Contoso Industries — Manufacturing

TL;DR

Contoso Industries: 127 VMs, 22 SQL databases, 14 .NET apps. ERP and MES follow Stabilize (months 1–4); customer portal and supply chain dashboard follow Transform (months 3–8). Fabric supports a near-real-time supply chain dashboard.

Contoso Industries is a mid-sized manufacturer with 15 production facilities across Europe. Their IT estate has grown organically over two decades — reliable, but increasingly unable to support the business decisions that leadership needs to make.

The Challenge

Contoso’s business is under pressure from multiple directions:

• Supply chain disruption — Global events have exposed the fragility of Contoso’s just-in-time supply chain. Leadership wants near-real-time visibility into production, inventory, and supplier performance.
• Aging infrastructure — The on-premises data center runs 127 Windows Server VMs, including a critical ERP system built on .NET Framework 4.6 with a SQL Server 2016 backend.
• No analytics capability — Production data is trapped in application databases. Monthly reports are generated manually from spreadsheet exports. There is no self-service BI and no predictive capability.
• Skills gap — The IT team excels at keeping the lights on but has limited cloud experience.

Why now?

Contoso’s CEO has committed to the board that the company will have near-real-time supply chain visibility within 18 months. The existing infrastructure cannot deliver this. The modernization program is not an IT initiative — it is a board-level strategic priority.

The Assessment

Azure Migrate reveals the estate:

Category	Count	Finding
Windows Server VMs	127	85 can migrate as-is, 30 need OS upgrade, 12 are idle
.NET applications	14	10 are .NET Framework 4.x, 4 are already .NET 6+
SQL Server databases	22	18 compatible with SQL MI, 4 need feature remediation

The Path Decision

Workload	Path	Rationale
ERP system (core manufacturing)	Stabilize	Business-critical, stable, no appetite for code changes
MES (shop floor execution)	Stabilize	Tightly coupled to ERP, move together
Customer portal (order tracking)	Transform	Customer-facing, needs elastic scale for peak ordering
Supply chain dashboard	Transform	New development — build cloud-native from day one

Execution

Stabilize (Months 1-4):

• Migrate 85 VMs to Azure in 4 waves
• Migrate ERP and MES databases to SQL Managed Instance
• Enable SQL MI Mirroring to Fabric for production data

Transform (Months 3-8):

• Modernize customer portal: .NET Framework → .NET 8, containerize
• Build new supply chain dashboard as a cloud-native application
• Both backed by Azure SQL Database, mirrored to Fabric

The Payoff

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graph TB
  classDef azure   fill:#0078d4,stroke:#005a9e,color:#fff
  classDef onelake fill:#742774,stroke:#5a1e5a,color:#fff
  classDef bi      fill:#fde8f9,stroke:#742774,color:#3a003a
  subgraph h1["Stabilize"]
    ERP[("ERP → SQL MI")]:::azure
    MES[("MES → SQL MI")]:::azure
  end
  subgraph h2["Transform"]
    PORTAL[("Customer Portal → Azure SQL DB")]:::azure
    SUPPLY[("Supply Chain App → Azure SQL DB")]:::azure
  end
  FAB(["Fabric OneLake"]):::onelake
  PBI["Power BI<br/>Supply Chain Dashboard"]:::bi
  ML["Data Science<br/>Predictive Maintenance"]:::bi
  ERP -->|"Mirror"| FAB
  MES -->|"Mirror"| FAB
  PORTAL -->|"Mirror"| FAB
  SUPPLY -->|"Mirror"| FAB
  FAB --> PBI
  FAB --> ML
  style h1 fill:#e6f3ff,stroke:#0078d4
  style h2 fill:#e6f3ff,stroke:#0078d4

Business outcomes:

• Near-real-time supply chain visibility — delivered to the CEO’s dashboard in Power BI, with replication latency validated during rollout
• Predictive maintenance models trained on actual production data in Fabric, prioritizing equipment risk before maintenance windows
• Infrastructure cost reduction measured through Azure Migrate business-case assumptions, right-sizing, reservations, and PaaS adoption where appropriate
• IT team upskilled in Azure operations and Fabric analytics