ONTOLOGY BASED INTEGRATION OF TRAINING SYSTEMS

The Electrical Power Production Operators Domain

Ricardo Molina-González, Guillermo Rodríguez-Ortíz, Víctor-Hugo León-Sagahón

Gerencia de Sistemas Informáticos, Instituto de Investigaciones Eléctricas, Reforma 113, Cuernavaca, Morelos, Mexico

Jaime-Israel Paredes-Rivera

Subdirección de Generación, Comisión Federal de Electricidad, Don Manuelito 11, Col Olivar de los Padres, Mexico D.F.

Keywords: Training, Skills, Talent, Innovation, Ontology, Competitiveness, Knowledge Management, Competence.

Abstract: An ontology based approach to loosely integrate independent training management systems is presented.

The three systems are: the traditional training management system, the labour skills management system

and the talent and innovation management system. The method first represents the data of each of the three

independent systems using a simplified ontology structure, and then the integration relationships among the

systems are specified and implemented.

1 INTRODUCTION

Derived from governmental policies related to

competitiveness and considering its strategic

planning objectives, CFE (Comisión Federal de

Electricidad -the National Electric Utility in Mexico,

a 70,000 employee power company) has establish an

strategic program to improve its human capital and

to align it with its mission and future vision. This

program promotes learning and innovation at CFE

which is the enterprise responsible for generating,

transmitting and distributing electricity through out

the Mexican nation.

Continously, CFE has invested significant

amount of work to train and develop its human

capital related to electric power generation, with

efforts mainly concentrated on learning acquisition

(to enhance the specialization level), the workers

academic grades improvement, and the value

contribution through innovation as the basis for

competitiveness.

During the last 5 years, CFE has evolved its

training system by integrating its traditional

administrative model with training based on

competences. The implementation of this new

system is supported by powerful computer systems

where

 Information is managed to follow up both face-

to-face and on-line training.

 Information is maintained on the training

alignment with the mission and functions of the

company

 Applicable labor skills standards to electricity

generation are registered

 Labor competence certificates granted to the

workers are registered

 It is supported the elaboration, storage and

recovery of instructional contents for self

training trough Internet

 The talent and innovation management is

supported

With this new Human Capital Management

System (HCMS) for the electrical generation

operators, the workers are involved in a repetitive

cycle between the learning (training) process and the

innovation process (Miller, 2007) with emphasis on

value creation to guaranty financial profitability.

In this paper we describe the ontology based

approach used to integrate the traditional training

model and the competence based training model

with the talent and innovation management to

manage information about human capital.

Section two presents the HCMS model that

integrates learning, talent and innovation

management, the model is described both from the

conceptual and functional points of view. In section

Molina-González R., Rodríguez-Ortíz G., León-Sagahón V. and Paredes-Rivera J. (2010).

ONTOLOGY BASED INTEGRATION OF TRAINING SYSTEMS - The Electrical Power Production Operators Domain.

In Proceedings of the 5th International Conference on Software and Data Technologies, pages 49-56

DOI: 10.5220/0003016800490056

 SciTePress

three, the management systems are described and the

ontology models for each system is introduced, and

finally the integration method is presented.

2 THE HUMAN CAPITAL

MANAGEMENT SYSTEM

The Human Capital Management System can be

described using two models: a conceptual model and

a functional model.

2.1 The Conceptual Model

As mentioned in (Miller, 2007), learning stimulates

innovation, in the form of information and

knowledge. And in return, innovation gives birth to

new learning and knowledge. One way to experience

the relationship between learning and innovating is

to tune into our own breathing rhythms. Learning

and innovating go together just like inhaling and

exhaling:

 Inhaling = learning: acquiring, creating and

sharing new knowledge; converting knowledge

to wisdom.

 Exhaling = innovating: generating, deciding

upon, implementing and celebrating innovative

responses to opportunities and challenges.

Figure 1: Learning, Talent and Innovation.

The HCMS conceptual model is shown in figure 1.

The aim of the Learning Process is to train and

develop human capital.

With the Talent Management Module CFE

identifies the capacity of the workers and using

career training and development preserves their

talent.

The innovation management system supports

decisions about the workers value contributions to

CFE stimulating the creation of personal and

organizational innovations.

The information systems integrate learning,

talent and innovation using interactive technologies

as explained in the following section.

2.2 The Functional Model

From the functional point of view, the HCMS is

composed of an integrated set of four interactive

information management systems as shown in

Figure 2: the traditional training management

system, the competence based training system, the

talent management system and the innovation

management system.

Figure 2: Information Systems.

3 ONTOLOGY MODELS

This section describes the component systems of the

HCMS and their ontology models.

3.1 The Traditional Training Model

For more than 20 years, CFE has executed a training

program that includes a coherent and comprehensive

group of standards developed internally and aimed

to achieve the training of its 70,000 employees and

with the contractual objective of offering 10 training

days per year for each employee. The norms are

ICSOFT 2010 - 5th International Conference on Software and Data Technologies

classified in four groups:

Planning:

 Position Profiles

 Training Batteries

 Training profile per worker

 Individual Knowledge Matrix

 Individualized Training Program

 Specific problem solution oriented program

Organization and Integration:

 Revision, consolidation and authorization of the

annual training program

 Instructors' development

Execution:

 Execution of the annual training program

 Budget

 Reports

 Statistical

Control:

 Construction and application of diagnostic

evaluations

 Creation and application of partial evaluations

 Application of reaction evaluations

 Elaboration of the courses reports

 Transcript release of credit courses

 The worker's evaluation in his position

 Transcript release of labor abilities (aptitude

record)

 Transcript updating of labor abilities and their

upgrade for the current position and/or

immediate superior position.

The objective of the traditional program is to

obtain an annual training program of individualized

courses with impact in the productivity indexes.

CFE classifies the hundreds of employee

positions of its organizational chart into organic

groups or levels (from I to XII) depending on the

position responsibility (director, manager,

department boss, operator, clerk, etc.), the position

participation in business processes (generation,

transmission, distribution) or support processes, if

the position is unionized, and the remuneration level.

Each position in the organization has an assigned

profile that it includes one or more specialties, for

example,

 Maintenance (mechanic, instrumentation, elec-

tric)

 Operation (analysis and results, engineering,

chemical)

 Planning (supply, analysis, studies)

 Services (billing)

 Etc.

The specialties are classified in levels that match

with the academic levels: secondary, high school,

primary technician, secondary technician, bachelor,

graduate, master, and doctorate.

In this fashion, the annual training needs are

detected to proceed to the assignment of dates for

each course and to conform the training program

which typically includes approximately 40,000

courses per year. The aptitude records are good for

performance evaluation, to cover vacancies, or for

incentive assignments.

The ontology model to describe the data for the

traditional training model is shown in figure 3.

Figure 3: The Ontology Model for the Traditional Training

Model.

3.2 The Labor Skills Model

A competency or labor skill is a specific capacity to

perform a productive function in different labor

contexts on the basis of obtaining quality results in

the corresponding productive sector. A productive

sector is a part of the society that specializes in some

type of activity, for example, agriculture, health, or

energy.

ONTOLOGY BASED INTEGRATION OF TRAINING SYSTEMS - The Electrical Power Production Operators Domain

In contrast with a traditional training system, the

main objective of a job skills management system or

program is to certify individuals in knowledge,

skills, expertise, abilities, and attitudes appropriate

for specific enterprise productive functions

independent of how they acquired them. This means

that a labor competencies program may or may not

be supported by a training system.

The ways and means that a person uses to

acquire his skills is not the concern of a labor

competency program, however, this program has to

have evaluation tools and systems to make sure that

a candidate for certification has or not the

knowledge, abilities and attitudes required for

performing a position for a particular productive

function.

The Job Skills Technical Standard (JSTS)

management module of the system includes the

productive functions map for the electric sector, the

collaboration mechanisms for the JSTS

development, printing and publishing, and the

content structures to manage their storage.

A Job Skill Technical Standard (JSTS) is defined

and developed by a Job Skill Standard Committee

(compose of methodologists, technicians and

specialists, among other) authorized by CFE, and

approved by the National Council for Job Skills

Standardization and Certification (CONOCER,

Spanish initials) and sanctioned by the Public

Education and the Work and Social Affairs

Secretaries of State. A JSTS establishes, for

repeated and common use in the whole Mexican

States territory, the characteristics and the guidelines

for the evaluation of capacity or labor competence.

In Figure 4, a semantic model is shown for the

normalization management that has been

implemented with a relational database management

system.

The methodologists generate the knowledge

included in a JSTS; they are the experts in a

productive function contained in the company’s

functional map. Roughly, the functional map of the

CFE is a functions hierarchy or tree where the

functions corresponding to the highest level are four:

1. To operate the equipment for electric power

generation, transmission, transformation and

distribution.

2. To maintain the equipment for electric power

generation, transmission, transformation and

distribution under operating conditions.

3. To manage the operation and energy transactions

of the Power Electrical System.

4. To provide the electric power utility service.

Figure 4: Semantic Model for job skills normalization.

It is not the intention of this paper to show the

complete functional map for each one of the four

mentioned highest level functions, it would be very

extensive. All functions are composed of sub-

functions; a short fraction of the tree for the function

2 is as follows:

2.1 To plan the maintenance of the equipment for

electric power generation, transmission,

transformation and distribution.

2.2 To carry out the maintenance of the equipment

for electric power generation, transmission,

transformation and distribution.

2.3 To determine the maintenance effectiveness of

the equipment for electric power generation,

transmission, transformation and distribution.

Breaking down function 2.2 we reach (sub)

function 2.2.1.4.2 “To carry out the mechanical

maintenance of steam turbines” where several

standards is at hand, one of those is the norm:

CCFE0628.01 Mechanical maintenance of steam

turbines with high and low pressure cylinder.

In this way, CFE had to elaborate a JSTS for

each one of the lowest level productive functions of

the company (approximately two hundred norms). In

turn, the elements of a norm include evaluation

instruments that are the performance criteria related

with categories, in such a way that for an element

different skills evidences can be assessed, either for

abilities, for knowledge or for attitudes. One can

observe that to work in a company position, a person

will have to be certified in several labor

competences.

Each standardized competence or labor skill has

assigned a performance level according to the

ICSOFT 2010 - 5th International Conference on Software and Data Technologies

English NVQ system (National Vocational

Qualification). This classification of the

competences is in five performance levels, based on

different variables: complexity of the behavior,

variety of acting in different contexts, autonomy and

responsibility, requirement level, and of the

collaboration management of other people and

resources.

The levels imply progressive domain of these

variables as the competence level increases, the

smallest level of complexity and of variation is level

one, and level five is the highest.

Level 1: Competence performance is of different

labor activities most routine and predictable.

Level 2: Competence is in a significant scale of

labor activities carried out in different contexts.

Some are complex not routine. It should demonstrate

certain responsibility and autonomy and frequently

collaboration of others (team work).

Level 3: Competence is in a wide range of activities,

in different contexts. Most complex and not routine,

should demonstrate responsibility and autonomy,

frequently to control and to direct.

Level 4: Competence is in a wide range of labor

activities, in very different labor contexts, great

responsibility and autonomy, to take the

responsibility of the work of others and for

assignment of resources.

Level 5: Competence implies the application of great

quantity of fundamental and technical principles in

varied unpredictable contexts. It demands the

employee's autonomy, to take the responsibility in

great measure for the work of others and

considerable assignment of resources.

The elaboration of a specific norm is assigned to

a group that is integrated with a leader and a certain

number of technicians. The group uses a

methodological manual that contains a set of

templates or formats for the elaboration of standards.

The ontology model to describe the data for the

Labor Skills

model is shown in figure 5

Figure 5: Ontology for the Labor Skills Model.

3.3 The Talent and Innovation Model

The talent management model, conceptually

integrates the talent flow in periods that a worker

passes through from his entrance to CFE, until his

retirement (Figure 6). The training processes and

professional development, should consider the talent

management aspects that guarantee the investment

profitability as part of the intellectual capital of the

organization.

Figure 6: Worker Talent Flow (from hiring through

retirement).

As talent management, we classify the time

periods of the worker trajectory in:

NH: New Hired Worker

Worker identified in their attitudes according to

generations M or Y with time value to be classified

this way between 1 and 5 years of employment.

EW: Experienced Worker

Worker identified in their attitudes like

generation X with a time value to be classified this

way between 6 and 24 years of employment.

RP: Retirement Process Worker

Worker identified in their attitudes like

generation X or B-B with a time value to be

classified this way between 25 and 30 years of

employment.

RP: Repositioned Worker

Worker identified in their attitudes like

generation B-B with a time value to be classified this

way between 31 or more years of employment.

These time periods mentioned can be defined in

agreement with specialists, so that they can be

changed according to the perception that the

company has of them in a given moment.

The structural elements of the talent management

model are shown in figure 7.

ONTOLOGY BASED INTEGRATION OF TRAINING SYSTEMS - The Electrical Power Production Operators Domain

The training (knowledge, abilities and attitudes)

and the development represent or describe the

worker's talent throughout their trajectory or flow in

the company.

Training generally follows the worker's position

training battery and the battery of the immediately

higher position. The training is mainly aimed to

increase knowledge.

Figure 7: Structural elements of the talent management

model.

The skills training is of field practices with low,

high and very high intensity, with improvements

verification in the field, as well as the new

approaches of mental and psycho-motion abilities.

The attitude is what is expected on the part of the

worker in the various stages belonging to the

corresponding period of his labor trajectory.

Examples of attitudes are: personal interest toward

work, interest to participate in their position,

involved in work groups, and concern to engage in a

career plan.

Other attitudes are: to assume responsibility in

CFE, will to accept new challenges, to accept

responsibility as value and will to innovate and

create projects and improvements as well as to

promote learning and positive criticism for

improvement. Each attitude can show up in levels:

ATTITUD LEVELS:

Level 1 - Reception

Level 2 - Answer

Level 3 - Appraisement

Level 4 - Organization

Level 5 – Characterization

The development includes the opportunities

offered to the worker during the corresponding

period, development examples are: technical careers,

bachelor and graduate degree levels, technical,

administrative and executive competence

certification, and global competences, also,

specialization, short courses, research, author, tutor,

instructor and advisory skills development.

Experience is a key indicator in a talent

management system and the experience is measured

with the time dedicated to activity, where results

were obtained and there were a productive

performance in the different positions and

competences of the worker (xxxxx see section of

indicators).

The worker acts with objectives and clear

indicators, he knows the tracking processes, he

commits to his achievement and it accumulates

merits for results of his experience in the work.

The worker manages information and locates a

great deal of knowledge objects with related value to

the context of his work, like procedures, work

instructions, training manuals, recordings,

presentations, maps of contents, frequent searches in

internet and outstanding places, glossaries, initials,

data or corporate contents, convenient web links,

publications, historical information, norms, real

cases, examples, references to books, indexes,

summaries, etc.

The worker can contribute value to the company

by creating new knowledge objects or documents,

and this contribution can be evaluated financially

through a cost benefit analysis of the contribution.

For innovation management, we propose a

variation of the Values Analysis Model Process that

is the precursor to the Measure, Analyze, and

Improve process that in turn is the basis of the Six

Sigma improvement process. The HCMS innovation

process is composed of the following phases:

 A creative idea is captured through a coorporate

portal

 Headquarters pre-evaluates the idea

 An assigned operating area evaluates the idea

 A community of practice approves the idea and

sets up a project to implement the innovation

The ontology model to describe the data for the

talent model is shown in figure 8.

The talent and innovation management indicators

are a way of measuring the talent that the company

has, some indicators are an indirect way to express

the possible benefits of the value that the company

can obtain from its workers contribution.

ICSOFT 2010 - 5th International Conference on Software and Data Technologies

Figure 8: Ontology for the Talent Model.

The indicators are defined for the individual

worker and they can be integrated or accumulated

hierarchical or globally for the working center,

region and nation.

The proposed indexes of talent and innovation

management are:

Tenure in CFE (years of employment)

Experience, is the time and number of exercised

positions (functional and organic).

Labor period where the worker is (NH, EW, RP, or

RW)

Knowledge (Training)

 Current expenses in training

 Days of training

 Training percent in primary, secondary and

complementary functions

 Depth level of cognitive area (1, 2, 3, or 4)

Certified Competences or Skills (complexity levels:

1, 2, 3, 4, or 5)

 Practice percent (in primary, secondary and

complementary functions).

 Depth level in the psycho-motion area (1, 2, 3, or

4 )

Attitude

 Depth level in the attitude or affective area (1, 2,

3, 4, or 5)

Education, highest academic degree

Value Contribution and Innovation per worker

(documented cost benefit).

3.4 Integration

The real power of ontologies lies in the ability to

create relationships among classes and instances,

and to assign properties to those relationships that let

us make inferences about them (Jepsen 2009). As

mention in (Janev 2009), one application of

ontologies is on data integration, and data share and

reuse.

The Human Capital Management System loosely

integrates the Traditional Training Management

System (TTMS), the Labor Skills Management

System (LSMS), and the Talent and Innovation

Management System (TIMS). The information

systems that implement these models were

developed independently; however, to obtain data

consistency and integrity among the systems a data

integration effort was pursued.

The approach used was to develop an automatic

data extractor that gets data from one system and

inserts the data in another system, and if necessary

performs some processing on the data before the

integration into the other system. Table 1 shows

some examples of data extractor specifications of

source and target data items and the required

processing.

Table 1: Source and target data items.

TALENT

(Target)

Definition Source Processing

TRAINING BATTERY

KNOW-

LEDGE

Knowledge

training hours

for a position

profile

TRAINING

BATTERY

(TTMS )

Sum of the

knowledge

training hours

from a

position

profile

SKILLS

Skills training

hours for a

position profile

TRAINING

BATTERY

(TTMS)

Sum of the

skills hours

from a

position

profile

ATTITUD

Attitude

training hours

for a position

profile

TRAINING

BATTERY

(TTMS)

Sum of the

attitude

training hours

from a

position

profile

TRAINING PROFILE (KARDEX)

KNOW-

LEDGE

Knowledge

training hours

of an employee

TRAINING

PROFILE

(TTMS)

Sum of

knowledge

hours from

the training

profile

(kardex)

ONTOLOGY BASED INTEGRATION OF TRAINING SYSTEMS - The Electrical Power Production Operators Domain

Table 1: Source and target data items. (Cont.)

TRAINING PROFILE (KARDEX)

SKILLS

Skills training

hours of an

employee

TRAINING

PROFIL

(TTMS)

Sum of skills

hours from

the training

profile

(kardex)

ATTITUD

Attitude

training hours

of an employee

TRAINING

PROFILE

(TTMS)

Sum of

attitude

training hours

from the

training

profile

(kardex)

OTHERS

VALUE

CONTRIBU

-TION

Artefacts

(courses,

articles ) that

the employee

produces in

order to

increase the

company value

ARTEFAC

TVALUE

(LSMS)

None

CERTI-

FICATES

Certificates

awarded to the

employee

during his

trajectory

CERTIFIC

ATES

(LSMS)

None

EDUCA-

TION

Employee’s

education level

TRAINING

PROFILE

(TTMS)

None

4 CONCLUSIONS

An ontology based approach to loosely integrate

independent training management systems was

presented. The three independently developed

systems were loosely integrated: the traditional

training management system, the labour skills

management system and the talent and innovation

management system. The resultant Human Capital

Management System allows users to interactively

access information from the three systems without

the need of knowing which system they are

consulting.

The ontology analyses help us to integrate the

data and to support data share and reuse.

REFERENCES

Janev, V. and Vraneš, S., Semantic Web Technologies:

ready for adoption?, IEEE, IT Pro September/October

2009, pp. 22-27.

Jepsen, T. C., Just what is an ontology anyway?, IEEE, IT

Pro September/October 2009, pp. 22-27

Miller, W. C., The Innovation Process: Energizing values-

centered innovation from start to finish, IEEE-USA,

2007.

Values Analysis Model, Brecker Associates Inc:

Pittsburgh PA. 2007 http://www.brecker.com/

quality.htm.

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