Procurement Decision System

Abstract

A procurement decision-making system includes a transceiver, an input device, storage media, and a processor. The processor receives work order data via the transceiver or inputs work order data via the input device. A parsing module parses the work order data into calculable demand data. A standardization module converts the calculable demand data in different formats into a unified standard format. A condition setting module sets procurement constraints. A solution generation module generates procurement combination solutions based on the calculable demand data and procurement constraints. An evaluation module calculates the material utilization rate, remaining material weight, and procurement cost of the procurement combination solutions. A solution selection module selects a solution with practically feasible conditions based on the material utilization rate, remaining material weight, and procurement cost of the procurement combination solutions.

Description

Procurement Decision System Technical Field

[0001] This work is about a procurement decision-making system. Prior Technology

[0002] Generally, factories often face the challenge of receiving multiple orders within a short period, resulting in numerous work order documents. These work order documents are scattered across various systems, including Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Advanced Planning and Scheduling (APS), Warehouse Management System (WMS), external order systems, and Excel spreadsheets compiled by purchasing personnel. Due to the different data sources, the units of length, quantity, weight, and product specifications in these work order documents are often presented in different units or formats. This forces purchasing personnel to manually organize, convert, and compile each document into a single requirement document, which is prone to omissions or errors due to manual judgment or repetitive processing.

[0003] Furthermore, in the existing procurement operation model, factories have long followed established practices, purchasing only fixed lengths of raw materials (e.g., steel bars) for subsequent cutting. Procurement personnel typically rely on past experience or existing supply conditions to directly determine the fixed length and quantity of raw materials, without evaluating other possible raw material length configurations before implementing the procurement decision.

[0004] Furthermore, the supplier's available specifications, minimum purchase quantity, available batch size, and delivery time conditions are not converted into calculable parameterized constraints, making procurement decisions prone to relying on "past practices" or "judgments based on past experience." When management wants to compare the advantages and disadvantages of different procurement methods, they can only review them afterward through manual compilation or supplementary reports, making it difficult to quantify and compare the differences in material waste caused by "fixed length cutting" and other possible length configurations in subsequent cutting before implementing procurement decisions.

[0005] According to the above-mentioned procurement decision-making method, factories often only discover that there is too much leftover material, low material utilization rate and high loss cost after the procurement is completed and the actual cutting is done. This leads to additional procurement, rush orders, long-term accumulation of leftover material and an increase in overall material costs. Moreover, it is difficult to establish a traceable decision-making basis to explain "why the fixed length procurement plan was chosen in the first place". Summary of the Invention

[0006] The main purpose of this work is to provide a procurement decision-making system that can parse and convert work order data into a unified standard format.

[0007] Another objective of this invention is to provide a procurement decision-making system that can offer multiple procurement combination options to users or systems to evaluate various possible raw material length configurations.

[0008] Another objective of this work is to provide a procurement decision-making system that can provide management reports.

[0009] Another objective of this work is to provide a procurement decision-making system that can dynamically re-evaluate and adjust decisions regarding raw material procurement and cutting planning.

[0010] To achieve the aforementioned objectives, this invention provides a procurement decision-making system, including a transceiver, an input device, a storage medium, and a processor. The storage medium stores a plurality of modules. The processor is coupled to the transceiver, the input device, and the storage medium, receiving multiple work order data from different work orders or different sources via the transceiver, or inputting multiple work order data from different work orders or different sources via the input device, accessing and executing the modules, which include a parsing module, a standardization module, a condition setting module, a solution generation module, an evaluation module, and a solution selection module. The parsing module is used to parse the work order data from different work orders or different sources into multiple calculable requirement data, wherein the calculable requirement data have different formats. The standardization module is coupled to the parsing module and is used to convert the calculable requirement data with different formats into calculable requirement data in a unified standard format. The condition setting module is used to set procurement constraints. The scheme generation module is coupled to the standardization module and the condition setting module, and is used to generate multiple procurement combination schemes based on the computable demand data and procurement constraints in a unified standard format. The evaluation module is coupled to the scheme generation module, and is used to calculate the material utilization rate, surplus material weight, and procurement cost of the procurement combination schemes. The scheme selection module is coupled to the evaluation module, and is used to select one of the procurement combination schemes as the scheme with practically executable conditions based on the material utilization rate, surplus material weight, and procurement cost of the procurement combination schemes.

[0011] In some embodiments, the processor receives work order data from different work orders or different sources via the transceiver or inputs work order data from different work orders or different sources via the input device based on a triggering event. The triggering event includes a difference between the actual remaining length of the raw material after cutting and the original estimated value, a change in procurement conditions, a change in work order data, insertion of work order data, cancellation of work order data, an anomaly during the cutting simulation, or the inability to meet existing requirements during the cutting simulation, or a combination thereof.

[0012] In some embodiments, each work order data includes product category, product specifications, unit weight, finished product length, required quantity, unit price, or a combination thereof.

[0013] In some embodiments, the procurement restrictions include the procureable length range of raw materials, minimum procureable weight, quantity restrictions, unit price of raw materials, waste recycling price, specific suppliers, delivery dates, batch restrictions, or a combination thereof.

[0014] In some embodiments, the modules further include a report generation module coupled to the scheme selection module and used to generate a management report based on the scheme under the actual executable conditions.

[0015] The advantage of this invention is that it can parse and convert work order data from different work orders or different sources into a unified standard format. Purchasing personnel do not need to manually sort, convert and compile each work order into a requirement data, reducing omissions or errors caused by manual judgment or repetitive processing.

[0016] Furthermore, before a procurement decision is made, this invention provides multiple procurement combination options for users or systems to evaluate various possible raw material length configurations. Users or systems can compare the differences in material loss caused by the raw material length configurations of these procurement combination options in subsequent cutting based on material utilization, leftover material weight, and procurement cost.

[0017] In addition, this invention can provide management reports, allowing users to achieve their management needs by following these reports. For example, cutting raw materials according to the production practice combination cutting table, making cost-saving decisions through return on investment analysis reports, or making decisions with sustainable value through ESG environmental sustainability reports.

[0018] It is worth mentioning that this invention uses an event-triggered approach to dynamically re-evaluate and adjust raw material procurement and cutting planning decisions. Specifically, when a triggering event occurs, this invention retains the confirmed procurement and cutting results and only performs optimized simulation calculations on demand data that has not yet been executed or confirmed, in order to avoid unnecessary impact on existing decision results. Therefore, during the recalculation process, this invention generates multiple procurement combination schemes based on the latest demand data and procurement constraints, and simultaneously updates evaluation indicators such as material utilization rate, leftover material weight, procurement cost, and management reports for each procurement combination scheme. Through the above mechanism, this invention can dynamically adjust subsequent procurement and cutting recommendations without affecting existing procurement decisions, allowing users to continuously obtain optimal procurement decision support based on the latest situation, thereby achieving the goals of reducing material waste, optimizing cost structure, and improving decision accuracy. Simple Explanation of the Diagram

[0019] Figure 1 is a schematic diagram of the system structure of this invention. Figure 2 is a flowchart of the method used in this creation. Figure 3A is a schematic diagram of the input interface of the system in this invention. Figure 3B is a schematic diagram of the input interface in the manual input mode of Figure 3A. Figure 3C is a schematic diagram of the input interface of the Excel upload mode shown in Figure 3A. Figure 3D is a schematic diagram of the input interface of the AI ​​assistant dialogue mode in Figure 3A. Figure 4 is a schematic diagram of the input interface of the condition setting module in this creation. Figure 5 is a schematic diagram showing the evaluation results of the procurement combination scheme displayed by the evaluation module of this creation. Figure 6 is a schematic diagram of the production practice combination cutting table of this creation. Figure 7 is a schematic diagram of the investment return analysis report of this work. Figure 8 is a schematic diagram of the environmental sustainability report of this work. Implementation

[0020] The following diagrams and component symbols provide a more detailed explanation of the implementation of this invention, so that those familiar with the art can implement it after studying this manual.

[0021] Figure 1 is a schematic diagram of the system structure of this invention. As shown in Figure 1, this invention provides a procurement decision system, including a transceiver 10, an input device 20, a storage medium 30, and a processor 40.

[0022] Transceiver 10 transmits and receives signals wirelessly or via a wired connection. Transceiver 10 can also perform operations such as low-noise amplification, impedance matching, mixing, up or down frequency conversion, filtering, amplification, and similar functions.

[0023] The input device 20 can be a keyboard in conjunction with the system's built-in manual input mode, the system's built-in Excel upload mode, or an AI assistant dialogue mode (AI copilot).

[0024] Storage medium 30 is used to store various software, data, and code required for the operation of the procurement decision system. Storage medium 30 may be any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk drive (HDD), solid state drive (SSD), or similar components or combinations thereof, and is used to store a plurality of modules or various applications that can be executed by processor 40. Storage medium 30 stores a parsing module 31, a standardization module 32, a condition setting module 33, a solution generation module 34, an evaluation module 35, a solution selection module 36, a report generation module 37, and a database 38. The standardization module 32 is coupled to the parsing module 31, the solution generation module 34 is coupled to the standardization module 32 and the condition setting module 33, the evaluation module 35 is coupled to the solution generation module 34, the solution selection module 36 is coupled to the evaluation module 35, and the report generation module 37 is coupled to the solution selection module 36.

[0025] The processor 40 is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose microcontroller (MCU), microprocessor, digital signal processor (DSP), programmable logic controller (PLC), application-specific integrated circuit (ASIC), graphics processing unit (GPU), image signal processor (ISP), image processing unit (IPU), arithmetic logic unit (ALU), complex programmable logic device (CPLD), field programmable gate array (FPGA), or other similar elements or combinations thereof. The processor 40 is coupled to the storage medium 30 and accesses and executes various applications stored in the storage medium 30, as well as the parsing module 31, standardization module 32, condition setting module 33, scheme generation module 34, evaluation module 35, scheme selection module 36, report generation module 37, and database 38 stored in the storage medium 30.

[0026] Figure 2 is a flowchart of the method of this invention. Figure 3A is a schematic diagram of the input interface of the system of this invention. Figure 3B is a schematic diagram of the input interface of the manual input mode of Figure 3A. Figure 3C is a schematic diagram of the input interface of the Excel upload mode of Figure 3A. Figure 3D is a schematic diagram of the input interface of the AI ​​assistant dialogue mode of Figure 3A. Figure 4 is a schematic diagram of the input interface of the condition setting module 33 of this invention. Figure 5 is a schematic diagram of the evaluation module 35 of this invention displaying the evaluation results of the procurement combination scheme. Figure 6 is a schematic diagram of the production implementation combination cutting table of this invention. Figure 7 is a schematic diagram of the return on investment analysis report of this invention. Figure 8 is a schematic diagram of the environmental sustainability report of this invention. This invention provides a procurement decision-making method, including the following steps:

[0027] Step S10, as shown in Figures 1 and 2, involves the processor 40 receiving multiple work order data from different work orders or different sources via the transceiver 10 or inputting multiple work order data from different work orders or different sources via the input device 20 based on a trigger event. More specifically, the trigger event includes discrepancies between the actual remaining length of the raw material after cutting and the original estimated value, changes in procurement conditions, changes in work order data, insertion of work order data, cancellation of work order data, anomalies occurring during the cutting simulation, and the inability to meet existing requirements or combinations thereof during the cutting simulation. These work order data for different work orders are obtained from different customers, different delivery dates, or different finished product specifications. These work order data from different sources are obtained from an enterprise resource planning (ERP) management system, a manufacturing execution system (MES) advanced planning and scheduling (APS) system, a warehouse management system (WMS), an external order system, or manual input. Manual input includes manual input mode, Excel upload mode, and AI assistant dialogue mode (AI copilot), but this invention is not limited to these. Transceiver 10 receives work order data from ERP, MES, APS, WMS, and external order systems. As shown in Figures 3A and 3B, users can select manual input mode from the system's built-in input interface, and then input the work order data using the keyboard from the manual input mode input interface. As shown in Figures 3A and 3C, users can select Excel upload mode from the system's input interface, and then drag and drop the work order data files stored in database 38 to the Excel upload mode input interface or click upload. Supported formats for each work order data include .xlsx, .xls, and .csv. As shown in Figures 3A and 3D, users can select the AI ​​assistant dialogue mode from the system's input interface, and then input text commands into the AI ​​assistant dialogue mode input interface via the keyboard, such as "help me find multiple work order data from different work orders or different sources". The AI ​​assistant dialogue mode will then search for such work order data in database 38 according to the text commands.

[0028] Preferably, as shown in Figures 3B and 3C, each work order data includes product category, product specifications, unit weight, finished product length, required quantity, unit price, or a combination thereof; however, this work is not limited to this. For example, taking the procurement of steel bars as an example, Table 1 shows multiple work order data from different work orders or different sources, with each work order data including a combination of product category, product specifications, finished product length, and required quantity.

[0029] Table 1 Product Category Product Specifications Finished product length (cm) Quantity required (pieces) Reinforcing steel 10# SD420W 740 32 Reinforcing steel 10# SD420W 680 11 Reinforcing steel 10# SD420W 650 87 Reinforcing steel 10# SD420W 390 55 Reinforcing steel 10# SD420W 440 twenty three Reinforcing steel 10# SD420W 380 69 Reinforcing steel 10# SD420W 320 115 Reinforcing steel 10# SD420W 280 43 Reinforcing steel 10# SD420W 230 77 Reinforcing steel 10# SD420W 860 53

[0030] In Table 1, 10# indicates the steel bar number / diameter grade, commonly corresponding to approximately D10 (10 mm diameter) steel bars. SD420W is the steel grade (material specification) of the steel bar, SD indicates steel deformed, 420 indicates a yield strength of approximately 420 MPa, and W indicates weldability.

[0031] In some embodiments, the processor 40 can directly receive supply order data from different work orders or different sources via the transceiver 10 or input the supply order data from different work orders or different sources via the input device 20, without needing to be based on any triggering event.

[0032] Step S20, as shown in Figures 1 and 2, involves parsing module 31 into multiple work order data from different work orders or different sources, which are then converted into multiple calculable requirement data. These calculable requirement data have different formats. Specifically, the different formats refer to different units or representation methods; however, this invention is not limited to this. For example, when purchasing steel bars, the length units include meters, centimeters, millimeters, or table feet; the quantity units include pieces, bundles, sets, pieces, or cages; the weight units include kilograms or metric tons; and the product specifications include diameter (number), steel grade (strength), or weldability.

[0033] Step S30, as shown in Figures 1 and 2, involves the standardization module 32 converting the computationally readable requirement data in different formats into computationally readable requirement data in a unified standard format. For example, when purchasing steel bars, the standard format for the length of the steel bars is "centimeter," the standard format for the quantity of steel bars is "piece," the standard format for the weight of steel bars is "kilogram," and the standard format for the product specifications of the steel bars is a combination of diameter, steel type, and weldability.

[0034] Step S40, as shown in Figures 1 and 2, involves setting a procurement restriction condition in the condition setting module 33. Preferably, the procurement restriction condition includes the procureable length range of raw materials, minimum procurement weight, quantity limit, raw material unit price, scrap recycling price, specific supplier, delivery date, batch limit, or a combination thereof; however, this invention is not limited to these. The procurement restriction condition is established in the form of a "parameterized condition set" and can be dynamically adjusted according to different time points, order batches, or market conditions. For example, as shown in Figure 4, taking the procurement of steel bars as an example, the procureable length range of raw materials is between 900 cm and 1,600 cm, the minimum procurement weight is 15,000 kg, the raw material price is NT$20 per kg (not shown in the figure), and the scrap recycling price is NT$8 per kg (not shown in the figure).

[0035] In step S50, as shown in Figures 1 and 2, the scheme generation module 34 generates multiple procurement combination schemes based on the calculable demand data and procurement constraints in a unified standard format. Preferably, the scheme generation module 34 performs practical feasibility segmentation verification or segmentation calculations on each procurement combination scheme according to the resource unit scale of linear divisibility characteristics to generate corresponding segmentation configuration results. The linearly divisible resource refers to raw materials with one-dimensional length attributes (e.g., steel bars, pipes, or long strip metal materials), which can be cut and segmented along the length direction, with length as the unique allocation dimension; the segmentation behavior conforms to a linear additive relationship, and the sum of the lengths of each segment configuration must not exceed the length of the raw material; the minimum segmentation unit can be one centimeter or one millimeter. The partitioning and configuration results of each procurement combination scheme are specific cutting combination tables generated based on the correspondence between raw material length and required length. These tables include the raw material length, the finished product length combination for each raw material, the remaining length of each raw material, the number of pieces used in each cutting combination, the total quantity of finished products satisfied, and any remaining unallocated requirements (if any). These partitioning and configuration results of the procurement combination schemes can be directly used in actual cutting operations. For example, if the raw material length is 1520 cm, the partitioning and configuration result of the procurement combination scheme is: each raw material is cut into 860 cm lengths. 1 650 cm 1 With 10 centimeters remaining, this procurement combination plan uses a total of 170 raw materials to meet all requirements.

[0036] In step S60, as shown in Figures 1 and 2, the evaluation module 35 calculates the material utilization rate, surplus weight, and procurement cost of the procurement combination schemes based on the segmentation and configuration results. For example, as shown in Figure 5, taking the procurement of steel bars as an example, procurement combination scheme 1 includes the following information: raw material length is 1,200 cm, procurement quantity is 222 pieces, material utilization rate is 96.71% (i.e., 100% minus loss rate of 3.29%), surplus weight (i.e., loss weight) is 560 kg, and procurement cost (i.e., total cost) is NT$340,459; procurement combination scheme 2 includes the following information: raw material length is 1,400 cm, procurement quantity is 188 pieces, material utilization rate is 9... 7.88% (i.e., 100% deducting a loss rate of 2.12%), the remaining material weight (i.e., the loss weight) is 357 kg, and the procurement cost (i.e., the total cost) is NT$336,370; Procurement combination scheme 3 includes the following information: raw material length is 1,520 cm, procurement quantity is 170 pieces, material utilization rate is 99.7% (i.e., 100% deducting a loss rate of 0.3%), remaining material weight (i.e., the loss weight) is 50 kg, and procurement cost (i.e., the total cost) is NT$330,235. In some embodiments, the evaluation module 35 calculates the total procurement weight and the quantity of finished products that can be satisfied by the procurement combination schemes based on these procurement combination schemes.

[0037] In step S70, as shown in Figures 1 and 2, the scheme selection module 36 selects one of the procurement combination schemes as the scheme with practically feasible conditions based on the material utilization rate, leftover material weight, and procurement cost of the procurement combination schemes. For example, as shown in Figure 5, taking the procurement of steel bars as an example, in terms of material utilization rate, scheme 1 is the lowest, scheme 2 is the second highest, and scheme 1 is the highest; in terms of leftover material weight, scheme 1 is the highest, scheme 2 is the second highest, and scheme 3 is the lowest; in terms of procurement cost, scheme 1 is the highest, scheme 2 is the second highest, and scheme 3 is the lowest. Because procurement combination scheme 3 has the highest material utilization rate, the lowest leftover material weight, and the lowest procurement cost, it is the most economical, so the scheme selection module 36 recommends procurement combination scheme 3. The scheme selection module 36 can also recommend the procurement combination scheme with the highest material utilization rate, the lowest leftover material weight, and the lowest procurement cost from the remaining procurement combination schemes that can be practically cut or absorbed by the demand by excluding schemes that cannot be practically cut or absorbed. For example, as shown in Figure 5, taking the procurement of steel bars as an example, since both Option 1 and Option 2 are impractical to cut or cannot meet the demand, they must be excluded. Therefore, Option Selection Module 36 recommends Procurement Combination Option 3. Users can refer to the recommendation of Option Selection Module 36 and select Procurement Combination Option 3 as the practically feasible option. Alternatively, Option Selection Module 36 can automatically select Procurement Combination Option 3 as the practically feasible option. It is worth noting that excluded procurement combinations will not be included in the subsequent multiple available or recommended procurement combinations to highlight the substantial impact of the procurement decision on the output result, rather than simply ranking or presenting these procurement combinations numerically.

[0038] Step S80, as shown in Figures 1 and 2, generates a management report based on the actual executable conditions of the solution by the report generation module 37.

[0039] Preferably, as shown in Figure 6, the management report is a production practice combination cutting table, which includes raw material length, raw material quantity, and remaining material length. The number of pieces and the length of each step are matched. More specifically, taking the procurement of steel bars as an example, some users are concerned about how to actually cut the raw materials on the factory floor after purchasing them according to the selected procurement combination plan. Users can use the production practice combination cutting table to know how each type of raw material length can be combined and cut, how many finished pieces can be cut from each length, and whether there is any leftover material or waste. Therefore, users can easily cut raw materials according to the production practice combination cutting table.

[0040] Preferably, as shown in Figure 7, the management report is a return on investment (ROI) analysis report, which includes cost savings analysis, weight and wastage comparison, and annual benefit forecast. Cost savings analysis includes conventional procurement, reverse optimization, current savings, and percentage savings. Weight and wastage comparison includes conventional losses, reverse losses, reduced losses, and percentage reduction. Annual benefit forecast includes monthly savings, annual savings, and reduction in raw materials. More specifically, taking steel bar procurement as an example, some users are concerned with how much cost will be saved after procuring and cutting raw materials according to the selected procurement combination plan. Users can learn from the ROI analysis report that the report generation module 37 calculates the savings amount and ROI based on the cost, material savings, and benefit differences of different plans. Therefore, users can make cost-saving decisions through the ROI analysis report. Table 2 shows the calculation method of the ROI analysis report in the preferred embodiment.

[0041] Table 2 project Formula definition Actual calculation Baseline Scheme (Scheme 1) Waste Weight Wb 560 kg Optimization scheme (Scheme 3) Waste weight Wo 50 kg Material reduction W Wb Wo 560 50 510 kg Net value per unit of materials V P S 20 8 12 yuan / kg Return on Investment (ROI) W P S 510 12 6,120 yuan Cost Variance Verification (System) Rsingle Cbase Copt 340,459 330,235 10,224 yuan Monthly execution count Nm 4 times / month Monthly savings Rmonth Rsingle Nm 10,224 4 40,896 yuan Annual savings Ryear Rmonth 12 40,896 12 490,752 yuan Annual reduction in material weight Wyear W Nm 12 510 4 12 24,480 kg, approximately 24.5 metric tons Annual reduction in steel ( W Nm 12) 1000 (510 1 12) 1000 6.12 metric tons / year

[0042] Preferably, as shown in Figure 8, the management report is an ESG (Environmental, Social, and Governance) environmental sustainability report. This report includes material reduction, carbon reduction benefits, equivalent tree planting, resource savings, and annual ESG performance forecasts. More specifically, taking steel rebar procurement as an example, some users are concerned about whether raw material waste is reduced and whether it is better for the environment. Users can learn from the ESG environmental sustainability report that the report generation module 37 converts increased material utilization and reductions in waste and losses into carbon emission reduction and resource saving indicators. Therefore, users can make sustainable decisions based on the ESG environmental sustainability report. Table 3 is a calculation table of the ESG single-item benefits of the ESG environmental sustainability report in the preferred embodiment, and Table 4 is a calculation table of the ESG annual benefits of the ESG environmental sustainability report in the preferred embodiment.

[0043] Table 3 Indicator Items Formula definition Actual calculation Material reduction W 510 kg Carbon reduction (kg) W E 510 1.8 918 kg, approximately 920 kg (carbon dioxide) Carbon reduction (metric tons) ( W E) 1000 920 1000 0.92 metric tons (carbon dioxide) Equivalent tree planting ( W E) T 920 20 46 trees Save water resources W U 510 2 1,020 liters

[0044] Table 4 Indicator Items Formula definition Actual calculation Annual reduction in waste weight W N 510 48 24,480 kg, approximately 24.5 metric tons Annual carbon reduction (metric tons) ( W E N) 1000 (510 1.8 48) 1000 44.2 metric tons (carbon dioxide) Equivalent tree planting per year ( W E N) T (510 1.8 48) 20 2,208 trees

[0045] Therefore, this invention can parse and convert work order data from different work orders or different sources into a unified standard format. Purchasing personnel do not need to manually sort, convert and compile each work order into a requirement data, reducing omissions or errors caused by manual judgment or repetitive processing.

[0046] Furthermore, before implementing a procurement decision, this invention provides users or systems with multiple procurement combination options to evaluate various possible raw material length configurations. Users or systems can compare the differences in material loss resulting from subsequent cutting based on material utilization, leftover material weight, and procurement costs for these procurement combination options, thus achieving the following advantages: First, procurement personnel no longer need to follow established practices and only procure raw materials of a fixed length for subsequent cutting; second, procurement personnel no longer need to rely on past experience or existing supply conditions to directly decide on fixed raw material lengths and procurement quantities; third, management does not need to review the process afterward through manual compilation or supplementary reports; fourth, it avoids situations where the factory discovers excessive leftover materials, low material utilization, and high loss costs only after procurement is completed and actual cutting is performed, preventing additional procurement, rush orders, long-term accumulation of leftover materials, and increased overall material costs; fifth, it establishes a traceable decision-making basis to explain "why a specific procurement combination option was chosen in the first place."

[0047] In addition, this invention can provide management reports, allowing users to achieve their management needs by following these reports. For example, cutting raw materials according to the production practice combination cutting table, making cost-saving decisions through return on investment analysis reports, or making decisions with sustainable value through ESG environmental sustainability reports.

[0048] It is worth mentioning that this invention uses an event-triggered approach to dynamically re-evaluate and adjust raw material procurement and cutting planning decisions. Specifically, when a triggering event occurs, this invention retains the confirmed procurement and cutting results and only performs optimized simulation calculations on demand data that has not yet been implemented or confirmed, in order to avoid unnecessary impact on existing decision results. Confirmed procurement and cutting results refer to procurement combinations that have been confirmed and marked as unchangeable or have entered the actual execution process. Therefore, during the recalculation process, this invention will generate multiple procurement combination schemes based on the latest demand data and procurement constraints, and simultaneously update evaluation indicators such as material utilization rate, leftover material weight, procurement cost, and management reports for each procurement combination scheme. Through the above mechanism, this invention can dynamically adjust subsequent procurement and cutting recommendations without affecting existing procurement decisions, allowing users to continuously obtain optimal procurement decision support based on the latest situation, achieving the goals of reducing material waste, optimizing cost structure, and improving decision accuracy.

[0049] The above description is merely a preferred embodiment for explaining this work and is not intended to limit this work in any way. Therefore, any modifications or changes made to this work under the same creative spirit should still be included within the scope of protection intended by this work.

[0050] 10: Transceiver 20: Input device 30: Storage Media 31: Parsing Module 32: Standardized Module 33: Condition Setting Module 34: Solution Generation Module 35: Evaluation Module 36: Solution Selection Module 37: Report Generation Module 38: Database 40: Processor S10~S80: Steps

Claims

1. A procurement decision-making system, comprising: One transceiver; One input device; A storage medium that stores multiple modules; The transceiver, input device, and storage medium are coupled to a processor that receives multiple work order data from different work orders or different sources via the transceiver or inputs multiple work order data from different work orders or different sources via the input device, accesses and executes the modules, and the modules include: a parsing module for parsing the work order data from different work orders or different sources into multiple calculable requirement data, wherein the calculable requirement data have different formats; a standardization module coupled to the parsing module for converting the calculable requirement data with different formats into calculable requirement data in a unified standard format; a condition setting module for setting a procurement constraint; and a solution generation module coupled to the standardization module and the condition setting module for generating multiple procurement combination solutions based on the calculable requirement data in the unified standard format and the procurement constraint. An evaluation module, coupled to the scheme generation module, is used to calculate the material utilization rate, residual weight, and procurement cost of the procurement combination schemes based on the procurement combination schemes; and a scheme selection module, coupled to the evaluation module, is used to select one of the procurement combination schemes as the scheme with actual feasibility based on the material utilization rate, residual weight, and procurement cost of the procurement combination schemes.

2. The procurement decision-making system as described in claim 1, wherein, The processor receives work order data from different sources via the transceiver or inputs work order data from different sources via the input device based on a triggering event. The triggering event includes: a difference between the actual remaining length of the raw material after cutting and the original estimate; a change in procurement conditions; a change in work order data; insertion of work order data; cancellation of work order data; an anomaly during the cutting simulation; or the inability to meet existing requirements or a combination thereof during the cutting simulation.

3. The procurement decision-making system as described in claim 1, wherein, Each work order includes information such as product category, product specifications, unit weight, finished product length, required quantity, unit price, or a combination thereof.

4. The procurement decision-making system as described in claim 1, wherein, These procurement restrictions include the available length range of raw materials, minimum purchase weight, quantity limits, unit price of raw materials, scrap recycling price, specific suppliers, delivery dates, batch restrictions, or a combination thereof.

5. The procurement decision system as described in claim 1, wherein, These modules further include a report generation module coupled to the scheme selection module, which generates a management report based on the scheme under the actual executable conditions.

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