Operational problem
Shift production truth arrived late and manually. Cycle time lived in spreadsheets that froze under load. Efficiency numbers could be edited by hand. Supervisors could not see press status during the shift.
Proven Success · Injection · MENA
From late shift truth to live press visibility
A short anonymized story: operational pain, what shipped on the floor, how data was collected, and which outcomes we can actually stand behind — without naming the plant or inventing percentages.
Illustrative sceneProven Success · Injection
From shift pain to machine truth
Roles only. No plant names, no people. No unverified savings percentages.
Challenge
Prove live cycle counting on injection presses, tie counts to production activities, close activities with scrap, and build an honest path to OEE = Availability × Performance × Quality — without turning the whole platform into a cycle-time-only product.
What shipped
Capabilities on the floor
- 01Open
Open a production activity for the shift
- 02Count
Live cycles from the press signal
- 03Close + scrap
Official efficiency only after close
- 01Edge cycle devices on presses
- 02Live floor dashboard (Running / Not Running, gauges, Accepted / Over / Rejected bands)
- 03Production activity open → count → close + scrap
- 04Shift-oriented reporting from closed activities
- 05Foundation path to OEE A × P × Q from counted cycles and scrap
What operators and supervisors saw
Live dashboard after signal proof
Cycles from press signals into local storage and a live dashboard. Official efficiency used closed activities — not editable mid-shift theatre. Live views consumed open activities only.
Illustrative · anonymizedLive floor dashboard
LIVERunning3 / 4
Open activityShift A
Cycle bandAccepted / Over / Rejected
18.4s
Accepted
19.1s
Accepted
—
Idle
21.8s
Over
Visual of the field-validated dashboard pattern — not a live plant feed, and not an OEE uplift claim.
Counts from press signal to dashboard — official efficiency from closed activities only.
| Edge cycle counting | Live in-shift |
|---|---|
| Context | Counts from press signal to dashboard — official efficiency from closed activities only. |
OEE foundation
Honest data path: A × P × Q
What was field-validated is the collection path — not an invented OEE uplift %.
Availability
From running / stopped press signals during the shift.
Performance
From counted cycles vs standard time — not mid-shift manual edits.
Quality
From activity close + scrap — official path after close.
Impact and lessons
What we can stand behind
How decisions improved
- Supervisors could intervene during the shift when status or cycle bands drifted
- Operations leadership could challenge efficiency numbers sourced from machine activity
- Break-even conversations used press counts and material weight logic — without publishing unverified ROI %
Provable outcomes
- Field-validated live cycle monitoring on an injection pilot fleet
- Documented activity + scrap close path feeding OEE components
- Operator-visible floor status that matches plant reality when signals are healthy
Lessons learned
- A first industry win must not become the whole product identity
- Live dashboard trust dies if machine status is wrong
- Separate live (open) from official (closed) reporting
- Promote industry recipes through Intelligence Packs — not Core
Not claimed
- No invented OEE uplift percentage
- No unverified multi-million savings claims
- Not a Metron Core default — candidate for Injection Intelligence Pack only
← All Proven Success · Production Intelligence · Learn: cycle time
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Bring one production line, its current data sources, and a priority loss. We will map the measurement, workflow, and proof of value.