Process Minery
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Fusion Stabilization Application

Know whether your Fusion go-live is operationally stable. Before, during and after cutover.

A pre-packaged Celonis solution for Oracle customers in an EBS → Fusion migration. Monitor go-live, steer hypercare with data, and lay the foundation for AI-agent adoption.

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78%READINESS SCORE
Stability score
Ready
Pre go-live
Stable
Cutover
On track
Hypercare

From cutover anxiety to operational confidence

Most Oracle Fusion go-lives are judged on tickets and gut feel. The Fusion Stabilization Application replaces that with a quantified view of process stability - so leadership, the SI and the business see the same picture in real time.

  • Go-live risk is invisible in spreadsheets
    Status decks lag reality. Process data shows where Fusion is actually drifting from the design.
  • Hypercare burns weeks without data
    Without a stability baseline, teams chase tickets instead of root causes. Stabilization cuts the noise.
  • AI agents need a stable foundation
    You cannot automate what is not in control. Stabilization is the prerequisite for AI-agent adoption.
Book a stabilization scan Compare with Fit 2 Fusion

What you get

Six purpose-built views, one operating picture

Built on the Oracle Object-Centric Data Model and the existing Fit 2 Fusion and Procurement knowledge assets - so you start with proven KPIs, not a blank canvas.

Executive Go-Live Readiness

One stability score per domain, top risks for go-live and hypercare, and trend since cutover.

  • Stability score
  • Top risks
  • Cutover trend

Domain Cockpit: P2P / Procurement

Backlog, exception rate, automation rate, manual work and cycle-time drift in one operational view.

  • Open POs & approvals
  • Invoice & payment exceptions
  • Variants & rework

EBS vs Fusion Comparison

Compare process behaviour before and after migration - variant shift, new loops, changed durations.

  • Variant shift
  • New / lost activities
  • Throughput delta

Cutover & Hypercare Monitor

Daily and weekly go-live signals so issues are caught before they become escalations.

  • Stuck transactions
  • Interface delays
  • Failed matching

AI Readiness Foundation

See which processes and objects are stable enough to safely hand to an AI agent.

  • Stability per object
  • Root-caused exceptions
  • Agent-ready scope

Action Workbench

Prioritized findings with root cause, affected value, recommended action, owner and status.

  • Priority & owner
  • Recommended action
  • Status tracking

Scope

Procure-to-Pay first, then the rest of the value chain

We start where the data foundation is strongest and the go-live pain is sharpest - and extend from there.

Now - MVP

Procure-to-Pay & Procurement

Stabilization score, EBS vs Fusion comparison, hypercare findings and Action Workbench for P2P.

Next

Order-to-Cash / Receivables

Extend stabilization to revenue-side processes once the P2P foundation is live.

Later

Record-to-Report + AI agents

R2R coverage and AI-agent recommended actions once Catalog 3.0 / R2R foundation is available.

How it works

Connect. Score. Act.

    Step 1

    Connect

    Deploy the Oracle Object-Centric Data Model and reuse existing Fit 2 Fusion and Procurement Knowledge Models - no blank canvas.

    Step 2

    Score

    A reviewable stabilization model covers process stability, exception load, automation, cutover drift, data completeness, control risk and AI readiness.

    Step 3

    Act

    The Action Workbench turns findings into prioritized work with root cause, owner and recommended action - pre-, during and post-cutover.

Stabilization signals

The signals we surface from day one

A curated catalog of go-live and hypercare findings - each with severity, owner and recommended action.

Stuck transactionsFailed 2/3-way matchingInterface & ESS job delaysLate approvalsManual correctionsWorkflow & API errorsData completeness gapsControl & SoD risk

From stabilization to AI

Stable processes are the prerequisite for AI agents

AI-agent adoption fails on unstable processes. The AI Readiness view shows which objects and processes are predictable enough to delegate - and which exceptions already have a clear root cause, owner and recommended action. That is your safe scope for the first AI agents on Fusion.

Talk to us about your go-live

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