Should OPG Use a Quantitative or Qualitative Model to Assess HR Demand Forecasts for Technicians Over a Three-Year Period?

Should OPG Use a Quantitative or Qualitative Model to Assess HR Demand Forecasts for Technicians Over a Three-Year Period?

Shivkumar Raj (2204444)

University Canada West

HRMT:623 HR STRATEGY

Prof. Gifty Parker

4th August 2023

Table of Content

TOC o "1-1" h u HYPERLINK l _Toc15523 Introduction PAGEREF _Toc15523 h 3

HYPERLINK l _Toc15348 If OPG wants more accurate technician demand projections over the next three years, should it use a quantitative or qualitative model? PAGEREF _Toc15348 h 4

HYPERLINK l _Toc6021 The Recommended Form of Quantitative or Qualitative Model for OPG PAGEREF _Toc6021 h 6

HYPERLINK l _Toc6832 Conclusion PAGEREF _Toc6832 h 7

HYPERLINK l _Toc10308 Reference: PAGEREF _Toc10308 h 8

IntroductionIn today's competitive market, businesses are always looking for new ways to improve their processes and get ahead of the competition. Successful businesses often attribute their achievements in large part to their attention to human resource (HR) management. A company's ability to effectively devote resources and make educated decisions about recruitment, training, and development is greatly aided by an accurate forecast of HR demand. Ontario Power Generation (OPG), a global leader in the energy sector, understands the importance of improving its technician demand projections over the next three years in human resources. This foresightful plan is meant to coordinate staffing with anticipated needs and guarantee uninterrupted operations during this crucial time period. However, this raises the question of whether OPG should use a quantitative or qualitative model to gauge its need for technicians. In order to delve deeply into the topic at hand, it is necessary to examine both possibilities while keeping the implications on precision and trustworthiness in mind. Applying statistical methods to examine patterns in past data and make predictions about the future is at the heart of the quantitative method. OPG is able to gain insight from large datasets on topics such as project pipelines, retirements among the present staff pool, advances in technology affecting job roles, industry growth rates, and customer needs for electricity generation capacity expansion through the use of mathematical approaches such as regression analysis or time series forecasting techniques. However, qualitative approach rely on numbers or information that can be converted to numbers as their basis for analysis.

If OPG wants more accurate technician demand projections over the next three years, should it use a quantitative or qualitative model?The selection between a quantitative model and a qualitative model is contingent upon two primary factors: the level of uncertainty associated with the demand forecast and the quantity and intricacy of the data accessible for supporting the development of the demand forecast.

The utilization of quantitative models yielded more precise forecast estimates in comparison to qualitative methodologies (Goldstone, 2008). These models integrate diverse data points, including historical demand patterns, economic indicators, and workforce demographics, to enhance the accuracy and reliability of predictions. In addition, this tool facilitates the examination of various scenarios and the assessment of sensitivity, thereby providing valuable insights into the potential fluctuations in demand for technicians under different circumstances.

This study highlights the efficacy of quantitative models in capturing intricate relationships among various factors that influence the demand for technicians. Organizations such as OPG can develop a comprehensive understanding of the factors that drive technician demand growth or decline over time by utilizing statistical techniques such as regression analysis and time series forecasting. Furthermore, by incorporating accurately calibrated quantitative models alongside pertinent external data sources such as industry trends, OPG can attain exceptionally dependable forecasts that have the capacity to inform strategic workforce planning decisions.

Nevertheless, it is imperative to not overlook the counterarguments that highlight the advantages of qualitative methodologies in forecasting labour requirements for specialized positions like technicians. Exclusively relying on numerical data may fail to consider contextual intricacies and nuanced market dynamics that influence the supply and accessibility of proficient technicians in particular regions or sectors.

Quantitative models provide accurate and evidence-based predictions (Armstrong and Green, 2022), whereas qualitative approaches emphasize the significance of integrating expert knowledge and contextual information (Almalki, 2016). Given the intricate nature of projecting human resources (HR) demand for technicians spanning a duration of three years, it becomes apparent that attaining precise forecasts necessitates the utilization of a model capable of incorporating various internal and external factors that impact the supply and demand of technicians.

Therefore, it is recommended that OPG utilize a quantitative model to evaluate its technician demand, with the aim of enhancing its human resources demand projections. By utilizing historical data, economic indicators, scenario analysis, and statistical techniques such as regression analysis or time series forecasting, OPG will have the capacity to construct reliable predictive models that can effectively capture the complexities of technician demand patterns. Nevertheless, it is crucial to supplement these quantitative methodologies with qualitative perspectives provided by industry experts who possess implicit knowledge regarding regional labour markets and emerging industry trends.

The Recommended Form of Quantitative or Qualitative Model for OPGOPG can understand technician demand's complex dynamics by using both approaches.

This essay examined whether OPG should assess technician demand using a quantitative or qualitative model. We found that using one model may lead to inadequate insights and forecasting errors. Quantitative models like statistical data analysis and predictive algorithms offer unbiased numerical estimates based on historical trends (Shmueli, 2010). This method allows accurate projections and strategic technician recruitment and allocation. Qualitative models include expert opinions and market conditions. These models let OPG capture contextual nuances that statistical data cannot.

Qualitative assessments incorporate industry wisdom and external factors affecting technician demand (Carrington, 2016). OPG can improve forecast accuracy by combining these two complementary methods. Quantitative data-driven insights and qualitative knowledge will improve OPG technician HR planning decisions.

ConclusionIn conclusion, a quantitative or qualitative model for OPG technician HR demand forecasts would not capture all relevant factors. However, using both types simultaneously maximizes accuracy by drawing from data-driven analyses and industry-experienced expert judgement. It's important to note that this integrative approach requires extensive collaboration between OPG's stakeholders involved in forecasting processes at various organizational levels. OPG can maximize this hybrid model by encouraging interdisciplinary collaboration among HR specialists, departmental managers, data analysts, and subject matter experts across relevant domains.

Reference:Goldstone, Jack A., "Using Quantitative and Qualitative Models to Forecast Instability".

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Armstrong, J. Scott and Green, Kesten C.,2022, "Forecasting Methods and Principles: Evidence-Based Checklists".

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Almalki, Sami, 2016, "Integrating Quantitative and Qualitative Data in Mixed Methods ResearchChallenges and Benefits", Canadian Center of Science and Education.

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Linda Carrington, 2016, "A Qualitative Phenomenological Study of Employee Perceptions of the Impact of Layoffs", Walden University.

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