Digital Manufacturing Engineer - Manufacturing Data & Automation

Digital Manufacturing Engineer
- Manufacturing Data & Automation

The Digital Manufacturing Engineer
- Manufacturing Data & Automation is responsible for developing and implementing data-driven manufacturing solutions across machining, assembly, inspection, and test operations. This position will connect shop-floor equipment and manufacturing systems, automate the collection of production and process data, and transform that information into actionable manufacturing intelligence. The engineer will work closely with Manufacturing Engineering, Operations, Quality, Maintenance, and IT/OT to improve throughput, equipment utilization, labor productivity, quality, process capability, and overall manufacturing performance.

A key focus of the position will be establishing an integrated manufacturing data environment that connects CNC machine data, Renishaw probing results, assembly performance, quality information, test results, and ERP/MES data.

• Develop and maintain automated methods for collecting manufacturing data from CNC machine tools, assembly operations, inspection equipment, test systems, and other production assets.
• Establish connectivity to manufacturing equipment using technologies such as MTConnect, OPC UA, APIs, PLC interfaces, industrial gateways, MQTT, or similar communication technologies.
• Develop scalable methods for collecting, storing, structuring, and analyzing manufacturing data.
• Integrate shop-floor data with ERP, MES, PLM, QMS, SQL databases, manufacturing historians, and other enterprise systems.
• Establish common manufacturing-data definitions, naming conventions, data structures, and governance standards.
• Work with IT/OT resources to maintain secure and reliable connectivity between manufacturing equipment and business systems.

• Develop and support the collection and analysis of CNC machine data, including machine state, cycle time, alarms, downtime, spindle utilization, program status, tool information, and other process variables.
• Develop and/or support Renishaw on-machine probing applications used for in-process measurement, part verification, work-offset management, and process control.
• Capture and analyze Renishaw probing results for dimensional trending, SPC, process capability, tool-wear monitoring, process-drift detection, and continuous improvement.
• Develop automated methods for identifying abnormal machining conditions and process variation.
• Support the development of closed-loop manufacturing strategies where measurement results can be used to drive machining or process adjustments.
• Partner with CNC programmers, machinists, Manufacturing Engineering, and Quality Engineering to improve machining performance using manufacturing data.

• Develop automated methods for capturing and analyzing assembly-production information.
• Collect and analyze labor hours, standard versus actual hours, takt/cycle time, station performance, throughput, downtime, delays, rework, defects, and first-pass yield.
• Integrate information from digital torque systems, test equipment, PLCs, operator interfaces, electronic work instructions, and other connected assembly equipment where available.
• Link production information to work orders, part numbers, routings, operations, serial numbers, and production schedules to improve manufacturing traceability.
• Develop analytics to identify assembly bottlenecks, excessive wait/queue time, recurring delays, labor-efficiency losses, and capacity constraints.
• Analyze standard-versus-actual labor performance to identify opportunities for routing improvements, process changes, training, tooling, and workstation improvements.
• Combine production, quality, and test information to identify where defects and rework originate within the manufacturing process.

• Develop and maintain manufacturing dashboards using Power BI or similar business-intelligence platforms.
• Establish automated reporting for key manufacturing metrics including:
• OEE
• Machine utilization
• Standard versus actual hours
• Labor productivity
• Schedule attainment
• Cycle time
• Throughput
• Downtime
• First-pass yield
• Scrap and rework
• Process capability
• Quality trends
• Develop drill-down capability allowing manufacturing leadership to move from plant-level KPIs to individual departments, work centers, machines, operations, and products.
• Automate recurring manufacturing reports…