QSO 510 Absent causes by ill health

Name: QSO 510 Absent causes by ill health
Brand: Southern New Hampshire University
Price: 2 USD
Availability: InStock
Author: David Rodriguez

An assignment analyzing causes of absenteeism due to illness and its impact on operations.

David Rodriguez

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QSO 510 Absent causes by ill health
Analyze the impact of ill health on employee absenteeism, distinguishing between culpable and
non-culpable absenteeism. Use statistical data to support your analysis and discuss how
absenteeism due to ill health affects productivity and customer satisfaction in organizations.
Include a review of factors such as mental health, lifestyle, and seasonal influences.
Word Count: 1500-2000 words.

THESIS:
Ill health has a positive correlation with the periodicity of absent an person will do in his or her
company. Thus, if the health both mental and physical of a person is not good he will not go to
the office. It will cause a huge loss to the company as the essence of marketing and business is
the employer’s committed and regular work whose prime goal is the customer satisfaction. Thus,
ill health cause a genuine cause for the employee to not to go the office.
INTRODUCTION:
Absenteeism though most common but also most problematic issues faces by the employers
across the industry and sector. It includes both type of absenteeism culpable and non-culpable.
Basically, absenteeism impact on the employers is severe both in terms of monetary loss and
efficiency and effectiveness of work and customer satisfaction. People misses work for variety of
reasons and even without reason many times. When they do not attend office with a real reason it
is called non-culpable absenteeism and when they do not have any real reason to not go for their
work than it is called culpable absenteeism. Here, we are dealing with absent cases caused by ill
health that is a subpart of non-culpable or innocent absenteeism.
This problem is quite significant as it relates to the real day to day lifestyle which are caused by
various health issues including Alzheimer's and depressions. The pattern of lifestyle and the
social background of the employee decide how much day in a week he or she comes to office.
The people who are newly employed, having less morale and does not able to challenge the
workload are easily become the non-regular employ. This topic is even more significant than it
looks as when the company human resource (HR) department or manager does not try to support

the employ and looks for the solution but takes decision quickly to fire off the employ. Thus,
proper analysis of the topic using analytical and statistical tools is absolutely necessary.
STATISTICS AS A TOOL FOR ANALYSIS OF ABSENTEEISM:
Various research and studies were performed to get into the real problem and its consequences.
The most common and simple analysis has shown the health is the prime factor which increase
the graph of Absenteeism. Illness causes bed rest, appointment with the physician or doctor and
even injuries. These all turn to decrease the productivity of the company and also the utility
driven from each employee.
The major contributing factor for the illness is the season. For example, in the cold and flu
season less attendance is marked in the registers of the company. The data indicates that 98,000
employees employed in 12 most important occupation in the U.S. and came to conclusion that
77% of the people who fits into the survey definition are facing serious health disorders such as
Cancer, Asthma, diabetes and depressions. The statistics also shows that the mental health and
depression plays an important role in the absenteeism.
The loss due to absent causes by health issues accounts for low productivity which is around
worth of $84. This is really a challenging and most difficult task to handle by any company.
Though many trends which are evaluated by various studies and case studies it still needs a firm
catastrophic change in perception of the employee so that he or she can boost their mind each
morning that this is the job he or she has dreamt for and this is his or her passion
Data of absent causes by ill health

Sex Age Ht Wt
Pulse Chol Bmi
Medical
problems:
1 = Yes;
0 = No
Children?
Total
absent
days
level of risk
Male 58 70.8 169.1 68 522 23.8 1 4 22 Med
Male 22 66.2 144.2 64 127 23.2 0 2 28 High
Male 32 71.7 179.3 88 740 24.6 1 3 37 High
Male 31 68.7 175.8 72 49 26.2 1 1 33 High
Male 28 67.6 152.6 64 230 23.5 1 2 1 Low
Male 46 69.2 166.8 72 316 24.5 1 4 22 Med
Male 41 66.5 135 60 590 21.5 1 0 37 High
Male 56 67.2 201.5 88 466 31.4 1 4 17 Med
Male 20 68.3 175.2 76 121 26.4 0 2 15 Med
Male 54 65.6 139 60 578 22.7 0 4 20 Med
Male 17 63 156.3 96 78 27.8 1 0 12 Med
Male 52 73.1 191.1 56 250 25.2 0 4 14 Med
Male 25 67.6 151.3 64 265 23.3 1 1 16 Med
Male 29 68 209.4 60 273 31.9 1 2 11 Med
Male 17 71 237.1 64 272 33.1 1 0 2 Low
Male 41 61.3 176.7 84 972 33.2 1 4 10 Med
Male 52 76.2 220.6 76 75 26.7 1 0 39 High
Male 32 66.3 166.1 84 138 26.6 1 5 2 Low
Male 20 69.7 137.4 88 139 19.9 0 1 16 Med
Male 20 65.4 164.2 72 638 27.1 0 1 15 Med
Male 29 70 162.4 56 613 23.4 0 4 20 Med
Male 18 62.9 151.8 68 762 27 1 0 6 Low
Male 26 68.5 144.1 64 303 21.6 1 3 19 Med
Male 33 68.3 204.6 60 690 30.9 1 2 10 Med
Male 55 69.4 193.8 68 31 28.3 1 1 25 Med
Male 53 69.2 172.9 60 189 25.5 1 3 22 Med
Male 28 68 161.9 60 957 24.6 1 3 6 Low
Male 28 71.9 174.8 56 339 23.8 0 1 38 High
Male 37 66.1 169.8 84 416 27.4 1 1 26 Med
Male 40 72.4 213.3 72 120 28.7 1 4 20 Med
Male 33 73 198 84 702 26.2 0 4 16 Med
Male 26 68 173.3 88 1252 26.4 0 1 4 Low
Male 53 68.7 214.5 56 288 32.1 1 5 31 High
Male 36 70.3 137.1 64 176 19.6 0 5 18 Med
Male 34 63.7 119.5 56 277 20.7 1 1 30 High
Male 42 71.1 189.1 56 649 26.3 0 3 24 Med
Male 18 65.6 164.7 60 113 26.9 1 0 10 Med

Male 44 68.3 170.1 64 656 25.6 1 1 1 Low
Male 20 66.3 151 72 172 24.2 1 1 8 Low
Female 17 64.3 114.8 76 264 19.6 1 1 3 Low
Female 32 66.4 149.3 72 181 23.8 1 5 4 Low
Female 25 62.3 107.8 88 267 19.6 0 1 13 Med
Female 55 62.3 160.1 60 384 29.1 0 0 5 Low
Female 27 59.6 127.1 72 98 25.2 1 2 15 Med
Female 29 63.6 123.1 68 62 21.4 1 2 21 Med
Female 25 59.8 111.7 64 89 27.5 1 1 26 Med
Female 41 67.9 218.8 68 531 33.5 1 1 33 High
Female 32 61.4 110.2 68 130 20.6 1 0 3 Low
Female 31 66.7 188.3 80 175 29.9 0 1 8 Low
Female 19 64.8 105.4 76 44 17.7 1 1 20 High
Female 19 63.1 136.1 68 8 24 0 1 18 High
Female 23 66.7 182.4 72 112 28.9 1 3 50 High
Female 40 66.8 238.4 96 462 37.7 1 5 6 Low
Female 23 64.7 108.8 72 62 18.3 1 2 28 High
Female 27 65.1 119 68 98 19.8 1 1 37 High
Female 45 61.9 161.9 72 447 29.8 1 1 21 Med
Female 41 64.3 174.1 64 125 29.7 1 1 4 Low
Female 56 63.4 181.2 80 318 31.7 1 3 30 High
Female 22 60.7 124.3 64 325 23.8 1 2 16 Med
Female 57 63.4 255.9 80 600 44.9 0 2 18 Med
Female 24 62.6 106.7 76 237 19.2 0 3 20 High
Female 37 60.6 149.9 76 309 28.5 1 2 8 Low
Female 40 58.6 94.3 80 94 19.3 1 1 4 Low
Female 45 60.2 159.7 104 280 31 1 1 24 Med
Female 52 67.6 162.8 88 254 25.1 0 3 7 Low
Female 31 63.4 130 60 123 22.8 0 0 37 High
Female 32 64.1 179.9 76 596 30.9 1 2 4 Low
Female 23 62.7 147.8 72 301 26.5 1 2 18 Med
Female 23 61.3 112.9 72 223 21.2 1 1 11 Med
Female 47 58.2 195.6 88 293 40.6 1 4 35 High
Female 36 63.2 124.2 80 146 21.9 1 2 30 High
Female 34 60.5 135 60 149 26 0 1 11 Med
Female 37 65 141.4 72 149 23.5 1 5 27 Med
Female 18 61.8 123.9 88 920 22.8 1 0 10 Med
Female 29 68 135.5 88 271 20.7 1 3 4 Low
Female 48 67 130.4 124 207 20.5 0 2 1 Low

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University

Southern New Hampshire University

Subject

Operations Management

QSO 510 Absent causes by ill health

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