate, and reversely optimize the model.

10. A computer-readable storage medium for storing a program, characterized in that: When the program is executed, the steps of the full-link intelligent replenishment method for the automotive aftermarket described in any one of claims 1 to 6 are implemented.

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