Demand-driven restock order generation and restock plan scheduling method and system

Abstract

Disclosed are a demand-driven restock order generation and restock plan scheduling method and system. On the basis that the nature of a supply chain restock principle is revealed, a decoupling point restock order generation and restock plan scheduling method and system based on a supply and demand contract are provided. A proper order issuing time and a proper order quantity can be realized on the basis of future real demand and the fulfilment situation of past actual orders; it is ensured that a supply chain is not interrupted; decoupling point inventory is controlled to vary within the most rational range; three threshold value control lines, i.e., order forecast, order tracking early alarm and interruption alert, which are used by the supply chain to execute monitoring are also provided; the identification and measurement of a supply capacity gap are provided; on-hand inventory and an open order are evaluated on the basis of a supply and demand contract and future demand; and delivery-time review is performed on unconfirmed future demand on the basis of supply capacity.

Description

A demand-driven replenishment order generation and replenishment plan scheduling method and system Technical Field

[0001] The present invention relates to the technical field of supply chain planning, and in particular to a demand-driven replenishment order generation and replenishment plan scheduling method and system. Background Art

[0002] The Demand Driven Institute (DDI) proposed the Demand-Driven Material Requirements Planning (DDMRP) method based on a combination of the reorder point (ROP) method, traditional material requirements planning (MRP) methods, just-in-time (JIT) methods, and the theory of constraints (TOC). The Institute explicitly believes that the traditional MRP method is no longer suitable for the current VUCA supply chain environment and is precisely wrong. It is the culprit for the bimodal inventory distribution model and expedited overtime costs. The DDMRP method, which can automatically respond to changes in demand, should be used to generate and calculate supply orders.

[0003] The core logic of DDMRP for replenishing strategic inventory points (i.e., customer order decoupling points) is:

[0004] 1. Determine the Decoupled Lead Time (DLT) of the inventory point material

[0005] 2. Determine the expected order cycle (DOC) for the material

[0006] 3. Determine the minimum order quantity (MOQ) of the material

[0007] 4. Determine the material's lead time factor (LTF) based on the material type and decoupled lead time (DLT)

[0008] 5. Determine the Variability Factor (VF) by material type

[0009] 6. Calculate the average daily usage (ADU) of the material

[0010] 7. Calculate the three-color water level control line in the material buffer file: TOR = RZ = RZB + RZS = ADU·DLT·LTF + ADU·DLT·LTF·VF TOY = TOR + YZ = TOR + ADU·DLT TOG = TOY + GZ = TOY + max (ADU·DLT·LTF, ADU·DOC, MOQ)

[0011] 8. Calculate the Order Spike Quantity (OSQ)

[0012] Within the range of OSH (Order Spike Horizon) = DLT, find the demand quantity that is greater than OST (Order Spike Threshold). If there is, use it as the spike order quantity (OSQ). Otherwise, OSQ = 0.

[0013] OST is recommended

[0014] 9. Calculate Qualified Demand (QD): QD = Today's Demand + OSQ

[0015] 10. Calculate Net Flow Position (NFP)

[0016] NFP=OH+OO-QD, where OH=On-Hand Quantity, OO=On-Order Quantity

[0017] 11. Order generation calculation

[0018] If NFP>TOY, no order is generated; otherwise, the order quantity = TOG-NFP.

[0019] The average inventory on hand (AOH) at the decoupling point estimated by the DDMRP method is:

[0020] DDMRP requires manual dynamic adjustment to be effective in scenarios such as demand ramping and demand pinching. DDMRP's calculation of ADU suffers from the drawback of manually setting the sliding average window width and position, lacking rigorous logical support. DDMRP's identification and calculation of Order Spikes lack rigorous logical support. DDMRP's manual setting of LTF when DLT is known is unreasonable. DDMRP cannot guarantee an uninterrupted supply chain even if orders are executed completely on time. DDMRP will increase inventory at decoupling points. DDMRP's applicability is limited and cannot support scenarios such as material distribution for production lines and production work order generation with short order cycles. Summary of the Invention

[0021] Purpose of the invention: The purpose of the present invention is to provide a demand-driven replenishment order generation and replenishment plan scheduling method and system that solves the following technical problems. The technical problems to be solved by the present invention are:

[0022] Find an effective method with minimal manual parameter setting to achieve automatic scheduling calculation of DDMRP in all scenarios without the need for manual dynamic adjustment, ensuring uninterrupted supply chain and controlling inventory within the most reasonable range.

[0023] The connotation of an uninterrupted supply chain is: under the premise that the supplier strictly fulfills the order as required, based on the assumption of uniform consumption on the part of the demander, no matter what the scenario, it can be guaranteed that the inventory of the demander will not be lower than the safety stock.

[0024] The meaning of controlling inventory within the most reasonable range is: based on the assumption of uniform consumption on the demand side, no matter what the scenario, the inventory quantity in each order cycle will not exceed the inventory quantity limit; in each order cycle with incoming goods, there will be a point in time when the inventory will bottom out to the sum of the safety stock and the emergency backup stock or below.

[0025] Technical solution: The demand-driven replenishment order generation and replenishment plan scheduling method and system described in the present invention include the following steps: S1. Setting a demand object (Demand Object) based on the positions of the supplier and the demander in the supply network (Supply Network), wherein the demander provides material demand (Material Demand) and the supplier provides execution resources (Execution Resource); S2. Using a single numerical value ACU to condense and represent the near future material demand of the demand object; S3. Using the agreed order cycle (Agreed Order Cycle) as the time granularity, monitoring the current supply capacity (Supply Capacity) of the execution resource, the demander's opening on-hand inventory (On-Hand Quantity) and the supplier's opening open orders (On-Order Quantity), and establishing a timely updated buffer profile (Buffer Profile); S4. After allocating (Demand Dispatching) and prioritizing (Prioritized Ordering) the execution resource demand based on the demand of the demand object, considering the supply-demand balance equation (Supply-Demand Balancing) The constraints of Equation are used to dynamically match the demand object with execution resources and their supply capabilities, and generate corresponding replenishment orders and replenishment schedules.

[0026] Furthermore, using a single numerical value ACU to condense and characterize near-future material demand includes the following steps: S21. Obtain all known and real future material demands of the demand object, combine them with the current cumulative demand, pre-process them into a cumulative demand series, and group them by the agreed order cycle to obtain the actual cycle demand for each current and future order cycle; S22. Establish a moving average window for each order cycle on the actual cycle demand series, with the averaging window width being:

[0027] AWW=CLC+2, where

[0028] Wherein, OPO, DLT, and AOC are all parameters of the supply-demand contract (Supply-Demand Contract Parameters); S23. Within the sliding average window of the k-th order cycle, the average cycle usage is determined based on the assumption of uniform consumption by the demander. Average daily usage ADU k , Demand during the lead time DDLT k and Arithmetic average cycle usage AACU k+CLC+2 , During the period of preparing goods for large-volume demand waves, the average cycle usage ACU without considering stock preparation is determined according to the serial number mark of the large-volume demand stock preparation period of the order cycle. k :

[0029] Wherein, ACD i is the actual cycle demand of the i-th order cycle, SUI i is the serial number mark of the stock preparation period of the i-th order cycle.

[0030] Further, when the demand history is not obtained but the outstanding orders are obtained, the demand is extrapolated forward based on the same average cycle usage for the past demand. The specific formula is as follows:

[0031] Wherein, ACD k-1 represents the past demand to be extrapolated.

[0032] More strictly, if the actual cycle demands ACD k , ACD k+1, …, ACD k+m-1 of m (2 < m ≤ AWW) order cycles are known, then the actual cycle demand of the previous order cycle can be extrapolated forward through the second-order Chebyshev polynomial. The formula is as follows:

[0033] When m = 2, linear extrapolation is performed. The specific formula is as follows: ACD k-1 = 2ACD k - ACD k+1 .

[0034] The approximation and extrapolation of the second-order Chebyshev polynomial are based on the following principle:

[0035] Theorem: Let there be a sequence {y i} sampled at equal intervals with m (m > 2), i = 1, 2,..., m. Define the linear transformation matrix

[0036] Then

[0037] Among them, a, b, and c are the coefficients of the quadratic polynomial y = f(x) = ax 2 + bx + c, and y m+1 = f(x m+1 ) is the next function value obtained by backward extrapolation, and y0 = f(x0) is the previous function value obtained by forward extrapolation.

[0038] When m = 2, there is

[0039] Similarly, when insufficient historical demand data is obtained, the average cycle usage in the previous order cycle is obtained by the following formula:

[0040] More strictly, if the actual cycle demands ACD k , ACD k+1 , …, ACD k+m-1 of m (2 < m ≤ AWW) order cycles are known, and after extrapolating ACD k-1 by the method of claim 3, the average cycle usage in the previous order cycle can be calculated according to the definition, and the formula is as follows:

[0041] For the last AWW order cycles within the demand visibility period, calculate the arithmetic mean of their actual cycle demands, and then calculate the coefficient of dispersion:

[0042] Based on this coefficient of dispersion, predict the demand for AWW order cycles according to the following formula:

[0043] Correspondingly, we obtain the estimation of the average cycle usage of the DVH-CLC order cycle, and the formula is as follows:

[0044] When DVH < CLC, if the arithmetic average cycle usage AACU k-m+1 , AACU k-m+2 ,..., AACU k of the previous m (2 < m ≤ AWW) order cycles are known, then use the second-order Chebyshev polynomial to predict the arithmetic average cycle usage of the (k + 1)-th order cycle. However, to ensure that the supply chain does not interrupt and only increases or remains unchanged, the formula is as follows:

[0045] When m = 2, perform linear extrapolation prediction but also keep it only increasing or remaining unchanged. The specific formula is as follows: AACU k+1 = max(AACU k , 2AACU k - AACU k-1 ).

[0046] Arithmetic average cycle usage AACU based on extrapolation prediction k+1 Calculate ACD first according to the following formula k Recalculate and ACU k-1-CLC :

[0047] Furthermore, when describing the supply chain in S1, the following steps are included: S111, obtaining a supply network, the supply network including multiple supply chain nodes, and the supply chain nodes are connected by supply chain segments; S112, determining the supply chain potential according to the status of the supply chain node, and classifying the supply chain nodes according to the supply chain potential. Specifically, the supply chain potential is 1, indicating used inventory; the supply chain potential is 2, indicating in-use inventory; the supply chain potential is 3, indicating a line-side warehouse of materials to be consumed; the supply chain potential is 4, indicating a raw material supermarket or a semi-finished product buffer or a semi-finished product warehouse A supply chain potential of 5 indicates a main warehouse for raw and auxiliary materials; a supply chain potential of 6 indicates an external warehouse for raw and auxiliary materials or a supplier-managed inventory; a supply chain potential of 7 indicates a supplier's supply location or a customer's delivery location; a supply chain potential of 8 indicates a distribution center or a finished product consignment warehouse or a commodity supermarket; a supply chain potential of 9 indicates a main warehouse for finished products or a commodity warehouse; x and y represent the serial numbers of supply chain nodes with the same supply chain potential; S113, based on function, the supply chain is divided into a manufacturing enterprise supply chain, a commercial enterprise supply chain, and an industrial chain. The specific structure is as follows:

[0048] <Manufacturing Enterprise Supply Chain>

[0049] ::={7x6y|7x5y|6x5y|6x4y|6x3y|5x4y|5x3y|4x3y|3x2y|2x1y|1x4y|4x4y|4x3y|4x9y|9x8y|9x7y|8x7y}

[0050] <Commercial Enterprise Supply Chain>::={7x9y|9x8y|9x7y|8x7y}

[0051] <Industry Chain>::={T n zP u xP d y}

[0052] Among them, T n represents the n-level supplier of brand product manufacturers; z represents the supplier serial number of the same level, P u xP d y represents the supplier's internal supply chain.

[0053] Furthermore, when the demand object is established in S1, the following steps are specifically included: S121, dividing the target demand location into location, storage location, work center, production line, work station, and administrative division according to the demand situation of materials in the supply network; S122, combining the materials and the target demand locations one by one into multiple demand object types, specifically: demand object::={(material, location)|(material, storage location)|(material, work center)|(material, production line)|(material, work station)|(material, administrative division)}; wherein, (material, location) describes the delivery demand of materials (goods, finished products), thereby triggering the shipment of storage locations; (material, storage location) describes the outbound demand of materials (goods, finished products, semi-finished products, raw and auxiliary materials, tooling, containers) from a specific storage location (Storage Site), thereby triggering the replenishment of execution resources; (material, work center) describes the delivery demand of materials (finished products, semi-finished products) in a specific work center (Work (Material, Production Line) describes the production work order of materials (finished products, semi-finished products) at a specific production line (Production Line) or the distribution demand of materials (raw materials, auxiliary materials, semi-finished products, tooling, containers) to a specific production line; (Material, Work Station) describes the production kanban instruction order of materials (finished products, semi-finished products) at a specific work station (Work Station) or the distribution demand of materials (raw materials, auxiliary materials, semi-finished products, tooling, containers) to a specific work station; (Material, Administrative Division) describes the distribution demand of materials (supplies) distributed to a specific administrative division.

[0054] Furthermore, when enumerating the mapping between the demand objects and the execution resources, the following steps are specifically included: S131, subdividing the materials into material types such as commodities, finished products, semi-finished products, raw and auxiliary materials, tooling, and containers; subdividing the execution resources into resource types such as production lines, workstations, work centers, suppliers, finished product external warehouses, finished product main warehouses, commodity warehouses, raw and auxiliary material external warehouses, and raw and auxiliary material main warehouses; S132, when enumerating different material types, the mapping between the demand objects and the execution resources is specifically as follows:

[0055] ① Required object = (material, location), material type = finished product or commodity, execution resource: finished product external warehouse or finished product main warehouse or commodity supermarket or commodity main warehouse (the required object needs to be shipped);

[0056] ② When the demand object = (material, storage location), the material type = finished product or commodity, and the storage location supply chain potential = 8, the execution resource is: finished product main warehouse or commodity warehouse (replenishment of the demand object is required);

[0057] ③ When the demand object = (material, storage location), the material type = finished product, and the storage location supply chain potential = 9, the execution resource is: assembly work center, assembly production line, or assembly independent workstation (production and replenishment of the demand object is required);

[0058] ④ When the demand object = (material, storage location), the material type = commodity, and the storage location supply chain potential = 9, the execution resource is the supplier (purchase and replenishment of the demand object is required);

[0059] ⑤ When the demand object = (material, storage location), the material type = semi-finished product, and the storage location supply chain potential = 4, the execution resource is the work center or semi-finished product production line corresponding to the semi-finished product production, or the independent semi-finished product production station (production replenishment of the demand object is required);

[0060] ⑥ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 6, the execution resource is: supplier (purchase and replenishment of the demand object is required);

[0061] ⑦ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 5, the execution resource is: supplier (needs to purchase and replenish the demand object) or external raw and auxiliary material warehouse (needs to allocate and replenish the demand object);

[0062] ⑧ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 4, the execution resource is: the raw and auxiliary materials warehouse or external warehouse (replenishment of the demand object is required);

[0063] ⑨ When the demand object = (material, production line) and the material type = finished product or semi-finished product (production is scheduled on this production line), the execution resource is the production line (production of the finished product or semi-finished product needs to be scheduled);

[0064] ⑩ When the demand object = (material, production line) and the material type = raw and auxiliary materials, upstream semi-finished products, tooling, or containers, the execution resource is: storage location (replenishment of the demand object is required);

[0065] When the demand object = (material, workstation) and the material type = finished product or semi-finished product (the process route passes through the workstation), the execution resource is: workstation (production of the finished product or semi-finished product needs to be scheduled);

[0066] When the demand object = (material, workstation) and the material type = raw or auxiliary materials, upstream semi-finished products, tooling, or containers, the execution resource is the storage location (replenishment of the demand object is required).

[0067] When the demand object = (material, administrative division) and the material type = supplies, the execution resource is: storage location (requires distribution and replenishment of the demand object).

[0068] Furthermore, the supply capacity of the execution resources is determined, including the following steps: ① The demand objects and the execution resources are combined to form a scheduling object, and the agreed order cycle (AOC), order point offset (OPO), decoupled lead time (DLT), standard package quantity (SPQ), minimum order quantity (mOQ) and maximum order quantity (MOQ) attributes are set for each group of scheduling objects, and the time deviation factor (SVF) is evaluated based on the past performance of the execution resources. T ), quantity deviation factor (SVF Q ), quality deviation factor (SVF q );② The load of the execution resources in each order cycle caused by high-priority demand, expressed as the resource loading ratio (RLR). For a specific material, the maximum supply capacity of the execution resources in a specified order cycle i is:

[0069] Among them, when the execution resource is assigned POQ for a specific material in the i-th order cycle i The load factor will increase if the order

[0070] Furthermore, the time deviation factor, quantity deviation factor, and quality deviation factor are obtained by calculation or agreement; the calculation includes the following steps: obtaining past order completion status within the target time period; and calculating the time deviation factor, quantity deviation factor, and quality deviation factor within the target time period based on the past order completion status. The specific formula is as follows;

[0071] Where n is the actual demand period in the past [t B ,t0) total number of completed orders; t -i is the start time of the -i order cycle; τ -i is the actual order completion time of the -i order cycle; t B is the start time of the previous year, i.e. the starting time of the -b order cycle; ε(x) is a unit step function, and the ε(x) formula is: PFQ -i AFQ is the required delivery quantity for the -i order cycle. -i The actual delivery quantity of the -i order cycle; NGQ -i AFQ is the number of defective products delivered in the -i order cycle. -iis the actual delivery quantity in the -i-th order cycle.

[0072] Further, when it is found that the supply capacity of the execution resources cannot meet the demand of the corresponding order cycle (CLC order cycles are delayed), a reverse order method is adopted to find the Huge Demand Wave for advance stocking up, and at the same time, the supply capacity gap is detected. The specific steps are as follows:

[0073] ① Start detecting from the last order cycle of the future demand visibility period (Demand Visibility Horizon) as the detection boundary (Detection Boundary) one by one forward for each order cycle, and at the same time calculate the increments of the critical cycle demand (Critical Cycle Demand) and the resilience keeping demand (Resilience Keeping Demand) of the detected order cycle: CCD e = OFE·MOQ e-CLC ΔRKD e = ΔSSQ<f e-CLC + RTR·ΔACU e-CLC

[0074] ② Use the order cycle serial number e of the first corresponding actual cycle demand greater than the sum of the critical cycle demand and the increment of the resilience keeping demand as the initial value of the huge demand wave identification. If it does not exist, it means there is no huge demand wave, and the detection is aborted; if it exists, proceed to the next step;

[0075] ③ If MOQ e-CLC < mOQ, it indicates that the fragmented supply capacity is unavailable, e ← e + 1, and loop until e = DVH + 1 or MOQ e-CLC ≥ mOQ;

[0076] ④ Calculate the resilience keeping demand RKD of the e order cycle e ;

[0077] ⑤ Find the largest order cycle serial number s > -1 that satisfies the following formula:

[0078] ⑥ If the largest order cycle serial number s > -1 cannot be found, then identify the total supply capacity gap. The supply capacity gap can be evenly distributed among the 0, 1,..., e - CLC - 1 order cycles, and at the same time set s = 0; identifying the total supply capacity gap is achieved through the following formula:

[0079] ⑦ Mark the order cycles s, s+1, ..., e-CLC with the stocking up index, as shown in the following formula: SUI k =k-s+1,k=1,2,…,e-CLC-s+1;

[0080] ⑧If s>0, take s+CLC-1 as the start of a new massive demand detection boundary, continue detection, and repeat ① to ⑧.

[0081] After massive demand wave detection is complete, target inventory is generated at the start of each order cycle. This is a precise inventory plan based on known future demand and known supply capacity distribution:

[0082] In actual calculation, the calculation result needs to be rounded according to the material measurement accuracy. s=i-CLC-SUI i-CLC-1

[0083] QtyScale is the measurement accuracy of the material.

[0084] Furthermore, the safety stock quantity is used to deal with supplier delivery delays; a single value ACU is used to represent the near future demand, considering the minimum order quantity mOQ and the delivery time deviation factor SVF. T Determine the safety stock quantity. The specific formula is as follows: SSQ = sign (ACU) · (1-sign (SUI)) · SVF T max(ACU,mOQ)

[0085] Among them, when the average cycle usage ACU = 0, the safety stock quantity SSQ = 0; during the period of preparing for huge demand waves, the safety stock quantity SSQ = 0.

[0086] Furthermore, emergency reserve inventory is used to respond to urgent orders from demanders. Specifically, the RTR (urgent order tolerance) is introduced, and an RTR·ACU emergency reserve inventory is established. Generally, the limit is 0 ≤ RTR ≤ 1, but in special cases, RTR > 1 is allowed.

[0087] Furthermore, the supply and demand balance equation includes a first supply and demand balance equation, which is established to cope with conventional demand and resilience keeping demand. The equation is as follows: PIQ i +OFE·POO i +OFE·POQ i =(CLC+2)·ACU i+RKD i+CLC

[0088] In the equation, i=0,1,…,max(DVH,0) PSQ i =0 SSQ i =sign(ACU i ) SVF T max(ACU i ,mOQ) SUI j =0,j=0,1,…,max(DVH,0)

[0089] PIQ i ,POO i are the estimated available inventory quantity and the estimated open order quantity for the i-th order cycle; POQ i is the order quantity of the i-th order cycle, which is the required scheduling variable; OFE is the order fulfillment efficiency, OFE=(1-SVF Q )·(1-SVF q ); RKD i+CLC Maintain demand for supply chain resilience for the i+CLC order cycle; CMD i+CLC Capacity matching requirements for the i+CLC order cycle; PSQ i is the quantity of stock in reserve for the i-th order cycle. Since the first supply-demand balance equation is for normal demand and there is no huge demand beyond the supply capacity, the value is 0; SSQ i The safety stock for the i-th order cycle is used to cope with supplier delivery delays.

[0090] Furthermore, the supply and demand balance equation includes a second supply and demand balance equation, which is established to ensure a constant supply capacity of resources to cope with massive demand waves. The equation is as follows:

[0091] In the equation,

[0092] e is the identifier of any massive demand wave (order cycle number), which satisfies the following two conditions: ①ACD e >OFE·MOQ,②ACD e+1 ≤OFE·MOQ;

[0093] s=e-CLC-SUI e-CLC +1 is the starting order cycle required to prepare for this huge demand wave; PSQ e-CLC =0 SSQ e-CLC =sign(ACUe-CLC )·(1 - sign(SUI e-CLC ))·SVF T ·max(ACU e-CLC , mOQ)

[0094] PIQ s , POO s are the estimated available inventory quantity and the estimated outstanding order quantity in the s - th order cycle respectively; from the s - th order cycle to the (e - CLC - 1)-th order cycle, there are a total of (e - CLC - s) order cycles, and the planned order quantity is the maximum order quantity MOQ; POQ e-CLC is the order quantity in the (e - CLC)-th order cycle, which is the planned scheduling variable to be found; RKD e is the demand for maintaining supply chain resilience in the e - th order cycle; CMD e is the demand for capacity matching in the e - th order cycle; PSQ e-CLC is the reserve stock quantity in the (e - CLC)-th order cycle. Since SUI e-CLC+1 ≠1, its value is 0; SSQ e-CLC is the safety stock in the (e - CLC)-th order cycle, which enables immediate recovery to handle supplier delivery delays after the preparation for a huge demand wave is completed.

[0095] Furthermore, the supply - demand balance equation includes a third supply - demand balance equation. The third supply - demand balance equation is established to execute the variable supply capacity of resources to cope with huge demand waves, and the equation is as follows:

[0096] In the equation,

[0097] e is the identifier (order cycle number) of any huge demand wave, satisfying one of the following two conditions:

[0098] ① ACD e > OFE·MOQ e-CLC ≥ OFE·mOQ, and OFE·mOQ ≤ ACD e+1 ≤ OFE·MOQ e+1-CLC ;

[0099] ② MOQ e-CLC < mOQ, and MOQ e-CLC+1 ≥ mOQ, and makes ACD e′ > OFE·MOQ e′-CLC ≥ OFE·mOQ,

[0100] and for e′ < j ≤ e, there is MOQ j-CLC < mOQ;

[0101] s=e-CLC-SUI e-CLC +1 is the starting order cycle required to prepare for this huge demand wave; PSQ e-CLC =0 SSQ e-CLC =sign(ACU e-CLC )·(1-sign(SUI e-CLC SVF T max(ACU e-CLC ,mOQ)

[0102] PIQ s ,POO s They are the estimated available inventory quantity and the estimated open order quantity for the sth order cycle respectively; there are a total of e′-CLC-s order cycles from the sth order cycle to the e′-CLC-1th order cycle, and the planned order quantity is (1-ε(mOQ-MOQ j ))·MOQ j POQ e′-CLC is the order quantity of the e′-CLC order cycle, which is the required scheduling variable; from the e′-CLC+1 order cycle to the e-CLC order cycle, the planned order quantity is 0 due to the unavailability of the fragment supply capacity; RKD e Maintaining demand for supply chain resilience in the first e-order cycle; CMD e Capacity matching demand for the e-th order cycle; PSQ e-CLC The reserve quantity for the first e-CLC order cycle due to SUI e-CLC+1 ≠1, so the value is 0; SSQ e-CLC Provide safety stock for the first e-CLC order cycle, so that after the huge demand wave is completed, it can be restored immediately to cope with supplier delivery delays.

[0103] Furthermore, in steps S3 to S4, the buffer file includes the Green Top, Green Zone, Yellow Top, Yellow Zone, Red Top, Red Zone Base, Red Zone Safety, and Qualified Cycle Demand. Net flow compensation is introduced into the buffer based on the Projected Inventory Quantity, Projected On-Order Quantity, and the identified Supply Capacity Gap to dynamically adjust to the period of stocking for huge demand and its recovery. The specific process includes the following:

[0104] ① Define the top of the yellow zone (TOY) as

[0105] TOY at the top of the yellow zone is actually the net flow baseline, also known as the supply and demand balance line

[0106] ② Define the green zone depth (GZ) as GZ k =(1-sign(SUI k ))·RTR·ACU k

[0107] ③ Define the top of the green zone (TOG) as TOG k =TOY k +GZ k

[0108] TOG at the top of the green zone is actually the upper control line of net flow

[0109] ④ Define the yellow zone depth (YZ) as

[0110] ⑤ Define the top of the red zone (TOR) as TOR k =TOY k -YZ k

[0111] ⑥ Define the red safety zone depth (RZS) as RZS k =TOG k -OFE·MOQ k

[0112] The red safety zone depth RZS is actually the lower control line of net flow

[0113] ⑦ Define the red base zone depth (RZB) as RZB k =TOR k -RZS k

[0114] ⑧ Define order cycle effective demand (QCD) as QCD k =(1-sign(SUI k ))·ACU k +sign(SUI k )·OFE·MOQ k

[0115] ⑨ As a dynamic adjustment during the period of massive demand replenishment and its recovery, the net flow compensation (NFC) is defined as

[0116] in

[0117] Furthermore, net flow compensation is performed based on target inventory using the following formula:

[0118] in

[0119] ⑩Calculate Net Flow Position (NFP)

[0120] Among them, QtyScale is the measurement accuracy of the material (number of decimal places)

[0121] Generate planned order quantity (POQ)

[0122] Calculating the Supply Capacity Gap (SCG)

[0123] When SUI k =0, calculate the supply capacity gap according to the following formula; otherwise, retain the supply capacity gap calculated during the massive demand wave detection;

[0124] Calculate the Net Flow Threshold (NAT) for order notification

[0125] Calculate the expected fulfillment quantity (PFQ)

[0126] Among them, QtyScale is the measurement accuracy of the material (number of decimal places)

[0127] Calculate the Inventory Quantity Limit (IQL)

[0128] where s = k-CLC-SUI k-CLC-1

[0129] QtyScale is the measurement accuracy of the material (number of decimal places)

[0130] Calculate Inventory Quantity High (IQH)

[0131] where s = k-CLC-SUI k-CLC-1

[0132] Calculate Average Inventory Quantity (AIQ)

[0133] Calculating Benchmark Inventory Quantity (BIQ)

[0134] Furthermore, calculating benchmark inventory quantities based on inventory planning is more accurate:

[0135] Calculate the supply chain interruption alarm threshold (DAT)

[0136] Calculate the estimated available inventory quantity (PIQ) at the beginning of the next order cycle

[0137] Furthermore, determining the order of execution resources includes the following process: when the execution resource corresponding to the demand object is a drum point resource, and there is a supply capacity gap for the drum point resource, the execution resource is sorted in ascending order according to the corresponding assembly product customer priority, product family priority, and assembly product priority, and in descending order according to the value-added of the manufacturing process; when the execution resource corresponding to the demand object is a drum point resource, and there is no supply capacity gap for the drum point resource, the execution resource is sorted in descending order according to the value-added of the manufacturing process; when the execution resource corresponding to the demand object is not a drum point resource, the execution resource is sorted in descending order according to the value-added of the manufacturing process.

[0138] Furthermore, the added value of the execution resource manufacturing process is estimated regularly, and the estimation method includes the following steps:

[0139] ① The marginal contribution of the product is calculated by deducting the product standard cost unit price from the average sales price of sales orders within the foreseeable future demand period;

[0140] ② Calculate the difference between the standard cost of the assembly product and all levels of manufacturing components and the sum of the standard costs of sub-component materials as the non-material cost of the product. The standard cost of sub-component materials = standard cost unit price of sub-component × quantity used.

[0141] ③ Based on the proportion of non-material costs to marginal contribution, the marginal contribution is allocated to the assembly product and semi-finished parts at all levels as the value added of the manufacturing process of producing the product or semi-finished product; if the manufacturing process value added of a manufactured part has been estimated due to the marginal contribution allocation of other assembly products, the largest one is retained;

[0142] ④When the BOM and material standard cost change, repeat steps ① to ④.

[0143] Furthermore, the method for dynamically identifying the drum beat resource includes the following process:

[0144] ① Demand objects are clustered based on their mapping to execution resources. Demands not mapped to execution resources are grouped separately, with the corresponding group number 0. Scheduling calculations are subject only to the unified maximum order quantity (MOQ). For demands mapped to execution resources, the demand is automatically categorized based on the principle of non-overlapping execution resources, and group numbers 1, 2, ... are generated sequentially.

[0145] ② For each type of execution resource with a group number greater than 0, select the execution resource with the highest load rate in the previous order cycle as the drum point resource, and sort the multiple drum point resources in descending order of load rate to form a drum point resource list; wherein, the drum point resource list at a specific point in the future is determined based on the execution resource load of the corresponding order cycle.

[0146] Furthermore, when determining the clustering of the demand objects, when the mapping relationship between the execution resources and the demand objects changes, the grouping is automatically adjusted.

[0147] Furthermore, when the same demand object corresponds to multiple execution resources, the cumulative demand is adjusted through the following process:

[0148] ① Perform scheduling calculations based on the priority order of the execution resources mapped to the demand objects to obtain the estimated arrival quantity PFQ of the replenishment order;

[0149] ② When calculating the replenishment plan for the nth priority resource, the Cumulative Demand Adjustment (CDA) is calculated based on the PFQ of the scheduling result. At the same time, part of the demand object's demand is reserved for the remaining execution resources according to the Demand Dispatching Ratio (DDR):

[0150] Where BUT k,j is the starting time of the jth order cycle of the kth execution resource, i=0,1,2,…,l n ;RSS k is the scheduling status of the scheduling object corresponding to the kth execution resource (Resource Scheduling Status), 0 means scheduled, 2 means to be scheduled, ∑ k DDR k The sum of the demand allocation ratios of all available resources for the same demand object, which may not be equal to 100%; CTD i Cumulative True Demand (CTD) is the cumulative true demand of the demand object at the end of the i-th order cycle of the n-th execution resource.

[0151] ③ When calculating actual cycle demand, the cumulative demand adjustment caused by the scheduling results of high-priority resources is taken into account

[0152] ACD n,i =ε(CTD i +CDA n,i -CTD i-1 -CDA n,i-1 )(CTD i +CDA n,i -CTD i-1 -CDA n,i-1 ), where i = 0, 1, 2, ..., l n .

[0153] The delivery date review method for undetermined requirements of the present invention uses the undetermined requirements after delivery date review as the material requirements. The delivery date review method specifically includes the following steps:

[0154] ① Obtain the original demand of the demand to be determined and make the original demand participate in the actual cycle demand calculation;

[0155] ② Identify supply capacity gaps. If any exist, split the original demand for the pending demand (such as pending sales orders) and partially shift it back. Recalculate the pending demand based on the BOM expansion, and repeat step ① until there are no supply capacity gaps.

[0156] ③After the delivery review is completed, the adjustment strategy for the original requirements is output.

[0157] The supply chain status assessment method of the present invention assesses the supply chain execution status based on the inventory on hand and the outstanding orders, and is implemented through the following process:

[0158] ① Evaluate the inventory on hand OHQ = PIQ0 and generate the current assessment conclusion (SAC)

[0159] ②Evaluate the unfulfilled order OOQ = POO0 and supplement the current situation assessment conclusion (SAC).

[0160] ③When the on-hand inventory is high, give the percentage of inventory decline and the saved occupied inventory funds.

[0161] The order execution situation monitoring method described in this invention is used to monitor the execution situations of the replenishment order and the replenishment plan, and specifically includes the following processes:

[0162] ①List of current order cycle orders (POQ0>0), sorted by the reorder point time (t0 + OPO) and the planned priority to distinguish the issued status;

[0163] ②Daily plan, weekly plan, monthly plan, quarterly plan and annual plan of material requirements, summarized by the arrival point time

[0164] ③List of expected inventory at a specific future point (according to PIQ k );

[0165] ④Material kit inspection for specified products and quantities at a specific future point;

[0166] ⑤List of materials with order issuance (POQ1>0) in the next order cycle, sorted by the planned priority for early communication with execution resources;

[0167] ⑥List of materials with arrivals (PFQ0>0) in the current order cycle, sorted by the required arrival time (t0 + FPO), for the order follower's order following or order execution kanban display;

[0168] ⑦List of materials with supply chain interruptions (PIQ0<DAT0) in the current order cycle, sorted by the expected supply chain interruption time for the planner to adjust the plan (including arranging emergency rush orders) or supply chain interruption warning kanban display;

[0169] ⑧List of materials with demand in the previous order cycle and no demand in the current and future order cycles (l = -1), sorted in descending order by the on-hand inventory amount, for the planner or salesperson to timely perceive the impact of market changes on material demand;

[0170] ⑨Inventory list of materials with discontinued demand (l<-1) but still in stock, sorted in descending order by the occupied inventory funds amount, for the warehouse keeper to timely perceive the risk of material sluggishness.

[0171] The resource load monitoring and resource capacity planning method of the present invention is used to monitor and plan the supply of the execution resources, and specifically includes the following process:

[0172] ① Resource load table for current and specified future order cycles, sorted in descending order by load rate;

[0173] ② Load trend curve of specified resources;

[0174] ③ A list of resources that cause insufficient supply capacity, sorted in descending order by the percentage of missing capacity;

[0175] ④ A list of resources recommended for outsourcing (load rates are consistently below a specific threshold), sorted by average load rate.

[0176] The supply chain digital twin simulation method based on the future demand visible scheduling results of sub-components at each level expanded by BOM described in the present invention is characterized in that in step S1, a supply chain demand transmission mechanism is established through the BOM table and the material pegging table (Material Pegging Table), with the material as the entry, and according to the BOM table or the material pegging table (Material Pegging Table), the list of sub-components at all levels of the material and the corresponding scheduling results of sub-components at all levels of the material are found, and the material inventory changes from the current time to each future time point are simulated. Combined with the location coordinates of the locations of each storage location, the material replenishment, consumption and inventory changes over time are displayed on the GIS map or the designated background map.

[0177] Beneficial Effects: This invention ensures an uninterrupted supply chain while maintaining sufficient total supply capacity, and controls decoupling point inventory fluctuations within the most reasonable range. It also provides three threshold control lines for supply chain execution monitoring: order forecasts, order follow-up warnings, and interruption alerts. It identifies and measures supply capacity gaps. It evaluates on-hand inventory and open orders based on supply and demand contracts and future demand. It also conducts delivery reviews for unconfirmed future demand based on supply capacity. This invention has the following significant effects:

[0178] 1. This invention significantly reduces decoupling point inventory: This invention defines an achievable benchmark inventory formula. Compared with the average inventory on hand (AOH) formula proposed by DDI, inventory reduction is extremely significant due to the removal of the impact of the decoupling lead time (DLT) on inventory. The following table shows the DLT range from 1 day to 16 days (different lead time adjustment factors (LTFs) are assigned to short, medium, and long lead times). Please refer to Figure 13 for a comparison of the two methods. It can be seen that the method of this invention is superior in terms of process, calculation, and results.

[0179] Among them, the benchmark inventory formula is:

[0180] Average inventory on hand AOH formula:

[0181] 2. Guarantee of uninterrupted supply chain

[0182] In response to real future demand, under the premise that no supply capacity gap is detected, the replenishment orders and replenishment plans generated by the method of the present invention allow order execution resources to execute deviations (not on time, not in quantity) within the scope of contract tolerance and can also ensure the uninterrupted supply chain. This is because in addition to meeting the supply and demand balance needs, inventory is also reserved to meet the needs of maintaining supply chain resilience, including safety inventory to deal with supplier delivery delays, emergency backup inventory to deal with urgent orders from demanders, reserve inventory to deal with the huge demand waves that are about to be prepared, and capacity matching needs to deal with insufficient supply capacity in the next order cycle.

[0183] 3. The present invention can automatically detect huge demand waves and make reasonable stocking according to the maximum supply capacity and resource load, and make reasonable arrangements for stocking in advance.

[0184] 4. The target inventory formula of the present invention is an accurate inventory plan under the condition of known future demand and known supply capacity distribution. It can directly generate net demand by combining the inventory on hand and the outstanding orders.

[0185] 5. Planning and scheduling covers the entire visible period of demand and can simulate the future of the supply chain.

[0186] 6. Efficient supply chain execution monitoring, providing multiple execution monitoring methods such as advance order notification, current order warning, and supply chain interruption warning.

[0187] 7. Review of the delivery date of pending requirements: automatically review the pending requirements and automatically decompose and recommend extensions based on resource load if the delivery date is not met.

[0188] 8. Basically, there is no need to set subjective parameters. DLT, AOC, OPO, SPQ, mOQ, MOQ are all contract parameters between supply and demand. SVF T ,SVF Q ,SVF q It is obtained based on the evaluation of the actual execution of order execution resources in the past. The only thing that needs to be set subjectively is the customer's tolerance for urgent orders (RTR).

[0189] 9. Automatically identify drum resources and sort drum resource orders taking into account manufacturing value-added factors.

[0190] 10. In the case of a single demand with multiple execution resources, scheduling can be performed in priority order based on the supply and demand contracts of different execution resources to meet the total future demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0191] FIG1 is a schematic diagram of a supply chain replenishment model according to the present invention;

[0192] FIG2 is a schematic diagram showing the principle of determining the sliding average window width;

[0193] FIG3 is a schematic diagram of the first supply and demand balance equation (applicable to conventional demand replenishment);

[0194] Figure 4 is a schematic diagram of the principle of massive demand wave detection;

[0195] Figure 5 is a schematic diagram of the second supply and demand balance equation (applicable to large-scale demand wave stocking);

[0196] FIG6 is a schematic diagram of the third supply-demand balance equation (applicable to massive demand wave stocking with resource load constraints);

[0197] FIG7 is a schematic diagram of the principle of generating replenishment orders and replenishment plans;

[0198] Figure 8 is a schematic diagram of inventory control lines and estimated inventory monitoring;

[0199] Figure 9 is a supply and demand balance diagram;

[0200] Figure 10 is a schematic diagram of inventory improvement effects;

[0201] Figure 11 is the data model ER diagram;

[0202] Figure 12 is a logic block diagram of the scheduling system program;

[0203] Figure 13 is a comparison chart of the benchmark inventory formula and the AOH formula. DETAILED DESCRIPTION

[0204] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0205] The demand-driven replenishment order generation and replenishment plan scheduling method and system described in the present invention include the following steps: S1. Setting a demand object based on the positions of the supplier and the demander in the supply network, wherein the demander provides material requirements and the supplier provides execution resources; S2. Condensing the recent material requirements of the demand object using a single numerical value ACU; S3. Monitoring the current supply capacity of the execution resources, the demander's opening inventory on hand, and the supplier's opening open orders at the agreed order cycle as the time granularity, and establishing a timely updated buffer file; S4. After allocating and prioritizing the execution resources based on the requirements of the demand object, dynamically matching the execution resources and supply capacity for the demand object considering the constraints of the supply and demand balance equation, and generating a replenishment order and replenishment plan.

[0206] In S1, please refer to Figure 1, which shows the mathematical model of supply chain replenishment between suppliers and demanders. The supply chain replenishment model involves the following concepts and terms:

[0207]

Furthest Future True Demand Point

[0208] Significant Past Actual Demand Horizon: The period from the beginning of the previous year to the beginning of the order cycle at the current time, expressed as t B To represent the beginning of the previous year, use t0 to represent the starting point of the order cycle at the current moment, then the effective past actual demand period is the left closed and right open interval [t B ,t0);

[0209] [Future True Demand Visibility Horizon] - the period from the start of the order cycle at the current moment to the last real demand point, represented by the closed interval [t0,t L ]express;

[0210] Agreed Order Cycle (AOC) is the order cycle agreed upon by the supplier and the buyer, that is, how often the buyer issues an order to the supplier. This is an important part of the supply and demand contract and is expressed in t P represents the current moment, let t0=t B +b·AOC, The i-th order cycle (Cycle i) can be represented by [t0+i·AOC,t0+(i+1)·AOC), where Indicates that x is rounded down, i=-b,-b+1,…,-1,0,1,2,…,l;

[0211] Decoupled Lead Time (DLT) is the time from the time the order is placed by the buyer to the time the supplier delivers the ordered materials to the buyer. It is also an important part of the supply-demand contract.

[0212] [Order Point Offset] OPO (Order Point Offset) - the time from the start of the order cycle to the time when the order is issued. Obviously, 0≤OPO <AOC;

[0213]

Possible Order Points

[0214]

Fulfillment Point Offset

[0215] Among them, represents the ceiling of x, {x} represents the fractional part of x, and sign(x) is the sign function;

[0216]

Possible Fulfillment Points

[0217]

Averaging Window Width

[0218] The demand in the future true demand visibility period and the effective past actual demand period is the input for the DDMRP order generation and scheduling calculation. Considering the long lead time situation, the effective past actual demand period starts from the beginning of the previous year and ends at the starting point of the current order cycle. The future true demand visibility period starts from the starting point of the current order cycle and ends at the last true demand time point.

[0219] In S1, to accurately depict the supply and demand relationship, a supply and demand contract is established digitally. See Figure 1 for a graphical representation of the various concepts involved in the supply chain replenishment model, including the last real demand point, the effective past actual demand period, the future real demand visibility period, the agreed order cycle, the decoupled lead time, the order point offset, the possible order point, the arrival point offset, the possible arrival point, and the demand sliding average window width. The supply and demand contract between the supplier and the demander includes agreements on time, quantity, and service level:

[0220] (1) The time agreements include: Agreed Order Cycle (AOC): the buyer will only issue one order per order cycle (except for expedited orders due to the supplier's failure to fulfill the contract); Order Point Offset (OPO): the buyer will only issue orders at a fixed order point (relative to the starting point of the order cycle) per order cycle; Decoupled Lead Time (DLT): from the time the supplier receives the order, the supplier will deliver the required quantity of qualified products to the buyer on time after the DLT time (assuming there is no delay from the time the buyer places the order to the time the supplier accepts the order).

[0221] ⑵The quantity agreements are: Standard Pack Quantity SPQ (Standard Pack Quantity): the order quantity of the purchaser must be an integer multiple of SPQ; Minimum Order Quantity mOQ (Minimum Order Quantity): the order quantity of the purchaser must not be less than mOQ (mOQ must be an integer multiple of SPQ); Maximum Order Quantity MOQ (Maximum Order Quantity): the order quantity of the purchaser must not be greater than MOQ (MOQ must be an integer multiple of SPQ). This is the maximum supply capacity of the order execution resources within one order cycle.

[0222] ⑶ Service Level Agreement: Supply Variability Factor SVF: Allows the supplier to have a comprehensive order execution deviation of SVF×100% in terms of time, quantity, and quality, 0≤SVF<1; In addition, SVF∈[0,1) can be adjusted based on the supplier's [t B ,t0) order execution history estimation, the formula is as follows: SVF=1-(1-SVF T )·(1-SVF Q )·(1-SVF q )

[0223] Among them, n is the actual demand period in the past [t B ,t0)Total number of completed orders,τ -i is the actual order completion time of the -i order cycle, PFQ-i AFQ is the required delivery quantity for the -i order cycle. -i is the actual delivery quantity of the -i order cycle, NGQ -i is the number of defective products detected in the -i order cycle, and ε(x) is the unit step function

[0224] Rush-order Tolerance Ratio (RTR): allows the purchaser to place urgent orders within the demand timeframe (DTF) at a ratio of RTR × 100% of the order cycle usage. 0≤RTR<1.

[0225] In addition, based on the above contract parameters of the supply and demand contract, the following parameters can be further derived:

[0226] FPO (Fulfillment Point Offset)

[0227] CLC (Count of Lead Cycles)

[0228] Demand Time Fence DTF = t CLC+1 =t0+(CLC+1)·AOC

[0229] Supply Service Level SSL (Supply Service Level) SSL = 1-SVF = (1-SVF T )·(1-SVF Q )·(1-SVF q )

[0230] Order Fulfillment Effectiveness (OFE)

[0231] Furthermore, a single value ACU is used to condense the recent material demand of the demand object. Without losing rationality, the present invention characterizes the material demand based on the assumption of uniform consumption by the demander. Assuming that the inventory consumption of the demander is uniform within an order cycle, the order cycle consumption rate CCR (Cycle Consumption Rate) is

[0232] Then, referring to the idea of ​​calculating the average daily usage (ADU) of DDMRP, the present invention calculates the average cycle usage (ACU) of the k-th order cycle, which is used to centrally represent the recent material demand of the k-th order cycle. Without loss of generality, the present invention uses the AWW actual cycle usages around the k-th order cycle to perform a weighted average according to a certain weight calculation method to obtain the average cycle usage (ACU) of the k-th order cycle. k The specific steps include:

[0233] S21. Obtain all known and real future material requirements of the demand object, combine them with the current cumulative demand, pre-process them into a cumulative demand series (Cumulative Demand Series), and group them by the agreed order cycle to obtain the actual cycle demand (Actual Cycle Demand) of each current and future order cycle, recorded as ACD k , -b≤k≤l, ACD can be calculated based on the accumulation of cumulative demand at a certain point in time k ; Let CD(t) be the cumulative demand (Cumulative Demand) aggregated to time t, then ACD k =CD(t k +AOC)-CD(t k )=CD(t0+(k+1)·AOC)-CD(t0+k·AOC).

[0234] Let the Cumulative True Demand at the end of the k-th order cycle be CTD k =CD(t k + AOC)=∑ i≤k ACD i .

[0235] S22. Establish a sliding average window for each order cycle based on the actual order cycle demand sequence. The width of the sliding average window (Averaging Window Width) is:

[0236] AWW=CLC+2, where In the formula, OPO, DLT, and AOC are all parameters of the supply and demand contract;

[0237] Among them, the present invention proposes a method for determining the location of the optimal replenishment mechanism, the demand moving average window width, and the corresponding position of the moving average window. On the premise of strictly following the supply and demand contract, the correct time (Right Time) and the correct quantity (Right Quantity) are used as the target positioning of the replenishment mechanism of the present invention, and full-scenario support is required. Based on the "correct time", from an isolated demand special case, as shown in Figure 2, for an isolated demand (only one order cycle has demand), in order to achieve the target positioning of Right Time and Right Quantity, it is proved that the AWW calculated by the present invention is AWW = CLC + 2, and the proof process is as follows:

[0238] Let there be an isolated demand ACD in the k-th order cycle k >0, ACD i =0, i≠k, k>0, i=-b, -b + 1, …, k, then there is

[0239] Among them, w j is the weight coefficient, j = 1, 2, …, AWW, that is, the demand is recognized as early as in the (k - AWW + 1)-th order cycle, and at this time the order POQ is generated k-AWW+1 , according to the supply and demand contract, the order will be completed in the (k - AWW + 1 + CLC)-th order cycle, PFQ k-AWW+1+CLC = OFE·POQ k-AWW+1 , so there will be inventory (Projected available Inventory Quantity) at the beginning of the (k - AWW + 2 + CLC)-th order cycle: PIQ k-AWW+2+CLC = PFQ k-AWW+1+CLC = OFE·POQ k-AWW+1 .

[0240] To sum up, if k - AWW + 2 + CLC < k, that is, AWW > CLC + 2, the arrival is too early, violating the "correct time" constraint. Therefore, AWW ≤ CLC + 2; if k - AWW + 2 + CLC > k, that is, AWW < CLC + 2, the arrival is too late, resulting in the demand in the k-th order cycle not being met. Therefore, AWW ≥ CLC + 2; thus, the conclusion is obtained that the moving average window width AWW = CLC + 2.

[0241] In addition, the determination of the position of the demand moving average window is through the following process: considering the case where the maximum order cycle number l may be less than CLC, the starting position of the moving average window in the k-th order cycle is determined to be k + min(CLC + 1, l - k) - CLC - 1, and the ending position is k + min(CLC + 1, l - k), and the window width is CLC + 2.

[0242] S23. Within the sliding average window of the k-th order cycle, determine the average cycle usage based on the assumption of uniform consumption by the demand side. Average daily usage ADU k , Demand during lead time DDLT k and Arithmetic average cycle usage AACU k+CLC+2 . During the preparation period for large-demand waves, the average cycle usage ACU without considering preparation is determined according to the serial number mark of the large-demand preparation period of the order cycle. k :

[0243] Wherein, ACD i is the actual cycle demand of the i-th order cycle, SUI i is the serial number mark of the lead time of the i-th order cycle.

[0244] Further, when demand history is not obtained but unfulfilled orders are obtained, the demand is extrapolated forward based on the same average cycle usage for past demands. The specific formula is as follows:

[0245] Wherein, ACD k-1 represents the past demand to be extrapolated.

[0246] More strictly, if the actual cycle demands ACD k , ACD k+1 ,..., ACD k+m-1 of m (2 < m ≤ AWW) order cycles are known, then the actual cycle demand of the previous order cycle can be extrapolated forward through the second-order Chebyshev polynomial. The formula is as follows:

[0247] When m = 2, linear extrapolation is performed. The specific formula is as follows:

[0248] ACD k-1 = 2ACD k - ACD k+1 .

[0249] Similarly, when insufficient demand history data is obtained, the average cycle usage of the previous order cycle is obtained through the following formula:

[0250] More strictly, if the actual cycle demands ACD k , ACD k+1 ,..., ACD k+m-1 are known and ACD <000​

[0251] For the last AWW order cycles within the demand visibility period, calculate the arithmetic mean of their actual cycle demands, and then calculate the coefficient of dispersion:

[0252] Based on this coefficient of dispersion, predict the demands for AWW order cycles according to the following formula:

[0253] Correspondingly, we obtain the estimated average cycle usage for the DVH-CLC order cycle, with the formula as follows:

[0254] When DVH < CLC, if the arithmetic average cycle usage AACU of the past m (2 < m ≤ AWW) order cycles is known k-m+1 , AACU k-m+2 ,..., AACU k , then use the second-order Chebyshev polynomial to predict the arithmetic average cycle usage of the (k + 1)th order cycle, but to ensure that the supply chain does not interrupt and only increases or remains the same, the formula is as follows:

[0255] When m = 2, perform linear extrapolation prediction but also keep it only increasing or remaining the same. The specific formula is as follows: AACU k+1 = max(AACU k , 2AACU k - AACU k-1 ).

[0256] Based on the extrapolated arithmetic average cycle usage AACU k+1 , first calculate ACD according to the following formula k Then calculate and ACU k-1-CLC :

[0257] When describing the supply chain in S1, the following steps are included: S111, obtaining a supply network, which includes multiple supply chain nodes, and the supply chain nodes are connected by supply chain segments; S112, determining the supply chain potential according to the status of the supply chain node, and classifying the supply chain nodes according to the supply chain potential. Specifically, the supply chain potential is 1, indicating used inventory; the supply chain potential is 2, indicating in-use inventory; the supply chain potential is 3, indicating a line-side warehouse for materials to be consumed; the supply chain potential is 4, indicating a raw material supermarket or a semi-finished product buffer or a semi-finished product warehouse or A commodity supermarket or a product line side warehouse; a supply chain potential of 5 indicates a raw material warehouse; a supply chain potential of 6 indicates an external raw material warehouse or a supplier-managed inventory; a supply chain potential of 7 indicates a supplier supply location or a customer delivery location; a supply chain potential of 8 indicates a distribution center or a finished product consignment warehouse or a commodity supermarket; a supply chain potential of 9 indicates a finished product warehouse or a commodity warehouse; x and y represent the serial numbers of supply chain nodes with the same supply chain potential; S113, the supply chain is divided into a manufacturing enterprise supply chain, a commercial enterprise supply chain, and an industrial chain according to function, with the specific structure as follows:

[0258] <Manufacturing Enterprise Supply Chain>

[0259] ::={7x6y|7x5y|6x5y|6x4y|6x3y|5x4y|5x3y|4x3y|3x2y|2x1y|1x4y|4x4y|4x3y|4x9y|9x8y|9x7y|8x7y}

[0260] <Commercial Enterprise Supply Chain>::={7x9y|9x8y|9x7y|8x7y}

[0261] <Industry Chain>::={T n zP u xP d y}

[0262] Among them, T n Indicates the Tier n supplier of brand product manufacturers; z indicates the supplier serial number of the same level, P u xP d y represents the supplier's internal supply chain.

[0263] When the demand object is established in S1, the following steps are specifically included: S121, dividing the target demand location into location, storage location, work center, production line, work station, and administrative division according to the demand situation of materials in the supply network; S122, combining the materials and the target demand locations one by one into multiple demand objects, specifically: demand object::={(material, location)|(material, storage location)|(material, work center)|(material, production line)|(material, work station)|(material, administrative division)}; wherein, (material, location) describes the delivery demand of materials (goods, finished products), thereby triggering the shipment of storage locations; (material, storage location) describes the outbound demand of materials (goods, finished products, semi-finished products, raw and auxiliary materials, tooling, containers) from a specific storage location (Storage Site), thereby triggering the replenishment of execution resources; (material, work center) describes the delivery demand of materials (finished products, semi-finished products) in a specific work center (Work (Material, Production Line) describes the production work order of materials (finished products, semi-finished products) at a specific production line (Production Line) or the distribution demand of materials (raw materials, auxiliary materials, semi-finished products, tooling, containers) to a specific production line; (Material, Work Station) describes the production kanban instruction order of materials (finished products, semi-finished products) at a specific work station (Work Station) or the distribution demand of materials (raw materials, auxiliary materials, semi-finished products, tooling, containers) to a specific work station; (Material, Administrative Division) describes the distribution demand of materials (supplies) distributed to a specific administrative division.

[0264] When enumerating the mapping between the demand objects and the execution resources, the following steps are specifically included: S131, subdividing the materials into material types such as commodities, finished products, semi-finished products, raw and auxiliary materials, tooling, and containers; subdividing the execution resources into resource types such as production lines, workstations, work centers, suppliers, finished product external warehouses, finished product main warehouses, commodity warehouses, raw and auxiliary material external warehouses, and raw and auxiliary material main warehouses; S132, when enumerating different material types, the mapping between the demand objects and the execution resources is specifically as follows:

[0265] ① Required object = (material, location), material type = finished product or commodity, execution resource: finished product external warehouse or finished product main warehouse or commodity supermarket or commodity main warehouse (the required object needs to be shipped);

[0266] ② When the demand object = (material, storage location), the material type = finished product or commodity, and the storage location supply chain potential = 8, the execution resource is: finished product main warehouse or commodity warehouse (replenishment of the demand object is required);

[0267] ③ When the demand object = (material, storage location), the material type = finished product, and the storage location supply chain potential = 9, the execution resource is: assembly work center, assembly production line, or assembly independent workstation (production and replenishment of the demand object is required);

[0268] ④ When the demand object = (material, storage location), the material type = commodity, and the storage location supply chain potential = 9, the execution resource is the supplier (purchase and replenishment of the demand object is required);

[0269] ⑤ When the demand object = (material, storage location), the material type = semi-finished product, and the storage location supply chain potential = 4, the execution resource is the work center or semi-finished product production line corresponding to the semi-finished product production, or the independent semi-finished product production station (production replenishment of the demand object is required);

[0270] ⑥ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 6, the execution resource is: supplier (purchase and replenishment of the demand object is required);

[0271] ⑦ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 5, the execution resource is: supplier (needs to purchase and replenish the demand object) or external raw and auxiliary material warehouse (needs to allocate and replenish the demand object);

[0272] ⑧ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 4, the execution resource is: the raw and auxiliary materials warehouse or external warehouse (replenishment of the demand object is required);

[0273] ⑨ When the demand object = (material, production line) and the material type = finished product or semi-finished product (production is scheduled on this production line), the execution resource is the production line (production of the finished product or semi-finished product needs to be scheduled);

[0274] ⑩ When the demand object = (material, production line) and the material type = raw and auxiliary materials, upstream semi-finished products, tooling, or containers, the execution resource is: storage location (replenishment of the demand object is required);

[0275] When the demand object = (material, workstation) and the material type = finished product or semi-finished product (the process route passes through the workstation), the execution resource is: workstation (production of the finished product or semi-finished product needs to be scheduled);

[0276] When the demand object = (material, workstation) and the material type = raw or auxiliary materials, upstream semi-finished products, tooling, or containers, the execution resource is the storage location (replenishment of the demand object is required).

[0277] When the demand object = (material, administrative division) and the material type = supplies, the execution resource is: storage location (requires distribution and replenishment of the demand object).

[0278] Determining the supply capacity of execution resources includes the following steps: ① Combining demand objects with execution resources to form scheduling objects, setting agreed order cycle (AOC), order point offset (OPO), decoupled lead time (DLT), standard package quantity (SPQ), minimum order quantity (mOQ) and maximum order quantity (MOQ) attributes for each scheduling object, and evaluating the time deviation factor (SVF) based on the past performance of the execution resources. T ), quantity deviation factor (SVF Q ), quality deviation factor (SVF q );② The load of the execution resources in each order cycle caused by high-priority demand, expressed as the resource loading ratio (RLR). For a specific material, the maximum supply capacity of the execution resources in a specified order cycle i is:

[0279] Among them, when the execution resource is assigned POQ for a specific material in the i-th order cycle i The load factor will increase if the order

[0280] The time deviation factor, quantity deviation factor, and quality deviation factor are obtained by calculation or agreement. The calculation includes the following steps: obtaining the past order completion status within the target time period; and calculating the time deviation factor, quantity deviation factor, and quality deviation factor within the target time period based on the past order completion status. The specific formula is as follows;

[0281] Where n is the actual demand period in the past [t B ,t0) total number of completed orders; t -i is the start time of the -i order cycle; τ -i is the actual order completion time of the -i order cycle; t B is the start time of the previous year, i.e. the starting time of the -b order cycle; ε(x) is a unit step function, and the ε(x) formula is: PFQ -i AFQ is the required delivery quantity for the -i order cycle. -i The actual delivery quantity of the -i order cycle; NGQ -i AFQ is the number of defective products delivered in the -i order cycle. -i is the actual delivery quantity of the -i-th order cycle.

[0282] When it is found that the execution resource supply capacity cannot meet the requirements of the corresponding order cycle (with a delay of CLC order cycles), a reverse order method is used to find the Huge Demand Wave for advance stocking up, and at the same time, the supply capacity gap is detected. The specific steps are as follows:

[0283] ① Starting from the last order cycle of the future demand visibility horizon as the detection boundary, detect one by one forward for each order cycle, and at the same time calculate the increments of the critical cycle demand (CCD) and the resilience keeping demand (RKD) of the detected order cycle: CCD e = OFE·MOQ e-CLC ; ΔRKD e = ΔSSQ e-CLC + RTR·ΔACU e-CLC ;

[0284] ② Use the order cycle number e corresponding to the first cycle demand greater than the increments of the critical cycle demand and the resilience keeping demand as the initial value of the huge demand wave identification. If it does not exist, it means there is no huge demand, and the detection is aborted; if it exists, proceed to the next step;

[0285] ③ If MOQ e-CLC < mOQ, it means the fragmented supply capacity is unavailable, e ← e + 1, and loop until e = DVH + 1 or MOQ e-CLC ≥ mOQ;

[0286] ④ Calculate the resilience keeping demand RKD of the e order cycle e ;

[0287] ⑤ Find the largest order cycle number s > -1 that satisfies the following formula:

[0288] ⑥ If the largest order cycle number s > -1 cannot be found, then identify the total supply capacity gap. The supply capacity gap can be evenly distributed among the 0, 1,..., e - CLC - 1 order cycles, and at the same time set s = 0; the total supply capacity gap is identified through the following formula:

[0289] ⑦ Mark the order cycles s, s+1, ..., e-CLC with the stocking up index, as shown in the following formula: SUI k =k-s+1,k=1,2,...,e-CLC-s+1;

[0290] ⑧If s>0, take s+CLC-1 as the start of a new massive demand detection boundary, continue detection, and repeat ① to ⑧.

[0291] Here, huge demand refers to actual cyclical demand that exceeds the supplier's capacity within a single order cycle. Huge demand can occur multiple times within the visible future demand period, either continuously or intermittently. When huge demand exceeds the capacity within a single order cycle, suppliers must stock up in advance at their maximum supply capacity (stocking up). Continuous order cycle demand that requires continuous stocking at maximum supply capacity (except for the last stocking cycle) is called a wave of huge demands. A wave of huge demands is identified by the order cycle number of its last huge demand. For each wave of huge demand, we need to detect and calculate the order cycle number for which stocking must begin. The stocking-up lead cycles (SLC) are calculated by subtracting the minimum order cycle number required to stock up from the wave huge demand identifier.

[0292] After massive demand wave detection is complete, target inventory is generated at the start of each order cycle. This is a precise inventory plan based on known future demand and known supply capacity distribution:

[0293] In actual calculation, the calculation result needs to be rounded according to the material measurement accuracy. s=i-CLC-SUI i-CLC-1

[0294] QtyScale is the measurement accuracy of the material.

[0295] In the target inventory formula, the beginning inventory of each order cycle before the start of the huge demand wave only needs to meet the consumption before the current arrival point and reserve the resilience maintenance inventory. The resilience maintenance inventory includes the safety stock to cope with the supplier's delivery delay, the emergency standby inventory to cope with the urgent order insertion of the demander, the preparatory inventory to cope with the huge demand wave at the beginning of the next order cycle, and the capacity matching inventory to cope with the insufficient supply capacity in the next order cycle. During the huge demand wave, before the arrival order cycle corresponding to the last order cycle with substantial supply capacity (s + CLC + 1 ≤ i ≤ e'), the target inventory is the beginning inventory + total supply - total demand. When e' < i ≤ e, the target inventory needs to cover the demand and resilience maintenance demand corresponding to the fragmented supply capacity period. After the huge demand wave ends (i = e + 1), the target inventory resumes to the demand before the current arrival point plus the resilience maintenance demand.

[0296] Use the safety stock quantity to cope with the supplier's delivery delay; among them, use a single value ACU to represent the demand in the near future (Near Future), and consider combining the minimum order quantity mOQ and the delivery time deviation factor SVF T Determine the safety stock quantity, and the specific formula is as follows: SSQ = sign(ACU)·(1 - sign(SUI))·SVF T ·max(ACU, mOQ)

[0297] Among them, when the average cycle usage ACU = 0, the safety stock quantity SSQ = 0; during the huge demand wave stock preparation period, the safety stock quantity SSQ = 0.

[0298] The determination of the safety stock quantity formula is obtained by the following process. Based on the supply and demand contract service level agreement, the supplier is allowed to have a delivery time deviation of SVF T The impact of the order completion time deviation will be the entire order quantity. Therefore, the present invention sets the safety stock quantity SSQ (Safety Stock Quantity) for the kth order cycle of the demander as SSQ k = SVF T ·max(ACU k , OFE·mOQ). The first supply and demand balance equation is a "correct time, correct quantity" supply chain replenishment order generation and scheduling solution for the demand without excess supply capacity under the assumptions of uniform consumption by the demander and on-time delivery by the supplier. It is very rigid. Introducing the safety stock quantity SSQ increases the resilience of the supply chain execution in the horizontal direction (time dimension) and can tolerate the moderate deviation of the order delivery time according to the supplier's actual performance in the past. During the huge demand stock preparation period, the maximum supply capacity has been enabled for stock preparation, and the safety stock no longer makes sense, so it is set to 0.

[0299] At the same time, based on the uniform consumption assumption, the inventory quantity of the demander at the beginning of each order cycle must be no less than a critical inventory quantity. This critical inventory quantity CIQ (Critical Inventory Quantity) is the minimum inventory quantity at the beginning of the order cycle to ensure that the inventory quantity within the order cycle is no less than the safety stock quantity SSQ.

[0300] Obviously,

[0301] This is the critical inventory calculation formula that replaces the actual cycle demand with the average cycle usage. The actual critical inventory ACIQ (Actual Critical Inventory Quantity) should be

[0302] Emergency reserve inventory is used to respond to urgent orders from demanders. Specifically, the Rush-order Tolerance RTR is introduced, and an RTR·ACU emergency reserve inventory is established; under normal circumstances, the limit is 0≤RTR≤1, and in special circumstances, RTR>1 is allowed. The artificially set Rush-order Tolerance RTR provides support for urgent orders, which can support demand not exceeding RTR·ACU k Emergency order.

[0303] Whenever demand time t>DTF=t CLC+1 =t0+(CLC+1)·AOC's demands are grouped as normal demands and are not considered as emergency orders, because the calculation and generation of POQ0, POQ1, ... automatically take these demands into account. <t≤DTF=t CLC+1 This demand is an urgent order insertion. Since the order has already been issued and fulfillment has begun, this urgent order insertion requires appropriate reserved inventory to meet it. Supporting urgent orders provides an Available To Promise (ATP) beyond normal replenishment, increasing the resilience of supply chain execution in the vertical dimension (quantity dimension).

[0304] For stocking of large demand waves, although the total supply capacity is sufficient to meet the total demand of the wave, the uneven demand of each order cycle within the wave may lead to local supply chain disruptions during the large demand wave. To prevent this from happening, additional stocking according to the stocking requirement quantity is required in the order cycle before the start of the large demand wave stocking order cycle. The formula is as follows:

[0305] In the absence of massive demand, AWW = CLC + 2 represents a supply-demand balance cycle, which ensures that no supply chain disruption occurs when replenishment arrives in the current order cycle. However, if the maximum supply capacity of the next order cycle is lower than the maximum supply capacity of the current order cycle, replenishment in the next order cycle may not be able to meet demand after the replenishment arrival point of the current order cycle, leading to supply chain disruption. To address this, capacity matching requirements are introduced, using the following formula:

[0306] Furthermore, the supply and demand balance equation includes a first supply and demand balance equation, which is established to cope with conventional demand and resilience keeping demand. The equation is as follows: PIQ i +OFE·POO i +OFE·POQ i =(CLC+2)·ACU i +RKD i+CLC

[0307] In the equation, i=0,1,…,max(DVH,0) PSQ i =0 SSQ i =sign(ACU i ) SVF T max(ACU i ,mOQ) SUI j =0,j=0,1,…,max(DVH,0)

[0308] PIQ i ,POO i are the estimated available inventory quantity and the estimated open order quantity for the i-th order cycle; POQ i is the order quantity of the i-th order cycle, which is the required scheduling variable; OFE is the order fulfillment efficiency, OFE=(1-SVF Q )·(1-SVF q ); RKD i+CLC Maintain demand for supply chain resilience for the i+CLC order cycle; CMD i+CLC Capacity matching requirements for the i+CLC order cycle; PSQ i is the quantity of stock in reserve for the i-th order cycle. Since the first supply-demand balance equation is for normal demand and there is no huge demand beyond the supply capacity, the value is 0; SSQ i The safety stock for the i-th order cycle is used to cope with supplier delivery delays.

[0309] The process of establishing the first supply and demand balance equation is as follows:

[0310] As shown in Figure 3, if we do not consider the constraints of maximum supply capacity MOQ, minimum order quantity mOQ and standard packaging quantity SPQ, and assume that order execution resources can be executed 100% as planned, based on the assumption of uniform consumption on the demand side, the consumption of the i-th order cycle before the arrival of the delivery point is

[0311] Assume that at the start of the i-th order cycle, there is a projected available inventory quantity PIQ i and Projected On Order (POO) i , the order quantity (Projected Order Quantity) POQ issued in the i-th order cycle i According to the "correct quantity" constraint, after the arrival of the i+CLC order cycle, the starting inventory of the i+CLC+1 order cycle should be exactly equal to the consumption before the arrival of the i+CLC+1 order cycle, that is,

[0312] because

[0313] then

[0314] make

[0315] The present invention derives the first supply and demand balance equation

[0316] in, Essentially, it is CLC+2 order cycle demand ACD i ,ACU i+1 ,…,ACU i+CLC+1 It is the weighted average of , which can effectively characterize the near future demand (Near Future Demand) of the i-th order cycle. It can also be understood as The arithmetic mean of the actual cycle demand of the order cycle, so we can get ADU and ACU * The conversion relationship between them:

[0317] Due to the maximum order quantity (MOQ) constraint of the supply and demand contract, the first supply and demand balance equation is only applicable to the actual order cycle demand that does not exceed the corresponding order cycle supply capacity within the foreseeable future demand period, that is, ACD j ≤OFE·MOQ,j=0,1,2,…,l.

[0318] In addition to satisfying the supply and demand balance, in order to make the supply chain resilient, the right side of equation (1) is added with the resilience maintenance requirement to obtain the improved first supply and demand balance equation, that is, the first supply and demand balance equation of claim 13, which is as follows: PIQ i +OFE·POO i +OFE·POQ i =(CLC+2)·ACU i +RKD i+CLC (2)

[0319] Since there is no actual order cycle demand that exceeds the corresponding order cycle supply capacity within the foreseeable future demand period, ACU i and There is no substantial difference.

[0320] The improved first supply and demand balance equation (2) reveals the inherent law of supply chain planning, but it is not possible to directly solve the equation to obtain POQ. i Instead, we need to find the optimal solution to the following constrained optimization problem based on the supply and demand contract: minPOQ i (3)

[0321] Furthermore, the supply and demand balance equation includes a second supply and demand balance equation, which is established to ensure a constant supply capacity of resources to cope with massive demand waves. The equation is as follows:

[0322] In the equation,

[0323] e is the identifier of any massive demand wave (order cycle number), which satisfies the following two conditions: ①ACD e >OFE·MOQ,②ACD e+1 ≤OFE·MOQ;

[0324] s=e-CLC-SUI e-CLC +1 is the starting order cycle required to prepare for this huge demand wave; PSQ e-CLC =0 SSQ e-CLC =sign(ACU e-CLC )·(1-sign(SUI e-CLC SVF T max(ACU e-CLC ,mOQ)

[0325] PIQ s ,POO sThey are the estimated available inventory quantity and the estimated open order quantity for the sth order cycle respectively; there are a total of e-CLC-s order cycles from the sth order cycle to the e-CLC-1 order cycle, and the planned order quantity is the maximum order quantity MOQ; POQ e-CLC is the order quantity of the first e-CLC order cycle, which is the required planning and scheduling variable; RKD e Maintaining demand for supply chain resilience in the first e-order cycle; CMD e Capacity matching demand for the e-th order cycle; PSQ e-CLC The reserve quantity for the first e-CLC order cycle due to SUI e-CLC+1 ≠1, so the value is 0; SSQ e-CLC Provide safety stock for the first e-CLC order cycle, so that after the huge demand wave is completed, it can be restored immediately to cope with supplier delivery delays.

[0326] The following describes the process of establishing the second supply and demand balance equation: Let e ​​be the last order cycle number of any large demand wave, and s be the starting order cycle for stocking the large demand of this wave. In order to ensure that after stocking is completed to meet all the needs of the large demand wave, it can also meet the consumption before the arrival point of the e+1th order cycle, we obtain the second supply and demand balance equation for the stocking period of the large demand wave as follows:

[0327] The corresponding stocking plan order generation mechanism is as follows:

[0328] When j=s,s+1,…,e-CLC-1, POQ j =MOQ;

[0329] When j = e-CLC, if POQ j =mOQ, otherwise

[0330] During the preparation period for the huge demand wave, the impact of supplier delivery time deviation can be ignored, that is, there is no need to reserve safety stock. However, after the preparation for the huge demand wave is completed, the supply chain resilience should still be restored in the e+1 order cycle. The corresponding average cycle usage should be based on the e-CLC order cycle, that is, ACU e-CLC During the huge demand wave, the contribution of actual cycle demand to average cycle consumption is 0, so we get the formula for the modified average cycle consumption as

[0331] After adding supply chain execution resilience, the revised second supply and demand balance equation, namely the second supply and demand balance equation in claim 14, is as follows:

[0332] Most of the execution resources cannot guarantee a constant maximum supply capacity for each order cycle. Therefore, further, a third supply-demand balance equation is established to handle the huge demand wave with the variable supply capacity of the execution resources. The equation is as follows:

[0333] In the equation,

[0334] e is the identifier (order cycle number) of any huge demand wave, satisfying one of the following two conditions:

[0335] ① ACD e > OFE·MOQ e-CLC ≥ OFE·mOQ, and OFE·mOQ ≤ ACD e+1 ≤ OFE·MOQ e+1-CLC ;

[0336] ② MOQ e-CLC < mOQ, and MOQ e-CLC+1 ≥ mOQ, and makes ACD e′ > OFE·MOQ e′-CLC ≥ OFE·mOQ,

[0337] and e′ < j ≤ e, there is MOQ j-CLC < mOQ;

[0338] s = e - CLC - SUI e-CLC +1 is the starting order cycle for stocking up for this huge demand wave; PSQ e-CLC =0 SSQ e-CLC =sign(ACU e-CLC )·(1 - sign(SUI e-CLC ))·SVF T ·max(ACU e-CLC ,mOQ)

[0339] PIQ s ,POO s are the expected available inventory quantity and the expected outstanding order quantity of the s-th order cycle respectively; from the s-th order cycle to the e′ - CLC - 1 order cycle, there are e′ - CLC - s order cycles in total, and the planned order quantity is (1 - ε(mOQ - MOQ j )) · MOQ j ; POQ e′-CLCis the order quantity for the e'-CLC order cycle, which is the planned scheduling variable to be determined; from the e'-CLC+1 order cycle to the e-CLC order cycle, since the fragment supply capacity is unavailable, the planned order quantity is 0 for all; RKD e is the demand for maintaining the supply chain resilience for the e order cycle; CMD e is the demand for capacity matching for the e order cycle; PSQ e-CLC is the quantity of preparatory goods for the e-CLC order cycle. Since SUI e-CLC+1 ≠1, the value is 0; SSQ e-CLC is the safety stock for the e-CLC order cycle, enabling immediate recovery to handle supplier delivery delays after the preparation for a large demand wave is completed.

[0340] The establishment process of the third supply-demand balance equation is described below.

[0341] In the real supply chain planning scenario, often in the future demand visibility period, part of the supply capacity of the order execution resources has been occupied by high-priority demands, and it is impossible to fully prepare for large demands according to the maximum supply capacity. The decoupling point replenishment demand of a demand object can be met by multiple order execution resources. Suppose there are m order execution resources corresponding to the material demand of a specific demand object, {R k |k = 1, 2,..., m}, and the resource R k can have its own contract parameters with the demander DLT k , AOC k , OPO k , SPQ k , mOQ k , MOQ k , And the load rates of each order execution resource in the future demand visibility period are {RLR k,i |k = 1, 2,..., m; i = 0, 1, 2,..., l;}, so the maximum supply capacity of the resource R k in the future demand visibility period is:

[0342] For simplicity, still use MOQ to represent the maximum supply capacity of the resource R k in a single order cycle. The principle of detecting large demands considering the load constraints of order execution resources is as follows:

[0343] Suppose a large demand wave is identified by the order cycle e and needs to start preparing goods from the s order cycle. Since there may be e - e' order cycles without substantial supply capacity (MOQ j-CLC <mOQ, j = e'+1, e'+2,...2e), the planned scheduling variable to be determined must be POQ e′-CLC , and thus the third supply-demand balance equation is obtained:

[0344] The equation also reflects the unavailability of fragment supply capacity during the period of huge demand preparation.

[0345] After adding supply chain execution resilience, the revised third supply and demand balance equation is the third supply and demand balance equation described in claim 15, and the equation is as follows:

[0346] Furthermore, in steps S3 to S4, the buffer file includes the Green Top, Green Zone, Yellow Top, Yellow Zone, Red Top, Red Zone Base, Red Zone Safety, and Qualified Cycle Demand. Net flow compensation is introduced into the buffer based on projected inventory quantity, projected on-order quantity, and identified supply capacity gaps to address dynamic adjustments during the period of stocking up for massive demand and its recovery. Specifically, the process includes the following:

[0347] ① Define the top of the yellow zone (TOY) as

[0348] TOY at the top of the yellow zone is actually the net flow baseline, also known as the supply and demand balance line

[0349] ② Define the green zone depth (GZ) as GZ k =(1-sign(SUI k ))·RTR·ACU k

[0350] ③ Define the top of the green zone (TOG) as TOG k =TOY k +GZ k

[0351] TOG at the top of the green zone is actually the upper control line of net flow

[0352] ④ Define the yellow zone depth (YZ) as

[0353] ⑤ Define the top of the red zone (TOR) as TOR k =TOY k-YZ k

[0354] ⑥ Define the red safety zone depth (RZS) as RZS k =TOG k -OFE·MOQ k

[0355] The red safety zone depth RZS is actually the lower control line of net flow

[0356] ⑦ Define the red base zone depth (RZB) as RZB k =TOR k -RZS k

[0357] ⑧ Define order cycle effective demand (QCD) as QCD k =(1-sign(SUI k ))·ACU k +sign(SUI k )·OFE·MOQ k

[0358] ⑨ As a dynamic adjustment during the period of massive demand replenishment and its recovery, the net flow compensation (NFC) is defined as

[0359] in

[0360] Furthermore, net flow compensation is performed based on target inventory using the following formula:

[0361] in

[0362] ⑩Calculate Net Flow Position (NFP)

[0363] Among them, QtyScale is the measurement accuracy of the material (number of decimal places).

[0364] Generate planned order quantity (POQ)

[0365] Calculating the Supply Capacity Gap (SCG)

[0366] When SUI k =0, calculate the supply capacity gap according to the following formula; otherwise, retain the supply capacity gap calculated during the massive demand wave detection;

[0367] Calculate the Net Flow Threshold (NAT) for order notification

[0368] Calculate the expected fulfillment quantity (PFQ)

[0369] Among them, QtyScale is the measurement accuracy of the material (number of decimal places)

[0370] Calculate the Inventory Quantity Limit (IQL)

[0371] in

[0372] s=k-CLC-SUI k-CLC-1

[0373] QtyScale is the measurement accuracy of the material (number of decimal places)

[0374] Calculate Inventory Quantity High (IQH)

[0375] in

[0376] s=k-CLC-SUI k-CLC-1

[0377] Calculate Average Inventory Quantity (AIQ)

[0378] Calculating Benchmark Inventory Quantity (BIQ)

[0379] Furthermore, calculating benchmark inventory quantities based on inventory planning is more accurate:

[0380] Calculate the supply chain interruption alarm threshold (DAT)

[0381] Calculate the estimated available inventory quantity (PIQ) at the beginning of the next order cycle

[0382] Among them, the estimation of inventory lower limit, inventory upper limit, reasonable inventory, average inventory and setting of benchmark inventory are as follows.

[0383] The most reasonable inventory constraint requires an order cycle for arrival, and at the arrival point, the inventory reaches the safety stock (due to the constraints of SPQ and mOQ, it may not reach the SSQ, but it must fall between SSQ and SSQ + RTR·ACU). Therefore, the inventory quantity low limit IQL (Inventory Quantity Low Limit) is:

[0384] IQL k =SSQ k-CLC-1 =sign(ACU k-CLC-1 )·(1-sign(SUI k-CLC-1 SVF T ·max(AC Uk-CLC-1 ,OFE·mOQ)

[0385] Taking into account the impact of material measurement accuracy, the formula for the lower limit of inventory quantity is revised to:

[0386] where s = k-CLC-SUI k-CLC-1

[0387] QtyScale is the measurement accuracy of the material (number of decimal places)

[0388] The target inventory provides an accurate inventory plan that ensures an uninterrupted supply chain while matching supply capacity. Therefore, the formula for defining the inventory quantity high limit (IQH) based on the target inventory is as follows:

[0389] where s = k-CLC-SUI k-CLC-1

[0390] Average Inventory Quantity (AIQ) is based on the assumption of uniform consumption by the demander and takes the average value of inventory within the order cycle.

[0391] In the case of no arrival during the order cycle, then

[0392] Taking into account possible supply chain disruptions in actual implementation, the broader average inventory quantity formula is adjusted to:

[0393] The definition of reasonable inventory quantity RIQ (Reasonable Inventory Quantity) is the assumption that the demand of CLC+2 order cycles is evenly distributed (ACDk-CLC =ACD k-CLC+1 =…=ACD k+1 ), the median of the lower and upper limits of the order cycle inventory quantity, that is,

[0394] Define the benchmark inventory quantity BIQ (Benchmark Inventory Quantity), formula (8):

[0395] (8) gives the equation based on ADU, mOQ, SVF T The calculation formula for the benchmark inventory quantity of RTR shows that if the actual inventory is greater than BIQ, there is room for improvement. From this formula, it can be seen that inventory has nothing to do with the decoupling lead time. This means that as long as the supply and demand contract is followed and the method of the present invention is used, inventory can be effectively reduced, thereby reducing inventory holding costs and achieving the goal of reducing costs and increasing efficiency. Figure 10 compares the benchmark inventory quantity of the method of the present invention with the average inventory quantity on hand of the classic DDMRP method. For the sake of convenience, the inventory quantity is expressed as a multiple of the average demand of the agreed order cycle. It can be seen that no matter how much decoupling is required, the inventory of the method of the present invention has an extremely significant decrease. In fact, the method of the present invention reveals the independence of inventory and decoupling lead time through the supply and demand balance equation. It is sufficient to place orders in a timely and variable quantity according to the supply and demand contract.

[0396] Since the target inventory is a precise inventory plan that matches supply capacity, it is more accurate to calculate the benchmark inventory quantity based on the target inventory formula. The formula is as follows:

[0397] In addition, the above process involves the identification of demand suspension and inertia scheduling, the scope of scheduling calculation basis and the scope of scheduling results, the decomposition of the number of open orders at the start of the current order cycle, the calculation of buffer files and the determination of effective cycle demand, net flow compensation, net flow location and scheduling calculation. The specific content is as follows:

[0398] Identification of demand suspension and inertia scheduling issues:

[0399] DDMRP is a material requirement plan based on real demand. Real demand includes future real demand (including the current order cycle demand) and past actual demand. If no future real demand is received, that is, l<0, is it necessary to schedule calculations and generate replenishment orders POQ0 for the current order cycle?

[0400] We believe that when l<-1, it can be determined that the demand of the buyer has been terminated and there is no need to perform scheduling calculations to generate replenishment orders.

[0401] When l = -1, it indicates that the demander had actual demand in the previous order cycle. To prevent the situation where there is demand in the current order cycle and future order cycles but it has not been collected in a timely manner, we have added fault tolerance processing, that is, allowing inertial scheduling according to past actual situations. This inertia essentially means extrapolating while keeping the ACU unchanged. Of course, more strictly, the aforementioned second-order Chebyshev polynomial can be used for extrapolation. ACU i = ACU -CLC-2 , i = -CLC - 1, -CLC, …, -1, 0.

[0402] According to the previously corrected ACU calculation formula,

[0403] For the case of 0 ≤ l < CLC + 1, to prevent supply chain disruptions, we still perform inertial scheduling, that is, according to

[0404] extrapolate ACU i = ACU l-CLC-1 , i = l - CLC, l - CLC + 1, …, l.

[0405] The range of the scheduling calculation basis and the range of the scheduling results:

[0406] From the discussion in the previous section, it can be determined that, in addition to the supply-demand contract and its derivative parameters, among the past actual demands, only the data of a total of CLC + 3 order cycles from -CLC - 3, -CLC - 2, …, -1 are scheduling-related information (Relevant Information). Earlier data is only used to evaluate and calculate SVF T , SVF Q and SVF q , which has no direct effect on the scheduling calculation. The data of each order cycle in the future true demand visibility period are all the basis for the scheduling calculation. In addition, we also need to calculate ACD

[0407] by extrapolation l+1 .

[0408] Of course, more strictly, the ACU can be extrapolated through the second-order Chebyshev polynomial l-CLC .

[0409] So the entire range of the scheduling calculation basis is -CLC - 3, -CLC - 2, …, -1, 0, 1, 2, …, l + 1, a total of l + CLC + 5 order cycles.

[0410] The result of the scheduling calculation should be limited within the order cycle range of 0, 1,..., max(0, l - CLC), with a total of max(0, l - CLC)+1 order cycles. Among them, POQ0 is the replenishment order for the current order cycle, and POQ i is the planned order for future order cycles, where i = 1, 2,..., max(0, l - CLC)+1.

[0411] The decomposition problem of the quantity of outstanding orders at the start of the current order cycle:

[0412] If there is no breakdown of the outstanding order details, we need to decompose the current outstanding order quantity into the previous CLC order cycles based on the supply and demand contract in order to estimate the expected inventory at the start of the 1,..., CLC order cycles.

[0413] First, we need to obtain the average cycle usage ACU for the order cycles of -CLC, -CLC + 1,..., -1. If there is historical data, obtain it from the historical records; otherwise, we need to extrapolate forward.

[0414] From

[0415] So

[0416] Let ACU k-1 = ACU k , and we obtain the formula for extrapolating ACD forward based on the same ACU

[0417] The change in demand is controlled by some unknown non - linear dynamic system. The change in the actual cycle demand should follow the same pattern as the change in the average cycle usage. Therefore, without loss of rationality, the present invention uses equation (11) for the forward extrapolation of ACU and obtains

[0418] Considering factors such as the long - term trend and seasonal fluctuations of demand changes, more strictly, if the arithmetic average cycle usage AACU of m (2 < m ≤ AWW) order cycles is known k , AACU<00​​​​​​​​​​​​​​Arithmetic average cycle usage AACU based on extrapolation prediction k-1 Calculate ACD first according to the following formula k-2 Recalculate and ACU k-3-CLC :

[0421] In this way, extrapolate forward to obtain ACU -CLC ,ACU -CLC+1 ,…,ACU -1 , calculate the safety stock SSQ corresponding to each order cycle -CLC ,SSQ -CLC+1 ,…,SSQ -1 ,Then based on the improved first supply and demand balance equation, we derive the expected order quantity for each corresponding order cycle. PIQ1=PIQ0+PFQ0-ACD0=PIQ0+OFE·POQ -CLC -ACD0 PIQ k+1 =PIQ k +PFQ k -ACD k =PIQ k +OFE·POQ -CLC+k -ACD k

[0422] So we calculated POQ -CLC ,POQ -CLC+1 ,…,POQ -1 ,It should be noted that, during the iterative calculation process, it is necessary to verify that the sum should be equal to POO0.

[0423] but POQ -CLC+k+1 =…=POQ -1 =0

[0424] Calculation of buffer files and determination of effective cycle requirements:

[0425] DDMRP's decoupling point buffer files include the Red Zone Safety (RZS), Red Zone Base (RZB), Yellow Zone (YZ), Green Zone (GZ), and the scheduling engine control lines (Top of Red (TOR), Top of Yellow (TOY), and Top of Green (TOG). We reconstruct these buffer files using the backward-compatible revised first, second, and third supply-demand balance equations. For each k = 0, 1, 2, ..., max(l-CLC, 0)):

[0426] ⑴TOY at the top of the yellow zone

[0427] ⑵Green Zone GZ GZ k =(1-sign(SUI k ))·RTR·ACU k

[0428] During the period of huge demand preparation, there is no need to consider the demand for urgent orders

[0429] ⑶ Green zone top TOG TOG k =TOY k +GZ k

[0430] ⑷ Yellow Zone YZ

[0431] Here, we are compatible with the yellow zone calculation of DDMRP, YZ = ADU·DLT, but during the stocking period of huge demand waves, we define YZ k =OFE·MOQ k

[0432] ⑸ TOR TOR at the top of the red zone k =TOY k -YZ k

[0433] ⑹Red safety zone RZS RZS k =TOG k -OFE·MOQ k

[0434] This is the lower limit of net flow control. Once it is lower than it, it must be insufficient supply capacity.

[0435] ⑺Red base area RZB RZB k =TOR k -RZS k

[0436] The red basic area actually has no practical physical explanation for the planning and scheduling of replenishment orders. In order to be compatible with the current practices of DDMRP, it is retained for the time being.

[0437] Next, we discuss the calculation of Qualified Cycle Demand (QCD).

[0438] During normal replenishment, according to the modified first supply and demand equilibrium equation, QCD k =ACU k , and during the period of preparing for huge demand waves, QCD k =OFE·MOQ k , so the effective period demand QCD can be uniformly expressed as QCD k =(1-sign(SUI k ))·ACU k +sign(SUI k )·OFE·MOQ k

[0439] Net flow compensation, net flow position and schedule calculation:

[0440] Set POQ k The projected order quantity issued at the order point for the kth order cycle (PFQ); k is the Projected Fulfillment Quantity received at the arrival point in the kth order cycle; k is the Projected On-Order quantity at the start of the k-th order cycle.

[0441] To simplify calculations, we introduce the concept of Net Flow Compensation (NFC) to dynamically adjust the effective cycle demand during the massive demand preparation period and its corresponding recovery period.

[0442] The huge demand stocking period refers to SUI k For those order cycles k > 0, the huge demand stocking recovery period refers to the order cycle with huge demand wave stocking in the previous CLC-1 order cycle, that is, satisfying Those order cycles k.

[0443] Define Net Flow Position NFP k =PIQk +OFE·POO k -QCD k +NFC k

[0444] Let's discuss NFC k calculation:

[0445] From

[0446] we get NFP k =TOG k -OFE·POQ k -OFE·SCG k

[0447] So NFC k =TOG k -OFE·POQ k -OFE·SCG k -(PIQ k +OFE·POO k -QCD k ) = TOG k +QCD k -PIQ k -OFE·POO k -OFE·POQ k -OFE·SCG k

[0448] When the huge demand wave detection identifies the supply capacity gap, i.e., SCG k >0, there must be SUI k >0. At this time, QCD k =OFE·MOQ k , POQ k =MOQ k So, we have NFC k =TOG k -PIQ k -OFE·POO k -OFE·SCG k ;

[0449] When SUI k =0, MOQ k <mOQ, QCD k =ACU k , POQ k =0. So, we have NFC k =TOG k +ACU k -PIQ k -OFE·POOk ;

[0450] When SUI k > 0, MOQ k < mOQ, QCD k = OFE·MOQ k , POQ k = 0 Then there is NFC k = TOG k + OFE·MOQ k - PIQ k - OFE·POO k ;

[0451] During the huge demand wave replenishment period, when the remaining net demand of the wave plus the resilient demand after the wave ends exceeds the current maximum supply capacity, that is, SUI k > 0, NWD k + RKD e > OFE·MOQ k , it is necessary to replenish according to the maximum supply capacity, that is, QCD k = OFE·MOQ k , POQ k = MOQ k , then there is NFC k = TOG k - PIQ k - OFE·POO k

[0452] During the huge demand wave replenishment period, when the remaining net demand of the wave plus the resilient demand after the wave ends is less than or equal to 0, that is, SUI k > 0, NWD k + RKD e ≤ 0, it is necessary to make POQ k = 0, and since QCD k = OFE·MOQ k , we have NFC k = TOG k + OFE·MOQ k - PIQk - OFE·POO k ;

[0453] During the huge demand wave replenishment period, when the remaining net demand of the wave plus the resilient demand after the wave ends is not greater than the current supply capacity, that is, 0 < NWD k + RKD e ≤ OFE·MOQ k , at this time we hope to just achieve supply - demand balance, that is, we hope

[0454] PIQ k + OFE·POOk +OFE·POQ k =NWD k +RKD e , and at this time QCD k =OFE·MOQ k , so there is NFC k =TOG k +OFE·MOQ k -NWD k -RKD e ;

[0455] For any other situation, the net flow compensation NFC k =0.

[0456] Based on the above discussion, the general calculation formula for net flow compensation is as follows

[0457] where

[0458] Furthermore, since after the detection of the huge demand wave is completed, we have obtained the accurate inventory plan for dynamically matching the supply capacity, we can further perform net flow compensation according to the target inventory. The principle is as follows:

[0459] Since

[0460] Take the projected inventory PIQ k+CLC+1 as the target inventory, that is, TIQ k+CLC+1 =PIQ k+CLC+1 ,

[0461] So

[0462] When the huge demand wave detects a supply capacity gap (SCG k >0) and the current functional capacity is fragmented supply capacity (MOQ k <mOQ), the calculation of net flow compensation is exactly the same as the above discussion;

[0463] When SUI k =0, from QCD k =ACU k , we can get

[0464] When SUI k >0, and the remaining net demand NWD k >OFE·MOQ k at this time, the calculation of net flow compensation is also the same as the above discussion;

[0465] The rest of the situation during the period of huge demand replenishment, QCD k =OFE·MOQ k , we can get

[0466] In summary, we get the calculation formula for net flow compensation based on target inventory:

[0467] in

[0468] After solving the general net flow compensation calculation formula, the net flow position can be calculated according to the definition. However, considering the need to reduce the error propagation caused by measurement accuracy problems, the following formula is used:

[0469] Among them, QtyScale is the measurement accuracy of the material (number of decimal places).

[0470] After the net flow position calculation is completed, the expected number of orders to be issued can be calculated based on the revised first supply and demand balance equation, the revised second supply and demand balance equation, and the revised third supply and demand balance equation.

[0471] At this point, the regular replenishment order generation based on the modified first supply and demand balance equation, the stocking order generation for wave-like large-scale demand based on the modified second supply and demand balance equation, and the replenishment and stocking order generation based on the modified third supply and demand balance equation considering resource load constraints have been organically integrated.

[0472] Determining the order of execution resources includes the following process: when the execution resource corresponding to the demand object is a drum point resource, and there is a supply capacity gap for the drum point resource, the execution resources are sorted in ascending order according to the corresponding assembly product customer priority, product family priority, and assembly product priority, and in descending order according to the value-added of the manufacturing process; when the execution resource corresponding to the demand object is a drum point resource, and there is no supply capacity gap for the drum point resource, the execution resources are sorted in descending order according to the value-added of the manufacturing process; when the execution resource corresponding to the demand object is not a drum point resource, the execution resources are sorted in descending order according to the value-added of the manufacturing process.

[0473] The value-added of the execution resource manufacturing process is estimated regularly, and the estimation method includes the following steps:

[0474] ① The marginal contribution of the product is calculated by deducting the product standard cost unit price from the average sales price of sales orders within the foreseeable future demand period;

[0475] ② Calculate the difference between the standard cost of the assembly product and all levels of manufacturing components and the sum of the standard costs of sub-component materials as the non-material cost of the product. The standard cost of sub-component materials = standard cost unit price of sub-component × quantity used.

[0476] ③ Based on the proportion of non-material costs to marginal contribution, the marginal contribution is allocated to the assembly product and semi-finished parts at all levels as the value added of the manufacturing process for producing the order; if the manufacturing process value added of a manufactured part has been estimated due to the marginal contribution allocation of other assembly products, the largest value is retained;

[0477] ④When the BOM and material standard cost change, repeat steps ① to ④.

[0478] The method for dynamically identifying the drum beat resource includes the following steps:

[0479] ① Demand objects are clustered based on their mapping to execution resources. Demands not mapped to execution resources are grouped separately, with the corresponding group number 0. Scheduling calculations are subject only to the unified maximum order quantity (MOQ). For demands mapped to execution resources, the demand is automatically categorized based on the principle of non-overlapping execution resources, and group numbers 1, 2, ... are generated sequentially.

[0480] ② For each type of execution resource with a group number greater than 0, select the execution resource with the highest load rate in the previous order cycle as the drum point resource, and sort the multiple drum point resources in descending order of load rate to form a drum point resource list; wherein, the drum point resource list at a specific point in the future is determined based on the execution resource load of the corresponding order cycle.

[0481] When determining the clustering of the demand objects, the grouping is automatically adjusted when the mapping relationship between the execution resources and the demand objects changes.

[0482] Furthermore, when the same demand object corresponds to multiple execution resources, the cumulative demand is adjusted through the following process:

[0483] ① Perform scheduling calculations according to the priority order of the execution resources mapped to the demand objects to obtain the estimated replenishment order quantity PFQ;

[0484] ② When calculating the replenishment plan for the nth priority resource, the Cumulative Demand Adjustment (CDA) is calculated based on the PFQ of the scheduling result. At the same time, demand objects are reserved for the remaining execution resources according to the Demand Dispatching Ratio (DDR):

[0485] Where BUT k,jis the starting time of the jth order cycle of the kth execution resource, i=0,1,2,…,l n ;RSS k is the scheduling status of the scheduling object corresponding to the kth execution resource (Resource Scheduling Status), 0 means scheduled, 2 means to be scheduled, ∑ k DDR k The sum of the demand allocation ratios of all available resources for the same demand object, which may not be equal to 100%; CTD i Cumulative True Demand (CTD) is the cumulative true demand of the demand object at the end of the i-th order cycle of the n-th execution resource.

[0486] ③ When calculating actual cycle demand, the cumulative demand adjustment ACD caused by the scheduling results of high-priority resources is taken into account n,i =ε(CTD i +CDA n,i -CTD i-1 -CDA n,i-1 )(CTD i +CDA n,i -CTD i-1 -CDA n,i-1 ),i=0,1,2,…,l n .

[0487] The delivery date review method for undetermined requirements of the present invention uses the undetermined requirements after delivery date review as the order requirements. The delivery date review method specifically includes the following steps:

[0488] ① Obtain the original demand of the demand to be determined and make the original demand participate in the actual cycle demand calculation;

[0489] ② Identify supply capacity gaps. If any exist, split the original demand for the pending demand (such as pending sales orders) and partially shift it back. Recalculate the pending demand based on the BOM expansion and repeat step ① until there are no supply capacity gaps.

[0490] ③After the delivery review is completed, the adjustment strategy for the original requirements is output.

[0491] The inventory status evaluation method of the present invention is used to evaluate the inventory status and is implemented through the following process:

[0492] ① Evaluate the inventory on hand OHQ = PIQ0 and generate the current assessment conclusion (SAC)

[0493] ②Evaluate the outstanding orders OOQ=POO0 and supplement the current assessment conclusion (SAC)

[0494] ③When the on-hand inventory is on the high side, give the percentage of inventory decline and the savings in inventory capital occupancy

[0495] The method for monitoring the execution of orders according to the present invention is used to monitor the execution of the replenishment order and the replenishment plan scheduling, and specifically includes the following processes:

[0496] ①The list of current order cycle orders (POQ0>0), sorted according to the order point time (t0 + OPO) and the planned priority and distinguish the issued status;

[0497] ②The daily plan, weekly plan, monthly plan, quarterly plan and annual plan of material requirements are summarized according to the arrival point time

[0498] ③The list of expected inventory at a specific future point (according to PIQ k );

[0499] ④The material kit inspection for specified products and quantities at a specific future point;

[0500] ⑤The list of materials with order issuance (POQ1>0) in the next order cycle, sorted according to the planned priority for early communication with the execution resources;

[0501] ⑥The list of materials with arrivals (PFQ0>0) in the current order cycle, sorted according to the required arrival time (t0 + FPO), for the follow-up of the follow-up clerk or the display of the order execution kanban;

[0502] ⑦The list of materials with supply chain interruptions (PIQ0<DAT0) in the current order cycle, sorted according to the expected supply chain interruption time for the planner to adjust the plan (including arranging emergency rush orders) or the display of the supply chain interruption warning kanban;

[0503] ⑧The list of materials with demand in the previous order cycle and no demand in the current and future order cycles (l=-1), sorted in descending order according to the on-hand inventory amount, for the planner or salesperson to timely perceive the impact of market changes on material requirements;

[0504] ⑨The inventory list of materials with demand aborted (l<-1) but still having inventory, sorted in descending order according to the inventory capital occupancy amount, for the warehouse keeper to timely perceive the risk of material stagnation.

[0505] The resource load monitoring and resource capacity planning method according to the present invention is characterized in that it is used to monitor and plan the supply situation of the execution resources, and specifically includes the following processes:

[0506] ① Resource load table for current and specified future order cycles, sorted in descending order by load rate;

[0507] ② Load trend curve of specified resources;

[0508] ③ A list of resources that cause insufficient supply capacity, sorted in descending order by the percentage of missing capacity;

[0509] ④ A list of resources recommended for outsourcing (load rates are consistently below a specific threshold), sorted by average load rate.

[0510] The supply chain digital twin simulation method based on the future demand visible scheduling results of sub-components at each level expanded by BOM described in the present invention is characterized in that in step S1, a demand transmission mechanism is established through the BOM table and the material pegging table (Material Pegging Table), with the material as the entry, and according to the BOM table or the material pegging table (Material Pegging Table), the list of sub-components at all levels of the material and the corresponding scheduling results of sub-components at all levels of the material are found, and the material inventory changes from the current time to each future time point are simulated. Combined with the location coordinates of the locations of each storage location, the material replenishment, consumption and inventory changes over time are displayed on the GIS map or the designated background map.

[0511] FIG12 is a program logic block diagram of the scheduling system software implementation of the method of the present invention, wherein, when future demand is completely determined, the scheduling is calculated in two cycles according to demand priority and resource priority.

[0512] In summary, the following embodiments specifically describe the complete order generation and planning scheduling mechanism (steps and algorithms). Steps 01 to 24 of the decoupling point replenishment order generation and replenishment stocking planning scheduling mechanism of the present invention are as follows:

[0513] Step 01: Digitalize the supply and demand contract, i.e. set the following supply and demand contract parameters

[0514] Step 02: Evaluate past order execution, automatically calculate or manually set the following order execution variation factors, and then calculate the comprehensive supply variation factor, supply service level, and order fulfillment efficiency

[0515] If order execution deviation records are collected, the order execution deviation factor is calculated according to the following formula:

[0516] Among them, n is the actual demand period in the past [t B ,t0)Total number of completed orders,τ -iis the actual order completion time of the -i order cycle, PFQ -i AFQ is the required delivery quantity for the -i order cycle. -i is the actual delivery quantity of the -i order cycle, NGQ -i is the number of defective products detected in the -i-th order cycle, and ε(x) is a unit step function.

[0517] The comprehensive supply change factor is calculated as follows: SVF = 1-(1-SVF T )·(1-SVF Q )·(1-SVF q )

[0518] Supply service level: SSL = 1-SVF = (1-SVF T )·(1-SVF Q )·(1-SVF q )

[0519] Order fulfillment efficiency:

[0520] Step 03: Derived from the supply and demand contract, the following control parameters are calculated

[0521] The calculation formula is as follows: AWW=CLC+2

[0522] Step 04: Collect past actual and future demand in the form of cumulative demand version changes CD = {[t i ,t i+1 ),cd i |i=1,2,…,n}

[0523] In the formula, cd i Cut-off t i The cumulative demand at a certain point in time, n is the last demand, t n+1 =∞.

[0524] Step 05: Preprocess demand data and generate cumulative demand sequence according to order cycle

[0525] First, get the current time t P , and calculate the time point t at the beginning of last year B , determine the final demand time t L =t n

[0526] Secondly, calculate the starting time of the current order cycle

[0527] Then, calculate the order cycle number of the last demand point When l = , it indicates that the demand has been interrupted and the scheduling calculation is terminated; when l = -1, inertia scheduling calculation can be performed based on past actual demand.

[0528] Next, generate the order cycle sequence -CLC-3, -CLC-2,…, l+1

[0529] Finally, the Cumulative True Demand (CTD) corresponding to each order cycle is generated. for i=-CLC-3→l do CTD i ←CD(t0+(i+1)·AOC) end for

[0530] Step 06: Determine whether requirements are met

[0531] Since the resource load rate is dynamically maintained (for demands that are not mapped to execution resources, the maximum supply capacity for each future order cycle is MOQ), the cumulative supply capacity from the current order cycle to any future time point t is

[0532] Cumulative demand at any future time point t

[0533] For specific needs, with the shortest order cycle AOC min As the step size, find the smallest t such that CTD(t)>CSC(t). If it cannot be found, it means that future demand can be met and there is no supply capacity gap. Otherwise, there is a supply capacity gap.

[0534] Step 07: Prioritize drum resource requirements

[0535] For demands involving drum-point resources, if the satisfiability determination fails, the demand priority will be rearranged according to the priority of the customer, product family, and product; otherwise, the demand priority will be rearranged according to the value-added of the manufacturing process; for demands in each group that do not involve drum-point resources, the scheduling priority will be calculated according to the value-added of the manufacturing process.

[0536] Step 08: Calculate the Cumulative Demand Adjustment (CDA) resulting from high-priority resource scheduling.

[0537] Let n represent the execution resource scheduling priority of the same demand object, and the corresponding preset demand dispatching ratio is DDR n, let the resource scheduling status of each order execution resource of the same demand object be RSS n (The status value enumeration is: 0-scheduling calculation completed, 1-scheduling result expired, 2-accumulated demand updated, 3-scheduling calculation in progress, 4-actual demand terminated).

[0538] Step 09: Calculate the actual cycle demand (ACD) for i = -CLC-2 → l do ACD i ←ε(CTD i +CDA i -CTD i-1 -CDA i-1 )·(CTD i +CDA i -CTD i-1 -CDA i-1 ) end for

[0539] Step 10: Enter the inventory quantity on hand PIQ0 and the open order quantity POO0 at the starting point t0 of the current order cycle (Note: when CLC = 0, POO0 = 0) and initialize the estimated inventory quantity and estimated open order quantity for future order cycles.

[0540] Step 11: Calculate the maximum order quantity (MOQ) for each order cycle

[0541] Step 12: Calculate the Cumulative Supply Capacity (CSC) for each order cycle

[0542] Step 13: Multi-wave massive demand detection and stocking period sequence number SUI (Stocking Up Index) calculation

[0543] Step 14: Get the stocking period sequence number SUI from the historical records i ,i=-CLC-2,-CLC-1,..,-1.

[0544] If there is no historical record, the historical lead time flag is initialized to 0: for i=-CLC-2→-1do / / -CLC-3 order cycle does not need to consider SUI i ←0 end for

[0545] Step 15: Calculate the average cycle usage per order cycle

[0546] Step 16: if CLC=0 then goto Step 18

[0547] Step 17: Calculation of the breakdown of outstanding orders

[0548] Get the order quantity POQ of -CLC, -CLC+1, ..., -1 order cycle from historical data -CLC ,POQ -CLC+1 ,…,POQ -1 ;

[0549] Step 18: Calculate historical supply capacity gap

[0550] Step 19: Inventory planning calculation, calculate the target inventory at the start of each order cycle

[0551] Step 20: Calculation of red, yellow, and green water levels and net flow control lines

[0552] Step 21: Calculations related to inventory monitoring lines

[0553] Step 22: Calculate the Qualified Cycle Demand (QCD) and initialize the Net Flow Compensation (NFC).

[0554] Step 23: Scheduling calculations, iteratively generating Net Flow Compensation (NFC), Net Flow Position (NFP), Supply Plan Priority (SPP), Supply Capacity Gap (SCG), Projected Order Quantity (POQ), Projected Fulfillment Quantity (PFQ), Projected Inventory Quantity (PIQ), Inventory Quantity High Limit, Average Inventory Quantity, and Reasonable Inventory Quantity.

[0555] Step 24: Output replenishment orders and planned orders

[0556] In addition, the process of reviewing the delivery date of requirements to be determined is as follows:

[0557] Since the resource load rate is dynamically maintained (for demands that are not mapped to execution resources, the maximum supply capacity for each future order cycle is MOQ), the cumulative supply capacity from the current order cycle to any future time point t is:

[0558] Cumulative demand at any future time point t

[0559] Assume that the requirements to be confirmed are τ1,τ2,…,τ n There are n required delivery time points (τ1<τ2<…<τ n ), at the i-th delivery time, μ i requirements, corresponding to native requirements The demand is And there is Delivery time after initial review τ i ' ,j =τ i , the demand delivery review can be performed according to the following algorithm:

[0560] The process of evaluating the current status of inventory on hand and open orders is as follows:

[0561] ① Evaluate the inventory on hand OHQ = PIQ0 and generate the current assessment conclusion (SAC)

[0562] ②Evaluate the outstanding orders OOQ=POO0 and supplement the current assessment conclusion (SAC)

[0563] ③ When the inventory on hand is high, give the percentage of inventory reduction and the amount of capital saved on inventory.

[0564] The order execution abnormality impact assessment process proposed by the present invention is as follows: the order POQ0>0 of the current order cycle of a certain order execution resource cannot be executed for some reason, which will result in PFQ CLC =0,PIQ CLC+1 OFE·POQ0 is less than expected, that is

[0565] The estimation process of the manufacturing process value-added proposed by the present invention is as follows: the average sales unit price of sales orders within the visible period of future sales orders minus the standard cost unit price of the product is taken as the marginal contribution of the product; the difference between the total standard cost of the assembly product and manufacturing components at all levels and the standard cost of sub-component materials (sub-component standard cost unit price × usage) is calculated as the non-material cost of the product; the marginal contribution is allocated to the assembly and semi-finished components at all levels according to the proportion of non-material costs as the value-added of the manufacturing process for producing it; if the manufacturing process value-added of a certain manufacturing part has been estimated due to the marginal contribution allocation of other assembly products, the largest one is retained. In addition, the estimation of the manufacturing process value-added is carried out regularly (monthly is recommended). When the BOM and the standard cost of materials change, the re-estimation of the manufacturing process value-added is automatically triggered.

[0566] The supply chain digital twin process proposed in this invention is as follows:

[0567] Since the method of the present invention calculates the material requirement plan and resource plan for the visible period of future demand for all decoupling point materials, it is possible to use any finished product or semi-finished product as the entry point, find the list of its sub-components at all levels and their corresponding scheduling results based on the BOM table or material pegging table, simulate the changes in material inventory from the current time to various time points in the future, and combine the location coordinates of each storage location to display the changes in material supply, consumption and inventory over time on a GIS map or a specified background map.

[0568] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings:

[0569] Figure 1 provides a graphical representation of the various concepts involved in the supply chain replenishment model, including the last real demand point, the effective past actual demand period, the future real demand visibility period, the agreed order cycle, the decoupled lead time, the order point offset, the possible order point, the arrival point offset, the possible arrival point, and the demand sliding average window width.

[0570] As shown in Figure 2, for isolated demand (demand in only one order cycle), in order to achieve the target positioning of Right Time and Right Quantity, it is proved that AWW=CLC+2.

[0571] As shown in Figure 3, if the constraints of maximum supply capacity MOQ, minimum order quantity mOQ and standard packaging quantity SPQ are not considered, and it is assumed that order execution resources can be executed 100% as planned, the first supply and demand balance equation reveals the relationship between the current order cycle order quantity and the average cycle usage under the conditions of known on-hand inventory and known open orders.

[0572] As shown in Figure 4, the actual cycle demand in the 29th order cycle exceeds the maximum supply capacity of a single order cycle, OFE·MOQ. Therefore, a massive demand wave is identified. From the 25th order cycle to the 29th order cycle, it is necessary to prepare inventory according to the maximum supply capacity starting from the 17th order cycle to ensure that the supply chain is not interrupted in the 29th order cycle. The number of lead order cycles for preparation, SLC, is 29-17 = 12.

[0573] As shown in Figure 5, during the period of preparing for huge demand (order cycles 17 to 21), except for the last order cycle, orders need to be placed according to the maximum supply capacity. The order quantity of the last order cycle (order cycle 21) must satisfy the second supply and demand balance equation. In essence, it is to ensure that after the arrival of the huge demand preparation, the inventory is just enough to meet the demand before the arrival point of one order cycle after the huge demand wave (order cycle 30).

[0574] As shown in Figure 6, order fulfillment resources have already been partially occupied by high-priority demand in each order cycle. Only the remaining supply capacity can be scheduled to meet current demand. Significant demand detection indicates that order cycles 24 through 29 constitute a significant demand wave. Order cycles 16 through 20 must be stocked based on the remaining supply capacity. The order volume for cycle 21 must ensure that the beginning inventory for cycle 30 equals the safety stock plus the inventory reserved for urgent orders and the demand before the arrival point for cycle 30. The third supply and demand balance equation incorporates the first and second supply and demand balance equations, adding the safety stock and inventory reserved for urgent orders to enhance supply chain resilience.

[0575] As shown in Figure 7, for the actual demand of 40 order cycles containing huge demand waves, the green top (TOG), yellow top (TOY), and RZS (red safety zone) are calculated according to the method of the present invention as the upper control line, baseline, and lower control line of the net flow, the effective cycle demand, safety stock, and urgent order tolerance stock are calculated, the net flow compensation is calculated during the huge demand preparation period and the huge demand preparation recovery period, and then the net flow position is calculated, and the iterative function system method is used to generate orders for 40 order cycles in the future demand visible period. Starting from the 32nd order cycle, inertia scheduling is adopted (assuming the ACU is the same).

[0576] As shown in Figure 8, the estimated inventory quantity is controlled between the upper and lower limits of the inventory quantity, and the sum of the safety stock and the reserved urgent order stock constitutes the promised quantity.

[0577] As shown in Figure 9, the cumulative supply curve from the starting point of the current order cycle to the future is greater than the cumulative demand curve. The difference between the two is the expected inventory curve, which clearly shows the stocking situation for the huge demand wave.

[0578] Figure 10 compares the benchmark inventory levels of the proposed method with the average on-hand inventory levels of the classic DDMRP method. For ease of presentation, inventory levels are expressed as multiples of the average demand over the agreed order cycle. It can be seen that regardless of the amount of decoupling lead time, the proposed method achieves a significant reduction in inventory. Essentially, the proposed method, through the supply-demand balance equation, demonstrates the independence of inventory from decoupling lead time; orders can be placed at fixed times and in varying quantities according to the supply-demand contract.

[0579] FIG11 is an ER diagram of the data model of the method of the present invention. The types of demand objects include (material, storage location) tuple, (material, work center) tuple, (material, production line) tuple, (material, work station) tuple, (material, administrative division) tuple. The types of resources include work center, production line, work station, storage location, supplier (business partner). The transient attributes of demand objects include (1) tolerance for urgent orders, (2) time window width, (3) consumption time granularity, (4) resource group number, (5) priority level within the group, (6) quantity measurement accuracy, (7) standard packaging quantity, (8) 8) On-hand inventory quantity, (9) Cumulative inventory quantity, (10) Cumulative inventory surplus quantity, (11) Cumulative inventory outflow quantity, (12) Cumulative doubt quantity, (13) Cumulative scrap quantity, (14) Cumulative inventory loss quantity; transient attributes of the supply and demand relationship triple include (1) agreed order cycle, (2) order point offset, (3) decoupling lead time, (4) standard packaging quantity, (5) minimum order quantity, (6) maximum order quantity, (7) delivery time deviation factor, (8) delivery quantity deviation factor, (9) delivery quality deviation factor, (10) quantity measurement accuracy, (11) addition Urgent supply capacity, (12) Order economic batch, (13) Average cycle usage, (14) Average daily usage, (15) Stocking cycle number, (16) Red safety zone depth, (17) Red basic zone depth, (18) Red zone top water level, (19) Yellow zone depth, (20) Yellow zone top water level, (21) Green zone depth, (22) Green zone top water level, (23) Benchmark inventory quantity, (24) Benchmark inventory amount, (25) Inventory quantity lower limit, (26) Inventory quantity upper limit, (27) Average inventory quantity, (28) Safety stock quantity, (29) Urgent Order commitment quantity, (30) net flow compensation, (31) inventory quantity on hand at the beginning of the order cycle, (32) number of open orders at the beginning of the order cycle, (33) net flow position, (34) planning priority, (35) cumulative order quantity, (36) cumulative delivery quantity, (37) order notification threshold value, (38) follow-up warning threshold value, (39) interruption alarm threshold value, (40) demand allocation ratio, (41) cumulative demand adjustment, (42) cumulative number of orders, (43) cumulative delivery time deviation, (44) cumulative delivery quantity deviation, (45) cumulative number of defective deliveries.

[0580] FIG12 is a program logic block diagram of the scheduling system software implementation of the method of the present invention. When future demand is completely determined, the scheduling is calculated in two cycles according to demand priority and resource priority.

[0581] FIG13 is a comparative data table of the benchmark inventory quantity of the method of the present invention and the average inventory quantity on hand of the classic DDMRP method.

Claims

1. A demand-driven replenishment order generation and replenishment plan scheduling method, characterized in that: The following steps are involved: S1. Setting demand objects based on the positions of suppliers and buyers in the supply network, where the buyer provides material requirements and the supplier provides execution resources; S2. Use a single numerical value ACU to condense and represent the recent material demand of the demand object; S3. Monitor the current supply capacity of execution resources, the demander's opening inventory on hand, and the supplier's opening open orders based on the agreed order cycle, and establish a timely updated buffer file. S4. After allocating and prioritizing the execution resources according to the demands of the demand objects, dynamically matching the execution resources and their supply capabilities for the demand objects in accordance with the constraints of the supply-demand balance equation, and generating corresponding replenishment orders and replenishment plans.

2. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: Using a single numerical value ACU to condense and represent the recent material demand includes the following steps: S21. Obtain all known and real future material requirements of the demand object, combine them with the current cumulative requirements, pre-process them into a cumulative requirement sequence, and group them by the agreed order cycle to obtain the actual cycle requirements for the current and future order cycles; S22. Establish a sliding average window for each agreed order cycle based on the actual cycle demand sequence. The width of the sliding average window is: AWW=CLC+2, where Where, OPO, DLT, and AOC are supply and demand contract parameters; S23. Determine the average cycle usage within the sliding average window of the kth order cycle. Average daily dose ADU k 、DDLT during the lead period k and the arithmetic average cycle usage AACU k+CLC+2 During the huge demand wave stocking period, the average cycle usage ACU without stocking is determined based on the huge demand stocking period serial number mark of the order cycle. k : Where, ACD i is the actual cycle demand of the i-th order cycle, SUI i It is the serial number mark of the stocking period of the i-th order cycle.

3. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 2, characterized in that: When demand history is not obtained but open orders are obtained, the past demand is extrapolated forward based on the same average cycle usage. The specific formula is as follows: Where ACD k-1 Indicates past demand that needs to be extrapolated; More strictly, if the actual cycle demands ACD of m (2 < m ≤ AWW) order cycles are known k , ACD k+1 , …, ACD k+m-1 , then the actual cycle demand of the previous order cycle can be extrapolated forward using a second-order Chebyshev polynomial, and the formula is as follows: When m=2, linear extrapolation is performed, and the specific formula is as follows: ACD k-1 =2ACD k -ACD k+1 。 4. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 2, characterized in that: When insufficient historical demand data is obtained, the average cycle usage of the previous order cycle is obtained using the following formula: More strictly, if the actual cycle demands ACD of m (2 < m ≤ AWW) order cycles are known k , ACD k+1 , …, ACD k+m-1 , after extrapolating ACD by the method of claim 3 k-1 , the average cycle usage of the previous order cycle can be calculated by definition, and the formula is as follows: For the last AWW order cycles within the visible demand period, calculate the arithmetic mean of the actual cycle demand and then calculate the coefficient of dispersion: Based on this dispersion coefficient, the demand for AWW order cycles is predicted as follows: Correspondingly, we obtain the average cycle usage estimate of the DVH-CLC order cycle as follows: When DVH < CLC, if the arithmetic average cycle usage AACU for the past m (2 < m ≤ AWW) order cycles is known k-m+1 , AACU k-m+2 , …, AACU k , then the arithmetic average cycle usage for the (k + 1)-th order cycle is predicted using the second-order Chebyshev polynomial. However, to ensure that the supply chain does not break and only increases or remains the same, the formula is as follows: When m=2, linear extrapolation prediction is performed but it also keeps increasing but not decreasing. The specific formula is as follows: AACU k+1 =max(AACU k ,2AACU k -AACU k-1 ) Arithmetic average cycle usage AACU based on extrapolation prediction k+1 Calculate ACD first according to the following formula k Recalculate and ACU k-1-CLC :

5. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: When simulating the supply chain in S1, the following steps are included: S111. Obtain a supply network, where the supply network includes multiple supply chain nodes, and the supply chain nodes are connected by supply chain segments; S112. Determine the supply chain potential according to the supply chain node status, and classify the supply chain nodes according to the supply chain potential. Specifically, the supply chain potential of 1 indicates used inventory; the supply chain potential of 2 indicates in-use inventory; the supply chain potential of 3 indicates a line-side warehouse for materials to be consumed; the supply chain potential of 4 indicates a raw material supermarket, a semi-finished product buffer, a semi-finished product warehouse, a commodity supermarket, or a product line-side warehouse; the supply chain potential of 5 indicates a raw material main warehouse; the supply chain potential of 6 indicates a raw material external warehouse or a supplier-managed inventory; the supply chain potential of 7 indicates a supplier supply location or a customer delivery location; the supply chain potential of 8 indicates a distribution center, a finished product consignment warehouse, or a commodity supermarket; the supply chain potential of 9 indicates a finished product main warehouse or a commodity warehouse; x and y indicate the serial numbers of supply chain nodes with the same supply chain potential; S113. Divide the supply chain into manufacturing enterprise supply chain, commercial enterprise supply chain, and industrial chain according to function. The specific structure is as follows: <Manufacturing Enterprise Supply Chain> ::={7x6y|7x5y|6x5y|6x4y|6x3y|5x4y|5x3y|4x3y|3x2y|2x1y|1x4y|4x4y|4x3y|4x9y|9x8y|9x7y|8x7y} <Commercial Enterprise Supply Chain>::={7x9y|9x8y|9x7y|8x7y} <Industrial chain>::={T n zP u xP d y} Among them, T n represents the n-level supplier of brand product manufacturers; z represents the supplier serial number of the same level, P u xP d y represents the supplier's internal supply chain.

6. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: When the requirement object is established in S1, the following steps are specifically included: S121. Divide the target demand areas into locations, storage locations, work centers, production lines, workstations, and administrative divisions based on the demand for materials in the supply network; S122. Combining the materials and the target demand locations one by one into multiple types of demand objects, specifically: demand object::={(material, location)|(material, storage location)|(material, work center)|(material, production line)|(material, work station)|(material, administrative division)}; Among them, (material, location) describes the delivery demand of materials (commodities, finished products), thereby triggering the shipment from the storage location; (material, storage location) describes the demand for materials (commodities, finished products, semi-finished products, raw and auxiliary materials, tooling, containers) to be shipped out of a specific storage location, thereby triggering the replenishment of execution resources; (material, work center) describes the production plan of materials (finished products, semi-finished products) at a specific work center; (material, production line) describes the production work order of materials (finished products, semi-finished products) on a specific production line or the distribution demand of materials (raw and auxiliary materials, semi-finished products, tooling, containers) to a specific production line; (material, work station) describes the production kanban instruction order of materials (finished products, semi-finished products) at a specific work station or the distribution demand of materials (raw and auxiliary materials, semi-finished products, tooling, containers) to a specific work station; (material, administrative division) describes the distribution demand of materials (supplies) distributed to a specific administrative division.

7. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: When enumerating the mapping between the requirement object and the execution resource, the following steps are specifically included: S131. Subdivide the materials into commodity, finished product, semi-finished product, raw and auxiliary materials, tooling, containers and other material types; subdivide the execution resources into production line, workstation, work center, supplier, finished product external warehouse, finished product main warehouse, commodity warehouse, raw and auxiliary materials external warehouse, raw and auxiliary materials main warehouse and other resource types; S132. Enumerate the mapping between different material type requirement objects and execution resources. The specific situation is as follows: ① Required object = (material, location), material type = finished product or commodity, execution resource: finished product external warehouse or finished product main warehouse or commodity supermarket or commodity main warehouse (the required object needs to be shipped); ② When the demand object = (material, storage location), the material type = finished product or commodity, and the storage location supply chain potential = 8, the execution resource is: finished product main warehouse or commodity warehouse (replenishment of the demand object is required); ③ When the demand object = (material, storage location), the material type = finished product, and the storage location supply chain potential = 9, the execution resource is: assembly work center, assembly production line, or assembly independent workstation (production and replenishment of the demand object is required); ④ When the demand object = (material, storage location), the material type = commodity, and the storage location supply chain potential = 9, the execution resource is the supplier (purchase and replenishment of the demand object is required); ⑤ When the demand object = (material, storage location), the material type = semi-finished product, and the storage location supply chain potential = 4, the execution resource is the work center or semi-finished product production line corresponding to the semi-finished product production, or the independent semi-finished product production station (production replenishment of the demand object is required); ⑥ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 6, the execution resource is: supplier (purchase and replenishment of the demand object is required); ⑦ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 5, the execution resource is: supplier (needs to purchase and replenish the demand object) or external raw and auxiliary material warehouse (needs to allocate and replenish the demand object); ⑧ When the demand object = (material, storage location), the material type = raw and auxiliary materials, and the storage location supply chain potential = 4, the execution resource is: the raw and auxiliary materials warehouse or external warehouse (replenishment of the demand object is required); ⑨ When the demand object = (material, production line) and the material type = finished product or semi-finished product (production is scheduled on this production line), the execution resource is the production line (production of the finished product or semi-finished product needs to be scheduled); ⑩ When the demand object = (material, production line) and the material type = raw and auxiliary materials, upstream semi-finished products, tooling, or containers, the execution resource is: storage location (replenishment of the demand object is required); When the demand object = (material, workstation) and the material type = finished product or semi-finished product (the process route passes through the workstation), the execution resource is: workstation (production of the finished product or semi-finished product needs to be scheduled); When the demand object = (material, workstation) and the material type = raw or auxiliary materials, upstream semi-finished products, tooling, or containers, the execution resource is the storage location (replenishment of the demand object is required). When the demand object = (material, administrative division) and the material type = supplies, the execution resource is: storage location (requires distribution and replenishment of the demand object).

8. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: Determining the supply capacity of execution resources includes the following steps: ① Combine the demand objects with the execution resources, set the agreed order cycle (AOC), order point offset (OPO), decoupled lead time (DLT), standard packing quantity (SPQ), minimum order quantity (mOQ) and maximum order quantity (MOQ) attributes for each group, and determine the time deviation factor (SVF) based on the evaluation of the execution resource's past performance. T ), quantity deviation factor (SVF Q ), quality deviation factor (SVF q ); ② Based on the above parameters and the replenishment orders and replenishment plans for high-priority demand, calculate the load of the execution resource in each order cycle, expressed as the estimated labor hour ratio (RLR). For a specific material, the maximum supply capacity of the execution resource in a given order cycle i is: Among them, when the execution resource is assigned POQ for a specific material in the i-th order cycle i The load factor will increase if the order 9. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 8, characterized in that: The time deviation factor, quantity deviation factor, and quality deviation factor are obtained by calculation or agreement. The calculation includes the following steps: obtaining the past order completion status within the target time period; calculating the time deviation factor SVF within the target time period based on the past order completion status. T , quantity deviation factor SVF Q , quality deviation factor SVF q , the specific formula is as follows; Where n is the actual demand period in the past [t B ,t0) total number of completed orders; t -i is the start time of the -i order cycle; τ -i is the actual order completion time of the -i order cycle; t B is the start time of the previous year, i.e. the starting time of the -b order cycle; ε(x) is a unit step function, and the ε(x) formula is: PFQ -i AFQ is the required delivery quantity for the -i order cycle. -i The actual delivery quantity of the -i order cycle; NGQ -i AFQ is the number of defective products delivered in the -i order cycle. -i is the actual delivery quantity for the -ith order cycle.

10. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: When it is found that the execution resource supply capacity cannot meet the demand of the corresponding order cycle (there is a CLC order cycle delay), a reverse order method is used to find a large demand wave to prepare in advance and detect the supply capacity gap. Specifically, the following steps are included: ① Starting from the first order cycle DVH+1 after the last order cycle in the future demand visibility period as the detection boundary e, we detect each order cycle one by one, and at the same time calculate the increment of the critical cycle demand and the resilience maintenance demand of the detected order cycle: CCD e =FREE·MOQ e-CLC ΔRKD e =ΔSSQ e-CLC +RTR·ΔACU e-CLC ; ② The first order cycle number e corresponding to the actual cycle demand greater than the sum of the critical cycle demand and the resilience maintenance demand increment is used as the initial value of the huge demand wave identification. If it does not exist, it indicates that there is no huge demand wave and the detection is terminated; if it exists, proceed to the next step; ③ If MOQ e-CLC < mOQ, it indicates that the debris supply capacity is unavailable, e ← e + 1, and loop until e = DVH + 1 or MOQ e-CLC ≥ mOQ; ④ Calculate the resilience retention demand RKD of the e-order cycle e ; ⑤Find the largest order cycle number s>-1, so that the following formula is satisfied: ⑥ If the largest order cycle number s>-1 cannot be found, the total supply capacity gap is identified. The supply capacity gap can be evenly distributed over order cycles 0, 1, ..., e-CLC-1, while setting s = 0. The total supply capacity gap is identified using the following formula: ⑦ Label the order cycles s, s+1, ..., e-CLC with the stocking up index, as shown in the following formula: climb k =k-s+1,k=1,2,…,e-CLC-s+1; ⑧If s>0, use s+CLC-1 as the start of a new massive demand detection boundary, continue detection, and repeat ① to ⑧: After massive demand wave detection is complete, target inventory is generated at the start of each order cycle. This is a precise inventory plan based on known future demand and known supply capacity distribution: In actual calculation, the calculation result needs to be rounded according to the material measurement accuracy. s=i-CLC-SUI i-CLC-1 QtyScale is the measurement accuracy of the material.

11. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: Use safety stock to deal with supplier delivery delays; a single value ACU is used to represent recent material demand, taking into account the minimum order quantity mOQ and delivery time deviation factor SVF T Determine the safety stock quantity. The specific formula is as follows: SSQ = sign (ACU) · (1-sign (SUI)) · SVF T max(ACU,mOQ) Among them, when the average cycle usage ACU = 0, the safety stock quantity SSQ = 0; during the period of huge demand preparation, the safety stock quantity SSQ = 0.

12. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: Emergency reserve inventory is used to handle urgent orders from demanders. Specifically, an RTR (urgent order tolerance) is introduced, and an RTR·ACU emergency inventory is established. Generally, the limit is 0 ≤ RTR ≤ 1, but in special circumstances, RTR > 1 is allowed.

13. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: The supply and demand balance equation includes a first supply and demand balance equation, which is established to cope with conventional demand and resilience maintenance demand. The equation is as follows: PIQ i +OFE·POO i +OFE·POQ i =(CLC+2)·ACU i +RKD i+CLC In the equation, i=0,1,…,max(DVH,0) <h2 style=";text-align:left;direction:ltr">PSQ<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> =0 SSQ i =sign(ACU i )·SVF T ·max(ACU i ,mOQ) SUI j =0,j=0,1,…,max(DVH,0) PIQ i ,POO i are the estimated available inventory quantity and the estimated open order quantity for the i-th order cycle respectively; POQ i is the order quantity of the i-th order cycle, which is the required scheduling variable; OFE is the order fulfillment efficiency, OFE=(1-SVF Q )·(1-SVF q ); RKD i+CLC Maintain demand for supply chain resilience for the i+CLC order cycle; CMD i+CLC Capacity matching requirements for the i+CLC order cycle; PSQ i is the quantity of stock in reserve for the i-th order cycle. Since the first supply-demand balance equation is for normal demand and there is no huge demand beyond the supply capacity, the value is 0; SSQ i The safety stock for the i-th order cycle is used to cope with supplier delivery delays.

14. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: The supply and demand balance equation includes a second supply and demand balance equation, which is established to ensure a constant supply capacity of resources to cope with massive demand waves. The equation is as follows: In the equation, e is the identifier of any massive demand wave (order cycle number), satisfying the following two conditions: ①ACD e >OFF MOQ, ②ACD e+1 ≤OFF MOQ; s=e-CLC-SUI e-CLC +1 is the starting order cycle required to prepare for this huge demand wave; <h2 style=";text-align:left;direction:ltr">PSQ<h2 style=";text-align:left;direction:ltr"> e-CLC <h2 style=";text-align:left;direction:ltr"> =0 SSQ e-CLC =sign(ACU e-CLC )·(1-sign(SUI e-CLC ))·SVF T ·max(ACU e-CLC ,mOQ) PIQ s ,POO s are the estimated available inventory quantity and the estimated open order quantity for the sth order cycle respectively; There are a total of e-CLC-s order cycles from order cycle s to order cycle e-CLC-1. The planned order quantity is the maximum order quantity MOQ; POQ e-CLC is the order quantity of the first e-CLC order cycle, which is the required planning and scheduling variable; RKD e Maintaining demand for supply chain resilience in the first e-order cycle; CMD e Capacity matching demand for the e-th order cycle; PSQ e-CLC The reserve quantity for the first e-CLC order cycle due to SUI e-CLC+1 ≠1, so the value is 0; SSQ e-CLC Provide safety stock for the first e-CLC order cycle, so that after the huge demand wave is completed, it can be restored immediately to cope with supplier delivery delays.

15. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: The supply and demand balance equation includes a third supply and demand balance equation, which is established to implement the variable supply capacity of resources to cope with huge demand waves. The equation is as follows: In the equation, e is the identifier of any massive demand wave (order cycle number), which satisfies one of the following two conditions: ①ACD e >OFE·MOQ e-CLC ≥OFE·mOQ, and OFE·mOQ≤ACD e+1 ≤OFE·MOQ e+1-CLC ; ②MOQ e-CLC <mOQ, and MOQ e-CLC+1 ≥mOQ, and Make ACD e′ >OFE·MOQ e′-CLC ≥OFE·mOQ, and MOQ j-CLC <mOQ; s=e-CLC-SUI e-CLC +1 is the starting order cycle required to prepare for this huge demand wave; <h2 style=";text-align:left;direction:ltr">PSQ<h2 style=";text-align:left;direction:ltr"> e-CLC <h2 style=";text-align:left;direction:ltr"> =0 SSQ e-CLC =sign(ACU e-CLC )·(1-sign(SUI e-CLC ))·SVF T ·max(ACU e-CLC ,mOQ) PIQ s ,POO s are the estimated available inventory quantity and the estimated open order quantity for the sth order cycle respectively; There are e′-CLC-s order cycles from order cycle s to order cycle e′-CLC-1. The planned order quantity is (1-ε(mOQ-MOQ j ))·MOQ j POQ e′-CLC is the order quantity of the e′-CLC order cycle, which is the required scheduling variable; from the e′-CLC+1 order cycle to the e-CLC order cycle, the planned order quantity is 0 due to the unavailability of the fragment supply capacity; RKD e Maintaining demand for supply chain resilience in the first e-order cycle; CMD e Capacity matching demand for the e-th order cycle; PSQ e-CLC The reserve quantity for the first e-CLC order cycle due to SUI e-CLC+1 ≠1, so the value is 0; SSQ e-CLC Provide safety stock for the first e-CLC order cycle, so that after the huge demand wave is completed, it can be restored immediately to cope with supplier delivery delays.

16. The replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: In steps S3 to S4, the buffer zone file includes green top, green zone, yellow top, yellow zone, red top, red basic zone, red safety zone, and effective demand in the order cycle. Net flow compensation is introduced for the buffer zone based on the inventory on hand, the outstanding order volume, and the identified supply capacity gap to cope with dynamic adjustments during the period of stocking and recovery of huge demand. The following processes are included: ① Define the top of the yellow zone (TOY) as TOY at the top of the yellow zone is actually the net flow baseline, also known as the supply and demand balance line ②Define the green zone depth (GZ) as GZ k =(1-sign(SUI k ))·RTR·ACU k ③ Define the top of the green zone (TOG) as TOG k =TOY k +GZ k TOG at the top of the green zone is actually the upper control line of net flow ④ Define the yellow zone depth (YZ) as ⑤ Define the top of the red zone (TOR) as TOR k =TOY k -YZ k ⑥ Define the red safety zone depth (RZS) as <h2 style=";text-align:left;direction:ltr">RZS<h2 style=";text-align:left;direction:ltr"> k <h2 style=";text-align:left;direction:ltr"> =TOG<h2 style=";text-align:left;direction:ltr"> k <h2 style=";text-align:left;direction:ltr"> -OFE·MOQ<h2 style=";text-align:left;direction:ltr"> k The red safety zone depth RZS is actually the lower control line of net flow ⑦ Define the red base zone depth (RZB) as RZB k =TOR k -RZS k ⑧ Define order cycle effective demand (QCD) as QCD k =(1-sign(SUI k ))·ACU k +sign(SUI k )·OFE·MOQ k ⑨ As a dynamic adjustment during the period of massive demand replenishment and its recovery, the net flow compensation (NFC) is defined as in Furthermore, net flow compensation is performed based on target inventory using the following formula: in ⑩Calculate Net Flow Position (NFP) Among them, QtyScale is the measurement accuracy of the material (number of decimal places) Generate planned order quantity (POQ) Calculating the Supply Capacity Gap (SCG) When SUI k =0, calculate the supply capacity gap according to the following formula; otherwise, retain the supply capacity gap calculated during the massive demand wave detection; Calculate the Net Flow Threshold (NAT) for order notification Calculate the expected fulfillment quantity (PFQ) Among them, QtyScale is the measurement accuracy of the material (number of decimal places) Calculate the Inventory Quantity Limit (IQL) in s=k-CLC-SUI k-CLC-1 QtyScale is the measurement accuracy of the material (number of decimal places) Calculate Inventory Quantity High (IQH) in s=k-CLC-SUI k-CLC-1 Calculate Average Inventory Quantity (AIQ) Calculating Benchmark Inventory Quantity (BIQ) Furthermore, calculating benchmark inventory quantities based on inventory planning is more accurate: Calculate the supply chain interruption alarm threshold (DAT) Calculate the estimated available inventory quantity (PIQ) at the beginning of the next order cycle 17. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: Determining the priority of execution resources involves the following process: When the execution resource corresponding to the demand object is a drum point resource and there is a supply capacity gap in the drum point resource, the execution resources are sorted in ascending order according to the corresponding assembly product customer priority, product family priority, and assembly product priority, and in descending order according to the value-added of the manufacturing process; When the execution resource corresponding to the demand object is a drum point resource and there is no supply capacity gap for the drum point resource, the execution resource manufacturing process value-added is sorted in descending order; When the execution resource corresponding to the demand object is not a drum resource, it is sorted in descending order according to the value-added of the manufacturing process of the execution resource.

18. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 17, characterized in that: The value-added of the execution resource manufacturing process is estimated regularly, and the estimation method includes the following steps: ① The marginal contribution of the product is calculated by deducting the product standard cost unit price from the average sales price of sales orders within the foreseeable future demand period; ② Calculate the difference between the standard cost of the assembly product and all levels of manufacturing components and the sum of the standard costs of sub-component materials as the non-material cost of the product. The standard cost of sub-component materials = standard cost unit price of sub-component × quantity used. ③ Based on the proportion of non-material costs to marginal contribution, the marginal contribution is allocated to the assembly product and semi-finished parts at all levels as the value added of the manufacturing process of producing the product or semi-finished product; if the manufacturing process value added of a manufactured part has been estimated due to the marginal contribution allocation of other assembly products, the largest one is retained; ④When the BOM and material standard cost change, repeat steps ① to ④.

19. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 17, characterized in that: The method for dynamically identifying the drum beat resource includes the following steps: ① Demand objects are clustered based on their mapping to execution resources. Demands not mapped to execution resources are grouped separately, with the corresponding group number 0. Scheduling calculations are subject only to the unified maximum order quantity (MOQ). For demands mapped to execution resources, the demand is automatically categorized based on the principle of non-overlapping execution resources, and group numbers 1, 2, ... are generated sequentially. ② For each type of execution resource with a group number greater than 0, select the execution resource with the highest load rate in the previous order cycle as the drum point resource, and sort the multiple drum point resources in descending order of load rate to form a drum point resource list; wherein, the drum point resource list at a specific point in the future is determined based on the execution resource load of the corresponding order cycle.

20. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 19, characterized in that: When determining the clustering of the demand objects, the grouping is automatically adjusted when the mapping relationship between the execution resources and the demand objects changes.

21. The demand-driven replenishment order generation and replenishment plan scheduling method according to claim 1, characterized in that: When the same requirement object corresponds to multiple execution resources, the cumulative requirements are adjusted through the following process: ① Perform scheduling calculations based on the priority order of the execution resources mapped to the demand objects to obtain the estimated arrival quantity PFQ of the replenishment order; ② When calculating the replenishment plan for the nth priority resource, the cumulative demand is calculated based on the PFQ of the scheduling result, and the CDA is adjusted. At the same time, part of the demand of the demand object is reserved for the remaining execution resources according to the remaining execution resource demand allocation ratio DDR: Where BUT k,j is the starting time of the jth order cycle of the kth execution resource, i=0,1,2,…,l n ;RSS k is the scheduling status of the scheduling object corresponding to the kth execution resource, 0 means scheduled, 2 means to be scheduled, ∑ k DDR k The sum of the demand allocation ratios of all available resources for the same demand object, which may not be equal to 100%; CTD i The cumulative actual demand of the demand object at the end of the i-th order cycle of the n-th execution resource; ③ When calculating actual cycle demand, consider the cumulative demand adjustment caused by the scheduling results of high-priority resources ACD n,i = ε(CTD i + CDA n,i - CTD i-1 - CDA n,i-1 )(CTD i + CDA n,i - CTD i-1 - CDA n,i-1 ) where i = 0, 1, 2, …, l n .

22. A method for evaluating the delivery date of undetermined demand used in the demand-driven replenishment order generation and replenishment plan scheduling method according to any one of claims 1 to 21, characterized in that: The undetermined demand after the delivery review is used as the material demand. The delivery review method specifically includes the following steps: ① Obtain the original demand of the demand to be determined and make the original demand participate in the actual cycle demand calculation; ② Identify supply capacity gaps. If any exist, split the original demand for the pending demand (such as pending sales orders) and partially shift it back. Recalculate the pending demand based on the BOM expansion and repeat step ① until there are no supply capacity gaps. ③After the delivery review is completed, the adjustment strategy for the original requirements is output.

23. A current status assessment method for the demand-driven replenishment order generation and replenishment plan scheduling method according to any one of claims 1 to 21, characterized in that: Evaluate the supply chain execution status based on inventory on hand and open orders through the following process: ① Evaluate the inventory on hand OHQ = PIQ0 and generate the current assessment conclusion (SAC) ②Evaluate the outstanding orders OOQ=POO0 and supplement the current assessment conclusion (SAC) ③ When the inventory on hand is high, give the percentage of inventory reduction and the amount of capital saved on inventory.

24. A method for monitoring order execution status used in the demand-driven replenishment order generation and replenishment plan scheduling method according to any one of claims 1 to 21, characterized in that: To monitor the execution of the replenishment order and replenishment plan schedule, specifically The following processes are included: ① List of orders in the current order cycle (POQ0>0), sorted by order point time (t0+OPO) and planning priority Sorting, distinguishing the delivery status; ② Daily, weekly, monthly, quarterly and annual plans for material requirements, summarized by arrival time ③ The expected inventory list at a specific point in the future (based on PIQ k ); ④ Inspection of the completeness of materials of specified products and quantities at a specific time in the future; ⑤ The next order cycle has a bill of materials with order issuance (POQ1>0), according to the planning priority Sequencing, used for advance communication with execution resources; ⑥ The list of materials that have arrived (PFQ0>0) in the current order cycle is sorted by the required arrival time (t0+FPO) and used for merchandisers to follow orders or display on the order execution dashboard; ⑦Bill of materials with supply chain disruptions (PIQ0 < DAT0) in the current order cycle, according to the expected supply chain disruption time Sorting is used by planners to adjust plans (including arranging emergency orders) or display supply chain disruption warning boards; ⑧ A list of materials that were in demand in the previous order cycle but not in demand in the current or future order cycles (l = -1) is sorted in descending order by the amount of inventory on hand. This allows planners or sales personnel to timely understand the impact of market changes on material demand. ⑨ Inventory list of materials for which demand has been terminated (l<-1) but still in stock, sorted in descending order by the amount of inventory funds occupied, so that warehouse managers can timely identify the risk of material stagnation.

25. A resource load monitoring and resource capacity planning method for the demand-driven replenishment order generation and replenishment plan scheduling method according to any one of claims 1 to 21, characterized in that: Used to monitor and plan the supply of execution resources, specifically The following processes are included: ① Resource load table for current and specified future order cycles, sorted in descending order by load rate; ② Load trend curve of specified resources; ③ A list of resources that cause insufficient supply capacity, sorted in descending order by the percentage of missing capacity; ④ A list of resources recommended for outsourcing (load rates are consistently below a specific threshold), sorted by average load rate.

26. A supply chain digital twin simulation method for the demand-driven replenishment order generation and replenishment plan scheduling method according to any one of claims 1 to 21, based on the future demand forecast scheduling results of each level of sub-components in BOM expansion, characterized in that: In step S1, a supply chain demand transmission mechanism is established through the BOM table and material lookup table. With the material as the entry point, the list of material sub-components at all levels and the corresponding scheduling results of material sub-components at all levels are found according to the BOM table or material lookup table. The material inventory changes from the current time to various time points in the future are simulated. Combined with the location coordinates of each storage location, the material supply, consumption and inventory changes over time are displayed on the GIS map or the specified background map.

27. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 26 are implemented.

28. A device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 26 are implemented.

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