One of the essential tasks in the development of any system is the productivity assessment. It is essential to ensure that the entire system meets the requirements, is supplied timely, and is designed within the specified cost. It involves two crucial steps: anticipation and measurement of potential. Proper selection of performance measurement attributes is essential to this process. These measurement attributes, usually called “measures of effectiveness” or “MOEs”, provide quantifiable standards against which the system concept and implementation can be evaluated. In the early stages of a system’s life, forecasting is required to develop a technical feasibility assessment and specification. Towards the end of system implementation and development, performance measurement techniques play an essential role in system testing and validation.
Defining performance measures, it is necessary to point out that the term is closely related to another term applied to the evaluation of effectiveness – performance criteria of processes. The difference between these terms, while being relatively slight, is nevertheless essential. Performance measures are indicators derived from dimensional parameters (both physical and structural) and assess system performance attributes. The MOPs quantify a set of previously selected and sequenced parameters. Effectiveness indicators, unlike performance indicators, evaluate how a system performs in its environment, not only from a quantitative aspect but also from a qualitative one. Therefore, the MOP is usually a composite of the MOE.
Whereas performance indicators are the standards against which the performance of the system is to be compared, they are both mission- and scenario-dependent and should distinguish between options. Furthermore, they must realistically measure the goal or objective of the operation; otherwise, the system will not be fulfilling its purpose. The measure(s) of effectiveness should also reflect a change in a set of parameters. Finally, performance measures should be independent at the assessed level of analysis.
Among the main characteristics of performance indicators are task-orientation (the parameter relates directly to the system), measurability (the parameter can be calculated or evaluated), and realism (the parameter relates realistically to the system). In addition, there are also parameters such as certainty (the parameter is clearly defined, independent of subjective opinion) and consistency (the parameter refers to standards and analysis objectives). Sensitivity (the parameter reflects changes in system variables) and inclusivity (the parameter reflects those standards that are required by analysis work objectives) are also relevant criteria for performance indicators.
These parameters are introduced in the modeling process in conjunction with the case study requirements and the environmental conditions (Hamid, 2020). In addition to the basic parameters, time is also used as a performance measure. However, time is a parameter and, therefore, should not be used that way. Time is an independent variable, and the outcomes of processes occur as a function of time. Using time as an independent variable, the measure of effectiveness is the probability of completing a task within an appropriate amount of time.
Measures of effectiveness are the starting point for evaluating a system because it determines the set of parameters and their structure hierarchy during evaluation, determining the system’s performance. It is necessary to ensure that the performance measures reflect the purpose of the system. It is also essential to pay attention not to confuse the parameters and the measures. If properly defined and used, effectiveness measures play a crucial role in evaluating the system’s performance.
References
Hamid, K. (2020). Satisfaction, effectiveness, and extra effort as outcomes of veritable leadership and measures of effectiveness. Competition Forum, 18(1), 169-191.