Anonymous Manufacturer

As an enterprise pharmaceutical manufacturer managing operations across 20 global facilities and nearly 400,000 SKUs, the company faced mounting pressure from supply chain fragmentation, reactive maintenance practices, and disconnected IT and OT systems. Inventory days had climbed to 120, on-time delivery performance was unreliable, and production monitoring relied on manual processes that could not scale. In a regulated industry where traceability, uptime, and quality control carry compliance implications, the inability to detect component non-conformance early exposed the business to costly recalls and unplanned line stoppages.

The company deployed FactoryTalk InnovationSuite, powered by PTC — a Rockwell Automation platform that integrates edge-to-enterprise analytics, machine learning, IoT connectivity, and augmented reality into industrial operations. Implementation followed a use-case-led pilot model: connected enterprise specialists identified high-impact scenarios, validated them at individual facilities, then scaled globally. Predictive ML was applied specifically to injection molding assets — 35 machines of varying age and complexity — using IoT-collected sensor data to build a unified view of machine health and anticipate failures before they caused downtime. The platform unified previously siloed ERP, MES, scheduling, and OT data sources into a single connected system, enabling standardized workflows and cross-facility KPI dashboards.

The transformation delivered measurable improvements across supply chain and production performance:

• Supply chain on-time delivery reached 96%, up from prior underperformance
• Lead times were cut in half across the supply chain
• Inventory days reduced from 120 to 82, freeing working capital
• 30% annual capital avoidance captured through improved asset management
• 4–5% annual productivity improvement sustained year over year
• 75% reduction in line starved-condition downtime after work queue visibility was introduced
• 51% reduction in paste-related defects through ML-assisted quality inspection
• Predictive maintenance on legacy injection molding equipment achieved an 8% productivity gain

Supply chain traceability capabilities now enable the company to identify and isolate non-conforming components before broader production impact, with potential to reduce recalls by 80% or more.

• OT/IT convergence is a prerequisite, not a parallel track — connecting factory systems to enterprise data was the foundational step that made predictive ML viable.
• Pilot by use case, not by technology — deploying against specific operational problems (predictive maintenance, throughput, defect detection) kept scope manageable and ROI measurable before global rollout.
• Legacy equipment is not a blocker — IoT sensor overlays on aging machines delivered actionable data without full replacement investment.
• Traceability infrastructure pays dividends beyond compliance — the same connected supply chain architecture that improved delivery rates also enabled rapid isolation of quality events.
• Workforce adoption requires parallel investment — AR-based training and standardized digital work instructions were essential to sustaining gains as operations scaled across geographies.