Material insurance supply method and system applying supplier capacity matching coordination model

Abstract

The invention discloses a material supply insurance method and system applying a supplier productivity matching coordination model, and relates to the technical field of productivity coordination, and the method comprises the following steps: obtaining supplier historical supply data, and constructing a supply fluctuation curve based on a supply type according to the supplier historical supply data; dividing suppliers into a dynamic supply unit and a static supply unit according to the supply fluctuation curve and the fluctuation threshold; constructing a multi-objective optimization function with the minimum cost, the highest technical iteration speed and the minimum fluctuation, and constructing a supplier productivity matching coordination model according to the dynamic supply unit and the static supply unit by the multi-objective optimization function; and obtaining a current production plan, and outputting a material insurance supply strategy based on the production plan and the supplier capacity matching coordination model. The method has the beneficial effects that material supply is carried out through a stable static supply unit as much as possible, and the stability of material supply and the balance between cost and product evolution are ensured.

Description

Technical Field

[0001] The present application relates to the field of capacity coordination technology, and in particular to a material supply guarantee method and system using a supplier capacity matching coordination model. Background Art

[0002] Supply chain management is a core component of corporate operations. It involves the management of the entire process from raw material procurement, production and manufacturing, logistics distribution to the delivery of final products to customers. The supply of materials is directly related to the stable supply of raw materials, parts and other materials required in the production process of the enterprise. Once the supply of materials is interrupted, production activities will be seriously affected and may even lead to production stagnation. Therefore, ensuring the timely and sufficient supply of materials is the basis for maintaining the continuity of enterprise production.

[0003] When planning traditional material supply strategies, isolated and scattered indicators are often used to evaluate suppliers, such as cost, which lacks systematic integration and does not deeply consider the inherent relationship between the supplier's technical strength and supply stability. This one-sidedness makes it easy to fall into a short-term cost trap when selecting suppliers. Once market demand changes or technological innovation accelerates, suppliers who have only been shortlisted by low prices may not be able to provide materials that meet new requirements due to lagging technology, or due to their own lack of flexibility in production capacity, there will be fluctuations in supply quantity and time, causing the company's production to stagnate and production plans to fail to proceed smoothly.

[0004] In related technologies, although some supplier evaluation models take multiple factors into account, they lack effective differentiation between dynamic and static supply characteristics. In actual production, the market demands faced by enterprises are complex and changeable. If a fixed supplier combination and order allocation model is adopted, the dynamic change capabilities of different suppliers in different market scenarios are not fully considered. When market demand suddenly increases or fluctuates seasonally, suppliers who lack the ability to adjust production capacity may not be able to respond in time, while the resources of suppliers with flexible production capacity are not fully utilized, resulting in enterprises either facing insufficient supply or incurring excessive inventory costs.

[0005] The patent "A material supply guarantee method and electronic equipment", publication number: CN116307433A, publication date: June 23, 2023, specifically discloses: obtaining and identifying material demand information; when it is identified that the material demand information carries a target state identifier, using the material matching method corresponding to the target state identifier and outputting the corresponding material matching result; obtaining and identifying transaction information; when it is identified that the transaction information carries a target state identifier, using the material scheduling plan corresponding to the target state identifier and performing material allocation and logistics scheduling. This solution achieves material supply guarantee by selecting the material scheduling speed and range, but it relies on the supplier to have sufficient supply capacity. Once the supplier's supply capacity fluctuates or the production plan of the manufacturer fluctuates, the material supply guarantee cannot be guaranteed.

[0006] The patent "Method and system for early warning of material supply and demand of power grid enterprises based on orders", publication number: CN116051162A, publication date: May 2, 2023, specifically discloses: cost analysis of raw materials involved in the order; building a raw material price prediction model for the raw materials involved in the order based on the historical sequence of raw material prices, and obtaining raw material price prediction results based on the raw material price prediction model; obtaining the prediction length of the raw material price prediction model based on the actual delivery date and limit production time of the order; judging the order risk level within the prediction length and providing early warning information. This solution judges the supplier's supply capacity by the actual delivery date and limit production time of the order, but cannot reasonably allocate the supplier's supply based on the risk to avoid the order risk. Summary of the invention

[0007] In view of the fact that the material supply guarantee strategies in the prior art cannot take into account both supply stability and supply rationality, the present application provides a material supply guarantee method and system using a supplier capacity matching coordination model. Suppliers are divided into dynamic supply units and static supply units according to their fluctuations compared to overall supply fluctuations, and a multi-objective optimization function is constructed according to minimum cost, highest technology iteration speed and minimum fluctuation. The supplier capacity matching coordination model is constructed based on the dynamic supply units and static supply units using the multi-objective optimization function, so that materials can be supplied in stable static supply units as much as possible while ensuring that the allocation of supply units meets the cost and technology iteration speed, thereby ensuring the stability of material supply and the balance between cost and product evolution.

[0008] To achieve the above-mentioned technical objectives, a technical solution provided by the present application is a material supply guarantee method using a supplier capacity matching coordination model, comprising the following steps: S1: obtaining the supplier's historical supply data, and constructing a supply fluctuation curve based on the supply type according to the supplier's historical supply data; S2: dividing the suppliers into dynamic supply units and static supply units according to the supply fluctuation curve and the fluctuation threshold; S3: constructing a multi-objective optimization function with minimum cost, highest technology iteration speed and minimum fluctuation, and using the multi-objective optimization function to construct a supplier capacity matching coordination model based on the dynamic supply unit and the static supply unit; S4: obtaining the current production plan, and outputting a material supply guarantee strategy based on the production plan and the supplier capacity matching coordination model.

[0009] Furthermore, S1 also includes: obtaining historical supply data of suppliers, and dividing the historical supply data of suppliers according to supply type; performing cluster analysis on the historical supply data of suppliers corresponding to each supply type using a clustering algorithm to obtain cluster centers and cluster clusters; and constructing a supply fluctuation curve based on the supply type according to the cluster centers.

[0010] Furthermore, the construction of a supply fluctuation curve based on supply type according to cluster centers also includes: if there are multiple cluster centers at the same time, incorporating the cluster cluster corresponding to the cluster center into the additional factor influence set; extracting additional factor features from the additional factor influence set, and obtaining additional factor feature weights based on the supply quantity in the additional factor influence set; obtaining supply points at each time based on the additional factor feature weights and cluster centers; and constructing a supply fluctuation curve based on supply type with supply points and cluster centers.

[0011] Furthermore, S2 also includes: taking suppliers whose historical supply fluctuation values ​​compared to the supply fluctuation curve exceed the fluctuation threshold as dynamic supply units; taking suppliers whose historical supply fluctuation values ​​compared to the supply fluctuation curve are less than or equal to the fluctuation threshold as static supply units.

[0012] Furthermore, the multi-objective optimization function constructed with minimum cost, highest technology iteration speed and minimum fluctuation includes: constructing a minimum cost objective optimization function with procurement cost, transportation cost and inventory cost; constructing a highest technology iteration speed objective optimization function with product update frequency and product application degree; constructing a minimum fluctuation objective optimization function with the proportion of dynamic supply units and static supply units; constructing a multi-objective optimization function with the minimum cost objective optimization function, the highest technology iteration speed objective optimization function and the minimum fluctuation objective optimization function.

[0013] Furthermore, the construction of a supplier capacity matching coordination model based on dynamic supply units and static supply units using a multi-objective optimization function includes: obtaining historical production demand, and obtaining historical supplier coordination strategies based on historical production demand and supplier historical supply data; performing model training based on the multi-objective optimization function according to historical supplier coordination strategies and dynamic supply units and static supply units to obtain cost weights, technology iteration speed weights, and supply fluctuation weights, thereby constructing a supplier capacity matching coordination model.

[0014] Furthermore, it also includes: S51: obtaining historical production plans, and obtaining the production demand impact relationship between dynamic supply units and static supply units based on historical production plans and suppliers' historical supply data; S52: obtaining step-by-step fluctuation correlation based on the production sequence of the production plan and the production demand impact relationship; S53: obtaining the difference between the current actual production demand and the theoretical production demand in the production plan, and judging whether it is necessary to update the material supply strategy based on the difference value, and if so, executing S54; S54: updating the material supply strategy based on the current production sequence and step-by-step fluctuation correlation.

[0015] Furthermore, the S51 includes: obtaining the difference between the theoretical demand and the actual demand in the historical production plan, obtaining a first compensation value of the dynamic supply unit for the difference value and a second compensation value of the static supply unit for the difference value according to the distribution of supply quantities of the dynamic supply unit and the static supply unit at the corresponding moment, and constructing the production demand impact relationship between the dynamic supply unit and the static supply unit with the first compensation value, the second compensation value and the difference value.

[0016] Furthermore, S52 also includes: dividing the time series levels corresponding to the supply types according to the production plan, obtaining the time series levels with difference values, obtaining the step-by-step influence relationship according to the difference values ​​corresponding to the time series levels and the difference values ​​of subsequent time series levels, and obtaining the step-by-step fluctuation correlation according to the step-by-step influence relationship and the production demand influence relationship.

[0017] Furthermore, the S54 includes: calculating the difference value of the subsequent timing level based on the level-by-level impact relationship according to the timing level corresponding to the current production order; and updating the material supply strategy according to the difference value of the subsequent timing level and the production demand impact relationship.

[0018] Another technical solution provided by the present application is a material supply guarantee system that applies a supplier capacity matching coordination model, which is used to implement the material supply guarantee method that applies a supplier capacity matching coordination model as mentioned above, including: a dynamic and static screening module, which is used to construct a supply fluctuation curve based on the supply type according to the supplier's historical supply data, and divide the dynamic supply unit and the static supply unit accordingly; a model construction module, which is used to construct a multi-objective optimization function according to the minimum cost, the highest technology iteration speed and the minimum fluctuation, and use the multi-objective optimization function to construct a supplier capacity matching coordination model according to the dynamic supply unit and the static supply unit; a supply guarantee analysis module, which is used to obtain a material supply guarantee strategy for the current production plan according to the current production plan and the supplier capacity matching coordination model.

[0019] Another technical solution provided by the present application is a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a processing device, a material supply guarantee method based on the above-mentioned application supplier capacity matching coordination model is implemented.

[0020] The beneficial effects of the present application are as follows: 1. According to the supplier's historical supply data, a supply fluctuation curve is constructed based on the difference in supply types to highlight the supply situation of suppliers of each supply type under different conditions, and suppliers are divided into dynamic supply units and static supply units according to the fluctuation threshold, and stable supply and unstable supply types are distinguished. A dual-objective optimization function is constructed with minimum cost, highest technology iteration speed and minimum fluctuation to coordinate the best balance between minimum cost, highest technology iteration speed and minimum fluctuation. Based on the demand forecast data and the supplier capacity matching coordination model, the supplier capacity data is obtained to output the material supply guarantee strategy, so that the supply quantity of each supplier is reasonably allocated according to the supplier's capacity and demand, reducing the cost waste and production interruption risks caused by insufficient or oversupply, and improving the accuracy and reliability of material supply.

[0021] 2. The supply fluctuation curve corresponds to the supply type, and different supply types have different fluctuation thresholds. Based on the fluctuation of the entire supply type, the energy supply stability of all suppliers of the current type is obtained, thereby distinguishing between stable industrial chains and unstable industrial chains to avoid mutual influence between the two.

[0022] 3. Determine whether it is necessary to update the material supply strategy based on whether there is a difference between the current actual production demand and the theoretical production demand in the production plan, and ensure that the material supply strategy adapts to the actual implementation of the production plan to reduce corporate losses.

[0023] 4. According to the difference value of the subsequent timing level and the adjustable supply situation of the dynamic supply unit and the static supply unit, the supply quantity of the dynamic supply unit and the static supply unit is reasonably adjusted to reduce the loss of the producer. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flowchart of the material supply guarantee method using the supplier capacity matching coordination model for this application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below in conjunction with the drawings and examples. It should be understood that the specific implementation method described here is only an optimal embodiment of the present application, which is only used to explain the present application and does not limit the scope of protection of the present application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0026] like Figure 1 As shown in the figure, the material supply guarantee method using the supplier capacity matching coordination model includes the following steps: S1: Obtain the supplier's historical supply data and construct a supply fluctuation curve based on the supply type according to the supplier's historical supply data; S2: Divide suppliers into dynamic supply units and static supply units according to the supply fluctuation curve and fluctuation threshold; S3: Construct a multi-objective optimization function with minimum cost, highest technology iteration speed and minimum fluctuation, and use the multi-objective optimization function to construct a supplier capacity matching coordination model based on dynamic supply units and static supply units; S4: Obtain the current production plan and output the material supply guarantee strategy based on the production plan and supplier capacity matching coordination model.

[0027] In this embodiment, a supply fluctuation curve is constructed based on the differences in supply types according to the suppliers' historical supply data to highlight the supply conditions of suppliers of each supply type under different conditions, and suppliers are divided into dynamic supply units and static supply units according to the fluctuation threshold, and stable supply and unstable supply types are distinguished. A dual-objective optimization function is constructed with minimum cost, maximum technology iteration speed and minimum fluctuation to coordinate the best balance between minimum cost, maximum technology iteration speed and minimum fluctuation. Therefore, the supplier capacity data is obtained based on the supplier capacity matching coordination model according to the demand forecast data to output the material supply guarantee strategy, so that the supply quantity of each supplier is reasonably allocated according to the supplier capacity and demand, reducing the cost waste and production interruption risks caused by insufficient or oversupply, and improving the accuracy and reliability of material supply.

[0028] Wherein, step S1 also includes: Obtain suppliers' historical supply data and divide the suppliers' historical supply data according to supply type; Use clustering algorithms to perform cluster analysis on the historical supply data of suppliers corresponding to each supply type to obtain cluster centers and cluster clusters; Construct supply fluctuation curves based on supply types according to cluster centers.

[0029] The historical supply data at least includes the supply quantity, supply time and supply type. After preprocessing the supplier's historical supply data, the supplier's historical supply data is divided according to the supply type, such as dividing the chip supplier's historical supply data into a set of chip supply of supply type, and dividing the plastic shell supplier's historical supply data into a set of plastic shell supply of supply type. Because if the suppliers corresponding to the same supply type exclude the fluctuations caused by internal factors such as the increase or decrease of equipment and personnel, the fluctuations caused by external factors should have the same trend, such as the fluctuations in supplier production capacity caused by fluctuations in raw material output due to seasonal factors. The cluster center is a representative point in the multidimensional space for each type of data set, which reflects the typical characteristics of this type of data. In this embodiment, a representative point in the supply quantity and supply time of the historical supply data of suppliers of the same supply type reflects the common state of this supply type under similar external influencing factors. For example, there are three chip suppliers A1, A2, and A3, whose supply quantities at time t are a1, a2, and a3 respectively. At this time, the cluster center is a2. It can be seen that the supply quantities of chip suppliers A1 and A3 are close to a2, but there may be differences due to internal factors. The supply fluctuation curve of the entire historical moment is constructed in this way. This supply fluctuation curve is the fluctuation of the impact of external factors on the supplier's production capacity.

[0030] In other cases, there may be multiple cluster centers at the same time. In this case, the supply fluctuation curve based on the supply type constructed according to the cluster centers also includes: If there are multiple cluster centers at the same time, the cluster corresponding to the cluster center is included in the additional factor influence set; Extracting additional factor features from the additional factor impact set, and obtaining additional factor feature weights based on the supply quantity in the additional factor impact set; Obtain the supply points at each moment based on the additional factor feature weights and cluster centers; Construct supply fluctuation curve based on supply type with supply points.

[0031] In this case, the historical supply data also includes historical weather data and historical geographic location data. When there are multiple cluster centers at the same time, it means that there are multiple different supply modes at that time or different supply data feature groups are formed due to the influence of multiple different combinations of factors. For example, in a certain season, some suppliers may form a cluster center due to the special climate of the raw material production area (such as heavy rain affecting transportation), while other suppliers may form another cluster center at the same time due to their own equipment maintenance plans. Feature extraction is performed on the additional factor influence set to obtain additional factor features that lead to special supply situations, such as transportation obstruction, equipment maintenance, etc. Statistical analysis methods such as regression analysis are used to establish a relationship curve or model between the supply quantity and each additional factor feature, and calculate the relative importance of each additional factor feature in affecting the change in supply quantity, thereby obtaining the additional factor feature weight.

[0032] The supply points at each moment are obtained based on the additional factor feature weights and cluster centers, including: When there are multiple cluster centers, the weighted average of each cluster center is taken according to the feature weight of the additional factor to obtain the supply point; When there is a single cluster center, the cluster center is used as the supply point.

[0033] By taking weighted average of each cluster center according to the weight of additional factor features to filter out the interference of additional factor features, we can obtain the supply point without the interference of additional factors. At this time, this supply point reflects the supply capacity of the entire supply industry under non-special circumstances.

[0034] In this embodiment, step S2 further includes: Suppliers whose historical supply fluctuation values ​​exceed the fluctuation threshold compared to the supply fluctuation curve are regarded as dynamic supply units; Suppliers whose historical supply fluctuation values ​​compared to the supply fluctuation curve are less than or equal to the fluctuation threshold are taken as static supply units.

[0035] At this time, suppliers are divided into dynamic supply units and static supply units based on whether their historical supply fluctuations exceed the fluctuation threshold compared to the supply fluctuation curve. That is, suppliers with larger supply fluctuations compared to the overall fluctuations are classified as dynamic supply units, while suppliers that are only affected by the overall fluctuations are classified as static supply units. For static supply units, their supply stability is higher, but their flexibility in adjusting production capacity is lower; for dynamic supply units, their supply stability is poor, but their flexibility in adjusting production capacity is higher.

[0036] Wherein, step S2 further includes: Construct fluctuation thresholds based on the cost and production cycle corresponding to the supply type.

[0037] In this embodiment, the supply fluctuation curve corresponds to the supply type, and different supply types have different fluctuation thresholds. The energy supply stability of all suppliers of the current type is obtained based on the fluctuation of the entire supply type, thereby distinguishing between stable industrial chains and unstable industrial chains to avoid mutual influence between the two.

[0038] Constructing a multi-objective optimization function with minimum cost, maximum technology iteration speed and minimum fluctuation includes: Construct the minimum cost objective optimization function based on purchasing cost, transportation cost and inventory cost; Construct the highest technology iteration speed target optimization function based on product update frequency and product application degree; The minimum fluctuation objective optimization function is constructed based on the proportion of dynamic supply units and static supply units; A multi-objective optimization function is constructed with the minimum cost objective optimization function, the highest technology iteration speed objective optimization function and the minimum fluctuation objective optimization function.

[0039] Among them, the minimum cost objective optimization function constructed with procurement cost, transportation cost and inventory cost is: ; Where C represents the total cost, represents the price of purchasing a unit of product from the i-th supplier, represents the quantity of products purchased from the i-th supplier, n is the number of suppliers, represents the unit transportation cost of the product from the i-th supplier to the destination, represents the unit product inventory cost provided by the i-th supplier, represents the average inventory level of the product provided by the ith supplier.

[0040] The optimization function for constructing the highest technology iteration speed target based on product update frequency and product application degree includes: ; Among them, T represents the overall technology iteration speed, represents the product update weight, represents the number of products updated by the i-th supplier each year, Indicates the product application weight, Represents the production efficiency improvement ratio of the i-th supplier.

[0041] The minimum fluctuation objective optimization function constructed based on the proportion of dynamic supply units and static supply units includes: ; Among them, V represents the overall fluctuation, represents the dynamic supply unit set, ? is the conditional operator, that is, the conditional expression? True value: false value. If the value of the conditional expression is true, the value of the entire expression is the value of "true value", otherwise it is the value of "false value". Here, if the i-th supplier belongs to the dynamic supply unit in the dynamic supply unit set, then "1" is output, and if the i-th supplier belongs to the static supply unit in the static supply unit set, then "0" is output.

[0042] The multi-objective optimization function is constructed with the minimum cost objective optimization function, the highest technology iteration speed objective optimization function and the minimum fluctuation objective optimization function, including: A multi-objective optimization function is constructed based on cost weight and minimum cost target optimization function, technology iteration speed weight and maximum technology iteration speed target optimization function, supply fluctuation weight and minimum fluctuation target optimization function.

[0043] That is, the multi-objective optimization function is: ; Where F is a multi-objective optimization function, is the cost weight, is the weight of technology iteration speed, is the supply volatility weight.

[0044] In this embodiment, since the speed of technology iteration is expected to be as large as possible, it is described in the form of an inverse, so that the change trend in the overall function is consistent with the optimization direction of cost and supply fluctuations (the smaller the better), which is easy to calculate. The highest technology iteration speed highlights the supplier's tendency to update technology. Under the condition of consistent costs, the more motivated suppliers are to update, the more they can adapt to the development of technology, and the less likely they are to be affected by technical constraints and equipment lags, and their supply chains are more stable. Taking into account the cost, technology iteration speed, and fluctuations can ensure that production needs are met or even higher than production needs while avoiding unnecessary cost waste, while ensuring the stability and reliability of the supply chain. For example, if the production requires nuts with an accuracy of ±0.05, and there are two suppliers B1 and B2 with the same supply cost, but the nut accuracy of B1 can reach ±0.03, and the nut accuracy of B2 can only reach ±0.05, then B1 is selected as the supplier first, so that suppliers with better products can be selected for supply at the same cost.

[0045] The supplier capacity matching coordination model is constructed based on dynamic supply units and static supply units using a multi-objective optimization function, including: Obtain historical production demand data, and obtain historical supplier coordination strategies based on the historical production demand data and the supplier's historical supply data; According to the historical supplier coordination strategy and dynamic supply units and static supply units, model training is performed based on multi-objective optimization functions to obtain cost weights, technology iteration speed weights, and supply fluctuation weights, thereby constructing a supplier capacity matching coordination model.

[0046] The production demand data of the unit in each stage in the past is obtained as the historical production demand data, and the historical production demand data is correlated with the historical supply data of the corresponding supplier to analyze, and the supplier coordination strategy adopted for different production tasks in history is obtained by comparing the production demand and the actual supply situation. The historical supplier coordination strategy and the corresponding dynamic supply unit and static supply unit related data are substituted into the multi-objective optimization function for model training. In this embodiment, the particle swarm optimization algorithm can be used to obtain the values ​​of the cost weight, the technology iteration speed weight, and the supply fluctuation weight. In other cases, training methods such as genetic algorithms can also be used.

[0047] Therefore, by calculating the value of the multi-objective optimization function according to the current production plan, the optimal supplier capacity matching coordination plan can be determined, that is, how to allocate orders to different dynamic supply units and static supply units to achieve the best balance between cost, technology iteration speed and supply fluctuations, meet production needs while improving the overall performance of the supply chain.

[0048] As a second embodiment of the present application, the material supply guarantee method using the supplier capacity matching coordination model also includes: S51: Obtain a historical production plan, and obtain the production demand impact relationship between the dynamic supply unit and the static supply unit based on the historical production plan and the supplier's historical supply data; S52: Obtaining step-by-step fluctuation correlation based on the production sequence of the production plan and the production demand impact relationship; S53: Obtain the difference between the current actual production demand and the theoretical production demand in the production plan, and determine whether the material supply strategy needs to be updated based on the difference. If so, execute S54; S54: Update the material supply strategy based on the current production sequence and the correlation of step-by-step fluctuations.

[0049] Since the production plan may fluctuate due to the actual production situation, and although the static supply unit is more stable, it is difficult to adapt to the sudden increase in supply demand, or it cannot accept the decrease in supply demand due to the fixed orders, while the dynamic supply unit is more flexible and can accept the increase or decrease in supply demand. Therefore, based on the actual fluctuations of the historical production plan and the supplier's historical supply data, the changes in the dynamic supply unit and the static supply unit corresponding to the fluctuations in production demand are obtained, that is, when the production demand changes, the collaborative relationship between the dynamic supply unit and the static supply unit under different production demand changes. For example, when the production demand increases less, the static supply unit can accept a slight increase in orders, but when the production demand decreases, the static supply unit does not accept a decrease in orders, or, when the production demand increases more, the static supply unit cannot adapt, while the dynamic supply unit can meet the increased production demand.

[0050] At this time, the historical production plan is obtained, and the production demand impact relationship corresponding to the dynamic supply unit and the static supply unit is obtained based on the historical production plan and the supplier's historical supply data, including: The difference between the theoretical demand and the actual demand in the historical production plan is obtained, and the first compensation value of the dynamic supply unit for the difference value and the second compensation value of the static supply unit for the difference value are obtained according to the distribution of the supply quantities of the dynamic supply unit and the static supply unit at the corresponding moment. The first compensation value, the second compensation value and the difference value are used to construct the production demand impact relationship between the dynamic supply unit and the static supply unit.

[0051] In this embodiment, the first compensation value, the second compensation value and the difference value are used to construct the supply amount that the dynamic supply unit and the static supply unit can compensate for with production fluctuations. Thus, step S52 also includes: Divide the time series levels corresponding to the supply types according to the production plan, obtain the time series levels with difference values, obtain the level-by-level impact relationship based on the difference values ​​corresponding to the time series level and the difference values ​​of subsequent time series levels, and obtain the level-by-level fluctuation correlation based on the level-by-level impact relationship and the production demand impact relationship.

[0052] In the production plan, all the means of production cannot be used at the same time, and there must be a sequence of use. At this time, the timing levels corresponding to the supply types are divided according to the sequence of use of the supply products in the production plan. For example, the supply type corresponding to the first supply product is taken as the first timing level of the production plan. If there are processes that are carried out at the same time, such as the assembly of bolts and nuts, both bolts and nuts are required at this time, so the bolts and nuts are at the same timing level. However, some production can only be carried out after the completion of the previous production. At this time, the timing level corresponding to the supply product required for the subsequent production is taken as the subsequent timing level of the timing level corresponding to the supply product required for the previous production.

[0053] At this time, if there is a difference value at a certain time series level, it means that the production plan has fluctuated. Therefore, the level-by-level impact relationship is obtained based on the difference value between this time series level and the subsequent time series level, and the impact of the current time series level difference on the subsequent supply adjustment is obtained. Then, according to whether there is a difference value between the current actual production demand and the theoretical production demand in the production plan, it is judged whether it is necessary to update the material supply strategy to ensure that the material supply strategy changes adaptively with the actual implementation of the production plan and reduce corporate losses.

[0054] Step S54 includes: According to the timing level corresponding to the current production sequence, the difference value of the subsequent timing level is calculated based on the level-by-level impact relationship; Update the material supply strategy based on the difference values ​​at the subsequent time series levels and the impact of production demand.

[0055] According to the difference value of the subsequent timing level and the adjustable supply situation of the dynamic supply unit and the static supply unit, the supply quantity of the dynamic supply unit and the static supply unit is reasonably adjusted to reduce the loss of the producer.

[0056] As a third embodiment of the present application, a material supply guarantee system using a supplier capacity matching coordination model includes: The dynamic and static screening module is used to construct a supply fluctuation curve based on the supplier's historical supply data and divide the supply into dynamic supply units and static supply units. A model building module is used to build a multi-objective optimization function based on minimum cost, maximum technology iteration speed and minimum fluctuation, and to build a supplier capacity matching coordination model based on dynamic supply units and static supply units using the multi-objective optimization function; The supply guarantee analysis module is used to obtain the material supply guarantee strategy for the current production plan based on the current production plan and the supplier capacity matching coordination model.

[0057] In this embodiment, the dynamic and static screening module collects detailed records including supply quantity, supply time, supply product specifications and corresponding supply type, macroeconomic data of the supplier's region, industry raw material price fluctuation data, and major event records of the supplier's own enterprise from various information systems within the enterprise (such as enterprise resource planning ERP, procurement management system, logistics management system, etc.) and databases or data reports provided by external suppliers as supplier historical supply data.

[0058] The pre-processed production plan data is input into the supplier capacity matching coordination model. The supplier capacity matching coordination model performs detailed capacity matching calculations and order allocation optimization based on multiple factors, such as whether each supplier currently belongs to a dynamic supply unit or a static supply unit, as well as its capacity status, cost, technology iteration speed, and supply fluctuations, and outputs a material supply guarantee strategy that meets production needs.

[0059] In other embodiments, since the production plan may not be able to well reflect the production demand, a production material forecasting model can be constructed based on the producer's historical production data, market sales data, industry trend data and macroeconomic data. The production material forecasting model is used to output the supply product type, supply product quantity, demand time distribution and demand uncertainty range based on the current production plan data. The output of the production material forecasting model is used as a supplier capacity matching coordination model to improve the advance output of future material supply strategies, ensure that the corresponding materials can be supplied in time when needed, and improve the accuracy and timeliness of material supply.

[0060] As a fourth embodiment of the present application, a computer-readable storage medium is also provided for storing a computer program or instruction. When the computer program or instruction is executed by a processing device, the material supply guarantee method of the above-mentioned application supplier capacity matching coordination model is implemented. The computer-readable storage medium can be any available medium that can be stored by a computing device or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state hard disk), etc.

[0061] The specific implementation method described above is a preferred implementation method of the material supply method and system using the supplier capacity matching coordination model of this application, and is not intended to limit the specific implementation scope of this application. The scope of this application includes but is not limited to this specific implementation method. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A material supply guarantee method using a supplier capacity matching coordination model, characterized by: The steps include: S1: Obtain the supplier's historical supply data and construct a supply fluctuation curve based on the supply type according to the supplier's historical supply data; S2: Divide suppliers into dynamic supply units and static supply units according to the supply fluctuation curve and fluctuation threshold; S3: Construct a multi-objective optimization function with minimum cost, highest technology iteration speed and minimum fluctuation, and use the multi-objective optimization function to construct a supplier capacity matching coordination model based on dynamic supply units and static supply units; S4: Obtain the current production plan and output the material supply guarantee strategy based on the production plan and supplier capacity matching coordination model.

2. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 1 is characterized by: The S1 further comprises: Obtain suppliers' historical supply data and divide the suppliers' historical supply data according to supply type; Use clustering algorithms to perform clustering analysis on the historical supply data of suppliers corresponding to each supply type to obtain cluster centers and cluster clusters; Construct supply fluctuation curves based on supply types according to cluster centers.

3. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 2 is characterized by: The step of constructing a supply fluctuation curve based on supply type according to the cluster center further includes: If there are multiple cluster centers at the same time, the cluster corresponding to the cluster center is included in the additional factor influence set; Extracting additional factor features from the additional factor impact set, and obtaining additional factor feature weights based on the supply quantity in the additional factor impact set; Obtain the supply points at each moment based on the additional factor feature weights and cluster centers; The supply fluctuation curve based on supply type is constructed with supply points and cluster centers.

4. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 1 is characterized by: The S2 further includes: Suppliers whose historical supply fluctuation values ​​exceed the fluctuation threshold compared to the supply fluctuation curve are regarded as dynamic supply units; Suppliers whose historical supply fluctuation values ​​compared to the supply fluctuation curve are less than or equal to the fluctuation threshold are taken as static supply units.

5. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 1 is characterized by: The multi-objective optimization function constructed with minimum cost, maximum technology iteration speed and minimum fluctuation includes: Construct the minimum cost objective optimization function based on purchasing cost, transportation cost and inventory cost; Construct the highest technology iteration speed target optimization function based on product update frequency and product application degree; The minimum fluctuation objective optimization function is constructed based on the proportion of dynamic supply units and static supply units; A multi-objective optimization function is constructed with the minimum cost objective optimization function, the highest technology iteration speed objective optimization function and the minimum fluctuation objective optimization function.

6. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 5, characterized in that: The supplier capacity matching coordination model is constructed based on the dynamic supply unit and the static supply unit using the multi-objective optimization function, including: Obtain historical production demand, and obtain historical supplier coordination strategy based on historical production demand and supplier historical supply data; According to the historical supplier coordination strategy and dynamic supply units and static supply units, model training is performed based on multi-objective optimization functions to obtain cost weights, technology iteration speed weights, and supply fluctuation weights, thereby constructing a supplier capacity matching coordination model.

7. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 1 is characterized by: Also includes: S51: Obtain a historical production plan, and obtain the production demand impact relationship between the dynamic supply unit and the static supply unit based on the historical production plan and the supplier's historical supply data; S52: Obtaining step-by-step fluctuation correlation based on the production sequence of the production plan and the production demand impact relationship; S53: Obtain the difference between the current actual production demand and the theoretical production demand in the production plan, and determine whether the material supply strategy needs to be updated based on the difference. If so, execute S54; S54: Update the material supply strategy based on the current production sequence and the correlation of step-by-step fluctuations.

8. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 7 is characterized by: The S51 includes: The difference between the theoretical demand and the actual demand in the historical production plan is obtained, and the first compensation value of the dynamic supply unit for the difference value and the second compensation value of the static supply unit for the difference value are obtained according to the distribution of the supply quantities of the dynamic supply unit and the static supply unit at the corresponding moment. The first compensation value, the second compensation value and the difference value are used to construct the production demand impact relationship between the dynamic supply unit and the static supply unit.

9. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 8, characterized in that: The S52 further includes: Divide the time series levels corresponding to the supply types according to the production plan, obtain the time series levels with difference values, obtain the level-by-level impact relationship based on the difference values ​​corresponding to the time series level and the difference values ​​of subsequent time series levels, and obtain the level-by-level fluctuation correlation based on the level-by-level impact relationship and the production demand impact relationship.

10. The material supply guarantee method using the supplier capacity matching coordination model as claimed in claim 9, characterized in that: The S54 includes: According to the timing level corresponding to the current production sequence, the difference value of the subsequent timing level is calculated based on the level-by-level impact relationship; Update the material supply strategy based on the difference values ​​at the subsequent time series levels and the impact of production demand.

11. A material supply guarantee system using a supplier capacity matching coordination model, used to implement the method according to any one of claims 1 to 10, characterized in that: include: The dynamic and static screening module is used to construct a supply fluctuation curve based on the supplier's historical supply data and divide the supply into dynamic supply units and static supply units. A model building module is used to build a multi-objective optimization function based on minimum cost, maximum technology iteration speed and minimum fluctuation, and to build a supplier capacity matching coordination model based on dynamic supply units and static supply units using the multi-objective optimization function; The supply guarantee analysis module is used to obtain the material supply guarantee strategy for the current production plan based on the current production plan and the supplier capacity matching coordination model.

12. A computer-readable storage medium, characterized in that: The storage medium stores a computer program or instruction, and when the computer program or instruction is executed by a processing device, the method according to any one of claims 1 to 10 is implemented.

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