Human Resources Policy Analysis

Human Resources Policy Analysis

Data Interpretation

The data shows that the organization has 11 departments with a total workforce of 78 employees. Further, the data shows the departments in the organization have varied workforces based with the work involved. The departments in the organization are Accounts Planning, Account Service, Finance and administration, Human Resources and Facilities as well as IT, Logistics, and Management. Other departments are Management staff, Manufacturing, research. Sales and web development. The data indicates that the salary scale of staff is based on the department with the component of basic pay, overtime, and bonus pay (King, 2016). Further, the bonus pay across departments stands at US $ 2,000 but overtime varies. The highest-paid staffs are in management followed by the IT and the least paid staff takes home US $ 42,000 in salary. Moreover, few employees are earning overtime with those in the manufacturing department earning the highest overtime of US $ 27,000 and the least overtime awarded to the employees in the organization is US $ 1,100. From the data, it is prudent to note that most employees in the same department were recruited at the same time. For example, those working in Account planning were recruited in August 1987. The question that emerges from the data analysis is whether the employees working in the same department had the same level of qualification, skills, and experience at the time of recruitment? This question will seek to unearth why most employees in the same department earn a similar salary (King, 2016). Also, why are the essential employee salary not matched with the services they provide in the organization? This question will reveal the underlying factors that influenced their recruitment.

Data Analysis

The data has made me think that there exists laxity among employees since the salary award in the various departments is not reflective of the employees’ performance or score and the key role they play in the organization. Also, the employees in the organization are not well motivated to work for the organization since all of them receive similar bonus pay of US $ 2,000. As a result, the employees are less likely to engage in overtime work since they are not well compensated. The data fails to shed more light on the criteria used to determine the amount of bonus pay accruing to each employee (Bråthen, & Halpern, 2012). Moreover, the data is silent on the determinant of overtime pay. The qualitative data presents an organization that is overstaffed in some departments with a poorly salary determination scale. For example, in the Sales department, all the members of staff are earning similar salaries and bonuses despite one being a team leader. Moreover, the salary does not reflect the score of each employee with the one with a score of 4 getting a paycheck similar to that with a score of 2 and 3. Also, the team leader being the pacesetter should get a higher score which is not the case (Paré, & Tremblay, 2007). Primary and secondary data on the employees’ recruitment process and their qualifications will prove vital in data analysis. This is because it will reveal the salary for each cadre based on the skills set.

Conclusion and related outcomes

From the interpretation of data and analysis, the organization should consider merging interrelated departments. For example, web development, and research can be merged with IT while account planning can be merged with sales. This will free the resources used in manning these departments (Bongomin, et al, 2020). The organization can also introduce a performance-based pay scheme where those staff who fail to meet targets over a specified period can be terminated. With a 10% reduction in the budget, I will merge related departments and introduce performance-based compensation. This will reduce redundancy among staff and create room for overtime as well as higher bonuses for lean staff’ will defend my decisions by linking it to employee performance hence eliminating bias.

References

Bongomin, O., Gilibrays Ocen, G., Oyondi Nganyi, E., Musinguzi, A., & Omara, T. (2020). Exponential disruptive technologies and the required skills of industry 4.0. Journal of Engineering2020.

Bråthen, S., & Halpern, N. (2012). Air transport service provision and management strategies to improve the economic benefits for remote regions. Research in Transportation Business & Management4, 3-12.

King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review15(4), 487-495.

Paré, G., & Tremblay, M. (2007). The influence of high-involvement human resources practices, procedural justice, organizational commitment, and citizenship behaviors on information technology professionals’ turnover intentions. Group & organization management32(3), 326-357.

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