Availability
Breakdowns, changeover, starving and blocking consume planned run time.
Bennacer
Industry 4.0 · Internship case study
Production Intelligence with Aprizo / MES
A practical path from disconnected shop-floor signals to faster loss detection, traceability and predictive manufacturing.
01
Problem
A loss can hide between systems.
Production, quality, downtime, material and maintenance signals can exist without a shared loss hierarchy, trusted KPI logic or timely decision route. A dashboard can look polished while still disagreeing with the line.
“How might one defensible event model turn disconnected signals into owned, time-critical action?”
Performance
Micro-stops, speed loss and cycle variation erode the ideal rate.
Quality
NOK, scrap, rework and retest can be counted without common context.
Flow
WIP, waiting and bottlenecks appear when clocks, states and identifiers disagree.
ERP, MES, machine, quality and maintenance signals before reconciliation.
02
Process
Connect and reconcile before predicting.
The sequence is deliberate: preserve raw events, transform visibly, version business rules and make every KPI point traceable to its lineage before introducing automation or prediction.
01Source
02Canonical event
03Semantic measure
04Alert
05Owner
06Action
07Outcome
01
Stage 01
Scope + map
Map ERP, MES, machines, quality and maintenance into one loss tree.
Exit evidenceSystem and event map
02
Stage 02
Reconcile
Align clocks, states, IDs, output totals and scheduled windows.
Exit evidenceTrusted baseline
03
Stage 03
Diagnose
Stratify downtime, bottlenecks, recurring NOK and traceability exceptions.
Exit evidencePriority loss logic
04
Stage 04
Prototype
Build role views, drill-down, alert rules and a genealogy path.
Exit evidenceDecision cockpit
05
Stage 05
Pilot
Test one line and one countermeasure with a named owner and stop criteria.
Exit evidenceMeasured intervention
06
Stage 06
Control
Handover KPI rules, review cadences and a validated predictive backlog.
Exit evidenceGoverned operating loop
KPI system
OEE, FPY, scrap, cycle gap and downtime defined before visualization.
Production event star
Canonical events and visible transformation from source to decision.
03
Solution
A production intelligence layer.
A governed semantic model makes OEE, FPY, scrap, cycle gap and downtime comparable. Role-based views connect the headline metric to the station, product, event, source record and accountable response.
KPI contract
Each measure has a business definition, event-state logic, exclusions, source, owner, cadence and reconciliation test.
Event lineage
A production star links order, product, station, asset, material lot, quality result and downtime reason.
Unit genealogy
Supplier lot → receipt → kitting → station events → test → finished unit → shipment.
Predictive gates
Descriptive, diagnostic, predictive and prescriptive maturity only advance when the prior evidence gate is passed.
LIVE PORTFOLIO MODEL
Production Intelligence with Aprizo / MES
Synthetic portfolio data—replace with verified project records before public use.
Measurement plan · targets, not results
Trust first. Then speed.
The pilot aims to improve operations without hiding variance, changing test rules or creating false predictive confidence. Targets remain hypotheses until verified on approved data.
04
Evidence
The dossier, opened up.
The web story keeps the document’s words, engineering logic and visual evidence—then lets the reader move through them at their own speed.
Open visualBottleneck analysis
Downtime Pareto and shift trend using illustrative values.
Open visualProduction cockpit
A drill-down path from site to source record and action.
Open visualVirtual-line experiment
Scenario testing and green KPIs before a controlled physical pilot.
Supplied source captures
The briefing behind the reconstruction.
02 / FULL DOSSIER
Download PDF Keep the complete engineering narrative.
The supplied notes name Aptiv Kenitra and ‘Aprizo’. The official Dassault Systèmes product is Apriso; the exact platform name must be confirmed. All charts and targets are illustrative.