WO2020110131A1

WO2020110131A1 - Method and crew allocation system for allocating a field technician for executing a work order

Info

Publication number: WO2020110131A1
Application number: PCT/IN2018/050787
Authority: WO
Inventors: Anusha Pradeep MUJUMDAR; Ramamurthy Badrinath; Swarup Kumar Mohalik; Vijaya Yajnanarayana
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2018-11-27
Filing date: 2018-11-27
Publication date: 2020-06-04

Abstract

A method and a crew allocation system (100) for allocating a field technician for executing a work order on equipment (102) in a communication network (104). A set of tasks to be performed when executing the work order is identified and skills required for performing the identified tasks are also determined. Competencies of a set of candidate field technicians with respect to the required skills are further obtained (1:3), e.g. from a stored competency matrix, and at least one of the candidate field technicians is allocated (1:4) for executing (1:6) the work order on the equipment, based on the required skills and obtained competencies. The competency of the allocated field technician(s) may be updated (1:8) based on an execution report generated after execution of the work order.

Description

METHOD AND CREW ALLOCATION SYSTEM FOR ALLOCATING A FIELD TECHNICIAN FOR EXECUTING A WORK ORDER

Technical field

The present disclosure relates generally to a method and a crew allocation system for allocating a field technician for executing a received work order on equipment in a communication network.

Background

Communication networks of today such as wireless networks are typically very complex and contain many different nodes, elements and components, herein generally referred to as“equipment”, which are used to provide various communication services for users and subscribers and also for monitoring and surveillance of the network and its performance. Due to the complexity and many parts of such a communication network, various problems and issues are bound to occur in the network from time to time which often require some appropriate action by a field technician, e.g. on site, to remedy or“fix” the problem or issue. A problem in the network may involve faulty equipment that needs to be attended to, such as repaired, restored, replaced or upgraded, in order to eliminate or at least somehow address the problem.

When such a problem or issue is detected in a communication network, a work order is generated to address the problem and it is necessary to allocate a hopefully competent person to execute the work order, often locally on site where the cause for the problem or issue is known or assumed to be located. Such a person is commonly referred to as a field technician which term will be used in this description. The work order may comprise one or more specific tasks which effectively define the work order and there are typically a plurality of field technicians available for execution of various work orders in the network.

However, the allocation of a suitable field technician may not be optimal and it typically requires a certain amount of manual actions which must be made to obtain information about suitability of a number of candidate field technicians. A field technician is then selected and allocated to execute the work order based on whatever information that could be obtained, although without being confident as to whether the selected person is really able to execute the work order successfully. In practice, field technicians are typically selected by a person purely based on availability without consideration of their skills. Otherwise, manual efforts are involved to assess the skills possessed by the candidate field technicians compared to the skills required for the work order. Summary

It is an object of embodiments described herein to address at least some of the problems and shortcomings outlined above. It is possible to achieve this object and others by using a method and a crew allocation system as defined in the attached independent claims.

According to one aspect, a method that may be performed by a crew allocation system is provided for allocating a field technician for executing a work order on equipment in a communication network. In this method, a set of tasks to be performed when executing the work order are identified. Certain skills required for performing the identified tasks are also determined. Then, competencies of a set of candidate field technicians are obtained with respect to the required skills, and at least one of the candidate field technicians are allocated for executing the work order, based on the required skills and obtained competencies.

According to another aspect, a crew allocation system is arranged to allocate a field technician for executing a work order on equipment in a communication network. The crew allocation system is configured to identify a set of tasks to be performed when executing the work order, and to determine what skills are required for performing the identified tasks. The crew allocation system is further configured to obtain competencies of a set of candidate field technicians with respect to the required skills, and to allocate at least one of the candidate field technicians for executing the work order, based on the required skills and obtained competencies. When using either of the above method and crew allocation system, it is an advantage that the most suitable field technician can be automatically allocated for each new specific work order based on information about the competencies of the candidate field technicians, without requiring any manual efforts to achieve the allocation. Such competency information can also be automatically updated as the field technicians acquire more and more experience from executed work orders, to ensure continued accurate and automatic crew allocation for work orders.

The above method and crew allocation system may be configured and implemented according to different optional embodiments to accomplish further features and benefits, to be described below.

A computer program is also provided comprising instructions which, when executed on at least one processor in the above crew allocation system, cause the at least one processor to carry out the method described above. A carrier is also provided which contains the above computer program, wherein the carrier is one of an electronic signal, an optical signal, a radio signal, or a computer readable storage medium.

Brief description of drawings

The solution will now be described in more detail by means of exemplary embodiments and with reference to the accompanying drawings, in which:

Fig. 1 is a communication scenario illustrating an overall procedure where a crew allocation system allocates a field technician for a work order, according to some example embodiments.

Fig. 2 is a flow chart illustrating a procedure that may be performed by a crew allocation system, according to further example embodiments.

Fig. 3A is an example of a requirements matrix which may be used in the procedures of Figs 1 and 2, according to further example embodiments.

Fig. 3B is an example of a competency matrix which may be used in the procedures of Figs 1 and 2, according to further example embodiments.

Fig. 3C is an example of a task skills matrix which may be used in the procedures of Figs 1 and 2, according to further example embodiments.

Fig. 4 is a procedure diagram illustrating how a crew allocation system may operate using a task skills matrix, a requirements matrix and a competency matrix, according to further example embodiments. Fig. 5 is a procedure diagram illustrating how a requirements matrix may be generated, according to further example embodiments.

Fig. 6 is a block diagram illustrating how a crew allocation system may be structured, according to further example embodiments. Detailed description

Embodiments are provided herein to automatically produce a relevant and useful allocation of a field technician for executing a work order on equipment in a communication network, without requiring any manual efforts in the actual allocation procedure. This can be achieved by identifying which skills are required from a field technician to manage to execute the work order, and checking what competencies a set of candidate field technicians possess. The field technicians’ registered competencies basically determine whether they are fit to execute this particular work order or not. By matching the field technicians’ competencies with the required skills, a field technician that has (e.g. the best) matching competencies can be identified and allocated for executing the work order. The solution will now be described in terms of functionality in a crew allocation system which may be implemented in one or more suitable nodes within the communication network, such as an Operation Support System, OSS, or in some external node or system which is operative to support the network with providing field technicians for executing various jobs, as defined by work orders, on equipment therein. “Crew allocation system” is used herein as a descriptive term for a function that allocates field technicians for executing work orders, although it could alternatively be named a field technician selecting unit or any other similar term.

For example, a work order may be generated in response to an alarm or the like indicating that some equipment is not operating as expected or required. Further, a work order may contain one or more instructions indicating tasks and operations that need to be performed by one or more field technicians, in order to get the work order executed. The process of triggering and generating a work order lies somewhat outside the scope of this disclosure, and it is basically just assumed that the crew allocation system receives the work order in a suitable manner which is subject to practical implementation. In brief, the crew allocation system analyses the work order and identifies a set of tasks which need to be performed in order to execute the work order. The crew allocation system then determines which skills are required for the tasks, and also which competencies are available from a set of candidate field technicians. The required skills may be determined by applying a machine learning model or algorithm on the work order, which has been trained by a history of previously executed work orders.

An example of how the crew allocation system may be used in practice will now be described with reference to the communication scenario illustrated in Fig. 1 involving the crew allocation system 100. It is shown that a piece of equipment 102 in a communication network 104 is surveilled by a surveillance center 106, e.g. by means of sensors that measure performance of the equipment. Throughout this disclosure, the term “equipment” is used to denote any part of the network that may need attendance by a field technician, which may involve any repairing, adjusting, configuring, upgrading, software “debugging”, measuring and/or inspecting operations, to mention some illustrative but non-limiting examples. Without limitation, the equipment 102 may be any part of the network 104 such as, e.g., a network node, a communication link, a switch, a database, a server, a processor, an antenna, a radio transceiver, a power amplifier, a cooling system, and so forth.

In this example, the crew allocation system 100 communicates with the surveillance center 106 and with a crew center 108 which manages a crew of field technicians, herein referred to as a set of candidate field technicians 110, which are assumed to be available for executing various work orders depending on their individual competencies and also depending on what skills are required to execute the respective work orders. The crew center 108 may also be referred to as a dispatch office which is a commonly used term in this field. Further, the crew allocation system 100 may be a part of the crew center 108, or vice versa. The embodiments described herein may be employed in a procedure as follows.

A first action 1:1 illustrates that an alarm is issued from the equipment 102, e.g. when some abnormal or unwanted condition is detected by means of one or more sensors, not shown, arranged in or near the equipment 102. Such sensors may be employed to measure or observe some quantity or aspect that indicates or affects performance, e.g. temperature, data throughput, buffered data, power consumption, physical position, damage, missing parts, to mention some illustrative but non-limiting examples.

When the surveillance center 106 detects the alarm from equipment 102, a work order is generated to somehow act on the alarm in a suitable manner, e.g. to eliminate a cause for the alarm and to achieve or restore a desired performance. As said above, how an alarm is triggered and how a work order is generated go beyond the scope of the embodiments herein. It is assumed that one or more field technicians are required to execute the work order, e.g. locally on site or remotely depending on the work order. Executing a work order can also be referred to as a field service operation, FSO, or just “job” for short.

Another action 1:2 illustrates that the crew allocation system 100 receives or otherwise obtains the work order which requires allocation of one or more field technicians. In this example, only one field technician is allocated for simplicity although it can be understood that the procedure is also applicable for allocating more than one field technician for the job.

In a next action 1:3, the crew allocation system 100 retrieves information from a database 100A where certain useful data about field technicians and task types is maintained, e.g. in the form of a“competency matrix” and a “task skills matrix”, respectively. Briefly described, the competency matrix contains skill ratings for the candidate field technicians with respect to a set of predefined skills, thus indicating which competencies the candidate field technicians possess, while the task skills matrix indicates whether a skill is required or not for a set of predefined task types, which task types can be mapped to what tasks are required by the work order. Although matrices are used herein as a practical example, the skill ratings and the required skills for different task types could be maintained in any other suitable form and the solution is not limited in this respect. Further, the matrices described herein could also be referred to as tables.

The crew allocation system 100 analyzes the work order and thus identifies a set of tasks that need to be performed in order to execute the work order, herein referred to as required tasks. As mentioned above, the required tasks may be determined by applying a machine learning model or algorithm on the work order. The crew allocation system 100 then determines what skills are required for the tasks identified in this particular work order, e.g. by generating a“requirements matrix” which indicates a level of skill that is required for performing each task of the work order. The requirements matrix described herein is thus work order specific by reflecting the skill requirements for this particular work order. The above-mentioned competency matrix, task skills matrix and requirements matrix will be described in more detail later below with reference to examples thereof shown in Figs 3A-C, respectively.

Based on the required tasks and the competencies of the candidate field technicians, the crew allocation system 100 is able to determine which of the candidate field technicians is best suited to perform the required tasks and execute the work order, by matching the skill ratings of the candidate field technicians 110 with the required levels of skills in the requirements matrix. In practice, this determination may also take other information about the field technicians into consideration such as availability, work load, present whereabouts and need for training, which are however outside the scope of the embodiments described herein.

In a next action 1:4, the crew allocation system 100 sends to the crew center 108 a suitable message, instruction or notification which basically allocates the determined and hopefully best suited field technician for executing the work order, and this allocation is thus at least based on the required skills and the candidate competencies obtained in action 1:3. It can also be said that the crew allocation system 100 in this action initiates execution of the work order by the allocated field technician 110A. In response to the latter action, the crew center 108 dispatches the allocated field technician 110A in an action 1:5, for execution of the work order at the equipment 102 in a following action 1:6.

After having executed the work order, the allocated field technician 110A may report the results to the crew center 108 which accordingly generates an execution report that is sent to the crew allocation system 100 in an action 1:7. Execution reports as such are already used in conventional procedures, also called closure reports. The execution report contains information about how successful execution of the work order and/or the individual tasks has been, e.g. in terms of completed tasks, the time it has taken to complete the work order and/or the individual tasks, any performance improvement that could be detected after the work order execution, and so forth. It may thus basically be assumed that information about how well the field technician 110A has executed the work order, can be extracted from the execution report.

Another action 1:8 illustrates that the crew allocation system 100 updates the competency matrix based on the received execution report, that is in an entry for the field technician 110A where one or more of his/her skill ratings could be updated depending on the results of the work order execution (e.g. successful or unsuccessful, satisfactory or not so satisfactory, etc.) as indicated in the execution report. This way, the competency matrix can be continuously kept up-to-date as the candidate field technicians therein acquire more and more experience from executed work orders, so that the competency matrix can be used to accurately allocate the most suitable field technician for each new specific work order.

It is also an advantage that the competency matrix is flexible and can easily be updated, e.g. by adding or removing field technicians as well as adding or removing skill types and adjusting the technicians’ skill ratings according to any suitable granularity or resolution. The crew allocation system 100 may further update the task skills matrix in action 1:8, e.g. if the execution report indicates that the requirement of skill for a certain task type needs to be changed, a new task type needs to be added, or that a certain skill has been found to be required or not relevant for a task type.

An example of how the solution may be employed in terms of actions performed by a crew allocation system such as the crew allocation system 100, is illustrated by the flow chart in Fig. 2 which will now be described with further reference to Fig. 1, although this procedure is not limited to the example of Fig. 1. Fig. 2 thus illustrates a procedure that may be performed by the crew allocation system 100, for allocating a field technician for executing a work order on equipment 102 in a communication network 104. In this figure, field technician is denoted“FT” for short. Some optional example embodiments that could be used in this procedure will also be described.

A first action 200 illustrates that the crew allocation system 100 identifies a set of tasks to be performed when executing the work order. In other words, the identified set of tasks need to be performed in order to execute the work order. In another action 202, the crew allocation system 100 further determines which skills are required for performing the identified tasks. This action may be performed by applying a machine learning model on the work order, which has been trained by a history of previously executed work orders. An example of how this action could be performed will be described in more detail later below.

In another action 204, the crew allocation system 100 also obtains competencies of a set of candidate field technicians with respect to the required skills, e.g. from the above- described competency matrix which may be maintained in a database 100A or the like. In another action 206, the crew allocation system 100 allocates at least one of the candidate field technicians for executing the work order, based on the required skills and obtained competencies. A further action 208 illustrates that the crew allocation system 200 may initiate execution of the work order by the allocated field technician(s) 110A. Actions 200- 208 basically correspond to actions 1:3 - 1:4 of Fig. 1.

The crew allocation system 200 may further update the competency of the allocated at least one field technician, e.g. in the above-mentioned database 100A, as shown in action 210 which corresponds to the above-described action 1:8.

Some examples of optional embodiments that may be employed in the above procedure of Fig. 2 will now be described. It was described above that a competency matrix and a task skills matrix may be maintained in database 100A, and that these matrices may be updated after an executed work order. In one example embodiment, said competencies of the candidate field technicians may thus be obtained from a database 100A which is updated for the at least one allocated field technician based on an execution report generated after the work order has been executed. In another example embodiment, the database 100 A may comprise a competency matrix where the competencies of the candidate field technicians 110 are maintained in terms of skill ratings for the candidate field technicians and a set of predefined skills. In another example embodiment, the competency matrix may be updated for the at least one allocated field technician when the work order has been executed.

It was also mentioned above that a requirements matrix may be generated for the work order, which indicates a level of skill that is required for performing each task of the work order. Another example embodiment may thus be that the skills required for performing the identified tasks are determined by generating a requirements matrix for the work order, the requirements matrix comprising a level of skill required for performing each task of the work order. Fig. 3A illustrates a simplified example of how such a requirements matrix 300 may be structured. In this requirements matrix 300, a number of tasks tl, t2, t3 ... required for executing the work order are identified and a required level of skill is specified for each required task. The required level of skill for a task may have a value between 0 indicating that no skill is required for performing this task, and 1 indicating that full skill is required for performing this task, although any other type of skill levels may be used, e.g. represented by any number of letters A-X to indicate different levels of skill. The skill levels could have any granularity or resolution which is subject to implementation.

In the example of Fig. 3 A, a medium level 0.5 of skill si is required for performing task tl while skill s2 is not required at all (level 0) for performing that task tl. Further, skill s3 is not required at all (level 0) for performing tasks t2 and t3, while the highest level 1 of that skill s3 is required for performing task tl.

Fig. 3B further illustrates a simplified example of how a competency matrix 302 may be structured. In the competency matrix 302, a number of candidate field technicians FT1, FT2, FT3 ... are represented and a value of skill rating is specified for each of a set of predefined skills SI, S2, S3 ... . In this example, the skill rating values can vary between 0 indicating no skill and 1 indicating full skill, although any other type of skill rating values may be used, e.g. represented by any number of letters A-X to indicate different levels of skill. In the example of Fig. 3B, the field technician FT1 is very experienced regarding skill SI and possesses a quite high skill rating of 0.9 for skill SI. On the other hand, FT1 is a novice regarding skill S3 and consequently possesses a skill rating of 0 for skill S3. For example, if an execution report indicates that FT1 has performed skill S3 successfully in a newly executed work order, his/her skill rating for skill S3 may be upgraded to 0.1 in the competency matrix 302.

In another example embodiment, the at least one field technician may be allocated by matching the skill ratings of candidate field technicians in the competency matrix with the required levels of skills in the above-mentioned requirements matrix. Thereby, a field technician that possesses high skill ratings for the skills that are actually required for the work order can be identified and allocated for the job. In another example embodiment, the levels of skill in the requirements matrix may be predicted by applying a machine learning model on the work order, wherein the machine learning model has been trained by a history of previously executed work orders.

In another example embodiment, the skills required for performing the identified tasks may be determined based on a task skills matrix indicating whether a skill is required or not for a set of predefined task types. In another example embodiment, the task skills matrix may be updated if execution of the work order indicates that the requirement of skill for a task type needs to be changed.

Fig. 3C illustrates another simplified example of how a task skills matrix 304 may be structured. In the task skills matrix 304, a set of task types Tl, T2, T3 ... are identified and it is indicated whether a number of predefined skills SI, S2, S3 ... are required or not relevant for each task type. In this example, skills SI and S3 are required for task type Tl, skills SI and S2 are required for task type T2, and skill S3 is required for task type T3. This task skills matrix 304 may be used as input together with the competency matrix 302 when generating a requirements matrix 300 for a particular work order.

An example of how the crew allocation system described herein may operate when receiving a new work order“WO”, will now be described with reference to the procedure diagram in Fig. 4. It is assumed that a competency matrix 302 and a task skills matrix 304 are maintained as described above and can be accessed by the crew allocation system. In this example, the WO is first analysed in operation 400 using information in the task skills matrix 304, wherein tasks required for the WO are identified and a requirements matrix 300 is generated as an outcome of the analysis 400.

In a next operation 402 of crew allocation, at least one field technician is allocated for the job by matching information in the competency matrix 302 with information in the requirements matrix 300 in the manner described above. A further operation 404 illustrates the actual execution of the WO by the allocated field technician(s), which results in an execution report 406. The execution report 406 is used as a basis for automatically updating skills of the allocated field technician(s) in the competency matrix 302 in the manner described above, and possibly also for updating the task types and skills in the task skills matrix 304 if the report indicates that such an update is motivated. It should be noted that the competency matrix 302 and the task skills matrix 304 can also be updated manually at any time, e.g. by network specialists or crew managers, which however goes beyond the scope of this disclosure.

Another example of how the crew allocation system described herein may operate when creating the requirements matrix 300 of Fig. 4, will now be described in more detail with reference to the procedure diagram in Fig. 5. First, information and features of an incoming WO are extracted in an operation 500, possibly using information from the task skills matrix 304 as input. Then, a Root Cause Analysis, RCA, is performed in an operation 502, to find a set of possible root causes for the WO. In a typical situation, experts may analyse incidents mentioned in the work order and hypothetically identify the possible root causes that might have led to the respective incident. It is also possible to use historical information, e.g. substation X has a history of battery failure during storms, to hypothetically identify root causes and prioritize them. The technicians need to validate the hypotheses in the field and resolve the root causes of the incident.

In a following“solutioning” operation 504, the root causes found in 502 are mapped to different task types that need to be performed to resolve the WO. For example, if a root cause points to a battery failure, a task involving backup battery inspection will be generated. Thereby, the tasks required for the WO have been identified which are used as input to an operation 506 of determining which skills and levels thereof are required for performing the identified tasks. Further input for the determining operation 506 may include information from the task skills matrix 304 which indicates whether the different skills are required for the identified tasks. A machine learning model“ML” may also be applied for predicting the levels of skill for the identified tasks in the requirements matrix in operation 506. Finally, the outcome of operation 506 is the generated requirements matrix 300.

Some further features and examples of how the above-described embodiments may be used, will now be discussed.

The above-described task skills matrix and competency matrix may in the beginning be initialized with either default values or values specified by human experts. Subsequently, the feedback from WO execution reports are used to update the matrices continuously over time so that they converge to accurate and up-to-date values.

The determination of required skills and skill levels for a WO may be performed in the above operation 506 by a submodule or the like in the crew allocation system. It was mentioned above that a history of previous WOs and their execution reports can be used to train a machine learning model to predict the levels of the skills in the requirements matrix. The training set in this case may include records of the following format:

[date, time, weather, taskl, instrument involved, tools, task2, instrument involved, tools, ..]

[taskl, skill 1, level 1, task2, skill2, level2, ...] i.e. the requirements matrix contains ( task,, skill_j) levels

Known machine learning methods using Neural Networks can be used to build the machine learning model out of the training data. Note that when the task skills matrix is changed, the training data (expected output) will also change. Hence the old training data should be estimated and the machine learning model re-trained according to the new task skills matrix.

The updating of the competency matrix may be performed in the above action 210 by a competency submodule or the like in the crew allocation system, using information in the execution report. For example, the execution report may contain the following information to be used as parameter values:

A) Resolution success

B) Time to resolve C) Support duration

A“fulfilment rating” FR indicating how well the WO was executed can then be calculated for a field technician as a normalized weighted sum of the above parameter values, as follows:

FR = - wl *nl *( time ) - w2 *n2 *( support duration ) + w3 *n3 *( resolution success ), where the above wfs are weights for the parameters and the above n s are the normalization factors. It should be noted that low values of B) and C) and a high value of A) will result in a high FR.

Since there is possibly a trade-off between the time it takes to resolve a WO and quality of resolution, it may be desirable to offset the rating by an amount inversely proportional to the length of an interval between a WO resolution after a certain fault in the equipment and a later WO which points to a recurrence of the fault. The RCA computation in the above operation 502 can provide information on a possibly faulty execution of the tasks in an earlier WO, the field technician assigned to that task and the above time interval. whenever there is a new WO, the rating FR may be updated by FR = FR w * 1/interval

Here, the above parameter“interval” denotes the time between two successive WOs, i.e. interval = time(new WO) - time(previous WO). For practicality, the interval is fixed. Faults occurring before this interval do not affect the rating of the field technician but can be attributed to regular wear and tear unless they are software faults. The allocation of a field technician may be performed in either of the above action

206 and operation 402 by an allocation submodule or the like in the crew allocation system. An example of how the crew allocation may be done on the basis of the competency matrix, will now be described in terms operations 1-4 as follows. As noted above, the crew allocation may in practice depend on a large number of circumstances, although this disclosure is focused on the constraints dictated by the competency matrix and requirements matrix.

Operation 1

Compute MAXFT[s] = max_j FTj[s], for all j, 1 <= j <= m, where {FTl...FTm} is a set of candidate field technicians, FTs.

MAXFT[s] denotes the maximum skill level available in a group of FTs for each skill s. Operation 2

Compute MAXWO[s] = max_i WO[t_j, s], for all j, 1 <= j <= n, where t_j are all the tasks.

MAXWO[s] denotes the maximum level required for tasks in the WO for each skill s.

Operation 3 Add the constraints for all skills s, MAXFT[s] >= MAXWO[s]

These constraints ensure that the allocated crew has sufficient competency to handle the specific work order.

Operation 4 (optional)

Optionally, add an objective function as: - minimize s (||MAXFT[s] - MAXWO[s]||_2) This type of mathematical operation is generally known as the L2 norm, e.g. the L2 norm of X is written as ||X||_2. This operation may be performed in this context to assert that the above L2 norm (square of the difference in competency and skills) is to be minimized. When this objective function is added, it may be ensured that the crew has just the needed level of competency, i.e. not too high, meaning that highly skilled field technicians are not allocated to tasks requiring substantially lower skill level.

By employing at least some of the above-described embodiments and examples, any of the following advantages 1-5 may be achieved:

1. It is possible to find and allocate the field technician(s) that is/are best suited to execute a specific work order in a rapid and reliable manner and without requiring any manual efforts.

2. The proposed solution is feedback driven and can automatically take into account the actual performance of field technicians, thus reflecting more accurate skill levels of the field technicians than what can be derived from subjective considerations.

3. Crew allocation is performed automatically using up-to-date information about the field technicians’ competencies, e.g. as specified in the competency matrix, thus leading to more efficient use of the available field technicians.

4. This solution is flexible to easily account for new measures of competency as well as new task types, whenever they are recognised and established.

5. In addition, the competency matrix described herein may also be used as a basis for making various personnel decisions such as scheduling trainings, workforce planning etc., which however lie outside the embodiments herein.

The block diagram in Fig. 6 illustrates a detailed but non-limiting example of how a crew allocation system 600 may be structured to bring about the above-described solution and embodiments thereof. In this figure, the crew allocation system 600 corresponds to the crew allocation system 100 described for Fig. 1 and may be configured to operate according to any of the examples and embodiments of employing the solution as described herein, where appropriate. The crew allocation system 600 is shown to comprise a processor“P”, a memory“M” and a communication circuit“C” with suitable equipment for transmitting and receiving information and messages in the manner described herein. The communication circuit C in the crew allocation system 600 thus comprises equipment configured for communication using a suitable protocol for the communication depending on the implementation. The solution is however not limited to any specific types of messages or protocols. For example, the crew allocation system 600 may be adapted to communicate with a crew center 108 or the like for notifying the allocation of at least one field technician, and with a surveillance center 106 or the like for receiving the work order described herein.

The crew allocation system 600 is, e.g. by means of units, modules or the like, configured or arranged to perform at least some of the actions of the flow chart in Fig. 2 as follows.

The crew allocation system 600 is arranged to allocate a field technician for executing a work order on equipment in a communication network. As said above, allocate a field technician is not limited to a single field technician but should be understood as allocation of one or more field technicians. The crew allocation system 600 is configured to identify a set of tasks to be performed when executing the work order. This operation may be performed by an identifying module 600A in the crew allocation system 600, as illustrated in action 200. The identifying module 600A could alternatively be named an analysing module or task module. The crew allocation system 600 is further configured to determine skills required for performing the identified tasks. This operation may be performed by a determining module 600B in the crew allocation system 600, as illustrated in action 202. The determining module 600B could alternatively be named a logic module or requirement module. The crew allocation system 600 is further configured to obtain competencies of a set of candidate field technicians with respect to the required skills. This operation may be performed by an obtaining module 600C in the crew allocation system 600 as illustrated in action 204. The obtaining module 600C could alternatively be named a competency module or skills module. The crew allocation system 600 is further configured to allocate at least one of the candidate field technicians for executing the work order, based on the required skills and obtained competencies. This operation may be performed by an allocating module 600D in the crew allocation system 600 as illustrated in action 206. The allocating module 600D could alternatively be named a selecting module or evaluation module.

The crew allocation system 600 may be further configured to update the competency of the allocated field technician(s), e.g. in a competency matrix, when the work order has been executed. This operation could be performed by an updating module 600E in the crew allocation system 600 as illustrated in action 208. The updating module 600E could alternatively be named a database module or maintaining module.

It should be noted that Fig. 6 illustrates various functional modules in the crew allocation system 600 and the skilled person is able to implement these functional modules in practice using suitable software and hardware equipment. Thus, the solution is generally not limited to the shown structure of the crew allocation system 600, and the functional modules therein may be configured to operate according to any of the features, examples and embodiments described in this disclosure, where appropriate.

The functional modules 600A-E described above may be implemented in the crew allocation system 600 by means of program modules of a computer program comprising code means which, when run by the processor P causes the crew allocation system 600 to perform the above-described actions and procedures. The processor P may comprise a single Central Processing Unit (CPU), or could comprise two or more processing units. For example, the processor P may include a general purpose microprocessor, an instruction set processor and/or related chips sets and/or a special purpose microprocessor such as an Application Specific Integrated Circuit (ASIC). The processor P may also comprise a storage for caching purposes.

The computer program may be carried by a computer program product in the crew allocation system 600 in the form of a memory having a computer readable medium and being connected to the processor P. The computer program product or memory M in the crew allocation system 600 thus comprises a computer readable medium on which the computer program is stored e.g. in the form of computer program modules or the like. For example, the memory M may be a flash memory, a Random-Access Memory (RAM), a Read-Only Memory (ROM) or an Electrically Erasable Programmable ROM (EEPROM), and the program modules could in alternative embodiments be distributed on different computer program products in the form of memories within the crew allocation system 600. The solution described herein may be implemented in the crew allocation system 600 by a computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the actions according to any of the above embodiments and examples, where appropriate. The solution may also be implemented at the crew allocation system 600 in a carrier containing the above computer program, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.

While the solution has been described with reference to specific exemplifying embodiments, the description is generally only intended to illustrate the inventive concept and should not be taken as limiting the scope of the solution. For example, the terms“crew allocation system”,“field technician”,“equipment”,“work order”,“task” and“machine learning model” have been used throughout this disclosure, although any other corresponding entities, functions, and/or parameters could also be used having the features and characteristics described here. The solution is defined by the appended claims.

Claims

CLAIMS:

1. A method for allocating a field technician for executing a work order on equipment (102) in a communication network (104), the method comprising:

- identifying (200) a set of tasks to be performed when executing the work order, - determining (202) skills required for performing the identified tasks,

- obtaining (204) competencies of a set of candidate field technicians with respect to the required skills, and

- allocating (206) at least one of the candidate field technicians for executing the work order, based on the required skills and obtained competencies.

2. A method according to claim 1, wherein said competencies of the candidate field technicians are obtained (1:3) from a database (100A) which is updated (210, 1:8) for the at least one allocated field technician based on an execution report (1:7) generated after the work order has been executed.

3. A method according to claim 2, wherein the database comprises a competency matrix (302) where the competencies of the candidate field technicians are maintained in terms of skill ratings for the candidate field technicians and a set of predefined skills.

4. A method according to claim 3, wherein the competency matrix is updated for the at least one allocated field technician when the work order has been executed.

5. A method according to any of claims 1-4, wherein the skills required for performing the identified tasks are determined by generating a requirements matrix (300) for the work order, the requirements matrix comprising a level of skill required for performing each task of the work order.

6. A method according to claim 3 and 5, wherein the at least one field technician is allocated by matching the skill ratings of candidate field technicians in the competency matrix with the required levels of skills in the requirements matrix.

7. A method according to claim 6, wherein the levels of skill in the requirements matrix are predicted by applying a machine learning model on the work order, wherein the machine learning model has been trained by a history of previously executed work orders.

8. A method according to any of claims 1-7, wherein the skills required for performing the identified tasks are determined based on a task skills matrix (304) indicating whether a skill is required or not for a set of predefined task types.

9. A method according to claim 8, wherein the task skills matrix is updated if execution of the work order indicates that the requirement of skill for a task type needs to be changed.

10. A crew allocation system (600) arranged to allocate a field technician for executing a work order on equipment in a communication network, wherein the crew allocation system is configured to:

- identify (600A) a set of tasks to be performed when executing the work order,

- determine (600B) skills required for performing the identified tasks, - obtain (600C) competencies of a set of candidate field technicians with respect to the required skills, and

- allocate (600D) at least one of the candidate field technicians for executing the work order, based on the required skills and obtained competencies.

11. A crew allocation system (600) according to claim 10, wherein the crew allocation system is configured to obtain said competencies of the candidate field technicians from a database, and to update said database for the at least one allocated field technician based on an execution report generated after the work order has been executed.

12. A crew allocation system (600) according to claim 11, wherein the database comprises a competency matrix where the competencies of the candidate field technicians are maintained in terms of skill ratings for the candidate field technicians and a set of predefined skills.

13. A crew allocation system (600) according to claim 12, wherein the crew allocation system is configured to update the competency matrix for the at least one allocated field technician when the work order has been executed.

14. A crew allocation system (600) according to any of claims 10-13, wherein the crew allocation system is configured to determine the skills required for performing the identified tasks by generating a requirements matrix for the work order, the requirements matrix comprising a level of skill required for performing each task of the work order.

15. A crew allocation system (600) according to claim 12 and 14, wherein the crew allocation system is configured to allocate the at least one field technician by matching the skill ratings of candidate field technicians in the competency matrix with the required levels of skills in the requirements matrix.

16. A crew allocation system (600) according to claim 16, wherein the crew allocation system is configured to predict the levels of skill in the requirements matrix by applying a machine learning model on the work order, wherein the machine learning model has been trained by a history of previously executed work orders.

17. A crew allocation system (600) according to any of claims 10-16, wherein the crew allocation system is configured to determine the skills required for performing the identified tasks based on a task skills matrix indicating whether a skill is required or not for a set of predefined task types.

18. A crew allocation system (600) according to claim 17, wherein the crew allocation system is configured to update the task skills matrix if execution of the work order indicates that the requirement of skill for a task type needs to be changed.

19. A computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out the method according to any one of claims 1-9.

20. A carrier containing the computer program of claim 19, wherein the carrier is one of an electronic signal, optical signal, radio signal, or computer readable storage medium.