Strategic Workforce Planning Under Uncertainty

2021 ◽  
Author(s):  
Patrick Jaillet ◽  
Gar Goei Loke ◽  
Melvyn Sim
Keyword(s):  
Organizational Structure ◽  
Structure Analysis ◽  
Operations Research ◽  
Workforce Planning ◽  
Data Driven ◽  
Planning Under Uncertainty ◽  
Organizational Renewal

A new study in the INFORMS journal Operations Research proposes a data-driven model for conducting strategic workforce planning in organizations. The model optimizes for recruitment and promotions by balancing the risks of not meeting headcount, budget, and productivity constraints, while keeping within a prescribed organizational structure. Analysis using the model indicates that there are increased workforce risks faced by organizations that are not in a state of growth or organizations that face limitations to organizational renewal (such as bureaucracies).

Resilience of Complex Adaptive Systems: A Pedagogical Framework for Engineering Education and Research

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
T. Agami Reddy
Keyword(s):  
Complex Adaptive Systems ◽  
Operations Research ◽  
Adaptive Systems ◽  
Census Data ◽  
Data Driven ◽  
External Shocks ◽  
Essential Services ◽  
Complex Adaptive ◽  
Complete Failure ◽  
And Performance

Abstract The discourse on resilience, currently at the forefront of research and implementation in a wide variety of fields, is confusing because of its multi-disciplinary/spatial/temporal nature. Resilience analysis is a discipline that allows the assessment and enhancement of the coping and recovery behaviors of systems when subjected to short-lived high-impact external shocks leading to partial or complete failure. This paper, meant for pedagogical teaching and research formulation, starts by providing an overview of different aspects of resilience in general and then focuses on communities and regions that are complex adaptive systems (CAS) involving multiple engineered infrastructures providing essential services to local inhabitants and adapted to available natural resources and social requirements. Next, for objective analysis and assessment, it is proposed that resilience be characterized by four different quantifiable sub-attributes. This paper then describes the standard technocentric manner in which different temporal phases during and in the aftermath of disasters are generally visualized and analyzed, and discusses how these relate to reliability and risk analyses. Subsequently, two prevalent types of frameworks are described and representative literature reviewed: (i) those that aim at improving general resilience via soft methods such as subjective means (interviews, narratives) and census data, and (ii) those that are meant to enhance specific resilience under certain threat scenarios using hard/objective methods such as data-driven analysis and performance-predictive modeling methods, akin to resource allocation problems in operations research. Finally, the need for research into an integrated framework is urged; one that could potentially combine the strengths of both approaches.

scholarly journals A study on Efficient Modelling in Higher Education Academic Workforce Using Simulation

2021 ◽  
Vol 2 (6) ◽  
pp. 7-14
Author(s):  
Nethal K. Jajo ◽  
Shelton Peiris
Keyword(s):  
Higher Education ◽  
Operations Research ◽  
Discrete Event ◽  
Workforce Planning ◽  
Policy Changes ◽  
Event Simulation ◽  
Education Academic ◽  
Academic Workforce ◽  
The University

This paper shows how the Operations Research (OR) tools (in modelling and simulation) can be modified, applied in planning and their understanding of any long-term impacts due to sudden policy changes. The proposed approach is particularly useful to investigate the movements of the university academics and their impacts on changes in research, research funding, teaching and services as they are the integral parts of the career at any level. We argue that the Discrete Event Simulation (DES) approach can be used to model such dynamics in Higher Education Academic Workforce Model (HEAWM) and show that it can provide a comprehensive projection of future requirements within the context of career progression. Consequently, this HEAWM allows universities to interrogate factors influencing the academic workforce planning as this process often requires the new attributes to be tracked in the model which is difficult with other OR models. It is shown that this approach is easy to apply via DES and creation of the corresponding HEAWM provides better understanding of the factors that will influence the future workforce than the existing results.

Models and methods for workforce planning under uncertainty: Optimizing U.S. Army cyber branch readiness and manning

Omega ◽  
2020 ◽  
Vol 92 ◽  
pp. 102171 ◽  
Author(s):  
Nathaniel D. Bastian ◽  
Brian J. Lunday ◽  
Christopher B. Fisher ◽  
Andrew O. Hall
Keyword(s):  
Workforce Planning ◽  
Planning Under Uncertainty

Comparing and choosing organizational structures: A multicriteria methodology

2003 ◽  
Vol 22 (4) ◽  
pp. 185-195
Author(s):  
Lucio Biggiero ◽  
Domenico Laise
Keyword(s):  
Decision Theory ◽  
Organizational Structure ◽  
Utility Function ◽  
Decision Maker ◽  
Operations Research ◽  
Organizational Structures ◽  
Relevant Aspect ◽  
Scientific Approach ◽  
Outranking Methods ◽  
Multicriteria Methodology

To the extent an organizational structure is the outcome of an intentional and rational choice, such a choice is complex. A relevant aspect of such a complexity is the number of different and usually conflicting criteria employed by the decision maker. The application of traditional and pragmatic approaches implies the choice of one type of organizational structure through multiple assessing criteria. A multicriteria choice is involved even in a purely scientific approach: different organization theories suggest different criteria. The standard decision theory, based on the maxi-minimization of some utility function, cannot deal with multicriteriality. The task can be fulfilled in a rigorous and formal way by outranking methods, which are a branch of operations research. With an application to the problem of choosing the organizational structure, a tutorial example of outranking methods is discussed, addressing the larger theoretical framework where they can be placed and developed in management science.

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