Manpower Planning for Demand Forecasting of Faculty Members using Trend Analysis and Regression

Abstract

Abstract:Employing adequate manpower is one of the major concerns of modern organizations. As Faculty members having specific characteristics, they are not available when necessary and this requires planning in order to predict their demand in time. In this research, using trend analysis as one of the quantitative method for estimation, first the predictive variables including BA,MA and PhD students also the published articles are predicted for five years, then by using regression equations models, the faculty members by rank, age and gender are forecasted. The findings showed that associate professors, aged 46 to 55 years are the most necessary manpower to be employed. Also the percentage of female faculty members shows significant growth.

The Impact of Enhancing the Academic Performance on Student Satisfaction of Private Business Faculties: New Business Model for Egyptian Private Universities

“…The research results go hand in hand with the previous literature regarding the important role of the academic staff and the curriculum in the overall satisfaction of students with the university as in (Weerasinghe & Dedunu, 2017); (Dong & Lucey, 2013); (McCaffery, 2018); (Aref & Sabah, 2015); (Brown, 2009). The results of the study prove the satisfaction with professor performance has strong significant correlation with satisfaction with curriculum and strong significant positive correlation with overall satisfaction.…”

“…McCaffery 2018; Aref & Sabah (2015); Brown (2009); Abdul Rahim, (2015) noticed that the factors that are best indicators for academic staff teaching at a higher education institution are: -how a course is built (structure) and curriculum -teaching techniques -Assessment techniques -The relationship between the students and the instructor Calvo-Porral, et al (2013); Bazargan (1999) argue that the bases for education in universities of a country depends on the environment and governmental legislation under which a university carries out its tasks and depends on the skills and knowledge of the teaching staff and the students in addition to the curriculum and other resources available.…”

Influence of Workforce Planning on Organizational Performance in the Manufacturing Industry of Lagos, Nigeria

“…Therefore, to a large extent, an organization’s future performance depends on the decision made by HR/manpower managers through his/her predictive planning activities. Hence, the need for HR/manpower managers to be strategic about the future based on past decisions and understanding of both micro and macroeconomic variables of the functioning of the labour market (Aref & Sabah, 2015; CIPD, 2010; Scott et al, 2011; Timms, 2013;). This study exhibits workforce planning as a means for the manufacturing sector to understand and anticipate the impact of demographic, technological and policy trends on service requirements.…”

“…Its importance cuts across sectors; for manufacturing sectors which this research study is focused on, it is meant to balance workforce, materials and a training and deployment of personnel on the basis of need, affordability and product demand. Its role in the manufacturing sector cannot be underestimated as it helps to forecast the requirement of workforce and plan for the acquisition, retention and effective utilization of employees and at the same time ensure that the needs of the company of both people and material are met at all times (Aref & Sabah, 2015; Dessler, 2015; Kingir & Mesci, 2010). Its essence is to forecast likely outcomes, which guide against disruption of a production process, so that ‘interventions or contingency plans can be prepared to ameliorate the impact of unfavourable trends on labour market outcomes or fluctuation’ (Scott et al, 2011).…”

Improved grey‐level correlation feature and neural network model for medical resource requirement prediction

“…In the medical resource forecasting of information modelling ideas, the diversity and unknowns of influencing factors have led to the difficulty and uncertainty of medical resource requirement forecasting (Sinha et al, 2016). Common resource requirement sequence forecasting models include multiple regression models (Aref & Sabah, 2015), time‐series extrapolation forecasting models (Li et al, 2020), grey forecasting feature models (Zeng, 2017), and machine learning models such as support vector machines (Liu et al, 2011) or BP neural networks (Yong et al, 2017). These models have different application scenarios for different resource requirements.…”