Karen Botelho
Faria
Universidade Candido
Mendes, Brazil
E-mail:
karenbotelhofaria123@gmail.com
Ana Carla de
Souza Gomes dos Santos
Instituto
Federal do Rio de Janeiro, Brazil
E-mail:
anacarla.engenharia@gmail.com
Layanne Andrade
Mendonça
Instituto
Federal Fluminense, Brazil
E-mail:
layanne.mendoca@iff.edu.br
Augusto da Cunha
Reis
CEFET, Brazil
E-mail:
augusto@aaa.puc-rio.br
Sérgio Augusto
Faria Salles
Instituto
Federal Fluminense, Brazil
E-mail:
safsalles@hotmail.com
Submission:
29/03/2018
Accept:
29/03/2018
ABSTRACT
Companies
seek to improve service level, aiming to meet the most demanding customers that
enable it survives in a complex and competitive marketplace. The article
presents a study in a company of repair and maintenance of industrial equipment
and instruments, located in Campos dos Goytacazes (RJ), aiming to identify the
factors that influence the execution time of the operators' activities in the
manometer calibration process. For this scope, the study of times and movements
and statistical analyzes, such as Linear Regression and ANOVA, were applied.
The survey revealed that operators who have completed their technical courses
in mechanics perform the activities in standard time greater than those who
have not yet completed and that working on board and being married do not
change the execution time. It also revealed that employees of lower age and
longer experience are preferable because of shorter operating times and that
the age was statistically significant, revealing that the most appropriate age
to have a shorter time is approximately 27 years.
Keywords: time-motion study; productivity;
regression; ANOVA
1. INTRODUCTION
Service
sector has faced a decrease of 3.2% on volume of services rendered in July 2017
in comparison with the same month a year ago and a decrease of 0.8% in
comparison with its previous month (IBGE,
2017). As
a result, companies at a high level of competitiveness are aiming to meet
customer requirements and remain survivor in the marketplace.
Some standardization
tools such as time and motion study (TMS), first proposed by Frederick Taylor,
provides competitiveness for a company and is still currently applied in
several organizations (NOVASKI;
SUGAI, 2002; SILVA; GUIMARÃES; VERALDO JÚNIOR, 2014; SIMÕES; FENNER;
ESPERANCINI, 2014). This study
enables reduction of the cycle time, best control over the process,
standardization of the way of work and an increase in production capacity in
view of encouraging competitive differentiation.
According
to Figueiredo,
Oliveira and Santos (2011), time and
motion study is applied in order to eliminate unnecessary efforts when
performing a given operation by establishing a certain way to perform it and
thus obtain methods to assure an overall improving of the process.
By
applying TMS it is also possible to identify which operators are performing the
activities above or below the value established by the company (SANTOS et al.,
2015). However, it is extremely important to find out what are the factors that
influence this performance time. This sort of analysis is possible through
statistical techniques, such as ANOVA (for qualitative data) and Linear Regression
(for quantitative data).
Therefore,
the aim of this article is to provide an application of time and motion studies
to identify the factors that influence the execution time of the activities
performed by the operators. The study was carried out in a company that
provides equipment repair, maintenance and repair service according to NR-13,
located in Campos dos Goytacazes, specifically in the calibrating gauge
process.
This
paper is structured in five sections as follows. Section 1 is the introductory
one in which the article is contextualized. Section 2 reviews time and motion
study key concepts in which the study is based on. In Section 3, the
methodology proposed for the study is described. The results and subsequent
discussions are provided in Section 4 and, finally, the paper is concluded in
Section 5.
2. TIME AND MOTION STUDY
According
to Chiavenato (2014) and Santos et al. (2015),
time and motion studies were first suggested in 1881 by Frederick Taylor while
working at the Midvale Steel Company. After becoming chief operating engineer,
he decided to change the management system in a way that the interests of both
factory floor workers and high managers would not conflict.
Soon,
he realized that the great obstacle was the unfair workload given to the workforce
and got permission to apply a certain amount of money in a scientific study in
order to determine the best production time. He chose two healthy and efficient
workers and aimed to find out what a “fair day’s work” meant for an efficient
worker; i.e., the best way a man could do his job year after year successfully.
Thus,
Taylor discovered that the energy spent during the execution of heavy jobs were
correlated with its rest periods, frequencies and durations and also
established that there would always be a faster method and a better instrument
to be found and improved by means of scientific analysis and time/motion study.
Barnes (2004)
indicates that time and motion study is used to determine the time required for
a properly trained and qualified operator perform a specific task as well as
its most appropriate execution method. Moreover, he states that time and motion
study has the following purposes: (i) to find the most appropriate method for
performing a task, (ii) standardize this method, (iii) determine the time taken
by a given operator to perform the task at a normal pace, and (iv) guide and
train the operator in the chosen method.
According
to Peinado and Graeml (2007),
time and motion studies are not only applied to define a better way of perform
a task, but also to determine the productive capacity of an organization and
elaborate production and cost reduction programs, among other applications.
For
the work to be executed in a better and more efficient way, it must be analyzed
under a specific method. The first step is to breakdown the operation into
smaller and observable elements. Then, the number of repetitions is defined and
the operator’s time for each element is measured and recorded using a stop
watch. This analysis is used to determine the performance time of the overall
productive process, each task and each operator (CHIAVENATO, 2014; SANTOS et al., 2015). The
observed time is relevant if the time to be measured exceeds five seconds.
Thus, with the assistance of a stop watch, each step is timed several times so
that the arithmetic mean can be calculated (JACOBS; CHASE, 2012).
After
that, it is necessary to calculate the number of cycles (N), so the number of
elements collected in the study is valid and it can arrive at an accurate
average time. In other words, the formula determines the minimum number of
repetitions. A number greater than or equal to the value provided by the
formula is statistically feasible and the average time found can be used.
Equation 1 determines the minimum sample number:
(1)
Where:
N:
Number of cycles;
Z:
Standard normal distribution coefficient;
R:
Sample’s range;
Er:
Relative error;
: Coefficient related to number of times previously observed;
: Arithmetic mean of observed times.
It is
acceptable to use a confidence interval between 90 and 95% to determine the
standard normal distribution coefficient (Z), and the relative error (Er) with
a margin between 5 and 10%. The values of Z and d2 are set out in
Tables 1 and 2 respectively:
Table
1: Standard
normal distribution coefficient (Z)
Probability |
90% |
91% |
92% |
93% |
94% |
95% |
96% |
97% |
98% |
99% |
Z |
1.65 |
1.70 |
1.75 |
1.81 |
1.88 |
1.96 |
2.05 |
2.17 |
2.33 |
2.58 |
Source:
Adapted from Peinado and Graeml (2007)
Table
2: Coefficient
related to number of times previously observed
N |
2 |
3 |
5 |
5 |
6 |
7 |
8 |
9 |
10 |
d2 |
1.128 |
1.693 |
2.059 |
2.326 |
2.534 |
2.704 |
2.847 |
2.970 |
3.078 |
Source:
Adapted from Peinado and Graeml (2007)
The
next step is to calculate the performance rating of each operator using the
average time of the productive process, called observed time (OT), and the
average time of each operator, called normal time (NT). OT is equivalent to a
normal rate of 100%. Therefore, the speed of each operator is calculated by the
Equation 2 below:
(2)
Where:
NT: Normal Time;
OT: Observed Time;
PR: Performance rating of each operator.
There
are some reasons that may lead the worker to be at an above-standard rate, for
example: fatigue, lack of practice, family problems, lack of motivation,
company time, inadequate training or lack of training, age, work environment
and so on. Thus, the performance rating calculation reveals whether the
operator is: slow (above 100%); fast (below 100%); or in the ideal time (equal
to 100%).
According
to Martins and Laugeni (2015), it is
impossible for an operator to work all day without interruption or break. Thus,
breaks during working hours are granted to workers so that they can meet their
physiological and personal needs, thereby minimizing their fatigue and even
resulting in increased productivity when they return to their jobs. Allowances
or tolerance (p) is made by means of
the sum of time that the company grants its employees, including time for
delays and alimentation, due to the workload according to Equation 3 below:
(3)
After
calculating the allowance time (p), the tolerance factor (TF) is calculated by
Equation 4 below:
(4)
Where:
TF: Tolerance factor;
p:
Allowances.
The
tolerance factor (TF) obtained is added to the OT and NT of each operator,
resulting in the standard time (ST), according to Equations 5 and 6 below:
(5)
(6)
Where
ST= Standard time;
NT= Normal Time;
TF= Tolerance factor;
OT= Observed Time.
These two calculations differ from each other since
the calculation of the ST of the operator is the multiplication of the NT by
the TF. The standard time of the company is calculated by multiplying the OT by
the TF.
2.1.
Linear
regression and time and motion study
According
to Golberg and Cho (2004), the regression analysis consists of
obtaining an equation that tries to explain the relationship among variables.
In order to establish an equation that represents this analysis, it is
necessary to draw a graph, called a scatter diagram, in order to verify how the
values of the criterion variable (Y) depends on the variation of the
independent variables (X).
It is
observed whether the points in the diagram drawn fit the model line. For the
study to be consistent, the model chosen must match what happens in practice,
containing only the factors that are relevant to the explanation of the
phenomenon. The linear regression models can be, among others, first degree or
second degree, as shown in Equations 7 and 8, respectively:
(7)
Where:
is the value
observed at the i-th level of variable X;
is the regression coefficient;
is the constant of the regression;
is the i-th level of variable X;
is the error associated with the difference
between the observed value and the estimated value for the same level i of the
variable X.
This
model is a first-degree polynomial function whose graph is a straight line. The
positive coefficient indicates that the function is increasing, i.e., increasing
the levels of the variable X studied, the response variable is also increased.
However, if the regression coefficient is negative, it means the lower the
factor level, the higher the response variable according to Equation 8 below:
(8)
Where:
is the value observed at the i-th level of
variable X;
and are the
regression coefficients;
is the
regression constant;
is the i-th level of variable X;
is the i-th level of variable X squared;
is the associated error that means the
difference between the observed value and the estimated value for the same
level i of the variable X.
The
adjustment of the regression by a second-degree model occurs when the data do
not behave around a line but a parabolic curve.
The
process of identifying the optimal level of the factor that generates a greater
or lesser value of the dependent variable is called optimization. A first-degree
model with a positive coefficient, adjusted for a given range of factor levels,
has a higher response value for the higher factor level of this interval. If
the regression coefficient is negative, the highest value of the response
variable is obtained using the lowest level of the factor studied.
A
second-degree model, whose graph is a parabola, will have a maximum value if
the coefficient associated with
the quadratic term is negative. Otherwise, the adjusted equation will have a
minimum value. The optimization process of this model occurs through the
identification of the value of the level of the factor that maximizes or
minimizes the dependent variable, denoted and calculated
by Equation 9:
(9)
To determine
the maximum or minimum value of the dependent variable, the factor must be simply replaced in the adjusted
equation.
The
coefficient of determination is a fundamental
parameter in determining the best adjusted model. This can have a value between
0 and 1 and the closer it is to 1, the better the model explains the variation
of the data. If is close to 0, it implies that there are other
causes that did not enter the model, but should be studied or that another
model should be adjusted.
By
associating the application of linear regression to time and motion study, it
is possible to identify which are the factors that affect the execution time of
the operators. Each operator has an average time to perform the task, however
when calculating the standard time of the operator and comparing it to the
standard time of the company, it is concluded that some operators are slow and
others are faster.
3. RESEARCH METHODOLOGY
This section describes the research
method adopted in this paper so that all steps can be replicated in future
studies.
3.1.
Data
collection
As
highlighted by Amato and Reis (2016) a case study methodology allows an investigation of
current events in a practical and holistic way in which the researcher has
little or no control over the object in question. The research is classified
according to its nature, since it aimed to generate new knowledge from a local
practical application. To achieve this objective, this research is a case study
which was conducted taking into account the unit of analysis that is the
calibration process of manometers.
Regarding
the approach, the research is characterized as both quantitative and
qualitative, since it covered variables that were translated into numbers and
has subjective characteristics. From the point of view of the objective, the
research is classified as explanatory, because "it aims to identify the
factors that determine or contribute to the occurrence of the phenomena" (SILVA; MENEZES, 2005).
Initially,
a research was done on the topics that this article possesses so that the
reader could clearly understand its purpose. After, the study object was
chosen. The chosen process was the calibration of manometers based on the great
representation that this operation has in the company's revenue.
Then,
a survey, a division of the activities that compose the calibration of a
manometer and the selection of five operators were made. This selection was
made at random, according to company availability. Therefore, eight visits were
made in loco, being five in the month of September and three in the month of
November of 2016, for the accomplishment of the timekeeping.
3.2.
Data
processing and analysis
The
stages related to time and motion study were performed: operators’ activities
time were recorded using a stopwatch and a drawing board; observed time (OT)
were calculated to each small operation and to the overall production process;
normal time (NT) and performance rate of each operator were defined; tolerance
factor (FT) was determined; and finally, standard time (TP) was calculated. In
view of the variability of the operators' times, it became impossible to
calculate the number of cycles.
The
data were submitted to statistical analysis aiming to identify the factors, or
independent variables that may interfere with the standard time response
variable of the operators. Assuming normality, data independence and
homogeneity of variance, an Analysis of Variance (ANOVA) was performed with
qualitative levels factors. In this situation, it was intended to know whether
the factors such as completed technical mechanical course, children, marital
status and/or work situation, influence operators’ performance. Each factor has
two qualitative levels, described in the table below:
Table 3: Qualitative levels
|
Factors |
|||||||
Married |
Children |
Work situation |
Education |
|||||
Levels |
Yes |
No |
Yes |
No |
Onshore |
Offshore |
Complete |
Incomplete |
The
hypotheses of interest in an ANOVA are as follows:
In
this formula, μ1 stands for the mean of the "yes" or
"completed" level of the factor under analysis and μ2, stands for
"no" or "not completed" level. If the p-value calculated is
lower than the established level of significance (α), the hypothesis of equal
means is rejected; thus, a difference between factor levels is detected. This
means that changing the "yes" to "no" or
"completed" to "not completed" level of the factor under
analysis causes change in the standard time.
The
results were interpreted comparing the averages of the studied levels. Those
with lower values were recommended, indicating a shorter activity operating
time.
For
the quantitative analysis, the standard time was used as dependent variable as
a function of the factors of experience, age and quantity of children of the
operators. The table below presents the factors and their respective levels
used in the analysis:
Table 4: Experience time, age and number of children
Factors |
|||
Levels |
Experience time (months) |
Age (years) |
Number of children |
18 24 30 36 60 |
20 23 32 33 40 |
0 1 2 |
Correlation
analysis was used to analyze the strength of the relationship between the
factors and the standard time. Subsequently, a first degree or second degree
linear regression adjustment was made. For the factors that were statistically
significant (p-value <α) in the fit of the first-degree model, an adjustment
for a second-degree model was then made, since they could have a parabolic
behavior. The model with highest coefficient of determination (R²) was the one
defined as the best represent of the situation, indicating that this model
explains better the variation of the data collected.
For
the adjustment of the first-degree and quadratic linear equations, the models
represented in Equations 7 and 8 were used respectively. For the second-degree
adjustments, the optimization was done by calculating the value of the vertex
of the parabola () that generates
a better standard time. This value was calculated using Equation 9.
For
all analyzes, the significance level was defined as 5% (α = 0.05) and Microsoft
Office Excel and Statistica 7.0 were used.
4. RESULTS AND DISCUSSION
The
research was conducted in a company that provides inspection, maintenance and
calibration services of industrial equipment and instruments, focused on safety
and technology improvement for more than 17 years, helping companies to achieve
excellence in operation performance. The company is located in Campos dos
Goytacazes/RJ and currently has 88 employees.
Time
and motion study was performed and the calibration time of the manometer was
measured considering five employees who were observed during the process.
Calibration
time included the time the manometer is put into the calibration pump until its
final phase, which is the photo and labeling stage. After placing the pressure
gauge on the pump, the calibration process occurs as follows:
·
The pressure gauge is installed in the pump: the
operator installs the pressure gauge on the pump, checks for any type of
leakage and waits for Bourdom to settle by injecting and withdrawing pressure,
while it begins to fill its certificate with the equipment code and its
pressure;
·
1st Calibration cycle: this is where the operator
handles the pump so that it injects pressure into the pressure gauge, which
will cause the pressure indicators to appear according to the progress of the
1st cycle;
·
2nd calibration cycle: this cycle is only performed to
check the values obtained in the first cycle;
·
Finalization of the certificate: in this step, the
operator transcribes for the certificate the values found and calculated and
also checks if the data put in the certificates are correct and;
·
Instrument finalization: the operator places a label
on each gauge containing the calibration date and its validity, which is 3
months, in addition to placing the instrument code. Photographs of all
calibrated manometers are also taken for archiving and controlling processes.
These photos go along with the certificate for the clients.
4.1.
Work
measurement
For
each activity, five manometers and five operators were used. For each activity
of the calibration process, there is a legend used to simplify the
interpretation of the table below: A1 (manometer installation at pump), A2 (1st
calibration cycle), A3 (2nd calibration cycle), A4 (certificate completion) and
A5 (finalization of the instrument). In addition, it follows the NT for each
operator.
Table 5: Normal time
for each operator
A1 |
C1 |
C2 |
C3 |
C4 |
C5 |
NT (s) |
Operator 1 |
164 |
120 |
86 |
95 |
136 |
120.2 |
Operator 2 |
223 |
140 |
160 |
180 |
147 |
170.0 |
Operator 3 |
138 |
143 |
107 |
105 |
114 |
121.4 |
Operator 4 |
160 |
120 |
136 |
141 |
132 |
137.8 |
Operator 5 |
135 |
110 |
114 |
115 |
116 |
118.0 |
A2 |
|
|
|
|
|
|
Operator 1 |
88 |
92 |
72 |
85 |
76 |
82.6 |
Operator 2 |
120 |
142 |
82 |
121 |
124 |
117.8 |
Operator 3 |
94 |
89 |
77 |
109 |
90 |
91.8 |
Operator 4 |
86 |
83 |
75 |
80 |
75 |
79.8 |
Operator 5 |
74 |
74 |
75 |
81 |
76 |
76.0 |
A3 |
|
|
|
|
|
|
Operator 1 |
83 |
64 |
63 |
70 |
63 |
68.6 |
Operator 2 |
85 |
106 |
140 |
95 |
119 |
109.0 |
Operator 3 |
95 |
67 |
70 |
119 |
120 |
94.2 |
Operator 4 |
101 |
77 |
69 |
76 |
79 |
80.4 |
Operator 5 |
60 |
66 |
60 |
82 |
62 |
66.0 |
A4 |
|
|
|
|
|
|
Operator 1 |
43 |
39 |
40 |
43 |
46 |
42.0 |
Operator 2 |
52 |
39 |
60 |
70 |
74 |
59.0 |
Operator 3 |
73 |
73 |
41 |
48 |
49 |
56.8 |
Operator 4 |
42 |
65 |
70 |
44 |
46 |
53.4 |
Operator 5 |
50 |
58 |
56 |
57 |
55 |
55.2 |
A5 |
|
|
|
|
|
|
Operator 1 |
90 |
122 |
80 |
60 |
35 |
77.4 |
Operator 2 |
90 |
69 |
100 |
99 |
106 |
92.8 |
Operator 3 |
51 |
43 |
38 |
50 |
37 |
43.8 |
Operator 4 |
48 |
43 |
46 |
45 |
44 |
45.2 |
Operator 5 |
39 |
40 |
42 |
38 |
37 |
39.2 |
Table
6 shows the OT of each activity of the manometer calibration process and the OT
of the overall production process.
Table 6: Average time
of activities and production process
Activities |
OT of operation(s) |
OT of overall production process |
A1 |
133.5 |
419.7 |
A2 |
89.6 |
|
A3 |
83.6 |
|
A4 |
53.3 |
|
A5 |
59.3 |
4.2.
Tolerance
factor
To
calculate the tolerance factor (TF) it was necessary to determine the idle time
(p). The company gives employees 15 minutes for coffee-break in the morning for
coffee, 15 minutes for coffee-break in the afternoon, 10 minutes in the early
hours and 10 minutes on their return from lunch. A total of 50 minutes at a
workload of 8 hours a day, which results in a p of 0.104 and a FT of 1.116.
4.3.
Standard
time of operators, activities and overall production process
The
tolerances granted by the company were added to the average time of each
activity and each operator (table 5) and then the average time of the
production process (table 6), resulting in tables 7 and 8, respectively.
Table 7:
Operators’ Standard Time
Standard Time (s) |
A1 |
A2 |
A3 |
A4 |
A5 |
Operator 1 |
134.1 |
92.2 |
77.0 |
47.1 |
86.4 |
Operator 2 |
190.0 |
131.4 |
121.6 |
66.0 |
104.0 |
Operator 3 |
135.5 |
102.4 |
105.1 |
63.4 |
49.0 |
Operator 4 |
154.0 |
89.1 |
90.0 |
60.0 |
50.4 |
Operator 5 |
131.7 |
85.0 |
73.7 |
61.6 |
44.0 |
Table 8: Standard time of activities and production
process
Activities |
ST of operation(s) |
OT of overall production process |
A1 |
149.0 |
468.4 |
A2 |
100.0 |
|
A3 |
93.3 |
|
A4 |
59.5 |
|
A5 |
66.2 |
4.4.
Operators’
performance rating
The
performance rating was calculated for each operator in order to compare them
with the normal rate of the company. It is worth mentioning that if the
operator is above 100% it means he is slow, if it is below 100% it is because
he is agile and if operator is equal to 100% it means that he is performing his
task exactly in the company’s average time. The following table represents, in
percentage, the performance rating of the operators considering the productive
process.
Table 9: Operators’
performance rating in percentage
Operators |
Performance rating on the production process |
Operator 1 |
83% |
Operator 2 |
117% |
Operator 3 |
87% |
Operator 4 |
85% |
Operator 5 |
76% |
4.5.
Qualitative
analysis
Evaluating
the data collected, it was noticed that the factor “Married” was directly
related to the factor “Children”, so that every married operator had at least
one child and the unmarried did not have any. For this reason, one of the
factors was removed from the analysis, remaining only the factor “Married”. The
table below shows the means and standard deviation of each factor level
studied, as well as the p-values obtained by ANOVA:
Table 10: Mean,
standard deviation and p-value of each factor’s level
Factors |
Mean and standard deviation |
p-value |
|
Yes/ Complete |
No/Incomplete |
||
Married |
483.45±99.41 |
445.84±55.17 |
0.2891 |
Offshore |
476.42±89.25 |
436.36±64.14 |
0.3580 |
Education |
501.31±94.36 |
419.06±31.61 |
0.0145* |
*it
presented a significant difference with p-value < 0.05
According
to ANOVA, only the training factor presented a significant difference, i.e.,
p-value <0.05. Therefore, operators whom have already completed their
technical courses in mechanics perform the activities in standard time greater
than those that have not yet completed. This result was contrary to
expectations, but may be related not to the fact that the academic training
interferes with the execution of the activities, but to the fatigue of the work
routine, accumulation of tasks, pressure from the top management, among others.
Consequently, it is not possible to state the causes that affected the trained
operator to perform the activity in a larger standard time and a more detailed
study would be necessary. The other factors presented p-value> 0.05,
therefore, the hypotheses of equality between the averages were rejected, that
is, working on board and being married did not change the standard time.
4.6.
Quantitative
analysis
For
the study of factors of quantitative levels, the age and time of operator
experience were taken into consideration. The number of children was withdrawn
from the analysis since the fact of having children does not change the
standard time, as already written in item 4.5.
The
correlation analysis identifies a directly proportional relationship (positive
correlation: r = 0.54) between the employee's standard time and his age and
inversely proportional between the standard time and his experience time
(negative correlation: r = -0, 31). With the interpretation of these
correlations, it is expected that employees of smaller age and greater time of
experience would be preferable because they have shorter operating times.
However,
it can be considered that these relations are weak, the latter being even
smaller. Then, a linear regression was performed with a first-degree model to
identify the cause and effect relationship between these factors and the
standard time.
As a
result of the analysis of variance for regression, the time of experiment was
not significantly (p-value> 0.05) the cause of the variation of the
operators’ standard time. In contrast, age was statistically significant
(p-value <0.05) with Due to the R² value obtained
being considered low, an adjustment was made for a second-degree model, with
results in the table below:
Table
11: R² values
Factor (X) |
First-degree model |
R² |
Second-degree model |
R² |
Experience time Age |
Y = -1.79X + 528.46 Y = 6.23x+284.01 |
0.1 0.29 |
- Y=1.31X2-71.35X+364.05 |
- 0.72 |
The
second-degree model was chosen to represent the data, since it presented higher
value of R² than the first-degree model. This model, represented graphically by
a parabola of upwards concave, suggests an optimal value of age so that one has
a minimum time. The graph below represents the standard time as a function of
age, according to the proposed quadratic model.
|
Figure
1: Graphic of Standard
Time versus Age of operators |
Optimization
indicates the most appropriate age to have a shorter standard time is
approximately 27 years, which generates an approximate optimum standard time of
393 min.
5. CONCLUSIONS
This
case study presented the application of time and motion study, in association
with the statistical techniques ANOVA and linear regression, in the process of
manometer calibration, in the city of Campos dos Goytacazes/RJ.
When
comparing the standard time of each operator in relation to the standard time
of the company, it was possible to identify which are the slower and faster
operators. With the application of ANOVA it was revealed that operators who
have already completed their technical courses in mechanics perform the activities
in standard time greater than those that have not yet completed.
Also,
factors such as working on board and being married do not change the standard
time. When performing the Linear Regression, it was verified that the
experience time was not significant to influence in the variation of the
operators’ standard time and that the age was statistically significant,
revealing that the age most suitable to have a smaller standard time is
approximately 27 years.
For
the company in question this information is of extreme importance since it is a
service provider and one of the definitions of service is the variability.
Thus, the results show the variability among operators. There are two ways in
which the company can charge an offshore service: per day, operators will board
the ship or platform to perform the service for a certain period of time
previously established and; for productivity, an equipment inspector goes on
board before summoning operators to measure how many equipment there is on
board that need calibration.
So,
the crew goes aboard without a set date to return and the more equipment the
operators calibrate, the higher the profit. According to the Commercial
Director, productivity contracts are much more profitable and represent a large
part of the contracts performed, so time and motion study revealed a standard
time for the calibration of onboard gauges and statistical analyzes revealed
the factors which most influence this execution time.
It is
suggested that all the employees who works onboard should be trained to reach
this standard time and thus lead to greater productivity, where more gauges
will be calibrated in a shorter time; more contracts closed in one period;
increased reliability due to reduced time staff is on board; and cost reduction.
For future research, it is recommended that a greater number of operators must
be analyzed, as well as the application in other market segments.
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