Priority-based routing with strict deadlines and server flexibility under uncertainty

Parvin, Hoda; Bose, Abhijit; Oyen, Mark P. Van

doi:10.1109/wsc.2009.5429275

Cited by 7 publications

(15 citation statements)

References 7 publications

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“…We have surpassed their work to i) reach human level accuracy using advanced ensemble techniques for automated dispatch, ii) scale it to hundreds of resolver groups and iii) incorporate retraining strategies to adapt to changing data. Several other researchers have studied different aspects of the problem of routing tickets to resolver groups [12] [13] [9]. The work in [13] approaches the problem by mining resolution sequence data and does not access ticket description at all.…”

Section: Related Workmentioning

confidence: 99%

“…This work also focuses on ticket transfers between groups (given an initial assignment) without looking at the ticket text content. The work in [9] is different and approaches the problem from a queue perspective. This is more related to the issue of service times and becomes particularly relevant when the ticket that has been dispatched to a group needs to be assigned to an agent.…”

Section: Related Workmentioning

confidence: 99%

“…Several factors make the dispatcher's job challenging such as requirement of knowledge of the IT portfolio being managed, roles and responsibilities of the individual groups, ability to quickly parse the ticket text describing the problem and map it to the right group, which is often not straightforward given the heterogeneous and informal nature of the problem description. A number of different approaches have been proposed for automating ticket dispatch [2] [12] [13] [9]. Although automated email assignment may look like a simple text classification problem at first glance it becomes quite complex and challenging when considered at industry scale.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cognitive System to Achieve Human-Level Accuracy in Automated Assignment of Helpdesk Email Tickets

Mandal¹,

Malhotra

Agarwal³

et al. 2018

Service-Oriented Computing

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Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution. The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources. Our system has been deployed in production of three major service providers and currently assigning over 40,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of about 23000 man-hours of effort per annum.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cognitive System to Achieve Human-Level Accuracy in Automated Assignment of Helpdesk Email Tickets

Mandal¹,

Malhotra

Agarwal³

et al. 2018

Service-Oriented Computing

View full text Add to dashboard Cite

show abstract

“…Given the fact that inefficiencies in dispatch have serious business consequences, there has been a lot of interest in automating the assignment process, that is, the dispatching of a ticket to an appropriate resolver group based on the problem description. A number of different approaches have been proposed for automating ticket dispatch (Agarwal, Sindhgatta, and Sengupta 2012) (Shao et al 2008a) (Shao et al 2008b) (Parvin, Bose, and Van Oyen 2009). The tickets may be raised in different manners such as through voice, web forms or e-mails to a centralized helpdesk team.…”

Section: Introductionmentioning

confidence: 99%

Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI

Mandal

Malhotra

Agarwal

et al. 2019

AAAI

View full text Add to dashboard Cite

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of a classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum. Till date, our deployed system has already served more than 700,000 tickets in production.

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“…IT service delivery problem is more complex than call center problems. (Pravin et al 2009) [8]. In a typical call center, the SLA is a function of the waiting time whereas in an IT environment, the SLA is the total time taken to serve a request.…”

Section: Introductionmentioning

confidence: 99%

Workforce planning for Global Network Delivery Model

Raut

Padmanabhan

Somasundaram

et al. 2014

2014 IEEE International Conference on Industrial Engineering and Engineering Management

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A steep competition in IT services and customers' demand for high quality of service makes IT service providers to investigate new ways of delivering services. To gain competitive advantage, Tata Consultancy Services Limited proposes Global Network Delivery Model (GNDM) to deliver services seamlessly and uniformly for global customers from multiple delivery locations such as India, China, Europe, North America and Latin America. This paper focuses on workforce planning for multiple locations while aiming to meet time-based Service Level Agreement (SLA) for each location. Based on workload and time-based SLA, we propose a mathematical model to determine optimal work-force size for each location and each shift. A real-life IT service process is considered to evaluate the performance of the proposed model. The proposed model is compared with current practice and benefits explained.

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Priority-based routing with strict deadlines and server flexibility under uncertainty

Cited by 7 publications

References 7 publications

Cognitive System to Achieve Human-Level Accuracy in Automated Assignment of Helpdesk Email Tickets

Cognitive System to Achieve Human-Level Accuracy in Automated Assignment of Helpdesk Email Tickets

Automated Dispatch of Helpdesk Email Tickets: Pushing the Limits with AI

Workforce planning for Global Network Delivery Model

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