report - effect of fatigue due to repetitive work

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The Effects of Continuous Repetitive Work on the Quality and Efficiency of the Manufacturing

Process

Introduction

Manufacturing is the production of merchandise for use or sale using labor and machines, tools,

chemical and biological processing, or formulation. The term may refer to a range of human activity,

from handicraft to high tech, but is most commonly applied to industrial production, in which raw

materials are transformed into finished goods on a large scale. Such finished goods may be used for

manufacturing other, more complex products, such as aircraft, household appliances or automobiles, or

sold to wholesalers, who in turn sell them to retailers, who then sell them to end users – the "consumers".

The United States is the world's second largest manufacturer, with a 2010 industrial output of

approximately $1,696.7 billion. In 2008, its manufacturing output was greater than that of the

manufacturing output of China and India combined, despite manufacturing being a very small portion of

the entire U.S economy, as compared to most other countries.

The manufacturing sector is closely connected with engineering and industrial design. In its

earliest form, manufacturing was usually carried out by a single skilled artisan with assistants, however

today it is usually a much larger process and is carried out by a multitude of people with skills in various

aspects of engineering. Manufacturing supports an estimated 17.6 million jobs in the United States or

about one in six private-sector jobs. More than 12 million Americans (or 9 percent of the workforce) are

employed directly in manufacturing. Manufacturing industries are complex organizations that require a

lot of human-machine interactions. Hence, human errors that come from the mishandling defective

products and machine failures can become dreadfully prevalent in manufacturing industries. Many of the

work environments that workers have to deal with are noisy, stressful and may include repetitive tasks.

An organization that does not have an ergonomic system or process may have allowed a gap to

exist between the worker's characteristics and the functional requirements of their job. Ergonomics is

undoubtedly an important social and technological proponent for people to improve their quality of work,

work satisfaction and safety. In short, there have been many studies on human error. However, these

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studies are not fundamentally correlated to any particular factor. Therefore, it is crucial for researchers to

highlight the effects of several factors on human error in the manufacturing industry. The factors that

possibly affect human error may include stress, repetition, fatigue and work environment.

Fatigue is a major safety concern in many fields, but especially in transportation and

manufacturing because fatigue can result in disastrous accidents. Fatigue is considered an internal

precondition for unsafe acts because it negatively affects the human operator's internal state. Fatigue

Research has generally focused on pilots, truck drivers, and shift workers. This study therefore attempts to

quantify the effects of fatigue on repetitive activities in the manufacturing field by using human factors

approach to analyze both mental and physical load.

Scope

Due to repetitive work, we believe, that fatigue sets in. This may lead to reduction in speed of the

work being done and thus efficiency decreases. Also, repetitive work in wrong posture or ergonomically

unbeneficial workstations not only leads to higher levels of fatigue but health problems like

musculoskeletal disorders. To do the same task day in and day out can lead to reduction in concentration

and over confidence which may affect the quality of a product. In scenarios involving manufacturing of

critical components, like pressure gauges in aircrafts, this lack of quality control can have disastrous

effects, even loss of lives. Also, the fatigue and loss of concentration can lead to safety issues of the

worker itself in scenarios involving use of hazardous equipment, like welding machines.

The scope of this project is to study the effects of fatigue caused by repetitive work on quality of

product and efficiency of worker.

Objective

To analyze the effect of continuous repetitive work on the quality of work and efficiency of

manufacturing process. Testify the effectiveness of proposed solutions.

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Literature Review

Much work has been conducted in the field of minimizing fatigue. Gershon et al [1] tested two

methods in reducing fatigue experienced by vehicle drivers. The methods tested were the effectiveness of

energy drinks and dexterity activity as fatigue counter measures. They concluded that drinking energy

drinks had a positive effect on counteracting fatigue. Thus, for our experiment participants in the control

group were asked not to consume energy drinks and drinks with caffeine. Gershon et al [2] further

elaborated by conducting a survey on professional and nonprofessional drivers and observed the most

effective fatigue coping behaviors. Some noteworthy techniques included listening to the radio and

talking, so using that information we chose not to engage the participant in conversation once the

experiment had begun (except to initiate breaks). Dadi G. B et al [3] studied the effects of information

presentation and how it relates to mental workload. They concluded that while there was no significant

mental load difference between using 2D drawings, 3D CAD drawings and 3D printed models, the most

effective way of conveying this information was through training. They also concluded that 3D models

are a better learning tool than either 2D drawings or CAD drawings. As a result for the experiment, the

training phase that the participant undergoes has an instructional process that involves a 3d model as a

learning tool. Saito K [4] remarked that fatigue manifests itself in various forms and cannot be measured

or indicated by a single test or a change of only one function. It is therefore important in the assessment of

fatigue that one should not only make physiological and psychological measurements, but also relate the

findings to various factors which influence fatigue. In conclusion, fatigue should be evaluated by a

multidisciplinary approach rather than simply measured. Many tools have been used in the past to

measure fatigue, some more effective than others, for instance Hosokawa et al [5] studied the effect of

room illumination and other conditions on the portable fatigue meter, by employing it in several different

fatigue measuring experiments. The results suggested that the portable fatigue meter did have a

significant age bias in measurement. Fatigue is assumed to increase with work time as a study conducted

by Kakizaki and Kurimori [6] , which studied the changes in mental workload and fatigue during a one

day transcribing task and concluded that mental workload becomes heavier and subjective feeling of

fatigue increases as the working hours become longer . Most occupational tasks involve some level of

mental/cognitive processing in addition to physical work. Mehta and Agnew [7] conducted a study to

quantify the interactive effects of physical and mental workload on muscle endurance. Changes in

muscular capacity were determined by measuring EMG and heart rate among other parameters. The study

conclusively showed that physical capacity is adversely affected by mental workload. Higher levels of

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mental workload have detrimental effects to the subject’s performance, increasing human error and injury

and reducing work efficiency. Fatigue, regardless how high or low, does show noticeable effects to a

subjects work performance. To this end, Barker and Maury [8] examined the effects of multiple

dimensions (both high and low types) of fatigue simultaneously on the human body. They concluded that

higher physical fatigue adversely affected measures of physical and mental performance, whereas higher

mental fatigue had a positive effect on one measure of mental performance. Overall, these results suggest

causal effects between manipulated levels of mental and physical fatigue and task-induced changes in

mental and physical performance. A paper by Mehta and Agnew [9] shows that fatigue has an undeniable

relationship with repetitive work. The study examined the effect of repetitive work on shoulder fatigue

and how they related to both mental and physical demands. The literature collected provided key insight

into the relationship between fatigue, mental and physical workloads, as well as fatigue measurement

techniques, the trend’s in measurement, etc. vital to carrying out our experiment. It was also found that, in

continuous repetitive work, both physical and mental fatigues have negative effects on the worker’s

production rate. The more tired you are, the less efficient you are at work. By scheduling shift duration

and rearranging the breaks properly, worker’s fatigue and stress level can be reduced. As what human

factors engineering emphasizes on, designing a scientific schedule helps increase the efficiency of worker

in repetitive works.

Tecnomatix, a software by Siemens, allows ergonomic analysis and also the creation and modification

virtual environment. It also allows the dimensioning and positioning the biomechanical model of human

and creation of work operation simulations. For analysis and evaluation of workload in virtual

environment, we can use a few tools like: Fatigue Analysis, Force Solver, Lower Back Analysis, Manual

Handling Limits, NIOSH etc. Using this software, we can realize the corrective measures in virtual

environment and their results can be followed in real time while changing the basic parameters.

Case Study

The study took place in an industry in India, which does the work of machining and

manufacturing of spare parts for industries. These parts are huge in terms of size and weight. The work is

done according to the customer specifications. Raw material is converted into finished product or semi-

finished product.

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For part in consideration, an axle wheel, the machining process requires two workers. Once the

raw material is received it is loaded on to the large lathe machines to be machined. It takes about 48 hours

to manufacture a single product with 2 workers working in shifts. The workers have to work in 12 hour

shifts and get single 1 hour break and single 15 minutes break during this shift.

Machining on the lathe includes the following processes -

1. Loading the raw material with the help of the crane – 1 hour

2. Aligning the raw material - 0.5 hour

3. Ready the tool according to the job - 1 hour

4. Machining the raw material which will take up to 40 hours (includes 3hr break, 6hr machine idle time, second tool to be made ready 1hr)

5. Unloading - 0.2hours.

After this the product goes for Drilling, then Clearance fit which involves heating the part and a

lot of precision work and finally finishing operations are done.

The working environment is in a large shed with concrete flooring and walls, work goes on the

floor with no platform. The shed is made up of steel sheets with proper ventilation. This process involves

is a lot of repetitive work happening continuously for a long time. The product needs to be manufactured

with a quality of 15% error on the tolerance or it has to be reworked. For example, if the product is

obtained with error on diameter of up to 0.1 – 0.2mm then this product requires rework to be done.

Finishing required would be triple delta finish, and if there is any error then rework needed again. This

leads to increase in mental load. Physical load on workers is also high due to the nature of work involved.

Proficiency of the workers in this industry:

1. Semiskilled labor with 2-3 years of experience. These workers get no training

2. Skilled labor with about 8 years of experience and theoretically require no training.

3. Unskilled labor with no more than a year of experience, and hence trained for about 3 months.

The unit is just 2.5 years old and has various products. However, due to the processes involved, we identified this as a situation where, in a few years, quality and efficiency will be major issues as a result of fatigue due to repetitive work.

Experiment Design and Implementation

Permission for conducting experiment in the factory was not achieved. It was also not in our scope to conduct an experiment involving the actual manufacturing process in college due to availability of skilled

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subjects and IRB permission guidelines. Hence, we decided to simulate manufacturing process using Lego toys. The experiment was designed to induce fatigue in subjects by making them continuously assemble airplane models (repetitive work). The research method used was between-subjects. Thus the 10 subjects were divided in 3 groups - Control group, Group E1 with reduced mental load and Group E2 with reduced physical load. The aim of the experiment was to study the effects of reduction in mental or physical load on productivity and quality in a repetitive process which induces fatigue. Also, breaks and shifts were scheduled differently for Control and the Experimental (E1, E2) groups. The detailed procedure of experiment is as follows:

1. Subjects are advised not to have any energy or caffeinated drinks just prior to starting experiment.

2. Initially, all subjects complete a survey regarding hours of sleep they had, current mental and

physical state.

3. Each subject gets training for 15 minutes in assembling the airplane model with help of 2D

drawings and 3D model. This is to avoid learning period delays in actual experiment. Subjects

complete assembly of model at least once in this period while primarily using the 2D drawing.

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4. Subjects were asked to sign a standard Consent form for participation in the experiment.

5. Physiological signals of subjects are continuously monitored:

Mental Workload � Skin Conductance, heartbeat rate � Skin Conductor Sensors, BVP Physical Workload � Muscle strain, heartbeat rate � EMG, BVP These sensors are to be attached to the subject using standard procedures prior to starting the experiment.

6. During the experiment, subjects are to continuously assemble airplane models from given blocks

for a period of 30 minutes (working time). The number of models created and the quality in terms

of correctness and fit is recorded to measure productivity and efficiency

7. For comparison purposes, subjects divided in 3 groups:

a. Control Group – 4 Subjects

i. A table is provided for assembling the aircraft model. A 2D drawing explaining the assembly procedure is provided on the table

ii. The building blocks are unsorted and collected in single bin. These are placed on a chair behind the subject and slightly to the right at a distance of around 3 feet.

iii. The subject has to perform the experiment standing up. iv. Single break of 5 minutes is provided after 15 minutes of work done. Thus, total time for

experiment, including break, is 35 minutes

Subject Standing

Physiological Sensors Attached

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b. E1 Group – 3 Subjects

i. For this group, the raw material is sorted depending on shape and color in different bins. This is done to reduce mental workload.

ii. Also, for same reason, a partial 3D model is provided along with 2D drawing on the table. Actual prototype of model makes assembling process much easier.

iii. The subject is still standing with blocks placed at a distance behind him (no change in physical load).

iv. Two breaks of 2.5 minutes are given after 10 and 20 minutes respectively. So, total time for experiment, including break, is 35 minutes.

c. E2 Group – 3 Subjects

i. Here, the building blocks are unsorted again but placed on the table where assembly is done

ii. Subject is sitting on a chair near the table to reduce physical load.

iii. Only 2D drawing of assembly process is provided, so no change in mental workload

iv. Two breaks of 2.5 minutes are given after 10 and 20 minutes respectively. Thus, total time for experiment, including break, is 35 minutes.

Sorted Blocks

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8. Each subject completes the NASA-TLX form at end of experiment.

Data Collection

1. Skin Conductance Sensor

30 seconds intervals mean value

● Increasing trend for all subjects

● Highest in control group

● Stress level reduced in solution groups

● Stress level lowest in physical solution group

Subject is sitting

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2. EMG


● As we see the EMG levels from the plot, for the control group the increase in the trend is quite evident than the physical group, and towards the end increases significantly than the Mental group.

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● Looking at the E2 physical group the solutions provided worked successfully as there no significant increase in the stress levels.

● EMG data for the E1 mental group remains the same while showing smaller decrease in the stress levels.

● EMG data for certain subjects, as we see from graph 1, had lower levels of stress even from control group.

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3. BVP (HR)


● Heart rate is indication of stress induced in body.

● Highest variation of heart rate was in control group

● Fatigue lowest in physical solution group

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4. Production Rate and Quality

Data Analysis

How decrease in physical and mental workloads would impact the physical and mental stress.

Skin Conductance Sensor (how physical and mental workload would affect mental stress)

Here is the initial statistical data:

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However, to eliminate the effects from unusual polar values, it is better to analyze the data within 95% confidential interval:

Group Type 95%CI

Variable N Mean StDev Lower Upper Range

Control

S1 C 60 10.592 1.369 10.238 10.946 0.708

S2 C 60 12.284 2.283 11.694 12.874 1.18

S6 C 60 1.5406 0.3162 1.4589 1.6223 0.1634

S8 C 60 9.648 2.131 9.097 10.199 1.102

Average 60 8.51615 1.5248 8.121975 8.910325 0.78835

Experimental 1

(visual/mental)

S3 E1 60 5.779 2.796 5.057 6.502

1.445

S4 E1 60 2.0083 0.3793 1.9104 2.1063 0.1959

S5 E1 60 7.838 2.345 7.232 8.444

1.212

Average 60 5.208433 1.8401 4.733133 5.6841 0.950967

Experimental 2

(physical)

S7 E2 60 4.201 1.074 3.924 4.479

0.555

S9 E2 60 4.933 0.79 4.729 5.137

0.408

S10 E2 50 0.283 0.2451 0.2133 0.3526 0.1393

Average 56.7 3.139 0.703033 2.955433 3.322867 0.367433

Summary:

In view of the average value for entire group, control group had the largest mental stress,

then the first experimental group, and the second experimental group had the least mental stress.

So the result is that decreasing physical workload is the most effective way to decrease the mental

stress, which reduced by (3.139-8.516)/8.516 = 63.1%; decreasing mental workload is the second

effective method to weaken the mental stress, which decreased by (5.208-8.516)/8.516 = 38.8%.

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The subjects in the first experimental group had the largest range on average (most

fluctuate), while the subjects in the second experimental group had the least average range (most

flat). This result shows that the reduction on physical workload can make mental stress less time

sensitive (mental stress increases slowly as time goes by), whereas the reduction on mental

workload makes mental stress more time sensitive (mental stress increases quickly as time goes

by). This result has been foreseen in the beginning, because decreasing the mental workload

significantly increased the production rate, by which the labor did more work than the labors in

control group and the second experimental group (reducing physical workload).

EMG (how physical and mental workload would affect muscle strain)


Group Type 95% CI


Control

S1 C 60 93.95 17.34 89.47 98.43 8.96

S2 C 60 68.45 13.84 64.87 72.02 7.15

S6 C 60 19.125 4.249 18.027 20.223 2.196

S8 C 60 57.85 9.95 55.28 60.42 5.14

Average 60 59.84375 11.34475 56.91175 62.77325 5.8615

Experimental 1

(visual/mental)

S3 E1 60 44.57 25.49 37.98 51.15 13.17

S4 E1 60 44.57 25.49 37.98 51.15 13.17

S5 E1 60 40.189 6.131 38.605 41.773 3.168

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Average 60 43.10967 19.037 38.18833 48.02433 9.836

Experimental 2

(physical)

S7 E2 60 18.78 5.692 17.309 20.25 2.941

S9 E2 60 25.358 3.357 24.491 26.225 1.734

S10 E2 50 43.8 8.99 41.24 46.35 5.11

Average 56.66667 29.31267 6.013 27.68 30.94167 3.261667

Analyze the data within 95% confidential interval:

Summary:

In view of the average EMG value for each group, the method of reducing physical workload in the

second experimental group notably decreased the muscle strain by 49.3% and the method of reducing

mental workload in the first experimental group decreased the muscle strain by 28%.

From the range values, it can be concluded that the increasing speed of muscle strain is in this order:

experimental 1 > control group > experimental 2. This means that the second method of reducing

physical workload not only caused less muscle strain, but also led to a much slower increasing speed for

muscle strain; whereas reducing the mental workload brought up higher increasing speed for muscle

stain than the control group did, although it caused less average strain. This phenomenon could still be

explained by the fact that reducing mental workload remarkably increased the production rate which

simultaneously caused the labor to do more work.

BVP/ Converted Heart Beat Rate (how physical and mental workload would affect physical stress and mental stress)


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Analyze the data within 95% confidential interval:

Group Type 95% CI


Control

S1 C 60 93.25 9.06 90.91 95.59 4.68

S2 C 60 100.148 7.207 98.286 102.01 3.724

S6 C 60 104.34 8.09 102.25 106.42 4.17

S8 C 60 110.43 3.992 109.399 111.461 2.062

Average 60 102.042 7.08725 100.2113

103.8703 3.659

Experimental 1 visual/mental

S3 E1 60 95.86 7.89 93.82 97.89 4.07

S4 E1 60 110.334 5.313 108.962 111.706 2.744

S5 E1 60 104.34 8.09 102.25 106.42 4.17

Average 60 103.5113

7.097667

101.6773

105.3387

3.661333

Experimental 2 physical

S7 E2 60 92.176 5.443 90.77 93.582 2.812

S9 E2 60 98.441 7.425 96.523 100.359 3.836

S10 E2 50 90.66 9.97 87.83 93.49 5.66

Average 56.66667 93.759 7.61266

7 91.7076

7 95.8103

3 4.10266

7

Summary:

Because the simulation experiments were not asking the subjects to perform heavy

workload, the proposed methods of two experimental groups would not have obvious impact to

the heart beat rate. As the workload gets heavier, the impacts could be amplified.

In view of the average heart beat rate for each of the three groups, the method of

decreasing mental workload in the first experimental group slightly increased the heart beat rate

by 1.5%, whereas the method of decreasing physical workload decreased the heart beat rate by

8.5%. This shows that reducing the physical workload can keep the heart beat rate at a lower level,

whereas decreasing the mental workload could propel the heart beat rate.

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Overall Tendency:

Conducted the regression analysis for each group, using time as the predictor, then it showed the

following regression functions:

Sensor type Regression functions Revealed truth

Skin Conductance

(mental stress)

ConAve = 6.09 + 0.00265 Time Proposed methods in experimental group #1 caused the fastest increasing speed for metal stress; methods in experimental group #2 led to the slowest increasing speed. E1Ave = 2.16 + 0.00333 Time

E2Ave = 1.64 + 0.00196 Time

EMG

(muscle strain)

ConAve = 54.4 + 0.00598 Time Proposed methods in experimental group #1 caused the fastest increasing speed for muscle strain; methods in experimental group #2 led to the slowest increasing speed. E1Ave = 14.7 + 0.0310 Time

E2Ave = 25.9 + 0.00281 Time

HR

(heart beat rate)

ConAve = 6.09 + 0.00265 Time Proposed methods in experimental group #1 caused decreasing heart beat rate; methods in experimental group #2 led to the fastest increasing speed. E1Ave = 104 - 0.000589 Time

E2Ave = 90.6 + 0.00389 Time

How subjects react to the Break

Control group took one 5-minute-break at after 15 minutes (900 seconds) of working, and the two

experimental groups took two 2.5-minute-breaks after 10 minutes (600 seconds) and 20 minutes (1,200

seconds) of working respectively. All devices were stopped recording data while break time.

For all the three groups, all the three parameters after break(s) were higher than their previous

values. But the increasing speed after breaks was slightly decreased.

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NASA-TLX & Performance Analysis

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Summary

All Scales have comparatively high value with low production rate in Control Group.

In E1 group, Mental Demand, temporal demand and frustration level are lower as raw material is sorted.

Performance is also higher with no mistakes in production

For E2 group, Physical Demand is greatly reduced leading to some reduction in mental demand and good

performance.

Subject 9 from E2 group has highest production rate with 1 wrong product. This is great example of

reduction is efficiency due to repetitive work and high mental load.

Product correctness was highest for E1 group with minimum mental load while production rate was

highest for E2 group with minimum physical load

Average Mental Demand

Physical Demand

Temporal Demand

Performance Effort Frustration Level

Total Products

Correct Products

C 11.5 11.25 15 12.75 13 10 3.5 1.75

E1 8 9 9.66 16.33 9.33 5 6 6

E2 9 7.33 12.33 10 7.6 5.33 6.33 5.66

Correlation Analysis

Correlation analysis was conducted to determine what aspects of the subject’s mental state both

before and during the experiment most affected the performance of the process. Correlation analysis was

conducted between pre activity questionnaire and various production parameters as shown in table C1.

The ratio of fit to total products yields no use full information so it is left out. The first analysis was

conducted to analyze how the mental load before the experiment affected the performance.

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Table C1 - Correlation coefficients between pre-activity questionnaires and production performance

How many hours of sleep do you typically get?

How many hours of sleep have you gotten in the last three nights, on average

Ratio of question 2 to question 1

Compared to your normal stress levels, how stressed are you currently?

Rate your current level of mental exhaustion

Rate your current level of physical exhaustion

Number of products made 0.27686223 0.0976023 -0.2158401 -0.2187286 0.3877801 0.109778

Ratio # of correct products/# of products 0.44708719 0.0032210 -0.5408434 -0.1273518 -0.0640726 0.0229444

Correlation analysis was then conducted between various production parameters and the NASA TLX scores shown in table C2. This analysis was conducted to analyze how the mental load during the experiment affected the performance

Table C2 - Correlation coefficients between NASA-TLX and production performance

Number of products Ratio of # of correct to total

Mental -0.120319494 -0.20677037

physical 0.13266678 -0.556626577

Temporal 0.157498592 -0.524494735

performance 0.486694585 0.097363545

effort -0.304109384 -0.908957768

frustration -0.265793844 -0.804454641

Similarly another correlation analysis was conducted to check what conditions of the participant before the experiment affect the TLX scores entered after the experiment. Table C3 compares the 6 questions of the pre questionnaires with the 6 rating scales of the TLX scale.

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Table C3 - Correlation coefficients between pre-activity questionnaires and NASA-TLX

How many hours of

sleep do you typically get?

How many hours of sleep have you

gotten in the last three nights, on

average Ratio of question 2

to question 1

Compared to your normal stress

levels, how stressed are you

currently?

Rate your current level of mental

exhaustion

Rate your

current level of physical exhaustion

Mental 0.018107606 -1.15689E-17 0.108210659 0.435767464 0.139517176 0.4407297

Physical -0.172349211 -0.193888125 0.071886139 0.147421927 0.024723409 0.264359416

Temporal -0.357197379 -0.139178 0.399548114 0.520976443 0.360402923 0.52907555

performance -0.38828218 -0.544643452 -0.131990083 -0.642808629 -0.19349128 -0.265403341

Effort -0.400064015 -0.096788796 0.482086592 0.216540879 -0.101929438 0.101929438

frustration -0.468542925 -0.253782363 0.355989854 0.486664263 -0.06681531 0.233853587

Summary

The effort required and the frustration experienced by the subject was inversely related (High negative correlation) to how “correctly” the products were being made. In other words, the more frustrated the subject was, the fewer products were made correctly. Similarly the greater the level of effort the subject required doing the experiment; the fewer products were made correctly. High levels of stress prior to the experiment have negative (Moderate correlation) effects on the performance during the experiment

Contrary to conventional understanding of how sleep affects performance, all the participants consistently scored themselves (Performance TLX score) higher when they had slept fewer hours the previous few days. One possibility that could be inferred is that people are poor at judging their own performance when they are sleep deprived.

This conclusion may be backed up by a study conducted by the department of physiology at Clemson University

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Conclusion

Physiological Measures

1) Fatigue and stress can be lowered if better working conditions are provided.

2) Both methods employed in groups E1 and E2 were effective in decreasing the muscle strains and

mental stresses. E2 is more powerful. E2 decreases the heart rate whereas E1 slightly increases it.

3) E2 can cause flatter trends, E1 causes more choppy trends.

4) A combination of methods in E1 and E2 group is proposed as solution.

5) Breaks did not show effectiveness in decreasing fatigue, but the breaks did cause lower rates of

increase.

Performance

6) Better working conditions ergonomically - sorted, automated and well planned processes - can help in

reducing mental and physical workload considerably.

7) This will lead to higher productivity with much lower rate of defects in products.

8) Frustration level (-0.8), amount of effort required (-0.91) and Hours of sleep (0.45) affected the

quality, while the level of mental exhaustion (0.5) affected the quantity of work.

Limitation and Work Extension

Limitation

1.1 Sample size was too small

While the ideal sample size for this experiment was 30, the actual number of participants

in this experiment was 10 due to availability of equipment (sensors), human subject resources and

time limitations. The sample size was not large enough and the individual variances may have

affected the results of the experiment. Though the results showed some trends for some

parameters and differences within and between groups, larger sample size might show more

obvious trend and bigger differences in the results.

1.2 Sample diversity

The experiment study was done on a group of people in same age group (20 – 25 years).

The subjects were from varied backgrounds (not mechanical or industrial engineering) with

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almost zero experience of working in a manufacturing unit. Experiment done with actual workers

may give different results

1.3 Workstation design limitation

The original workstation design was to try to simulate the real manufacturing workshop

in the case study as much as possible. Because subjects were required to be attached with sensors

in the experiment and the laboratory setting was fixed, the workstation for the simulation was

relatively simple. The distance between Lego parts and the workstation was not long enough.

Enough amount of fatigue could not be generated.

1.4 Manufacturing process simulation design limitation

To make sure no harm would be done to human subjects and to stick to the IRB

agreement, the simulation experiment required no more than minimum physical risk to the

subjects. In this situation, it was hard to produce proper amount of mental and physical fatigue

while keeping subjects at a safe and risk-free condition. Although the Lego assembly experiment

we chose for the simulation could satisfy the basic requirement of the simulation, the fatigue and

mental workload created were not big enough to show strong effects of our solution groups’

results.

1.5 Real time motions not simulated

Real time motions of the workers and the working time need to be considered to get the

real time data of the work being done and also the workers mental and physical states.

1.6 Experiment duration was too short

The duration of experiment was also not long enough due to same reasons. The 30

minutes experiment could not simulate the real manufacturing process which usually takes hours

for each shift. The limited duration could only create small amount of fatigue and mental

workload.

1.7 Pre-learning process

The pre-learning process was essential to our experiment because the simulation process

required good familiarity of the work. We could only give each participant only 15 to 20 minutes

of pre-learning to make one plane due to laboratory scheduling. In the experiment we found that,

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most of the participants found it hard to assemble the first two planes and it took almost half of

the time of the experiment. The effects of learning phase were not eliminated entirely for the

simulation and the results were affected correspondently.

1.8 Proper training of TLX self-evaluation scores

The NASA-TLX scores for human subjects showed great individual bias. Even with

detailed explanation of our group, some participants found it hard to evaluate their workload

properly. The scale scores for different participants were different. In addition, the bias in TLX

self-evaluation had effects on our experiment results. The expected correlations could not be

showen.

Extension of Work

With improvements and modification of the experiment, the solution groups may show more

significant difference in reducing fatigue and mental workload.

The results in this experiment can be used in designing more ergonomic-friendly workstation and

rescheduling shift breaks to reduce the fatigue and mental workload in real manufacturing unit. The

efficiency and production of manufacturing can also be increased.

The comparison between real labors and simulation participants could be done. Real labors in the

industry could be stronger and older than the participants due to their proficiency and experience.

References

[1] Gershon. P., Shinar. D., Ronen. A., 2009, “Evaluation of experience-based fatigue countermeasures”, Ben-Gurion University of the Negev, Beer Sheva, P.O.B. 653, Beer Sheva 84105, Israel

[2] Gershon. P., Shinar. D., Oron-Gilad. T., Parmet. Y., Ronen. A., 2010,“Usage and perceived effectiveness of fatigue countermeasures for professional and nonprofessional drivers”, Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

[3]Dadi. G. B., Goodrum P. M., Taylor T. R. B., Carswell C.M, 2014, “Cognitive workload demands using 2d and 3d special engineering information formats”, Journal of Construction Engineering and Management,Vol.140(5), pp.04014001

[4] Saito, K., April 1999 “Measurement of fatigue in industries” , Industrial health, Vol.37 (2), pp.134-42

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[5] Hosokawa, T., Mikami, K., Saito, K., 1997, “Basic study of the portable fatigue meter: effects of illumination distance from eyes and age”. Ergonomics 40, pp. 887-94.

[6]Kakizaki, T., Oka, T. and Kurimori, S,. 1992, “Changes in mental workload and fatigue during performance of a mental task”. An experiment in 8-h self-paced transcribing task, Japanese Journal of Industrial Health, Vol 34, pp.565-573

[7] Mehta, Rk ; Agnew, Mj, 2012 Aug, “ Influence of mental workload on muscle endurance, fatigue, and recovery during intermittent static work” European Journal Of Applied Physiology, , Vol.112(8), pp.2891-2902

[8] Barker, L, M., Nussbaum, M, A., September 2011, “The effects of fatigue on performance in simulated nursing work”, Ergonomics, Vol.54 (9), pp.815-29

[9] Mehta, R., Agnew, M., 2012, “Effects of physical and mental demands on shoulder muscle fatigue”, Journal Of Prevention Assessment & Rehabilitation, Vol.41, pp.2897-2901

report - effect of fatigue due to repetitive work

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