scholarly journals A General Framework to Compare Announcement Accuracy: Static vs. LES-Based Announcement

2020 ◽  
Author(s):  
Achal Bassamboo ◽  
Rouba Ibrahim
Keyword(s):  
Operations Management ◽  
Call Center ◽  
Service Providers ◽  
Waiting Times ◽  
Real Life ◽  
Weighted Average ◽  
Superior Performance ◽  
Service Discipline ◽  
Priority Service ◽  
Real Time Information

Service providers often share delay information, in the form of delay announcements, with their customers. In practice, simple delay announcements, such as average waiting times or a weighted average of previously delayed customers, are often used. Our goal in this paper is to gain insight into when such announcements perform well. Specifically, we compare the accuracies of two announcements: (i) a static announcement that does not exploit real-time information about the state of the system and (ii) a dynamic announcement, specifically the last-to-enter-service (LES) announcement, which equals the delay of the last customer to have entered service at the time of the announcement. We propose a novel correlation-based approach that is theoretically appealing because it allows for a comparison of the accuracies of announcements across different queueing models, including multiclass models with a priority service discipline. It is also practically useful because estimating correlations is much easier than fitting an entire queueing model. Using a combination of queueing-theoretic analysis, real-life data analysis, and simulation, we analyze the performance of static and dynamic announcements and derive an appropriate weighted average of the two which we demonstrate has a superior performance using both simulation and data from a call center. This paper was accepted by Vishal Gaur, operations management.

The Important Role of Time Limits When Consumers Choose Their Time in Service

2022 ◽  
Author(s):  
Pnina Feldman ◽  
Ella Segev
Keyword(s):  
Operations Management ◽  
Service Providers ◽  
Waiting Times ◽  
Consumer Welfare ◽  
Welfare Benefit ◽  
Time Limits ◽  
Main Challenge ◽  
Speed Up ◽  
Time In Service

A main challenge that service providers face when managing service systems is how to generate value and regulate congestion at the same time. To this end, classical queueing models suggest managers charge per-use fees and invest in capacity to speed up the service. However, in discretionary services, in which consumers value time in service and choose how long to stay, per-use fees result in suboptimal performance and speeding up does not apply. We study a queueing model of a service provider and rational consumers who are heterogenous in their requirements for service duration. Consumers incur disutility from waiting and choose whether to join and how long to spend in service. We consider time limits as a novel mechanism that may help in controlling congestion. Time limits put a cap on the maximum time that customers can spend in service. We analyze their effectiveness when combined with two price schemes: per-use fees and price rates. Time limits are effective because they reduce time in service and impact waiting times and joining behavior. Revenue maximizing firms and social planners who maximize social welfare benefit from implementing time limits in addition to price rates. Social planners who seek to maximize consumer welfare, however, focus on regulating congestion and should, therefore, offer the service for free but implement time limits if congestion levels are high. The attractiveness of time limits goes further. We show that time limits are not only a useful lever that works well when combined with simple price mechanisms, but they are in fact optimal when congestion is high. Service providers can achieve the first-best outcome and extract all customer surplus by coupling a time limit with an optimal price mechanism. The attractiveness of time limits stems from their ability to reduce not only the average time spent in service, but also its variance. This is highly effective in settings in which customers’ service times impose externalities on others’ waiting times. Thus, we conclude that providers of discretionary services should set time limits when congestion is an issue. This paper was accepted by Vishal Gaur, operations management.

Don’t Call Us, We’ll Call You: An Empirical Study of Caller Behavior Under a Callback Option

2020 ◽  
Author(s):  
Brett A. Hathaway ◽  
Seyed M. Emadi ◽  
Vinayak Deshpande
Keyword(s):  
Service Quality ◽  
Waiting Time ◽  
Operations Management ◽  
Call Center ◽  
Structural Model ◽  
Waiting Times ◽  
System Throughput ◽  
Service Channel ◽  
Data Driven Approach ◽  
Alternative Service

Although call centers have recently invested in callback technology, the effects of this innovation on call center performance are not clearly understood. In this paper, we take a data-driven approach to quantify the operational impact of offering callbacks under a variety of callback policies. To achieve this goal, we formulate a structural model of the caller decision-making process under a callback option and impute their underlying preferences from data. Our model estimates shed light on caller preferences under a callback option. We find that callers experience three to six times less discomfort per unit of time while waiting for callbacks than while waiting in queue, suggesting that offering callbacks can increase service quality by channeling callers to an alternative service channel where they experience less discomfort while waiting. However, after controlling for expected waiting times, callers generally prefer waiting in a queue over accepting a callback and waiting offline. This suggests that managers of this call center may want to spend efforts in educating their customers on the benefits of the callback option. Using the callers’ imputed preferences, we are able to conduct counterfactual analyses of how various callback policies affect the performance of this call center. We find that in this call center, offering to hold the callers’ spot in line or to call back within a window (guaranteed timeframe) reduces average online waiting time (the average time callers wait on the phone) by up to 71% and improves service quality by decreasing callers’ average incurred waiting cost by up to 46%. Moreover, we find that offering callbacks as a demand postponement strategy during periods of temporary congestion reduces average online waiting time by up to 86%, increases service quality by up to 54%, and increases system throughput by up to 2.1%. This paper was accepted by Vishal Gaur, operations management.

scholarly journals MINIMIZING USER AND OPERATOR COSTS OF SINGLE LINE BUS SERVICE USING OPERATIONAL STRATEGIES

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 993-1004
Author(s):  
Chunyan Tang ◽  
Avishai (Avi) Ceder ◽  
Shengchuan Zhao
Keyword(s):  
Supply And Demand ◽  
Waiting Times ◽  
Real Life ◽  
Single Line ◽  
Significant Saving ◽  
Operational Strategies ◽  
Passenger Demand ◽  
Trade Offs ◽  
Bus Service ◽  
Real Time Information

This work presents a methodology for minimizing costs involved in the operation of a single line bus service. The model developed is based on optimal implementation of operational strategies tailored to passenger demand for a bi-directional single bus line. As a result, the commonly used timetable for Full Route Operation (FRO) will have to change to accommodate three types of strategies: short turn, limited stop, and mixed strategy (a combination of short turn and limited stop). The use of operational strategies will better match supply and demand, and will thus improve operation efficiency. The optimization model determines which trips of the given FRO timetable will be implemented with given strategies considering the trade-offs between passenger and operator costs. Moreover, in applying the model, the availability of real time information for passengers is considered in the calculation of waiting times. The proposed model is interpreted in the context of a small example, which serves as an explanatory devise. Then, it is applied to a real life case study in Dalian, China. The results show an indication that a significant saving could be attained by the use of multiple strategies. These savings were especially observed in the reduction of operational costs involved with the saving of travel times and running empty seats.

We're So Bad It's Funny - Effects of Using Humour in the Marketing Communication of Low-Quality Service Providers

2020 ◽  
Vol 4 (2-3) ◽  
pp. 84-99
Author(s):  
Ilias Danatzis ◽  
Jana Möller ◽  
Christine Mathies
Keyword(s):  
Service Quality ◽  
Service Providers ◽  
Purchase Intentions ◽  
Service Performance ◽  
Superior Performance ◽  
Service Research ◽  
Marketing Communication ◽  
Quality Service ◽  
Customer Expectations ◽  
Service Outcomes

Low-quality service providers who are unable or unwilling to compete through superior performance increasingly use humour in their marketing communication to generate positive service outcomes. Yet it remains unclear whether using humour to communicate poor service quality is indeed effective. Based on an online experiment in the context of budget hotels, this study finds that using humour to deliberately communicate poor service quality leads to higher purchase intentions and service quality evaluations by reducing both technical and functional service quality expectations. Theoretically, this study extends humour and service research by providing first empirical evidence for the viability of using humour as an effective tool for leveraging customer expectations of service quality rather than improving service performance. Managerially, these insights highlight how reducing customer expectations is an alternative strategy for attracting new customers and for achieving superior quality evaluations.

Drivers, Riders, and Service Providers: The Impact of the Sharing Economy on Mobility

2021 ◽  
Author(s):  
Saif Benjaafar ◽  
Harald Bernhard ◽  
Costas Courcoubetis ◽  
Michail Kanakakis ◽  
Spyridon Papafragkos
Keyword(s):  
Operations Management ◽  
Service Providers ◽  
Sharing Economy ◽  
Car Ownership ◽  
Full Time ◽  
Ride Sharing ◽  
Finite Set ◽  
Nonatomic Game ◽  
Payoff Functions ◽  
The Impact

It is widely believed that ride sharing, the practice of sharing a car such that more than one person travels in the car during a journey, has the potential to significantly reduce traffic by filling up cars more efficiently. We introduce a model in which individuals may share rides for a certain fee, paid by the rider(s) to the driver through a ride-sharing platform. Collective decision making is modeled as an anonymous nonatomic game with a finite set of strategies and payoff functions among individuals who are heterogeneous in their income. We examine how ride sharing is organized and how traffic and ownership are affected if a platform, which chooses the seat rental price to maximize either revenue or welfare, is introduced to a population. We find that the ratio of ownership to usage costs determines how ride sharing is organized. If this ratio is low, ride sharing is offered as a peer-to-peer (P2P) service, and if this ratio is high, ride sharing is offered as a business-to-customer (B2C) service. In the P2P case, rides are initiated by drivers only when the drivers need to fulfill their own transportation requirements. In the B2C case, cars are driven all the time by full-time drivers taking rides even if these are not motivated by their private needs. We show that, although the introduction of ride sharing may reduce car ownership, it can lead to an increase in traffic. We also show that traffic and ownership may increase as the ownership cost increases and that a revenue-maximizing platform might prefer a situation in which cars are driven with only a few seats occupied, causing high traffic. We contrast these results with those obtained for a social welfare-maximizing platform. This paper was accepted by Charles Corbett, operations management.

Personalized Priority Policies in Call Centers Using Past Customer Interaction Information

2021 ◽  
Author(s):  
Brett Alan Hathaway ◽  
Seyed Morteza Emadi ◽  
Vinayak Deshpande
Keyword(s):  
Customer Service ◽  
Operations Management ◽  
Bayesian Learning ◽  
Structural Model ◽  
Call Centers ◽  
Waiting Times ◽  
Operational Performance ◽  
System Throughput ◽  
History Data ◽  
Individual Level

To increase revenue or improve customer service, companies are increasingly personalizing their product or service offerings based on their customers' history of interactions. In this paper, we show how call centers can improve customer service by implementing personalized priority policies. Under personalized priority policies, managers use customer contact history to predict individual-level caller abandonment and redialing behavior and prioritize them based on these predictions to improve operational performance. We provide a framework for how companies can use individual-level customer history data to capture the idiosyncratic preferences and beliefs that impact caller abandonment and redialing behavior and quantify the improvements to operational performance of these policies by applying our framework using caller history data from a real-world call center. We achieve this by formulating a structural model that uses a Bayesian learning framework to capture how callers’ past waiting times and abandonment/redialing decisions affect their current abandonment and redialing behavior and use our data to impute the callers’ underlying primitives such as their rewards for service, waiting costs, and redialing costs. These primitives allow us to simulate caller behavior under a variety of personalized priority policies and hence, collect relevant operational performance measures. We find that, relative to the first-come, first-served policy, our proposed personalized priority policies have the potential to decrease average waiting times by up to 29% or increase system throughput by reducing the percentage of service requests lost to abandonment by up to 6.3%. This paper was accepted by Vishaul Gaur, operations management.

scholarly journals THE ACCESSIBILITY OF EUROPEAN REGIONS AND AIRPORT NETWORK

2015 ◽  
Vol 6 (2) ◽  
pp. 87-109 ◽  
Author(s):  
Renato Redondi ◽  
Paolo Malighetti ◽  
Stefano Paleari
Keyword(s):  
Travel Time ◽  
High Speed ◽  
Waiting Times ◽  
Weighted Average ◽  
Air Transport ◽  
High Speed Train ◽  
Territorial Unit ◽  
Average Travel Time ◽  
Direct Flight ◽  
Total Travel Time

The objective of this work is to evaluate the accessibility of European municipalities by air transport. We focus on travels that typically require the use of air transport by computing the quickest paths between any pair of municipalities separated by more than 500 km. The total travel time includes three components: i) travel by car or High Speed Train to reach the origin airport, ii) travel by air from the origin airport to the destination airport, including waiting times when no direct flight is available and iii) travel by car or High Speed Train from the destination airport to the municipality of destination. For each territorial unit, we calculate the population-weighted average travel time to reach any other municipality in Europe.

scholarly journals Hub location problem in round-trip service applications

2020 ◽  
Author(s):  
Omar Kemmar ◽  
Karim Bouamrane ◽  
Shahin Gelareh
Keyword(s):  
Economies Of Scale ◽  
Location Problem ◽  
Service Providers ◽  
Real Life ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Hub Location ◽  
Service Networks ◽  
Hub Location Problem ◽  
Hub And Spoke

In this paper, we introduce a new hub-and-spoke structure for service networks based on round-trips as practiced by some transport service providers. This problem is a variant of Uncapacitated Hub Location Problem wherein the spoke nodes allocated to a hub node form round-trips (cycles) starting from and ending to the hub node. This problem is motivated by two real-life practices in logistics wherein  runaway  nodes and  runaway  connections with their associated economies of scale were foreseen to increase redundancy in the network. We propose a mixed integer linear programming mathematical model with exponential number of constraints. In addition to the separation routines for separating from among exponential constraints, we propose a hyper-heuristic based on reinforcement learning and its comparable counterpart as a variable neighborhood search. Our extensive computational experiments confirm efficiency of the proposed approaches.In this paper, we introduce a new hub-and-spoke structure for service networks based on round-trips as practiced by some transport service providers. This problem is a variant of Uncapacitated Hub Location Problem wherein the spoke nodes allocated to a hub node form round-trips (cycles) starting from and ending to the hub node. This problem is motivated by two real-life practices in logistics wherein  runaway  nodes and  runaway  connections with their associated economies of scale were foreseen to increase redundancy in the network. We propose a mixed integer linear programming mathematical model with exponential number of constraints. In addition to the separation routines for separating from among exponential constraints, we propose a hyper-heuristic based on reinforcement learning and its comparable counterpart as a variable neighborhood search. Our extensive computational experiments confirm efficiency of the proposed approaches.

scholarly journals Price effect analysis and pre-reseravtion scheme on electric vehicle charging networks

2019 ◽  
Vol 9 (6) ◽  
pp. 5586
Author(s):  
Junghoon Lee ◽  
Gyung-Leen Park
Keyword(s):  
Electric Vehicles ◽  
Service Providers ◽  
Real Life ◽  
Price Effect ◽  
Effect Analysis ◽  
Electric Vehicle Charging ◽  
Demand Patterns ◽  
Charging Stations ◽  
Occupancy Rates ◽  
Charging Demand

<p>This paper investigates the price effect to the charging demand coming from electric vehicles and then evaluates the performance of a pre-reservation mechanism using the real-life demand patterns. On the charging network in Jeju city, the occupancy rates for 3 price groups, namely, free, medium-price, and expensive chargers, are separated almost evenly by about 9.0 %, while a set of chargers dominates the charging demand during hot hours. The virtual pre-reservation scheme matches electric vehicles to a time slot of a charger so as not only to avoid intolerable waiting time in charging stations systematically but also to increase the revenue of service providers, taking into account both bidding levels specified by electric vehicles and preference criteria defined by chargers. The performance analysis results obtained by prototype implementation show that the proposed pre-reservation mechanism improves the revenue of service providers by up to 9.5 % and 42.9 %, compared with the legacy FCFS and reservation-less walk-in schemes for the given performance parameter sets.</p>

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