Workforce Optimization and its Impact on Performance: A Discussion

This discussion post delves into the concept of workforce optimization (WO) and its significance in enhancing performance management and operational efficiency, particularly within the healthcare sector. The post references a study by Bastian et al. (2015) on the Army Medical Department (AMEDD), which utilized a goal programming approach to optimize workforce planning, specifically focusing on the challenges of determining the appropriate number of promotions and hires. The discussion highlights the use of the Objective Force Model (OFM) to improve transparency and decision-making. Furthermore, the post provides a real-world example of workforce optimization through Alan Joyce, CEO of Qantas Airways, emphasizing the importance of customer satisfaction and employee training. The post also identifies a research gap, suggesting the need for further planning to model changing workforce stochastically for providing better support to downsizing decisions.

Running head: DISCUSSION
Discussion
Name of the Student
Name of the University
Author note

1DISCUSSION
For the promotion of operational efficiency, workforce optimization (WO) is done to
have an insight into the performance of workforce focusing on experiences of customers. WO is
done to enhance performance management so that there is better view of overall workforce
making work easier and faster. As described in the paper by Bastian et al., (2015) the
determination of appropriate number of promotions and hires in each medical specialty is a tiring
and complex task. AMEDD Personnel Proponency Directorate (APPD) used manual approach
for projecting these tasks supporting 30-year old life cycle. To meet the demands of the
healthcare organization, objective force model (OFM) was proffered by APPD optimizing the
workforce planning of AMEDD. In this optimization process, the employees are selected based
on preferences engaging them to the fullest in the forecasting, planning and evaluation of daily
scheduling tasks. Unboundedness, infeasibility and instability are the problems that are
associated with optimization that is corrected permitting better transparency offered by OFM
(López-Santana et al., 2016).
The main objective of this OFM was to enhance better transparency for AMEDD
personnel decision makers and effective projection of optimal number of officers who were
working to meet the demands of the current workforce. As APPD was previously functioning
manually, this new model helped to conduct work quickly for the decision-making in the
organization.
The strength of paper is that OFM used by APPD for the workforce optimization by
medical professionals was explicitly explained with stochastically variants used to Medical
Specialist Corps. Several methods are explained in the article where expert workforce decision
making estimate correct promotion and hires that achieved personnel structure showing great
significance. The study has research gap when further planning is required to model changing

2DISCUSSION
workforce stochastically for providing better support to downsizing decisions. For example, Alan
Joyce, CEO of Qantas Airways, Australia implemented the strategy of staff optimization that
tripled its customer satisfaction and experiences. He did market research, invested in service
innovations and most importantly, gave training to the employees for winning ultimate customer
experience and employee job satisfaction (aib.edu.au, 2018).

3DISCUSSION
References
aib.edu.au. (2018). AIB Featured Business Leader - Alan Joyce , AIB Official Blog. Official Blog
- Australian Institute of Business. Retrieved 11 January 2018, from
http://www.aib.edu.au/blog/business-leaders/aib-featured-business-leader-alan-joyce/
Bastian, N. D., McMurry, P., Fulton, L. V., Griffin, P. M., Cui, S., Hanson, T., & Srinivas, S.
(2015). The AMEDD uses goal programming to optimize workforce planning
decisions. Interfaces, 45(4), 305-324.
López-Santana, E., Akhavan-Tabatabaei, R., Dieulle, L., Labadie, N., & Medaglia, A. L. (2016).
On the combined maintenance and routing optimization problem. Reliability Engineering
& System Safety, 145, 199-214.