Service Operations for Justice-On-Time: A Data-Driven Queueing Approach

32 Pages Posted: 27 Jan 2021 Last revised: 21 Sep 2023

See all articles by Nitin Bakshi

Nitin Bakshi

University of Utah

Jeunghyun Kim

Korea University Business School

Ramandeep S. Randhawa

University of Southern California

Date Written: September 15, 2023


Limited resources in the judicial system can lead to costly delays, stunted economic development, and even failure to deliver justice. Using the Supreme Court of India as an exemplar for such resource-constrained settings, we apply ideas from service operations to study delay. Specifically, court dynamics constitute a case-management queue, whereby each case may experience multiple service encounters spread across time, but all are necessarily with the same server. Our goal is to elucidate the drivers of congestion, focusing on metrics such as the expected case-disposition time (delay) and expected number of cases awaiting adjudication (pendency), and leverage this understanding to recommend operational interventions.

We employ data-driven calibrated simulations to model the analytically intractable case-management queue. The life cycle of a case comprises two stages: pre-admission (before determining its merit for detailed hearings) and post-admission. Our methodology allows us to capture the queueing dynamics in which the judges are shared resources across the two stages. It also permits modeling of holiday capacity, which is flexibly tailored to address any surplus work that spills over from the regular year. We find that the second stage of this judicial queue is overloaded, but holiday capacity creates a perception of stability by steadying performance metrics.

The sources of inefficiency that drive congestion include a misalignment between scheduling guidelines and judicial capacity, coupled with the requirement to schedule hearings in advance. Together, these factors inhibit utilization of shared capacity across the two-stage judicial queue. We demonstrate how interventions that account for these inefficiencies can successfully tackle judicial delay. In particular, scheduling to improve the allocation of time across pre- and post-admission cases can cut down the expected delay by as much as 65%.

Keywords: judicial delay, case-management queues, data-driven simulation

JEL Classification: C44, C63

Suggested Citation

Bakshi, Nitin and Kim, Jeunghyun and Randhawa, Ramandeep S., Service Operations for Justice-On-Time: A Data-Driven Queueing Approach (September 15, 2023). Available at SSRN: or

Nitin Bakshi (Contact Author)

University of Utah ( email )

1655 Campus Center Drive
Salt Lake City, UT Utah 84112
United States

Jeunghyun Kim

Korea University Business School ( email )

Anam-Dong, Seongbuk-Gu
Seoul 136-701, 136701

HOME PAGE: http://

Ramandeep S. Randhawa

University of Southern California ( email )

Marshall School of Business
BRI 401, 3670 Trousdale Parkway
Los Angeles, CA 90089
United States

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