Operational Excellence Turnaround: A 120-Day Case Study
An anonymized case study on how a mid-market services firm improved on-time delivery and reduced rework through operational excellence. Includes timeline, metrics, and key decisions.
Cabrillo Club
Editorial Team · February 21, 2026 · 6 min read

Operational Excellence Turnaround: A 120-Day Case Study
For a comprehensive overview, see our CMMC compliance guide.
A mid-market professional services organization (roughly 1,000 employees, distributed delivery teams, and a mix of fixed-fee and time-and-materials work) hit an inflection point: growth had outpaced its operating model. Projects were still shipping, but predictability was eroding—missed handoffs, inconsistent estimation, and “hero culture” were becoming the default risk controls.
This case study is anonymized by design. The scenario is real, the numbers are real to the engagement, and the lessons are broadly transferable for leaders pursuing operational excellence without disrupting delivery.
The Challenge: Predictability Broke Before Performance Did
The organization’s leadership team wasn’t reacting to a single failure; they were reacting to a pattern.
Symptoms observed across delivery (8 weeks of baseline data):
- On-time milestone delivery: 62% (defined as hitting committed milestone dates within ±3 business days)
- Rework rate: 18% of delivery hours (time spent on defect fixes, requirement clarifications, and redo work)
- Cycle time (intake to kickoff): median 19 days
- Context switching: 2.7 concurrent projects per billable contributor on average
- Escalations: ~14 per month reaching director level or above
Business impact (what made it urgent):
- Fixed-fee margins were compressing due to unplanned rework.
- Forecasting accuracy was low, driving staffing volatility.
- Customer satisfaction was uneven; some accounts were stable while others were “always red.”
Constraints that shaped the approach:
- No appetite for a “big bang” process overhaul.
- Teams operated across time zones; standardization had to be lightweight.
- Tooling was fragmented (multiple ticketing/project systems across practices).
Root causes (confirmed through interviews and artifact review):
- Unclear operating cadence: Teams lacked a consistent rhythm for planning, risk review, and decision-making.
- Ambiguous “definition of ready/done”: Work entered execution with incomplete inputs; completion criteria varied by team.
- Estimation inconsistency: Different practices used different estimation models; historical data was not systematically reused.
- Handoffs without ownership: Cross-functional dependencies existed, but accountability for outcomes was diffuse.
The leadership team’s central question was pragmatic: How do we improve reliability and throughput without slowing teams down?
The Approach: Diagnose the System, Then Design for Adoption
The engagement was structured around a principle common in operational excellence programs: optimize the system, not the people. The goal was to reduce friction and variability where it mattered most—intake, planning, execution, and escalation.
Timeline (120 days)
- Weeks 1–2: Discovery and baseline
- Interviews with delivery leads, PMO, finance, and account leadership
- Data collection across project tools and time tracking
- Value-stream mapping across intake → kickoff → build → QA → release
- Weeks 3–4: Design and decision points
- Define operating model changes
- Select metrics and governance cadence
- Pilot selection (two delivery pods across different practices)
- Weeks 5–10: Pilot implementation
- Implement standard work, dashboards, and escalation paths
- Weekly retrospectives and adjustments
- Weeks 11–16: Scale and embed
- Rollout to additional teams
- Train managers on coaching and continuous improvement routines
- Stabilize reporting and handoffs
Key decision points
- Standardize the “minimum viable process,” not everything.
Leadership chose a small set of non-negotiables (intake criteria, planning cadence, escalation rules) and left teams flexibility in execution.
- Fix intake quality before optimizing delivery speed.
Early analysis showed that upstream ambiguity was driving downstream rework.
- Use operational metrics tied to business outcomes.
Metrics were selected to connect directly to margin, predictability, and customer experience.
What we measured (and why)
- On-time milestone delivery (predictability)
- Rework rate (quality and requirements clarity)
- Cycle time from intake to kickoff (responsiveness)
- Work-in-progress (WIP) per contributor (flow efficiency)
- Escalation volume and age (governance effectiveness)
To avoid “metric theater,” each metric had an explicit action tied to it (e.g., WIP thresholds triggered replanning; rework thresholds triggered a requirements review).
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Cabrillo Club
Editorial Team
Cabrillo Club is a defense technology company building AI-powered tools for government contractors. Our editorial team combines deep expertise in CMMC compliance, federal acquisition, and secure AI infrastructure to produce actionable guidance for the defense industrial base.


