A Markovian Study of Manpower Planning in the Soft-Drink Industry in Nigeria

A Markovian approach to the analysis of data pertaining to recruitment, active staff wastage and retirement collected over a period of five years from a soft drink manufacturing company based in Lagos, Nigeria, is presented. Our results suggest that although the company studied has a long term employment policy, staff who retire from the system are disproportionately small 15 to 26 % compared to those who leave through wastage (74-85)%. This paper proposes a review of the current manpower policy to moderate the perceived imbalance in policy structure. The author is convinced that the method advocated is effective as a decision support instrument for solving manpower planning problems in industrial organizations.http://dx.doi.org/10.4314/njt.v33i4.1

Approximate Dynamic Programming Algorithms for United States Air Force Officer Sustainment

The United States Air Force (USAF) officer sustainment system involves making accession and promotion decisions for nearly 64 thousand officers annually. We formulate a discrete time stochastic Markov decision process model to examine this military workforce planning problem. The large size of the motivating problem suggests that conventional exact dynamic programming algorithms are inappropriate. As such, we propose two approximate dynamic programming (ADP) algorithms to solve the problem. We employ a least-squares approximate policy iteration (API) algorithm with instrumental variables Bellman error minimization to determine approximate policies. In this API algorithm, we use a modified version of the Bellman equation based on the post-decision state variable. Approximating the value function using a post-decision state variable allows us to find the best policy for a given approximation using a decomposable mixed integer nonlinear programming formulation. We also propose an approximate value iteration algorithm using concave adaptive value estimation (CAVE). The CAVE algorithm identities an improved policy for a test problem based on the current USAF officer sustainment system. The CAVE algorithm obtains a statistically significant 2.8% improvement over the currently employed USAF policy, which serves as the benchmark

RECRUITMENT AND PROMOTION SCHEDULE—AN ANALYSIS OF THE ROYAL AUSTRALIAN AIR FORCE AIR INTELLIGENCE ANALYST EMPLOYMENT CATEGORY

In 2015, the Royal Australian Air Force (RAAF) instituted Plan Jericho, a comprehensive plan to transform the RAAF into the world’s first 5th Generation Air Force. As a key contribution to realize Plan Jericho, the Director General Personnel–Air Force is proposing how to structure and manage the workforce. During the initial workforce review, the project team identified a gap in the Air Intelligence Analyst (AIA) workforce. This thesis develops a Markov model to forecast the number of AIA recruits needed to meet the RAAF AIA workforce demand through 2030. This thesis further examines the estimated Time-in-Grade (TIG) for promotion of AIAs based on historical separation behavior. Data was collected from the Australian Defence Force’s Human Resource Data Warehouse for three AIA Streams from 2002 to 2018. The Markov model forecasts the RAAF needs to recruit 173 personnel in Stream A, 404 personnel in Stream B, and 438 personnel in Stream C from fiscal year (FY) 19 through FY30 to meet the total AIA workforce demand. The model also provides managerially relevant measurements of expected TIG for promotion. The model, however, has some limitations due to the limited state-space and small sample size, and consequently, should be reviewed yearly. As one of the few personnel models of its type within the RAAF, it will provide a valuable tool for workforce planning and enable the realization of Plan Jericho.http://archive.org/details/recruitmentandpr1094564845Captain, Royal Australian Air ForceApproved for public release; distribution is unlimited

Fuzzy system dynamics and optimization with application to manpower systems

The dynamics of human resource recruitment and training in an uncertain environment creates a challenge for many policy makers in various organisations. In the presence of fuzzy manpower demand and training capacity, many companies fear losing critical human resources when their employees leave. As such, the development of effective dynamic policies for recruitment and training in a fuzzy dynamic environment is imperative

Study of Muslims in marital system using markov chain simple exponential smoothing (MCses) technique

The Markov Chain (MC) model is a popular mathematical model used to observe the flow of data in a system. It can also be used to forecast future values for short-term period. However, most previous studies do not focus on the accuracy of the forecast values. Integration of the MC model and Simple Moving Average (SMA) technique is known to produce higher forecast accuracy than the classical MC model for the case of long-term projection with known previous data. However, Simple Exponential Smoothing (SES) technique is more flexible than SMA because it uses a smoothing constant. Therefore, this study develops modeling steps for MC model in the case of limited data and short-term projection by integrating MC model with SES (MCsEs). The MCsEs hybrid model is used to enhance the MC model and improve the accuracy of the forecast values. Four error measures used to determine the accuracy of this model are mean absolute deviation, mean absolute percentage deviation, mean absolute percentage error and mean square error. This study uses a sample of 6061 Muslim couples data in Pendang, Kedah who are in the marital system for the year 2013 and 2014. The number of Muslims in subsequence year according to gender and age categories is forecasted using proposed MCsEs hybrid model. Comparison with MC and MCsMA models indicates that the developed MCsEs hybrid model has better forecast accuracy. Therefore, the MCsEs hybrid model is the most appropriate model to forecast the number of Muslims in the marital system according to gender and age categories for the year 2014. This model can be used for short-term projection in cases with limited data and is applicable in various fields

United States Air Force Officer Manpower Planning Problem via Approximate Dynamic Programming

The United States Air Force (USAF) is concerned with managing its officer corps to ensure sufficient personnel for mission readiness. Manpower planning for the USAF is a complex process which requires making decisions about accessions. Uncertainty about officer retention complicates such decisions. We formulate a Markov decision process model of the Air Force officer manpower planning problem (AFO-MPP) and utilize a least squares approximate policy iteration algorithm as an approximate dynamic programming (ADP) technique to attain solutions. Computational experiments are conducted on two AFO-MPP instances to compare the performance of the policy determined with the ADP algorithm to a benchmark policy. We find that the ADP algorithm performs well for the basis functions selected, providing policies which reduce soft costs associated with shortages and surpluses in the force

Application of Absorbing Markov Chains to the Assessment of Education Attainment Rates within Air Force Materiel Command Civilian Personnel

Increasing the education levels of an organization is a common response when attempting to improve organizational performance; however, organizational performance improvements are seldom found when the current and future workforce education levels are unknown. In this research, absorbing Markov chains are used to probabilistically forecast the educational composition of the Air Force Materiel Command civilian workforce to enable organizational performance improvements. Through the purposeful decoupling of effects resulting from recent workforce arrivals and education level progressions, this research attempts to determine the implications that stationarity assumptions have throughout the model development process of an absorbing Markov chain. The results of the analysis indicate that the four combinations of stationarity assumptions perform similarly at representing the historical data and that the forecasted educational attainment rates of the Air Force Materiel Command civilian workforce are expected to increase significantly

Human resource planning practices in the Omani Public Sector: An exploratory study in the Ministry of Education in the Sultanate of Oman

Human resource planning (HRP) is the management process that helps organisations prepare for the number of employees and the types of skills needed to achieve organisational goals and objectives. In short, the aim of HRP is to have the right people in the right place at the right time. However, unlike the private sector, HRP practices are not widespread in the public sector. Literature on the ways in which HRP is conducted in public sector organisations has been limited to date. While the process of moving from traditional models of public management to New Public Management (NPM), and the Resource-Based View (RBV) approach implies the need for emphasising the central role of the Human Resource Management (HRM) function, the question of how public-sector organisations implement HRP remains largely unanswered in the existing management literature. The focus of this study was to explore the current practices of HRP in the Ministry of Education (MoE) in Oman, an unexplored context, in order to gain an understanding of good practice, and recommend further improvements. An interpretive case study methodology was adopted for this study which enabled the researcher to gain access to the tacit knowledge held by experienced practitioners who are involved in HRP processes in the MoE. The analysis of data collected through interviews with key informants revealed that despite the implementation of some strategies, the MoE did not formulate or implement a comprehensive HRP approach. The focus for the MoE remains on operational and annual requirements with only few attempts made to incorporate HRP into strategic planning efforts or to involve HRP professionals in strategic planning processes. Strategic and operational HRP practices in the MoE have lagged the good practices highlighted in the literature. The results from this study also indicate that HRP professionals lack the ability, knowledge, and skills necessary to develop and implement effective HRP practices. The study found that HRP in the MoE is influenced by both external and internal factors. The external factors were government policies, the legal context, the labour market and the economy, while the internal factors included organisational structure and culture. Through cross-comparison and alignment of MoE practices with those best practices identified in the literature, the key characteristics of good HRP practices in Oman’s MoE were identified. This study begins to address this issue by attempting to use RBV and NPM theories to explain how HRP practices are currently recognised and used in publicsector organisations. The implications of the study suggest that having HRP in place is conducive to improving the competitiveness of the organisation. Moreover, under the principles of NPM, the study has been able to show how people at both strategic and operational levels of public organisations adopt, develop and manage the new concept of strategic HRP to continually improve organisational performance. This calls for researchers and those interested in the theory to give particular attention to the development of the skills and competencies of HRP professionals, including the skills needed to explore the ways that HRP is used to achieve competitive advantage. Further, in order to facilitate the effective adoption and application of NPM reforms, efforts should be made to prepare public-sector organisations well in terms of their culture, policies, rules and regulations