REDUCING CUSTOMER WAIT TIME AND IMPROVING PROCESSES AT ABC’s ATV RENTALS

This project serves to explore the system bottlenecks of a small, family owned ATV rental company. The main objective is to reduce the average time a customer spends in the system, focusing on customer wait time as well as other areas that can be improved. This was done by collecting time studies and inputting the values into simulation software, which was run to represent the current system as well as various other possible scenarios encountered by rental companies. While creating the simulation, adaptive techniques were incorporated into the simulation. These techniques aim to increase the durability and reusability of the simulation for future use. An example of incorporating adaptive simulation is through having the simulation software draw values from an Excel spreadsheet. This example of adaptive simulation targets the efficiency of use, as values and formulas are easier to calculate and visualize in Excel than the simulation software. Through the scenarios created in the simulation software, the main system bottleneck was discovered to be the company’s trailer fleet size. Several scenarios were then created to further explore the theory and resulted in confirming it. The results of this analysis conclude that to reduce customer wait time in the system, the company should increase its fleet size by one trailer. A secondary, no cost solution is to eliminate ATV load/unload times by moving ATVs to the dunes prior to customer arrival instead of loading them on a customer by customer basis

Fluid Approximation of a Call Center Model with Redials and Reconnects

In many call centers, callers may call multiple times. Some of the calls are re-attempts after abandonments (redials), and some are re-attempts after connected calls (reconnects). The combination of redials and reconnects has not been considered when making staffing decisions, while ignoring them will inevitably lead to under- or overestimation of call volumes, which results in improper and hence costly staffing decisions. Motivated by this, in this paper we study call centers where customers can abandon, and abandoned customers may redial, and when a customer finishes his conversation with an agent, he may reconnect. We use a fluid model to derive first order approximations for the number of customers in the redial and reconnect orbits in the heavy traffic. We show that the fluid limit of such a model is the unique solution to a system of three differential equations. Furthermore, we use the fluid limit to calculate the expected total arrival rate, which is then given as an input to the Erlang A model for the purpose of calculating service levels and abandonment rates. The performance of such a procedure is validated in the case of single intervals as well as multiple intervals with changing parameters

A Note on an M/M/s Queueing System with two Reconnect and two Redial Orbits

A queueing system with two reconnect orbits, two redial (retrial) orbits, s servers and two independent Poisson streams of customers is considered. An arriving customer of type i, i = 1, 2 is handled by an available server, if there is any; otherwise, he waits in an infinite buffer queue. A waiting customer of type i who did not get connected to a server will lose his patience and abandon after an exponentially distributed amount of time, the abandoned one may leave the system (lost customer) or move into one of the redial orbits, from which he makes a new attempt to reach the primary queue, and when a customer finishes his conversation with a server, he may comeback to the system, to one of the reconnect orbits where he will wait for another service. In this paper, a fluid model is used to derive a first order approximation for the number of customers in the redial and reconnect orbits in the heavy traffic. The fluid limit of such a model is a unique solution to a system of three differential equations

Waiting time predictors for multi-skill call centers

International audienceWe develop customer delay predictors for multi-skill call centers that take into inputs the queueing state upon arrival and the waiting time of the last customer served. Many predictors have been proposed and studied for the single queue system, but barely any predictor currently exists for the multi-skill case. We introduce two new predictors that use cubic regression splines and artificial neural networks, respectively, and whose parameters are optimized (or learned) from observation data obtained by simulation. In numerical experiments, our proposed predictors are much more accurate than a popular heuristic that uses as a predictor the delay of the last customer of the same type that started service

Implementing a service software to relieve waiting line frustration

Service companies have used waiting lines as a way of buffering the constant flow of providing a service for a long time. The introduction of hurdles, line numbers, time based appointments and virtualization have all worked for better and faster service, but the problem of long waiting lines has so far not been solved. The subject of this work is a startup company which has built a queue management service that enables smartphone users to wait in line remotely. The startup team to which I belong has built a product around the promise of allowing people to use the waiting time in their own terms. Therefore making the the issue of long period between request for service and actual service less important from customer perspective. By developing a minimum viable product for validating the market we have discovered that the customer pain of long queues does not convert into service provider’s pain: the combination of long waiting time, recurrence of the wait and option for replacement are rare combination that exists only on theoretical level. This work will concentrate on the findings on introducing new technology of remote queuing to the market and the specifics of waiting lines and the conditions where this technology would be most adaptable. This study maps out all the methods the team used for introducing the technology, all the findings and the conclusions that were made during the process. By testing different methods a model was developed that describes the problems of introducing new technologies that require personal hardware support from both parties in the context of physical serviceshttp://tartu.ester.ee/record=b2656163~S1*est2023-05-0

Sharing delay information in service systems: a literature survey

Service providers routinely share information about upcoming waiting times with their customers, through delay announcements. The need to effectively manage the provision of these announcements has led to a substantial growth in the body of literature which is devoted to that topic. In this survey paper, we systematically review the relevant literature, summarize some of its key ideas and findings, describe the main challenges that the different approaches to the problem entail, and formulate research directions that would be interesting to consider in future work

Reducing Wait Time Prediction In Hospital Emergency Room: Lean Analysis Using a Random Forest Model

Most of the patients visiting emergency departments face long waiting times due to overcrowding which is a major concern across the hospital in the United States. Emergency Department (ED) overcrowding is a common phenomenon across hospitals, which leads to issues for the hospital management, such as increased patient s dissatisfaction and an increase in the number of patients choosing to terminate their ED visit without being attended to by a medical healthcare professional. Patients who have to Leave Without Being Seen (LWBS) by doctors often leads to loss of revenue to hospitals encouraging healthcare professionals to analyze ways to improve operational efficiency and reduce the operational expenses of an emergency department. To keep patients informed of the conditions in the emergency room, recently hospitals have started publishing wait times online. Posted wait times help patients to choose the ED which is least overcrowded thus benefiting patients with shortest waiting time and allowing hospitals to allocate and plan resources appropriately. This requires an accurate and efficient method to model the experienced waiting time for patients visiting an emergency medical services unit. In this thesis, the author seeks to estimate the waiting time for low acuity patients within an ED setting; using regularized regression methods such as Lasso, Ridge, Elastic Net, SCAD and MCP; along with tree-based regression (Random Forest). For accurately capturing the dynamic state of emergency rooms, queues of patients at various stage of ED is used as candidate predictor variables along with time patient s arrival time to account for diurnal variation. Best waiting time prediction model is selected based on the analysis of historical data from the hospital. Tree-based regression model predicts wait time of low acuity patients in ED with more accuracy when compared with regularized regression, conventional rolling average, and quantile regression methods. Finally, most influential predictors for predictability of patient wait time are identified for the best performing model