Material online collection method, system and equipment

Abstract

The invention discloses a material online collection method, system and device, and relates to the technical field of material purchasing, and the method comprises the steps: obtaining the current purchasing demand information, and generating a recommended supplier and a supplier portrait corresponding to each to-be-purchased material according to the current purchasing demand information and historical purchasing data; initializing a plurality of supplier combinations based on the recommended suppliers, and determining basic scores of the supplier combinations according to supplier portraits corresponding to the recommended suppliers; performing pressure test simulation on each supplier combination, and generating an elastic score of each supplier combination according to a simulation result; and sorting the supplier combinations in combination with the basic score and the elastic score, and determining a target supplier combination according to a sorting result. The supplier combinations are quantitatively evaluated through the dynamic scoring model and pressure test simulation, intelligent supplier combination recommendation is achieved, potential supply risks can be avoided in advance, and the ability of enterprises to cope with emergencies is improved.

Description

Technical Field

[0001] This application relates to the field of materials procurement technology, and in particular to a method, system and equipment for online procurement of materials. Background Technology

[0002] With increasing market competition and expanding production scale, enterprises' needs for material procurement are becoming increasingly diversified and complex, making traditional procurement models increasingly unable to meet their development requirements. The material procurement process involves multiple aspects, including supplier selection, cooperation review, and portfolio optimization. It requires a comprehensive consideration of various factors to select suppliers and ensure the stability of material supply.

[0003] Existing supplier selection methods often focus on static analysis of suppliers' historical data, making it difficult to accurately predict supplier performance under different scenarios. However, due to the constant changes in market environment and supplier conditions, especially during stressful or unforeseen events (such as extreme weather or logistical disruptions), the existing supplier portfolio determined based on static scoring may not be the optimal combination in the actual environment, leading to significant supply disruption risks for enterprises in the face of unforeseen circumstances. Summary of the Invention

[0004] The main purpose of this application is to provide an online procurement method, system, and equipment for materials, which aims to solve the technical problem that existing supplier combinations determined based on static scoring are difficult to adapt to changing environments.

[0005] To achieve the above objectives, this application proposes an online procurement method for materials, the method comprising: Obtain current procurement needs information, and generate recommended suppliers and supplier profiles for each material to be procured based on the current procurement needs information and historical procurement data; Several supplier combinations are initialized based on each of the recommended suppliers, and the basic score of each supplier combination is determined according to the supplier profile corresponding to each of the recommended suppliers. Stress test simulations are performed on each of the aforementioned supplier combinations, and resilience scores are generated for each of the aforementioned supplier combinations based on the simulation results; The supplier combinations are ranked by combining the base score and the flexibility score, and the target supplier combination is determined based on the ranking results.

[0006] In one embodiment, the step of generating recommended suppliers and supplier profiles for each material to be procured based on the current procurement demand information and historical procurement data includes: Based on the current procurement demand information, a number of materials to be procured are determined, and the historical suppliers corresponding to each of the materials to be procured and their corresponding recommendation weights are queried from the historical procurement database. The recommendation weights are generated based on the number of times each of the historical suppliers has cooperated in the past. Historical suppliers whose corresponding recommendation weight is higher than the preset recommendation threshold are identified as recommended suppliers; Obtain supplier information for each of the recommended suppliers, and generate a supplier profile for each of the recommended suppliers based on the supplier information.

[0007] In one embodiment, before the step of querying the historical suppliers and corresponding recommendation weights for each of the materials to be procured from the historical procurement database, the following steps are included: Initialize the recommendation weights based on the historical number of times each of the aforementioned historical suppliers has been selected. User behavior influence factors are generated based on the number of times users delete and manually add users for each of the historical suppliers, and the recommendation weights are updated based on the user behavior influence factors to obtain the recommendation weights for each of the historical suppliers.

[0008] In one embodiment, before the step of initializing several supplier combinations based on each of the recommended suppliers and determining the basic score of each supplier combination according to the supplier profile corresponding to each of the recommended suppliers, the method further includes: Based on the supplier profiles and preset screening rules, the recommended suppliers are screened to obtain the candidate suppliers for each material to be purchased. The preset filtering rule is a rule for determining whether the supplier profile corresponding to each of the recommended suppliers simultaneously meets the dimensional indicator conditions in multiple preset dimensions. Accordingly, the step of initializing several supplier combinations based on each of the recommended suppliers and determining the basic score of each supplier combination according to the supplier profile corresponding to each of the recommended suppliers includes: Several supplier combinations are initialized based on each of the candidate suppliers, and the basic score of each supplier combination is determined according to the supplier profile corresponding to each candidate supplier.

[0009] In one embodiment, the supplier profile consists of several preset dimensions and corresponding indicator values, and each preset dimension includes: material dimension, evaluation dimension, qualification dimension and price dimension; The step of filtering each recommended supplier according to the supplier profile and preset filtering rules to obtain candidate suppliers for each material to be purchased includes: Based on the supplier profiles of each recommended supplier, determine whether the supplier profiles of each recommended supplier simultaneously meet the dimensional indicator conditions of each preset dimension; If the index values ​​of each preset dimension in the supplier profile of the recommended supplier all meet the dimension index conditions of a certain material to be purchased, the recommended supplier is determined to be a candidate supplier for a certain material to be purchased. If the index values ​​of each preset dimension in the supplier profile of the recommended supplier do not meet the dimension index conditions of any of the materials to be purchased, it is determined that the recommended supplier is not a candidate supplier corresponding to each of the materials to be purchased.

[0010] In one embodiment, the step of initializing several supplier combinations based on each of the candidate suppliers and determining the basic score of each supplier combination according to the supplier profile corresponding to each of the candidate suppliers includes: According to a preset combination optimization strategy, several supplier combinations are generated based on each of the candidate suppliers. The preset combination optimization strategy includes: cost priority strategy, risk diversification strategy, and collaborative optimization strategy. Obtain the index values ​​of each preset dimension in the supplier profile corresponding to each candidate supplier, and generate the basic score of each supplier combination by combining the dimension weight allocation relationship determined by the preset combination optimization strategy.

[0011] In one embodiment, the step of performing stress test simulations on each of the supplier combinations and generating a resilience score for each supplier combination based on the simulation results includes: Construct a stress test scenario library, which includes instances of various stress scenarios and their corresponding dimensional impact parameters; Monte Carlo simulation modeling technology is used to generate several random disturbance scenarios based on the stress test scenario library. Each random disturbance scenario consists of at least one stress scenario instance. Based on the dimensional impact parameters corresponding to the stress scenario instance in the random disturbance scenario, generate elasticity index values ​​for each preset dimension, and determine the elasticity score of each supplier combination based on each elasticity index value.

[0012] In one embodiment, the step of ranking each supplier combination by combining the base score and the resilience score, and determining the target supplier combination based on the ranking result, includes: The base score and the flexibility score are summed to obtain the comprehensive score for each supplier combination; Based on the comprehensive scores from highest to lowest, the top few supplier combinations are selected as candidate supplier combinations. The candidate supplier combinations are presented to the user so that the user can select the target supplier combination from the candidate supplier combinations.

[0013] Furthermore, to achieve the above objectives, this application also proposes an online procurement system for materials, the system comprising: The demand import module is used to obtain current procurement demand information and generate recommended suppliers and supplier profiles for each material to be procured based on the current procurement demand information and historical procurement data. The initial scoring module is used to initialize several supplier combinations based on each of the recommended suppliers, and to determine the basic score of each supplier combination according to the supplier profile corresponding to each of the recommended suppliers; The resilience scoring module is used to perform stress test simulations on each of the supplier combinations and generate a resilience score for each supplier combination based on the simulation results. The comprehensive ranking module is used to rank each supplier combination by combining the basic score and the elastic score, and to determine the target supplier combination based on the ranking results.

[0014] In addition, to achieve the above objectives, this application also proposes an online procurement device for materials, the device comprising: a memory, a processor, and an online procurement program for materials stored in the memory and executable on the processor, the online procurement program for materials being configured to implement the steps of the online procurement method for materials as described above.

[0015] This application discloses an online procurement method for materials. The method involves acquiring current procurement demand information and generating recommended suppliers and supplier profiles for each material to be procured based on this information and historical procurement data. Several supplier combinations are initialized based on each recommended supplier, and a basic score for each combination is determined according to its corresponding supplier profile. Stress tests are simulated on each supplier combination, and an elasticity score is generated based on the simulation results. The supplier combinations are then ranked using both the basic and elasticity scores, and a target supplier combination is determined based on the ranking results.

[0016] This application comprehensively considers suppliers' historical data and current procurement needs to generate accurate recommended suppliers and their profiles, providing a comprehensive foundational assessment for supplier portfolio selection. Simultaneously, it introduces stress test simulations to quantitatively evaluate the resilience of each supplier portfolio under extreme scenarios, effectively compensating for the shortcomings of existing procurement methods in risk prediction and making procurement decisions more forward-looking and reliable. On the one hand, accurate recommendations and portfolio initialization help quickly locate high-quality supplier resources, improving procurement efficiency; on the other hand, the integration of resilience scoring enables the early avoidance of potential supply risks in complex market environments, ensuring the continuity and stability of enterprise material supply, thereby enhancing the enterprise's competitiveness in supply chain management and its ability to respond to emergencies. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the online procurement method for materials in this application; Figure 2 This is a flowchart illustrating the second embodiment of the online procurement method for materials in this application. Figure 3 This is a flowchart illustrating the third embodiment of the online procurement method for materials in this application; Figure 4 This is a schematic diagram of the module structure of the online procurement system for the materials in this application; Figure 5 This is a schematic diagram of the online procurement equipment for the materials in this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] This application provides an online procurement method for materials, referencing... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the online procurement method for materials according to this application. In this embodiment, the method includes steps S10 to S40: Step S10: Obtain current procurement demand information, and generate recommended suppliers and supplier profiles for each material to be procured based on the current procurement demand information and historical procurement data.

[0024] It should be noted that the executing entity in this embodiment can be a computer service device with data processing, program execution, and interactive display functions, such as a mobile phone, tablet computer, laptop computer, online procurement server, etc., or other electronic devices capable of accessing the online procurement system and implementing the online procurement method of this application. Here, an electronic device capable of accessing the online procurement system (referred to as the "procurement system") is selected as an example to explain the various embodiments of this application.

[0025] It should be understood that the current procurement needs information can include the specifications, quantity, delivery time, and budget of the materials that the company currently needs to purchase (i.e., the materials to be purchased). The historical procurement data can include the materials purchased by the company in the past and the suppliers selected, as well as past supplier performance records (on-time rate, qualification rate, price fluctuations), production capacity data, and geographical distribution.

[0026] Suppliers selected by the company in its past procurement activities can be directly used as current recommended suppliers, and corresponding supplier profiles can be generated based on the aforementioned historical procurement data. These supplier profiles can consist of several preset dimensions and corresponding indicator values. Each preset dimension can include: material dimension, evaluation dimension, qualification dimension, and price dimension.

[0027] In addition, since users can remove or add recommended suppliers based on actual circumstances and manually select other suppliers from the full supplier list, user actions can be a factor in generating recommended suppliers. If a recommended supplier is frequently removed by the user, the recommendation of that supplier will decrease.

[0028] Specifically, step S10 includes: steps S101~S103: Step S101: Based on the current procurement demand information, determine a number of materials to be procured, and query the historical suppliers and corresponding recommendation weights for each of the materials to be procured from the historical procurement database. The recommendation weights are generated based on the number of times each of the historical suppliers has cooperated in the past.

[0029] It should be noted that historical procurement data can be stored in the historical procurement database in the form of a data table structure. For example, the data table structure corresponding to a certain historical supplier can store the following correspondence: supplier ID is SUP-001, material type is ABS plastic, number of historical cooperations is 8, most recent cooperation date is 2025-01-01, performance rating is 4.5 / 5, and price volatility is 8%.

[0030] The recommendation weight corresponding to this historical supplier can then be calculated using the recommendation weight calculation formula: .

[0031] In the formula, represents the circle coefficient that the user pre-defined based on historical experience, for example... =0.5、 =0.3、 =0.2.

[0032] In addition, after initializing the recommendation weights based on the historical selection counts of each historical supplier, user behavior influence factors can be generated based on the number of times users delete and manually add users corresponding to each historical supplier. The recommendation weights can then be updated based on the user behavior influence factors to obtain the recommendation weights corresponding to each historical supplier.

[0033] Specifically, the system can record user actions when using the supplier recommendation function, including the number of times a recommended supplier is deleted and the number of times a supplier is manually added. For example, a purchasing agent deleted supplier A's recommendation record twice and manually added supplier B's record once in the past month.

[0034] The system can also be pre-set to decrease the recommendation acceptance index each time a recommended supplier is deleted, and increase the recommendation acceptance index each time a supplier is manually added. For example, deleting a recommended supplier once will decrease the recommendation acceptance index by 5%; manually adding a supplier once will increase the recommendation acceptance index by 3%.

[0035] Therefore, the system can update the initial recommendation weights based on user behavior influencing factors. These updated recommendation weights comprehensively consider the supplier's historical performance and the user's actual operational preferences, providing a more accurate data foundation for subsequent supplier profiling and portfolio evaluation. For example, supplier A's initial recommendation weight was 80%, but because a user deleted its recommendation record twice, the recommendation acceptance index decreased by 10%, and the final recommendation weight was adjusted to 70%.

[0036] Step S102: Identify historical suppliers whose corresponding recommendation weight is higher than the preset recommendation threshold as recommended suppliers.

[0037] It should be noted that the preset recommended threshold can also be pre-defined by the user based on historical experience. Multiple thresholds can be set based on different material types of materials to be purchased, or they can be uniformly set to the same threshold. This embodiment does not impose any restrictions on this.

[0038] In practice, the recommended weight of historical suppliers for each material to be procured can be compared with a preset recommendation threshold. Historical suppliers with a weight higher than the threshold are identified as candidate suppliers.

[0039] Step S103: Obtain supplier information for each of the recommended suppliers, and generate a supplier profile for each of the recommended suppliers based on the supplier information.

[0040] It should be understood that the supplier information can also be extracted from the aforementioned historical database, which may include material dimensions (such as material type and material quality), evaluation dimensions (performance score, delivery delay status, and collaboration status), qualification dimensions (material production license level), and price dimensions (price volatility and historical quotations).

[0041] In its implementation, the system can generate supplier profiles for each recommended supplier based on the acquired supplier information, utilizing data processing and analysis technologies. This creates a comprehensive file or tag for each supplier, presenting their characteristics and performance in key aspects in an intuitive and comprehensive manner. Users can also quickly understand the strengths and weaknesses of suppliers by viewing their profiles, providing strong support for subsequent supplier portfolio selection and evaluation.

[0042] Step S20: Initialize several supplier combinations based on each of the recommended suppliers, and determine the basic score of each supplier combination according to the supplier profile corresponding to each of the recommended suppliers.

[0043] It should be noted that recommended suppliers for each material to be purchased can be combined according to certain rules (such as the geographical distribution of suppliers, the categories of products supplied, etc.) to achieve the initialization of the supplier combination scheme.

[0044] For each combination, the system can extract the rating indicators (such as material rating, evaluation rating, qualification rating and price rating) of each recommended supplier from the supplier profile, and perform weighted summation according to preset weights (such as material [X]%, evaluation [Y]%, qualification [Z]%, and price [P]%) to calculate the basic score of each supplier combination, reflecting the comprehensive performance of the combination under normal procurement conditions.

[0045] Step S30: Perform stress test simulations on each of the supplier combinations and generate a resilience score for each supplier combination based on the simulation results.

[0046] It should be noted that several stress test scenarios can be pre-built, such as: road closure scenarios, raw material supply disruption scenarios, production accident scenarios, extreme weather scenarios, etc.

[0047] To accurately simulate the supplier's performance under the aforementioned stress test scenarios, basic supplier information, historical supplier data, supplier network data, and supplier emergency response capability data can be collected, and simulations can be performed using professional supply chain simulation software or customized simulation models.

[0048] First, the simulation parameters can be initialized: based on the stress test scenario and supplier data, the initial parameters of the simulation can be set, such as the lockdown time, the degree of raw material shortage, and the scope of impact of production accidents. Then, the stress test scenario can be run in the simulation software to observe the supply performance of the supplier combination in the simulated environment, including indicators such as delivery delay, order completion rate, inventory consumption rate, and quality fluctuation.

[0049] Finally, various data and indicators during the simulation process were recorded in detail, and key performance indicators of the supplier portfolio under stress test scenarios, such as on-time delivery rate, order completion rate, inventory turnover rate, and quality pass rate, were calculated, which were then used as the elasticity score of each supplier portfolio.

[0050] Step S40: Sort each supplier combination by combining the basic score and the elasticity score, and determine the target supplier combination based on the sorting results.

[0051] It should be noted that the weighting coefficients of the basic score and the flexible score can be set (e.g., the weight of the basic score is [M]%, the weight of the flexible score is [N]%, and M+N=100), the comprehensive score of each supplier combination can be calculated, and the combinations can be sorted according to the comprehensive score, and the top few combinations can be selected as candidate target supplier combinations and output.

[0052] Finally, users can further review and evaluate the candidate supplier portfolio (such as conducting on-site inspections and communicating with suppliers) to ultimately determine the target supplier portfolio. This ensures that the supply of materials is stable, timely, and reliable while meeting procurement needs, thereby enhancing the company's competitiveness and risk management capabilities in supply chain management.

[0053] This embodiment comprehensively considers suppliers' historical data and current procurement needs to generate accurate recommended suppliers and their profiles, providing a comprehensive foundational assessment for supplier portfolio selection. Simultaneously, it introduces stress test simulations to quantitatively evaluate the resilience of each supplier portfolio under extreme scenarios, effectively compensating for the shortcomings of existing procurement methods in risk prediction and making procurement decisions more forward-looking and reliable. On the one hand, accurate recommendations and portfolio initialization help quickly locate high-quality supplier resources, improving procurement efficiency; on the other hand, the integration of resilience scoring enables the early avoidance of potential supply risks in complex market environments, ensuring the continuity and stability of enterprise material supply, thereby enhancing the enterprise's competitiveness in supply chain management and its ability to respond to emergencies.

[0054] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the online procurement method for materials in this application.

[0055] In this embodiment, in order to further screen the recommended suppliers and ensure that the suppliers participating in the combination have high quality and suitability, the following steps are included before step S20: Step S01: Based on the supplier profiles and preset screening rules, screen the recommended suppliers to obtain the candidate suppliers for each material to be purchased.

[0056] It should be noted that since the supplier profile consists of material, evaluation, qualification, and price dimensions, along with their corresponding indicator values, this preset screening rule can be a series of rules designed to determine which recommended suppliers can become candidate suppliers. The core of these rules lies in judging whether the recommended supplier's profile simultaneously meets the corresponding indicator conditions in multiple preset dimensions.

[0057] It should be understood that each dimension has corresponding indicator conditions used to measure whether the supplier's performance in that dimension meets the requirements. The system can compare each indicator in the supplier profile of each recommended supplier with the dimensional indicator conditions of the preset dimensions one by one.

[0058] For example, for supplier A, the system can check whether its historical quotations are within the specified range, whether the price volatility meets the requirements, whether the material production license level reaches the preset level, whether the product quality pass rate reaches [specific percentage]%, whether the average delivery time is within the allowable number of days, and whether the number of delayed deliveries meets the regulations, etc.

[0059] Furthermore, different dimensional indicator conditions can be set for different materials to be procured, and this embodiment does not impose any restrictions on this.

[0060] Specifically, based on the supplier profiles of each recommended supplier, it is determined whether each recommended supplier's supplier profile simultaneously meets the dimensional indicator conditions of each preset dimension. If the indicator values ​​of each preset dimension in the supplier profile of a recommended supplier meet the dimensional indicator conditions of a certain material to be purchased, the recommended supplier is determined to be a candidate supplier for that material to be purchased; if the indicator values ​​of each preset dimension in the supplier profile of a recommended supplier do not meet the dimensional indicator conditions of any material to be purchased, the recommended supplier is determined not to be a candidate supplier for any material to be purchased.

[0061] Accordingly, step S20 includes: Step S200: Initialize several supplier combinations based on each of the candidate suppliers, and determine the basic score of each supplier combination according to the supplier profile corresponding to each candidate supplier.

[0062] It should be noted that, firstly, several supplier combinations can be generated based on each candidate supplier according to a preset combination optimization strategy. This preset combination optimization strategy may include: cost priority strategy, risk diversification strategy, and collaborative optimization strategy.

[0063] When generating supplier combinations, the system can first consider cost factors, selecting a combination of suppliers that can provide cost-effective supplies. Costs include not only the purchase price but also other related expenses such as transportation and warehousing costs. For example, the system might look for supplier combinations with lower prices and optimized transportation routes to ensure that the overall procurement process is cost-effective.

[0064] To mitigate supply risks, the system can also avoid concentrating excessive procurement on a few suppliers. By diversifying the supplier portfolio, the risk of supply chain disruptions due to problems with a single supplier (such as production accidents or financial difficulties) can be reduced. For example, the system might limit the maximum supply proportion of a single supplier in the portfolio, ensuring that multiple suppliers share the supply load.

[0065] Collaborative optimization strategies are used to select supplier combinations by considering the synergies among suppliers in the supply process. For example, some suppliers may be geographically close, which helps reduce transportation time and costs; or some suppliers may have complementary advantages in logistics and distribution, enabling more efficient material distribution. The system can prioritize supplier combinations that can improve the overall supply chain efficiency through collaborative work.

[0066] It should be understood that after obtaining several supplier combinations consisting of various candidate suppliers, for each candidate supplier, the system retrieves specific indicator values ​​(material score, evaluation score, qualification score, and price score) from its supplier profile across preset dimensions (material dimension, evaluation dimension, qualification dimension, and price dimension). These indicator values ​​are quantitative data on the supplier's performance in various key aspects, providing a basis for subsequent scoring calculations.

[0067] Next, based on the preset combination optimization strategy, the system can assign corresponding weights to each preset dimension. For example, under a cost-first strategy, the price dimension may be given a higher weight; under a risk diversification strategy, the weight of the material dimension may increase; and under a collaborative optimization strategy, the weight of dimensions related to the collaborative capabilities between suppliers will be higher. The weight allocation reflects the importance of each dimension to the overall performance of the supplier portfolio under different strategies.

[0068] Finally, the acquired indicator values ​​and corresponding dimension weights can be used to generate a basic score for each supplier combination through a weighted calculation method. The basic score comprehensively reflects the overall performance of the supplier combination in multiple aspects such as cost, risk, and synergy. For example, a supplier combination may have an advantage in price (high score) but be slightly inferior in supply stability (low score). A balanced comprehensive score can be obtained through weighted calculation, helping the purchaser to comprehensively evaluate the advantages and disadvantages of different supplier combinations.

[0069] This embodiment accurately identifies candidate suppliers that meet procurement requirements, ensuring the quality and suitability of the supplier portfolio. By comprehensively evaluating candidate suppliers' performance across multiple key dimensions, a scientifically sound supplier portfolio score is generated, improving the scientific rigor and reliability of procurement decisions. Furthermore, the screening and scoring process in this embodiment comprehensively considers the suppliers' overall capabilities, avoiding overlooking potential risks due to excellent performance in a single dimension, further enhancing the efficiency and effectiveness of the procurement process and strengthening the stability and competitiveness of the enterprise's supply chain.

[0070] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the online procurement method for materials in this application.

[0071] In this embodiment, to specifically illustrate how to perform stress testing on the initialized supplier combination, step S30 further includes: steps S301~S303: Step S301: Construct a stress test scenario library, which includes instances of various stress scenarios and their corresponding dimensional impact parameters.

[0072] It should be understood that this stress test scenario library can include several scenario types and corresponding scenario instances, with corresponding occurrence probabilities set for each scenario type. Parameter settings may differ between different scenario instances within the same scenario type. Scenario types can include: road closure scenarios, raw material supply disruption scenarios, production accident scenarios, extreme weather scenarios, etc. Furthermore, corresponding dimensional impact parameters can be set for each stress scenario instance. These dimensional impact parameters quantify the potential impact on the supplier portfolio across various key dimensions under a specific stress scenario.

[0073] For example, in a raw material supply disruption scenario, dimensional impact parameters might include the magnitude of the decrease in supply capacity, the number of days of delivery delay, and the degree to which product quality is affected. Specifically, the decrease in supply capacity is [X]%, the number of days of delivery delay increases by [specific number of days], and the product quality pass rate decreases by [corresponding percentage].

[0074] Step S302: Using Monte Carlo simulation modeling technology, generate several random disturbance scenarios based on the stress test scenario library. Each random disturbance scenario consists of at least one stress scenario instance.

[0075] It should be noted that Monte Carlo simulation modeling technology is a computer simulation method that simulates the behavior of complex systems through random sampling and statistical analysis. In this embodiment, this technology can be used to generate several random disturbance scenarios based on a stress test scenario library. The specific steps are as follows: First, stress scenario instances can be randomly selected from a stress test scenario library, and the intensity and duration of the scenario can be determined based on their corresponding probability distribution. Then, through multiple random samplings, several different random disturbance scenarios are generated. Each random disturbance scenario consists of at least one stress scenario instance, and the combination of scenarios and parameter settings are different. This allows the simulation process to cover various possible real-world scenarios, improving the comprehensiveness and reliability of the simulation results.

[0076] The random disturbance scenario can be a single stress scenario instance or a combination of multiple stress scenario instances. For example, a stress scenario combination that simultaneously includes raw material shortages and transportation disruptions could be generated to simulate the performance of a supplier combination when facing multiple adverse factors.

[0077] Step S303: Generate elasticity index values ​​for each preset dimension based on the dimensional impact parameters corresponding to the stress scenario instance in the random disturbance scenario, and determine the elasticity score of each supplier combination based on each elasticity index value.

[0078] It should be understood that for each supplier combination, the elasticity index value of each supplier in each random disturbance scenario can be calculated based on the dimensional impact parameter in each preset dimension (material dimension, evaluation dimension, qualification dimension and price dimension).

[0079] For example, in the evaluation dimension, the elasticity index value can be expressed as the proportion of the supplier combination that can still maintain supply in the case of raw material shortage, or as the increase in the number of days of delivery delay; in the material dimension, the elasticity index value can be expressed as the degree of decline in product quality pass rate, etc.

[0080] In practical implementation, a pre-defined weighting scheme (which can be pre-set by the user based on historical data, expert experience, or corporate strategy) can be used to comprehensively evaluate the resilience performance of the supplier portfolio across different dimensions based on various resilience index values. A resilience score for the supplier portfolio is then generated through weighted summation. The higher the resilience score, the stronger the resilience of the supplier portfolio in the face of stressful scenarios, meaning it is more effective in responding to various potential risks and maintaining stable supply performance.

[0081] Further, after obtaining the resilience score, step S40 specifically includes: steps S401~S403: Step S401: Sum the base score and the flexibility score to obtain the comprehensive score of each supplier combination.

[0082] It should be understood that, based on factors such as the company's procurement strategy and the current market environment, the weights of the baseline score and the flexibility score in the overall score can be pre-set for different market conditions. For example, when market supply is stable, the baseline score can be given a higher weight (e.g., 60%), and the flexibility score a lower weight (e.g., 40%); while when the market is volatile or the risk is high, the weight of the flexibility score can be appropriately increased.

[0083] It should be noted that the baseline score reflects the overall performance of the supplier portfolio under normal procurement conditions, while the resilience score reflects the supplier portfolio's ability to withstand stress test scenarios (such as raw material shortages, transportation disruptions, etc.). By summing the baseline score and resilience score of each supplier portfolio according to preset weights, a comprehensive score for that supplier portfolio is obtained. This integrates the results of two different aspects into a single quantitative indicator, providing a basis for subsequent ranking and decision-making.

[0084] Step S402: Select the top few supplier combinations as candidate supplier combinations according to the comprehensive score from high to low.

[0085] It should be understood that the number of candidate supplier combinations to be screened can be determined in advance based on factors such as the specific needs of the enterprise's procurement task, budget constraints, time requirements, and considerations for the diversity of the supplier portfolio. For example, a large and complex procurement task may require more candidate combinations to provide a wider range of choices; while for a small and simple task, it may only be necessary to screen the top few combinations.

[0086] In practice, the selection process can proceed sequentially from the supplier combination with the highest overall score, based on the ranking results, until a preset number of candidates is reached. These selected supplier combinations represent the best overall performance, balancing basic conditions and resilience.

[0087] Step S403: Present each of the candidate supplier combinations to the user so that the user can select the target supplier combination from the candidate supplier combinations.

[0088] It should be understood that the shortlisted candidate supplier portfolios can be presented to users (such as purchasing personnel, purchasing committees, etc.) in a clear and intuitive manner. The presentation can include detailed information for each candidate supplier portfolio, such as the name of each candidate supplier in the portfolio, their supply capacity, price level, quality assurance measures, delivery time, flexibility score details (performance under different stress scenarios), and overall score.

[0089] It should be noted that key indicator data for each candidate supplier combination can be listed in tabular form to facilitate horizontal comparison by users; at the same time, charts (such as bar charts, line charts, etc.) can also be provided to intuitively present the differences between the candidate supplier combinations in terms of basic scores and flexible scores, helping users to grasp key information more quickly.

[0090] In practice, users can obtain several candidate supplier combinations and detailed information from the system's interactive interface, and then determine the target supplier combination that best meets the project requirements and corporate interests from the candidate supplier combinations, providing a clear direction for actual procurement execution.

[0091] This embodiment can organically combine the basic score and flexible score of the supplier portfolio, and through scientific sorting and screening, and by giving full consideration to the user's subjective judgment, finally determine the optimal target supplier portfolio, helping enterprises to achieve efficient and reliable material procurement.

[0092] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the online procurement method of materials in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0093] This application also provides an online procurement system for materials. Please refer to... Figure 4 , Figure 4 This is a schematic diagram of the module structure of the online procurement system for materials in this application. The system includes: The demand import module 401 is used to obtain current procurement demand information and generate recommended suppliers and supplier profiles for each material to be procured based on the current procurement demand information and historical procurement data. The initial scoring module 402 is used to initialize several supplier combinations based on each of the recommended suppliers, and to determine the basic score of each supplier combination according to the supplier profile corresponding to each of the recommended suppliers. The resilience scoring module 403 is used to perform stress test simulations on each of the supplier combinations and generate a resilience score for each of the supplier combinations based on the simulation results. The comprehensive ranking module 404 is used to rank each supplier combination by combining the basic score and the elastic score, and to determine the target supplier combination based on the ranking result.

[0094] This embodiment comprehensively considers suppliers' historical data and current procurement needs to generate accurate recommended suppliers and their profiles, providing a comprehensive foundational assessment for supplier portfolio selection. Simultaneously, it introduces stress test simulations to quantitatively evaluate the resilience of each supplier portfolio under extreme scenarios, effectively compensating for the shortcomings of existing procurement methods in risk prediction and making procurement decisions more forward-looking and reliable. On the one hand, accurate recommendations and portfolio initialization help quickly locate high-quality supplier resources, improving procurement efficiency; on the other hand, the integration of resilience scoring enables the early avoidance of potential supply risks in complex market environments, ensuring the continuity and stability of enterprise material supply, thereby enhancing the enterprise's competitiveness in supply chain management and its ability to respond to emergencies.

[0095] In addition, this application also provides an online procurement device for materials, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to execute the online procurement method for materials in the above embodiment 1.

[0096] The following is for reference. Figure 5 , Figure 5This is a schematic diagram of the online procurement equipment for materials in this application. The online procurement equipment for materials in this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., as well as fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The online procurement equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0097] like Figure 5 As shown, the online procurement equipment may include a processor 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the online procurement equipment. The processor 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the online procurement equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows online procurement equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0098] The sequence numbers of the above embodiments of the present invention are merely for description and do not represent the superiority or inferiority of the embodiments. They are only some embodiments of this application and are not intended to limit the scope of this application. All equivalent structural transformations made under the technical concept of this application and based on the content of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the protection scope of this application.

Claims

1. A method for online procurement of materials, characterized in that, The method includes: Obtain current procurement needs information, and generate recommended suppliers and supplier profiles for each material to be procured based on the current procurement needs information and historical procurement data; Several supplier combinations are initialized based on each of the recommended suppliers, and the basic score of each supplier combination is determined according to the supplier profile corresponding to each of the recommended suppliers. Stress test simulations are performed on each of the aforementioned supplier combinations, and resilience scores are generated for each of the aforementioned supplier combinations based on the simulation results; The supplier combinations are ranked by combining the base score and the flexibility score, and the target supplier combination is determined based on the ranking results.

2. The method as described in claim 1, characterized in that, The step of generating recommended suppliers and supplier profiles for each material to be procured based on the current procurement demand information and historical procurement data includes: Based on the current procurement demand information, a number of materials to be procured are determined, and the historical suppliers corresponding to each of the materials to be procured and their corresponding recommendation weights are queried from the historical procurement database. The recommendation weights are generated based on the number of times each of the historical suppliers has cooperated in the past. Historical suppliers whose corresponding recommendation weight is higher than the preset recommendation threshold are identified as recommended suppliers; Obtain supplier information for each of the recommended suppliers, and generate a supplier profile for each of the recommended suppliers based on the supplier information.

3. The method as described in claim 2, characterized in that, Before the step of querying the historical suppliers and their corresponding recommendation weights for each of the materials to be procured from the historical procurement database, the following steps are included: Initialize the recommendation weights based on the historical number of times each of the aforementioned historical suppliers has been selected. User behavior influence factors are generated based on the number of times users delete and manually add users for each of the historical suppliers, and the recommendation weights are updated based on the user behavior influence factors to obtain the recommendation weights for each of the historical suppliers.

4. The method as described in claim 2, characterized in that, Before the step of initializing several supplier combinations based on each of the recommended suppliers and determining the basic score of each supplier combination according to the supplier profile corresponding to each of the recommended suppliers, the method further includes: Based on the supplier profiles and preset screening rules, the recommended suppliers are screened to obtain the candidate suppliers for each material to be purchased. The preset filtering rule is a rule for determining whether the supplier profile corresponding to each of the recommended suppliers simultaneously meets the dimensional indicator conditions in multiple preset dimensions. Accordingly, the step of initializing several supplier combinations based on each of the recommended suppliers and determining the basic score of each supplier combination according to the supplier profile corresponding to each of the recommended suppliers includes: Several supplier combinations are initialized based on each of the candidate suppliers, and the basic score of each supplier combination is determined according to the supplier profile corresponding to each candidate supplier.

5. The method as described in claim 4, characterized in that, The supplier profile consists of several preset dimensions and corresponding indicator values. Each preset dimension includes: material dimension, evaluation dimension, qualification dimension and price dimension. The step of filtering each recommended supplier according to the supplier profile and preset filtering rules to obtain candidate suppliers for each material to be purchased includes: Based on the supplier profiles of each recommended supplier, determine whether the supplier profiles of each recommended supplier simultaneously meet the dimensional indicator conditions of each preset dimension; If the index values ​​of each preset dimension in the supplier profile of the recommended supplier all meet the dimension index conditions of a certain material to be purchased, the recommended supplier is determined to be a candidate supplier for a certain material to be purchased. If the indicator values ​​of each preset dimension in the supplier profile of the recommended supplier do not meet the dimension indicator conditions of any of the materials to be purchased, it is determined that the recommended supplier is not a candidate supplier corresponding to each of the materials to be purchased.

6. The method as described in claim 5, characterized in that, The step of initializing several supplier combinations based on each of the candidate suppliers and determining the basic score of each supplier combination according to the supplier profile corresponding to each candidate supplier includes: According to a preset combination optimization strategy, several supplier combinations are generated based on each of the candidate suppliers. The preset combination optimization strategy includes: a cost priority strategy, a risk diversification strategy, and a collaborative optimization strategy. Obtain the index values ​​of each preset dimension in the supplier profile corresponding to each candidate supplier, and generate the basic score of each supplier combination by combining the dimension weight allocation relationship determined by the preset combination optimization strategy.

7. The method as described in claim 5, characterized in that, The step of performing stress test simulations on each of the supplier combinations and generating a resilience score for each supplier combination based on the simulation results includes: Construct a stress test scenario library, which includes instances of various stress scenarios and their corresponding dimensional impact parameters; Monte Carlo simulation modeling technology is used to generate several random disturbance scenarios based on the stress test scenario library. Each random disturbance scenario consists of at least one stress scenario instance. Based on the dimensional impact parameters corresponding to the stress scenario instance in the random disturbance scenario, generate elasticity index values ​​for each preset dimension, and determine the elasticity score of each supplier combination based on each elasticity index value.

8. The method as described in claim 7, characterized in that, The step of ranking each supplier combination by combining the base score and the flexibility score, and determining the target supplier combination based on the ranking results, includes: The base score and the flexibility score are summed to obtain the comprehensive score for each supplier combination; Based on the comprehensive scores from highest to lowest, the top few supplier combinations are selected as candidate supplier combinations. The candidate supplier combinations are presented to the user so that the user can select the target supplier combination from the candidate supplier combinations.

9. An online procurement system for materials, characterized in that, The system includes: The demand import module is used to obtain current procurement demand information and generate recommended suppliers and supplier profiles for each material to be procured based on the current procurement demand information and historical procurement data. The initial scoring module is used to initialize several supplier combinations based on each of the recommended suppliers, and to determine the basic score of each supplier combination according to the supplier profile corresponding to each of the recommended suppliers. The resilience scoring module is used to perform stress test simulations on each of the supplier combinations and generate a resilience score for each supplier combination based on the simulation results. The comprehensive ranking module is used to rank each supplier combination by combining the basic score and the elastic score, and to determine the target supplier combination based on the ranking results.

10. An online procurement device for materials, characterized in that, The device includes: a memory, a processor, and an online procurement program for materials stored in the memory and executable on the processor, the online procurement program for materials being configured to implement the steps of the online procurement method for materials as described in any one of claims 1 to 7.

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