Claims:1. A computer implemented task effort estimator comprising:
an intransient repository configured to store a predetermined set of processing rules, parsing rules, rules for establishing complexity measure of a task, rules for determining pseudo factor (ß) for said task, rules for determining the productivity factor of workers assigned to the task, and predefined tool/technology data;
a processor configured to receive the rules from said repository and further configured to generate component specific processing commands;
an input module configured to receive, from a user, a report corresponding to the task requirements and details of workers assigned to perform the task along with the seniority level, the proficiency and the competency of each of said workers;
a parser having (i) a crawler configured to traverse across the report, (ii) an extractor configured to extract words and phrases from said report, learnt during a training phase, corresponding to technical components, aesthetic components, and mixed technical & aesthetic components required for carrying out and completion of the task and (iii) an aggregator configured to discreetly aggregate and assign a value to said technical components (Ti), said aesthetic components (Aj) and mixed technical & aesthetic components (TAk), respectively based on predefined rules stored in said repository and under the influence of system commands generated by said processor and further compute a task specification (TS) using the assigned values for the respective components based on the equation:
TS= ?_(i=1)^n¦T_i +?_(j=1)^n¦A_j - ?_(k=1)^n¦?TA?_k ;
a task categorizer configured to receive from the extractor the extracted technical component (Ti), aesthetic components (Aj), and mixed technical & aesthetic components (TAk), said task categorizer having a mensuration tool configured to receive rules for determining complexity measure of a task from said repository and system commands from said processor and thereby calculate the complexity of said task and assign task category (TC) based on the calculated complexity;
a pseudo factor determiner configured to receive said rules for determining the pseudo factor for the task, said task requirements and said predefined tool/technology data, said pseudo factor determiner having a first mapper configured to map said task requirements with the predefined tool/technology data, to compute a pseudo factor (ß);
a productivity factor determiner configured to receive from said input module, said details of the workers, said determiner having (i) a second mapper configured to receive the rules for determining the productivity and map the said details of workers under the influence of system commands and assign a productivity factor to each of said worker based on said mapping (ii) a productivity aggregator configured to compute an aggregated productivity factor (PF) based on the productivity factor of each of said worker; and
an effort calculator configured to receive the task specification value (TS) from said parser, the task category from said task categorizer, the pseudo factor (ß) from said pseudo factor determiner, the productivity factor (PF) from said productivity determiner and further configured to estimate under the influence of processing commands from said processor effort requirement for the task based on the equation:
Estimated Effort = (TS * TC +ß)/ PF.
2. The computer implemented task effort estimator, as claimed in claim 1 in which the category of the task is segregated into simple (Zi), medium (Zii) and complex (Ziii) and the task categorizer is configured to compute the task category on the basis of the equation:
TC = ni (Zi) + nii (Zii) + niii (Ziii)
wherein n represents count of the task in the particular category.
3. The computer implemented task effort estimator, as claimed in claim 1 in which the system is configured to identify a stabilization factor d and the task categorizer is configured to calculate the task category TC using the equation:
TC = (1- d) * (niZi + niiZii + niiiZiii)
4. The computer implemented task effort estimator, as claimed in claim 1 in which the ß factor defines any one of the following parameters, tools display design similarity, write back mechanism, BI and other external factors that have an impact on the estimation.
5. The computer implemented task effort estimator, as claimed in claim 1 in which the productivity factor determiner is configured to determine the productivity factor (PF) of the workers based on the equation
PF= SR_RP * SR_ix + MR_RP * MR_jx + JR_RP * JR_kx+ ER_RP* ER_lx
wherein ix, jx, kx, lx represent the number of senior workers (SR), medium level workers (MR), junior level workers (JR) and entry level workers (ER) and SR_RP, MW_RP, JW_RP and EW_RP represents the productivity factor with respect to senior workers (SR), medium level workers (MR), junior level workers (JR) and entry level workers (ER).
6. The system as claimed in claim 1, wherein a weightage factor (µ) is the weightage factor among the competencies based on the task requirement.
7. The system as claimed in claim 1, wherein the productivity factor is assigned to each of said worker within a productivity factor range.
8. A computer implemented method for estimating effort for a task, said method comprising:
storing a predetermined set of processing rules, parsing rules, rules for establishing complexity measure of a task, rules for determining pseudo factor (ß) for said task, and rules for determining the productivity factor of workers assigned to the task, and predefined tool/technology data;
receiving the rules and generating component specific processing commands;
receiving a report corresponding to the task requirements and details of workers assigned to perform the task along with the seniority level, the proficiency and the competency of each of said workers;
traversing across the report and extracting words and phrases from said report, learnt during a training phase, corresponding to technical components (Ti), aesthetic components (Aj) and mixed technical & aesthetic components (TAk), required for carrying out and completion of the task;
aggregating and assigning a value to said technical components (Ti), said aesthetic components (Aj) and said mixed technical & aesthetic components (TAk) respectively based on predefined rules stored in said repository and under the influence of system commands generated by said processor and further compute a task specification (TS) using the assigned values for the respective components;
receiving the extracted technical component (Ti), aesthetic components (Aj), and mixed technical & aesthetic components (TAk), rules for determining complexity measure of a task and system commands, and calculating the complexity of said task and assign task category (TC) based on the calculated complexity;
receiving said rules for determining the pseudo factor for the task, said task requirements and said predefined tool/technology data, said pseudo factor determiner having a first mapper configured to map said task requirements with the predefined tool/technology data to compute a pseudo factor (ß);
receiving the details of the workers, the rules for determining the productivity and mapping the said details of workers under the influence of system commands and assigning a productivity factor to each of said worker based on said mapping and computing an aggregated productivity factor (PF) based on the productivity factor of each of said worker;
receiving the task specification value, the task category, the pseudo factor (ß), the productivity factor (PF) and estimating under the influence of processing commands from said processor effort requirement for the task based on the equation:
Estimated Effort = (TS * TC +ß)/ PF , Description:TECHNICAL FIELD
The present invention relates to a computer implemented system for estimating effort for a task.
BACKGROUND
Managing a task efficiently, such as a project, is required for its effective execution. An integral part of managing a task is estimating the effort required for completing it. Hitherto, tools do not exist which can automate the effort estimation process. This estimation is normally performed empirically by the manager. Hence, there is a need for a system and tool for precisely estimating the effort required for a task.
OBJECTS
Some of the objects of the present disclosure aimed to ameliorate one or more problems of the prior art or to at least provide a useful alternative are listed herein below.
An object of the present disclosure is to provide an automated effort estimating system which can estimate the effort required for a task with precision.
SUMMARY
This summary is provided to introduce concepts related to a computer implemented system and a method for estimating effort for a task, which is further described below in the detailed description. This summary is not intended to identify essential features of the disclosure nor is it intended for use in determining or limiting the scope of the disclosure.
In an embodiment, what is disclosed herein is a computer implemented task effort estimator comprising:
an intransient repository configured to store a predetermined set of processing rules, parsing rules, rules for establishing complexity measure of a task, rules for determining pseudo factor (ß) for the task, and rules for determining the productivity factor of workers assigned to the task;
a processor configured to receive the rules from the repository and further configured to generate component specific processing commands;
an input module configured to receive, from a user, a report corresponding to the task and details of workers assigned to perform the task along with the seniority level, the proficiency and the competency of each of the workers;
a parser having (i) a crawler configured to traverse across the report, (ii) an extractor configured to extract words and phrases from the report, learnt during a training phase, corresponding to technical component, aesthetic components, and mixed technical & aesthetic components required for carrying out and completion of the task and (iii) an aggregator configured to discreetly aggregate and assign a value to the technical component, aesthetic components, and mixed technical & aesthetic components respectively based on predefined rules stored in the repository and under the influence of system commands generated by the processor and further compute a task specification (TS) using the assigned values for the respective components;
a task categorizer configured to receive from the extractor, the extracted technical component, aesthetic components, and mixed technical & aesthetic components, the task categorizer having a mensuration tool configured to receive rules for determining complexity measure of a task from the repository and system commands from the processor and thereby calculate complexity of the task and assign task category (TC) based on the calculated complexity;
a pseudo factor determiner configured to receive the rules for determining the pseudo factor for the task, and compute under the influence of processing commands from the processor, a pseudo factor (ß) for the task;
a productivity factor determiner configured to receive from the input module, the details of the workers, the determiner having (i) a second mapper configured to receive the rules for determining the productivity and map the details of workers under the influence of system commands and assign a productivity factor to each of the worker based on the mapping (ii) a productivity aggregator configured to compute an aggregated productivity factor (PF) based on the productivity factor of each of said worker; and
an effort calculator configured to receive the task specification value (TS) from the parser, the task category from the task categorizer, the measure of the pseudo factor (ß) from the pseudo factor determiner, the productivity factor (PF) from the productivity determiner and further configured to estimate under the influence of processing commands from the processor effort requirement for the task based on the equation:
Estimated Effort = (TS * TC +ß)/ PF.
The disclosure also envisages a method for estimating the effort required for a task. The method includes receiving reports corresponding to the task and details of workers assigned to perform the task along with the seniority level, the proficiency and the competency of each of said workers as input from a user. The method further includes extracting technical components, aesthetic components, and mixed technical & aesthetic components, from the task and computing a task specification based on the technical components and aesthetic components. Furthermore, the method includes calculating the complexity measure of the task and assigning a task category based on the calculated complexity measure. Still further, the method comprises determining a pseudo factor in relation to the task. The method includes computing the productivity factor corresponding to the workers working on the task. Using the information computed above the method includes estimating the effort required for the task based on the task specification, the task category and the productivity factor and the pseudo factor.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
The computer implemented system and method for estimating effort for a task, of the present disclosure will now be described with the help of the accompanying drawings, in which:
Figure 1 illustrates a network implementing a system for estimating effort for a task, according to an embodiment of the present disclosure;
Figure 2 illustrates a report complexity measure chart for categorization of a report, according to an embodiment of the present disclosure; and
Figure 3 illustrates a method for estimating effort for a task, according to an embodiment of the present disclosure.
It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION
The present disclosure relates to a system and a method for estimating effort for task.
Unless specifically stated otherwise as apparent from the following discussions, it is to be appreciated that throughout the present disclosure, discussions utilizing terms such as “storing” or “receiving” or “providing” or “generating” or “determining”, “computing” or “comparing” and the like, refer to the action and processes of a computer system, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
The systems and methods are not limited to the specific embodiments described herein. In addition, modules of each system and each method can be practiced independently and separately from other modules and methods described herein. Each module and method can be used in combination with other modules and other methods.
Fig. 1 illustrates a network implementation of a system 102 for estimating effort for a task.
In one embodiment, the system 102 includes intransient repository 130. The intransient repository 130 may be coupled to the processor 110. Further, the intransient repository 130 is configured to store a predetermined set of processing rules, parsing rules, rules for establishing complexity measure of a task, rules for determining a pseudo factor (ß) for the task and rules for determining the productivity factor of workers assigned to the task.
In one embodiment, the system 102 includes at least one processor 110. The processor 110 communicates with the intransient repository 130 to receive the pre-determined set of processing rules and is further configured to generate specific processing commands. The processor 110 can be one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the intransient repository 130.
The system 102 further includes data repository 160. Further, the data repository 160 may include global data, classification data, technical and aesthetic data, tools/ technology capability data and other data. In an embodiment, the global data refers to all the data related to plurality of processes executed by the system 102. In an embodiment, the classification data refers to all the data required for the classification of the reports and tasks. In an embodiment the technical and aesthetic data refers to all the data required for determination of the technical components, aesthetic components, and mixed technical & aesthetic components from the reports. In an embodiment, the tools/ technology capability data refers to data such as processing powers, threshold values, physical requirement / limitations of the tool, and the like.
Further, the system 102 includes an input module 142, a parser 150, a task categorizer 170, a pseudo factor determiner 180, a productivity factor determiner 190 and an effort calculator 198.
According to the present embodiment, the input module 142 is configured to receive, from a user, a report corresponding to a task and details of worker assigned to perform the task along with the seniority level, the proficiency and the competency of each of the workers.
According to present embodiment, the parser 150 comprises: a crawler 152, an extractor 154 and an aggregator 156. The parser 150 is configured to cooperate with the processor 110 to receive the processing commands. The crawler 152 is configured to traverse across the report. The extractor 154 is configured to extract words and phrases, corresponding to technical components (Tj) and aesthetic components (Aj) required for carrying out and completion of the task. These words and phrases are typically learned by the system during a learning/training phase, where a correspondence is created between specific words and phrases in a report to correspond to specific technical or aesthetic components required for a task. Further the extractor 154 is configured to extract words and phrases, corresponding to mixed technical & aesthetic components (TA¬k) such as highlighted conditional background based on business logic. The use of a technical component to implement aesthetic component decreases the (?_(j=1)^n¦A_j ). For example the use of tool feature like “format painter” reduces the look and feel effort.
In an embodiment, the technical components may comprise a set of pre ETL (Extract, Transform, and Load), package modeling activity, report level SQL design and the like. In another embodiment, the technical component is represented by (Ti).
In an embodiment, the parser 150 may comprise a data processing framework, a data modeling framework and a reporting framework. The data processing framework provides the definition, generation and execution of standard and custom data. The modeling framework helps in standardization of data and in increasing the performance. The modeling framework creates containers/classes/scripts across the data to achieve standardization. The reporting framework is based on the data processing framework and data modeling framework, helps to define scalable design for the projects.
In another embodiment the parser 150 may comprise a data display framework (data look and feel, data presentation, data formatting) and a data delivery framework (Output format: PDF, Excel, Data vehicles like Mobile or tablets app, UI embedded).
In an embodiment, the data display framework defines the font, size alignment, display pattern etc. of the project data. The data display specification may be pre-decided which helps in achieving the uniformity and standardization. In another embodiment, the data delivery framework defines the delivery container and delivery type. The delivery container for the project could be a web portal with BI reports embedded in it or a GUI created for accessing.
In another exemplary embodiment, the input module 110 receives multiple reports (Report A, Report B, Report C....Report E) from users, wherein the reports comprise task requirements and details of the workers assigned to perform the tasks. The parser 150 having the extractor 154, then extracts the technical components (Tj), the aesthetic components (Aj¬) and the mixed technical & aesthetic components (TAk) from the received reports (Report A- Report E). The technical components (Tj) comprise multiple technical tasks t1-t5, the aesthetic components (Aj) comprise multiple aesthetic tasks a1- a5 and the mixed technical & aesthetic components (TAk) comprise multiple technical and aesthetic tasks ta1-ta2. This embodiment is further illustrated in table 1.

Table 1
Technical Component
(Tj) Aesthetic Component
(Aj) Technical & Aesthetic Component (TAk)
t 1 t 2 t 3 t 4 t 5

a 1 a 2 a 3 a 4 a 5

ta 1 ta 2
Report A 1 1 1
Report B 1 1 1 1
Report C 1 1 1 1 1
Report D 1 1 1 1 1 1
Report E 1 1 1 1 1 1 1 1 1

The aggregator 156 is configured to discreetly assign values (Ti) to each of the technical components extracted from the report and aggregate all the values by summation: ( ?_(i=1)^n¦T_i ). Similarly, the aesthetic components (Aj) are valued and aggregated: (?_(j=1)^n¦A_j ) respectively, based on predefined rules stored in the repository 130 under the influence of processor 110. Further, the aggregator 156 is configured to value the mixed technical & aesthetic components (TAk) and aggregate the same as: ?_(k=1)^n¦?TA?_k . The aggregator 156 is further configured to compute a task specification (TS) using the assigned values from the respective components.
In another embodiment, the task specification (TS) is computed as:
TS= ?_(i=1)^n¦T_i +?_(j=1)^n¦A_j - ?_(k=1)^n¦?TA?_k .
In an exemplary embodiment, the aggregator 156 discreetly aggregates and assigns values to the technical components (Ti), aesthetic components (Aj), and mixed technical & aesthetic components (TAk), present in table 1. Further, the aggregator 156 computes the task specification (TS) for reports A, B, C, D and E. This is further illustrated in table 2.

Table 2

Technical Component summation?_(i=1)^n¦T_i Aesthetic Component summation?_(j=1)^n¦A_j Technical & Aesthetic component summation?_(k=1)^n¦?TA?_k Task Specification TS= ?_(i=1)^n¦T_i +?_(j=1)^n¦A_j - ?_(k=1)^n¦?TA?_k . (in person hour )
Report A 10 2 0 12
Report B 10 4 0 14
Report C 16 6 0 22
Report D 14 10 0 24
Report E 28 20 2 46

According to the present embodiment, the task categorizer 170 is configured to receive the technical components, aesthetic components and, mixed technical & aesthetic components from the extractor 154. The task categorizer 170 comprises a mensuration tool 172. The mensuration tool 172 is configured to receive rules for determining complexity measure of a task from the repository 130 and system commands from the processor 110 to calculate the complexity of the task and assign category (TC) based on the calculated complexity.
In an embodiment, the task categorizer 170 categorizes the task in to simple Zi, medium Zii and complex Ziii task categories.
Figure 2 illustrates an exemplary embodiment of present disclosure wherein a task complexity measure graph is utilized to assign the task category for a task. The X-axis of the task complexity measure chart represents the count of technical component present in the task and the Y- axis represents the aesthetic component present in the task. Based on the mapping of the aesthetic component, the technical component and mixed technical & aesthetic components, the task category is determined.
In another embodiment, the task category may be equated based on this equation:
TC= niZi + niiZii + niiiZiii
Wherein ni, nii and niii represents the count of simple Zi, medium Zii and complex Ziii tasks in a report.
In another exemplary embodiment, task categories of reports are identified by the task categorizer 170, based on the technical components, the aesthetic components and the mixed technical & aesthetic components present in the reports. This embodiment is further illustrated in table 3, wherein TC Count represents the number of reports in different task categories.
Table 3
TC Analysis TC Count (No of Reports)
Simple -I ( Zi) 40
Simple –II ( Zi) 20
Medium-I ( Zii) 25
Medium –II ( Zii) 10
Complex -I ( Ziii) 5

In one another embodiment, the task categorizer 170 may use stabilization factor d while determining a project category. In an embodiment, the task category may be computed as:
TC = (1- d) * (niZi + niiZii + niiiZiii)
wherein stabilization factor d is dependent upon the task category (TC). As the team skill improves and a task gets standardized, the stabilization factor d is also considered. Worker skill enhancement and a task standardization over the N number of tasks leads to a decrease in time spent on design and development. This embodiment is further illustrated in table 3.1.
Table 3.1
Stabilization factor d Task Categorization N Effective Task Categorization ( 1- d ) N
5% 10 0.95N
10% 20 0.90N
15% 40 0.85N
18% 70 0.82N
20% 110 0.80N
In an exemplary embodiment, a stabilization factor d is considered while computing TS*TC for multiple reports, wherein TS represents the task specification and TC represents the task category. The stabilization factor d is used by the task categorizer while determining the project category. Table 2 and Table 3 are referred to obtain values of TS and TC. As the team skill improves and a task gets standardized over time, the task will require comparatively less effort. This measure is defined by the stabilization factor d. For example, if d is 15%, the effort required to complete a task is 15% less than what would initially be required. This embodiment is further illustrated in table 4.
Table 4
Task Category Stabilization Factor d TS*TC TS*TC with stabilization factor d
(in person hour )
Simple-I 15% 480 408
Simple -II 10% 280 252
Medium-I 10% 550 495
Medium -II 5% 240 228
Complex -I 0% 230 230
TOTAL SUM 1780 1613

According to the present embodiment, the pseudo factor determiner 180 is configured to receive processing commands from said processor 110 for determining the pseudo factor (ß), the task requirement from the input module and the predefined tool/technology data. The pseudo factor determiner 180 having a first mapper 182 is configured to map task requirements with the predefined tool/technology data under the influence of processing commands determines the pseudo factor (ß) for the task. In an embodiment, the pseudo factor determiner maps the tools/technology capability data with the predefined tool/technology data to compute a pseudo factor. The pseudo factor ß defines Out of the Box, other BI tool display design similarity, write back mechanism in BI and other external factors have an impact on requirement estimation. The pseudo factor defines the gap between user expectation and delivery output to an existing tool/ technology. In exemplary embodiment, the pseudo factor (ß) is 150 person hours for write back to database mechanism.
According to the present embodiment, the productivity factor determiner 190 is configured to cooperate with the processor 110 to receive processing commands and with the input module 142 to receive the details of the workers working on the task. The productivity factor determiner 190 comprises a second mapper 192 configured to receive the rules for determining the productivity and map the details of workers under the influence of processing commands from the processor 110 and assign a productivity factor to each of said worker based on the mapping. The productivity factor determiner 190 further comprises a productivity aggregator 194. The productivity aggregator 194 is configured to compute an aggregated productivity factor (PF) based on the productivity factor of the set of workers working on the task.
In an embodiment, the competency and proficiency of each worker is represented by a competency- proficiency table. Table 5 shows an exemplary embodiment of the competency-proficiency table. The A and B row in the table represents the primary competency which could be functional, technical or Industry specific. The M and N rows in the Competency-Proficiency Table represent the secondary competency which covers generic skills like Teamwork, logical analysis, interpersonal, leadership and the like. The E0 in the table 5 represents the entry level knowledge, whereas E3 represents industry expert level knowledge.
Table 5

E3 E2 E1 E0
Primary Competency A 10 5 3 1
B 10µ 5µ 3µ 1
Secondary Competency M 4 2 1 XX
N 4 2 1 XX

In an embodiment, weightage factor (µ) adds weightage among primary competency based on the task requirement. The µ factor helps in differentiating between A and B competency based on a project requirement.
In an exemplary embodiment, table 6 represents a worker role mapping table. The worker role mapping table helps in classification of workers according to their proficiency level or depth of knowledge required for each role. In an exemplary embodiment the top two competencies of table 5 are summed together to compute the competency-evaluation scores. Further, based on the competency-evaluation scores and the worker hierarchy, a productivity factor is assigned for each worker within the productivity factor range.

Table 6
Worker Hierarchy Competency-Evaluation Score Productivity Factor Range
Senior Worker (SW) Greater than 12 ?SW?_RP =1.2 -1.4
Mid-Level Worker (MW) 7 < Comp = 12 ?MW?_RP=0.9-.1.2
Junior Worker (JW) 3 = Comp = 7 ?JW?_RP =0.75- 0.9
Entry Worker (EW) Less than 3 ?EW?_RP =0.6-0.75

In an embodiment, the productivity factor determiner 190 determines the productivity factor of workers based on the table 5 (competency-proficiency table) and table 6 (worker role mapping table) and this equation:
PF= SW_RP * SW_ix + MW_RP * MW_jx + JW_RP * JW_kx+ EW_RP* EW_lx
Where in ix, jx, kx, lx represent number of senior workers (SR), medium level workers (MR), junior level workers (JR) and entry level workers (ER) and SW_RP, MW_RP, JW_RP and EW_RP represents the productivity factor with respect to senior workers (SR), medium level workers (MR), junior level workers (JR) and entry level workers (ER).
In an exemplary embodiment, table 7 illustrates a primary competency, a secondary competency, a µ factor and a µ2 factor of a worker assigned to perform a task.
Table 7
Competencies Input Comments

Primary Competency A BI Reporting Sample skillset for delivery purpose
Primary Competency B ETL Knowledge ''
Secondary Competency A Logical Analytics Skill Set measurement parameter
Secondary Competency B Reasoning
µ factor 0.8 B/A valuation Factor
µ2 factor 0.5 C/A valuation Factor
Competency-Proficiency Score 2 Score of Top 2 competency used for RP

In an exemplary embodiment, the primary proficiency (PC A-C) and the secondary proficiency (SPC A-B) for multiple workers category A1-A5 is illustrated in table 8.
Table 8
Worker Category
Primary Proficiency Secondary Proficiency
PC A PC B PC C SPC A SPC B
A1 E3 E2 E2
A2 E2 E2 E1
A3 E2 E2 E2
A4 E1 E2 E2
A5 E2 E3 E1

In another exemplary embodiment, the productivity factor of multiple workers is determined based on the worker competency (refer table 7), the worker proficiency (refer table 8) and the seniority level (refer table 6). Further, the productivity factor determiner 190 determines the aggregated productivity factor for the team of workers category A1-A5. This embodiment is further illustrated in table 9.
Table 9
Worker category Total Score Top 2 Comp Score Resource Hierarchy Productivity Factor (PF) Number of workers Productivity Factor
Range Value
A1 16 14 SR 1.2 to 1.4 1.3 1 1.3
A2 10 9 MR 0.9 to 1.2 1 3 3
A3 9.5 7.5 MR 0.9 to 1.2 0.9 2 1.8
A4 6.9 4.9 JR 0.75 to 0.9 0.82 3 2.46
A5 14 13 SR 1.2 to 1.4 1.2 1 1.2
Total 10 9.76

According to the present embodiment, the effort calculator 198 is configured to receive task specification value (TS) from the parser 150, the task category TC from the task categorizer 170, the measure of pseudo factor from the pseudo factor determiner 180 and the productivity factor (PF) from the productivity factor determiner 190. The effort calculator 198 is further configured to estimate effort requirement for the task under the influence of the processing commands from the processor 110 based on the equation:
Estimated Effort = (TS *TC + ß)/PF
wherein TS represents the task specification, TC represents the task category, ß represents the pseudo factor and PF represents the productivity factor.
In an exemplary embodiment, the effort for a task is estimated based on the data from table 4 and table 9. The values for TS*TC and TS*TC with stabilization factor d are obtained from table 4. The productivity factor PF is obtained from the table 9. When for a particular task, pseudo factor (ß) is 150, TS*TC with d is 1613 and productivity factor PF is 9.76, the estimated effort is 22.58 days. This embodiment is illustrated in table 10.
Table 10
TS *TC 1780
TS*TC with d 1613
pseudo factor (ß) 150
Productivity factor PF
(Per days) 9.76
Estimated Efforts (Days) 22.58

In another exemplary embodiment, the task effort estimator can be used for estimating the effort required for manufacturing of an automobile.
The intransient repository of the task effort estimator, stores a predetermined set of processing rules, rules for establishing a complexity measure with respect to different task involved in manufacturing of the automobile, rules for determining the pseudo factors for the tasks and rules for determining the productivity of different workers working on the tasks related to manufacturing of the automobile.
The input module is configured to receive different reports from a user, regarding the requirements for manufacturing of the automobile and work description and details of the workers assigned to perform the task along with the seniority level, proficiency level and competency level. For example: there are reports which contain information regarding the mechanical components such as brakes, transmission, tires, engine, sensors, electrical connections etc. Further, there are reports regarding the aesthetic components such as interiors, seating, color of automobile, headlights etc. Additionally, there is a report regarding the mixed technical & aesthetic components such as automobile body design, bumper design, headlight console design, which defines the aesthetic features of automobile such as looks and also the technical features such as aero-dynamicity & weight of the automobile. The workers from different domains are required for completion of different tasks such as engine installation, interiors, painting, structure design, sensor installation etc. Further, the reports also contain seniority level, competency level and the proficiency level of the workers of each domain.
The crawler present in the parser module traverses across the task specification report and extracts words and phrases from the reports, corresponding to the technical tasks, aesthetic tasks and, technical & aesthetic tasks. The aggregator present in the parser, further assigns the values to the tasks and calculates the aggregated value for the technical tasks, aesthetic tasks, and technical & aesthetic tasks based on the predefined rules stored in the repository.
Further, the task categorizer receives the technical tasks, aesthetic tasks and technical & aesthetic tasks from the extractor. The task categorizer comprises a mensuration tool for calculating the complexity measures of the tasks and assigning a task category based on the calculated complexity.
A pseudo factor determiner determines the pseudo factor for the task related to manufacturing of the automobile. The pseudo factor defines the external factors which have an impact on requirement estimation like non-automobile enhancement, customization of fuel tank for racing purpose and the like. The pseudo factor is calculated by mapping the tool/technology capability data present in the repository with the tasks present in reports accepted by the input module.
Further, a productivity factor determiner receives the details of the workers from the input module and includes a mensuration tool which determines the productivity factor of the workers working on the task.
Effort calculator receives the task specification from the aggregator module, the task category from the task categorizer, the pseudo factor from the pseudo factor determiner and the productivity factor of the workers from the productivity factor determiner module, and calculates the estimated effort required (in days or hours) for manufacturing of automobile based on the following equation:
Estimated Effort = (TS * TC +ß)/ PF.
Wherein TC represents task category, TS represents task categorization, ß represents the pseudo factor and PF represents the productivity factor.
Figure 3 illustrates a method 200 for estimating effort requirement for projects according to an embodiment of the present disclosure. The method 200 may be described in the general context of computer executable instructions. Generally, the computer executable instructions include routines, programs, objects, components, data structures, procedures, and modules, functions, which perform particular functions or implement particular abstract data types. The method 200 can also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. In a distributed computing environment, computer executable instructions are located in both local and remote computer storage media, including memory storage devices.
The order in which the method 200 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 200, or an alternative method. Additionally, individual blocks may be deleted from the method 200 without departing from the spirit and scope of the method described herein. Furthermore, the method 200 can be implemented in any suitable hardware, software, firmware, or combination thereof.
Referring to the method 200, at block 202, a predetermined set of processing rules, parsing rules, rules for establishing complexity measure of a task, rules for determining pseudo factor (ß) for the task, and rules for determining the productivity factor of workers assigned to the task are stored. In an embodiment, the predetermined set of rules, and other rules are stored in the intransient repository 130.
At block 204, the rules are received and entity specific processing commands are generated. In an embodiment, the processor 110 is configured to receive the rules and generate entity specific processing commands.
At block 206, a report corresponding to the task and details of workers assigned to perform the task along with the seniority level, the proficiency and the competency of each of the workers is received. In an embodiment, the input module 142 is configured to receive the report corresponding to the task and details of workers assigned to perform the task along with the seniority level, the proficiency and the competency of each of the workers.
At block 208, the report is traversed and words and phrases from the report, learnt during a training phase, corresponding to technical components, aesthetic components and mixed technical & aesthetic components, required for carrying out and completion of the task are extracted. In an embodiment, the crawler 152 is configured to traversing across the report and the extractor 154 is configured to extract words and phrases corresponding to the technical components, aesthetic components and mixed technical & aesthetic components in the task.
At block 210, values to the technical components, the aesthetic components and the mixed technical & aesthetic components, respectively based on predefined rules are assigned and aggregated and a task specification (TS) is computed using the assigned values for the respective components. In an embodiment, the aggregator 156 is configured to aggregate and assign value to the technical components, the aesthetic components and the mixed technical & aesthetic components respectively based on predefined rules.
At block 212, the technical components, aesthetic components and mixed technical & aesthetic components are received, and the complexity measure of a task is determined, and assigned a task category (TC) based on the calculated complexity. In an embodiment, a task categorizer is configured to receive the extracted technical components, aesthetic components and mixed technical & aesthetic components and to determine the complexity measure of a task, and assign a task category (TC) based on the calculated complexity.
At block 214, the rules for determining the pseudo factor for the task are received, and under the influence of processing commands, a pseudo factor (ß) for the task is computed. In an embodiment, a pseudo factor determiner is configured to receive the rules for determining the pseudo factor for the task, and computing under the influence of processing commands, a pseudo factor (ß) for the task.
At block 216, the rules for determining the productivity are received and the details of workers are mapped under the influence of system commands and a productivity factor are assigned to each of the workers based on the mapping and computing an aggregated productivity factor (PF) based on the productivity factor of each of said worker. In an embodiment, the productivity factor determiner 190 is configured to receive the rules for determining the productivity and the second mapper 192 maps the details of workers under the influence of system commands and assigning productivity factor to each of the worker based on the mapping, and the productivity aggregator 194 compute an aggregated productivity factor (PF) based on the productivity factor of each of said worker.
At block 218, under the influence of processing commands from the processor, the effort requirement for a task based on the equation: Estimated Effort = (TS * TC +ß)/ PF. In an embodiment, the effort calculator 198 is configured to estimating effort requirement for the task based on the equation:
Estimated Effort = (TS * TC +ß)/ PF.
The systems and methods are not limited to the specific embodiments described herein. In addition, components of each system and each method can be practiced independently and separately from other components and methods described herein. Each component and method can be used in combination with other components and other methods.
Throughout the description and claims of this complete specification, the word “comprise” and variations of the word, such as “comprising” and “comprises,” means “including but not limited to,” and is not intended to exclude, for example, other additives, components, integers or steps. “Exemplary” means “an example of” and is not intended to convey an indication of a preferred or ideal embodiment. “Such as” is not used in a restrictive sense, but for explanatory purposes.
For a firmware and/or software implementation, the methodologies can be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. Any machine readable medium tangibly embodying instructions can be used in implementing the methodologies described herein. For example, software codes and programs can be stored in a memory and executed by a processing unit.
In another firmware and/or software implementation, the functions may be stored as one or more instructions or code on a non-transitory computer-readable medium. Examples include computer-readable media encoded with a data structure and computer-readable media encoded with a computer program. The computer-readable media may take the form of an article of manufacturer. The computer-readable media includes physical computer storage media. A storage medium may be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer; disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blue-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.