Working Paper 3: A Framework for Extremely Delayed Cases

Differentiated Case Management for the Indian Judiciary


A crucial factor in the ever-increasing pendency burden on courts is the fact that a significant number of these cases remain pending for multiple years. The National Judicial Data Grid (“NJDG”) notes that nearly 4.4 crore cases are currently pending across district courts in the country, with approximately 1.1 crore of those cases (nearly 24%) having been pending for more than 5 years. Such “extremely delayed” cases are often listed before courts with decreasing frequency over the years, until they ultimately disappear within the system. 

The reasons for such cases getting lost in the system are myriad. Lack of ability in one or both the parties to push for steady progress of a case, presence of perverse incentives with one or both the parties to keep the case pending, lack of proactive measures by the courts to ensure time-bound progress in cases – these are some of the reasons why cases may continue to remain pending, ultimately reflecting poorly on the ability of the Indian courts to deliver timely justice.   

Although the problem is well known, very few measures have been taken to ensure that extremely delayed cases are tackled systemically and in a targeted manner. Old cases continue to be listed alongside fresh cases without any regard for their previous progress, the underlying causes for delay and specific requirements. However, in October 2023, the Supreme Court in Yashpal Jain v. Sushila Devi & Ors. brought this issue to the forefront, calling for greater coordination at all High Courts between the Bench and the Bar to identify cases pending for more than 5 years and take measures for speedy disposal. For this directive to yield positive results, it is high time for the judiciary to adopt a more nuanced approach to tackle these cases. The Differentiated Case Management (“DCM”) framework outlined in this paper seeks to aid the judiciary in enforcing its intent, by relying on a well thought-out strategy based on judicial data to ensure that the court is able to make timely and effective interventions.

Issues Identified 

Presently, our courts lack a mechanism for:

  • Identifying and analysing the varying reasons for delay for extremely delayed cases;
  • Customising strategies at various levels based on the reasons for delay and specific requirements of a case; 
  • Identifying the specific stakeholders who must coordinate with the Bench to enable effective case progress in different circumstances; and
  • Harnessing the potential of judicial data and technology to ensure that any framework which is adopted is smart and  effective. 

Proposed Solutions 

The DCM framework outlined in this paper takes a two-fold approach:

  1. For any DCM strategy to be effective, judicial data pertaining to pendency and reasons for delay must first be aggregated, collected, and analysed. To this end, templatisation of a portion of order sheets and the creation of data dashboards, which provide information at a glance pertaining to a case’s prior progress and history, pendency statistics, and reasons for delay at each step, would be necessary. 
  2. Once the data has been made available in this manner, the following steps would have to be undertaken: 
    1. Creating different tracks for future case progress (priority track, median track, regular track, and dormant cases track) based on the extent to which the reasons for delay are within the court’s immediate control to tackle; 
    2. Identifying cases which have passed the 5+ year mark, and segregating them into these tracks based on certain metrics; and 
    3. Following customised strategies devised for each track, based on an underlying analysis of reasons for delay. 

These metrics and strategies would have to be re-examined periodically to ensure that the DCM strategy effectively tackles extremely delayed cases. 

Below is a flowchart depicting the DCM framework for extremely delayed cases:

The paper provides suggestions for templatisation and the creation of data dashboards. Further, it provides for an illustrative list of potential metrics for segregating cases into different tracks, and the nature of strategies which may be used for future progress for each track, depending on the most common reasons for delay which have been identified through data analysis. 

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