Challenge 1
Bill of Materials and Supply Chain Visibility
Modern manufacturing supply chains can involve hundreds or thousands of component suppliers across multiple tiers. BOM data exists in PLM systems, procurement data in ERP, quality data in QMS, and supplier relationship data in SRM platforms. Each system may use different part numbering schemes, supplier identifiers, or commodity codes.
Industry experience suggests that getting complete visibility into where components come from, their quality history, lead times, and alternative sources can require navigating multiple systems. When supply disruptions occur, rapidly identifying affected products and alternative sourcing options becomes critical, but may require manual data compilation from disconnected systems
Challenge 2
Production Floor to Business System Integration
Manufacturing execution systems (MES), SCADA systems, and OT networks on the production floor often operate separately from IT business systems like ERP, PLM, and quality management. This OT/IT divide can create delays in visibility, for instance, business systems may not reflect real-time production status, and production systems may not have visibility into order changes or material availability.
Practitioners report that bridging OT/IT data gaps often requires custom integration code or manual data transfer processes. Changes to either production systems or business systems can break these integrations, requiring ongoing maintenance effort.
Challenge 3
Quality Data and Traceability
Quality data is generated across the value chain: incoming inspection data in receiving systems, in-process measurements from production equipment, final inspection results in QMS, and customer quality feedback in CRM or warranty systems. For regulated industries or high-reliability products, demonstrating complete traceability from raw material lot to finished product to customer can be critical.
Creating this traceability chain typically requires connecting data from procurement, warehouse management, MES, quality systems, and shipping/logistics platforms. Organizations note that quality investigations can require significant effort to manually trace affected lots, particularly when issues span multiple production batches or facilities.
Challenge 4
Equipment and Maintenance Data Fragmentation
Equipment data exists in multiple forms: design specifications in engineering systems, asset hierarchies in ERP, maintenance histories in CMMS, performance data in historians or IoT platforms, and condition monitoring data from predictive maintenance sensors. Different departments may maintain separate equipment lists with varying levels of detail.
Industry practitioners observe that maintenance teams may struggle to access equipment design specifications, while engineering teams may lack visibility into maintenance histories. This fragmentation can limit the effectiveness of predictive maintenance initiatives that require combining design data, operational performance, and maintenance patterns.
Challenge 5
Engineering Change Management Across Systems
Engineering changes can impact multiple systems simultaneously: product designs in PLM, BOMs in ERP, work instructions in MES, quality specifications in QMS, and potentially supplier contracts in procurement systems. Ensuring that changes propagate completely and consistently across all affected systems can be challenging.
Organizations report that incomplete change propagation can lead to production using outdated specifications, quality checks based on superseded standards, or procurement ordering obsolete components. Manual change coordination processes may be time-intensive and subject to communication gaps between departments.
