To perform an economic analysis of waiting lines and define their economic costs, it is necessary to consider several variables. These include the waiting cost per period for each unit, the average number of units in the system, the service cost per period for each server, the number of servers, and the total cost per period (Anderson et al., 2015). In most cases, the service cost per time is easy to quantify, as it constitutes a proportion of the costs incurred when providing services.
Waiting cost per time is harder to quantify, as waiting per se does not involve immediate economic losses, but if lines become too long, customers will eventually choose other service providers and, thus, the costs will be incurred (Anderson et al., 2015). If one seeks to avoid it, it is necessary to assign a specific cost to a waiting period, which may be higher or lower depending on how important it is for the company t reduce waiting time (Anderson et al., 2015). Thus, defining the economic costs of waiting lines involves a combination of straightforward calculations based on precisely known variables and rough estimations.
One may use such decision criteria as customer experience and customer dissatisfaction when choosing to expand services and, thus, reduce waiting time. Predicting customer dissatisfaction is possible through a hybrid model combining agent-based and discrete-event models (Alvarado-Valencia, Silva, & Montoya-Torres, 2017). Agent-based elements of the model are better suited to predict customer reaction on the micro-level, which can then be used for overreaching conclusions on the macro-level (Alvarado-Valencia et al., 2017). If the model demonstrates the levels of customer dissatisfaction with lines to be higher than acceptable, expanding services to reduce waiting lines is necessary.
References
Alvarado-Valencia, J. A., Silva, G. C. T., & Montoya-Torres, J. R. (2017). Modeling and simulation of customer dissatisfaction in waiting lines and its effects. Simulation: Transactions of the Society for Modeling and Simulation International, 93(2), 91–101.
Anderson, D. R., Sweeney, D. J., Williams, T. A, Camm, J. D., Cochran, J. J., Fry, M. J., & Ohlmann, J. W. (2015). Quantitative Methods for Business (13th ed.). Boston, MA: Cengage.