An adaptive project management method

Abstract

The application discloses a kind of self-adapting project management methods, including constructing the multi-level approval process model of each node of project execution;Real-time monitoring to approval process model, capture and store the approval exception handling data of approval process model;Based on the performance index of the approval process model of approval data and approval exception handling data of approval process model;And when performance index is less than preset value, multiple approval process model sets are constructed based on current performance index by approval process planning algorithm based on current performance index;The performance index of each approval process model in approval process model set is calculated, and the highest approval process model of index score is screened out as optimal approval process model;Self-adapting approval workflow is realized, and approval efficiency and accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of project management technology, specifically to an adaptive project management method. Background Technology

[0002] In project management, traditional fixed approval processes are inefficient and inflexible, and can no longer meet the needs of modern enterprises. In existing technologies, the implementation of approval processes in project management is mostly achieved by establishing approval processes that meet various business processes. This mainly includes: defining the approval process, clarifying the specific steps of the approval process, determining the positions of the personnel involved in the approval, approval nodes, approval conditions, etc.; and configuring approval process rules, establishing corresponding rules for the approval process according to business needs.

[0003] However, this method is only applicable to approval processes with fixed business scenarios. In actual use, as business operations continue to develop and change, this method cannot adapt to new business needs, leading to delays in the approval process or inaccurate approval results. Therefore, an adaptive project management method is urgently needed to solve the above problems. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive project management method to improve the efficiency and accuracy of traditional approval workflows, which are often inefficient and unable to adapt to the ever-evolving business needs of enterprises.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: an adaptive project management method, comprising the following steps:

[0006] Construct a multi-level approval process model for each node of project execution;

[0007] Real-time monitoring of the approval process model, capturing and storing approval anomaly handling data of the approval process model;

[0008] The performance metrics of the approval process model are calculated based on approval data and approval exception handling data.

[0009] When the performance index is less than the preset value, multiple sets of approval process models are constructed based on the current performance index using an approval process planning algorithm.

[0010] Calculate the performance index of each approval process model in the set of approval process models, and select the approval process model with the highest index score as the optimal approval process model.

[0011] Prior to this, the approval process model includes defining approval levels and setting approval positions, determining the approval authority and approval process path for different levels.

[0012] Priority is given to linking and saving the model data of the approval process model with the approval position information during real-time monitoring.

[0013] Priority is given to exception handling data, which includes approval timeouts and exceeding the preset number of rejections.

[0014] Prioritize calculating the model performance index P based on data such as approval results at all levels, number of rejections, approval time, and exception handling. M :

[0015]

[0016] Among them, R i It is the approval result of the i-th approval node in the approval model M; T i N is the approval time used for the i-th approval node; i E represents the number of approval rejections at the i-th approval node. i ω is the number of exceptions handled at the i-th approval node, and N is the review rejection evaluation factor; r ω t ω c ω e These are weighting coefficients, and the four weighting coefficients have a value range of (0, 1). The sum of the four weighting coefficients is 1, which respectively represent the weights of the approval result, the time consumption of the review process, the number of approval rejections, and the number of exceptions handled.

[0017] Preferred, the preset value is set based on the historical approval data of the approval process model.

[0018] Prior to this, the approval process planning algorithm specifically involves generating a complete approval process model M. i Determine the approval nodes m at each approval level. ij After each new approval node is added, the performance index of the generated process model is calculated according to the process model performance index calculation formula. when The performance index P of the current approval process model is less than that of the current approval process model. M If the current approval node is removed, other approval nodes are added; this continues until a set of multiple approval process models M = {M1, M2, ..., M} is obtained. n}

[0019] Prioritize and dynamically adjust the approval process in real time and visualize it for the optimal approval process model.

[0020] Beneficial effects: This invention initializes the task approval process model and configures model parameters including approval level and approval position; records the approval data of the current model during the execution of the approval process model; finally, it judges the execution performance of the current model based on the approval process calculation formula to optimize the current model and thus achieve adaptive function; it compares the performance indicators of the current approval process model with the performance indicators of the historical model, and generates the final approval process model by combining the approval path planning algorithm, thus realizing an adaptive approval workflow. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0022] In the attached diagram:

[0023] Figure 1 This is a schematic diagram of the adaptive approval workflow of the present invention;

[0024] Figure 2 This is a flowchart illustrating the generation of the optimal approval process model in this invention;

[0025] Figure 3 This is a visual approval process model diagram generated by the present invention. Detailed Implementation

[0026] The embodiments of the present invention will now be described with reference to the accompanying drawings. The terminology used in the embodiments section is for illustrative purposes only and is not intended to limit the scope of the invention. The embodiments of this application will now be described with reference to the accompanying drawings.

[0027] Example: Figure 1 As shown, an adaptive project management method includes the following steps:

[0028] Step S1: Create a multi-level approval process model for each node of the project execution, including defining approval levels and setting approval positions, and determining the approval authority and approval process path for different levels.

[0029] The process model for creating approval tasks can be designed by the user or initialized by uploading a process model file; the uploaded process model file can be in .bpmn format or in the form of a compressed file.

[0030] Approval levels are mainly used to set the number of approval operations required in the approval process, and approval positions are the personnel responsible for performing the approvals for each approval level.

[0031] Step S2: Monitor the task approval progress in real time, associate the model data of the approval process model with the approval position information, and persistently store it in the database;

[0032] Step S3: Configure model anomaly handling monitoring. When the approval of a process node times out or is rejected multiple times in a row, capture the approval anomaly handling data information of the approval model and record the approval process node information, including the approval position and the anomaly data.

[0033] Step S4: Calculate the model's performance metrics using the performance metric calculation formula for the approval process model; the specific calculation formula is as follows:

[0034] Record the audit result of the current node R i The average of the review results of each node in the process model is taken as the review result evaluation factor R, that is:

[0035]

[0036] The number of times a process model node is rejected is a negative impact on the approval process; therefore, the fewer the rejections, the greater the contribution to performance metrics should be. The rejection evaluation factor N can be calculated as follows:

[0037]

[0038] Recorded exception handling data includes task approval timeouts and multiple rejections of task approval nodes; when a task approval time T... i When the threshold t set for the current node is exceeded, the system will record this approval as an exception; the system will also record the number N times each approval node in the approval process model is rejected. i When N is rejected at a certain approval stage i When the threshold n set for the current node is exceeded, the system will record this approval as an exception; this reflects the negative impact of the number of exceptions in the process on performance; the fewer the number of exceptions, the better the performance. The exception handling evaluation factor E can be calculated as follows:

[0039]

[0040] Time consumed during the review process in Indicates the time of entering the current approval stage. This indicates the completion time of the approval process. The total time for executing the current approval task workflow model M is the sum of the review times T of each node. Record the reciprocal T of the total time of the approval task workflow model M. e ;Right now:

[0041]

[0042] An evaluation formula for an approval process model was adopted, and the performance index P of the model was derived by comparing it with historical approval data. M The calculation formula is as follows:

[0043] P M =ω r R+ω t T e -ω c N-ω e E;

[0044] Right now:

[0045]

[0046] Among them, R i It is the approval result of the i-th approval node in the approval model M; T i N is the approval time used for the i-th approval node; i E represents the number of approval rejections at the i-th approval node. i ω is the number of exceptions handled at the i-th approval node, and N is the review rejection evaluation factor; r ω t ω c ω e These are weighting coefficients, and the four weighting coefficients have a value range of (0, 1). The sum of the four weighting coefficients is 1, which respectively represent the weights of the approval result, the time consumption of the review process, the number of approval rejections, and the number of exceptions handled.

[0047] The model performance indicators are calculated based on data such as approval results at all levels, number of rejections, approval time, and exception handling. The calculation results are used to determine whether the current approval process model still meets the preset value requirements. The preset values ​​are set based on the historical approval data of the approval process model. If the performance indicators do not meet the expected requirements, step S5 is performed.

[0048] Step S5: Using an approval process planning algorithm, select approval positions at each approval level that meet the requirements by traversing the job list to obtain the approval process model with the optimal performance indicators, and adjust the approval process model in real time; when the approval process model needs to be adjusted and optimized, use the approval process planning algorithm to obtain an optimal approval process model, such as... Figure 2 As shown, the algorithm process includes:

[0049] (1) Data collection: According to steps S2 and S3 in claim 1, the historical approval data of the current approval process model is obtained, including the approval results of process nodes, approval time, number of approval rejections, and exception handling. And the performance indicators of the current model calculated in claim 4 are also included.

[0050] (2) Define model variables, including: a set of approval process models M, and performance indicators for each approval process model in the set. Each approval node in the approval process model is denoted as i, where i is the level of the approval process, and j is the approval node selected at the i-th level. A complete approval model includes everything from the approval start node to the approval end node; that is:

[0051] (3) Determine the constraints, which stipulate that the performance index of the generated approval process model cannot be less than the performance index P of the current process model; that is:

[0052] (4) Generate an approval process model set: To generate a complete approval process model, it is necessary to determine the approval nodes at each approval level. After each new approval node is added, the performance index of the generated process model needs to be calculated according to the process model performance index calculation formula. If it is less than the current process model's performance index P, then the newly added approval node does not meet the constraints, and the approval node is removed. Continue to add other approval nodes; and so on until multiple approval process model sets M = {M1, M2, ..., M} are obtained. n}

[0053] (5) Select the optimal approval process model; calculate the performance index of each approval process model in the set of approval process models according to the performance calculation formula, and select the approval process model with the highest performance index score from the set M as the optimal approval process model.

[0054] Step S6: After obtaining the optimal approval process model, the approval process can be dynamically adjusted in real time, and a visualized approval process model can be generated to improve model performance. The generated approval process model diagram is shown below. Figure 3 As shown;

[0055] The method disclosed in this invention, when applied in practice, improves the efficiency of enterprise R&D project approval, and enhances the accuracy and flexibility of the approval process. It effectively reduces the time and cost consumed by R&D projects in the approval process. This method can adapt to the ever-changing business needs of enterprise R&D projects.

[0056] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. For those skilled in the art, after learning the contents described in the present invention, several equivalent changes and substitutions can be made without departing from the principle of the present invention. These equivalent changes and substitutions should also be considered to fall within the protection scope of the present invention.

Claims

1. An adaptive project management method, characterized in that, Includes the following steps: Construct a multi-level approval process model for each node of project execution; Real-time monitoring of the approval process model, capturing and storing approval anomaly handling data of the approval process model; The performance metrics of the approval process model are calculated based on the approval data and approval exception handling data of the approval process model. as well as When the performance index is less than the preset value, multiple sets of approval process models are constructed based on the current performance index using an approval process planning algorithm. Calculate the performance index of each approval process model in the set of approval process models, and select the approval process model with the highest index score as the optimal approval process model. The performance index P of the calculation model is based on the approval results at all levels, the number of rejections, the approval time, and the data on exception handling. M : Among them, R i It is the approval result of the i-th approval node in the approval model M; T i N is the approval time used for the i-th approval node; i E represents the number of approval rejections at the i-th approval node. i ω is the number of exceptions handled at the i-th approval node, and N is the review rejection evaluation factor; r ω t ω c ω e These are weighting coefficients, and the four weighting coefficients range from (0, 1). The sum of the four weighting coefficients is 1, representing the weights of the approval result, the time consumed in the review process, the number of approval rejections, and the number of exceptions handled, respectively. The approval process planning algorithm specifically involves generating a complete approval process model M. i Determine the approval nodes m at each approval level. ij After each new approval node is added, the performance index of the generated process model is calculated according to the process model performance index calculation formula. when The performance index P of the current approval process model is less than that of the current approval process model. M If the current approval node is removed, other approval nodes are added; this continues until a set of multiple approval process models M = {M1, M2, ..., M} is obtained. n ]; The optimal approval process model dynamically adjusts the approval process in real time and visualizes it.

2. The adaptive project management method according to claim 1, characterized in that: The approval process model includes defining approval levels and setting approval positions, determining the approval authority and approval process path for different levels.

3. The adaptive project management method according to claim 1, characterized in that: The real-time monitoring process also includes linking and saving the model data of the approval process model with the approval position information.

4. The adaptive project management method according to claim 1, characterized in that: The exception handling data includes approval timeouts and exceeding the preset number of rejections.

5. The adaptive project management method according to claim 1, characterized in that: The preset value is set based on the historical approval data of the approval process model.

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