Method for improving production supply efficiency based on dynamic MRP model algorithm

Abstract

The invention relates to the technical field of workflow data processing, in particular to a method for improving production supply efficiency based on a dynamic MRP model algorithm. According to the method for improving the production supply efficiency based on the dynamic MRP model algorithm, a latest plan data result is obtained through a material quantity auxiliary model, calculation of a BOM list based on a general MRP algorithm and loop iteration; when the enterprise manual operation data exceed the obtained latest plan data threshold value range, recording the error value data of the enterprise manual operation exceeding the threshold value to a threshold value exceeding result; the error correction auxiliary model feeds back a threshold exceeding result to the Internet of Things equipment for error correction; and after actual execution, eliminating error value data, updating the acquired data, and continuously outputting a corrected MRP result. According to the method for improving the production supply efficiency based on the dynamic MRP model algorithm, dynamic change factors in production supply are integrated, an MRP result is simulated and iteratively optimized, a supply and demand plan is automatically simulated, fed back and updated, MRP calculation steps are counterfeited, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain, and in particular to a method for improving production supply efficiency based on a dynamic MRP model algorithm. Background Art

[0002] Material Requirements Planning (MRP) calculation is a management technology that is based on sales forecasts and orders, using information such as bill of materials, inventory data and master production schedule to calculate the required quantities and time of various materials.

[0003] However, the current common processing methods only consider inherent data such as inventory and plans, and do not take into account data such as plans and production capacity that change dynamically and are frequently adjusted. In addition, there is no feedback on subsequent execution results and error updates after the calculation, resulting in frequent manual recalculation and release, which is time-consuming and labor-intensive, and results in frequent errors.

[0004] How to integrate the dynamic operation data of each business node in real time based on MRP and perform automatic error correction is an issue that needs to be addressed urgently.

[0005] Based on this, the present invention proposes a method for improving production supply efficiency based on a dynamic MRP model algorithm. Summary of the invention

[0006] In order to make up for the defects of the prior art, the present invention provides a simple and efficient method for improving production supply efficiency based on a dynamic MRP model algorithm.

[0007] The present invention is achieved through the following technical solutions:

[0008] A method for improving production supply efficiency based on a dynamic MRP model algorithm, characterized in that it includes the following steps:

[0009] Step S1: Classify the collected MRP input parameters into three categories: third-party external data, enterprise manual operation data, and IoT device data;

[0010] In step S1, the third-party external data includes upstream supplier data and downstream distributor data;

[0011] The manual operation data of an enterprise includes its own production line data, warehousing data and logistics data;

[0012] IoT device data includes operational data input by the device, such as automatic RFID codes, delivery codes, etc.

[0013] Step S2: Establish an independent model based on third-party external data as a material quantity auxiliary model;

[0014] The material quantity auxiliary model takes the enterprise as the central node, obtains the current and future material supply quantities of the upstream as the input flow, and obtains the current and future material production and consumption quantities of the downstream as the output flow.

[0015] Step S3: Calculate the BOM (Bill of Materials) list based on the general MRP algorithm through the material quantity auxiliary model to obtain the input and output detailed material quantities of the enterprise's production and sales plan, and record them as the plan data result A;

[0016] Step S4: Based on the production and sales plan cycle of the enterprise, the calculation logic in step S3 is iterated in a loop, and the plan data result A is updated synchronously as the data based on the latest plan;

[0017] Step S5, establishing a trigger for the enterprise manual operation data collected in step S1, and customizing the latest plan data threshold in the trigger according to the latest plan data result A in step S4; the trigger condition is when the enterprise manual operation data exceeds the threshold range of the latest plan data obtained; after the trigger, the error value data of the enterprise manual operation exceeding the threshold is recorded in the exceeding threshold result B;

[0018] The latest plan data threshold is customized by the user according to the latest plan data result A. For example, the upper and lower thresholds of the latest plan data result A are 80% to 120% respectively.

[0019] Step S6: establishing an independent model based on the IoT device data collected in step S1 as an auxiliary model for error correction;

[0020] The error correction auxiliary model is used to feed back the over-threshold result B to the IoT device, and perform error correction on the over-threshold result B in step S5;

[0021] The processing logic of the error correction auxiliary model is: determine whether the error value data of the enterprise manual operation exceeding the threshold recorded in the exceeding threshold result B is positive or negative:

[0022] If it is a positive value, it means that the current enterprise manual operation data exceeds the threshold of the latest planned data, and the excess result is fed back to the IoT device, issuing a job delay notification;

[0023] If it is a negative value, it means that the current manual operation data of the enterprise is lower than the threshold of the latest planned data. The shortage result will be fed back to the IoT device and a replenishment notification will be issued.

[0024] Step S7: Apply IoT devices according to the feedback results of the error correction auxiliary model. After actual execution, eliminate the corresponding error value data in the queue in the over-threshold result B, and update the third-party external data, enterprise manual operation data and IoT device data;

[0025] Step S8, jump to step S4, and loop steps S4 to S7 to continuously output the corrected MRP results.

[0026] A system for improving production supply efficiency based on a dynamic MRP model algorithm, comprising:

[0027] The data collection and classification module is responsible for collecting data and classifying the collected MRP input parameters into three categories: third-party external data, enterprise manual operation data, and IoT device data;

[0028] The third-party external data includes upstream supplier data and downstream distributor data;

[0029] The enterprise manual operation data includes its own production line data, warehousing data and logistics data;

[0030] The IoT device data includes operation data input by the device, such as automatic RFID codes, delivery codes, etc.

[0031] The material quantity auxiliary model building module is responsible for building an independent model based on third-party external data as a material quantity auxiliary model;

[0032] The material quantity auxiliary model takes the enterprise as the central node, obtains the current and future material supply quantities of the upstream as the input flow, and obtains the current and future material production and consumption quantities of the downstream as the output flow.

[0033] The planning data calculation module is responsible for calculating the BOM (Bill of Materials) list through the material quantity auxiliary model based on the general MRP algorithm, obtaining the input and output detailed material quantities of the enterprise's production and sales plan, and recording them as planning data result A;

[0034] Based on the production and sales planning cycle of the enterprise, the above calculation logic is iterated and the planning data result A is updated synchronously as the data based on the latest plan;

[0035] The trigger module is used to customize the latest plan data threshold according to the latest plan data result A, and when the enterprise manual operation data exceeds the latest plan data threshold range, the enterprise manual operation over-threshold error value data is recorded to the over-threshold result B;

[0036] The error correction auxiliary model building module is responsible for building an independent model based on IoT device data as an error correction auxiliary model;

[0037] The error correction auxiliary model is used to feed back the over-threshold result B to the IoT device and perform error correction on the over-threshold result B;

[0038] The processing logic of the error correction auxiliary model is: determine whether the error value data of the enterprise manual operation exceeding the threshold recorded in the exceeding threshold result B is positive or negative:

[0039] If it is a positive value, it means that the current enterprise manual operation data exceeds the threshold of the latest planned data, and the excess result is fed back to the IoT device, issuing a job delay notification;

[0040] If it is a negative value, it means that the current manual operation data of the enterprise is lower than the threshold of the latest planned data. The shortage result will be fed back to the IoT device and a replenishment notification will be issued.

[0041] The execution and feedback module is responsible for applying IoT devices based on the feedback results of the error correction auxiliary model. After actual execution, it eliminates the corresponding error value data in the queue of the over-threshold result B, updates third-party external data, enterprise manual operation data and IoT device data, and continuously outputs the corrected MRP results.

[0042] A device for improving production and supply efficiency based on a dynamic MRP model algorithm, characterized in that it includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method steps described above when executing the computer program.

[0043] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the method steps described above are implemented.

[0044] The beneficial effects of the present invention are as follows: the method for improving production and supply efficiency based on the dynamic MRP model algorithm comprehensively considers the dynamic changing factors in production and supply, simulates and iterates to optimize the MRP results, automatically simulates and feeds back to update the supply and demand plan, simplifies the MRP calculation steps, and improves the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] 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 description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Attached Figure 1It is a schematic diagram of a method for improving production and supply efficiency based on a dynamic MRP model algorithm of the present invention. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a 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 should fall within the scope of protection of the present invention.

[0048] The method for improving production supply efficiency based on a dynamic MRP model algorithm comprises the following steps:

[0049] Step S1: Classify the collected MRP input parameters into three categories: third-party external data, enterprise manual operation data, and IoT device data;

[0050] In step S1, the third-party external data includes upstream supplier data and downstream distributor data;

[0051] The manual operation data of an enterprise includes its own production line data, warehousing data and logistics data;

[0052] IoT device data includes operational data input by the device, such as automatic RFID codes, delivery codes, etc.

[0053] Step S2: Establish an independent model based on third-party external data as a material quantity auxiliary model;

[0054] The material quantity auxiliary model takes the enterprise as the central node, obtains the current and future material supply quantities of the upstream as the input flow, and obtains the current and future material production and consumption quantities of the downstream as the output flow.

[0055] Step S3: Calculate the BOM (Bill of Materials) list based on the general MRP algorithm through the material quantity auxiliary model to obtain the input and output detailed material quantities of the enterprise's production and sales plan, and record them as the plan data result A;

[0056] Step S4: Based on the production and sales plan cycle of the enterprise, the calculation logic in step S3 is iterated in a loop, and the plan data result A is updated synchronously as the data based on the latest plan;

[0057] Step S5, establishing a trigger for the enterprise manual operation data collected in step S1, and customizing the latest plan data threshold in the trigger according to the latest plan data result A in step S4; the trigger condition is when the enterprise manual operation data exceeds the threshold range of the latest plan data obtained; after the trigger, the error value data of the enterprise manual operation exceeding the threshold is recorded in the exceeding threshold result B;

[0058] The latest plan data threshold is customized by the user according to the latest plan data result A. For example, the upper and lower thresholds of the latest plan data result A are 80% to 120% respectively.

[0059] Step S6: establishing an independent model based on the IoT device data collected in step S1 as an auxiliary model for error correction;

[0060] The error correction auxiliary model is used to feed back the over-threshold result B to the IoT device, and perform error correction on the over-threshold result B in step S5;

[0061] The processing logic of the error correction auxiliary model is: determine whether the error value data of the enterprise manual operation exceeding the threshold recorded in the exceeding threshold result B is positive or negative:

[0062] If it is a positive value, it means that the current enterprise manual operation data exceeds the threshold of the latest planned data, and the excess result is fed back to the IoT device, issuing a job delay notification;

[0063] If it is a negative value, it means that the current manual operation data of the enterprise is lower than the threshold of the latest planned data. The shortage result will be fed back to the IoT device and a replenishment notification will be issued.

[0064] Step S7: Apply IoT devices according to the feedback results of the error correction auxiliary model. After actual execution, eliminate the corresponding error value data in the queue in the over-threshold result B, and update the third-party external data, enterprise manual operation data and IoT device data;

[0065] Step S8, jump to step S4, and loop steps S4 to S7 to continuously output the corrected MRP results.

[0066] The system for improving production and supply efficiency based on dynamic MRP model algorithm includes:

[0067] The data collection and classification module is responsible for collecting data and classifying the collected MRP input parameters into three categories: third-party external data, enterprise manual operation data, and IoT device data;

[0068] The third-party external data includes upstream supplier data and downstream distributor data;

[0069] The enterprise manual operation data includes its own production line data, warehousing data and logistics data;

[0070] The IoT device data includes operation data input by the device, such as automatic RFID codes, delivery codes, etc.

[0071] The material quantity auxiliary model building module is responsible for building an independent model based on third-party external data as a material quantity auxiliary model;

[0072] The material quantity auxiliary model takes the enterprise as the central node, obtains the current and future material supply quantities of the upstream as the input flow, and obtains the current and future material production and consumption quantities of the downstream as the output flow.

[0073] The planning data calculation module is responsible for calculating the BOM (Bill of Materials) list through the material quantity auxiliary model based on the general MRP algorithm, obtaining the input and output detailed material quantities of the enterprise's production and sales plan, and recording them as planning data result A;

[0074] Based on the production and sales planning cycle of the enterprise, the above calculation logic is iterated and the planning data result A is updated synchronously as the data based on the latest plan;

[0075] The trigger module is used to customize the latest plan data threshold according to the latest plan data result A, and when the enterprise manual operation data exceeds the latest plan data threshold range, the enterprise manual operation over-threshold error value data is recorded to the over-threshold result B;

[0076] The error correction auxiliary model building module is responsible for building an independent model based on IoT device data as an error correction auxiliary model;

[0077] The error correction auxiliary model is used to feed back the over-threshold result B to the IoT device and perform error correction on the over-threshold result B;

[0078] The processing logic of the error correction auxiliary model is: determine whether the error value data of the enterprise manual operation exceeding the threshold recorded in the exceeding threshold result B is positive or negative:

[0079] If it is a positive value, it means that the current enterprise manual operation data exceeds the threshold of the latest planned data, and the excess result is fed back to the IoT device, issuing a job delay notification;

[0080] If it is a negative value, it means that the current manual operation data of the enterprise is lower than the threshold of the latest planned data. The shortage result will be fed back to the IoT device and a replenishment notification will be issued.

[0081] The execution and feedback module is responsible for applying IoT devices based on the feedback results of the error correction auxiliary model. After actual execution, it eliminates the corresponding error value data in the queue of the over-threshold result B, updates third-party external data, enterprise manual operation data and IoT device data, and continuously outputs the corrected MRP results.

[0082] The device for improving production supply efficiency based on a dynamic MRP model algorithm comprises a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method steps described above when executing the computer program.

[0083] The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described above are implemented.

[0084] Compared with the existing technology, the method for improving production supply efficiency based on the dynamic MRP model algorithm has the following characteristics:

[0085] First, unlike the traditional fixed calculation MRP model, it introduces real-time dynamic change factors in the supply chain. The change factors update the MRP in real time. After calculation, it automatically feeds back and updates the real-time demand data for the execution results, and also simultaneously updates the data for errors caused by supply failure during the operation process.

[0086] Second, MRP analysis and calculation is not performed once and for all at a fixed period. Instead, multiple parameters are considered based on upstream and downstream situations, and MRP is updated in real time through real-time dynamic changes in the supply chain.

[0087] Third, unlike the traditional manual MRP calculation and multiple manual interventions, the data processed by manual data is transferred to the IoT terminal for secondary processing, and the terminal is used to assist in decision-making for manual normal and error data, simulate iterative optimization of MRP results, and automatically simulate feedback to update the supply and demand plan;

[0088] Fourth, unlike the traditional MRP calculation, which requires recalculation of production errors, the errors are processed separately and the corrected results are reintegrated into the business operations. Errors caused by supply failures during the operation are also automatically updated simultaneously, solving the problem of complicated manual MRP calculation steps and large errors in the results.

[0089] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for improving production supply efficiency based on a dynamic MRP model algorithm, characterized in that: The following steps are involved: Step S1: Classify the collected MRP input parameters into three categories: third-party external data, enterprise manual operation data, and IoT device data; Step S2: Establish an independent model based on third-party external data as a material quantity auxiliary model; Step S3: Calculate the BOM list through the material quantity auxiliary model based on the general MRP algorithm to obtain the input and output detailed material quantities of the enterprise's production and sales plan, and record them as the planning data result A; Step S4: Based on the production and sales plan cycle of the enterprise, the calculation logic in step S3 is iterated in a loop, and the plan data result A is updated synchronously as the data based on the latest plan; Step S5, establishing a trigger for the enterprise manual operation data collected in step S1, and customizing the latest plan data threshold in the trigger according to the latest plan data result A in step S4; the trigger condition is when the enterprise manual operation data exceeds the threshold range of the latest plan data obtained; after the trigger, the error value data of the enterprise manual operation exceeding the threshold is recorded in the exceeding threshold result B; Step S6: establishing an independent model based on the IoT device data collected in step S1 as an auxiliary model for error correction; The error correction auxiliary model is used to feed back the over-threshold result B to the IoT device, and perform error correction on the over-threshold result B in step S5; Step S7: Apply IoT devices according to the feedback results of the error correction auxiliary model. After actual execution, eliminate the corresponding error value data in the queue in the over-threshold result B, and update the third-party external data, enterprise manual operation data and IoT device data; Step S8, jump to step S4, and loop steps S4 to S7 to continuously output the corrected MRP results.

2. The method for improving production and supply efficiency based on a dynamic MRP model algorithm according to claim 1, characterized in that: In step S1, the third-party external data includes upstream supplier data and downstream distributor data; The manual operation data of an enterprise includes its own production line data, warehousing data and logistics data; IoT device data includes operation data input by the device.

3. The method for improving production and supply efficiency based on a dynamic MRP model algorithm according to claim 1, characterized in that: The material quantity auxiliary model takes the enterprise as the central node and obtains the current and future material supply quantities of the upstream as input flow; Get the current and future material production and consumption quantities of the downstream as output flow.

4. The method for improving production and supply efficiency based on a dynamic MRP model algorithm according to claim 1, characterized in that: The processing logic of the error correction auxiliary model is: determine whether the error value data of the enterprise manual operation exceeding the threshold recorded in the exceeding threshold result B is positive or negative: If it is a positive value, it means that the current enterprise manual operation data exceeds the threshold of the latest planned data, and the excess result is fed back to the IoT device, issuing a job delay notification; If it is a negative value, it means that the current manual operation data of the enterprise is lower than the threshold of the latest planned data. The shortage result will be fed back to the IoT device and a replenishment notification will be issued.

5. A system for improving production and supply efficiency based on a dynamic MRP model algorithm, characterized by: include: The data collection and classification module is responsible for collecting data and classifying the collected MRP input parameters into three categories: third-party external data, enterprise manual operation data, and IoT device data; The material quantity auxiliary model building module is responsible for building an independent model based on third-party external data as a material quantity auxiliary model; The planning data calculation module is responsible for calculating the BOM list through the material quantity auxiliary model based on the general MRP algorithm, obtaining the input and output detailed material quantities of the enterprise's production and sales plan, and recording them as planning data result A; Based on the production and sales planning cycle of the enterprise, the above calculation logic is iterated and the planning data result A is updated synchronously as the data based on the latest plan; The trigger module is used to customize the latest plan data threshold according to the latest plan data result A, and when the enterprise manual operation data exceeds the latest plan data threshold range, the enterprise manual operation over-threshold error value data is recorded to the over-threshold result B; The error correction auxiliary model building module is responsible for building an independent model based on IoT device data as an error correction auxiliary model; The error correction auxiliary model is used to feed back the over-threshold result B to the IoT device and perform error correction on the over-threshold result B; The execution and feedback module is responsible for applying IoT devices based on the feedback results of the error correction auxiliary model. After actual execution, it eliminates the corresponding error value data in the queue of the over-threshold result B, updates third-party external data, enterprise manual operation data and IoT device data, and continuously outputs the corrected MRP results.

6. The system for improving production and supply efficiency based on a dynamic MRP model algorithm according to claim 5 is characterized in that: The third-party external data includes upstream supplier data and downstream distributor data; The enterprise manual operation data includes its own production line data, warehousing data and logistics data; The IoT device data includes operation data input by the device.

7. The system for improving production and supply efficiency based on a dynamic MRP model algorithm according to claim 5, characterized in that: The material quantity auxiliary model takes the enterprise as the central node and obtains the current and future material supply quantities of the upstream as input flow; Get the current and future material production and consumption quantities of the downstream as output flow.

8. The system for improving production and supply efficiency based on a dynamic MRP model algorithm according to claim 5, characterized in that: The processing logic of the error correction auxiliary model is: determine whether the error value data of the enterprise manual operation exceeding the threshold recorded in the exceeding threshold result B is positive or negative: If it is a positive value, it means that the current enterprise manual operation data exceeds the threshold of the latest planned data, and the excess result is fed back to the IoT device, issuing a job delay notification; If it is a negative value, it means that the current manual operation data of the enterprise is lower than the threshold of the latest planned data. The shortage result will be fed back to the IoT device and a replenishment notification will be issued.

9. A device for improving production supply efficiency based on a dynamic MRP model algorithm, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method according to any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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