Anonymous Metals Manufacturer

A multi-division metals manufacturer — producing both raw steel materials and finished parts for consumer and industrial applications — faced a fragmented digital landscape where each division operated independently, at different stages of technology maturity. Melt, roll, and processing operations lacked unified visibility, making it impossible to identify cross-divisional improvement opportunities. Without a coherent IT/OT integration strategy, the company risked falling behind competitors already leveraging data-driven operations. The absence of a shared corporate framework meant inefficiencies in labor productivity, scrap generation, tooling wear, and unplanned downtime were compounding across the enterprise with no systemic path to address them.

The manufacturer engaged Rockwell Automation to design and execute a digital transformation roadmap using the Connected Enterprise framework. The process began with structured assessments across all divisions — evaluating existing IT/OT infrastructure, network architecture, and operational gaps through workshops with C-suite, finance, operations, engineering, and IT stakeholders. Rather than imposing a uniform solution, the approach mapped shared opportunities while respecting each division's distinct requirements. Identified implementations included IoT-enabled automation modernization to unlock data from existing plant equipment, edge analytics software for real-time streaming process data and anomaly detection, model predictive control to optimize melt processes and energy consumption, augmented reality tools for operator performance, and device analytics for real-time asset health monitoring. A corporate center of excellence was proposed to enable cross-divisional knowledge sharing and continuous improvement.

The digital transformation initiative delivered measurable operational improvements across the enterprise:

• 13% reduction in scrap, achieved through better process visibility and real-time anomaly detection on production lines
• 12% reduction in unplanned downtime, enabled by device analytics and predictive monitoring of equipment health
• 8% improvement in labor productivity, supported by augmented reality tools and data-driven operator guidance
• 10% reduction in tooling costs, resulting from optimized process control and reduced wear from out-of-spec conditions

The results validated the cross-divisional strategy: improvements compounded across multiple cost centers simultaneously rather than requiring trade-offs between divisions.

• Assess before implementing: Operational and infrastructure assessments across all divisions were essential for identifying where data connectivity gaps created the largest business risk.
• Stakeholder alignment drives adoption: Engaging C-suite, finance, operations, and IT early reduced the organizational resistance that derails most large-scale digital transformation efforts.
• Existing equipment is an underutilized data source: Modernizing current automation for connectivity often yields faster ROI than full equipment replacement.
• Edge analytics enables real-time response: Streaming process data analyzed at the edge — rather than in the cloud — is critical for time-sensitive anomaly detection in metals production.
• A corporate center of excellence sustains gains: Sharing results and learnings across divisions prevents improvements from being siloed and accelerates adoption of what works.