AN ANALYSIS ON FAIR LABOUR PRACTICES OF COMPANY ABC

INTRODUCTION

The fair labor standard act is a federal law that establishes minimum wage overtime pay, recordkeeping and child labor standards for private and public sector employees.

It is very important for companies to adopt fair labor practices for the benefit of the employees and the company’s growth. Here are some of the benefits of fair labor practices:

• One of the key benefits of fair labor practices is improved productivity. When workers are treated fairly and with respect, they are more likely to be motivated and engaged in their work.
• Another benefit of fair labor practices is improved morale. Workers who feel that they are being treated fairly are more likely to have a positive attitude towards their work and their employer.
• Fair labor practices can also help to reduce employee turnover. When workers are treated fairly and provided with adequate benefits and compensation, they are less likely to leave their jobs in search of better opportunities. This can help to reduce the costs associated with recruiting and training new employees.
• Additionally, fair labor practices can help to improve the reputation of a company. By demonstrating a commitment to treating workers fairly, companies can enhance their brand and attract customers who value ethical and socially responsible business practices.

PROBLEM STATEMENT

The HR manager of Company ABC asked for my assistance as a business analyst in summarizing the information that he has about the company’s employees to know how the company’s income level measures up with the furniture industry’s standard, amongst other information about the employees.

OVERVIEW DASHBOARD

This Dashboard shows specific insights about the distribution of gender, minority and majority staff, salary growth over the months of working for the company amongst others.

This shows the comparison of the 95% confidence interval between the industry standard sample and company ABC income distribution. showing the top and bottom interval of salary distribution.

This first visual shows the distribution of employees among the 3 Job categories in Company ABC.

The second visual shows the percentage distribution of the employees by their years of Education.

DATA INSIGHTS

• The confidence interval comparison shows that company ABC’s salary range is within the standard salary range of the employees in the furniture industry with a %marginal error of 13%.
• The average salary of the Male employees is 58% higher than that of the Female employees even though there’s a closely similar distribution between majority and minority employees of both gender.
• 40% of the employees have 12years education which is the highest while 0.21% have 21yrs of education which is the lowest percentage of staff.

RECOMMENDATION

• Company ABC should ensure there’s equal income level between the female employees and their male counterparts. To ensure the female staff are highly motivated to work.
• They should maintain the general income level of the company as it is of the right standard and this will help maintain employee motivation and efficiency.

TOOLS AND FORMULAS USED

• Microsoft Excel and PowerBI tools were used for the Data collection and analysis of this project.
• The columns in the dataset include: Emp_ID, Gender, Birthdate, Educ_yrs, Job_Cat, Salary, Sal_Begin, Job_Time, Prev_Exp, Minority.
• The other columns and measure were created using Data Analytic Expressions (DAX) in PowerBI. The formulas include:
• Avg_Female_salary = CALCULATE(AVERAGE(‘Employee data’[Salary]), FILTER(‘Employee data’, ‘Employee data’[Gender] = “f”))
• Avg_Male_salary = CALCULATE(AVERAGE(‘Employee data’[Salary]), FILTER(‘Employee data’, ‘Employee data’[Gender] = “m”))
• Female_ Staff = CALCULATE(COUNT(‘Employee data’[Gender]), FILTER(‘Employee data’, ‘Employee data’[Gender] = “f”))
• Male_Staff = CALCULATE(COUNT(‘Employee data’[Gender]), FILTER(‘Employee data’, ‘Employee data’[Gender] = “m”))
• Minority_Female_Staff = CALCULATE(COUNT(‘Employee data’[Gender]),FILTER(‘Employee data’, ‘Employee data’[Gender] = “f”), FILTER(‘Employee data’, ‘Employee data’[Minority] = 1))
• Minority_Male_Staff = CALCULATE(COUNT(‘Employee data’[Gender]),FILTER(‘Employee data’, ‘Employee data’[Gender] = “m”), FILTER(‘Employee data’, ‘Employee data’[Minority] = 1))
• Majority_Female_Staff = CALCULATE(COUNT(‘Employee data’[Gender]),FILTER(‘Employee data’, ‘Employee data’[Gender] = “f”), FILTER(‘Employee data’, ‘Employee data’[Minority] = 0))
• Majority_Male_Staff = CALCULATE(COUNT(‘Employee data’[Gender]),FILTER(‘Employee data’, ‘Employee data’[Gender] = “m”), FILTER(‘Employee data’, ‘Employee data’[Minority] = 0))