Multi-module dynamic cooperative stock demand calculation method

Abstract

The invention discloses a multi-module dynamic cooperative stock demand calculation method, which comprises the following steps that: a data acquisition module acquires sales volume prediction, current inventory and future arrival plan data, and generates a net demand sequence of multiple weeks; the parameter dynamic configuration module adjusts key parameters for stock demand calculation, and key parameter changes are synchronized to the stock demand collaborative calculation module; the stocking demand collaborative calculation module receives the net demand sequence and the key parameters, and performs the following steps: aligning the effective period of each transportation mode with the net demand sequence; determining a priority sequence of the transportation modes based on a transportation priority dynamic rule, distributing a demand interval for each transportation mode by adopting an interval locking algorithm, and calculating a stock quantity; calculating a demand gap rate based on the total stock quantity and the net demand of the corresponding week, and triggering reverse adjustment when the demand gap rate exceeds a set threshold value; and synchronizing a calculation result to a service visualization system. According to the invention, the automatic and precise calculation of the stock demand is realized.

Description

Technical Field

[0001] This invention relates to the field of material requirements planning (MRP) technology, and in particular to a multi-module dynamic collaborative method for calculating inventory requirements. Background Technology

[0002] In MRP (Material Requirements Planning) inventory management practices, enterprises need to simultaneously handle multi-dimensional data such as sales forecasts, inventory levels, and transportation plans. However, existing technologies have the following core shortcomings: 1. Fragmented Module Data and Severe Time-Sequence Misalignment: In the existing solution, functional modules such as demand calculation, transportation timeliness configuration, and safety stock management operate independently, requiring manual transcription and transmission of data. For example, the demand calculation module outputs "total monthly demand," while the transportation module requires "weekly transportation plan." During manual conversion, time-sequence misalignment is prone to occur (such as misassigning "week 3 demand" to "week 4 transportation"), resulting in inventory preparation being too early or too late. According to industry statistics, such error rates are as high as 18%-25%.

[0003] 2. Key parameters are statically fixed and have poor adaptability: Parameters such as transportation timeliness (first-leg L / T) and safety stock are mostly stored as fixed values ​​and cannot be dynamically adjusted according to actual scenarios. For example, the transportation timeliness of large items is used as that of standard items (which actually requires an additional 2-3 weeks), or the safety stock is not expanded with the seasonal demand peak, resulting in a stockout rate of more than 30% during peak seasons and an increase of 20% in inventory backlog costs during off-seasons.

[0004] 3. Disconnect between transportation priorities and demand allocation: Transportation mode priorities are only statically ranked (e.g., "air freight > fast ship > slow ship") and are not linked to demand cycles. High-cost transportation modes (such as air freight) are often used to cover long-term demand (accounting for over 40%), resulting in inflated transportation costs; while low-cost transportation modes (such as slow ships) are used for short-term demand, causing a shortage risk of over 35%.

[0005] 4. Lack of end-to-end collaboration and delayed response: The entire process, from "demand update → parameter adjustment → inventory recalculation," requires manual intervention, with a response cycle of 24-48 hours. In scenarios with drastic demand fluctuations, such as peak seasons for cross-border e-commerce, delayed inventory planning can lead to missed replenishment windows. According to research, such losses account for 5%-8% of a company's annual revenue.

[0006] The essence of the above problems is the lack of a dynamic collaboration mechanism between modules. Existing technologies can only optimize a single function point and cannot form a closed-loop linkage of "data-parameter-decision". It is urgent to break through this limitation by deep coupling of multiple modules. Summary of the Invention

[0007] In view of this, the purpose of this invention is to propose a multi-module dynamic collaborative inventory demand calculation method, which can be applied to scenarios such as cross-border e-commerce and manufacturing that require comprehensive handling of the complex relationship between "demand fluctuations, transportation timeliness, inventory buffering, and cost optimization". Through a multi-module dynamic collaborative mechanism, the method realizes the automated and accurate calculation of inventory demand, solving the problems of module isolation, response lag, and imbalance between cost and timeliness in traditional solutions.

[0008] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows: This invention provides a multi-module dynamic collaborative inventory demand calculation method. It achieves real-time data transmission and logical linkage between the data acquisition module, the parameter dynamic configuration module, and the inventory demand collaborative calculation module via a data bus. The method includes the following steps: Step 1: The data acquisition module automatically collects weekly sales forecast data, current inventory data, and future delivery plan data. Based on the collected data, it generates a net demand sequence for multiple future weeks with a unified time dimension of one week. When inventory fluctuations exceed a set threshold, the net demand sequence is recalculated. Step 2: The parameter dynamic configuration module configures and dynamically adjusts the key parameters used for inventory preparation demand calculation, including the first-leg transportation timeliness matrix, safety stock, and dynamic transportation priority rules. All changes to key parameters are synchronized to the inventory preparation demand collaborative calculation module in real time through the data bus. Step 3: The inventory preparation demand collaborative calculation module receives the net demand sequence and key parameters, and executes the following three-layer collaborative logic to calculate the inventory preparation demand: Step 31: Align the effective cycle of each mode of transport with the net demand sequence; Step 32: Determine the priority order of transportation modes based on the aforementioned transportation priority dynamic rules, and use the interval locking algorithm to allocate demand intervals to each transportation mode according to the priority order and calculate the stock preparation quantity based on the net demand of the allocated demand intervals. Step 33: Based on the calculated total inventory and the net demand for the corresponding week, calculate the demand gap rate for each week. When the demand gap rate exceeds the set threshold, trigger a temporary reverse adjustment of the key parameters. Step 4: The inventory demand collaborative calculation module synchronizes the calculation results to the business visualization system via the data bus.

[0009] Furthermore, in step 1, the data acquisition module automatically collects weekly sales forecast data, current inventory data, and future delivery plan data. Based on the collected data, it generates a net demand sequence for multiple future weeks with a unified time dimension of one week. Specifically, this includes: Step 11: The data acquisition module automatically collects sales forecast data, current inventory data, and future delivery plan data every week; a1) The sales forecast data is generated using a mechanism that combines a fusion algorithm model with manual correction; The fusion algorithm model combines a time series model and a machine learning model. The time series model is used to capture sales trends and periods, while the machine learning model is used to fit nonlinear factors. The outputs of the two models are weighted according to a preset weight ratio to obtain an initial predicted value. It provides a visual interface that allows business personnel to modify the initial forecast based on market experience. The revised initial forecast automatically overwrites the original initial forecast and saves the revision record. a2) The current inventory data includes in-stock inventory synchronized in real time from the warehouse management system and in-transit inventory synchronized from the transportation management system with an estimated arrival time. a3) The future delivery plan data includes the expected delivery data of purchase orders that have been placed, collected from the procurement management system; Step 12: Set a unified time base, define the start and end times of each week through the weekly boundary calibration algorithm, and attach a standardized week timestamp to all collected data; Step 13, according to the formula: D n = P n - (I0+ΣT k ) +ΣA m Calculate the net demand D for the next nth week. n Among them, P n Let I0 be the sales forecast demand for week n, and ΣT be the current available inventory. k ΣA represents the total in-transit inventory with an estimated arrival time no later than week n, where k is the batch number of the in-transit inventory. m This is the total allocated inventory that has been locked by sales orders but has not yet been shipped, and is expected to be held for no later than week n, where m is the allocation record number; Step 14: Synchronize the calculated net demand sequence to the inventory demand collaborative calculation module in real time via the data bus.

[0010] Furthermore, in step 1, triggering a recalculation of the net demand sequence when inventory fluctuations exceed a set threshold specifically includes: The data acquisition module is connected to the inventory management system through the application programming interface of the inventory management system, and the fluctuation threshold of the inventory fluctuation range is set. The inventory management system calculates the fluctuation range of each inventory item in real time. The formula for calculating the fluctuation range is: Fluctuation Range = |Current Inventory - Last Collected Inventory| / Last Collected Inventory × 100%; When the fluctuation range of a certain inventory item is detected to exceed the fluctuation threshold in real time, the data acquisition module is automatically triggered to execute the formula: Dn = P n - (I0+ΣT k ) +ΣA m Recalculate all relevant net requirements items; The recalculated net demand sequence is synchronized to the inventory demand collaborative calculation module via the data bus.

[0011] Furthermore, in step 2, the first-leg transportation timeliness matrix is ​​used to dynamically correct the deviation rate between the actual timeliness and the basic timeliness value, specifically including: Step 211: The first-leg transportation timeliness matrix is ​​configured with basic timeliness T according to four dimensions: destination warehouse, transportation mode, cargo specifications and origin. The configuration method supports manual input or automatic synchronization through the logistics provider's system interface. Step 212: Record the historical transportation time within the past set period under each of the four-dimensional combinations, and calculate the average value as the actual time. Step 213: Compare the actual delivery time with the baseline delivery time value weekly, and calculate the deviation rate. Deviation rate = |Actual delivery time - Baseline delivery time T| / Baseline delivery time T × 100%; Step 214: When the deviation rate continuously reaches the set number of weeks and exceeds the set proportion, the base time T of the four-dimensional combination is automatically updated to the corrected base time T'. The corrected base time T' = base time T + (actual time - base time T) × K, where K is the smoothing coefficient configured according to user needs.

[0012] Furthermore, in step 2, the safety stock is used to adjust for the volatility of the net demand sequence, specifically including: Step 221: Safety stock supports two-level configuration at the SKU level and its category level. The SKU level configuration has higher priority than the category level. When both the SKU and category levels exist and their values ​​are different, the SKU level configuration takes precedence. When no SKU level is configured, the highest-level category level of its category is automatically matched. Step 222: Calculate the demand volatility between consecutive weeks based on the net demand sequence. The volatility of week n = |D n - D n-1 | / D n-1 ×100%, where D n For the net demand in week n, D n-1 This represents the net demand for week n-1; for the volatility of week 1, the average volatility of the same period in history is used instead. Step 223: Set the volatility trigger threshold and the consecutive trigger count threshold; maintain an independent counter, and when the weekly volatility of an inventory item is detected to exceed the trigger threshold for the first time, the counter starts to accumulate; Step 224: If the number of times the volatility indicated by the counter exceeds the trigger threshold reaches the consecutive trigger number threshold, the safety stock S of the inventory item is automatically adjusted to S' = S×α, where S' is the adjusted safety stock and α is the adjustment coefficient configured according to user needs. Step 225: After the adjustment is completed, the counter will automatically reset.

[0013] Furthermore, the dynamic transportation priority rule in step 2 is used to link with the net demand sequence, specifically including: Step 231: Provide at least two basic templates: a cost-priority template and a time-priority template; The default transportation methods for the cost-priority template are ordered as follows: slow ship, fast ship, and air freight, with a built-in cost threshold constraint. The default transportation methods for the time-priority template are ordered as follows: air freight, fast ship, and slow ship, with a built-in cost upper limit constraint. Step 232: Support users in setting custom trigger conditions based on one or more of the following: net demand level, time node, and cargo attributes; Step 233: The core parameters in the custom triggering condition, including the set net demand and seasonal time nodes, are directly obtained from the net demand sequence or its derived data; when the net demand sequence is updated, causing the custom triggering condition to be met, the priority of the transportation mode corresponding to the basic template used is automatically switched.

[0014] Furthermore, step 31 specifically includes: By matching week timestamps, the basic timeliness and safety stock are respectively bound to each week of the net demand sequence, and the effective period for each mode of transportation to cover future demand is calculated. The starting point of the effective period is the current week plus the sum of the corresponding basic timeliness and safety stock; the ending point of the effective period is the last week covered by the net demand sequence. The effective period is calculated as follows: for any mode of transportation, the period covering future demand from the start to the end of the effective period is taken as the effective period.

[0015] Furthermore, step 32 specifically includes: Step 321: Sort all available transportation modes from high to low according to the currently effective transportation priority dynamic rules; Step 322: Calculate the demand range for each transportation mode using an interval locking algorithm, prioritizing them from highest to lowest priority. b1) For the highest priority mode of transport, its demand range is from the start of its own effective period to the end of its effective period; b2) For any transportation mode that is not the highest priority, set an emergency demand period, calculate the effective period for each transportation mode to cover future demand, and determine whether the effective period is partially or entirely within the emergency demand period. If not, its demand interval is from the start point to the end point of its own effective period. If yes, the start point of its demand interval is the start point of its own effective period, and the end point of its demand interval is the start point of the effective period of the transportation mode with higher priority minus one week. If the calculated start point of the demand interval is greater than the end point of the demand interval, then the transportation mode has no demand interval to allocate in this instance. Step 323: Each mode of transport allocates only the net demand of the corresponding week in the net demand sequence as its inventory quantity within its demand range; Formula for calculating inventory quantity: Inventory quantity E for a certain mode of transportation in week n n =D_{starting point of effective period + n-1}; Step 324: When the first-leg transportation timeliness or safety stock of any transportation mode is dynamically adjusted, resulting in a change in its effective cycle, steps 321 to 323 are automatically re-executed to recalculate and update the allocable demand range and stock quantity for all relevant transportation modes.

[0016] Furthermore, step 33 specifically includes: Step 331, Define the demand gap rate for week n = (D n -ΣE n ) / D n ×100%, where D n For the net demand in week n, ΣE n This represents the total amount of stock prepared for all modes of transport in week n. Step 332: Automatically perform a full-cycle demand gap ratio check at set intervals; when the net demand sequence or key parameters are updated, immediately trigger a full-cycle demand gap ratio check. Step 333: If the verification finds that the demand gap rate in any future week exceeds a set threshold, a reverse adjustment strategy is executed in a preset order. The reverse adjustment strategy includes: c1) Prioritize temporarily expanding the effective coverage of high-priority transportation modes in specific scenarios: Select transportation modes with sufficient current capacity and an effective cycle start time ≤ 5 weeks from the gap cycle start time, and temporarily advance the start time of their effective cycle by 1-2 weeks; c2) If the demand gap rate still does not meet the target after executing step c1, the basic timeliness of the scenario-based low-priority transportation method will be temporarily shortened: select the transportation method with the lowest absolute priority, temporarily reduce its basic timeliness value by the predetermined percentage, and advance the start of its effective period by 1-2 weeks. c3) If the demand gap rate exceeds the limit threshold, the priority of air transport will be temporarily increased; Step 334: The parameter changes generated after executing the reverse adjustment strategy are only used for the current round of inventory demand calculation.

[0017] Furthermore, step 4 specifically includes: Step 41: After completing the calculation, the inventory demand collaborative calculation module will synchronize the calculation results, including demand gap rate analysis and adjustment suggestions, to the business visualization system for display via the data bus. Step 42: Control the response cycle from data update to calculation result output within the set time.

[0018] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: 1. It solves the problem of time sequence misalignment caused by fragmented module data. Through timestamp alignment mechanism and dynamic verification, the demand gap rate is reduced, and the time dimension of demand, timeliness and inventory are unified, improving the accuracy of inventory preparation. It completely solves the problem of time sequence misalignment caused by manual transcription, and the effect is particularly significant in the multi-warehouse inventory preparation scenario of cross-border e-commerce. 2. Solve the problem of static and fixed key parameters, and build dynamic adjustment rules for first-leg transportation timeliness matrix, safety stock, and transportation priority. It supports automatic switching between peak and off-peak seasons, standard / large items and other scenarios. It can adapt to the needs of different industries such as cross-border e-commerce, 3C manufacturing, and fresh food cold chain without secondary development, and the scenario adaptability is significantly enhanced. 3. To address the disconnect between transportation priorities and demand allocation, establish a linkage mechanism between priorities and demand cycles, optimize the allocation of transportation resources, reduce the proportion of high-cost transportation methods used, and use them only to cover short-term emergency needs, thereby reducing the company's annual transportation costs. 4. To address the lack of end-to-end collaboration, an automatic triggering and data synchronization mechanism between modules is designed to shorten the response cycle, improve response speed, and reduce the risk of stockouts during peak seasons. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is an execution flowchart of a multi-module dynamic collaborative inventory demand calculation method provided in an embodiment of the present invention.

[0021] Figure 2This is a logical architecture diagram of the data acquisition module, parameter dynamic configuration module, and inventory demand collaborative calculation module provided in this embodiment of the invention. Detailed Implementation

[0022] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention adopts a "collaborative hub + distributed modules + data bus" architecture, with three core modules: a data acquisition module, a dynamic parameter configuration module, and a collaborative calculation module for inventory requirements. Cross-module data transfer and logical linkage are achieved through a real-time data bus. The architecture logic is as follows: Figure 2 As shown.

[0024] Please see Figure 1 The present invention discloses a multi-module dynamic collaborative inventory demand calculation method, which realizes real-time data transmission and logical linkage between the data acquisition module, the parameter dynamic configuration module, and the inventory demand collaborative calculation module through a data bus. The method includes the following steps: Step 1: The data acquisition module automatically collects weekly sales forecast data, current inventory data, and future delivery plan data. Based on the collected data, it generates a net demand sequence for multiple future weeks with a unified time dimension of one week. When inventory fluctuations exceed a set threshold, the net demand sequence is recalculated. As a dynamic data source, the data acquisition module breaks through the traditional static acquisition mode, generates a weekly net demand sequence with consistent time sequence, and captures inventory fluctuations in real time, providing an accurate data source for collaborative computing.

[0025] In this embodiment, the data acquisition module in step 1 automatically collects weekly sales forecast data, current inventory data, and future delivery plan data. Based on the collected data, it generates a net demand sequence for multiple future weeks with a unified time dimension of one week. Specifically, this includes: Step 11: The data acquisition module automatically collects sales forecast data, current inventory data, and future delivery plan data every week; a1) The sales forecast data is generated using a mechanism that combines a fusion algorithm model with manual correction; The fusion algorithm model combines a time series model (ARIMA) and a machine learning model (LightGBM). The time series model is used to capture sales trends and cycles, while the machine learning model is used to fit non-linear factors (such as promotions and seasonality). The outputs of the two models are weighted according to a preset weight ratio (e.g., the outputs of the time series model and the machine learning model are weighted in a 7:3 ratio) to obtain the initial predicted value. It provides a visual interface that allows business personnel to modify the initial forecast based on market experience (such as sudden promotions and supply chain risks). The modified initial forecast automatically overwrites the original initial forecast and saves the modification record. a2) The current inventory data includes in-stock inventory synchronized in real time from the warehouse management system and in-transit inventory synchronized from the transportation management system with an estimated arrival time. a3) The future delivery plan data includes the expected delivery data of purchase orders that have been placed, collected from the procurement management system; Step 12: Set a unified time base (the time unit is strictly unified to "week"). Define the start and end times of each week through the week boundary calibration algorithm (e.g., the start time of each week is Monday 00:00 and the end time is Sunday 24:00 to eliminate cross-system time zone deviation). Add a standardized week timestamp to all collected data (format: "year - W week", such as "week 1 = 2025-W40", to ensure that the time dimension of all data is unified to "week"). Step 13, according to the formula: D n = P n - (I0+ΣT k ) +ΣA m Calculate the net demand D for the next nth week. n (Unit: piece); where P n Let I0 be the sales forecast demand (units) for week n, and I0 be the current available inventory (units). ΣT k ΣA represents the total in-transit inventory with an estimated arrival time no later than week n, where k is the batch number of the in-transit inventory. m This is the total allocated inventory that has been locked by sales orders but has not yet been shipped, and is expected to be held for no later than week n, where m is the allocation record number; Step 14: Synchronize the calculated net demand sequence to the inventory demand collaborative calculation module in real time via the data bus.

[0026] In this embodiment, step 1, which triggers a recalculation of the net demand sequence when inventory fluctuations exceed a set threshold, specifically includes: Connect the data acquisition module to the inventory management system through the application programming interface (API) of the inventory management system, and set the fluctuation threshold of inventory fluctuation (default 5%). The inventory management system calculates the fluctuation range of each inventory item in real time. The formula for calculating the fluctuation range is: Fluctuation Range = |Current Inventory - Last Collected Inventory| / Last Collected Inventory × 100%; When the fluctuation range of a certain inventory item is detected to exceed the fluctuation threshold in real time, the data acquisition module is automatically triggered to execute the formula: D n = P n - (I0+ΣT k ) +ΣA m Recalculate all relevant net requirements items; The recalculated net demand sequence is synchronized to the inventory demand collaborative calculation module via the data bus. During data bus transmission, the weekly timestamp format of the data in the inventory demand collaborative calculation module is automatically verified. If there is non-whole-week data (such as 3-day inventory changes), it is converted into equivalent weekly data (3 days = 3 / 7 ≈ 0.43 weeks) through the "non-whole-week data intelligent conversion algorithm" to ensure consistency with the time dimension of other modules.

[0027] Step 2: The parameter dynamic configuration module configures and dynamically adjusts key parameters used for inventory demand calculation, including the first-leg transportation timeliness matrix, safety stock, and dynamic transportation priority rules. All key parameter changes are synchronized to the inventory demand collaborative calculation module in real time via the data bus. When parameters change, the inventory demand collaborative calculation module is automatically triggered to recalculate (response delay <30 seconds), ensuring that parameters and demands are dynamically adapted. The data bus synchronization adopts an event-driven mechanism: "event-driven + intelligent judgment of impact scope" mode. When data changes occur in the data acquisition module or the parameter dynamic configuration module, an event carrying the changed content and impact scope (such as carrying parameter type, modification scope, etc.) is automatically generated. Recalculation scope: Only the part of the inventory demand collaborative calculation module that should be affected by the impact scope is recalculated (e.g., if the timeliness of a certain transportation method in a certain warehouse is modified, only the inventory demand related to that transportation method in that warehouse is recalculated), rather than a global recalculation, thus improving response efficiency. The parameter dynamic configuration module serves as a linkage parameter pool, constructing a multi-dimensional dynamic parameter system to achieve intelligent adjustment of first-leg transportation timeliness, safety stock, and transportation priority, and to link with the net demand sequence in real time.

[0028] In this embodiment, the first-leg transportation timeliness matrix in step 2 (first-leg transportation typically refers to the first segment of logistics transportation from the supplier or production site to the destination warehouse. Transportation timeliness refers to the time required from shipment to receipt. The matrix represents a multi-dimensional timeliness configuration table, used to dynamically calculate transportation time based on different combinations of transportation conditions. The first-leg transportation timeliness matrix supports dynamic adjustment of transportation time based on transportation conditions, improving the accuracy and adaptability of inventory preparation plans) is used to dynamically correct the deviation rate between actual timeliness and basic timeliness values, avoiding transportation plan deviations caused by using static values. Specifically, it includes: Step 211: The first-leg transportation timeliness matrix is ​​configured with a basic timeliness T according to four dimensions: destination warehouse, transportation mode, cargo specifications and origin. The configuration method supports manual input (users fill in the basic timeliness value according to the four dimensions in the system interface) or automatic synchronization through the logistics provider system interface (connecting to the logistics provider system API interface to automatically obtain real-time transportation timeliness data and fill it in); the two configuration methods can be switched between each other.

[0029] Step 212: Record the historical transportation timeliness within the past set period (preferably the past 4 weeks) under each of the four-dimensional combinations, and calculate the average value as the actual timeliness to ensure data stability; Step 213: Compare the actual delivery time with the baseline delivery time T weekly, and calculate the deviation rate. Deviation rate = |Actual delivery time - Baseline delivery time T| / Baseline delivery time T × 100%; Step 214: When the deviation rate continuously reaches the set number of weeks and exceeds the set proportion (e.g., the deviation rate exceeds 10% for 2 consecutive weeks), the basic timeliness T of the four-dimensional combination is automatically updated to the corrected basic timeliness T'. The corrected basic timeliness T' = basic timeliness T + (actual timeliness - basic timeliness T) × K, where K is a smoothing coefficient configured according to user needs. It can be configured as K = 0.8 to avoid overly aggressive timeliness adjustments (e.g., if the actual timeliness of "large items + slow ships + warehouse A + cargo from location B" is 2 weeks longer than the configured value for a long time, the system will automatically increase the basic timeliness T value of the combination by 1.6 weeks).

[0030] In this embodiment, the safety stock in step 2 is used to adjust for the volatility of the net demand sequence, specifically including: Step 221: Safety stock supports two-level configuration at the SKU (SKU is the smallest unit for uniquely identifying and managing goods in warehousing and logistics management. Each SKU represents a specific combination of product attributes) and its corresponding category dimension (deeply bound to demand fluctuation linkage rules). The dimension priority rule is: SKU dimension configuration has higher priority than category dimension. When both SKU and category dimensions exist and their values ​​differ, the SKU dimension configuration takes precedence. When no SKU dimension is configured, the highest-level category dimension of its corresponding category is automatically matched (e.g., if the SKU belongs to "turn signal lights," the "turn signal lights" category configuration is matched first; if none exists, the "auto parts" category is matched, and so on upwards). The rules for linking demand fluctuations are as follows: Step 222: Calculate the demand volatility between consecutive weeks based on the net demand sequence. The volatility of week n = |D n - D n-1 | / D n-1 ×100%, where D n For the net demand in week n, D n-1 This represents the net demand for week n-1; for the volatility of week 1, the average volatility of the same period in history is used instead (e.g., the average volatility of the first week of the same quarter over the past 3 years). Step 223: Set the volatility trigger threshold (configurable to 20%) and the consecutive trigger number threshold (configurable to 3 times); maintain an independent counter, and when the weekly volatility of a certain inventory item is detected to exceed the trigger threshold for the first time, the counter starts to accumulate; Step 224: If the number of times the counter indicates that the volatility exceeds the trigger threshold consecutively reaches the consecutive trigger number threshold, the safety stock S (unit: piece) of the inventory item is automatically adjusted to S' = S×α, where S' is the adjusted safety stock (unit: piece), and α is the adjustment coefficient configured according to user needs, which can be configured as α=1.2. Step 225: After adjustment, the counter automatically resets. For example: the counter starts accumulating when volatility exceeds 20% (configurable) for the first time. If it is triggered 3 times consecutively, an adjustment is performed (S'=S×1.2, where 1.2 is a configurable adjustment coefficient). After adjustment, the counter automatically resets. If volatility continues in the 4th week, it needs to accumulate 3 times again before triggering the next adjustment to avoid frequent oscillations.

[0031] In this embodiment, the dynamic transportation priority rule in step 2 is used to link with the net demand sequence, specifically including: Step 231: Provide at least two basic templates: a cost-priority template and a time-priority template; The default transportation methods of the cost-priority template are ordered as follows: slow ship, fast ship, and air freight, with built-in cost threshold constraints (e.g., the maximum transit time for slow ships is no more than 45 days, and if it exceeds this, it will automatically be upgraded to fast ship) to balance cost and timeliness. The default transportation methods for the time-priority template are ordered as follows: air freight, fast ship, and slow ship, with a built-in cost limit constraint (e.g., air freight costs should not exceed 20% of the total transportation costs) to prevent cost overruns. Step 232: Support users to set custom trigger conditions based on one or more of the following: net demand level, time node, and cargo attributes; for example, "when D1>100 pieces, enable time-sensitive priority", "automatically increase the priority of express shipments during the peak season from June to August", and "fragile items are enabled with time-sensitive priority by default". Step 233: The core parameters in the custom triggering condition, including the set net demand and seasonal time nodes, are directly obtained from the net demand sequence or its derived data; when the net demand sequence is updated, causing the custom triggering condition to be met, the priority of the transportation mode corresponding to the basic template used is automatically switched.

[0032] Step 3: The inventory preparation demand collaborative calculation module (central collaborative logic, function: as the collaborative core, based on the input of the real-time data bus, calculates the inventory preparation demand through a three-layer logic of "timing alignment - priority allocation - dynamic verification", realizing deep module coupling) receives the net demand sequence and key parameters, and executes the following three-layer collaborative logic to calculate the inventory preparation demand: Step 31: Align the effective cycle of each mode of transport with the net demand sequence; In this embodiment, step 31 specifically includes: By matching week timestamps, the basic timeliness and safety stock are respectively bound to each week of the net demand sequence. The effective period for each mode of transportation to cover future demand is calculated. The starting point of the effective period is the current week plus the sum of the corresponding basic timeliness and safety stock, i.e., the starting point of the effective period = current week + basic timeliness + safety stock; the ending point of the effective period is the last week covered by the net demand sequence, such as: net demand sequence (D1~D2). 48 If ), then the effective period ends in week 48; The effective period is calculated as follows: for any mode of transportation, the effective period is the period of future demand covered from the start of the effective period to the end of the effective period (e.g., if the current time for a slow ship is week 1, the basic transit time is 12 weeks, and the safety stock is 2 weeks, then the starting point of the effective period for a slow ship is 1+12+2=15 weeks, and the effective period covers the demand from week 15 onwards).

[0033] Step 32: Determine the priority order of transportation modes based on the aforementioned transportation priority dynamic rules, and use the interval locking algorithm to allocate demand intervals to each transportation mode according to the priority order and calculate the stock preparation quantity based on the net demand of the allocated demand intervals; the demand intervals are dynamically adjusted according to the effective period of the highest priority transportation mode; In this embodiment, step 32 specifically includes: Step 321: Based on the currently effective dynamic rules for transportation priority, sort all available modes of transportation from high to low (e.g., time-priority template: air freight > fast ship > slow ship). Step 322: Calculate the demand range for each transportation mode using an interval locking algorithm, prioritizing them from highest to lowest priority. b1) For the highest priority mode of transportation (such as air freight), its demand range is from the start of its own effective period to the end of the effective period (e.g., if the current week for air freight is week 1, the basic delivery time T = 2 weeks, and the safety stock S = 1 week, then the start of the effective period = week 1 + 2 + 1 = week 4, the end of the effective period = week 48, and the effective period is from week 4 to week 48, thus locking in this demand range). b2) For any non-highest priority mode of transport, set an emergency demand period (e.g., weeks 4-14), calculate the effective period for each mode of transport to cover future demand, and determine whether the effective period is partially or entirely within the emergency demand period. If not, its demand range is from the start to the end of its own effective period; if so, the start of its demand range is the start of its own effective period, and the end of its demand range is the start of the effective period of the previous mode of transport with a higher priority minus one week. That is, the demand range of a non-highest priority mode of transport = its own effective period start ~ the start of the effective period of the previous priority mode of transport - 1. If the calculated start of the demand range is greater than the end of the demand range, then the mode of transport has no demand range to allocate in this instance (e.g., the effective period for fast ships is weeks 9-48 (of which weeks 9-14 are within the emergency demand period), and the effective period for the previous priority air transport is weeks 4-48, then the demand range that fast ships can allocate is weeks 9-3, there is no effective range, and no demand is allocated; if the effective period for slow ships is weeks 15...). Weeks 15 to 48 are outside the urgent demand period; the allocable demand range is from week 15 to week 48, and this demand range is locked in. Step 323: Each mode of transport allocates only the net demand of the corresponding week in the net demand sequence as its inventory quantity within its demand range; Formula for calculating inventory quantity: Inventory quantity E for a certain mode of transportation in week n n =D_{start of effective period + n-1} (only if the demand for that week is not covered by high priority transportation).

[0034] Step 324: When the basic transit time or safety stock of any mode of transport is dynamically adjusted, resulting in a change in its effective period, steps 321 to 323 are automatically re-executed to recalculate and update the demand range and inventory of all relevant modes of transport. When the effective period of a high-priority mode of transport changes, the allocatable range of low-priority modes of transport is automatically recalculated (e.g., if the basic transit time of air freight is corrected to 3 weeks, the effective period becomes week 5 to week 48, and the demand range of express shipping is updated to week 9 to week 4, there is still no effective range). Specific example (cost priority template: slow ship > fast ship > air freight, urgent demand cycle is week 4-14): Slow ship effective period: Week 15 to Week 48, allocating demand from Week 15 to Week 48 (the highest priority mode of transportation, whose demand range is its own effective period); Clipper's effective period: Week 9 to Week 48. The allocable demand range is Week 9 to Week 14. The demand in this range is allocated (for non-highest priority transportation methods, Weeks 9 to 14 of their effective period are within the emergency demand period, and their demand range = the start of their own effective period to the start of the effective period of the previous priority transportation method - 1, i.e., Weeks 9 to 14). Air freight effective period: Week 4 to Week 48. The allocable demand range is Week 4 to Week 8. Allocate demand within this range (for non-highest priority transportation methods, Weeks 4 to 14 of their effective period are within the emergency demand period, and their demand range = the start of their own effective period to the start of the effective period of the previous priority transportation method - 1, i.e., Weeks 4 to 8). Step 33: Based on the calculated total inventory and the net demand for the corresponding week, calculate the demand gap rate for each week. When the demand gap rate exceeds the set threshold, trigger a temporary reverse adjustment of the key parameters. In this embodiment, step 33 specifically includes: Step 331, Define the demand gap rate for week n = (D n -ΣE n ) / D n ×100%, where D n For the net demand in week n, ΣE n This represents the total amount of stock prepared for all modes of transport in week n. Step 332: Automatically perform a full-cycle demand gap ratio check at set intervals (e.g., 15 minutes); when the net demand sequence or key parameters are updated, immediately trigger a full-cycle demand gap ratio check. Step 333: If the verification finds that the demand gap rate in any future week exceeds a set threshold, a reverse adjustment strategy is executed in a preset order. The reverse adjustment strategy includes: c1) Prioritize temporarily increasing the effective coverage of high-priority transportation modes in specific scenarios: Select transportation modes with sufficient current capacity and an effective cycle start time ≤ 5 weeks from the gap cycle (e.g., slow ships, whose effective cycle start time is week 15, 5 weeks from week 10 of the gap cycle, making adjustment feasible), and temporarily advance the start time of their effective cycle by 1-2 weeks (e.g., advance the effective cycle start time of slow ships from week 15 to week 13-14). I. Criteria for Determining Sufficient Transportation Capacity Core definition: The current allocated cargo volume for a mode of transportation does not exceed 80% of its maximum carrying capacity (with a 20% flexibility reserve to avoid overloading). Key parameter calculation: Maximum carrying capacity (hereinafter referred to as "maximum capacity"): 1.2 is the highest actual stocking volume in a single week over the past 3 months for this mode of transportation (1.2 is an elasticity coefficient to adapt to short-term demand fluctuations). Allocated Cargo Volume: The total cargo volume currently allocated for this mode of transport across all effective demand periods (e.g., for slow vessels, the total cargo volume allocated for weeks 15-48 is ΣD). 15 ~D 48 ); Judgment formula: Allocated cargo volume ÷ maximum transport capacity ≤ 80% → judged as sufficient transport capacity; Example verification: In the slow ship example, assuming the slow ship's highest weekly inventory in the past 3 months is 1000 pieces, the maximum capacity = 1000 × 1.2 = 1200 pieces; the slow ship has allocated a total of 800 pieces of inventory for weeks 15-48, 800 ÷ 1200 ≈ 66.7% ≤ 80% → it is determined that the capacity is sufficient.

[0035] II. Definition and Calculation Logic of Gap Cycle Core definition: The gap cycle refers to "the first week in which the demand gap rate continuously exceeds a set threshold (default 5%)", which is the core time node that triggers the reverse adjustment; Calculation premise: First, calculate the demand gap ratio for each week using the formula: Demand gap ratio for week n = (D n -ΣE n ) / D n ×100%, where D n For the net demand in week n, ΣE n This represents the total amount of stock prepared for all modes of transport in week n. Calculation steps: Step 1: Iterate through all future weeks (e.g., weeks 4-48) and calculate the gap rate for each week; Step 2: Filter out weeks with a gap rate > 5% (set threshold). If there are two or more consecutive weeks with a gap rate exceeding the standard, take the first week that exceeds the standard as the "gap cycle". Step 3: If the gap rate exceeds the standard only in a single week (without continuity), then that week is directly determined as a gap cycle; Example verification: Net demand D in week 10 10 = 1000 units, total inventory quantity ΣE 10 = 920 items.

[0036] The logic for "Week 10 of the gap cycle" is: Week 10 gap rate = (D 10 - ΣE 10 ) / D 10 ×100%=8%>5%, and the gap rate exceeds the standard for consecutive weeks 10-11 → The first week exceeding the standard (week 10) is determined to be a gap cycle.

[0037] c2) If the demand gap rate still does not meet the target after executing step c1 (e.g., gap rate > 5%), then temporarily shorten the basic transit time of the scenario-based low-priority transportation mode: select the transportation mode with the lowest absolute priority (e.g., slow ship), temporarily reduce its basic transit time value by a predetermined percentage (e.g., reduce the basic transit time value of slow ship from 14 weeks to 12.6 weeks by 10%), and advance the start of its effective cycle by 1-2 weeks (e.g., advance from week 13-14 to week 12-13) to expand the effective coverage to fill the gap. c3) If the demand gap rate exceeds the limit threshold, the priority of air transport will be temporarily increased (only activated when the demand gap rate is >10%). Priority definitions (divided into two categories, adapted to different scenarios): ① Absolute priority (globally fixed): Air freight (high) > Fast ship (medium) > Slow ship (low) (ranked by the faster the delivery time and the higher the cost, the higher the priority); ② Contextualized Priority (for gap correction only): Scenario-based high priority: Transportation methods that are not currently at full capacity and can cover the gap (feasibility first). Scenario-based low priority: The transportation method with the lowest absolute priority and the lowest cost of time-saving compression (cost priority).

[0038] Step 334: The parameter changes generated after executing the reverse adjustment strategy are only used for the current round of inventory demand calculation and are not permanently modified to avoid affecting subsequent normal calculations. The reverse adjustment record is kept separately for traceability.

[0039] Step 4: The inventory demand collaborative calculation module synchronizes the calculation results to the business visualization system via the data bus.

[0040] In this embodiment, step 4 specifically includes: Step 41: After completing the calculation, the inventory demand collaborative calculation module will synchronize the calculation results, which include demand gap rate analysis and adjustment suggestions (such as "the gap rate in week 10 is 8%, it is recommended to advance the effective cycle start of the fast ship by 1 week"), to the business visualization system for display via the data bus. Step 42: Control the response cycle from data update to calculation result output within a set time (e.g., within 15 minutes).

[0041] From a research and development perspective, the module collaboration mechanism of this invention faces multiple technical barriers that are easily overlooked or difficult to implement by those not skilled in the art: The technical complexity of time-series alignment lies in the inherent heterogeneity of time units for demand, timeliness, and inventory (demand is measured in "months," timeliness in "days," and inventory in "real-time"). Research and development must overcome challenges such as "intelligent conversion of non-weekly data" (e.g., converting 3 days of inventory changes to 0.43 weeks) and "cross-system timestamp synchronization" (resolving time zone discrepancies between different databases). For example, to ensure complete consistency of the time range for "week n" across modules, a "weekly boundary calibration algorithm" needs to be developed. This process requires 6-8 weeks of iterative testing and is not a simple unit conversion.

[0042] The deviation correction of the first-leg transportation timeliness matrix, which involves dynamic parameter adjustment, requires the establishment of a ternary model of "historical data - configured value - demand fluctuation" (rather than single-dimensional correction). The linkage of safety stock fluctuations needs to balance "response sensitivity" and "adjustment stability" (to avoid frequent oscillations). For example, when the demand volatility exceeds the threshold, it is necessary to first verify whether the fluctuation is caused by sudden orders (non-trend changes) before deciding whether to adjust the safety stock. This logic needs to integrate statistics and business rules and cannot be directly derived through the existing single parameter configuration function.

[0043] The priority-based interval locking algorithm only covers the rule of "preceding cycle gaps" for low-priority transportation. It requires real-time comparison of the effective cycles and demand sequences of multiple transportation modes. During development, issues such as "interval overlap detection" and "rapid gap location" need to be addressed. For example, when the effective cycle of a slow ship is extended by one week due to parameter adjustments, the coverage interval of the fast ship needs to be automatically shrunk to ensure no duplicate allocation. This process involves the design of a dynamic interval tree data structure, which is far more complex than static sorting.

[0044] In summary, the technical features of this invention require overcoming multiple technical obstacles such as timing alignment, dynamic correction, and interval allocation. Its collaborative logic is a creative solution formed through extensive testing and iteration during the research and development process, rather than a simple superposition of existing technologies.

[0045] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A multi-module dynamic collaborative inventory demand calculation method, characterized in that, Real-time data transmission and logical linkage are achieved between the data acquisition module, the parameter dynamic configuration module, and the inventory demand collaborative calculation module via a data bus. The method includes the following steps: Step 1: The data acquisition module automatically collects weekly sales forecast data, current inventory data, and future delivery plan data. Based on the collected data, it generates a net demand sequence for multiple future weeks with a unified time dimension of one week. When inventory fluctuations exceed a set threshold, the net demand sequence is recalculated. Step 2: The parameter dynamic configuration module configures and dynamically adjusts the key parameters used for inventory preparation demand calculation, including the first-leg transportation timeliness matrix, safety stock, and dynamic transportation priority rules. All changes to key parameters are synchronized to the inventory preparation demand collaborative calculation module in real time through the data bus. Step 3: The inventory preparation demand collaborative calculation module receives the net demand sequence and key parameters, and executes the following three-layer collaborative logic to calculate the inventory preparation demand: Step 31: Align the effective cycle of each mode of transport with the net demand sequence; Step 32: Determine the priority order of transportation modes based on the aforementioned transportation priority dynamic rules, and use the interval locking algorithm to allocate demand intervals to each transportation mode according to the priority order and calculate the stock preparation quantity based on the net demand of the allocated demand intervals. Step 33: Based on the calculated total inventory and the net demand for the corresponding week, calculate the demand gap rate for each week. When the demand gap rate exceeds the set threshold, trigger a temporary reverse adjustment of the key parameters. Step 4: The inventory demand collaborative calculation module synchronizes the calculation results to the business visualization system via the data bus.

2. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, In step 1, the data acquisition module automatically collects weekly sales forecast data, current inventory data, and future delivery plan data. Based on the collected data, it generates a net demand sequence for multiple future weeks with a unified time dimension of one week. Specifically, this includes: Step 11: The data acquisition module automatically collects sales forecast data, current inventory data, and future delivery plan data every week; a1) The sales forecast data is generated using a mechanism that combines a fusion algorithm model with manual correction; The fusion algorithm model combines a time series model and a machine learning model. The time series model is used to capture sales trends and periods, while the machine learning model is used to fit nonlinear factors. The outputs of the two models are weighted according to a preset weight ratio to obtain an initial predicted value. It provides a visual interface that allows business personnel to modify the initial forecast based on market experience. The revised initial forecast automatically overwrites the original initial forecast and saves the revision record. a2) The current inventory data includes in-stock inventory synchronized in real time from the warehouse management system and in-transit inventory synchronized from the transportation management system with an estimated arrival time. a3) The future delivery plan data includes the expected delivery data of purchase orders that have been placed, collected from the procurement management system; Step 12: Set a unified time base, define the start and end times of each week through the weekly boundary calibration algorithm, and attach a standardized week timestamp to all collected data; Step 13: According to the formula: D n = P n - (I0+ΣT k ) +ΣA m Calculate the net demand D for the next nth week. n Among them, P n Let I0 be the sales forecast demand for week n, and ΣT be the current available inventory. k ΣA represents the total in-transit inventory with an estimated arrival time no later than week n, where k is the batch number of the in-transit inventory. m This represents the total allocated inventory that has been locked by sales orders but has not yet been shipped, and is expected to be held for no later than week n, where m is the allocation record number; Step 14: Synchronize the calculated net demand sequence to the inventory demand collaborative calculation module in real time via the data bus.

3. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, Step 1, which triggers a recalculation of the net demand sequence when inventory fluctuations exceed a set threshold, specifically includes: The data acquisition module is connected to the inventory management system through the application programming interface of the inventory management system, and the fluctuation threshold of the inventory fluctuation range is set. The inventory management system calculates the fluctuation range of each inventory item in real time. The formula for calculating the fluctuation range is: Fluctuation Range = |Current Inventory - Last Collected Inventory| / Last Collected Inventory × 100%; When the fluctuation range of a certain inventory item is detected to exceed the fluctuation threshold in real time, the data acquisition module is automatically triggered to execute the formula: D n = P n - (I0+ΣT k ) +ΣA m Recalculate all relevant net requirements items; The recalculated net demand sequence is synchronized to the inventory demand collaborative calculation module via the data bus.

4. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, In step 2, the first-leg transportation timeliness matrix is ​​used to dynamically correct the deviation rate between the actual timeliness and the basic timeliness value, specifically including: Step 211: The first-leg transportation timeliness matrix is ​​configured with basic timeliness T according to four dimensions: destination warehouse, transportation mode, cargo specifications and origin. The configuration method supports manual input or automatic synchronization through the logistics provider's system interface. Step 212: Record the historical transportation time within the past set period under each of the four-dimensional combinations, and calculate the average value as the actual time. Step 213: Compare the actual delivery time with the baseline delivery time value weekly, and calculate the deviation rate. Deviation rate = |Actual delivery time - Baseline delivery time T| / Baseline delivery time T × 100%; Step 214: When the deviation rate continuously reaches the set number of weeks and exceeds the set proportion, the base time T of the four-dimensional combination is automatically updated to the corrected base time T'. The corrected base time T' = base time T + (actual time - base time T) × K, where K is the smoothing coefficient configured according to user needs.

5. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, In step 2, the safety stock is used to adjust for the volatility of the net demand sequence, specifically including: Step 221: Safety stock supports two-level configuration at the SKU level and its category level. The SKU level configuration has higher priority than the category level. When both the SKU and category levels exist and their values ​​are different, the SKU level configuration takes precedence. When no SKU level is configured, the highest-level category level of its category is automatically matched. Step 222: Calculate the demand volatility between consecutive weeks based on the net demand sequence. The volatility of week n = |D n -D n-1 | / D n-1 ×100%, where D n For the net demand in week n, D n-1 This represents the net demand for week n-1; for the volatility of week 1, the average volatility of the same period in history is used instead. Step 223: Set the volatility trigger threshold and the consecutive trigger count threshold; maintain an independent counter, and when the weekly volatility of an inventory item is detected to exceed the trigger threshold for the first time, the counter starts to accumulate; Step 224: If the number of times the volatility indicated by the counter exceeds the trigger threshold reaches the consecutive trigger number threshold, the safety stock S of the inventory item is automatically adjusted to S' = S×α, where S' is the adjusted safety stock and α is the adjustment coefficient configured according to user needs. Step 225: After the adjustment is completed, the counter will automatically reset.

6. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, The dynamic transportation priority rules in step 2 are used to link with the net demand sequence, specifically including: Step 231: Provide at least two basic templates: a cost-priority template and a time-priority template; The default transportation methods for the cost-priority template are ordered as follows: slow ship, fast ship, and air freight, with a built-in cost threshold constraint. The default transportation methods for the time-priority template are ordered as follows: air freight, fast ship, and slow ship, with a built-in cost upper limit constraint. Step 232: Support users in setting custom trigger conditions based on one or more of the following: net demand level, time node, and cargo attributes; Step 233: The core parameters in the custom triggering condition, including the set net demand and seasonal time nodes, are directly obtained from the net demand sequence or its derived data; when the net demand sequence is updated, causing the custom triggering condition to be met, the priority of the transportation mode corresponding to the basic template used is automatically switched.

7. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, Step 31 specifically includes: By matching week timestamps, the basic timeliness and safety stock are respectively bound to each week of the net demand sequence, and the effective period for each mode of transportation to cover future demand is calculated. The starting point of the effective period is the current week plus the sum of the corresponding basic timeliness and safety stock; the ending point of the effective period is the last week covered by the net demand sequence. The effective period is calculated as follows: for any mode of transportation, the period covering future demand from the start to the end of the effective period is taken as the effective period.

8. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, Step 32 specifically includes: Step 321: Sort all available transportation modes from high to low according to the currently effective transportation priority dynamic rules; Step 322: Calculate the demand range for each transportation mode using an interval locking algorithm, prioritizing them from highest to lowest priority. b1) For the highest priority mode of transport, its demand range is from the start of its own effective period to the end of its effective period; b2) For any transportation mode that is not the highest priority, set an emergency demand period, calculate the effective period for each transportation mode to cover future demand, and determine whether the effective period is partially or entirely within the emergency demand period. If not, its demand interval is from the start point to the end point of its own effective period. If yes, the start point of its demand interval is the start point of its own effective period, and the end point of its demand interval is the start point of the effective period of the transportation mode with higher priority minus one week. If the calculated start point of the demand interval is greater than the end point of the demand interval, then the transportation mode has no demand interval to allocate in this instance. Step 323: Each mode of transport allocates only the net demand of the corresponding week in the net demand sequence as its inventory quantity within its demand range; Formula for calculating inventory quantity: Inventory quantity E for a certain mode of transportation in week n n =D_{starting point of effective period + n-1}; Step 324: When the first-leg transportation timeliness or safety stock of any transportation mode is dynamically adjusted, resulting in a change in its effective cycle, steps 321 to 323 are automatically re-executed to recalculate and update the allocable demand range and stock quantity for all relevant transportation modes.

9. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, Step 33 specifically includes: Step 331, Define the demand gap rate for week n = (D n -ΣE n ) / D n ×100%, where D n For the net demand in week n, ΣE n This represents the total amount of stock prepared for all modes of transport in week n. Step 332: Automatically perform a full-cycle demand gap ratio check at set intervals; when the net demand sequence or key parameters are updated, immediately trigger a full-cycle demand gap ratio check. Step 333: If the verification finds that the demand gap rate in any future week exceeds a set threshold, a reverse adjustment strategy is executed in a preset order. The reverse adjustment strategy includes: c1) Prioritize temporarily expanding the effective coverage of high-priority transportation modes in specific scenarios: Select transportation modes with sufficient current capacity and an effective cycle start time ≤ 5 weeks from the gap cycle start time, and temporarily advance the start time of their effective cycle by 1-2 weeks; c2) If the demand gap rate still does not meet the target after executing step c1, the basic timeliness of the scenario-based low-priority transportation method will be temporarily shortened: select the transportation method with the lowest absolute priority, temporarily reduce its basic timeliness value by the predetermined percentage, and advance the start of its effective period by 1-2 weeks. c3) If the demand gap rate exceeds the limit threshold, the priority of air transport will be temporarily increased; Step 334: The parameter changes generated after executing the reverse adjustment strategy are only used for the current round of inventory demand calculation.

10. The multi-module dynamic collaborative inventory demand calculation method as described in claim 1, characterized in that, Step 4 specifically includes: Step 41: After completing the calculation, the inventory demand collaborative calculation module will synchronize the calculation results, including demand gap rate analysis and adjustment suggestions, to the business visualization system for display via the data bus. Step 42: Control the response cycle from data update to calculation result output within the set time.

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