A demand priority calculation method, system and computer readable storage medium

Abstract

This invention relates to the field of production planning and scheduling technology, and particularly to a method, system, and computer-readable storage medium for calculating demand priority. The calculation method includes the following steps: constructing a multi-dimensional evaluation model; setting a custom external configuration file, wherein the external configuration file contains interval scores for each dimension, dynamic weights for each dimension, and priority mapping rules; based on the input dimension values ​​or dimension types of the demands to be prioritized, reading the intervals and corresponding interval scores for each dimension from the external configuration file, and reading the dynamic weights for each dimension from the external configuration file; calculating the score for each dimension based on the interval scores and dynamic weights, and calculating a comprehensive score by combining the scores of all dimensions; and outputting the priority segment corresponding to the comprehensive score according to the priority mapping rules of the external configuration file. The external configuration file enables customized evaluation dimensions for different business scenarios, and an independent rule configuration file enables dynamic loading and differentiated adaptation of the calculation logic.

Description

Technical Field

[0001] This invention relates to the field of production planning and scheduling technology, and in particular to a method, system, and computer-readable storage medium for calculating demand priority. Background Technology

[0002] In the field of production planning and scheduling, traditional decision-making regarding demand prioritization primarily relies on manual order scheduling, which reveals its limitations when facing complex and ever-changing production environments. As the manufacturing industry undergoes digital transformation, enterprises are gradually shifting from "subjective experience-based judgment" to "objective quantitative assessment" and "algorithmic dynamic decision-making." When factory production resources are limited and production demand is high, demand prioritization becomes particularly important, serving as one of the conditions for algorithmic decision-making regarding the order in which demands are satisfied when capacity is insufficient.

[0003] Production demands vary significantly across different business scenarios, making it difficult for a single, fixed evaluation model to adapt to diverse management needs. For example, simply sorting orders by delivery date in ascending order, or using hard-coded if-else statements to define a few branching rules (such as prioritizing strategic clients). Another example is a customer order priority ranking method disclosed in Chinese patent application CN118134587A, which uses customer criticality, order profitability, and delivery urgency as ranking indicators with corresponding fixed evaluation levels. If business strategies change, the matrix needs to be reconstructed and feature values ​​recalculated, resulting in poor flexibility.

[0004] Therefore, it is necessary to provide a computational method for prioritizing requirements that is highly flexible, scalable, and computationally efficient. Summary of the Invention

[0005] The purpose of this invention is to propose a method, system, and computer-readable storage medium for calculating demand priority. It enables customized evaluation dimensions for different business scenarios through external configuration files and achieves dynamic loading and differentiated adaptation of calculation logic through independent rule configuration files.

[0006] To achieve this objective, the present invention adopts the following technical solution:

[0007] A method for calculating demand priority includes the following steps:

[0008] A multidimensional evaluation model is constructed, and a custom external configuration file is set, which includes interval scores for each dimension, dynamic weights for each dimension, and priority mapping rules.

[0009] Based on the input dimension values ​​or dimension types to be prioritized, read the ranges and corresponding range scores of each dimension in the external configuration file, as well as the dynamic weights of each dimension in the external configuration file.

[0010] The score for each dimension is calculated based on the interval score and dynamic weights, and the overall score is calculated by combining the scores of all dimensions.

[0011] Based on the priority mapping rules of the external configuration file, output the priority level corresponding to the comprehensive score.

[0012] Furthermore, the external configuration file is an Excel worksheet, which contains multiple worksheet tabs, including a dimension definition tab, an interval score rule tab, a weight parameter tab, and a priority mapping rule tab.

[0013] The dimension definition tab stores the range corresponding to the dimension's value or type.

[0014] Furthermore, the external configuration file undergoes multi-layered verification after being updated, including verification of the reasonableness of the numerical range, consistency verification, weight and 100% verification, and logical reasonableness verification.

[0015] Furthermore, after the external configuration file is updated, it replaces the original external configuration file, and the calculation of requirement priority is performed based on the updated external configuration file.

[0016] Furthermore, priority scores are set for each dimension. In the multidimensional evaluation model, when the combined scores of the priority scores of each dimension are the same, the final score is calculated by combining the priority scores of each dimension.

[0017] Furthermore, in the steps of calculating the comprehensive score by combining the scores of each dimension, and in the steps of calculating the final score by combining the priority scores of each dimension, the calculation method is either linear weighted summation or weighted geometric mean.

[0018] Furthermore, when the value of the dimension is calculated from historical periodic data, a threshold range is set; if the calculation result exceeds the threshold range, it is considered abnormal data and an abnormal prompt is output.

[0019] The values ​​of the aforementioned dimensions, calculated from historical periodic data, are reused when prioritizing requirements within the period.

[0020] Furthermore, the dimensions of the multidimensional evaluation model include, but are not limited to, a combination of customer classification, demand forecast accuracy, product marginal value, order delivery urgency, order amount, customer historical cooperation contribution, product safety and compliance level, material availability rate, production process difficulty coefficient, new product sampling identification, customer satisfaction, strategic project identification, inventory turnover days, energy load sensitivity, and return rate.

[0021] A demand priority calculation system for implementing the demand priority calculation method, the calculation system comprising:

[0022] The model building module is used to build a multidimensional evaluation model based on the number and definition of dimensions input by the interaction module. The multidimensional evaluation model includes a formula for calculating the comprehensive score.

[0023] The configuration parsing module is used to parse the external configuration files in the storage module, send the interval scores and dynamic weights of the dimensions to the weighted calculation module, and send the priority mapping rules to the priority mapping module;

[0024] The weighted calculation module calculates the score for each dimension based on the interval score and dynamic weight, and combines the scores of all dimensions to obtain the comprehensive score.

[0025] The priority mapping module obtains the priority segment corresponding to the comprehensive score according to the priority mapping rules, and sends the priority segment to the interaction module;

[0026] The storage module is used to store external configuration files;

[0027] The interaction module is used to input numerical values ​​or dimension types for each dimension, modify external configuration files in the storage module, and input the number of dimensions and the definition of each dimension.

[0028] A computer-readable storage medium for storing an executable program that, when executed, implements the method for calculating the requirement priority.

[0029] The technical solution provided by this invention may include the following beneficial effects:

[0030] This solution uses weighted quantitative scoring of multiple evaluation dimensions to uniformly map each dimension to a standardized interval score. After obtaining the comprehensive score of each dimension through dynamic weights, it outputs the priority level according to the priority mapping rule, thereby eliminating human subjective cognitive bias and achieving decision objectivity.

[0031] Dimension definitions, interval scores, weight percentages, and priority mapping rules are all externalized to the configuration file, eliminating the need for algorithm developers to access them. Business personnel can maintain and adjust the external configuration file to adjust priority strategies, making the application of priority algorithms more flexible and adaptable to changes in business strategies. Detailed Implementation

[0032] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the present invention.

[0033] An embodiment of the present invention provides a method for calculating demand priority, which includes the following steps:

[0034] A multidimensional evaluation model is constructed, and a custom external configuration file is set, which includes interval scores for each dimension, dynamic weights for each dimension, and priority mapping rules.

[0035] Based on the input dimension values ​​or dimension types to be prioritized, read the ranges and corresponding range scores of each dimension in the external configuration file, as well as the dynamic weights of each dimension in the external configuration file.

[0036] The score for each dimension is calculated based on the interval score and dynamic weights, and the overall score is calculated by combining the scores of all dimensions.

[0037] Based on the priority mapping rules of the external configuration file, output the priority level corresponding to the comprehensive score.

[0038] Since the dimension definitions, interval scores, weight proportions, and priority mapping rules are all externalized in an external configuration file, when business strategies are adjusted, only the configuration needs to be modified, without modifying the source code or recompiling and deploying. This enables dynamic adjustment and updating of the calculation logic, significantly reducing business iteration costs. Furthermore, each dimension can be flexibly configured to adapt to the differentiated evaluation needs of various industries.

[0039] Specifically, each dimension interval refers to dividing attribute features across different dimensions into multiple numerical intervals or categories, with pre-defined interval scores for each interval. The number and definition of dimensions, numerical intervals or categories, interval scores, and dynamic weights can be adjusted via external configuration files based on business strategy adjustments. Furthermore, prioritizing calculations based on external configuration files results in a smaller code size, easier deployment, lower database storage pressure, and improved computational efficiency, making it suitable for various business scenarios across industries such as manufacturing, power, and retail.

[0040] As an example, one embodiment of the present invention is as follows: Based on the input of multiple business dimension information for priority calculation of demand, the latest weight parameters in the configuration file are first parsed and read; the read weights are assigned to the corresponding evaluation dimensions; after obtaining the scores of each dimension of the current business object, the dynamic weights read from the external configuration file are directly applied for calculation, for example: Comprehensive score = Customer classification score × Dynamic weight A + Prediction accuracy score × Dynamic weight B + Product marginal amount score × Dynamic weight C. Finally, according to the priority mapping rules, the priority segment corresponding to the comprehensive score is output. This calculation mechanism completely decouples business rules from the underlying code. When the business strategy changes, for example, if the company decides to focus more on product profit in the current stage and needs to increase the weight of the "product marginal amount" dimension from 30% to 50%, business personnel only need to modify the configuration in the "Demand Priority Check" template (external configuration file) and upload it, and click the "Update Demand Priority" button of the system to automatically apply the new weight rules for calculation without modifying the underlying source code or recompiling and deploying.

[0041] In one embodiment of the present invention, the external configuration file is an Excel worksheet. The Excel worksheet contains multiple tabs, including a dimension definition tab, a range scoring rule tab, a weight parameter tab, and a priority mapping rule tab. The dimension definition tab stores the ranges corresponding to the dimensions' values ​​or categories. Splitting the rules in the Excel worksheet makes the rule boundaries clear, facilitating business personnel to categorize and maintain the rules, quickly locate and modify items, and reduce the probability of configuration errors.

[0042] In one embodiment of the present invention, the external configuration file undergoes multi-layered verification after updating, including numerical range rationality verification, consistency verification, weight and 100% verification, and logical rationality verification. The pre-verification mechanism after the external configuration file update improves the stability of the calculation process, and the multi-layered verification accurately locates errors, facilitating quick modification by business personnel. Specifically, numerical range rationality verification checks the size logic of interval thresholds, such as prohibiting the lower limit of the interval from being greater than or equal to the upper limit, negative interval scores, and weight values ​​between 0 and 100%. Consistency verification ensures that the dimension names in the weight tab and interval score tab completely match the dimension definition tab, with no undefined or missing dimensions. Logical rationality verification checks that priority segment intervals have no overlap or gaps, ensuring that the obtained comprehensive score matches a unique priority segment.

[0043] In one embodiment of the present invention, the updated external configuration file replaces the original external configuration file, and the priority calculation of the requirements is performed based on the updated external configuration file. The external file update process does not require restarting the service or recompiling and redeploying.

[0044] In one embodiment of the present invention, priority scores are set for each dimension. In the multi-dimensional evaluation model, when the overall scores of the priority scores for each dimension are the same, the final score is calculated by combining the priority scores of each dimension. This tie-breaking rule is also externally configurable, allowing for flexible adjustment of the priority score values ​​according to business priorities. For example, three dimensions can be set: customer classification, demand forecast accuracy, and product marginal cost, with priority scores set for each dimension's intervals. When the overall scores of two demands are completely identical, the overall score is recalculated by adding the priority scores to the interval scores corresponding to each dimension.

[0045] In one embodiment of the present invention, a priority dimension is set, and when multiple requirements have the same priority level, the requirement with a higher interval score in the priority dimension is prioritized over other requirements. For example, the customer classification dimension is set as the priority dimension, and the requirement with a higher score in the customer classification dimension is prioritized over other requirements.

[0046] In one embodiment of the present invention, the steps of calculating the overall score by combining the scores of each dimension, and calculating the final score by combining the priority scores of each dimension, are performed using either a linear weighted summation method or a weighted geometric mean method. The linear weighted summation method allows a high score in a single dimension to boost the overall score, making it more suitable for production scenarios; the weighted geometric mean method can suppress extremely low scores and balance the scores of each dimension, making it suitable for scenarios requiring balanced management of multiple indicators, such as power load dispatching and retail distribution.

[0047] In one embodiment of the present invention, when the value of the dimension is calculated from historical periodic data, a threshold range is set; if the calculation result exceeds the threshold range, it is regarded as abnormal data and an abnormal prompt is output.

[0048] The values ​​of the aforementioned dimensions, calculated from historical periodic data, are reused when prioritizing requirements within the period.

[0049] Threshold validation of historical periodic data can identify invalid business data. For example, the demand forecast accuracy is calculated as follows: Demand Forecast Accuracy = 1 - (|Actual Value - Forecast Value| / Actual Value * 100%). The actual value is the actual sales data of the previous historical period provided to the customer, and the forecast value is the value calculated in the demand plan for the corresponding period. The threshold range for demand forecast accuracy is [0.6-1]. If the calculated value is greater than 1, it indicates that the data input is incorrect; if the calculated value is less than 0.6, it proves that the accuracy of the aforementioned forecast value is too low and needs to be verified.

[0050] In one embodiment of the present invention, the dimensions of the multidimensional evaluation model include, but are not limited to, a combination of customer classification, demand forecast accuracy, product marginal value, order delivery urgency, order amount, customer historical cooperation contribution, product safety and compliance level, material availability rate, production process difficulty coefficient, new product sampling identification, customer satisfaction, strategic project identification, inventory turnover days, energy load sensitivity, and return rate.

[0051] The dimensions are divided into two main categories: enumerated qualitative dimensions and continuous numerical dimensions. When adding a new dimension, a record needs to be added to the dimension definition tab in the external configuration file, along with the corresponding interval score and dynamic weight.

[0052] As an example, the evaluation dimensions of the multidimensional evaluation model are customer classification, demand forecast accuracy, and product marginal value. The definitions and interval scores of the three dimensions are shown in the table below:

[0053] Customer Classification:

[0054]

[0055] Demand forecast accuracy:

[0056]

[0057] Product marginal amount:

[0058]

[0059] Dynamic weight settings:

[0060]

[0061] Priority mapping rules:

[0062]

[0063] When the input business requirements data or categories are: customer classification maturity / strategy, demand forecast accuracy of 63%, and product marginal amount of 3000, the corresponding interval scores are 100, 70, and 100 points. Based on the dynamic weight data of the three dimensions, the comprehensive score is calculated as: 100*40% + 70*30% + 100*30% = 91 points. Priority mapping rule: 91 points corresponds to a requirement priority of 1. Business personnel arrange the execution order of business according to the priority level.

[0064] As an example, the method of this invention is applied to manufacturing industries, such as PCB manufacturing plants. These plants face concurrent demands from multiple customers: receiving impedance tightening changes for automotive ADAS boards (high safety requirements), initial prototyping of mobile phone HDI boards (extremely tight delivery times), and requirements for etching parameter optimization due to internal quality anomalies (to prevent defective products from leaving the factory).

[0065] For customer classification, the automotive ADAS board corresponds to "Mature / Strategic" and scores 100 points, the mobile HDI board corresponds to "Cultivation / Key" and scores 90 points, and internal optimization needs correspond to "Mature / Observation" and score 50 points. Based on the previous month's forecast and actual values ​​for each customer, the demand forecast accuracy rates are calculated as follows: Automotive ADAS board 92% (100 points), Mobile HDI board 84% (90 points), and Internal optimization needs 63% (70 points), with a corresponding interval score of 80. Business personnel configure product margins based on demand, and the corresponding interval scores are: Automotive ADAS board 100 points, Mobile HDI board 70 points, and Internal optimization needs 80 points. Using a dynamic weighting of 40% for customer classification, 30% for demand forecast accuracy, and 30% for product margins, the overall score is calculated as follows: Automotive ADAS board 100 points, Mobile HDI board 84 points, and Internal optimization needs 65 points. According to the priority mapping rules, the priority for Automotive ADAS board is 1, for Mobile HDI board it is 2, and for Internal optimization needs it is 4.

[0066] As an example, in the energy and power industry, the power grid faces a power shortage during the summer peak season. Given limited power generation and storage capacity, it needs to rationally allocate electricity demand from different sectors such as industry, residential, medical, and commercial. This involves setting three dimensions: customer classification, demand forecast accuracy, and electricity costs for each customer. Different customer classifications are as follows: 100 points for power supply to a top-tier hospital ICU (life-threatening); 90 points for power supply to a wafer fab production line (power outage means scrapping); 90 points for power supply to a residential community (people's livelihood needs); and 70 points for power supply to a commercial complex (non-essential). The accuracy scores for demand forecasting are: 100 points for top-tier hospital ICU, 90 points for wafer fab production line, 90 points for residential community, and 100 points for commercial complex. The cost range scores for each customer are: 80 points for top-tier hospital ICU, 90 points for wafer fab production line, 100 points for residential community, and 90 points for commercial complex. Based on a dynamic weighted customer classification (40%), demand forecast accuracy (30%), and electricity cost (30%), a comprehensive score was calculated. The scores were: ICU power supply for tertiary hospitals (94 points), wafer fab production line power supply (93 points), residential community power supply (90 points), and commercial complex power supply (88 points). According to the priority mapping rules, the priority for ICU power supply for tertiary hospitals was 1, for wafer fab production line power supply (1 point), for residential community power supply (1 point), and for commercial complex power supply (2 points). The priority of ICU power supply for tertiary hospitals, wafer fab production line power supply, and residential community power supply was then ranked according to their customer classification scores.

[0067] Accordingly, the present invention also provides a demand priority calculation system for implementing the demand priority calculation method. The calculation system includes a model building module, a configuration parsing module, a weighted calculation module, a priority mapping module, a storage module, and an interaction module.

[0068] The model building module constructs a multidimensional evaluation model based on the number and definitions of dimensions input from the interaction module. This multidimensional evaluation model includes a formula for calculating the comprehensive score. The model building module supports online addition, deletion, and deactivation of evaluation dimensions. The configuration parsing module parses external configuration files stored in the storage module, sending the dimension interval scores and dynamic weights to the weighted calculation module, and the priority mapping rules to the priority mapping module. The configuration parsing module incorporates multi-layered verification logic, synchronously performing consistency checks on weights, intervals, and dimensions during the parsing process. The weighted calculation module calculates the score for each dimension based on the interval score and dynamic weights, and combines the scores of all dimensions to obtain the comprehensive score. The weighted calculation module includes a built-in periodic indicator caching submodule to cache repeatedly calculated indicators such as periodic prediction accuracy; it also includes a tie-weighting calculation submodule to differentiate the sorting order when comprehensive scores are the same.

[0069] The priority mapping module obtains the priority tier corresponding to the comprehensive score based on the priority mapping rules and sends the priority tier to the interaction module. The storage module stores external configuration files. The interaction module allows users to input numerical values ​​or dimension types for each dimension, modify the external configuration files in the storage module, and input the number and definition of each dimension. The interaction module provides configuration file upload and download, one-click priority update, and highlighted display of abnormal data, and also provides visual operations for business users.

[0070] Accordingly, the present invention also provides a computer-readable storage medium for storing an executable program, which, when executed, implements the method for calculating the demand priority.

[0071] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0072] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0074] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method of calculating demand priority, characterized by, Includes the following steps: A multidimensional evaluation model is constructed, and a custom external configuration file is set, which includes interval scores for each dimension, dynamic weights for each dimension, and priority mapping rules. Based on the input dimension values ​​or dimension types to be prioritized, read the ranges and corresponding range scores of each dimension in the external configuration file, as well as the dynamic weights of each dimension in the external configuration file. The score for each dimension is calculated based on the interval score and dynamic weights, and the overall score is calculated by combining the scores of all dimensions. Based on the priority mapping rules of the external configuration file, output the priority level corresponding to the comprehensive score.

2. The method of claim 1, wherein, The external configuration file is an Excel worksheet, which contains multiple worksheet tabs, including a dimension definition tab, an interval score rule tab, a weight parameter tab, and a priority mapping rule tab. The dimension definition tab stores the range corresponding to the dimension's value or type.

3. The method of claim 2, wherein the demand priority is calculated based on a ratio of a number of the first type of data to a number of the second type of data. The external configuration file is updated and then subjected to multi-layered verification, including verification of the reasonableness of the numerical range, consistency verification, weight and 100% verification, and logical reasonableness verification.

4. The method of claim 3, wherein, The updated external configuration file replaces the original external configuration file, and the priority calculation of the requirements is performed based on the updated external configuration file.

5. The method of claim 1, wherein, Each dimension is assigned a priority score. In the multidimensional evaluation model, when the combined scores of the priority scores of each dimension are the same, the final score is calculated by combining the priority scores of each dimension.

6. The method of claim 5, wherein, In the steps of calculating the overall score by combining the scores of each dimension, and in the steps of calculating the final score by combining the priority scores of each dimension, the calculation method is either linear weighted summation or weighted geometric mean.

7. The method of calculating a demand priority according to claim 6, wherein, When the value of the dimension is calculated from historical periodic data, a threshold range is set; if the calculation result exceeds the threshold range, it is considered abnormal data and an abnormal prompt is output. The values ​​of the aforementioned dimensions, calculated from historical periodic data, are reused when prioritizing requirements within the period.

8. The method of claim 7, wherein the demand priority is calculated based on a ratio of a number of the first type of data to a number of the second type of data. The dimensions of the multidimensional evaluation model include, but are not limited to, a combination of customer classification, demand forecast accuracy, product marginal value, order delivery urgency, order amount, customer historical cooperation contribution, product safety and compliance level, material availability rate, production process difficulty coefficient, new product sampling identification, customer satisfaction, strategic project identification, inventory turnover days, energy load sensitivity, and return rate.

9. A demand priority calculation system, characterized by, A method for calculating demand priority according to any one of claims 1 to 8, the calculation system comprising: The model building module is used to build a multidimensional evaluation model based on the number and definition of dimensions input by the interaction module. The multidimensional evaluation model includes a formula for calculating the comprehensive score. The configuration parsing module is used to parse the external configuration files in the storage module, send the interval scores and dynamic weights of the dimensions to the weighted calculation module, and send the priority mapping rules to the priority mapping module; The weighted calculation module calculates the score for each dimension based on the interval score and dynamic weight, and combines the scores of all dimensions to obtain the comprehensive score. The priority mapping module obtains the priority segment corresponding to the comprehensive score according to the priority mapping rules, and sends the priority segment to the interaction module; a storage module for storing an external configuration file; an interaction module for inputting values of each dimension or category of each dimension, modifying the external configuration file in the storage module, and inputting the number of dimensions and definitions of each dimension.

10. A computer readable storage medium for storing an executable program, characterized in that, When the executable program is executed, the method for calculating the demand priority according to any one of claims 1-8 is implemented.

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