COI.
search Request for Consideration arrow_forward menu
close Submit Innovation
close
Manufacturing verified Verified Outcome TRL 9

Production Line & Supply Chain Digital Twin

domain Client: A premier luxury automotive manufacturer handshake Provider: Celonis (ProcessGold) schedule Deploy: Q2 2020 (Expansion)
93 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

Moving beyond back-office finance, the automaker applied process mining to the physical production line. By ingesting data from vehicle sensors and assembly line PLCs, they visualized the flow of every car through the paint shop and assembly. This identified micro-stoppages and rework stations that were causing systemic delays.

rate_review Analyst Verdict

"Convergence of IT and OT. Using process mining on physical object flows (cars) rather than digital documents (invoices) unlocks massive value in manufacturing. It allows for a 'Digital Twin' of the process itself, enabling real-time bottleneck removal in the factory."

lock
Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

Production delays in the paint shop rippled through the entire assembly line, causing missed delivery slots. Traditional SCADA systems showed machine status (Up/Down) but not the *process flow* context (e.g., 'Why do cars of Color X always get reworked at Station Y?').

psychology The Solution

Ingested data from tracking points (RFID) throughout the plant. Visualized the path of every vehicle body. Identified that specific custom configurations caused bottlenecks at the rework station. Adjusted the production sequencing algorithm to smooth the flow.

settings_suggest Technical & Deployment Specs

Integrations
MES, SAP
Deployment Model
Private Cloud
Data Classification
Operational / IoT
Estimated TCO / ROI
High
POC Summary (2018-01-01 to 2019-01-01)

"Pilot at Munich plant."

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Data Volume High Edge aggregation of sensor data before cloud ingestion.
Process Complexity Medium Focusing scope on specific shops (e.g., Paint) first.

trending_up Impact Trajectory

Audited value realization curve

Monitoring of thousands of vehicles daily Verified Outcome
Primary KPIReduction in rework cycle time
Audit CycleThroughput increase in critical bottlenecks

policy Compliance & Gov

  • Standards: N/A
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Operational / IoT

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
lock
verified_user
Technical Audit
PDF • Audited
lock

Related Intelligence

article
Generative AI Clinical Scribe
View Outcome →
article
Order-to-Cash (O2C) Throughput Optimization
View Outcome →
article
Augmented Reality (AR) Virtual Try-On
View Outcome →
Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for A premier luxury automotive manufacturer without exposing sensitive IP or identities.

Private
lock_person

1. Raw Evidence

Audit ID: #PRIV-946
Evidence: Direct SQL Logs
Public
public

2. Verified Asset

Outcome: Verified
Ref ID: #COI-946

Strategic Action Center

Identify your current stage and take the next step.

rocket_launch
Replicate This Success
Want similar results? Request a deployment consultation.
psychology_alt
Submit Challenge
Have a different problem? Submit your problem statement.
publish
Publish Case Study
Submit your own verified evidence.
thumb_up
Verify Impact
Audit your existing solution.