Plastic raw material supply chain data filing method and system

Abstract

The invention discloses a plastic raw material supply chain data filing method and system, relates to the technical field of data processing, and solves the problem that in an existing management system, workers do not have strict obligation to check and verify the accuracy of data in the data filing process, so that the working efficiency is greatly improved. In order to solve the problem of careless omission during manual input due to the fact that abnormal key indexes are subjected to secondary proofreading, the possibility of data omission and manual operation errors is greatly reduced, and the accuracy of recorded data is further ensured; the accuracy and reliability of data are greatly improved through a layer-by-layer analysis mechanism, the problem of careless omission of workers during data filing caused by wrong data is also greatly avoided, meanwhile, the system automatically conducts judgment according to historical data, preliminary screening and judgment of the data can be completed without manual intervention, and the efficiency is improved. And the manual intervention frequency in daily operation is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for filing data of a plastic raw material supply chain. Background Art

[0002] Efficient management of the plastic raw material supply chain is crucial to the production and operation of plastic manufacturing companies. In the existing supply chain management system, companies usually use various information tools, such as ERP (Enterprise Resource Planning System), WMS (Warehouse Management System) and other software for management. These systems can help companies track and manage data from the purchase of raw materials to the sale of finished products.

[0003] In the current plastic raw material supply chain management, various departments within the enterprise will generate different types of data, including procurement data, production data, inventory data, transportation data, and sales data. These data are critical to the normal operation of the supply chain and decision support. However, in the existing management system, although each department is responsible for data collection and recording, when entering data for filing, the staff is not strictly obliged to check and verify the accuracy of the data. This situation mainly occurs when the data is complicated, the information flow is not smooth, or the system's automatic verification function is insufficient, resulting in omissions during manual entry;

[0004] Therefore, the occurrence of various data filing errors in the plastic raw material supply chain often has a negative impact on the company's supply chain efficiency, customer service and operating costs. These errors sometimes lead to inventory backlogs, production delays, order losses and even customer loss, causing huge economic losses to the company. Therefore, how to ensure the accuracy of data filing and how to establish an efficient data filing and verification mechanism have become issues that need to be urgently addressed in the current plastic raw material supply chain management. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a plastic raw material supply chain data filing method and system, which solves the problem of omissions in manual data entry due to the fact that in the existing management system, the staff has no strict obligation to check and verify the accuracy of the data during the data filing process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A plastic raw material supply chain data filing method, comprising:

[0007] Step 1: Take the plastic raw material company as the target company, and classify the data of plastic raw materials according to the company's supply chain structure, and then determine the key indicators of each data type; where each structure in each supply chain corresponds to one type of data;

[0008] Step 2: Collect and summarize the key indicators of each type of data, analyze and judge the key indicators of each type of data collected, and determine the abnormal key indicators in each type of data collected this time;

[0009] Step 3: Perform a secondary determination on the abnormal key indicator in step 2 to determine whether it is an erroneous key indicator;

[0010] Step 4: Identify all incorrect key indicators, extract the specific values ​​of the incorrect key indicators, and conduct manual proofreading with the staff of the corresponding supply chain department;

[0011] Step 5: Record the specific data of normal key indicators and key indicators after manual proofreading.

[0012] Preferably, in step 1, the key indicator of each data type is preset, and the key indicator of each data type is one or more.

[0013] Preferably, in step 2, the method of determining the abnormal key indicator is:

[0014] S1: Select key indicators as analysis indicators in turn;

[0015] S2: After determining the analysis index, obtain the specific value Hs of the analysis index collected this time;

[0016] S3: Then obtain the specific value h collected by the analysis indicator for the previous n times i , where i=1, ..., n, and n is a preset value;

[0017] S4: Get n sets of values ​​h i After that, calculate the average value Hp, and calculate the corresponding n groups of values ​​h by the formula i The specific value of the floating value G is determined by the following formula:

[0018]

[0019] Then the floating value G is judged. When G exceeds B1, the value h of group n is i Delete and recalculate the G value until the G value does not exceed the B1 value, and delete h i The number of determines the judgment value Hped;

[0020] S5: Obtain the specific value Hs and the evaluation value Hped of the analysis index collected in step S2 and calculate the size of |Hs-Hped|, and determine the key index of the abnormality according to the size of |Hs-Hped|;

[0021] S6: Through the process of steps S1-S5, determine the abnormal key indicators among the key indicators of each type of data.

[0022] Preferably, in step S4, the value h i The specific method of deletion is:

[0023] Follow |h i -Hp|Get the corresponding h in order from large to small i Value, each time you get an h i When the value is i The value of is deleted, and after deletion, the remaining h is recalculated i G value, and re-compared with B1, when G value also exceeds U value, according to |h i -Hp|Select the next h in descending order i The value is deleted and the G value is recalculated until the G value does not exceed the B1 value, where B1 is the preset value.

[0024] Preferably, in step S4, according to deleting h i The specific method for determining the evaluation value is as follows:

[0025] Confirm to delete h i The specific number of values ​​d, calculate the ratio Q of d to n, when Q does not exceed the preset value B2, calculate nd h i The average value of the values ​​is taken as the judgment value Hped;

[0026] When Q exceeds the preset value B2, calculate the d h to be deleted i The average value is taken as the judgment value Hped;

[0027] Among them, B2 is the preset value.

[0028] Preferably, in step S5, the specific method of determining the critical indicator of the abnormality according to the size of |Hs-Hped| is:

[0029] When |Hs-Hped|≤g, it means that the specific value Hs of the analysis indicator collected this time is within a reasonable range;

[0030] When |Hs-Hped|>g, it means that the specific value Hs of the analysis indicator collected this time is abnormal, and the analysis indicator is marked as an abnormal key indicator;

[0031] Among them, g is the size of the error range.

[0032] Preferably, in step 3, the specific method of determining whether it is an error key indicator is:

[0033] P1: Obtain abnormal key indicators in sequence as judgment indicators;

[0034] P2: Then obtain the specific value x of the previous n times of the judgment indicator i ;

[0035] P3: According to the specific value x i The i-th moving average MA is obtained by the following formula i ,

[0036]

[0037] Among them, MA i is the moving average of the i-th item, k is the size of the moving window, x i-j is the real data of the ijth;

[0038] P4: Based on the specific value x collected this time i+1 , the moving average MA is predicted by the formula in step P3 i+1 ;

[0039] Then calculate C i =|x i -MA i |The results for C i Sort from largest to smallest, and put C at the first place i As the judgment value R; at the same time, calculate the specific value x collected this time i+1 And this moving average MA i+1 The difference Cs = |x i+1 -MA i+1 |;

[0040] P5: Compare Cs with the judgment value R to determine the critical indicator of the error.

[0041] Preferably, in step P5, Cs is compared with the determination value R in the following manner:

[0042] If Cs>R, the judgment indicator is determined to be an incorrect key indicator;

[0043] If Cs≤R, the judgment indicator is determined to be a normal key indicator.

[0044] A plastic raw material supply chain data filing system, comprising:

[0045] The key indicator determination module is used to classify the data of plastic raw materials according to the company's supply chain composition structure, and then determine the key indicators of each data type;

[0046] The abnormal key indicator analysis module is used to collect and summarize the key indicators of each type of data, analyze and judge the key indicators of each type of data collected, and determine the abnormal key indicators of each type of data in this collection;

[0047] An error key indicator determination module is used to perform a secondary determination on the abnormal key indicator to determine whether it is an error key indicator;

[0048] The manual proofreading module is used to extract the specific values ​​of the key indicators of the errors and conduct manual proofreading with the staff of the corresponding supply chain link department;

[0049] The data filing and entry module is used to file the specific data of normal key indicators and key indicators after manual proofreading.

[0050] The present invention provides a plastic raw material supply chain data filing method and system. Compared with the prior art, it has the following beneficial effects:

[0051] (1) The present invention significantly improves the accuracy of data filing by clarifying the collection and analysis process of key indicators; through the classification and collection of key indicators in the first step, the data in the supply chain of the plastic raw material company is classified and processed according to departments, and the procurement, production, inventory, transportation and sales data generated by each department have corresponding key indicators; this classification method not only improves the accuracy of data management, but also avoids the risk of data confusion; by setting parameters such as floating value G and preset value B1 to detect and correct data anomalies, the correctness of the data is ensured;

[0052] (2) The present invention greatly reduces the possibility of data omission and manual operation errors by performing secondary proofreading on abnormal key indicators, further ensuring the accuracy of the filed data; the layer-by-layer analysis mechanism greatly improves the accuracy and reliability of the data, and also greatly avoids the problem of omissions caused by staff in data filing due to erroneous data;

[0053] (3) When data anomalies occur, the system automatically makes judgments based on historical data without human intervention to complete preliminary screening and judgment of the data. At the same time, after confirming the key indicators of the error, the manual proofreading module will be called for human intervention, which reduces the frequency of manual intervention in daily operations. Compared with traditional methods, this system not only shortens the data processing time, but also reduces the complexity of operations and the probability of errors, thereby effectively saving operating costs and improving the management efficiency of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described below in conjunction with the accompanying drawings.

[0055] Figure 1A method flow chart of a plastic raw material supply chain data filing method of the present invention;

[0056] Figure 2 This is a structural framework diagram of a plastic raw material supply chain data filing system of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0058] Embodiment 1

[0059] See also Figure 1 , the present invention provides a plastic raw material supply chain data filing method, including;

[0060] Step 1: Take the plastic raw material company as the target company, and classify the data of plastic raw materials according to the company's supply chain structure, and then determine the key indicators of each data type; where each structure in each supply chain corresponds to one type of data;

[0061] Specifically, the key indicators of each data type are determined by professional staff, and each data type may have one or more key indicators;

[0062] For example, the key indicators of procurement data can be: the number of plastic raw material purchase orders and the plastic raw material purchase cost per piece;

[0063] It should be noted that the supply chain structure is generally composed of various departments in the supply chain, including the procurement department, production department, logistics department and sales department. When the corresponding plastic raw material data is classified, it is divided into different data categories according to different departments, including procurement data, production data, inventory data and sales data.

[0064] Step 2: Collect and summarize the key indicators of each type of data, analyze and judge the key indicators of each type of data collected, and determine the abnormal key indicators in each type of data collected this time;

[0065] The key indicators of abnormality are determined as follows:

[0066] S1: Select key indicators as analysis indicators in turn;

[0067] S2: After determining the analysis index, obtain the specific value Hs of the analysis index collected this time;

[0068] S3: Then obtain the specific value h collected by the analysis indicator for the previous n times i , where i=1, ..., n, n is a preset value, and the specific parameter value is set by professional staff. In this embodiment, n is 60;

[0069] S4: Get n sets of values ​​h i After that, calculate the average value Hp, and calculate the corresponding n groups of values ​​h by the formula i The specific value of the floating value G is determined by the following formula:

[0070]

[0071] Then the floating value G is judged. When G exceeds B1, the value h of group n is i Delete and recalculate the G value until the G value does not exceed the B1 value, and delete h i The number of determines the judgment value;

[0072] The specific method of deleting values ​​is as follows:

[0073] Follow |h i -Hp|Get the corresponding h in order from large to small i Value, each time you get an h i When the value is i The value of is deleted, and after deletion, the remaining h is recalculated i G value, and re-compared with B1, when G value also exceeds U value, according to |h i -Hp|Select the next h in descending order i The value is deleted and the G value is recalculated until the G value does not exceed the B1 value, where B1 is the preset value and the specific parameters are set by professional staff;

[0074] According to delete h i The specific method of determining the evaluation value is as follows:

[0075] Confirm to delete h i The specific number of values ​​d, calculate the ratio Q of d to n, when Q does not exceed the preset value B2, calculate nd h i The average value of the values ​​is taken as the judgment value Hped;

[0076] When Q exceeds the preset value B2, calculate the d h to be deleted i The average value is taken as the judgment value Hped;

[0077] Among them, B2 is the preset value, and the specific parameters are set by professional staff;

[0078] S5: Obtain the specific value Hs of the analysis indicator collected in step S2 and the evaluation value Hped to calculate the value of |Hs-Hped|. When |Hs-Hped|≤g, it means that the specific value Hs of the analysis indicator collected this time is within a reasonable range;

[0079] When |Hs-Hped|>g, it means that the specific value Hs of the analysis indicator collected this time is abnormal, and the analysis indicator is marked as an abnormal key indicator;

[0080] Among them, g is the size of the error range, and the specific parameters are set by professionals;

[0081] S6: Determine abnormal key indicators among the key indicators of each type of data through the process of steps S1-S5;

[0082] By clarifying the collection and analysis process of key indicators, the accuracy of data filing is significantly improved; through the classification and collection of key indicators in the first step, the system classifies the data in the plastic raw material company's supply chain by department, and the procurement, production, inventory, transportation and sales data generated by each department have corresponding key indicators; this classification method not only improves the accuracy of data management, but also avoids the risk of data confusion; by setting parameters such as floating value G and preset value B1 to detect and correct data anomalies, the correctness of the data is ensured;

[0083] Step 3: Perform a secondary determination on the abnormal key indicator in step 2 to determine whether it is an erroneous key indicator;

[0084] The specific method to determine whether it is a key indicator of error is:

[0085] P1: Obtain abnormal key indicators in sequence as judgment indicators;

[0086] P2: Then obtain the specific value x of the previous n times of the judgment indicator i ;

[0087] It should be noted that h i Represents the specific data of the analysis indicator, and x i To determine the specific data of the indicator, the two have different meanings, but the expression methods are similar, and the total amount n remains unchanged;

[0088] P3: According to the specific value x i The i-th moving average MA is obtained by the following formula i ,

[0089]

[0090] Among them, MA iis the moving average of the ith item, k is the size of the moving window, and the specific parameters are determined by professional staff. In this embodiment, k is 3, x i-j is the real data of the ijth;

[0091] P4: Based on the specific value x collected this time i+1 , the moving average MA is predicted by the formula in step P3 i+1 ;

[0092] Then calculate C i =|x i -MA i |The results for C i Sort from largest to smallest, and put C at the first place i As the judgment value R; at the same time, calculate the specific value x collected this time i+1 And this moving average MA i+1 The difference Cs = |x i+1 -MA i+1 |;

[0093] P5: Compare Cs with the judgment value R:

[0094] If Cs>R, the judgment indicator is determined to be an incorrect key indicator;

[0095] If Cs≤R, the judgment indicator is determined to be a normal key indicator;

[0096] For example, the key indicator of procurement data is the number of plastic raw material purchase orders. Set n = 10. The number of purchase orders for the past 10 times is shown in the following table:

[0097] Number of times (i) Order quantity (xi) 1 100 2 105 3 102 4 108 5 110 6 112 7 115 8 117 9 120 10 125

[0098] The specific value collected this time is 140, and the moving average MA is determined by the formula i+1 is 128.33, and then the i-th moving average MA is calculated by the formula i The calculation results are shown in the following table:

[0099] Number of times (i) Order quantity (xi) <![CDATA[Moving Average (MA i )]]> 1 100 - 2 105 - 3 102 102.33 4 108 105.00 5 110 106.67 6 112 110.00 7 115 112.33 8 117 114.67 9 120 117.33 10 125 120.67

[0100] Through the above table, the judgment value R is determined to be 4.33;

[0101] Cs is 11.67, and compared with the judgment value R, it is determined that the judgment indicator is an incorrect key indicator;

[0102] By rechecking abnormal key indicators, the possibility of data omissions and manual operation errors is greatly reduced, further ensuring the accuracy of the recorded data; the layer-by-layer analysis mechanism greatly improves the accuracy and reliability of the data, and also greatly avoids the problem of omissions caused by staff in data recording due to erroneous data;

[0103] Step 4: Identify all incorrect key indicators and extract the specific values ​​of the incorrect key indicators. Manually check with the staff of the corresponding supply chain department to eliminate the errors in the work;

[0104] Step 5: The staff will record the specific data of normal key indicators and key indicators after manual proofreading;

[0105] Embodiment 2

[0106] See also Figure 2 In the specific implementation process, this embodiment is based on the first embodiment and is different from the first embodiment in that this embodiment also includes a plastic raw material supply chain data filing system;

[0107] The key indicator determination module is used to classify the data of plastic raw materials according to the company's supply chain composition structure, and then determine the key indicators of each data type;

[0108] The abnormal key indicator analysis module is used to collect and summarize the key indicators of each type of data, analyze and judge the key indicators of each type of data collected, and determine the abnormal key indicators of each type of data in this collection;

[0109] An error key indicator determination module is used to perform a secondary determination on the abnormal key indicator to determine whether it is an error key indicator;

[0110] The manual proofreading module is used to extract the specific values ​​of the key indicators of the errors and conduct manual proofreading with the staff of the corresponding supply chain link department;

[0111] The data filing and entry module is used to file the specific data of normal key indicators and key indicators after manual proofreading;

[0112] When data anomalies occur, the system automatically makes judgments based on historical data without manual intervention to complete preliminary screening and judgment of data. At the same time, after confirming the key indicators of errors, the manual proofreading module will be called for manual intervention, which reduces the frequency of manual intervention in daily operations. Compared with traditional methods, this system not only shortens the data processing time, but also reduces the complexity of operations and the probability of errors.

[0113] Embodiment 3

[0114] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0115] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0116] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for filing data of a plastic raw material supply chain, characterized in that: include: Step 1: Take the plastic raw material company as the target company, and classify the data of plastic raw materials according to the company's supply chain structure, and then determine the key indicators of each data type; where each structure in each supply chain corresponds to one type of data; Step 2: Collect and summarize the key indicators of each type of data, analyze and judge the key indicators of each type of data collected, and determine the abnormal key indicators in each type of data collected this time; Step 3: Perform a secondary determination on the abnormal key indicator in step 2 to determine whether it is an erroneous key indicator; Step 4: Identify all incorrect key indicators, extract the specific values ​​of the incorrect key indicators, and conduct manual proofreading with the staff of the corresponding supply chain department; Step 5: Record the specific data of normal key indicators and key indicators after manual proofreading.

2. A plastic raw material supply chain data filing method according to claim 1, characterized in that: In step 1, the key indicator of each data type is preset, and the key indicator of each data type is one or more.

3. A plastic raw material supply chain data filing method according to claim 2, characterized in that: In step 2, the key indicators of the anomaly are determined as follows: S1: Select key indicators as analysis indicators in turn; S2: After determining the analysis index, obtain the specific value Hs of the analysis index collected this time; S3: Then obtain the specific value h collected by the analysis indicator for the previous n times i , where i=1, ..., n, and n is a preset value; S4: Get n sets of values ​​h i After that, calculate the average value Hp, and calculate the corresponding n groups of values ​​h by the formula i The specific value of the floating value G is determined by the following formula: Then the floating value G is judged. When G exceeds B1, the value h of group n is i Delete and recalculate the G value until the G value does not exceed the B1 value, and delete h i The number of determines the judgment value Hped; S5: Obtain the specific value Hs and the evaluation value Hped of the analysis index collected in step S2 and calculate the size of |Hs-Hped|, and determine the key index of the abnormality according to the size of |Hs-Hped|; S6: Through the process of steps S1-S5, determine the abnormal key indicators among the key indicators of each type of data.

4. A plastic raw material supply chain data filing method according to claim 3, characterized in that: In step S4, the value h i The specific method of deletion is: Follow |h i -Hp|Get the corresponding h in order from large to small i Value, each time you get an h i When the value is i The value of is deleted, and after deletion, the remaining h is recalculated i G value, and re-compared with B1, when G value also exceeds U value, according to |h i -Hp|Select the next h in descending order i The value is deleted and the G value is recalculated until the G value does not exceed the B1 value, where B1 is the preset value.

5. A plastic raw material supply chain data filing method according to claim 4, characterized in that: In step S4, according to the deletion of h i The specific method for determining the evaluation value is as follows: Confirm to delete h i The specific number of values ​​d, calculate the ratio Q of d to n, when Q does not exceed the preset value B2, calculate nd h i The average value of the values ​​is taken as the judgment value Hped; When Q exceeds the preset value B2, calculate the d h to be deleted i The average value is taken as the judgment value Hped; Among them, B2 is the preset value.

6. A plastic raw material supply chain data filing method according to claim 5, characterized in that: In step S5, the specific method of determining the critical indicator of the abnormality according to the size of |Hs-Hped| is: When |Hs-Hped|≤g, it means that the specific value Hs of the analysis indicator collected this time is within a reasonable range; When |Hs-Hped|>g, it means that the specific value Hs of the analysis indicator collected this time is abnormal, and the analysis indicator is marked as an abnormal key indicator; Among them, g is the size of the error range.

7. A plastic raw material supply chain data filing method according to claim 1, characterized in that: In step 3, the specific method of determining whether the key indicator is an error is: P1: Obtain abnormal key indicators in sequence as judgment indicators; P2: Then obtain the specific value x of the previous n times of the judgment indicator i ; P3: According to the specific value x i The i-th moving average MA is obtained by the following formula i , Among them, MA i is the moving average of the i-th item, k is the size of the moving window, x i-j is the real data of the ijth; P4: Based on the specific value x collected this time i+1 , the moving average MA is predicted by the formula in step P3 i+1 ; Then calculate C i =|x i -MA i |The results for C i Sort from largest to smallest, and put C at the first place i As the judgment value R; at the same time, calculate the specific value x collected this time i+1 And this moving average MA i+1 The difference Cs = |x i+1 -MA i+1 |; P5: Compare Cs with the judgment value R to determine the critical indicator of the error.

8. A plastic raw material supply chain data filing method according to claim 7, characterized in that: In step P5, Cs is compared with the determination value R as follows: If Cs>R, the judgment indicator is determined to be an incorrect key indicator; If Cs≤R, the judgment indicator is determined to be a normal key indicator.

9. A plastic raw material supply chain data filing system, characterized in that: include: The key indicator determination module is used to classify the data of plastic raw materials according to the company's supply chain composition structure, and then determine the key indicators of each data type; The abnormal key indicator analysis module is used to collect and summarize the key indicators of each type of data, analyze and judge the key indicators of each type of data collected, and determine the abnormal key indicators of each type of data in this collection; An error key indicator determination module is used to perform a secondary determination on the abnormal key indicator to determine whether it is an error key indicator; The manual proofreading module is used to extract the specific values ​​of the key indicators of the errors and conduct manual proofreading with the staff of the corresponding supply chain link department; The data filing and entry module is used to file the specific data of normal key indicators and key indicators after manual proofreading.

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