Waiting time predictors for multi-skill call centers

Thiongane, Mamadou; Chan, Wyean; L’Ecuyer, Pierre

doi:10.1109/wsc.2015.7408410

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…In order to compare the proposed system and the system without queue reservation, the percentage of improvement from the system without queue reservation is used as the performance measurement. The percentage of improvement is denoted as %improvement and calculated by (10).…”

Section: Performance Measurementmentioning

confidence: 99%

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Enhance System Utilization and Business Revenue with AI-based Queue Reservation System

Limlawan¹,

Anussornnitisarn²

2021

IJMLC

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Queue management is a crucial part of service industry. Business has to deal with the uncertainty of arrival customer and the trade-off between the costs of providing capacity and customer satisfaction. Matching service capacity with the arrival customer is difficult so the system utilization is low in the low periods and customers have to wait for a long time in the peak period. Due to long waiting, some customer abandons the queue before receiving service. Customer abandonment affects business revenue and the system utilization. To relieve the effect of the customer abandonment, this paper aims to propose Artificial Neural Network based waiting time predictor with queue reservation system. The proposed system increases the system utilization and increase customer satisfaction at the same time. Instead of abandonment, customers can reserve their place in queue if the waiting time is too long and they can do other activities while waiting. Moreover, the proposed system provides the accurate estimated waiting time to each customer instead of queue-length. The accurate waiting time enables arrival customers to better decide on the reservation options. 95% of our predicted waiting time is accurate within 5 minutes. Comparing to the system without the queue reservation system, the system utilization and the number of the arrival customer completed service improve 13% and 54% respectively

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Section: Performance Measurementmentioning

confidence: 99%

“…Many techniques such as the historical based predictor, Queue-length based predictor, machine learning based predictor, artificial neural network is applied to predict the waiting time [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Enhance System Utilization and Business Revenue with AI-based Queue Reservation System

Limlawan¹,

Anussornnitisarn²

2021

IJMLC

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“…Much of the current research focuses on inbound voice calls, but others address skills-based routing and the potential to allow calls to overflow from one group to another under heavy load [17][18][19][20][21][22]. The channels available may extend beyond voice to multimedia techniques such as e-mail and web chat, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Using their approach, the probability of waiting, when abandonment is taken into account is given by:

P_{A} = \frac{M (true \frac{N}{θ T_{s}}, \frac{λ}{θ}) P_{B}}{1 + (M (true \frac{N}{θ T_{s}}, \frac{λ}{θ}) - 1) P_{B}}

where θ is the abandonment rate ( 1/θ is the average degree of patience exercised by the caller) and P B is given by Erlang's loss formula. The term

M false(x, y false)

is given as

M false(x, y false) = 1 + \sum_{j = 1}^{\infty} \frac{y^{j}}{{false\prod}_{k = 1}^{j} false(x + k false)}

Much of the current research focuses on inbound voice calls, but others address skills‐based routing and the potential to allow calls to overflow from one group to another under heavy load [17–22]. The channels available may extend beyond voice to multimedia techniques such as e‐mail and web chat, e.g.…”

Section: Related Workmentioning

confidence: 99%

Holistic assessment of call centre performance

Smith¹,

Schormans

2017

IET Networks

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In modern call centres 60-70% of the operational costs come in the form of the human agents who take the calls. Ensuring that the call centre operates at lowest cost and maximum efficiency involves a trade-off of the cost of agents against lost revenue and increased customer dissatisfaction due to lost calls. Modelling the performance characteristics of a call centre in terms of the agent queue alone misses key performance influencers, specifically the interaction between channel availability at the media gateway and the time a call is queued. A blocking probability at the media gateway, as low as 0.45%, has a significant impact on the degree of queuing observed and therefore the cost and performance of the call centre. Our analysis also shows how abandonment impacts queuing delay. However, the call centre manager has less control over this than the level of contention at the media gateway. Our commercial assessment provides an evaluation of the balance between abandonment and contention, and shows that the difference in cost between the best and worst strategy is £130K per annum, however this must be balanced against a possible additional £2.98 m exposure in lost calls if abandonment alone is used.

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“…Jouini et al further extend wait modelling by considering customers with different priorities, proposing two delay estimators that consider a cost function [JAK * 15]. Thiongane et al investigate waiting times by considering multi-skilled call centres using artificial neural networks to predict delays [TCl15]. Ibrahim further extends the research into queueing systems by considering the randomness in the number of available agents to receive calls [Ibr18].…”

Section: Queueingmentioning

confidence: 99%

Visualisation of Large-Scale Call-Centre Data

Rees¹

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The contact centre industry employs 4% of the entire United King-dom and United States’ working population and generates gigabytes of operational data that require analysis, to provide insight and to improve efficiency. This thesis is the result of a collaboration with QPC Limited who provide data collection and analysis products for call centres. They provided a large data-set featuring almost 5 million calls to be analysed. This thesis utilises novel visualisation techniques to create tools for the exploration of the large, complex call centre data-set and to facilitate unique observations into the data.A survey of information visualisation books is presented, provid-ing a thorough background of the field. Following this, a feature-rich application that visualises large call centre data sets using scatterplots that support millions of points is presented. The application utilises both the CPU and GPU acceleration for processing and filtering and is exhibited with millions of call events.This is expanded upon with the use of glyphs to depict agent behaviour in a call centre. A technique is developed to cluster over-lapping glyphs into a single parent glyph dependant on zoom level and a customizable distance metric. This hierarchical glyph repre-sents the mean value of all child agent glyphs, removing overlap and reducing visual clutter. A novel technique for visualising individually tailored glyphs using a Graphics Processing Unit is also presented, and demonstrated rendering over 100,000 glyphs at interactive frame rates. An open-source code example is provided for reproducibility.Finally, a novel interaction and layout method is introduced for improving the scalability of chord diagrams to visualise call transfers. An exploration of sketch-based methods for showing multiple links and direction is made, and a sketch-based brushing technique for filtering is proposed. Feedback from domain experts in the call centre industry is reported for all applications developed.

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Waiting time predictors for multi-skill call centers

Cited by 12 publications

References 21 publications

Enhance System Utilization and Business Revenue with AI-based Queue Reservation System

Enhance System Utilization and Business Revenue with AI-based Queue Reservation System

Holistic assessment of call centre performance

Visualisation of Large-Scale Call-Centre Data

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