EBITDA Leak Map Framework | Platform Performance Partners

Works at both levels

Platform CEO & ops leadership — operational correction

PE sponsor & board — EBITDA protection and recovery

Operating partner — field-to-finance translation

The Core Insight

What this framework is built on

"The leak that matters most is the one active on this platform right now. Every platform leaks differently — the job is to find which of the nine is running hottest and fix it structurally, not symptomatically."

There is no universal ranking of which leakage area hits hardest. Dispatch kills utilization on one platform. Billing velocity destroys cash conversion on another. Pricing discipline is the problem on a third. What is consistent across every PE-backed MEP platform is this: the leakage is structural, it is measurable, it is present before the P&L reflects it, and it requires field-level diagnosis — not financial analysis — to find it. This framework provides the diagnostic structure to identify, score, and prioritize which leaks are active and what structural corrections will move EBITDA.

Where Leakage Occurs — The Field-to-Invoice Flow

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STEP 1

Dispatch

Job assigned to tech — load balancing, routing, priority decisions

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STEP 2

Field execution

Tech performs work — labor productivity, scope control, first-time fix

→

STEP 3

Closeout & handoff

Job documented — field-to-office handoff, pricing execution, closeout completeness

STEP 4

Invoice & capture

Revenue recognized — billing velocity, decision latency, supervision accountability

EBITDA leakage occurs at every transition point in this flow. The P&L captures the outcome — not the cause.

The 9 Leakage Areas — Full Diagnostic Map

Dispatch & load balancing

Field execution layer · Step 1

Ops signal

EBITDA direct

How it leaks

Inefficient routing wastes 1–3 billable hours per tech per day

Load imbalance — some techs at capacity, others underutilized

Wrong tech dispatched to job — skill mismatch drives callbacks

Data signals

Time-to-assign >30 min average

Idle hours >10% of available hours

Overtime rising while utilization holds flat

Structural fix

Dispatch authority and routing rules defined and documented

Skill-based dispatch matching implemented

Utilization reviewed daily — not monthly

Scope control & change work

Field execution layer · Step 2

Both signals

EBITDA direct

How it leaks

Scope additions performed without change order — revenue lost

Verbal approvals with no paper trail — T&M exposure uncontrolled

Field tech absorbs scope creep without escalating

Data signals

% tickets with scope notes vs. billed <70%

Change order capture rate <75%

Job margin variance >5 points budgeted vs. closed

Structural fix

Change order policy defined — dollar threshold for field authority

Scope documentation required for job closeout — no closeout without it

CO capture rate tracked by tech and by branch

Labor productivity

Field execution layer · Step 2

Ops signal

EBITDA direct

How it leaks

Wrench time below 55% of available hours — windshield time absorbing margin

Parts runs, admin time, and waiting not tracked or managed

Productivity differences between techs not visible or addressed

Data signals

Wrench time % <55% of available hours

Revenue per tech below platform benchmark

Jobs per day per tech declining quarter-over-quarter

Structural fix

Wrench time defined and measured by individual tech

Non-billable time categories tracked — parts runs, travel, admin

Tech productivity reviewed in weekly supervisor cadence

First-time fix rate & rework

Field execution layer · Step 2

Both signals

EBITDA direct

How it leaks

Callbacks cost full labor and travel — zero additional revenue

Rework consumes tech capacity that would otherwise be billable

Pattern callbacks indicate systemic skill or parts availability issue

Data signals

First-time fix rate <85%

Callback rate >8% of completed jobs

Callback rate not tracked by tech or job type

Structural fix

Callback rate tracked by tech, job type, and branch

Root cause review on all callbacks above threshold

Parts availability and tech skill gaps addressed systematically

Supervisor span & decision latency

Leadership layer · All steps

Ops signal

EBITDA direct

How it leaks

Supervisors managing too many techs — oversight quality drops

Decisions waiting >7 days create cascading field delays

Escalation reaching CEO level for field-level decisions

Data signals

Supervisor span >10 techs per supervisor

Avg days open per escalation >5 days

CEO calendar shows field-level decisions 3+ days per week

Structural fix

Supervisor span target defined — 6–8 techs per supervisor

Decision authority matrix documented and enforced

Escalation SLA defined — no decision open >48 hours at field level

Pricing execution

Field & office layer · Steps 2–3

Both signals

EBITDA direct

How it leaks

Quoted vs. realized GM variance driven by field-level exceptions

Discounting without approval process — margin given away at the job level

Price book not current — techs quoting from outdated rates

Data signals

Quoted vs. realized GM variance >3 points

Price exceptions not tracked centrally

GM% declining while revenue holds flat or grows

Structural fix

Price book updated and locked — exceptions require approval

Quoted vs. realized GM tracked by tech, by branch, by job type

Pricing discipline reviewed in weekly ops cadence

Billing & closeout velocity

Office layer · Step 4

Finance signal

Cash & margin

How it leaks

Delayed invoicing extends cash conversion cycle

Incomplete closeouts create billing disputes and write-offs

WIP accumulation masks true revenue recognition timing

Data signals

Days to invoice >12 days average

% jobs with missing closeout >15%

WIP balance growing faster than revenue

Structural fix

Closeout completion required before tech can receive next dispatch

Days-to-invoice target set — reviewed weekly by billing lead

WIP reviewed in ops cadence — aging WIP escalated within 48 hours

Field-to-office handoffs

Transition layer · Steps 3–4

Both signals

Margin & cash

How it leaks

Unstructured handoffs lose scope, materials, and pricing context

Billing team invoices from incomplete field documentation

Disputes arise from mismatched field vs. office records

Data signals

% jobs with missing closeout documentation >20%

Billing disputes or credits issued >3% of invoices

Back-and-forth between field and office on job records

Structural fix

Closeout checklist standardized — materials, scope, time, photos

Handoff protocol between field and billing defined in writing

Dispute rate tracked and reviewed in weekly ops cadence

Deferred decisions (>7 days)

Leadership layer · All steps

Ops signal

EBITDA direct

How it leaks

Every deferred decision holds up downstream execution

Pricing exceptions, hire decisions, and scope calls sitting open

Decision latency compounds across all 8 other leakage areas

Data signals

Avg days open per escalation >7 days

Repeat escalations on same topic — no resolution

Field team working around decisions rather than waiting

Structural fix

Decision register maintained — all open decisions tracked with owner and deadline

48-hour SLA on field-level decisions — 5 days max for leadership decisions

Decision velocity reviewed in weekly leadership cadence

Leakage Heat Map — Scoring Template

Leakage area

Severity

Data available

Fix priority

01 — Dispatch & load balancing

02 — Scope control & change work

03 — Labor productivity

04 — First-time fix & rework

05 — Supervisor span & decision latency

06 — Pricing execution

07 — Billing & closeout velocity

08 — Field-to-office handoffs

09 — Deferred decisions (>7 days)

Severe — immediate intervention

Active — address within 30 days

Managed — monitor

Not yet assessed

How Leakage Appears — By Audience

Operational signals — CEO & ops see these first

These appear before the P&L reflects the damage

Overtime %

Watch: >15% of labor hours

Callback rate

Watch: >8% of completed jobs

Wrench time %

Watch: <55% of available hours

Decision latency

Watch: >7 days avg open escalation

Days to invoice

Watch: >12 days average

Financial signals — sponsor & board see these

These appear after the operational damage has been running

GM% variance

Watch: quoted vs. realized >3 pts

EBITDA vs. plan

Watch: 2+ consecutive misses

WIP growth

Watch: growing faster than revenue

Revenue per tech

Watch: declining QoQ

Forecast variance

Watch: narrative explanation pattern

Diagnostic Questions — Finding the Active Leak

Where does margin first appear to compress — at dispatch, in the field, at closeout, or at invoice?

Probe: If the answer is "at month-end when we close the books," the field-to-finance translation is broken. The leak's origin is never visible at that point.

Which of the 9 leakage areas are currently tracked with a metric — and which are managed by feel?

Probe: Unmeasured leakage areas are always active. If it's not tracked, it's leaking.

In the last 90 days, which leakage area has driven the most operational firefighting?

Probe: Where leadership is spending reactive time is almost always where the active leak is running hottest.

If you recovered 2 points of gross margin — which leakage area would you fix first to get there?

Probe: If the answer is "pricing" or "billing" without data to support it, the platform is guessing. The structural diagnosis has not been done.

Which leakage areas compound each other on this platform — and in what sequence?

Probe: Dispatch strain drives overtime which drives callback rates which drives rework which drives billing delays. The sequence tells you where to intervene first.

PPP Position — How This Framework Is Used

How Platform Performance Partners uses this in an engagement

"Every platform leaks differently. The job is not to apply a generic fix — it is to find which of the nine is running hottest on this platform right now and correct it structurally before it hits the P&L."

The EBITDA Leak Map is the spine of the Field-to-EBITDA Diagnostic — every engagement begins here regardless of entry point

Scoring produces the one-page heat map deliverable — the primary output the board and CEO act on

Works at both levels simultaneously — CEO uses it to manage operations, sponsor uses it to monitor EBITDA protection

Connects directly to Board Readout slides 2, 4, and 8 — risk profile, field execution, and root cause summary