The present invention relates to a process estimation apparatus and method for estimating a process performed by a worker.
Generally, when manufacturing a product, a plurality of types of work are performed. For example, in order to generate printed matter, it is necessary to perform work composed of a plurality of processes such as printing, binding, cutting, and inspection. Managing an estimate versus actual results has been widely performed by generating a work plan in advance based on the priority of each process and the deadline of printed matter, performing each process based on the plan, and then inputting the work results of each process. However, if the number of processes constituting the work increases or the amount of work increases, there is a problem that, because there is an increase in results that must be inputted, the load on a worker who inputs results increases.
If a process performed by a worker can be automatically estimated, the above-mentioned problem can be solved. For example, the position and orientation of a worker, the operation log of each apparatus, a prior plan, and the like are collected for each time and converted into features which are used, together with the work process actually performed by the worker (correct answer data) at each time, as training data for machine learning. By generating an estimator using the training data, it is possible to estimate when and what processes a worker has performed by giving the above-mentioned features to the estimator at an arbitrary time. Of the information available for estimation, information about the plan is particularly useful. This is because, when generating the plan, the worker commits to work on the basis of the plan. Unless special circumstances arise, it is considered highly likely that a process performed by the worker at each time is any process that is planned to be around that time. Even in the case where a delay occurs in the work and it becomes difficult to perform the work as planned in advance, by regenerating the best plan at the time of occurrence of the delay, it can be used as highly reliable information in an estimation technique.
For example, Japanese Patent Laid-Open No. 2005-78288 has a technique for using plan information for an estimation. Japanese Patent Laid-Open No. 2005-78288 proposes a method of estimating an actual position of a user using position information obtained from a single position positioning technique, and plan information. In this method, the priorities of various types of information are set in advance, and information having a high priority is used as an estimation result. That is, by raising the priority of plan information in advance, it is possible to output a result that takes into account the plan information at the time of estimation.
In addition, Japanese Patent Laid-Open No. 2007-306179 is a different example of using plan information in estimation. Japanese Patent Laid-Open No. 2007-306179 also proposes a method of using plan information for position estimation. In the method of Japanese Patent Laid-Open No. 2007-306179, a system side determines a method for using plan information in consideration of the certainty of a plan, and then performs position estimation.
In general, in an estimation technique that uses plan information, whether or not accurate plans can be gathered is important because it has a large influence on an estimation accuracy that uses the plans. Therefore, if a delay occurs due to a sudden event such as a failure of an apparatus, and it becomes difficult to perform work as planned in advance, it is desirable to regenerate the plan at that point and ensure the accuracy of the plan information.
However, in an actual manufacturing site, when it becomes impossible to perform work as planned in advance, planning is not necessarily performed again. If processing steps for planning again as a work flow at a site have not been converted into rules, each worker may respond without planning again at their discretion. For example, if a delay has occurred in a plan, a person in charge of the work determines and copes on their own with how to proceed with the subsequent work, depending on the level of delay and how high the urgency or the importance of the delayed process is in the overall plan. If the importance/urgency of the delayed process is low and does not affect other work plans, there is a strong tendency for the work to continue with the scheduled work time period in the order of work processes scheduled in the prior plan. On the other hand, in a situation where the urgency or importance of a delayed process in the overall plan is high and the delayed situation affects the delivery deadline of the manufacturing, there is a strong tendency to cope by working in a shorter amount of time than planned in any process. Even in the above-described example, if there is explicit replanning, information on actions that a worker can take is included as reliable information in the plan information. Therefore, even in a case where the actions that a worker can take differ depending on the circumstances of the site as described above, accurate estimation can be expected by using the plan information as one piece of information used by an estimator. However, it is difficult to accurately estimate without planning again.
In the method described in Japanese Patent Laid-Open No. 2005-78288, it is necessary to decide in advance which information should be prioritized when estimating. Therefore, when it is set that plan information is preferentially used, the estimation accuracy is lowered in a situation where the plan is not correct. Since whether or not a plan is correctly maintained by performing planning again when a delay occurs differs depending on the user, the above-mentioned problem cannot be solved.
In Japanese Patent Laid-Open No. 2007-306179, the system side determines how to use the plan information in consideration of the certainty of the plan, but countermeasures in the case where the certainty of the plan is low are uniform. Therefore, it is not appropriate to apply this system to events in which the content of the work differs depending on the situation as described above. In particular, the prior art cannot deal with manufacturing sites in which action in response to delay of plan varies depending on the degree of importance and/or urgency of delayed work (whether or not they are included in a pre-created critical path in a plan, whether or not the critical path has enough margin or is about to reach its turnaround).
That is, in the conventional technique, when planning is not performed again in a situation where work is not proceeding as planned, plan information cannot be handled appropriately in order to estimate a process performed by a worker. It should be noted that it is theoretically possible to use a machine learning algorithm that considers the weight of each feature to train weighting for when a delay occurs due to a sudden matter. However, as a problem in actual operation of such an algorithm, it is difficult to generate an appropriate estimation model unless sufficient data for training is collected.
Certain embodiments of the present invention provide a technique capable of appropriately creating a feature related to a plan used in process estimation by machine learning without re-planning even in a situation where work is not proceeding as planned in advance.
As a means for solving the above problems, a process estimation apparatus according to an embodiment of the present invention has the following configuration.
A process estimation apparatus operable to, by machine learning using information regarding a plan as one of features, estimate a process being performed by a worker, the apparatus comprising: a progress information accepting unit configured to accept progress information of each process configuring a plan that is generated in advance; an importance/urgency decision unit configured to decide an importance/urgency of each process that configures the plan that is generated in advance; and a calculation method decision unit configured to decide a method of calculating a feature related to a plan in accordance with the progress information accepted by the progress information accepting unit and the importance/urgency decided by the importance/urgency decision unit.
By virtue of embodiments of the present invention, it is possible to appropriately create a feature related to a plan used in process estimation by machine learning without re-planning even in a situation where work is not proceeding as planned in advance. Accordingly, the accuracy of the process estimation can be improved.
Further features of the present invention will become apparent from the following description of exemplary embodiments (with reference to the attached drawings).
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention, and together with the description, serve to explain the principles of the invention.
Below, with reference to the attached drawings, a detailed explanation is given for the present invention based on preferred embodiments. Note that the configurations described in the following embodiments are merely examples, and the present invention is not limited to the illustrated configuration. In addition, the same or equivalent components, members, and processing illustrated in the respective drawings are denoted by the same reference numerals, and duplicate descriptions are omitted as appropriate. In the drawings, some members which are not important for explanation are omitted.
A proposed process estimation apparatus is constructed at a site where a product is produced by a person or an apparatus based on a plan. In the following description, a printing factory is taken as an example, but there is no limitation to this, and construction may be performed at other production sites.
A communication unit 101 functions for other functional blocks of the process estimation apparatus 1 to perform data communication with an apparatus/machine/DB/access point in a factory which are to be connected to via the local area network 19. An estimated/actual management unit 102 receives and manages the plan information generated by the plan generation apparatus 10. More specifically, the order table 410 and the plan table 420 are received and held in the process estimation apparatus 1. In addition, the estimated/actual management unit 102 manages estimation results of processes performed at respective times that are estimated by the process estimation unit 114, which will be described later, in association with the plan information as the actual result information for the plan information. A work definition information acquisition unit 103 acquires information on a work definition from the work definition information DB 8, and provides this information to other functions in the process estimation apparatus 1. Specifically, information of the work definition information table 310, the process table 320, the worker table 330, and the machine table 340 is acquired. A progress information accepting unit 104 receives progress information of work with respect to a plan at a printing site from an external apparatus/system, the estimated/actual management unit 102, or the input device 11 of the process estimation apparatus 1. An importance/urgency decision unit 105 decides the importance or urgency of a process for which work is scheduled based on information held by the estimated/actual management unit 102 or information received from the input device 11 of the process estimation apparatus 1. A calculation method decision unit 106 decides a method of calculating a feature value relating to a plan which is to be used in machine learning or deep learning. At this time, information of the work definition information, a work progress degree, and the importance/urgency of a process for which work is scheduled is received from the work definition information acquisition unit 103, the progress information accepting unit 104, and the importance/urgency decision unit 105, and the calculation method is decided based on these pieces of information. A feature that relates to a plan is expressed by the magnitude of the possibility of each estimation candidate process being a correct answer, which is calculated based on the plan information. A specific procedure for deciding a calculation method will be described later with reference to
A sensor information acquisition unit 108 receives measurement results of various sensors that perform measurement on the portable information terminal 5, and user information of the portable terminal. A machine operation log acquisition unit 109 acquires operation logs from machines such as the printing machine 2, the bookbinding machine 3, and the cutting machine 4 that operate in the factory. A position information acquisition unit 110 acquires position information of the portable information terminals 5 that are estimated by the position estimation apparatus 7. Since the portable information terminals are held by workers working in the printing factory, the position information of the workers is acquired. The feature generation unit 107 receives various data from the estimated/actual management unit 102, the sensor information acquisition unit 108, the machine operation log acquisition unit 109, and the position information acquisition unit 110, and generates features used in machine learning and deep learning. Note that a method decided by the calculation method decision unit 106 is followed when generating a feature related to the plan. An overview of a feature generated by the feature generation unit 107 and the procedure for generating the feature will be described later with reference to
A feature data storage unit 111 stores data of a feature generated by the feature generation unit 107. To features for each time generated by the feature generation unit 107, the correct answer data assigning unit 112 assigns, as correct answer data for machine learning, a process actually performed at the printing site at each time. The estimation model generation unit 113 generates an estimator for process estimation by taking, as training data, the features to which correct answer data has assigned by the correct answer data assigning unit 112. The process estimation unit 114 uses the estimator generated by the estimation model generation unit 113 to estimate, with respect to a feature for any time for which correct answer data stored by the feature data storage unit 111 has not been assigned, a work process that was performed at that time. In addition, the process estimation unit 114 transmits an estimation result to the estimated/actual management unit 102.
A column 601 stores time information for specifying which time data each row of the feature table 600 corresponds to. The information stored in a column 602 and a column 603 expresses, as information on a plane, where the portable information terminals 5 are located in the printing factory, and indicates an x coordinate and a y coordinate in the printing factory, respectively. The information of the columns 602 and 603 is generated by the feature generation unit 107 based on the position information, estimated by the position estimation apparatus 1, of the portable information terminals 5 at each time. Information stored in a column 604 represents, as information on a plane, a direction in which the portable information terminals 5 face, and indicates a value between 1 and 360 (unit: degrees). The information stored in the column 604 is generated by the feature generation unit 107 based on information, which is acquired by the sensor information acquisition unit 108, on a direction at each time point from the portable information terminals 5. Although features relating to the position and orientation is provided in the feature table 600 as information in a plane, that is, two-dimensional information, it may be provided as stereoscopic information, that is, three-dimensional information. Information as to whether or not the printing machine 2, the bookbinding machine 3, and the cutting machine 4 are operating is stored in a column 605, a column 606, and a column 607, respectively. Specifically, a value of 0 when a machine is operating and a value of 1 when the machine is not operating is stored as a feature. Though omitted in
A column 608 stores a value representing a possibility that the estimation target is performing the printing process at each time. Similarly, the columns 609, 610, and 611 store values representing the possibility that the estimation target is performing a bookbinding process, a cutting process, and an inspection process, respectively. Though omitted in
Using
The distributions 711 to 744 are generated based on information of a distribution pattern definition table 800 illustrated in
The distribution base (b), the procedure for generating the distribution base (b), the distribution base (c) and the procedure for generating the distribution base (c) in the present embodiment will be described with reference to
In an embodiment, the distribution base (b) is a normal distribution. The box in
On the other hand, in the present embodiment, the distribution base (c) is a distribution in which a portion of the first half monotonically increases, an intermediate portion is a constant value, and a portion of the second half monotonically decreases. The box in
Description will be given of a specific method of deciding the distribution of the portions before 13:00 and after 17:00 (hereinafter, each may be represented as a “tail of the distribution” or a “tail portion”). As written in the column 804 of the table 800, the standard deviation of the tail having the pattern ID of 12 is 3. Therefore, first, a normal distribution having an average of 0 and a standard deviation of 3 is generated, and scaling is performed so that the maximum value of the generated normal distribution becomes 0.7 for the planned portion. For where the distribution after scaling is less than or equal to 0, the distribution of the part before 13:00 in
Description will be given for handling in the case where a plurality of processes of the same type are scheduled in the row 700. Firstly, a distribution is generated by the above-described procedure for each process scheduled at each location. In this case, at each time, there are as many values representing the magnitude of the possibility of a process as the number of distributions. In the present embodiment, the total of the values of the possibilities from the respective distributions at each time is defined as the magnitude of the possibility at that time. Note that the magnitude of the possibility at each time may be defined by another method. Configuration may be taken to, at each time, calculate, from only the distribution generated from the nearest scheduled process, the magnitude of the possibility of that process at that time.
Note that distributions other than those described above may be base distributions. For example, a Poisson distribution, a distribution defined by a non-parametric method, or any other generally defined distribution may be used. Further, in the example of
The process estimation unit 114 according to the present invention estimates the type of the process at each time by using a trained estimation model in machine learning. Description will be given regarding generation of an estimation model in machine learning using the feature table 600 described above, the accuracy of the generated estimation model, and actual estimation using the estimation model. It is a first object of the process estimation unit of the present invention to estimate, in real time, a process being performed by an estimation target. However, the content described in the present invention is also applicable to non-real-time estimation.
First, a procedure for generating an estimation model will be described. In order to generate the estimation model, feature data obtained by digitizing features included in various data collected from the printing site is generated for each time. With respect to the generated feature data for each time, the type of a process actually performed by a person who is an estimation target at that time is linked as correct answer data. In the example of
Next, the accuracy of the generated estimation model will be described. The feature data with correct answer data that is not used at a time of training is input as test data to the above-described generated estimation model to cause estimation to be actually performed. The estimation accuracy of the estimation model can be obtained from an estimation result estimated by the estimation model and the correct answer data associated with the input feature data. There are several methods to represent estimation accuracy. For example, there is a reproducibility for expressing the estimation system by indicating how much an XYZ process can be correctly estimated from among data for which the correct answer is the XYZ process. In addition, there is a suitability rate for expressing the estimation accuracy by indicating how many of the results estimated by the estimation model to be the XYZ process are actually the XYZ process.
Next, a description will be given of processing for a case of actually estimating using the generated estimation model. In actual estimation, the feature data of the columns 602 to 607 for the time of a target of estimation and the feature data related to the plans for the columns 608 to 611 for this time are input to the estimation model, and an estimation result is obtained. At this time, when the progress information accepting unit 104 has not received information such as a delay in the progress of the estimation target (information that work is not proceeding as planned), the feature data relating to the plan is generated based on the latest plan that is generated in advance by the plan generation apparatus 10. However, when the progress information accepting unit 104 receives information such as the progress of the estimation target has been delayed, features related to the plan are generated according to the importance/urgency of the process in which the delay or the like has occurred. The estimation model performs estimation by taking the re-generated feature data as input data. Hereinafter, detailed processing related to the generation of features related to the plan will be described.
Hereinafter, details of how features relating to the plan which are used for estimation by the estimation model are re-generated will be described with reference to
In step S601, the calculation method decision unit 106 generates a distribution to be applied based on a prior plan generated by the plan generation apparatus 10. This step is executed when the plan generation apparatus 10 has first generated a plan to be followed by the worker who is the estimation target, or when the plan generation apparatus 1 has re-planned while the worker is working. A specific procedure for generating the distribution in this step is as described with reference to
In step S602, the calculation method decision unit 106 determines whether the plan generation apparatus 10 has re-generated the plan. More specifically, the content of the table 420 of the estimated/actual management unit 102 when this step was last performed is compared with the table 420 when this step is performed this time, and it is determined whether or not there is a difference in the location of the plan information. If the plan has been re-generated, the processing transitions to step S601. If it has not been regenerated, the processing transitions to step S603.
In step S603, the calculation method decision unit 106 acquires the progress from the progress information accepting unit 104. When a user of the process estimation apparatus 1 inputs information on work progress with respect to the plan, the progress information accepting unit 104 stores the information in the primary storage apparatus 14. Then, the progress information accepting unit 104 presents the latest progress information to be held in response to a request from the calculation method decision unit 106. Since when the information on the work progress is updated depends on the user who uses the process estimation apparatus 1, even if the work is delayed, such information is not always provided to the calculation method decision unit 106. The progress information is configured by “progress status” and “progress value”. Types of progress status include “as planned,” “delayed”, and “ahead of schedule”. “As planned” is a status indicating that the process planned at the current time is being performed as scheduled. “Delayed” is a status indicating that the work is delayed and the process scheduled for the current time has not yet been performed. “Ahead of schedule” is a status indicating that the process planned at the current time has been started ahead of the plan. The progress value is a value for expressing the degree of the progress status, and is expressed, for example, in units of minutes. For example, when the status of the progress is “delayed” and the progress value is 10, it indicates that the previous plan is delayed by 10 minutes and the start time of a process originally scheduled to be performed at the current time is delayed by 10 minutes according to calculations.
In the present embodiment, a user explicitly inputs progress information, but progress information may be provided to the progress information accepting unit 104 by another method in actual operation. For example, the progress information may be calculated from the operation logs of the machines 2 to 4 that are acquired by the machine operation log acquisition unit. Despite the fact that a printing process for an order was planned to start from 10:00, it is assumed that the fact that the printing for the order started from 9:45 was described in the log output by the printing machine 1. In this case, it is possible to generate progress information indicating that the printing process started 15 minutes ahead of schedule. When there is the appearance of a case in which, from the information on the estimation accuracy of the estimation model, an estimation result is sufficiently reliable (for example, the estimation accuracy is equal to or greater than a predetermined value), the progress information may be generated from this information. For example, when the accuracy of estimating the bookbinding process is high in both reproducibility and suitability, and when the estimation model estimates that the bookbinding process is being performed, this information can be handled as reliable information. If it is estimated that the bookbinding process was performed at 9:50 with respect to a plan where the bookbinding process was scheduled to be from 10:00, it is possible to generate progress information indicating that the bookbinding process has been performed 15 minutes in advance.
In step S604, it is determined whether the status of the progress has changed from “delayed” to “as planned”. If the status changes from “delayed” to “as planned”, the processing transitions to step S601. Otherwise, the processing transitions to step S605.
In step S605, the calculation method decision unit 106 determines whether or not the status of the progress has changed from “as planned” to “delayed”. If it has changed from “as planned” to “delayed”, the processing transitions to step S606. Otherwise, the processing transitions to step S614.
In step S606, the calculation method decision unit 106 acquires the importance/urgency of the processes planned after the current time in the prior plan, which are decided by the importance/urgency decision unit 105. In the present embodiment, it is determined whether or not the importance/urgency of each process is high, based on whether or not the process is included in a critical path. A critical path is a sequence of processes that effectively decide a plan for all work at a printing site. Delays in the process on the critical path result in delays in the final work completion time of the entire printing site. Therefore, it is considered that a processes on the critical path has a high importance/urgency. In the present embodiment, the importance/urgency of a process included in the critical path is set to be high, and the importance/urgency of a process not included are set to be low. In actual operation, the importance/urgency may be decided by another method.
For example, in consideration of the delivery deadline of printed matter, the importance/urgency may be set high in the case where the process is on the critical path for the entire plan and the delivery deadline of the printed matter will not be met if the process is delayed. The delivery deadline information can be specified by referring to the column 417 of the table 410. Alternatively, instead of the delivery deadline, the importance/urgency may be decided based on whether the process is on the critical path of a plan, and whether printed matter generated by the process is an express order. Whether this is for an express order can be specified by referring to the column 314 of the table 310.
In step S607, the calculation method decision unit 106 determines whether or not each process is included in the critical path. If the process is included in the critical path, the processing transitions to step S610. If the process is not included in the critical path, the processing transitions to step S608.
In step S608, the calculation method decision unit 106 simulates a situation for if the work were continued in accordance with the work time periods of the processes planned in the prior plan.
In step S609, the calculation method decision unit 106 determines whether or not the result of performing the simulation in step S608 is that a delayed process would consequently be included in the critical path. If a delayed process would be included in the critical path, the processing transitions to step S610. If a delayed process would not be included in the critical path, the processing proceeds to step S612.
In step S610, the calculation method decision unit 106 generates a virtual plan for the processes included in the critical path. A virtual plan (hereinafter also referred to as a virtual plan) is a plan that an estimation target is likely to follow when they perform work. A virtual plan may differ from a plan that is generated in advance. Details of the processing of this step will be described later with reference to
In step S611, the calculation method decision unit 106 generates distributions by deciding the distribution to apply to each process included in the critical path. Details of the processing of this step will be described later with reference to
In step S612, the calculation method decision unit 106 generates a virtual plan for the processes not included in the critical path. Details of the processing of this step will be described later with reference to
In step S613, the calculation method decision unit 106 generates distributions by deciding the distribution to apply to each process not included in the critical path. Details of this step will be described later with reference to
In step S614, the calculation method decision unit 106 determines whether process estimation by the process estimation unit 114 has ended. Whether or not the process estimation has ended is determined based on whether or not the estimation target is present at the printing site, based on the position information of the portable information terminal 5 held by the worker who is the estimation target. In the present embodiment, it is assumed that the process estimation has ended when the worker as the estimation target has left the printing site. It should be noted that the determination may be made by other methods. For example, the user of the process estimation apparatus 1 may explicitly instruct the end of the process estimation processing via the input device 11. When this ends, the processing flow illustrated in
In step S615, the calculation method decision unit 106 stops the processing and waits for a predetermined period of time (for example, one second), and then transitions to step S602.
The processing of step S610 to step S613 in
In step S621, the calculation method decision unit 106 acquires variation information of the unit work time periods of the respective processes. Specifically, the value of the standard deviation with respect to the unit work time period of a process is acquired with reference to the information stored in the column 327 of the process table 320. A process for which variation information is acquired is a process after the current time in the row 122 which configures the prior plan of the worker 1 who is the estimation target.
In step S622, the calculation method decision unit 106 calculates an amount of time by which to shorten each process. Based on the standard deviation of each process acquired in step S621, it is decided by how much the work of each process should be shortened. In the present embodiment, it is considered that the smaller the value of the ratio (unit work time period÷standard deviation), the easier it is to make the work time period shorter than the planned time when executing the process. That is, when a delay occurs, the amount of time by which to shorten each process is calculated for the processes included in the critical path of the worker 1 after the current time. The amount of time by which to shorten each process is calculated by solving an equation obtained from the above-mentioned ratio of each process, the length of the work time period of each process in the prior plan, and the sum total of the amount of time that must be shortened among all processes. Note that the total amount of time that must be shortened across all the processes is an amount of time indicated by the progress value acquired from the progress information accepting unit 104.
The row 123 of
In step S623, the calculation method decision unit 106 generates a virtual plan based on the shortened work time period of each process.
In step S625, the calculation method decision unit 106 acquires information of the distribution pattern definition table 800.
In step S626, the calculation method decision unit 106 acquires the distribution base and parameters of the respective processes from the column 803 and the column 804 of the table 800.
Hereinafter, step S627 to step S629 are performed for each process in the virtual plan.
In step S627, with respect to the process in the virtual plan, the calculation method decision unit 106 generates a distribution based on the distribution pattern of the process which was specified in step S626.
In step S628, with respect to the process in the prior plan, the calculation method decision unit 106 generates a distribution based on the distribution pattern of the process which was specified in step S626.
In step S629, the calculation method decision unit 106 generates a distribution having two average features from the two distributions that relate to the process and were generated in step S627 and step S628.
Supplemental description for step S629 will be given with reference to
According to the method described above, when a distribution is generated for a virtual plan, it is possible to generate a distribution in which a deviation of the plan is allowed more than when generating by the method described in
Note that, in actual operation, a method of generating a distribution that allows deviation in a plan is not limited to that illustrated in
In step S624, the calculation method decision unit 106 slides the times for performing the work of each process in the direction of travel of time by the amount of time of the delay. Row 123 of
In step S630, the calculation method decision unit 106 acquires information of the distribution pattern definition table 800.
In step S631, the calculation method decision unit 106 acquires the distribution base and parameters of the respective processes from the column 803 and the column 804 of the table 800.
Hereinafter, step S632 is performed for each process in the virtual plan.
In step S632, with respect to the process in the virtual plan, the calculation method decision unit 106 generates a distribution for the process which was specified in step S631. The method of generating the distribution is according to the procedure described with reference to
In the present embodiment, description was given for processing of the calculation method decision unit 106 by taking as an example a case where the progress of work is delayed. However, based on the concept described in the present embodiment, it is possible to provide an appropriate method of calculating a feature related to the plan even in a case where the progress of the work is otherwise. Hereinafter, some examples other than the case where the work progress is delayed will be described with reference to
By virtue of the technique described above, it is possible to appropriately generate a feature related to a plan used in process estimation by machine learning without re-planning even in a situation where work is not proceeding as planned in advance.
Description is given for an example of a second embodiment of the present invention. In the present embodiment, an example of generating the above-mentioned virtual plan and distribution based on past actual results will be described.
In the first embodiment, the method of generating a virtual plan is uniquely decided in accordance with the status of the progress and the importance/urgency. Actually, however, when a process included in a critical path is delayed, it is not necessarily the case that work is performed each time by shortening the work time period of each process. In some cases, work is performed by shortening the work time period so as not to affect the entire plan, and in other cases, the work is continued with the work time period allocated in the prior plan without worrying about the delay.
In the first embodiment, the distribution generated for each process is generated based on a specific distribution pattern decided from the process ID. However, there may be other optimal distribution patterns in a specific situation.
In the present embodiment, description is given for an example of a method of generating a virtual plan and a distribution optimized for each worker based on knowledge obtained by analyzing the tendency of each worker from actual results from the past.
When generating a distribution for each process, each distribution base/parameter associated with the process is used to generate a distribution in the manner described in the first embodiment. For a process for which there are a plurality of distribution bases/parameters, a distribution is generated by combining distributions based on the contribution rate of each distribution. Note that the contribution rate is a value (a statistic) statistically calculated from past actual result information.
When a virtual plan is generated, the virtual plan is generated by a virtual plan pattern linked to the corresponding progress status and the level of the importance/urgency. Note that, when a plurality of virtual plan patterns are linked, individual virtual plans are generated independently. With respect to the generated virtual plan, the distribution is generated by referring to the table 820 illustrated in
Description is given for an example of a third embodiment of the present invention. In the present embodiment, the process estimation apparatus 1 displays a screen that enables a user to grasp (or is capable of allowing a user to grasp) a prior work plan, an estimation result (actual results for the plan), the virtual plan described above, and information on a distribution used for the generation of a feature related to the plan. According to the present embodiment, it is possible to externally verify whether or not a distribution is changed at a time of estimation.
The display unit 131 displays information relating to a work plan generated in advance and actual result information linked thereto that are managed by the estimated/actual management unit 102. The work plan is a work schedule for the estimation target at each time, and is information described in the table 420. The actual result information is the type of the process, estimated by the process estimation unit 114, for which the estimation target is working on at each time. The display unit 131 can also display the virtual plan used at the time of estimation, and the distribution generated based on the prior work plan and the virtual plan. Note that the function of the display unit 131 is realized by the output device 12 included in the process estimation apparatus 1.
The correction unit 132 provides a function for a user to explicitly correct an estimation result and an envisioned plan after the estimation. The estimation result in accordance with the process estimation unit 114 includes an error corresponding to the estimation accuracy of the estimator. When a user is able find a result that was erroneously estimated, by correcting the result to a correct result, the result can be reused for a purpose such as training data for an estimation model. With regard to the envisioned plan, if it is possible to record whether it is envisioned how the worker who is the estimation target will perform work as correct answer data for the envisioned plan, it is possible to use this when generating an envisioned plan thereafter. In other words, it is possible to improve the accuracy of a result of generating an envisioned plan.
In the worker information display area 201, information of an estimation target of the process estimation unit 114, which is a target of information display by the management screen 200, is displayed.
In the display content selection area 202, check boxes 203 to 206 for specifying what information to display on the management screen 200 are arranged. When the check box 203 is checked, information on a prior plan is displayed. When the check box 204 is checked, information on an estimation result is displayed. When the check box 205 is checked, information on a virtual plan is displayed. When the check box 206 is checked, information on distributions used when calculating the feature related to the plan is displayed.
In the prior plan display area 207, when the prior plan display check box 203 is checked, the work schedule of the estimation target, which is described in the worker information, is displayed. The displayed work schedule is a plan that is generated in advance by the plan generation apparatus 10. In the example of
In the present embodiment, description has been given for an example of displaying on a screen information as to whether or not a method of calculating a feature relating to the plan used at the time of estimation has changed. Note that display may be performed by another method as long as the format of the display is easy for the user to identify. For example, when the estimated/actual management unit 102 uses a relational DB to record the estimation result, a column (column) indicating whether or not the distribution is changed may be provided in a table for storing estimation results. That is, DB design may be performed so that a user can grasp which distribution pattern was used at the time of estimation by looking at the table or in accordance with an SQL (Structured Query Language) operation by the user.
Although embodiments have been described above in detail, it is possible to have an embodiment of the present invention as, for example, a system, an apparatus, a method, a program, a recording medium (a storage medium), or the like. Specifically, the present invention may be applied to a system configured from a plurality of devices (for example, a host computer, an interface devices, an imaging apparatus, a web application, and the like), or may be applied to an apparatus that includes a single device.
It is needless to say that the object of the present invention is achieved by the following steps. That is, a recording medium (or a storage medium) on which program code (computer program) of software for realizing the functions of the above-described embodiments is recorded is supplied to a system or an apparatus. Such a storage medium is, of course, a computer-readable storage medium. Then, a computer (or a CPU or an MPU (Micro Processing Unit)) of the system or apparatus reads out and executes the program code stored in the recording medium. In this case, the program code itself read out from the recording medium realizes the functions of the above-described embodiments, and the recording medium on which the program code is recorded constitutes the present invention.
Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like.
While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.
This application claims the benefit of Japanese Patent Application No. 2018-235905, filed Dec. 17, 2018, which is hereby incorporated by reference herein in its entirety.
Number | Date | Country | Kind |
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2018-235905 | Dec 2018 | JP | national |