Open AccessResources Allocation in Service Planning Using Discrete-Event Simulation
Author(s) -
Mauricio Becerra Fernández,
Milton M. Herrera,
Cristian Trejos,
Olga Rosana Romero
Publication year - 2021
Publication title -
ingenieria y universidad/ingenieria y universidad
Language(s) - English
Resource type - Journals
eISSN - 0123-2126
pISSN - 2011-2769
DOI - 10.11144/javeriana.iued25.rasp
Subject(s) - computer science , service (business) , discrete event simulation , process (computing) , resource allocation , event (particle physics) , service quality , operations research , point (geometry) , quality (philosophy) , quality of service , resource (disambiguation) , service level , operations management , process management , reliability engineering , simulation , business , engineering , marketing , telecommunications , computer network , philosophy , physics , geometry , mathematics , epistemology , quantum mechanics , operating system
Objective: Calculate the required personnel and resources needed to fulfill the service promise agreed with the customer. Methods and materials: This paper presents a discrete event simulation (DES) model developed to select and implement a Point of Sale (POS) for a company providing financial products. First, the paper shows the characterization of the system components and times per process. Then, hypothesis testing and goodness-of-fit statistics are estimated. Subsequently, the simulation scenarios assess the times between arrivals and the number of commercial advisers. Results and discussion: This model allows us to assess the allocation of resources to fulfill the service promise, which is that 80 % of customers must be served within one hour or less. This paper provided the service isoquants allowing us to observe the behavior of the performance metrics (service promise fulfillment) among different scenarios. Conclusions: The use of DES techniques allows for the evaluation of the assignment of personnel to achieve the fulfillment of the service promise, including facilities, equipment, and the evaluation of related processes. These methods can be extended to the analysis of resource allocation in the development of other processes, observing the relationship between service quality and operating costs.