Case Study
Solving the Terminal Bottleneck: Accelerating Case Reviews via Pattern Recognition
70% reduction in review time; cleared systemic backlog growth.
Define
Problem Statement
In second-level support, the daily cycle was a race against a growing backlog. The task was straightforward but grueling: scan finance department cases, check for updates, reassign them to agents, and add contextual notes.
The Constraints:
- Legacy Tech: A command-line-only terminal system.
- Strict Compliance: Data Protection Act (DPA) rules meant nothing could be written down.
- Tribal Knowledge: The process was undocumented and entirely manual.
Measure
Baseline Data
A single backlog cycle took a full 8-hour shift, and the backlog was growing faster than it could be cleared. The bottleneck wasn’t the operator’s judgment - it was the speed of typing sequential commands for 200 cases.
Analyse
Root Cause Analysis
The process was slow because the terminal required a full sequence of commands (open booking, go to comments, page down) just to see if a case had changed.
We identified two major inefficiencies:
- Ghost Work: 80% of cases had no updates, yet required the full command sequence to verify.
- Repetitive Entry: Physical typing speed was the primary constraint. Manual retyping of standard comments was redundant and draining.
Improve
Solution Design
We moved from sequential manual entry to a high-speed, pattern-based workflow in three phases:
1. Template-Based Batching
We identified standard reassignment language and created a set of note templates. Instead of typing case-by-case, we batched 10–15 sequences in a separate text editor, filled in the specific case details, and pasted the entire block into the terminal for multi-line execution.
2. Visual Pattern Recognition
We learned to identify “no-change” cases by the specific visual output of the terminal. By scanning for visual flags rather than executing the full command sequence on every file, we could skip the 80% of cases that required no action.
3. Buffer Optimization
By finding the “sweet spot” of the terminal’s input buffer, we could paste batches of 10–20 cases at once. While the terminal processed the automation, the operator could manually pre-scan the next batch, creating a continuous flow.
Control
Results
The transition from manual typing to visual oversight transformed the role from data entry to exception handling.
| Metric | Before | After | Improvement |
|---|---|---|---|
| Full backlog review time | 8 hrs | 2–3 hrs | -70% |
| Typing time per case | 45 sec | 15 sec | -67% |
| Backlog manageability | No | Yes | Sustained |
Lessons Learned
- The bottleneck is often physical, not mental. The operator’s judgment was fast; the interface was slow. Solving for the “typing constraint” unlocked the productivity.
- Scanning beats executing. Learning to visually recognize a “null” state saved more time than any script or batch.
- Batch size is a technical constraint. We found that 10–15 cases was the limit; any more and the terminal buffer would fill, causing missed cases.
- Speed reduces fatigue. By automating the routine, the operator had more energy to focus on the complex cases that actually required human intervention.