mechanical engineers

PANORAMA OF THE MARKET DEMAND FOR MECHANICAL ENGINEERS IN SOUTH AMERICAN COUNTRIES

Ronald A. Cardenas

Faculty of Mechanical Engineering, Universidad Nacional de Ingeniera

Lima, Peru [email protected]

Kevin S. Bello Faculty of Mechanical Engineering, Universidad Nacional de Ingeniera


Alexander R. Valle

Faculty of Mechanical Engineering, Universidad Nacional de Ingeniera


Elizabeth R. Villota Section of Mechanical Engineering, Pontificia Universidad Catlica del

Peru Lima, Peru

[email protected]

Alberto M. Coronado Faculty of Mechanical Engineering, Universidad Nacional de Ingeniera


ABSTRACT

Many educational institutions employ surveys in order to

identify what majors to offer or what competencies to

emphasize in their curricula. Different from a survey, we

present an analysis of the labor market needs based on data

collected from job ads available in the Internet. Tools of

natural language processing (NLP) and statistical techniques

have been employed to handle the job ads. For Peru, Chile and

Colombia, a detailed panorama of the market demand has

been depicted: mechanical engineering appears among the top

most demanded engineering majors and maintenance is its

most frequent technical requirement; management (project,

quality and operations) related requirements also rank high,

together with a working knowledge of English. By using

diverse visualization techniques we can also show the social network of a major, where friendship is defined by the amount of job ads shared by any two majors.

1 INTRODUCTION During the past decade, many South American countries

have benefited from soaring commodity prices. Along with

their economic growth, their required working forces have

also grown in complexity and specialization. Many local

employers are having a hard time finding qualified workers,

especially in technical fields [1]. Educational institutions are

also having difficulty to supply the demanded labor.

Particularly, for these educational institutions, the challenge of

providing talented human labor to their local economies is

now bigger than ever, as commodity prices are down and

rising productivity is imperative, if sustained growth is desired

[2].

For educational institutions around the world, and South

America is not the exception, it is not always clear what

majors they should offer or what competencies they should

emphasize in their curricula; and it is also not self-evident

what the relative importance should be among foundational,

professional and emerging (entrepreneurial, innovation) skills

[3,4,5,6]. Some institutions have adopted the ABET program

outcomes and guide their curricula design towards meeting

those outcomes; however, to what extent can a developing

country use those ABET recommendations? and what about

incorporating their own economies needs into their

curriculum? or how much effort do they have to devote to

innovation? Different countries have different needs,

resources, cultures, and hence require different solutions [7].

Proceedings of the ASME 2015 International Mechanical Engineering Congress and Exposition IMECE2015

November 13-19, 2015, Houston, Texas

IMECE2015-51557

1 Copyright 2015 by ASME

Considering the specific case of mechanical engineering,

Wolfe [8] performed a survey among mechanical engineering

alumni of MIT. It was found that topics closely related to the

core undergraduate curriculum in mechanical engineering

were identified as the least used in their daily jobs. On the

other hand, topics such as, communications, teamwork,

independent thinking, professional skills and attitudes, and

personal skills and attitudes, were identified among the most

commonly used. Interestingly, the topics just mentioned also

were reported as being learnt mostly outside MIT. In addition,

three important aspects were stressed: 1) MIT graduates

pursued a wide variety of careers, 2) mechanical engineers

needed to learn more than just knowledge about the physical

world, and 3) engineers needed to have strong business skills,

such as communication.

An essential body of knowledge (BOK) for mechanical

engineering students was proposed by Jarosz & Busch-

Vishniac [9]. The authors dissected current curricula in

mechanical engineering, at nine US institutions, into a list of

required topics. Among the 64 topics deemed essential, 45%

correspond to engineering, 18% to mathematics, 15% to

physics, 12% to chemistry, 4% to computer science, 2% to

communication, 2% to statistics, 1% to economics, 1% to

ethics, and 0% to biology. The BOK obtained represents

what is currently taught, not necessarily what should be taught. In addition, it was observed that many of these topics reflect mechanical engineering in the seventeenth and

eighteenth centuries, not necessarily what is currently needed.

At present, universities across the world are engaged in a

race to increase their position in the academic rankings

currently available. This practice is now under increasing

scrutiny. Hill et al. [10] discuss the idea that the structure and

makeup of chemical engineering faculties in the US, and how

hiring and promotion is carried out, may affect the training of

undergraduate students regarding industrial applications. In

the same vein, Xue [11] analyses if there is in fact a STEM crisis or a STEM surplus in the US. It was reported that depending on the major, and sector, it can be one or another.

For example, in the academic job market, there was no

noticeable shortage of any discipline. On the other hand, in the

private sector, there was high demand for software developers,

petroleum engineers, data scientists and skilled trades.

Educational institutions tend to appeal to surveys to

employers, as well as alumni, in order to obtain a glimpse of

the labor market. Different from a survey, whose results are

tightly related to the quality and scope of questions performed,

in this study, we present an analysis of the labor market needs

based on data collected from job ads (tens of thousands)

available in the internet. Working with this data, we are able to

depict a detailed panorama of the market demand in three

South American countries: Peru, Chile and Colombia. The job

ads are handled using tools of natural language processing

(NLP), information retrieval and diverse statistical and

visualization techniques. Uni-, bi- and tri-gram-based

language models were used to extract the most common job

requirements, which can be readily examined as Wordclouds.

The remainder of this article is organized as follows. The

next section presents a brief discussion on the techniques used

to obtain, process and analyze natural language data.

Following, we present the results of our analysis. We start by

showing the market demand by country for diverse

engineering majors. Then we discuss the relationship among

mechanical engineering and other engineering majors taking

into account how often these majors share job ads. Next, we

present Wordclouds with the most common requirements for

mechanical engineers. The final section presents the

conclusions.

2 NATURAL LANGUAGE PROCESSING

2.1 Reference Corpus The corpus used in this work was built by extracting job

ads from several popular job search websites from Peru, Chile

and Colombia. Since the same job ad can be published in more

than one website, we consider it as repeated if the description

of the position is found within the last fifteen days in the

database. The time periods in which the data were extracted

are mentioned in Section 3.

This corpus consists of more than 250000 job ads for

Peru, 180000 for Colombia and 50000 for Chile. It contains

job ads from all areas in the market as published in the

websites.

2.2. Pre-processing Phase There is no standard format for job ads in Latin America.

The writing style is not formal and the text can surely contain

orthographic errors. Therefore, the data must be cleaned

before being classified. Techniques from natural language

processing (NLP) such as regular expressions (regex) and

tokenization were used to eliminate special symbols, HTML

tags, multiple spaces and so on. Furthermore, low frequency

words were mapped to a small set of tags, such as Number,

Date, LongWord, Url, PhoneNumber, among others.

2.3. Majors Classification The classifier was based on regular expressions. It

searches for major names surrounded by a pre-established set

of phrases, such as ...professional with major in... or ...student with major in.... The expression to match is constructed joining up to four sets of commonly seen phrases,

before and after the possible major name, in order to obtain as

much as possible relationships.

The 30 most popular engineering majors were accounted

for graduates from the three countries considered here. The

classifier relies on multi-word identifiers for each major

considered, in order to perform multi-classification. Each

major identifier set was tuned for each country.

The evaluation of the classifier was conducted in a semi-

automatic manner, since no annotated data was available. The


evaluation corpus consisted of 1000 randomly sampled job

ads. The accuracy for the top five most demanded engineering

majors in Peru can be appreciated in Table 1.

TABLE 1. Classifier accuracy for the top five most

demanded engineering majors in Peru

Engineering major Accuracy (%)

Industrial 94.01

Systems 89.49

Informatics 89.19

Electrical 88.02

Mechanical 88.10

2.4. Market Demand Visualization Two ways of visualizing the market demand were

selected. The first one, known as Treemap, shows the demand

per major in a space-constrained manner. Each engineering

major is represented as a rectangle with area proportional to its

demand.

The second one, known as Circleplot, allows us to

visualize interconnectedness among engineering majors taking

into account how often these majors share job ads. Each pair

of interconnected majors is linked by an arch. The arch

thickness is proportional to the interconnectedness between

majors, expressed as the number of shared job ads.

These graphics are shown in Section 3 for each country.

Interactive versions are available online for Peru [12,13],

Chile [14,15] and Colombia [16,17].

2.5. Requirements Ranking and Visualization Job requirements were modeled as keywords and ranked

using statistical scores. Potential keywords consisted of uni-

grams, bi-grams and tri-grams, filtered by a list of more than

3000 corpus tuned stopword phrases. This stopword set was

built combining standard Spanish stopwords, manually tuned

stopwords and smart stopwords. The smart stopwords were extracted with statistical techniques mentioned by Rose et al

[18].

Three ranking scores were compared. The first one was

term frequency tft, frequency of the term t in the entire corpus.

The main source of noise in this score was the fact that a

phrase can be mentioned multiple times, but only once as a

requirement. In order to deal with the ambiguity of uni-gram

terms, we perform a correction in uni-gram counts before

calculating their scores -only for terms in common with the

tri-gram list. This correction consisted in adding up the counts

of the tri-gram phrases in which the uni-gram term being

analyzed has the same meaning. The semantic relationship

was manually determined.

The second one was document frequency dfw, the number

of ads in the corpus that contains the word w. For this score,

there was no need for correction in uni-gram counts because

this score removes the noise from phrases mentioned in

different contexts, as was the case with the first scoring

method.

The third score compared was a discretized version of TF-

IDF score as implemented by Gutwin and Nevil-Manning

[19]. For the case of the TF score, we used the same procedure

to tackle the ambiguity of uni-gram terms as in the first

scoring method.

The second scoring method yielded better results for our

corpus. The main reason the third one did not performed well

was that it is biased by the length of the documents. In our

case, the document (job ad) body can contain detailed job

functions as well as description of benefits and working

conditions, resulting in ad lengths ranging from one to

hundreds of sentences. Given that requirements are usually

mentioned only once in the ad, the third score gave lower rank

to important requirements in large ads than in the short ones.

The requirement ranking was visualized as Wordclouds,

plotting the phrase font size proportional to its ranking score.

Wordclouds for mechanical engineering for the three countries

are presented in Section 4.

3 MARKET DEMAND BY COUNTRY The job ads obtained from the internet correspond to the

period June-September 2014, plus January-March 2015, for

the three countries considered here. It is important to observe

that different sources were used for obtaining the job ads for

the three countries, so we cannot conclude with these results

that there is more demand for engineering majors in one

country than another. In Fig. 1 we present the Treemap

corresponding to the Peruvian labor market demand

considering only engineering majors. There are several

features worth discussing. For example, it is very common for

a job ad to include more than one engineering major among its

requirements. Any prospective job seeker needs to have

majored in any of the specialties mentioned in the job ad. As a

consequence, if we add 19502 (Industrial Eng) + 8164

(Systems Eng) + 7296 (Informatics) + we will obtain 68682, which is larger than the total amount of job ads for

engineering majors (45368), since many job ads may be

counted more than once.

Also, it is important to observe that in Peru and some other

South American countries, Informatics and Systems

Engineering are closely related majors and most of the times it

is difficult to tell them apart. If we consider them as one, then

we have that mechanical engineering majors are currently the

fourth most demanded in Peru.


FIGURE 1: Peruvian labor market demand for engineering majors

In Fig. 2 we present the Treemap corresponding to the Chilean

labor market demand considering only engineering majors. In

this case, due to the sources we used to obtain the data, there is

a significant reduction in the number of job ads analyzed. As a

consequence, we may expect that this data corresponds to only

a rough description of the Chilean job market. However, the

most demanded engineering majors still present a great

resemblance with the Peruvian case, with a few differences in

the ranks.

In Fig. 3 we present the Treemap corresponding to the

Colombian labor market demand considering again only

engineering majors. Regarding the top majors, it can be seen

that the ranking presents a very close resemblance to the

Peruvian and Chilean cases.

FIGURE 2: Chilean labor market demand for engineering majors


FIGURE 3: Colombian labor market demand for engineering majors

As discussed above, it is very common to find more than one

engineering major as requirement in job ads. This information

can help us to build a social network kind of graph, in order to visualize the degree of connectedness among these diverse

technical disciplines. On the left side of Fig. 4, it is shown that

indeed there is a great degree of relationship among

engineering fields considering their common mention in job

ads, for the Peruvian job market case. For example, the two

most common connections of industrial engineering are

systems and mechanical engineering. On the right side of Fig.

4, it is shown the connections that are exclusive to mechanical

engineering. In this case, the majors that share more job ads

with mechanical engineering are: industrial (31%), electrical

(26%) and civil (10%) engineering. Mechanical engineering is

mentioned exclusively in only 10% of its job ads.

FIGURE 4: Relationship among Peruvian engineering majors. Left: all connections, right: mechanical engineering connections


FIGURE 5: Relationship among Chilean engineering majors. Left: all connections, right: mechanical engineering connections

FIGURE 6: Relationship among Colombian engineering majors. Left: all connections, right: mechanical engineering connections


Next, we analyze the Chilean job market case. In Fig. 5, on the

left side, we find again that there is a great level of

connectivity among diverse engineering majors. However,

since the sample for this country is relatively small, many of

the least frequent connections (corresponding to the thinner

links) are missing. On the right side of Fig. 5, we observe that

the most common majors related to mechanical engineering

are: electrical (33%), industrial (15%) and electronics (13%)

engineering.

In the case of the Colombian job market we obtained the

following results. On the right side of Fig. 6, we also observe a

great degree of connectivity among diverse majors. On the

right hand side of Fig. 6, we see that mechanical engineering

shares most of its job ads with: industrial (23%), electrical

(22%) and electronics (14%) engineering.

4 MECHANICAL ENGINEERING REQUIREMENTS During the time intervals considered in our analysis, in

the Peruvian labor market, mechanical engineering was

mentioned in 4388 job ads. Almost half of these ads (2141)

cited maintenance as one of its requirements (see Fig. 7). In

order to disambiguate the context in which this term is

employed, it is very useful to look at the related bi- and tri-

grams. For maintenance we found requirements such as: preventive maintenance, maintenance activities, maintenance management, maintenance plan, among others. Next, we have the requirement control, which can be traced to: quality control, control of projects, process control, among others. In the third place we find processes, which is related to: productive processes, industrial processes, process control, among others.

In the case of the Chilean labor market, in our sampling, we

obtained 424 job ads citing mechanical engineering as

requirement. Among them, almost half (232) mention

maintenance as a requirement (see Fig. 8). In this case, we find the following related mentions: preventive maintenance, industrial maintenance, maintenance activities, among others. Next, we have control, which is related to quality control, process control, among others. In third place, we find processes, which is traced to: production processes, manufacturing process, quality processes, among others.

FIGURE 7: Wordcloud for Peruvian mechanical engineering labor market requirements


FIGURE 8: Wordcloud for Chilean mechanical engineering labor market requirements

Finally, considering the case of the Colombian labor market,

we obtained that maintenance is once again the most common requirement with 910 job ads, out of a total of 1951

job ads asking for mechanical engineers (see Fig. 9). This first

requirement is associated to: preventive maintenance,

industrial maintenance, equipment maintenance, among others. Following, we find the requirement personal, which is mostly related to interpersonal skills. In third place, we find processes, which is linked to: process control, process quality, industrial process, among others.

FIGURE 9: Wordcloud for Colombian mechanical engineering labor market requirements

CONCLUSIONS From our analysis, we have identified that mechanical

engineering is among the most demanded engineering majors

in Peru, Chile and Colombia. An interesting feature we have

unveiled is the friendship between any two majors, defined

by the amount of job ads shared, which readily permits to

define something like a social network for majors. In this setting, mechanical engineering appears closely related to

industrial and electrical engineering majors, among others.

When analyzing the Wordclouds for mechanical engineering,

the technical requirement that is the most frequent is


maintenance. Management (project, quality and operations)

related requirements also rank high, together with a working

knowledge of English.

Our results can be used as a complementary tool to what

educational institutions already employ in order to dimension

their offer (labor supply) and to define their curriculum

design, i.e. surveys. It is up to each educational institution to

define the combination of skills that best supports its

educational objectives. Some institutions may lean towards

more technical skills (such as CAD, FEA, etc.), while others

may give more weight to soft and managerial skills.

This study represents the use of data analytics as a data-

driven transparency mechanism to align educational

institutions to the labor market. By providing this alignment,

our work can help to ensure conditions for steady economic

growth, better dimensioned university programs, satisfaction

for employers and graduates able to work. Overall, our study

can contribute to improve productivity by helping to provide

the talented work force South American countries require.

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