Paper on Quantitative Decision-Making: Adopting CBA for Optimal Outcomes

Decision-making when dealing with uncertain probability outcomes is quite challenging for many managers. However, adopting a suitable quantitative decision-making approach will enable the management to make sound decisions. In quantitative decision-making, a decision's numerical and statistical outcomes are evaluated to ascertain whether a decision will yield optimal results (Mosadeghi et al., 2015). Adopting CBA in decision-making is a crucial step to ensure the productivity of choice. Since CBA focuses on evaluating systems, projects, and decisions, it can be incorporated successfully into valuing intangible assets. More so, CBA, as a model, highlights the relevance of various choices and the cost of initiating the decisions.

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After the problem is identified, quantitative decision-making techniques are initiated to stipulate a criterion to be followed while resolving the issues. In CBA, all the possible solutions addressing the problem are highlighted. CBA allows for further evaluation of approaches, and the most outstanding resolution is adopted (Mosadeghi et al., 2015). Since the diagnostic imaging department comprises various entities such as Radiology, CT scan, and MRI, it is essential to identify the most needed image and how it can be sorted. More so, proper planning reduces the wastage of funds on non-essential services in this department.

The CBA decision-making model indicates the imaging department's feasibility and highlights the opportunity cost, enabling the board of directors to choose suitable approaches. This decision-making model allows financial metrics to be evaluated in terms of revenue generated and cost-savored based on the decision made to ascertain its suitability (Mosadeghi et al., 2015). Another fundamental characteristic of the CBA model encompasses other crucial intangible factors, such as employee satisfaction and morale. This is relevant in increasing productivity and profitability in the imaging department in a health facility. Furthermore, it adopts the most suitable options to give a competitive advantage in the stiff market, which improves the profits gained.

For the CBA model to effectively work in an organization, various entities must be considered to develop its framework. For instance, the issue affecting the organization should be clearly stated. More so, the required policy changes can be collected from questionnaires and other quantitative data collection techniques (Mosadeghi et al., 2015). The relationship between the problem being solved and the options considered is a critical approach to ensuring this model is functional. There are various categories that CBA falls into direct/indirect, tangible/intangible, and real (Tseng et al., 2018). Direct costs are linked to production in the organization. Indirect are fixed in nature and entail issues overhead of the department. Tangible costs are associated with identifiable assets such as infrastructure and rent costs. Intangible costs are unexplainable and are associated with customer satisfaction and productivity levels.

According to the patient’s suggestions acquired from the questionnaires and other sources, four discreet solutions are fundamental and are likely to improve this imaging department's profitability. The first solution proposed entailed rescheduling imaging timelines to accommodate more patients accessing the imaging sessions to avoid no-shows (Tseng et al., 2018). Secondly, increasing the number of imaging experts in the center would increase efficiency and productivity and increase the number of customers accessing the services. The third suggestion included introducing an electronic database to store all the health records. The fourth proposed solution entailed an increase in imaging equipment and tools to increase the number of patients served per day and reduce the time taken to help each patient.

However, all the proposed solutions had their strengths and weaknesses. Even though the solutions had defects, their forces were crucial in bringing changes to the imaging department. For instance, rescheduling imaging time will enable many patients to access the facility on time, reducing delayed treatment (Tseng et al., 2018). However, rescheduling a routine takes time and requires additional facilities and resources. More staff is needed to provide the necessary services, and these increase in cost. Secondly, increasing the number of experts increases the rate of service delivery. However, increasing the expert staff demands more expenditure which significantly affects profits (Tseng et al., 2018). Thirdly, introducing an electronic database fastens service delivery and active patient tracing. However, electronic databases are expensive to install and run. More so, they require expertise that many employees lack and hence require additional staffing. Lastly, increasing the equipment will accelerate service delivery but, on the contrary, increase the cost of operation.

Conclusion

In summary, Cost-Benefit Analysis was effective in providing solutions to this challenge. It allows for various choices to be exploited under keen observation and those with low costs and high benefits to be considered. Furthermore, CBA allows for tactical and strategic decision-making. All the decisions being made are provable by the board of directors since they have experimented. More so, saving on costs and increasing productivity attracts remarkable profits. CBA is fundamental in spotlighting any possible pitfalls in the decision to ensure preventing developing uncertain choices. More so, the analysis predicts possible outcomes with regard to the specific solutions. Therefore, all the submitted proposals are useful in providing a solution for profitability issues.

References

Mosadeghi, R., Warnken, J., Tomlinson, R., & Mirfenderesk, H. (2015). Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision-making model for urban land-use planning. Computers, Environment and Urban Systems, 49, 54-65. 10.1016/j.compenvurbsys.2014.10.001Tseng, M. L., Wu, K. J., Hu, J., & Wang, C. H. (2018). Decision-making model for sustainable supply chain finance under uncertainties. International Journal of Production Economics, 205, 30-36. 10.1016/j.ijpe.2018.08.024