Process analysis method and device and computer storage medium

Abstract

The invention discloses a process analysis method and device and a computer storage medium, and relates to the technical field of process analysis. The method comprises the following steps: S1, acquiring data of a plurality of process cases; s2, analyzing and processing the data based on a process mining algorithm to generate a process model so as to determine each node and node data in the process model; s3, calculating individual time consumption and average time consumption of each process case; bottleneck node identification is carried out on the process cases whose individual time consumption is greater than the average time consumption; identifying rework nodes in each process case; counting data of external factors in the bottleneck nodes and the rework nodes, and performing regression analysis to determine influence degrees of the external factors; and S4, feeding back the bottleneck nodes, the rework nodes and the influence degrees of the corresponding external factors.

Description

Technical Field

[0001] The present invention belongs to the field of process technology, and particularly relates to a process analysis method, device, and computer storage medium. Background Art

[0002] In today's industrial field, supply chain management plays a crucial role in the efficient operation of enterprises. Especially for power enterprises, the smooth operation of their supply chains is even more crucial for the stability and reliability of power supply. In particular, since the power supply chain involves many links and the processes are very complex, process management is of great importance. And an important part of process management is to conduct process analysis. In the prior art, data can often only be obtained by regularly collecting business reports, such as monthly procurement reports, inventory reports, etc. Therefore, the following situations have to be faced:

[0003] (1) Data is usually collected using spreadsheet software such as Excel, and usually only local data can be analyzed, and there is a lack of the ability for in-depth analysis, making it difficult to quantify key data;

[0004] (2) The analysis process of data has the characteristics of strong subjectivity and relies on industry experience.

[0005] In the above situations, it is difficult for the prior art to accurately find the problems existing in the process, and it is even more impossible to put forward targeted improvement suggestions. Therefore, the prior art has obvious deficiencies in analyzing business processes, lacking an effective method that can comprehensively and accurately analyze the entire process, and it is difficult to meet the urgent needs of enterprises for optimizing supply chain management and improving operation efficiency.

[0006] The information disclosed in this background art section is only intended to enhance the overall understanding of the present invention and should not be regarded as an admission or any form of implication that this information constitutes the prior art already known to those of ordinary skill in the art. Summary of the Invention

[0007] The object of the present invention is to solve the problem that the current process cannot be comprehensively and accurately analyzed to optimize the operation efficiency of enterprises, and to provide a process analysis method, device, and computer storage medium.

[0008] The first aspect of the present invention provides a process analysis method, including:

[0009] S1. Obtain data of a number of process cases;

[0010] S2. Analyze and process the data based on a process mining algorithm to generate a process model, so as to determine each node and node data in the process model;

[0011] S3. Calculate the individual time consumption and average time consumption of each process case; identify the bottleneck nodes for the process cases where the individual time consumption is greater than the average time consumption;

[0012] Identify the rework nodes in each process case;

[0013] Statistically analyze the data of external factors in each bottleneck node and rework node, and perform regression analysis to determine the influence degree of the external factors;

[0014] S4. Feedback each of the bottleneck nodes and each of the rework nodes, as well as the influence degree of the corresponding external factors.

[0015] In an embodiment of the present invention, the method for identifying the bottleneck node is: calculate the variance of the time consumption of each node, and if the variance exceeds the set threshold, it is determined that the current node is a bottleneck node.

[0016] In an embodiment of the present invention, the execution process of the bottleneck node is divided into a receiving process, an execution process, and a distribution process, and the time consumption ratio and time consumption fluctuation of each process are calculated respectively.

[0017] In an embodiment of the present invention, the method for identifying the rework node is: if the next step of the current node traces back to a previous node, it is determined that the previous node is a rework node.

[0018] In an embodiment of the present invention, the external factors include at least one of scenario factors, department factors, time period factors, and regional factors.

[0019] In an embodiment of the present invention, it further includes step S3-1: compare each process case with the process model, identify the skip nodes, and calculate the average time consumption and node ratio of each skip node, and the step S3-1 is executed after the step S2.

[0020] In an embodiment of the present invention, the step S3-1 further includes: compare each process case with the process model, identify the misaligned nodes, and calculate the average time consumption and node ratio of the misaligned nodes;

[0021] In an embodiment of the present invention, it further includes step S3-2: count the number of process cases consistent with the process model and calculate the ratio and average time consumption, and the step S3-2 is executed after the step S2.

[0022] The second aspect of the present invention provides a device, including a processor and a memory communicatively connected to the processor, where the memory stores computer instructions executable by the processor, and when the computer instructions are executed, the above process analysis method is implemented.

[0023] The third aspect of the present invention provides a computer storage medium storing computer instructions, which implement the above-mentioned process analysis method when executed.

[0024] Compared with the prior art, the technical effects achieved by the present invention are as follows: By processing and calculating data in dimensions such as time and scenario of the appearance, in-depth analysis of the data is realized, and the required results can be quantitatively represented, achieving an objective and accurate effect. At the same time, by obtaining various detailed calculation result analyses, the problems in the process can be found more accurately, so as to provide more accurate and effective improvement opinions. Through continuous iteration, the actual execution process increasingly approaches the standard process model, completely solving the problems existing in the process. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flowchart of a process analysis method according to an embodiment of the present invention.

[0026] Figure 2 is a flowchart of the process analysis method according to an embodiment of the present invention with step S3-1 added.

[0027] Figure 3 is a flowchart of the process analysis method according to an embodiment of the present invention with step S3-2 added.

[0028] Figure 4 is a flowchart of the process analysis method according to an embodiment of the present invention with step S3-1 and step S3-2 added. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] Unless otherwise clearly stated, throughout the specification and claims, the term "comprising" or its variations such as "comprises" or "including" etc. will be understood to include the stated elements or components, without excluding other elements or other components.

[0030] The technical solutions of the present invention are described below through specific embodiments. It should be understood that one or more steps mentioned in the present invention do not exclude the existence of other methods and steps before and after the combined steps, or other methods and steps can be inserted between these clearly mentioned steps. It should also be understood that these examples are only used to illustrate the present invention and not to limit the scope of the present invention. Unless otherwise specified, the numbers of the method steps are only for the purpose of identifying the method steps, rather than limiting the arrangement order of each method or the implementation scope of the present invention. The change or adjustment of their relative relationship can also be regarded as the scope in which the present invention can be implemented under the condition of no substantial change in technical content.

[0031] There are no specific restrictions on the sources of the raw materials and instruments used in the embodiments, and they can be purchased in the market or prepared according to the conventional methods well-known to those skilled in the art.

[0032] like Figures 1-4 As shown, the process analysis method according to the preferred embodiment of the present invention includes:

[0033] S1. Obtain data for several process cases.

[0034] A process case is the actual execution process of a project or business, which includes at least the name, time, and node direction of each node. The more process cases there are, the better the execution effect of subsequent steps will be.

[0035] S2. Analyze and process the data based on a process mining algorithm to generate a process model to determine each node and node data in the process model. The process model can be generated with the help of a process algorithm in the prior art, such as the AlphaMiner algorithm. The Alpha Miner algorithm determines the causal relationship through the direct follow-up relationship between activities, generates a Petri net model, extracts the frequency matrix and the causal matrix from the event log, and constructs a process model.

[0036] S3. Calculate the individual time consumption and average time consumption of each process case; identify the bottleneck nodes for the process cases whose individual time consumption is greater than the average time consumption.

[0037] If the individual time consumption exceeds the average time consumption, it means that the corresponding process case is inefficient, indicating that there is a problem in the execution process. In this case, it is necessary to specifically determine which node or nodes have problems. The bottleneck node identification method is: based on all process cases, the variance of the time consumption of each node is calculated. If the variance exceeds the set threshold, the current node is identified as a bottleneck node. As we all know, the larger the variance, the more drastic the data fluctuations, and the more unstable the execution cycle of the corresponding node, indicating that the node may be affected by certain factors and slow down the progress. Nodes with small variances indicate stable execution cycles. Even if the total time of the node is long, it should be regarded as necessary time rather than a problem with the node.

[0038] Furthermore, the rework nodes in each process case are identified. The identification method of the rework node is: if the next step of the current node traces back to the previous node, the previous node is considered to be the rework node. The backtracking of the node can be judged by the direction and order of the node, and can also be judged by whether a circular process appears in the DFG diagram. The rework node indicates that the work output result of the node does not meet the standard and needs to be reprocessed. This situation will actually drag down the overall process progress.

[0039] After identifying the bottleneck nodes and rework nodes, count the data of external factors for each bottleneck node and rework node, and conduct a regression analysis to determine the influence degree of each factor; feedback each of the bottleneck nodes and each of the rework nodes, as well as the influence degree of the corresponding external factors. The external data includes at least one of medium scenario factors, department factors, time period factors, and regional factors. However, the more factors included, the better, and the more comprehensive and profound the analysis of the causes of bottleneck nodes and rework nodes will be. The above non-quantitative parameters can be preferably quantified by methods such as the dummy variable method or the ordinal variable method during the regression analysis process.

[0040] Further, the execution process of the bottleneck node is divided into a receiving process, an execution process, and a distribution process, and the time proportion and time fluctuation of each process are calculated respectively. The receiving process represents the process from the completion of the previous node to the preparation for completion of this node. The distribution process represents the process from the completion of the operation of this node to the sending to the next node. If the time proportion of the receiving process and the distribution process is too high, it indicates that there are factors affecting work efficiency in the corresponding processes for a long time. If the time fluctuation is too large, it indicates that there are factors affecting work efficiency frequently in the corresponding processes.

[0041] On this basis, in order to make the analysis more in-depth and comprehensive, the following steps can also be executed:

[0042] S3-1. Compare each process case with the process model, that is, compare the actual execution process with the standard execution process. When inconsistencies are found in the comparison, identify skip nodes and / or misaligned nodes in the corresponding process case, and then calculate the average time consumption and node proportion of the skip nodes and misaligned nodes. It should be noted here that the process model mined by the process mining algorithm represents the standard execution process. However, in the specific execution process, not all process cases can be consistent with the process model, and there will be a few special cases, which means that there are errors in the execution process, and corresponding skip nodes and misaligned nodes may occur. A skip node means that one or some intermediate nodes are skipped between two nodes. For example, in the execution process of A→B→C, if B is skipped, it becomes A→C, where A is the skip node, indicating that there is a problem with the downstream direction of node A. A misaligned node means that a node appears in a position where it should not appear. For example, if A→B→C becomes A→C→B, then C is the misaligned node. When skip nodes and / or misaligned nodes appear, it indicates that there are abnormal situations in the process. If the average time consumption of the skip nodes and / or misaligned nodes is abnormal, and the proportion of the skip nodes or misaligned nodes is higher than expected, excluding data anomalies or accidental situations, it is necessary to further analyze whether it is caused by human factors (such as approval habits) or system factors (process configuration errors), and further evaluate whether to optimize approval rules, control logic, etc. The proportion here refers to the ratio of the number of a certain node with a certain number of skips or misalignments in many process cases to the total number of process cases.

[0043] S3-2. Count the number of process cases that are consistent with the process model and calculate the proportion and average time consumption. If the process case is consistent with the process model, it means that the execution process of the corresponding process case is standardized. If the proportion of standardized process cases is lower than expected, it means that it is necessary to check the compliance issues of the process execution, and even evaluate whether it is necessary to optimize or enforce the process standard. Counting the average time consumption is to more accurately understand the general execution time of standardized process cases and can be used as a reference.

[0044] Both step S3-1 and step S3-2 can be executed after step S2.

[0045] S4. Feedback each of the bottleneck nodes and each of the rework nodes, as well as the influence degree of the corresponding external factors. Thus, a process analysis process is completed. Subsequently, the staff can also execute the following steps to complete the process optimization.

[0046] S5. Improve the bottleneck nodes and rework nodes. Through regression analysis, the impact degree of each factor on the formation of bottleneck nodes can be intuitively, objectively, and accurately judged in a quantitative manner, so that the causes can be targeted for inspection and improvement. By decomposing and analyzing the execution process of bottleneck nodes, the factors causing bottleneck nodes can be more accurately located, and the analysis of problems will be more intuitive and accurate. After combining with the regression analysis results, the integration of multi-dimensional information and in-depth analysis of quantitative data can be achieved to provide accurate improvement suggestions. At the same time, compliance checks are also carried out based on the results of steps S3-1 and S3-2.

[0047] S6. Iteratively execute steps S1, S3, and S4 until the bottleneck nodes and rework nodes cannot be determined.

[0048] In this method, through processing and calculating data in dimensions such as time and scenario of the appearance, in-depth analysis of the data is achieved, and the required results can be quantitatively represented, achieving an objective and accurate effect. At the same time, through obtaining various detailed calculation result analyses, the problems in the process can be found more accurately, so as to provide more accurate and effective improvement suggestions. Through continuous iteration, the actual execution process increasingly approaches the standard process model, completely solving the problems existing in the process.

[0049] As an implementation of the above method, the present invention discloses an embodiment of a device, which corresponds to the above method embodiment. The device includes a processor and a memory communicatively connected to the processor. The memory stores computer instructions executable by the processor, and when the computer instructions are executed, the above process analysis method is implemented.

[0050] As an implementation of the above method, the present invention discloses an embodiment of a computer storage medium, which corresponds to the above method embodiment. The computer storage medium stores computer instructions, and when the computer instructions are executed, the above process analysis method is implemented.

[0051] The foregoing description of specific exemplary embodiments of the present invention is for purposes of illustration and exemplification. These descriptions are not intended to limit the invention to the precise forms disclosed, and obviously, many changes and variations are possible in light of the above teachings. The purpose of selecting and describing exemplary embodiments is to explain the specific principles of the invention and its practical applications, so that those skilled in the art can implement and utilize various different exemplary embodiments of the invention as well as various different selections and changes. The scope of the present invention is intended to be defined by the claims and their equivalents.

Claims

1. A process analysis method, characterized in that, Including: S1. Obtain data of a number of process cases; S2. Analyze and process the data based on a process mining algorithm to generate a process model, so as to determine each node and node data in the process model; S3. Calculate the individual time consumption and average time consumption of each process case; Identify bottleneck nodes for process cases with individual time consumption greater than the average time consumption; Identify rework nodes in each process case; Statistically analyze the data of external factors in each bottleneck node and rework node, and perform regression analysis to determine the influence degree of the external factors; S4. Feedback each of the bottleneck nodes and each of the rework nodes, as well as the influence degree of the corresponding external factors.

2. The process analysis method according to claim 1, wherein The method for identifying the bottleneck node is: calculate the variance of the time consumption of each node, and if the variance exceeds a set threshold, the current node is determined to be a bottleneck node.

3. The process analysis method according to claim 1, wherein: The execution process of the bottleneck node is divided into a receiving process, an execution process, and a distribution process, and the time consumption ratio and time consumption fluctuation of each process are calculated respectively.

4. The process analysis method according to claim 1, characterized in that The method for identifying the rework node is: if the next step of the current node traces back to a previous node, the previous node is determined to be a rework node.

5. The process analysis method according to claim 1, characterized in that, The external factors include at least one of scenario factors, department factors, time period factors, and regional factors.

6. The process analysis method according to claim 1, characterized in that It further includes step S3-1: Compare each process case with the process model to identify skip nodes, and calculate the average time consumption and node ratio of each skip node. The step S3-1 is executed after the step S2.

7. The process analysis method according to claim 6, wherein The step S3-1 further includes: Compare each process case with the process model to identify misaligned nodes, and calculate the average time consumption and node ratio of the misaligned nodes.

8. The process analysis method according to claim 1, wherein It further includes step S3-2: Statistically analyze the number of process cases consistent with the process model, and calculate the ratio and average time consumption. The step S3-2 is executed after the step S2.

9. A device, characterized in that, It includes a processor and a memory communicatively connected to the processor. The memory stores computer instructions executable by the processor. When the computer instructions are executed, the process analysis method described in any one of claims 1-8 is implemented.

10. A computer storage medium storing computer instructions, characterized in that, When the computer instructions are executed, the process analysis method described in any one of claims 1-8 is implemented.

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