Module 4 Background People Predictive Analytics Required Mat ✓ Solved

Module 4 Backgroundpeoplepredictive Analyticsrequired Materialsmorg

Analyze three problems related to linear equations involving employment data from the U.S. Bureau of Labor Statistics. For each problem, develop a linear equation based on given historical data, use it to make future predictions, and evaluate the realism of these predictions considering potential interfering factors not accounted for by the model. Clearly explain the steps used to derive the equations, support your analysis with research-based insights, and discuss the limitations and possible variations affecting future employment numbers in each industry.

Sample Paper For Above instruction

Introduction

Linear models serve as fundamental tools in predicting future trends based on historical data across various industries. Understanding how to construct these models and interpret their predictions critically is essential, especially in human resource management and workforce planning. This paper addresses three employment sectors—union workers, air transportation, and truck transportation—using linear equations derived from historical figures. We will assess the predictions’ realism and discuss possible factors that could influence actual future outcomes beyond what the models suggest.

Problem 1: Union Workers

a. Developing the Linear Model

Given data: 16.3 million union workers in 2000 and 14.7 million in 2018. Let y denote the number of union workers, and x the number of years since 2000. Therefore, x=0 corresponds to the year 2000, and x=18 corresponds to 2018. The two data points are (0, 16.3) and (18, 14.7).

Calculating the slope (m): m = (14.7 - 16.3) / (18 - 0) = -1.6 / 18 ≈ -0.089.

Using point-slope form: y - 16.3 = -0.089(x - 0), simplifying to y = -0.089x + 16.3.

b. Prediction for 2050

Year 2050 corresponds to x = 2050 - 2000 = 50.

Using the equation: y = -0.089(50) + 16.3 ≈ -4.45 + 16.3 = 11.85 million union workers.

c. Realism and Interfering Factors

The prediction suggests around 11.85 million union workers in 2050, assuming linear decline continues. However, such a projection oversimplifies workforce dynamics. Factors such as changes in labor policies, union popularity, economic shifts, automation, and industry restructuring can significantly alter future union membership numbers. For example, the decline in unionization has slowed in some sectors due to political and economic changes (BLS, 2020). Additionally, external shocks like major economic recessions or legislative reforms could accelerate or reverse trends, making the linear model an approximation that may not fully reflect future realities (Kalleberg & Vallas, 2018).

Problem 2: Air Transportation Industry

a. Developing the Linear Model

Initial data: 529,000 employees in 1990; 498,780 in 2018. x=0 for 1990 and x=28 for 2018.

Calculating slope: m = (498,780 - 529,000) / (28 - 0) = -30,220 / 28 ≈ -1,079.29.

Equation form: y - 529,000 = -1,079.29(x - 0), simplifying to y = -1,079.29x + 529,000.

b. Year When Employees Reach 400,000

Set y = 400,000: 400,000 = -1,079.29x + 529,000.

Solve for x: x = (529,000 - 400,000)/1,079.29 ≈ 129,000 / 1,079.29 ≈ 119.57 years.

Adding 119.57 years to 1990: 1990 + 119.57 ≈ 2109.57, approximately mid-2110.

c. Realism and Factors Influencing Future Employment

Predicting the industry to have 400,000 employees around 2110 assumes a constant rate of decline—an oversimplification given industry fluctuations. The actual workforce could be affected by technological innovations, automation reducing the need for human labor, deregulation, or economic crises. For example, increased automation in air travel maintenance and customer service could further reduce employment (International Air Transport Association, 2020). Conversely, industry expansion or resilience during economic downturns could stabilize or increase employment levels, indicating that the linear decline is a limited predictor of complex industry dynamics (Boeing, 2019).

Problem 3: Truck Transportation Industry

a. Developing the Linear Model

Data: 1.1 million workers in 1990; 1.5 million in 2018; x=0 for 1990, x=28 for 2018.

Slope: m = (1.5 - 1.1) / (28 - 0) = 0.4 / 28 ≈ 0.0143.

Equation: y - 1.1 = 0.0143(x - 0), thus y = 0.0143x + 1.1.

b. Future Workforce in 2028

Year 2028: x = 2028 - 1990 = 38.

y = 0.0143(38) + 1.1 ≈ 0.543 + 1.1 = 1.643 million employees.

To reach 2.5 million: set y = 2.5, solve for x: x = (2.5 - 1.1)/0.0143 ≈ 96.5 years after 1990, i.e., approximately 2086.

c. Realism and Potential Disruptions

Predicting 2.5 million workers by 2086 presumes continual growth, yet numerous factors could alter this trajectory. Automation in trucking, including unmanned vehicles, could reduce employment needs, similar to trends identified by the McKinsey Global Institute (2018). Economic shifts, transportation regulations, and environmental policies would also influence employment levels. Thus, while the linear trend offers an initial estimate, actual future employment might diverge significantly due to technological and socio-economic developments.

Conclusion

Linear models provide valuable initial approximations for understanding industry trends but must be applied with caution. External factors—technological advancements, policy changes, economic fluctuations—can substantially influence employment beyond simple projections. Therefore, while these models help forecast future scenarios, they should be complemented with qualitative insights and sensitivity analyses to account for uncertainties inherent in workforce dynamics.

References

• Boeing. (2019). World Air Traffic Forecast. Boeing Commercial Market Outlook.
• International Air Transport Association (IATA). (2020). Future of Aviation Industry Workforce. IATA Reports.
• Kalleberg, A. L., & Vallas, S. P. (2018). Precarious work and the future of organizational control. Annual Review of Sociology, 44, 461–482.
• McKinsey Global Institute. (2018). Artificial Intelligence: The Next Digital Frontier?
• Bureau of Labor Statistics (BLS). (2020). Industries at a Glance: Union Workers.
• U.S. Bureau of Labor Statistics. (2012-2022). Occupational Employment Statistics.
• Snyder, H. (2019). The Use of Predictive Analytics in Human Resources. Journal of HR Analytics, 4(1), 55–70.
• Vallas, S. P., & Schor, J. (2020). Work and Workers in the Age of Automation. Society and Industry Blog.
• WorldatWork. (2015). Implementing a New HRIS System: Challenges and Benefits. WorldatWork Articles.
• Jahan, S. (2014). Human Resources Information System (HRIS): A Theoretical Perspective. Journal of Human Resource and Sustainability Studies, 2, 33–39.