MGT602 Business Decision Analysis Report - Absenteeism Study

This report presents a business decision analysis focused on employee absenteeism within the IT sector. The study utilizes a dataset from the UCI Machine Learning Repository to investigate the factors influencing absenteeism. The analysis employs several decision-making tools, including regression analysis, ANOVA, and Pareto analysis, to identify significant variables and patterns. Regression analysis is used to determine the relationship between absenteeism and various factors, while ANOVA is applied to assess the variance across different categories. Pareto analysis helps pinpoint the most impactful factors contributing to absenteeism. The findings indicate that digestive system issues, specific days of the week (Monday, Tuesday, Wednesday), and employee characteristics (age, weight, BMI) are correlated with higher absenteeism rates. The report concludes by highlighting the successful application of statistical tools in identifying key factors, while also acknowledging limitations such as the dataset's sample size and potential lack of direct applicability to IT organizations. The study underscores the importance of data-driven decision-making in addressing workplace challenges and improving employee productivity.

BUSINESS DECISION ANALYSIS
Name of Student:
Name of University:
Author Note:

INTRODUCTION
Decision making has a vital role to play in the achievement of
various organizational goals. It can be considered as a pervasive
function of the mangers that is aimed at accomplishing all the
organizational goals. Decision making is referred to as a process
that helps in selecting best course of action from among many of
the available alternatives. It is the responsibility of the
management in an organization to take vital decisions and at the
same time ascertain that these are carried out in accordance with
the defined goals. Decision making can be considered as the
medium through which the managerial functions such as planning,
directing, controlling and organizing are controlled. It is important
at each and every level of management (Kocakulah et al., 2016).

PROBLEM DESCRIPTION
• Absenteeism of employees in the workplace
• Illness and injuries are to be considered
• Disengagement
• Burnout in IT industry
• Lack of flexibility

DATASET INFORMATION
• The dataset which is used for the research is
obtained from the UCI machine learning
• The attributes or variables of the dataset are
common for many organizations including IT
companies
• The data is a multivariate set of data and is
real integer values

DECISION MAKING TOOLS
The decision making tools help the business
leaders in taking important decisions
• Regression
• Anova
• Pareto analysis

REGRESSION ANALYSIS
• linear regression analysis performed to see
the relationship between the target
• The backward linear method is applied to find
the most significant variables

ANOVA
• Analysis of variance is of great use
• has a lot of significance in business
management.
• This will help to come to conclusions that in
turn will enhance efficiency
• performance of the IT firm.
• This will help in evaluating the performance

PARETO ANALYSIS
• This will help to identify the changes that need
to be done
• for increasing the enthusiasm of the employees.
• According to this analysis 20% of the factors are
frequent contributors to almost 80% of the
growth of the company.
• In this context it can be used to find out the
characteristic of the employees

RESULTS OF DECISION MAKING TOOLS
• the results of each of the three decision tools
shows results that matches with the results of
other tools
• The regression tool provides the most probable
variables that can effect absenteeism time in a
significant way
• Anova proves that variation of categories in
those variable causes significantly different
absenteeism

CONCLUSION
In conclusion it can be stated that the objective of the project has been
successfully met as the main factors responsible for the common
challenge in an IT workplace which is Absenteeism has been identified by
using a authenticated source of data and thus by analysis with statistical
decision making tools. The most absenteeism time is found for the
reasons of disease of digestive systems and people tends to absent more
on Monday, Tuesday and Wednesday. Also, people with two and three
has the higher absenteeism time than other and from regression
equation the people with more age and heavy weight and low body mass
index tends to increase the absenteeism time. However, it must be noted
that the findings may not exactly accurate as sample size of dataset is
only 740 and the data is collected from a courier company in Brazil which
may not have exactly same characteristics of an IT organization.

BIBLIOGRAPHY
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• UCI Machine Learning Repository: Absenteeism at work Data Set. (2020). Retrieved 18 January 2020, from
https://archive.ics.uci.edu/ml/datasets/Absenteeism+at+work
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