Process configuration optimization management method based on event probability

Abstract

The invention discloses a process configuration optimization management method based on event probability. Comprising the steps of defining events and counting the occurrence frequency of the events, calculating the event probability of each event relative to a corresponding employee, constructing a data matrix, calculating the per capita demand workload in any process, determining different workload levels and matching corresponding score levels, and determining the composition of the personnel participating in the corresponding process. And updating the occurrence probability of each event and the cumulative score of the personnel participating in the corresponding process, and periodically updating the data. According to the method, the event occurrence frequency corresponding to each process participant is counted, the event probability is calculated, data arrangement is carried out in a data matrix form, elements with good data representation or intersecting are marked by using colors, and different strategies are adopted for selection from UIDs represented by different event probabilities on the basis of the composition of subsequent process participants. Therefore, the participant configuration of the whole process is optimized, and the completion quality of the whole process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management and optimization, and in particular to a process configuration optimization management method based on event probability. Background Art

[0002] During the process of equipment installation and debugging or replacement of accessories, due to mistakes made by the assembly or debugging personnel and the difficulty of installing or replacing parts, some parts may fall into the machine or the debugging may not be in place, resulting in equipment not running smoothly or even damage during production, thus affecting production;

[0003] In each process of equipment assembly, debugging and maintenance, in actual scenarios, resources are allocated according to the degree of manpower and task intensity, without micro-management of the corresponding personnel's capabilities and proficiency in multiple processes, making it impossible to allocate resources more finely, and it is impossible to quantitatively analyze the results of subsequent work and trace the source of problems, resulting in the inability to optimize processes and resources in a targeted manner. To this end, we provide a process configuration optimization management method based on event probability. Summary of the invention

[0004] The purpose of the present invention is to provide a process configuration optimization management method based on event probability to solve the problems in the background technology.

[0005] The present invention can be implemented by the following technical solution: a process configuration optimization management method based on event probability, comprising the following steps:

[0006] Step 1: Divide different processes and define different events according to the equipment work results, and count the number of different events for each unique U ID set by each employee;

[0007] Step 2: Define the ratio of the number of times an event occurs after an employee participates in a process to the number of times the employee participates in the process as the event probability;

[0008] Step 3: construct a data matrix with the number of U IDs as the number of rows and the number of event types as the number of columns and fill in the data. In each column, use different colors to mark the U IDs with the smallest event probability and the top N positions and the U IDs with the largest event probability and the bottom M positions;

[0009] Step 4: Set different workload levels and match corresponding scoring levels, and calculate the per capita workload required for each actual process;

[0010] Step 5: Determine the personnel composition involved in the corresponding process;

[0011] Step 6: After each process is completed and before the next implementation of the same process, update the probability of each event and the cumulative score of the personnel involved in the corresponding process;

[0012] Step 7: Set participation restriction conditions for the U ID that executes a certain process.

[0013] A further technical improvement of the present invention is that the event types include part falling, abnormal wear of parts and automatic shutdown of equipment.

[0014] A further technical improvement of the present invention is that the number of event occurrences is bound to a specific employee. If a certain event occurs during a corresponding process or occurs between the current corresponding process and the next corresponding process, the occurrence of the event is attributed to the person involved in the corresponding process.

[0015] A further technical improvement of the present invention is that for the event probability definition process in step 2, when the number of times an employee participates in the corresponding process is less than the set number, the average event probability of the remaining employees who participate in the corresponding process more than the set number is used as the event probability of the event relative to the employee.

[0016] A further technical improvement of the present invention is that the workload level in step 4 is divided into five levels, and five scoring levels are set correspondingly, workload level I has the largest workload and the highest score; workload level V has the smallest workload and the smallest score.

[0017] A further technical improvement of the present invention is that: in step 4, the per capita required workload of each actual process is obtained by continuously dividing the required completion time and the required number of people required to complete the actual process by the total standard workload, wherein the total standard workload is obtained by continuously multiplying the standard labor time, the standard labor number and the labor intensity index of the process;

[0018] The standard working time and the standard number of workers are the standard set values ​​for the process. The labor intensity index is set according to the corresponding labor intensity level, and the labor intensity level is determined based on the experience of workers and experts.

[0019] A further technical improvement of the present invention is that the process of determining the composition of personnel involved in the corresponding process in step 5 includes:

[0020] Select a U ID with the lowest cumulative score from the first N idle U IDs to participate in this process;

[0021] The idle U IDs in the last M positions are selected to participate in this process through a quota application system;

[0022] The idle U IDs at positions N+1 to M-1 are used to make up the number of people, and the number of people is made up in order from the U IDs with the lowest scores.

[0023] A further technical improvement of the present invention is that the quota application system limits the number of people who have completed the requirements according to a certain ratio, and then selects the corresponding U ID with an earlier application time to participate in the process.

[0024] A further technical improvement of the present invention is that the setting process of the restriction conditions in step seven includes: sorting the cumulative scores updated by each U ID, and when a corresponding color mark appears in the last three digits of the cumulative score sorting, recording the process corresponding to the color mark, and when a certain U ID is recorded in the same process three times in a row, then in the process of executing the process thereafter, the U ID will no longer be used as an option for executing the process; when the U ID cannot participate in the processes that exceed the set proportion in the total process volume, the employee corresponding to the U ID is no longer qualified for the position.

[0025] A further technical improvement of the present invention is that it also includes periodic updating of data, specifically, automatically discarding data within an interval of equal length from the time when the corresponding U ID first generates data at a certain interval, and recalculating the event probability.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention divides the processes and defines the events that need to be operated and maintained, and counts the number of event occurrences corresponding to the employees participating in each process and calculates the event probability, then arranges the data in the form of a data matrix, uses different colors to mark the matrix elements with small event probabilities and large event probabilities, and adopts different strategies to select the UIDs with small event probabilities, large event probabilities, and probabilities between the two in the composition of the participants in the subsequent processes, and evaluates the employee performance in combination with the cumulative score and the event probability, thereby optimizing the configuration of the participants in the overall process and improving the completion quality of the entire process. DETAILED DESCRIPTION

[0029] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail as follows.

[0030] The process configuration optimization management method based on event probability specifically includes the following steps:

[0031] Step 1: Define events and count their occurrences

[0032] The installation, commissioning or parts replacement and maintenance of a certain equipment is divided into multiple processes OPn. Different events are defined for each process according to the equipment working results (excluding the interference of the quality factors of the parts themselves). Events include: parts falling, abnormal parts wear and automatic equipment shutdown, etc.

[0033] Set a unique UI for each employee involved in the above work, and record the personnel composition, working hours (including start and end time) of each corresponding process, and the number of occurrences of the corresponding event of the process, and build a mapping set of personnel composition, working hours and events with the employee UI as the starting index;

[0034] It should be noted that the number of event occurrences is bound to a specific employee. For an event A, if event A occurs during the corresponding process OPa or between the corresponding process and the next corresponding process, the occurrence of the event is attributed to the person involved in the corresponding process, and the number of occurrences is bound to the U ID of the person involved.

[0035] For example, when a consumable part needs to be installed or replaced, after determining the personnel composition for this process, if a part falls during the installation or replacement process and subsequent use, it is recorded as a part falling event; when a part is abnormally worn before the next such process, it is recorded as an abnormal wear event; when an equipment automatically shuts down between two such processes, and the reason for the automatic shutdown is related to the corresponding part, it is recorded as an equipment automatic shutdown event.

[0036] Step 2: Calculate the probability of each event relative to the corresponding employee

[0037] The ratio of the number of times a certain event occurs after the employee participates in a certain process to the number of times the employee participates in the process is defined as the event probability. In order to avoid large fluctuations in the probability data when the data sample is small, the event probability result is only bound to the employee when the data sample (i.e. the number of times the employee participates in the corresponding process) exceeds a certain number. Before that, the average event probability of all other employees exceeding the data sample is used as the event probability of the event relative to the employee.

[0038] Step 3: Construct the data matrix

[0039] Construct a data matrix with the number of U IDs as the number of rows and the number of event types as the number of columns, and fill the corresponding employees and their corresponding event probabilities into the data matrix;

[0040] In each column of the data matrix, different colors are used to mark the UIDs with the lowest event probability and the top N positions and the U IDs with the highest event probability and the bottom M positions;

[0041] Step 4: Calculate the average workload per person in any process

[0042] The total standard working time is calculated based on the standard working time and the standard number of workers for each process, and the labor intensity level corresponding to each process is determined based on the experience of workers and experts, and different labor intensity indexes are set for different labor intensity levels; the total standard working time is multiplied by the labor intensity level to obtain the total standard workload;

[0043] The work requirements of a specific process link include the required completion time and the required number of people to complete the task. The required completion time and the required number of people to complete the task are continuously divided by the total standard workload to obtain the required workload per person.

[0044] Step 5: Set different workload levels and match corresponding scoring levels

[0045] Five workload levels are set from large to small according to the workload. Workload level I has the largest workload, and workload level V has the smallest workload. Five scoring levels are set for each of the five workload levels. The higher the workload level, the higher the scoring level, and the higher the corresponding score.

[0046] Step 6: Determine the personnel composition involved in the corresponding process

[0047] According to the average workload required per person in step 4, it can be known that it is at a certain workload level and a score matching it is obtained; then a U ID with the lowest cumulative score (the cumulative score can be statistically analyzed at a weekly or monthly frequency) is selected from the first N idle U IDs to participate in this process, and the last M idle U IDs adopt a quota application system to participate in this process, and the idle U IDs at positions N+1 to M-1 make up the number of people, and the number of people is made up in order from the U IDs with the lowest scores;

[0048] It should be noted that the quota application system limits the number of applicants according to a certain ratio, and then selects the corresponding U ID with an earlier application time to participate in the process;

[0049] Step 7: Update the probability of occurrence of each event and the cumulative score of the personnel involved in the corresponding process

[0050] After each process is completed and before the next implementation of the same process, the probability of each U ID in the corresponding process is updated, and the cumulative score of each U ID participating in the corresponding process is calculated;

[0051] Step 8: Set participation restrictions for the U ID that executes a certain process

[0052] Sort the cumulative scores updated by each U ID, and observe the distribution of color marks with larger event probabilities and in the Mth position after sorting in the sorted data matrix; when the corresponding color mark appears in the last three positions of the cumulative score sorting, record the process corresponding to the color mark. When a U ID is recorded in the same process three times in a row, the association between the U ID and the process is untied. In the subsequent execution of the process, the U ID will no longer be used as an option for executing the process; when the U ID cannot participate in the processes exceeding the set proportion, the employee corresponding to the U ID is no longer qualified for the position.

[0053] Step 9: Update data periodically

[0054] Taking into account the proficiency and skill growth of employees, in order to avoid early data from distorting the overall data, the data within the same interval from the time when the corresponding U ID was first generated is automatically discarded at a certain interval (one quarter or half a year), and the event probability is recalculated to complete the periodic update of the data.

[0055] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A process configuration optimization management method based on event probability, characterized by: The steps include: Step 1: Divide different processes and define different events according to the equipment work results, and count the number of different events for each unique U ID set by each employee; Step 2: Define the ratio of the number of times an event occurs after an employee participates in a process to the number of times the employee participates in the process as the event probability; Step 3: construct a data matrix with the number of UIDs as the number of rows and the number of event types as the number of columns and fill in the data. In each column, use different colors to mark the UIDs with the smallest event probability and the top N positions and the UIDs with the largest event probability and the bottom M positions; Step 4: Set different workload levels and match corresponding scoring levels, and calculate the per capita workload required for each actual process; Step 5: Determine the personnel composition involved in the corresponding process; Step 6: After each process is completed and before the next implementation of the same process, update the probability of each event and the cumulative score of the personnel involved in the corresponding process; Step 7: Set participation restriction conditions for the UID that executes a certain process.

2. The process configuration optimization management method based on event probability according to claim 1 is characterized in that: The types of events described include parts falling, abnormal parts wear and automatic equipment shutdown.

3. The process configuration optimization management method based on event probability according to claim 1 is characterized in that: The number of occurrences of the event is bound to a specific employee. If a certain event occurs during the corresponding process or occurs between the corresponding process and the next corresponding process, the occurrence of the event is attributed to the person involved in the corresponding process.

4. The process configuration optimization management method based on event probability according to claim 1 is characterized in that: For the event probability definition process in step 2, when the number of times an employee participates in the corresponding process is less than the set number, the average event probability of the remaining employees who participate in the corresponding process more than the set number is used as the event probability of the event relative to the employee.

5. The process configuration optimization management method based on event probability according to claim 1 is characterized in that: The workload levels described in step 4 are divided into five levels, and five scoring levels are set correspondingly. Workload level I has the largest workload and the highest score; workload level V has the smallest workload and the smallest score.

6. The process configuration optimization management method based on event probability according to claim 1 is characterized in that: In step 4, the per capita required workload of each actual process is obtained by continuously dividing the required completion time and the required number of people in the actual process by the total standard workload, wherein the total standard workload is obtained by continuously multiplying the standard labor time, the standard labor number and the labor intensity index of the process; The standard working time and the standard number of workers are the standard set values ​​for the process. The labor intensity index is set according to the corresponding labor intensity level, and the labor intensity level is determined based on the experience of workers and experts.

7. The process configuration optimization management method based on event probability according to claim 1 is characterized in that: The process of determining the personnel composition involved in the corresponding process described in step 5 includes: Select a U ID with the lowest cumulative score from the first N idle U IDs to participate in this process; The idle U IDs in the last M positions are selected to participate in this process through a quota application system; The idle UIDs at positions N+1 to M-1 will be used to make up the number of people, and the number of people will be made up in order from the UIDs with the lower scores forward.

8. The process configuration optimization management method based on event probability according to claim 7 is characterized in that: The quota application system limits the number of people who have completed the requirements according to a certain ratio, and then selects the corresponding U ID with an earlier application time to participate in the process.

9. The process configuration optimization management method based on event probability according to claim 1 is characterized in that: The process of setting the restriction conditions described in step 7 includes: The cumulative scores updated by each U ID are sorted. When a corresponding color mark appears in the last three digits of the cumulative score ranking, the process corresponding to the color mark is recorded. When a U ID is recorded in the same process three times in a row, the U ID will no longer be an option for executing the process in the subsequent execution of the process. When the U ID cannot participate in the processes that exceed the set proportion in the total process volume, the employee corresponding to the UID is no longer qualified for the position.

10. The process configuration optimization management method based on event probability according to claim 1 is characterized in that: It also includes periodic updating of data, specifically, automatically discarding data within an interval of equal length from the time when the corresponding U ID first generated data at a certain interval, and recalculating the event probability.