Enterprise digital risk prevention and control system construction method and system

Abstract

The invention discloses an enterprise digital risk prevention and control system construction method and system, and relates to the technical field of data analysis systems. Multi-angle risk prevention and control analysis is lacked in the prior art. The system comprises a human risk identification unit which analyzes and calculates the daily average production capacity and the daily average attendance rate on a single production line, and evaluates the output level of each employee; the job age relevancy of the employees is analyzed, and demission risk parameters are calculated; a production cycle and a cycle production index are preset, the production index completion degree is analyzed and calculated, the minimum value in the production index completion degrees of all production lines is obtained and recorded as a theoretical production value, and human risk parameters are calculated; and the storage risk identification unit is used for acquiring the quantity of warehouse-out products in the storage products, calculating the sales ratio, constructing an ARIMA model of the warehouse-out quantity with respect to time, and analyzing and calculating to obtain a storage risk index. The technical scheme has the advantages of comprehensiveness, real-time performance, flexibility, accuracy, practicability and the like, and the risk prevention and control capability of a manufacturing enterprise can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis systems, and in particular to a method and system for constructing a digital risk prevention and control system for an enterprise. Background Art

[0002] Manufacturing enterprises refer to the type of enterprises whose core business is material processing, assembly and production, covering the whole process from raw material procurement to final product delivery. Such enterprises transform raw materials or semi-finished products into high value-added products through mechanization, automation or digital technology, and widely serve multiple industries, such as automobiles, electronics, machinery, chemicals, etc. Manufacturing enterprises focus on efficient production processes, precise quality control and supply chain collaboration to meet customer needs and market competition.

[0003] For manufacturing companies, assessing their own operating risks based on highly timely enterprise digital data is a powerful means to avoid the risk of supply and marketing imbalance. However, the existing risk prevention and control system can only conduct risk assessment on unilateral data. It lacks a multi-angle risk prevention and control system and its construction method, and is unable to conduct human and warehousing risk assessments for manufacturing companies at the same time, resulting in poor risk avoidance effects for manufacturing companies. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method and system for constructing a digital risk prevention and control system for an enterprise, which can effectively solve the problem of lack of multi-angle risk prevention and control analysis in the prior art.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a method and system for constructing a digital risk prevention and control system for an enterprise, which at least includes: The human risk identification unit analyzes and calculates the average daily production volume and average daily attendance rate on a single production line, divides the average daily production volume by the average daily attendance rate to obtain the output evaluation value and evaluates the output level of each employee; Analyze the correlation between employee output level and employee length of service, and calculate the resignation risk parameter based on the proportion of employees at each output level, turnover rate, and number of new employees, and select a calculation formula based on the correlation of length of service; The production cycle and cycle production index are preset. The production index completion degree is calculated by combining the average daily production volume of the production line and the resignation risk parameter. The minimum value of the production index completion degree of all production lines is obtained and recorded as the theoretical production value. The human risk parameter is calculated based on the theoretical production value. The warehouse risk identification unit obtains the number of outbound products in the warehouse and calculates the sales ratio, calculates the inventory ratio based on the theoretical daily output value, and constructs an ARIMA model of outbound volume with respect to time based on time series analysis; When the sales ratio is greater than the inventory ratio, the position reference value is calculated based on the difference between the sales ratio and the inventory ratio, and the total outbound quantity is calculated through the ARIMA model. The storage risk index is calculated by combining the position reference value, the total outbound quantity and the total quantity in storage; When the sales ratio is less than the inventory ratio, the full warehouse reference value is calculated based on the difference between the sales ratio, the inventory ratio and the preset full warehouse coefficient, and the warehouse risk index is calculated based on the total outbound quantity and the total quantity in storage.

[0006] This technical solution comprehensively considers the risks of manufacturing enterprises in multiple aspects such as manpower and warehousing, avoids the one-sidedness of risk assessment from a single angle, and thus improves the comprehensiveness and accuracy of risk assessment. Based on the digital data of the enterprise, it can evaluate the risks of manufacturing enterprises in real time, which helps enterprises to timely discover and respond to potential risks and improve the timeliness of risk prevention and control. The risk identification unit can flexibly select calculation formulas and evaluation methods according to different risk indicators and actual conditions, thereby improving the flexibility and applicability of risk assessment. By constructing statistical methods such as ARIMA models, it is possible to more accurately predict and analyze key data such as the company's outbound volume and production index completion, providing a more accurate basis for risk assessment. This technical solution is easy to implement and operate, and is suitable for all types of manufacturing enterprises, which helps enterprises improve their risk management level and enhance their market competitiveness. This technical solution has the advantages of comprehensiveness, real-time, flexibility, accuracy and practicality, which can significantly improve the risk prevention and control capabilities of manufacturing enterprises and provide strong guarantees for the sustainable development of enterprises.

[0007] Furthermore, the average daily production volume is calculated as follows: The employees on the same production line are marked as , where i represents the workstation number of each employee, i=1,2,3,…,j, j is the total number of employees on the production line, and the current daily processing volume, weekly processing volume, and monthly processing volume of the production line are recorded as , get the valid working days in the most recent week and the most recent month and record them as , substitute into the formula The average daily production is calculated .

[0008] Furthermore, the calculation process of the average daily attendance rate is as follows: Record the number of products processed by each employee every day as the daily output, obtain multiple daily outputs and calculate the average to get the output reference value of each employee. Suppose the output reference value of any employee is , let the employee's daily output be , substitute into the formula The calculation is performed in is the preset proportional coefficient, and the employee's single-day attendance index is obtained. ; The sum of the daily attendance index of each employee is calculated to obtain the actual attendance rate. Multiple actual attendance rates are obtained and the average value is calculated to obtain the average daily attendance rate.

[0009] Furthermore, the employee output level evaluation process is as follows: Get the maximum value among the employee output reference values and minimum value , substitute into the formula The range span is calculated in , and the output evaluation value is recorded as ; Substitute into the formula to calculate the primary interval , Intermediate , Advanced Zone , corresponding to output levels one, two and three respectively.

[0010] Furthermore, the calculation process of the resignation risk parameter is as follows: The proportion of employees with different output levels on the same production line is recorded as , where A, B, and C correspond to employees with output levels of level 1, level 2, and level 3, respectively. Set an evaluation cycle and obtain the employee turnover rate of the production line within the evaluation cycle. , obtain the proportion of employees at each output level among the resigned personnel and record them as , , ; When the length of service correlation is less than the preset correlation threshold, substitute into the formula Calculate the risk parameters of resignation ; When the length of service correlation is greater than or equal to the preset correlation threshold, substitute into the formula Calculate the risk parameters of resignation ; in is the preset weight coefficient, Indicates the number of employees admitted during the evaluation period.

[0011] Furthermore, the human risk parameter calculation process is as follows: The production cycle and cycle production indicators are preset to obtain the average daily production volume of each production line and turnover risk parameters , substitute into the formula The production index completion degree corresponding to each production line is calculated ,in is the preset constant coefficient, They represent the production cycle and the evaluation cycle respectively. Indicates that the value is not less than The smallest integer of ; Obtain the minimum value of the production index completion of all production lines and record it as the theoretical production value , substitute into the formula Calculate and obtain the human risk parameters , where e is a natural constant, It is a periodic production indicator.

[0012] Furthermore, the process of calculating the storage risk index based on the sales ratio and the storage ratio is as follows: Calculate the future based on ARIMA model Total outbound volume within the day ; When the sales ratio is greater than the inventory ratio, substitute the formula Calculate in and get the reference value of position ,in Indicates not less than The smallest integer, substituting into the formula The storage risk index is calculated by ; When the sales ratio is less than the inventory ratio, substitute the formula Calculate in and get the full warehouse reference value ,in Indicates not less than The smallest integer, substituting into the formula The storage risk index is calculated by ; in Indicates the total quantity of the current warehouse products. It is the preset full position coefficient.

[0013] Furthermore, it also includes a comprehensive assessment unit, which obtains the storage risk index and human risk parameters and performs normalization processing, and substitutes them into the formula The structured risk index is calculated by ,in is the preset weight coefficient, and are integers and satisfy .

[0014] Furthermore, it also includes a warehouse-out risk prevention and control unit, constructs a warehouse-out product system consisting of different batches of products, and optimizes the proportion of different batches of products in the warehouse-out product system based on the quality feedback data of different batches of products, including the following steps: S1: Record the product currently being analyzed as the target product and obtain the production interval between two adjacent batches of the target product , obtain the warranty period of the target product ,in Indicates the warranty period of the target product; S2: According to the production interval Warranty period Divided into multiple unit periods, expressed as , , …, , where k is a positive integer and , the serial numbers of the unit periods are 1, 2, ..., k+1, and a data set is bound to each unit period , n represents the unit period number corresponding to the set; S3: Products with quality problems are recorded as problem products. After-sales inspection is performed on the problem products and they are marked as quality problems or human problems. When the problem products are marked as quality problems, the sales time and after-sales time of the problem products are obtained and the time difference is calculated. Based on the time difference between the sales time and the after-sales time, they are divided into corresponding data sets; S4: Get the total number of elements in different data sets and substitute them into the formula Calculate in and get the interval distribution coefficient, where: represents the interval distribution coefficient; Representing a collection of data Total amount of internal elements; They represent the right and left endpoints of the unit period numbered n respectively; S5: Get any batch of products and record them as regulated products, get the sales volume and shelf time of the regulated products, calculate the time difference between the shelf time and the current time and record it as the sales time, get the serial number of the unit cycle to which the sales time belongs and record it as q, get the number of defective products in the regulated products and divide it by the sales volume of the regulated products and record it as the defective rate , substitute into the formula Calculate the defect risk value ,in Respectively represent the market share of regulated products; S6: Preset a defective risk threshold, compare the defective risk value of the regulated product with the defective risk threshold, and adjust the proportion of the regulated product in the outbound product system.

[0015] The method for constructing a digital risk prevention and control system for an enterprise is applied to the above-mentioned system for constructing a digital risk prevention and control system for an enterprise, and includes the following steps: Step 1: Establish multiple production lines based on the production process, calculate the average daily production volume and average daily attendance rate of each production line, divide the average daily production volume by the average daily attendance rate to obtain the output evaluation value, and evaluate the output level of each employee based on the output evaluation value; Step 2: Obtain the output level and length of service of different employees to calculate the length of service relevance. A relevant threshold is preset. When the length of service relevance is less than the relevant threshold, the resignation risk parameter of each production line is calculated based on the employee turnover rate, the number of employees admitted, and the proportion of employees at each output level. When the length of service relevance is greater than or equal to the relevant threshold, the resignation risk parameter is further calculated based on the proportion of employees at each output level among the resigned employees. Step 3: Calculate the completion degree of production indicators of each production line based on the resignation risk parameter, obtain the minimum value of the completion degree of production indicators of all production lines as the theoretical production value, analyze and calculate to obtain the human risk parameter, and when the human risk parameter is greater than the preset human risk threshold, perform human resource optimization on the production line corresponding to the theoretical production value; Step 4: Evaluate product storage risk based on product sales data and production data, analyze and calculate the storage risk index, and when the storage risk index is greater than the preset storage risk threshold, adjust the product output of the production line and calculate the enterprise structured risk index in combination with the human risk parameters; Step 5: Construct an outbound product system consisting of different batches of products, adjust the outbound quantity of each batch of products according to the outbound product system, and optimize the proportion of different batches of products in the outbound product system based on the quality feedback data of different batches of products.

[0016] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects: 1. The present invention calculates the human risk parameters by comprehensively considering the turnover rate, the number of new employees and the length of service correlation of employees on different production lines, which can help managers understand whether there is a risk of human resource loss that affects the production indicators of each production line. The larger the human risk parameter, the more hidden dangers of human resource loss there are in one or more of the many production lines, resulting in reduced production efficiency of the entire production process, thereby reminding managers to make timely adjustments to reduce the impact of human resource losses on production efficiency.

[0017] 2. The present invention predicts future outbound volume based on the time series analysis method, and further analyzes and calculates the inventory ratio based on the theoretical production value calculated by the human risk identification unit, compares the sales ratio and the inventory ratio, and selects different methods to calculate the warehousing risk index under different conditions to reflect whether the warehousing level is in a good stable state or whether there is a problem of excessive storage, so as to facilitate the staff to regulate product output and optimize the storage capacity.

[0018] 3. The defective risk value obtained by the present invention by obtaining the interval distribution coefficient and combining it with the defective rate and market share of the regulated product will increase as the quality of the regulated product decreases and the market share increases, thereby indirectly reflecting the degree of influence of a certain batch of target products on the company's product quality reputation. When the defective risk value is larger, it means that the batch of products has obvious quality problems and the impact range is wide, and it is necessary to timely optimize the market share, which is conducive to the staff to reduce the risk impact of each batch of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The present invention will be further described below in conjunction with the embodiments.

[0023] Embodiment 1: See also Figure 1 , the enterprise digital risk prevention and control system construction system shall at least include: The human risk identification unit divides the production process into multiple production processes. Each production process corresponds to a production line. Each production line is equipped with multiple employees to carry out the production work of the production process. Each employee is analyzed. The analysis process is as follows: The employees on the same production line are marked as , where i represents the workstation number of each employee, i=1,2,3,…,j, j is the total number of employees on the production line, and the current daily processing volume, weekly processing volume, and monthly processing volume of the production line (i.e., the number of products processed by the production line in the last full working day, the number of products processed in the last week, and the number of products processed in the last month) are recorded as , get the valid working days in the most recent week and the most recent month and record them as (Effective working days equals the total duration of the cycle minus holidays within the cycle), substitute into the formula The average daily production is calculated ; The number of products processed by each employee every day is recorded as the daily output (that is, the number of products produced on the production line in one day). Multiple daily outputs are obtained and the average is calculated to obtain the output reference value of each employee. Based on the output reference value of each employee, the attendance of each employee is evaluated: Assume that the output reference value of any employee is , let the employee's daily output be , substitute into the formula The calculation is performed in is the preset proportional coefficient, and the employee's single-day attendance index is obtained. (The single-day attendance index reflects the ratio of the employee's actual output to the predetermined output reference value, and is used to evaluate the employee's actual output on that day. It is applicable to situations where employees leave their jobs or take leave, and helps to truly evaluate the daily attendance rate); Calculate the sum of the attendance index of each employee every day to get the actual attendance rate of each day. Obtain multiple actual attendance rates (the actual attendance rates of each day in the last month) and calculate the average daily attendance rate by averaging them. The output evaluation value is calculated by dividing the average daily production by the average daily attendance rate. The output level of each employee is evaluated based on the output evaluation value, where: Get the maximum value of each employee's output reference value and minimum value , substitute into the formula The range span is calculated in , and the output evaluation value is recorded as , substitute into the formula to calculate the primary interval , Intermediate , Advanced Zone , corresponding to output levels one, two and three respectively; Get the interval to which each employee's output level belongs, and mark them as junior employees, mid-level employees, or senior employees (for example, when an employee's output level belongs to the junior interval, mark them as junior employees).

[0024] It should be noted that by evaluating the output level of each employee and marking it, managers can analyze the output capacity of each employee based on data. The higher the output level of an employee, the higher the benefits that the employee can bring to the company's production during the work process. Correspondingly, the higher the level of the employee, the higher the value to the company. The loss of high-value employees will have an impact on the company's human resources and cause the risk of human loss.

[0025] Furthermore, a turnover risk analysis is conducted for each production line, including: The proportion of employees with different output levels on the same production line is recorded as , where A, B, and C correspond to employees with output levels of level one, level two, and level three, respectively; Calculate the average length of service of employees at different output levels and record them as , substitute into the formula Calculate the length of service correlation ; It should be noted that the seniority correlation indirectly reflects the degree of correlation between the product output efficiency and the employee seniority on the production line. When the seniority correlation is less than a certain threshold, it means that the correlation between the employee output efficiency and seniority on the production line is low. When the turnover rate on the production line increases and human resource losses occur, it can be effectively compensated by recruiting and training new employees. Conversely, when the seniority correlation is greater than or equal to a certain threshold, it means that the employee output efficiency on the production line is positively correlated with the employee seniority. Accordingly, when the turnover rate on the production line increases, the output efficiency of the entire production line will be significantly reduced, and this reduction is difficult to compensate by training new employees, and the risk of human resource losses will be higher.

[0026] When the length of service relevance is less than a preset relevance threshold (in a specific embodiment, the value is 1), an evaluation period is set (in a specific embodiment, the evaluation period is 1 quarter), and the employee turnover rate of the production line within the evaluation period is obtained. (The turnover rate is the number of employees who left during the evaluation period divided by the average number of employees during the evaluation period). Substituting this into the formula Calculate the risk parameters of resignation ,in is a preset weight coefficient (in a specific embodiment, The value is 1, 2, or 3). Indicates the number of employees admitted during the evaluation period.

[0027] When the length of service relevance is greater than or equal to a preset relevance threshold (in a specific embodiment, the value is 1), the total number of employees who resigned during the evaluation period is obtained, and the proportion of employees at each output level among the resigned employees is calculated and recorded as , , , substitute it into the formula to calculate and get the resignation risk parameter The specific formula is ,in is a preset weight coefficient (in a specific embodiment, The value is 1).

[0028] It should be noted that the turnover risk parameter indirectly reflects the impact of personnel turnover on different production lines on the production line output efficiency. When the turnover risk parameter is larger, it means that there is a phenomenon of high-output personnel loss on the production line. If the manager does not take corresponding human resource management measures, the output efficiency of the production line will continue to decline, and it will be difficult to compensate by recruiting and training new employees, resulting in irreparable risk of human resource loss.

[0029] Furthermore, we conduct a human risk analysis on the production process: The production cycle and cycle production indicators (i.e. the production indicators that the company needs to complete within the production cycle) are preset. The average daily production volume and resignation risk parameters of each production line are obtained and substituted into the formula The production index completion degree corresponding to each production line is calculated ,in is a preset constant coefficient (in a specific embodiment, the value is 0.5), They represent the production cycle and the evaluation cycle respectively. Indicates that the value is not less than The smallest integer of ; Obtain the minimum value of the production index completion of all production lines and record it as the theoretical production value , substitute into the formula Calculate and obtain the human risk parameters , where e is a natural constant, It is a periodic production indicator.

[0030] It should be noted that by calculating the human risk parameters, managers can understand whether the current production lines have the risk of human resource loss that affects the production indicators of the production lines. The larger the human risk parameters are, it means that one or more of the many production lines have hidden dangers of human resource loss (that is, the turnover rate increases, and it is difficult to adjust the balance by recruiting employees), resulting in a decrease in the production efficiency of the entire production process (usually the production efficiency of each production line will affect each other. When the production efficiency of one of them decreases, the efficiency of the entire production process will decrease), thereby reminding managers to make timely adjustments to reduce the impact of human resource losses on production efficiency.

[0031] The storage risk identification unit evaluates product storage risks based on product sales data and production data, including: Obtain the theoretical production value and divide it by the production cycle to get the theoretical daily output value; The unsold products stored in the warehouse are recorded as storage products, and the sold products are recorded as outbound products. The sales ratio is calculated by dividing the daily outbound product quantity by the storage product quantity. Calculate the theoretical daily output value and divide it by the number of stored products to get the storage ratio , set an analysis period, obtain the number of products shipped out every day in the most recent analysis period as the daily shipment volume, perform time series analysis on the daily shipment volume, and construct an ARIMA model of the daily shipment volume with respect to time; It should be noted that time series analysis is a conventional data processing method in the existing technology. The main steps include data preparation, model selection, model evaluation, prediction and other links. It can give a specific model based on the relationship between data and time, and use the model to predict future data.

[0032] When the sales ratio is greater than the inventory ratio, substitute the formula Calculate in and get the reference value of position ,in Indicates not less than The smallest integer, based on the ARIMA model to calculate the future Total outbound volume within the day , substitute into the formula The storage risk index is calculated by ,in Indicates the total quantity of the current storage products; It should be noted that when the sales ratio is greater than the inventory ratio, the warehousing risk index reflects the ratio between the sales difference and the warehousing inventory in a certain period in the future. This relationship indirectly reflects whether the warehousing level is in a good and stable state, that is, whether the number of warehousing products will increase or decrease due to market fluctuations. Correspondingly, the warehousing risk will increase with the change in the number of warehousing products compared with the benchmark warehousing quantity (in this case, it may cause too much or too little inventory, which is not conducive to warehousing stability).

[0033] When the sales ratio is less than the inventory ratio, a full warehouse coefficient is preset (depending on the upper limit of the warehouse's storage capacity, in a specific embodiment, the value is 1), substitute into the formula Calculate in and get the full warehouse reference value ,in Indicates not less than The smallest integer, based on the ARIMA model to calculate the future Total outbound volume within the day , substitute into the formula The storage risk index is calculated by .

[0034] It should be noted that when the sales ratio is less than the inventory ratio, the warehousing risk index reflects the ratio between the sales difference and the total warehousing capacity in a certain period in the future. This relationship indirectly reflects whether there is a problem of excessive storage at the warehousing level, that is, whether the number of stored products will exceed the upper limit of warehousing capacity due to product backlogs. Accordingly, the warehousing risk will increase as the number of stored products exceeds the normal storage capacity of the warehouse (that is, the general inventory quantity of the warehouse) (in this case, there may be a risk of inventory exceeding the upper limit of warehouse storage).

[0035] The comprehensive assessment unit calculates the enterprise structured risk index based on the warehousing risk index and human risk parameters, where: Obtain the warehouse risk index and human risk parameters, normalize them, and substitute them into the formula The structured risk index is calculated by ,in is the preset weight coefficient, and are integers and satisfy .

[0036] Embodiment 2: Reference Figure 1 Based on the enterprise digital risk prevention and control system construction system in Example 1, in the actual application process, the warehouse inventory and shipment order are carried out in batches, which leads to the market share of products being mainly dominated by products of a certain batch. When a large number of quality problems occur in the products of this batch, the return and exchange rate of the products on sale will increase rapidly, thereby affecting the reputation of the enterprise and causing the risk of unsalable products. It is necessary to optimize the product shipment system.

[0037] Compared with the first embodiment, the present embodiment is different in that it further includes: The outbound risk prevention and control unit builds an outbound product system consisting of different batches of products, and optimizes the proportion of different batches of products in the outbound product system based on the quality feedback data of different batches of products, including the following steps: S1: Record the product currently being analyzed as the target product and obtain the production interval between two adjacent batches of the target product (Generally, it is a fixed period interval. When there are multiple production intervals of different lengths, take the average value) to obtain the warranty period of the target product ,in Indicates the warranty period of the target product; S2: According to the production interval Warranty period Divided into multiple unit periods, expressed as , , …, , where k is a positive integer and , the serial numbers of the unit periods are 1, 2, ..., k+1, and a data set is bound to each unit period , n represents the unit period number corresponding to the set; S3: Products with quality problems are recorded as problem products. After-sales inspection is performed on the problem products and they are marked as quality problems or man-made problems (after-sales inspection is an existing technology used to determine whether the problem of the problem product is caused by man-made factors or the quality of the product itself). When the problem product is marked as a quality problem, the sales time and after-sales time of the problem product (i.e., the time when the problem product enters the after-sales service process) are obtained and the time difference is calculated. Based on the time difference between the sales time and the after-sales time, it is divided into the corresponding data set; S4: Get the total number of elements in different data sets and substitute them into the formula Calculate in and get the interval distribution coefficient, where: represents the interval distribution coefficient; Representing a collection of data Total amount of internal elements; They represent the right and left endpoints of the unit period numbered n respectively; It should be noted that the interval distribution coefficients corresponding to different data sets reflect the distribution relationship between the quality problems of the target product and the time at which they appear. When the interval distribution coefficient is larger, it means that the quality problems of the target product will appear in this time period.

[0038] S5: Get any batch of products and record them as regulated products, get the sales volume and shelf time of the regulated products, calculate the time difference between the shelf time and the current time and record it as the sales time, get the serial number of the unit cycle to which the sales time belongs and record it as q, get the number of defective products in the regulated products and divide it by the sales volume of the regulated products and record it as the defective rate , substitute into the formula Calculate the defect risk value ,in Respectively represent the market share of regulated products (i.e. the proportion of regulated products among all target products on sale); S6: Preset a defective risk threshold. When the defective risk value of the regulated product is greater than the defective risk threshold, reduce the proportion of the regulated products in the outbound product system, and conduct quality inspection on the regulated products in the warehouse. When the defective risk value of the regulated product is less than the defective risk threshold, increase the proportion of the regulated products in the outbound product system. When the defective risk value of the regulated product is equal to the defective risk threshold, maintain the proportion of the regulated products in the outbound product system.

[0039] It should be noted that the defective risk value obtained by comprehensively calculating the interval distribution coefficient and the defective rate and market share of the regulated products will increase as the quality of the regulated products decreases (the defective rate increases) and the market share increases (the impact range is wider), thereby indirectly reflecting the impact of a batch of target products on the company's product quality reputation. The larger the defective risk value, the more obvious quality problems there are in this batch of products and the impact range is wide, and it is necessary to timely optimize the market share to reduce the risk impact of this batch of products.

[0040] The enterprise digital risk prevention and control system construction method proposed based on the enterprise digital risk prevention and control system construction system in the above-mentioned embodiments 1 and 2 includes the following steps: Step 1: Establish multiple production lines based on the production process, calculate the average daily production volume of each production line based on the daily, weekly and monthly processing volumes of the production line, obtain the average daily attendance rate of employees on the same production line, divide the average daily production volume by the average daily attendance rate to calculate the output evaluation value, and evaluate the output level of each employee based on the output evaluation value; Step 2: Obtain the output level and length of service of different employees to calculate the length of service relevance. A relevant threshold is preset. When the length of service relevance is less than the relevant threshold, the resignation risk parameter of each production line is calculated based on the employee turnover rate, the number of employees admitted, and the proportion of employees at each output level. When the length of service relevance is greater than or equal to the relevant threshold, the resignation risk parameter is further calculated based on the proportion of employees at each output level among the resigned employees. Step 3: Calculate the completion degree of production indicators of each production line based on the resignation risk parameter, obtain the minimum value of the completion degree of production indicators of all production lines as the theoretical production value, analyze and calculate to obtain the human risk parameter, and when the human risk parameter is greater than the preset human risk threshold, perform human resource optimization on the production line corresponding to the theoretical production value; Step 4: Evaluate product storage risk based on product sales data and production data, analyze and calculate the storage risk index, and when the storage risk index is greater than the preset storage risk threshold, adjust the product output of the production line and calculate the enterprise structured risk index in combination with the human risk parameters; Step 5: Construct an outbound product system consisting of different batches of products, adjust the outbound quantity of each batch of products according to the outbound product system, and optimize the proportion of different batches of products in the outbound product system based on the quality feedback data of different batches of products.

[0041] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. Enterprise digital risk prevention and control system construction system, characterized by include: The human risk identification unit analyzes and calculates the average daily production volume and average daily attendance rate on a single production line, divides the average daily production volume by the average daily attendance rate to obtain the output evaluation value and evaluates the output level of each employee; analyzes the correlation between the employee's output level and the length of service of the employee, based on the proportion of employees of each output level, the turnover rate and the number of new employees, and selects a calculation formula based on the correlation of length of service to calculate the risk parameter of resignation; presets the production cycle and cycle production indicators, combines the average daily production volume of the production line and the risk parameter of resignation to analyze and calculate the completion of the production indicators, obtains the minimum value of the completion of the production indicators of all production lines as the theoretical production value, and calculates the human risk parameters based on the theoretical production value; The storage risk identification unit obtains the number of outbound products in the storage products and calculates the sales ratio, calculates the storage ratio based on the theoretical daily output value, and constructs an ARIMA model of the outbound quantity with respect to time based on time series analysis; when the sales ratio is greater than the storage ratio, the position reference value is calculated according to the difference between the sales ratio and the storage ratio, and the total outbound quantity is calculated through the ARIMA model. The storage risk index is calculated by combining the position reference value, the total outbound quantity and the total quantity in storage; When the sales ratio is less than the inventory ratio, the full warehouse reference value is calculated based on the difference between the sales ratio, the inventory ratio and the preset full warehouse coefficient, and the warehouse risk index is calculated based on the total outbound quantity and the total quantity in storage.

2. The enterprise digital risk prevention and control system construction system according to claim 1 is characterized in that: The calculation process of daily average production is as follows: The employees on the same production line are marked as , where i represents the workstation number of each employee, i=1,2,3,…,j, j is the total number of employees on the production line, and the current daily processing volume, weekly processing volume, and monthly processing volume of the production line are recorded as , get the valid working days in the most recent week and the most recent month and record them as , substitute into the formula The average daily production is calculated .

3. The enterprise digital risk prevention and control system construction system according to claim 2 is characterized in that: The calculation process of daily average attendance rate is as follows: Record the number of products processed by each employee every day as the daily output, obtain multiple daily outputs and calculate the average to get the output reference value of each employee. Suppose the output reference value of any employee is , let the employee's daily output be , substitute into the formula The calculation is performed in is the preset proportional coefficient, and the employee's single-day attendance index is obtained. ; The sum of the daily attendance index of each employee is calculated to obtain the actual attendance rate. Multiple actual attendance rates are obtained and the average value is calculated to obtain the average daily attendance rate.

4. The enterprise digital risk prevention and control system construction system according to claim 3 is characterized in that: The employee output rating evaluation process is as follows: Get the maximum value among the employee output reference values and minimum value , substitute into the formula The range span is calculated in , and the output evaluation value is recorded as ; Substitute into the formula to calculate the primary interval , Intermediate , Advanced Zone , corresponding to output levels one, two and three respectively.

5. The enterprise digital risk prevention and control system construction system according to claim 2 is characterized in that: The calculation process of the resignation risk parameter is as follows: The proportion of employees with different output levels on the same production line is recorded as , where A, B, and C correspond to employees with output levels of level 1, level 2, and level 3, respectively. Set an evaluation cycle and obtain the employee turnover rate of the production line within the evaluation cycle. , obtain the proportion of employees at each output level among the resigned personnel and record them as , , ; When the length of service correlation is less than the preset correlation threshold, substitute into the formula Calculate the risk parameter of resignation ; When the length of service correlation is greater than or equal to the preset correlation threshold, substitute into the formula Calculate the risk parameters of resignation ; in is the preset weight coefficient, Indicates the number of employees admitted during the evaluation period.

6. The enterprise digital risk prevention and control system construction system according to claim 1 is characterized in that: The calculation process of human risk parameters is as follows: The production cycle and cycle production indicators are preset to obtain the average daily production volume of each production line and turnover risk parameters , substitute into the formula The production index completion degree corresponding to each production line is calculated ,in is the preset constant coefficient, They represent the production cycle and the evaluation cycle respectively. Indicates that the value is not less than The smallest integer of ; Obtain the minimum value of the production index completion of all production lines and record it as the theoretical production value , substitute into the formula Calculate and obtain the human risk parameters , where e is a natural constant, It is a periodic production indicator.

7. The enterprise digital risk prevention and control system construction system according to claim 1 is characterized in that: The process of calculating the storage risk index based on the sales ratio and storage ratio is as follows: Calculate the future based on ARIMA model Total outbound volume within the day ; When the sales ratio is greater than the inventory ratio, substitute the formula Calculate in and get the reference value of position ,in Indicates not less than The smallest integer, substituting into the formula The storage risk index is calculated by ; When the sales ratio is less than the inventory ratio, substitute the formula Calculate in and get the full warehouse reference value ,in Indicates not less than The smallest integer, substituting into the formula The storage risk index is calculated by ; in Indicates the total quantity of the current warehouse products. It is the preset full position coefficient.

8. The enterprise digital risk prevention and control system construction system according to claim 1 is characterized in that: It also includes a comprehensive assessment unit to obtain the storage risk index and human risk parameters and normalize them, and substitute them into the formula The structured risk index is calculated by ,in is the preset weight coefficient, and are integers and satisfy .

9. The enterprise digital risk prevention and control system construction system according to claim 1 is characterized in that: It also includes an outbound risk prevention and control unit, which builds an outbound product system consisting of different batches of products, and optimizes the proportion of different batches of products in the outbound product system based on the quality feedback data of different batches of products, including the following steps: S1: Record the product currently being analyzed as the target product and obtain the production interval between two adjacent batches of the target product , obtain the warranty period of the target product ,in Indicates the warranty period of the target product; S2: According to the production interval Warranty period Divided into multiple unit periods, expressed as , , …, , where k is a positive integer and , the serial numbers of the unit periods are 1, 2, ..., k+1, and a data set is bound to each unit period , n represents the unit period number corresponding to the set; S3: Products with quality problems are recorded as problem products. After-sales inspection is performed on the problem products and they are marked as quality problems or human problems. When the problem products are marked as quality problems, the sales time and after-sales time of the problem products are obtained and the time difference is calculated. Based on the time difference between the sales time and the after-sales time, they are divided into corresponding data sets; S4: Get the total number of elements in different data sets and substitute them into the formula Calculate in and get the interval distribution coefficient, where: represents the interval distribution coefficient; Representing a collection of data Total amount of internal elements; They represent the right and left endpoints of the unit period numbered n respectively; S5: Get any batch of products and record them as regulated products, get the sales volume and shelf time of the regulated products, calculate the time difference between the shelf time and the current time and record it as the sales time, get the serial number of the unit cycle to which the sales time belongs and record it as q, get the number of defective products in the regulated products and divide it by the sales volume of the regulated products and record it as the defective rate , substitute into the formula Calculate the defect risk value ,in Respectively represent the market share of regulated products; S6: Preset a defective risk threshold, compare the defective risk value of the regulated product with the defective risk threshold, and adjust the proportion of the regulated product in the outbound product system.

10. A method for constructing a digital enterprise risk prevention and control system, applied to the system for constructing a digital enterprise risk prevention and control system according to claims 1-9, characterized in that: The following steps are involved: Step 1: Establish multiple production lines based on the production process, calculate the average daily production volume and average daily attendance rate of each production line, divide the average daily production volume by the average daily attendance rate to obtain the output evaluation value, and evaluate the output level of each employee based on the output evaluation value; Step 2: Obtain the output level and length of service of different employees to calculate the length of service relevance. A relevant threshold is preset. When the length of service relevance is less than the relevant threshold, the resignation risk parameter of each production line is calculated based on the employee turnover rate, the number of employees admitted, and the proportion of employees at each output level. When the length of service relevance is greater than or equal to the relevant threshold, the resignation risk parameter is further calculated based on the proportion of employees at each output level among the resigned employees. Step 3: Calculate the completion degree of production indicators of each production line based on the resignation risk parameter, obtain the minimum value of the completion degree of production indicators of all production lines as the theoretical production value, analyze and calculate to obtain the human risk parameter, and when the human risk parameter is greater than the preset human risk threshold, perform human resource optimization on the production line corresponding to the theoretical production value; Step 4: Evaluate product storage risk based on product sales data and production data, analyze and calculate the storage risk index, and when the storage risk index is greater than the preset storage risk threshold, adjust the product output of the production line and calculate the enterprise structured risk index in combination with the human risk parameters; Step 5: Construct an outbound product system consisting of products from different batches, adjust the outbound quantity of each batch of products according to the outbound product system, and optimize the proportion of products from different batches in the outbound product system based on the quality feedback data of products from different batches.

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