Non-bidding supply chain visualization system and method based on multi-source data and inventory

Abstract

This invention relates to the field of non-tender inventory management technology, and discloses a non-tender supply chain visualization system and method based on multi-source data and inventory. The parameter acquisition module acquires real-time inventory levels, historical consumption records, and supply chain status parameters from multiple inventory units, and collects demand fluctuation indices and logistics channel smoothness indicators. The parameter processing module constructs a dynamic benchmark model based on historical consumption records and the demand fluctuation index to determine the supply stability coefficient. The inventory analysis module analyzes the real-time inventory levels and the expected inventory range output by the dynamic benchmark model, and calculates thresholds to adjust demand levels. The threshold revision module generates dynamic threshold adjustment instructions based on the threshold adjustment demand levels and inventory cost constraints. The visual display module generates visual charts based on the revised inventory warning thresholds, triggers warnings, and pushes optimization solutions to improve material allocation efficiency, reduce inventory costs, and support real-time collaborative decision-making by multiple roles.

Description

Technical Field

[0001] This invention relates to the field of non-tender inventory management technology, and more specifically, to a non-tender supply chain visualization system and method based on multi-source data and inventory. Background Technology

[0002] In non-tender centralized procurement management, insufficient supply chain visibility is a key issue hindering efficiency improvement. In existing technologies, data from various links in the supply chain (production, inventory, logistics, etc.) are scattered across different systems, forming information silos, leading to low inventory turnover, delayed material allocation, and low collaboration efficiency. Traditional solutions rely on manual data integration and static inventory models, which have the following drawbacks: 1. Weak data integration capabilities: Data across departments and systems is difficult to synchronize in real time, relying on manual integration, resulting in information transmission delays; 2. Insufficient dynamic response: Static models cannot adapt to fluctuations in market demand and changes in production plans; 3. Low coordination efficiency: Lack of joint reserve strategy, with both redundant inventory and stockout risk coexisting; 4. Poor visualization: Traditional dashboards only display basic indicators and lack multi-dimensional interactive and linked analysis functions. They lack intuitive multi-dimensional data display and have limited decision support capabilities. Summary of the Invention

[0003] This invention provides a non-tender supply chain visualization system and method based on multi-source data and inventory. By integrating multi-source data, dynamic inventory modeling, joint reserve strategy optimization and real-time visualization technology, it solves the problem of fragmented supply chain data in non-tender centralized procurement scenarios, improves material allocation efficiency, reduces inventory costs, and supports real-time collaborative decision-making by multiple roles.

[0004] To achieve the above objectives, this invention provides a non-tender supply chain visualization system based on multi-source data and inventory, comprising: The parameter acquisition module is used to acquire real-time inventory levels, historical consumption records, and supply chain status parameters of multiple inventory units through the inventory management information system at different time points, and simultaneously collect demand fluctuation index and logistics channel smoothness index in the external market environment. The parameter processing module is used to construct a dynamic benchmark model representing the inventory consumption pattern based on the historical consumption records and demand fluctuation index of each inventory unit, and to determine the supply stability coefficient by combining supply chain status parameters and logistics channel smoothness indicators. The inventory analysis module is used to perform deviation analysis between real-time inventory levels and the expected inventory range output by the dynamic benchmark model, and calculate the threshold adjustment demand level for each inventory unit in combination with the supply stability coefficient. The threshold revision module is used to adjust the demand level and preset inventory cost constraints according to the threshold, generate dynamic threshold adjustment instructions, and revise the inventory warning threshold of the corresponding inventory unit in real time. The visual display module is used to generate visual charts based on the revision results of inventory warning thresholds, trigger warnings, and push optimization solutions.

[0005] Furthermore, the parameter processing module is used for: Perform time-series decomposition on the historical consumption records of each inventory unit to extract trend components, periodic components, and random components; The baseline consumption rate is obtained by superimposing the trend component and the periodic component, and the dispersion of the random component is calculated as the demand fluctuation coefficient. The demand volatility index and the demand volatility coefficient are weighted and fused to obtain a comprehensive volatility representation. Based on the comprehensive volatility characterization, the benchmark consumption rate is extended to form the expected inventory range of the dynamic benchmark model.

[0006] Furthermore, the inventory analysis module is used for: The difference between the real-time inventory level and the midpoint of the expected inventory range is calculated as the inventory deviation. Combined with the preset inventory range, the deviation coefficient is determined. The threshold adjustment demand level for each inventory unit is calculated based on the deviation coefficient and the supply stability coefficient.

[0007] Furthermore, the inventory analysis module is used for: The difference between the real-time inventory level and the midpoint of the expected inventory range is calculated as the inventory deviation. Obtain the in-transit inventory level during the current replenishment cycle, and use the sum of the in-transit inventory level and the real-time inventory level as the total available inventory. The total available inventory is compared with the lower and upper limits of the expected inventory range. A negative deviation signal is generated when the total available inventory is lower than the lower limit and a positive deviation signal is generated when it is higher than the upper limit. The ratio of the absolute value of the inventory deviation to the expected inventory range width is used as the deviation coefficient.

[0008] Furthermore, the inventory analysis module is used for: The supplier delivery on-time rate and production plan completion rate are obtained from the supply chain status parameters, and the weighted average is used to obtain the supply reliability. The transportation timeliness fluctuation rate is calculated based on the logistics channel smoothness index, and the ratio of the transportation timeliness fluctuation rate to the supply reliability is used as the supply stability coefficient. Multiply the deviation coefficient by the supply stability coefficient to obtain the first product value. Multiply the first product value by the importance weight factor of the inventory unit to obtain the threshold adjustment demand level.

[0009] Furthermore, the importance weighting factor is calculated as follows: Obtain the criticality rating of the material corresponding to the inventory unit in the production plan, and map the criticality rating to a criticality value between 1 and 10. Calculate the ratio of the average inventory value per unit time to the total inventory value to obtain the capital occupation ratio and then normalize it. The importance weight factor is obtained by calculating the harmonic mean of the criticality value and the normalized capital occupation ratio.

[0010] Furthermore, the threshold revision module is used for: Set the inventory cost constraint as the upper limit of the sum of inventory holding costs and stockout costs; For inventory units whose threshold adjustment demand level is higher than the first preset level, calculate the safety stock adjustment increment; The safety stock adjustment increment is added to the current warning threshold to obtain a candidate new threshold. The expected inventory cost under the candidate new threshold is verified to meet the allowable upper limit constraint. If it does, a dynamic threshold adjustment instruction using the candidate new threshold is generated.

[0011] Furthermore, it also includes: Threshold adaptive module, used for: Within the preset evaluation period after the dynamic threshold adjustment instruction is executed, the actual number of stockouts and the number of inventory backlogs in each inventory unit are counted, and the inventory control performance index is calculated. When the inventory control performance index continues to fall below the preset target value, the parameter recalibration process of the dynamic benchmark model is triggered, the weight ratio of the demand fluctuation coefficient and the supply stability coefficient is recalculated, the prediction accuracy of the dynamic benchmark model is optimized, and the inventory threshold adjustment method is adaptively optimized.

[0012] Furthermore, the visual display module is used for: Determine the deviation ratio between the real-time inventory level and the revised inventory warning threshold, and determine the warning trigger level based on the combined mapping relationship between the deviation ratio and the threshold adjustment demand level. The warning trigger level includes normal monitoring level, attention reminder level and emergency intervention level. Based on the warning trigger level, differentiated visualization charts are generated. For the normal monitoring level, an inventory trend line chart and a threshold range strip chart are generated. For the attention alert level, an inventory unit heat map and a threshold revision trajectory map are generated. For the emergency intervention level, a three-dimensional topology map of inventory shortage risk and a replenishment urgency radar chart are generated. When the warning trigger level is the attention alert level, a first optimization plan is generated, which includes the suggested replenishment batch and the increase in safety stock. When the warning trigger level is the emergency intervention level, a second optimization scheme is generated. The second optimization scheme includes an emergency allocation path recommendation, an alternative supplier activation instruction, and a temporary relaxation strategy for inventory cost constraints. The differentiated visualization chart and the corresponding first optimization scheme or second optimization scheme are packaged into an early warning information package. Based on the early warning trigger level, the corresponding push channel and response time are selected, and the early warning information package is pushed to the inventory management terminal.

[0013] To achieve the above objectives, the present invention also provides a non-tender supply chain visualization method based on multi-source data and inventory, including: At different points in time, the real-time inventory levels, historical consumption records, and supply chain status parameters of multiple inventory units are obtained through the inventory management information system, while demand fluctuation index and logistics channel smoothness index are collected simultaneously in the external market environment. Based on the historical consumption records and demand fluctuation index of each inventory unit, a dynamic benchmark model representing the inventory consumption pattern is constructed, and the supply stability coefficient is determined by combining supply chain status parameters and logistics channel smoothness indicators. Deviation analysis is performed between real-time inventory levels and the expected inventory range output by the dynamic benchmark model, and the threshold adjustment demand level for each inventory unit is calculated in combination with the supply stability coefficient. Based on the threshold adjustment demand level and the preset inventory cost constraints, a dynamic threshold adjustment instruction is generated to revise the inventory warning threshold of the corresponding inventory unit in real time. Based on the revised inventory warning threshold, a visual chart is generated, triggering a warning and pushing out optimization solutions.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention discloses a non-tender supply chain visualization system and method based on multi-source data and inventory. The parameter acquisition module acquires real-time inventory levels, historical consumption records, and supply chain status parameters from multiple inventory units, and collects demand fluctuation indices and logistics channel smoothness indicators. The parameter processing module constructs a dynamic benchmark model based on historical consumption records and the demand fluctuation index to determine the supply stability coefficient. The inventory analysis module analyzes the real-time inventory levels and the expected inventory range output by the dynamic benchmark model, calculating thresholds to adjust demand levels. The threshold revision module generates dynamic threshold adjustment instructions based on the threshold adjustment demand levels and inventory cost constraints. The visual display module generates visual charts based on the revised inventory warning thresholds, triggers warnings, and pushes optimization solutions to improve material allocation efficiency, reduce inventory costs, and support real-time collaborative decision-making by multiple roles. Attached Figure Description

[0015] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This diagram illustrates the structure of a non-tendering supply chain visualization system based on multi-source data and inventory, as described in an embodiment of the present invention. Figure 2 The diagram illustrates a flowchart of a non-tender supply chain visualization method based on multi-source data and inventory in an embodiment of the present invention. Detailed Implementation

[0016] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0017] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0018] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0020] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0021] like Figure 1 As shown, embodiments of the present invention disclose a non-tendering supply chain visualization system based on multi-source data and inventory, including: The parameter acquisition module is used to acquire real-time inventory levels, historical consumption records, and supply chain status parameters of multiple inventory units through the inventory management information system at different time points, and simultaneously collect demand fluctuation index and logistics channel smoothness index in the external market environment. The parameter processing module is used to construct a dynamic benchmark model representing the inventory consumption pattern based on the historical consumption records and demand fluctuation index of each inventory unit, and to determine the supply stability coefficient by combining supply chain status parameters and logistics channel smoothness indicators. The inventory analysis module is used to perform deviation analysis between real-time inventory levels and the expected inventory range output by the dynamic benchmark model, and calculate the threshold adjustment demand level for each inventory unit in combination with the supply stability coefficient. The threshold revision module is used to adjust the demand level and preset inventory cost constraints according to the threshold, generate dynamic threshold adjustment instructions, and revise the inventory warning threshold of the corresponding inventory unit in real time. The visual display module is used to generate visual charts based on the revision results of inventory warning thresholds, trigger warnings, and push optimization solutions.

[0022] In this embodiment, different time nodes refer to preset data collection times such as 2:00 AM daily, 8:00 AM every Monday, and the first working day of each month, ensuring the timeliness and completeness of the data. These can also be adjusted adaptively according to actual needs. Real-time inventory refers to the actual quantity of materials stored in the warehouse, read in real-time through the inventory module interface of the enterprise resource planning system; historical consumption records refer to the daily outbound quantity and corresponding timestamps over the past 12 months, used to analyze consumption patterns; supply chain status parameters include supplier order confirmation timeliness (e.g., average 24 hours), production progress status (e.g., completion rate percentage), and shipping notification information (e.g., expected shipping date); demand fluctuation indices include industry seasonal fluctuation coefficients (e.g., a 30% increase in demand before and after the Spring Festival) and regional market demand change rates (e.g., monthly demand growth rate in East China); logistics channel smoothness indicators include transportation timeliness fluctuation rates (e.g., the percentage deviation between actual arrival time and promised time) and logistics node congestion indices (e.g., the proportion of backlogged cargo at ports).

[0023] In some embodiments of this application, the parameter processing module is used for: Perform time-series decomposition on the historical consumption records of each inventory unit to extract trend components, periodic components, and random components; The baseline consumption rate is obtained by superimposing the trend component and the periodic component, and the dispersion of the random component is calculated as the demand fluctuation coefficient. The demand volatility index and the demand volatility coefficient are weighted and fused to obtain a comprehensive volatility representation. Based on the comprehensive volatility characterization, the benchmark consumption rate is extended to form the expected inventory range of the dynamic benchmark model.

[0024] In this embodiment, the supply stability coefficient quantifies the reliability of the supply chain, with a value ranging from 0 to 1. The closer it is to 1, the more stable the supply.

[0025] In this embodiment, the time series decomposition uses the classical decomposition method, decomposing historical consumption records (such as daily consumption over the past 365 days) into three parts: a trend component reflecting long-term growth or decline trends (such as demand growth due to product lifecycle), calculated using a 12-day moving average; a periodic component reflecting fluctuations within a fixed period (such as higher consumption on Fridays and lower consumption at the end of each month), obtained by extracting 7-day and 30-day periodic components through spectral analysis and then superimposing them; and a random component reflecting unpredictable, accidental fluctuations, which is the residual sequence after subtracting the trend and periodic components from the original data. The baseline consumption rate is calculated by adding the values ​​of the trend and periodic components on the forecast date. The demand volatility coefficient is obtained by calculating the ratio of the standard deviation of the random component to the baseline consumption rate; for example, if the standard deviation is 10 units and the baseline rate is 100 units, the volatility coefficient is 0.1. The demand volatility index is derived from external market data, such as a seasonal adjustment coefficient of 1.2 given in industry reports (indicating a 20% increase in demand during peak season). The comprehensive volatility representation uses a weighted fusion: volatility coefficient with a weight of 60% and demand volatility index with a weight of 40%. For example, if the volatility coefficient is 0.1 and the demand index is 1.2, then the comprehensive volatility representation is 0.6 × 0.1 + 0.4 × 1.2 = 0.54. The range expansion method is as follows: multiply the baseline consumption rate by the replenishment cycle days (e.g., 7 days) to obtain the median expected inventory. Multiply the median expected inventory by (1 ± comprehensive volatility representation) to obtain the upper and lower limits, forming the expected inventory range. For example, if the median is 700 units and the volatility representation is 0.54, then the range is 322 to 1078 units.

[0026] The beneficial effects of the above technical solution are as follows: by extracting the trend, periodicity and randomness characteristics of consumption data through time series decomposition, a dynamic benchmark model that can reflect the inherent laws of consumption is constructed; by integrating the internal fluctuation coefficient and the external demand index, a response to changes in the market environment is achieved; and by using the interval expansion mechanism, the forecast uncertainty is quantified into an inventory interval, providing a scientific reference standard for subsequent deviation analysis.

[0027] In some embodiments of this application, the inventory analysis module is used for: The difference between the real-time inventory level and the midpoint of the expected inventory range is calculated as the inventory deviation. Combined with the preset inventory range, the deviation coefficient is determined. The threshold adjustment demand level for each inventory unit is calculated based on the deviation coefficient and the supply stability coefficient.

[0028] In some embodiments of this application, the inventory analysis module is used for: The difference between the real-time inventory level and the midpoint of the expected inventory range is calculated as the inventory deviation. Obtain the in-transit inventory level during the current replenishment cycle, and use the sum of the in-transit inventory level and the real-time inventory level as the total available inventory. The total available inventory is compared with the lower and upper limits of the expected inventory range. A negative deviation signal is generated when the total available inventory is lower than the lower limit and a positive deviation signal is generated when it is higher than the upper limit. The ratio of the absolute value of the inventory deviation to the expected inventory range width is used as the deviation coefficient.

[0029] In this embodiment, the expected inventory range midpoint is the arithmetic mean of the lower and upper limits, such as a midpoint of 200 units for the range [100, 300]. In-transit inventory refers to the quantity of goods ordered but not yet received, obtained through the purchase order module of the Enterprise Resource Planning (ERP) system. For example, there are currently 3 in-transit orders totaling 80 units. Deviation detection: When the lower limit (100 units) < available quantity (230 units) < upper limit (300 units), it is considered within the normal range, and no deviation signal is generated; when the available quantity < 100 units, a negative deviation signal is generated (indicating a risk of stockout); when the available quantity > 300 units, a positive deviation signal is generated (indicating a risk of overstocking). Deviation coefficient calculation method: For negative deviation, the coefficient is (lower limit - available quantity) / range width, such as (100-80) / 200 = 0.1; for positive deviation, the coefficient is (available quantity - upper limit) / range width, such as (350-300) / 200 = 0.25. The coefficient ranges from 0 to 1, with values ​​closer to 1 indicating a more severe deviation.

[0030] The beneficial effects of the above technical solution are: by introducing in-transit inventory to calculate the total available inventory, the decision-making bias caused by looking only at the current inventory is avoided; by using two-way deviation judgment (negative and positive), both stockout and backlog risks are identified simultaneously; and by using the deviation degree coefficient to quantify the severity of the risk, a continuous quantitative input is provided for subsequent demand level calculations, realizing an upgrade from simple threshold judgment to refined risk measurement.

[0031] In some embodiments of this application, the inventory analysis module is used for: The supplier delivery on-time rate and production plan completion rate are obtained from the supply chain status parameters, and the weighted average is used to obtain the supply reliability. The transportation timeliness fluctuation rate is calculated based on the logistics channel smoothness index, and the ratio of the transportation timeliness fluctuation rate to the supply reliability is used as the supply stability coefficient. Multiply the deviation coefficient by the supply stability coefficient to obtain the first product value. Multiply the first product value by the importance weight factor of the inventory unit to obtain the threshold adjustment demand level.

[0032] In this embodiment, the supplier's on-time delivery rate refers to the percentage of orders delivered on the promised date by the supplier in the past 6 months, such as 95%; the production plan completion rate refers to the ratio of the supplier's promised output to the actual output, such as 90%; when the two are weighted and averaged, the on-time delivery rate has a weight of 60% and the completion rate has a weight of 40%, resulting in a supply reliability of 0.6 × 0.95 + 0.4 × 0.90 = 0.93. The transportation timeliness volatility is calculated by dividing the standard deviation of the actual transportation time from the promised time in the past 6 months by the average transportation time. For example, if the standard deviation is 2 days and the average transportation time is 10 days, the volatility is 0.2. The supply stability coefficient is calculated by dividing the volatility of 0.2 by the supply reliability of 0.93, which is approximately 0.215. The larger this coefficient, the more unstable the supply. The deviation coefficient comes from the calculation result of claim 4, such as 0.15. The threshold adjustment demand level is calculated as follows: deviation coefficient 0.15 × supply stability coefficient 0.215 × importance weight 8.5 ≈ 0.27, and then multiplied by the normalization coefficient 37 (to map the result to the range of 0-10).

[0033] The beneficial effects of the above technical solution are: by comprehensively assessing supply reliability and transportation timeliness volatility, the uncertainty of the supply chain is quantified; by introducing an importance weighting factor, key materials are given priority attention, and threshold adjustment resources are tilted towards high-value, high-critical materials, thus optimizing the overall inventory structure.

[0034] In some embodiments of this application, the importance weighting factor is calculated as follows: Obtain the criticality rating of the material corresponding to the inventory unit in the production plan, and map the criticality rating to a criticality value between 1 and 10. Calculate the ratio of the average inventory value per unit time to the total inventory value to obtain the capital occupation ratio and then normalize it. The importance weight factor is obtained by calculating the harmonic mean of the criticality value and the normalized capital occupation ratio.

[0035] In this embodiment, the material criticality rating is derived from the company's material classification system, typically divided into three levels: critical (Category A), important (Category B), and general (Category C), further subdivided into 10 grades: core raw materials within critical materials are mapped to 10 (e.g., dedicated chips from a single supplier), auxiliary materials within critical materials are mapped to 8 (e.g., major components), important materials are mapped to 5 (e.g., standard parts), and general materials are mapped to 2 (e.g., office supplies). The capital occupation ratio is calculated by dividing the material's average inventory value (daily average inventory quantity × unit price) by the company's total inventory value of all materials. For example, if a material has an average inventory of 100,000 yuan and a total inventory of 10 million yuan, then the capital occupation ratio is 0.01 (1%). Normalization maps this ratio to a range of 1 to 10. For example, if the maximum capital occupation ratio is 20% (corresponding to 10), then 1% corresponds to 0.5. Considering that lower capital occupation should be given priority (to avoid capital stagnation), the reciprocal is taken and mapped to 9.5. The harmonic mean is calculated as 2 × keyness value × normalized capital occupation reciprocal / (keyness value + normalized capital occupation reciprocal). For example, if the keyness is 10 and the capital occupation mapping is 9.5, then the harmonic mean is 2 × 10 × 9.5 / (10 + 9.5) ≈ 9.74.

[0036] The beneficial effects of the above technical solution are: it balances the criticality of materials and capital efficiency through the harmonic averaging mechanism, avoiding excessive capital occupation due to the pursuit of criticality alone, or shortage of key materials due to the pursuit of capital efficiency alone; by quantifying importance into a weight factor in the range of 0-10, it realizes the quantitative ranking of inventory unit priorities, and provides a scientific basis for weight allocation for multi-objective optimization (service level and capital efficiency).

[0037] In some embodiments of this application, the threshold revision module is used for: Set the inventory cost constraint as the upper limit of the sum of inventory holding costs and stockout costs; For inventory units whose threshold adjustment demand level is higher than the first preset level, calculate the safety stock adjustment increment; The safety stock adjustment increment is added to the current warning threshold to obtain a candidate new threshold. The expected inventory cost under the candidate new threshold is verified to meet the allowable upper limit constraint. If it does, a dynamic threshold adjustment instruction using the candidate new threshold is generated.

[0038] In this embodiment, inventory holding costs include interest on capital tied up (6% annual interest rate), warehouse rent (2 yuan per square meter per day), insurance costs (0.5% of the value of goods), and depreciation (2% annual rate), totaling approximately 12% of the inventory value annually. Stockout costs include order losses (an average loss of 5000 yuan per stockout), additional costs for expedited purchases (20% higher than normal purchases), and customer reputation losses (equivalent to 2000 yuan per stockout). Based on historical statistics, the cost per stockout is approximately 8000 yuan. The allowable upper limit is set according to the company's financial budget, such as the annual total inventory cost not exceeding 15% of the total inventory value. The first preset level is set to 5.0 (range 0-10). When the demand level > 5.0, a threshold adjustment is triggered. The formula for calculating the safety stock adjustment increment is: basic adjustment amount × (demand level / 10) / supply stability coefficient. For example, if the basic adjustment amount is 100 units, the demand level is 8, and the supply stability coefficient is 0.3, then the increment is 100 × 0.8 / 0.3 ≈ 267 units. If the current warning threshold is 200 units, the candidate new threshold is 467 units. Expected inventory cost verification: Calculate the average inventory level under the new threshold (e.g., increasing from 300 units to 550 units). Holding costs increase (250 units × unit price × 12%), but the number of stockouts decreases (from an average of 5 times per year to 1 time, and stockout costs decrease from 40,000 yuan to 8,000 yuan). If the total cost change is lower than the allowed upper limit increase (e.g., the upper limit allows an increase of 50,000 yuan, but the actual increase is 30,000 yuan), an adjustment instruction is generated; if it exceeds the upper limit, the adjustment is based on the maximum allowed increment (e.g., only 150 units can be added, so the new threshold is 350 units).

[0039] The beneficial effects of the above technical solution are: by making cost constraints explicit as an allowable upper limit, inventory strategy is linked to the company's financial capabilities; and through the cost verification mechanism of candidate new thresholds, the economic feasibility of the adjustment plan is ensured, avoiding blind decision-making that simply pursues service level while ignoring cost-effectiveness.

[0040] In some embodiments of this application, it also includes: Threshold adaptive module, used for: Within the preset evaluation period after the dynamic threshold adjustment instruction is executed, the actual number of stockouts and the number of inventory backlogs in each inventory unit are counted, and the inventory control performance index is calculated. When the inventory control performance index continues to fall below the preset target value, the parameter recalibration process of the dynamic benchmark model is triggered, the weight ratio of the demand fluctuation coefficient and the supply stability coefficient is recalculated, the prediction accuracy of the dynamic benchmark model is optimized, and the inventory threshold adjustment method is adaptively optimized.

[0041] In this embodiment, the preset evaluation period is set to one month, meaning the threshold adjustment effect of the previous month is evaluated monthly. Actual stockout frequency refers to the number of days or orders where the total available inventory is below zero (considering that demand cannot be met even after goods are in transit); inventory backlog frequency refers to the number of days where the total available inventory exceeds the maximum inventory limit (e.g., set to 150% of the expected limit). The inventory control performance indicator is calculated as the harmonic mean of service level (1 - stockout rate) and inventory turnover rate. For example, if the stockout rate is 2% (service level 98%) and the inventory turnover rate is 6 times / year, the performance indicator is 2 × 0.98 × 6 / (0.98 + 6) ≈ 1.69. The preset target value is set to 1.5. If it is below 1.5 for three consecutive months, recalibration is triggered. The parameter recalibration process includes: rerunning the time series decomposition in claim 2 and updating the extraction parameters of trend, period, and random components; recalculating the weights of the demand fluctuation coefficient and the supply stability coefficient (originally 50% each, adjusted to 40% and 60% based on the latest data); and retraining the prediction algorithm of the dynamic benchmark model (e.g., upgrading from simple exponential smoothing to Holt-Winters three-parameter smoothing).

[0042] The beneficial effects of the above technical solution are as follows: by establishing an evaluation-feedback mechanism, continuous improvement of inventory strategy is achieved; by using the harmonic average evaluation of performance indicators, simultaneous control of stockouts and backlogs is mandated; by using a parameter recalibration process, the model can adapt to structural changes in the market environment (such as changes in demand patterns from stable to volatile); and by using an adaptive optimization closed loop, manual maintenance costs are reduced, ensuring the long-term effectiveness and robustness of the inventory management system.

[0043] Also includes: Threshold adaptive module, used for: Within the preset evaluation period after the dynamic threshold adjustment instruction is executed, the actual number of stockouts and the number of inventory backlogs in each inventory unit are counted, and the inventory control performance index is calculated. When the inventory control performance index continues to fall below the preset target value, the parameter recalibration process of the dynamic benchmark model is triggered, the weight ratio of the demand fluctuation coefficient and the supply stability coefficient is recalculated, the prediction accuracy of the dynamic benchmark model is optimized, and the inventory threshold adjustment method is adaptively optimized.

[0044] In this embodiment, the preset evaluation period is set to one month, meaning the threshold adjustment effect of the previous month is evaluated monthly. Actual stockout frequency refers to the number of days or orders where the total available inventory is below zero (considering that demand cannot be met even after goods are in transit); inventory backlog frequency refers to the number of days where the total available inventory exceeds the maximum inventory limit (e.g., set to 150% of the expected limit). The inventory control performance indicator is calculated as the harmonic mean of service level (1 - stockout rate) and inventory turnover rate. For example, if the stockout rate is 2% (service level 98%) and the inventory turnover rate is 6 times / year, the performance indicator is 2 × 0.98 × 6 / (0.98 + 6) ≈ 1.69. The preset target value is set to 1.5. If it is below 1.5 for three consecutive months, recalibration is triggered. The parameter recalibration process includes: rerunning the time series decomposition in claim 2 and updating the extraction parameters of trend, period, and random components; recalculating the weights of the demand fluctuation coefficient and the supply stability coefficient (originally 50% each, adjusted to 40% and 60% based on the latest data); and retraining the prediction algorithm of the dynamic benchmark model (e.g., upgrading from simple exponential smoothing to Holt-Winters three-parameter smoothing).

[0045] The beneficial effects of the above technical solution are as follows: by establishing an evaluation-feedback mechanism, continuous improvement of inventory strategy is achieved; by using the harmonic average evaluation of performance indicators, simultaneous control of stockouts and backlogs is mandated; by using a parameter recalibration process, the model can adapt to structural changes in the market environment (such as changes in demand patterns from stable to volatile); and by using an adaptive optimization closed loop, manual maintenance costs are reduced, ensuring the long-term effectiveness and robustness of the inventory management system.

[0046] In some embodiments of this application, the visual display module is used for: Determine the deviation ratio between the real-time inventory level and the revised inventory warning threshold, and determine the warning trigger level based on the combined mapping relationship between the deviation ratio and the threshold adjustment demand level. The warning trigger level includes normal monitoring level, attention reminder level and emergency intervention level. Based on the warning trigger level, differentiated visualization charts are generated. For the normal monitoring level, an inventory trend line chart and a threshold range strip chart are generated. For the attention alert level, an inventory unit heat map and a threshold revision trajectory map are generated. For the emergency intervention level, a three-dimensional topology map of inventory shortage risk and a replenishment urgency radar chart are generated. When the warning trigger level is the attention alert level, a first optimization plan is generated, which includes the suggested replenishment batch and the increase in safety stock. When the warning trigger level is the emergency intervention level, a second optimization scheme is generated. The second optimization scheme includes an emergency allocation path recommendation, an alternative supplier activation instruction, and a temporary relaxation strategy for inventory cost constraints. The differentiated visualization chart and the corresponding first optimization scheme or second optimization scheme are packaged into an early warning information package. Based on the early warning trigger level, the corresponding push channel and response time are selected, and the early warning information package is pushed to the inventory management terminal.

[0047] In this embodiment, the deviation ratio is calculated by dividing the difference between the real-time inventory level and the revised warning threshold by the revised warning threshold. For example, if the real-time inventory is 120 units and the revised threshold is 100 units, the deviation ratio is 0.2. The combined mapping relationship is set as follows: when the deviation ratio is between -0.1 and 0.1 and the threshold adjustment requirement level is below 3.0, it is a normal monitoring level; when the deviation ratio is between -0.2 and -0.1 or between 0.1 and 0.2 and the threshold adjustment requirement level is between 3.0 and 7.0, it is a warning level; when the deviation ratio is less than -0.2 or greater than 0.2 and the threshold adjustment requirement level is greater than 7.0, it is an emergency intervention level. For the inventory trend line chart of the normal monitoring level, time is used as the horizontal axis and real-time inventory level is used as the vertical axis to intuitively show the trend of inventory changes over time; the threshold interval band chart is centered on the revised warning threshold, with a certain range of band areas set above and below to clearly show the reasonable fluctuation range of inventory. The system features a heatmap of inventory units with varying risk levels, differentiated by color based on deviation ratios and threshold adjustment demand levels, with darker colors indicating higher risk. A threshold revision trajectory chart shows the revisions of warning thresholds over time, allowing managers to understand dynamic changes. A 3D topology map of inventory shortage risk at the emergency intervention level visually represents the shortage risk level of different inventory units, providing a more intuitive representation of inventory shortage distribution. A replenishment urgency radar chart assesses the urgency of replenishment from multiple dimensions (such as demand urgency and supply stability). The recommended replenishment batch size in the first optimization plan is calculated based on historical demand data and current inventory levels, while the safety stock increase is determined by combining the supply stability coefficient and threshold adjustment demand level. For example, based on historical data predicting a demand of 200 units for the next week, current inventory of 150 units, a supply stability coefficient of 0.3, and a threshold adjustment demand level of 6.0, the recommended replenishment batch size is 100 units, and the safety stock increase is 50 units. The second optimization plan recommends emergency allocation routes based on the inventory status and transportation distance of each warehouse to calculate the optimal allocation route. The alternative supplier activation command is generated by selecting suitable alternative suppliers from the company's supplier database and issuing activation commands. The temporary relaxation strategy for inventory cost constraints appropriately relaxes the allowable upper limit of the sum of inventory holding costs and stockout costs in emergency situations, based on the company's actual situation. The push channel for the early warning information package is selected according to the early warning trigger level: normal monitoring levels are pushed through the company's internal inventory management system; attention alert levels are pushed via SMS or email; and emergency intervention levels are pushed via instant messaging tools or telephone. The response time is also set according to the early warning trigger level: normal monitoring levels require viewing within 24 hours, attention alert levels require processing within 12 hours, and emergency intervention levels require response within 1 hour.

[0048] The beneficial effects of the above technical solution are as follows: by adjusting the combination mapping of deviation ratio and threshold to demand level, accurate hierarchical early warning of inventory status is achieved; differentiated visualization charts enable managers to quickly and intuitively understand the inventory situation; targeted optimization schemes provide effective countermeasures for different levels of early warning; reasonable push channels and response time settings ensure that early warning information can be transmitted and processed in a timely manner, improving the efficiency and accuracy of inventory management.

[0049] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0050] Correspondingly, such as Figure 2 As shown, this application also provides a non-tender supply chain visualization method based on multi-source data and inventory, including: S110: At different time points, the real-time inventory level, historical consumption records and supply chain status parameters of multiple inventory units are obtained through the inventory management information system, and the demand fluctuation index and logistics channel smoothness index in the external market environment are collected simultaneously. S120: Based on the historical consumption records and demand fluctuation index of each inventory unit, a dynamic benchmark model representing the inventory consumption pattern is constructed, and the supply stability coefficient is determined by combining supply chain status parameters and logistics channel smoothness indicators. S130: Perform deviation analysis between real-time inventory levels and the expected inventory range output by the dynamic benchmark model, and calculate the threshold adjustment demand level for each inventory unit in combination with the supply stability coefficient. S140: Based on the threshold adjustment demand level and the preset inventory cost constraints, generate a dynamic threshold adjustment instruction to revise the inventory warning threshold of the corresponding inventory unit in real time. S150: Based on the revised inventory warning threshold, generate a visual chart, trigger a warning, and push an optimization plan.

[0051] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0052] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0053] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A non-tender supply chain visualization system based on multi-source data and inventory, characterized in that: include: The parameter acquisition module is used to acquire real-time inventory levels, historical consumption records, and supply chain status parameters of multiple inventory units through the inventory management information system at different time points, and simultaneously collect demand fluctuation index and logistics channel smoothness index in the external market environment. The parameter processing module is used to construct a dynamic benchmark model representing the inventory consumption pattern based on the historical consumption records and demand fluctuation index of each inventory unit, and to determine the supply stability coefficient by combining supply chain status parameters and logistics channel smoothness indicators. The inventory analysis module is used to perform deviation analysis between real-time inventory levels and the expected inventory range output by the dynamic benchmark model, and calculate the threshold adjustment demand level for each inventory unit in combination with the supply stability coefficient. The threshold revision module is used to adjust the demand level and preset inventory cost constraints according to the threshold, generate dynamic threshold adjustment instructions, and revise the inventory warning threshold of the corresponding inventory unit in real time. The visual display module is used to generate visual charts based on the revision results of inventory warning thresholds, trigger warnings, and push optimization solutions.

2. The non-tender supply chain visualization system based on multi-source data and inventory as described in claim 1, characterized in that, The parameter processing module is used for: Perform time-series decomposition on the historical consumption records of each inventory unit to extract trend components, periodic components, and random components; The baseline consumption rate is obtained by superimposing the trend component and the periodic component, and the dispersion of the random component is calculated as the demand fluctuation coefficient. The demand volatility index and the demand volatility coefficient are weighted and fused to obtain a comprehensive volatility representation. Based on the comprehensive volatility characterization, the benchmark consumption rate is extended to form the expected inventory range of the dynamic benchmark model.

3. The non-tender supply chain visualization system based on multi-source data and inventory as described in claim 1, characterized in that, The inventory analysis module is used for: The difference between the real-time inventory level and the midpoint of the expected inventory range is calculated as the inventory deviation. Combined with the preset inventory range, the deviation coefficient is determined. The threshold adjustment demand level for each inventory unit is calculated based on the deviation coefficient and the supply stability coefficient.

4. The non-tender supply chain visualization system based on multi-source data and inventory as described in claim 3, characterized in that, The inventory analysis module is used for: The difference between the real-time inventory level and the midpoint of the expected inventory range is calculated as the inventory deviation. Obtain the in-transit inventory level during the current replenishment cycle, and use the sum of the in-transit inventory level and the real-time inventory level as the total available inventory. The total available inventory is compared with the lower and upper limits of the expected inventory range. A negative deviation signal is generated when the total available inventory is lower than the lower limit and a positive deviation signal is generated when it is higher than the upper limit. The ratio of the absolute value of the inventory deviation to the expected inventory range width is used as the deviation coefficient.

5. The non-tender supply chain visualization system based on multi-source data and inventory according to claim 3, characterized in that, The inventory analysis module is used for: The supplier delivery on-time rate and production plan completion rate are obtained from the supply chain status parameters, and the weighted average is used to obtain the supply reliability. The transportation timeliness fluctuation rate is calculated based on the logistics channel smoothness index, and the ratio of the transportation timeliness fluctuation rate to the supply reliability is used as the supply stability coefficient. Multiply the deviation coefficient by the supply stability coefficient to obtain the first product value. Multiply the first product value by the importance weight factor of the inventory unit to obtain the threshold adjustment demand level.

6. The non-tender supply chain visualization system based on multi-source data and inventory as described in claim 5, characterized in that, The importance weighting factor is calculated as follows: Obtain the criticality rating of the material corresponding to the inventory unit in the production plan, and map the criticality rating to a criticality value between 1 and 10. Calculate the ratio of the average inventory value per unit time to the total inventory value to obtain the capital occupation ratio and then normalize it. The importance weight factor is obtained by calculating the harmonic mean of the criticality value and the normalized capital occupation ratio.

7. The non-tender supply chain visualization system based on multi-source data and inventory according to claim 1, characterized in that, The threshold revision module is used for: Set the inventory cost constraint as the upper limit of the sum of inventory holding costs and stockout costs; For inventory units whose threshold adjustment demand level is higher than the first preset level, calculate the safety stock adjustment increment; The safety stock adjustment increment is added to the current warning threshold to obtain a candidate new threshold. The expected inventory cost under the candidate new threshold is verified to meet the allowable upper limit constraint. If it does, a dynamic threshold adjustment instruction using the candidate new threshold is generated.

8. The non-tender supply chain visualization system based on multi-source data and inventory according to claim 1, characterized in that, Also includes: Threshold adaptive module, used for: Within the preset evaluation period after the dynamic threshold adjustment instruction is executed, the actual number of stockouts and the number of inventory backlogs in each inventory unit are counted, and the inventory control performance index is calculated. When the inventory control performance index continues to fall below the preset target value, the parameter recalibration process of the dynamic benchmark model is triggered, the weight ratio of the demand fluctuation coefficient and the supply stability coefficient is recalculated, the prediction accuracy of the dynamic benchmark model is optimized, and the inventory threshold adjustment method is adaptively optimized.

9. The non-tender supply chain visualization system based on multi-source data and inventory according to claim 1, characterized in that, The visual display module is used for: Determine the deviation ratio between the real-time inventory level and the revised inventory warning threshold, and determine the warning trigger level based on the combined mapping relationship between the deviation ratio and the threshold adjustment demand level. The warning trigger level includes normal monitoring level, attention reminder level and emergency intervention level. Based on the warning trigger level, differentiated visualization charts are generated. For the normal monitoring level, an inventory trend line chart and a threshold range strip chart are generated. For the attention alert level, an inventory unit heat map and a threshold revision trajectory map are generated. For the emergency intervention level, a three-dimensional topology map of inventory shortage risk and a replenishment urgency radar chart are generated. When the warning trigger level is the attention alert level, a first optimization plan is generated, which includes the suggested replenishment batch and the increase in safety stock. When the warning trigger level is the emergency intervention level, a second optimization scheme is generated. The second optimization scheme includes an emergency allocation path recommendation, an alternative supplier activation instruction, and a temporary relaxation strategy for inventory cost constraints. The differentiated visualization chart and the corresponding first optimization scheme or second optimization scheme are packaged into an early warning information package. Based on the early warning trigger level, the corresponding push channel and response time are selected, and the early warning information package is pushed to the inventory management terminal.

10. A non-tender supply chain visualization method based on multi-source data and inventory, applied to the non-tender supply chain visualization system based on multi-source data and inventory as described in any one of claims 1-9, characterized in that, include: At different points in time, the real-time inventory levels, historical consumption records, and supply chain status parameters of multiple inventory units are obtained through the inventory management information system, while demand fluctuation index and logistics channel smoothness index are collected simultaneously in the external market environment. Based on the historical consumption records and demand fluctuation index of each inventory unit, a dynamic benchmark model representing the inventory consumption pattern is constructed, and the supply stability coefficient is determined by combining supply chain status parameters and logistics channel smoothness indicators. Deviation analysis is performed between real-time inventory levels and the expected inventory range output by the dynamic benchmark model, and the threshold adjustment demand level for each inventory unit is calculated in combination with the supply stability coefficient. Based on the threshold adjustment demand level and the preset inventory cost constraints, a dynamic threshold adjustment instruction is generated to revise the inventory warning threshold of the corresponding inventory unit in real time. Based on the revised inventory warning threshold, a visual chart is generated, triggering a warning and pushing out optimization solutions.

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