EMUG Tech delivers end-to-end smart factory and manufacturing transformation across six domain clusters: smart factory and MES implementation — DELMIA Apriso, Siemens Opcenter, OPC-UA, and SAP QM integration; robotics and automation — OLP programming for KUKA, Fanuc, and ABB, PLC programming, and cobot integration; AI and Quality 4.0 — computer vision inspection, predictive maintenance, SPC automation, and yield optimisation ML; digital twin — DELMIA manufacturing simulation, equipment digital twin, and virtual commissioning; SAP manufacturing integration — SAP PP, QM, PM/EAM, and SAP Digital Manufacturing; and lean manufacturing — VSM, SMED, TPM, DMAIC, and digital OEE programmes. All programmes follow the EMUG YIELD Smart Factory Engagement Model.

EMUG Tech’s computer vision inspection programmes follow a structured deployment: data collection — acquiring production images covering all product variants and defect types including rare defect classes; annotation — labelling defect instances with customer quality engineers to capture domain expertise; model training — YOLO v8 for detection tasks, custom CNN architectures for classification; edge deployment — NVIDIA Jetson AGX or Intel OpenVINO on industrial cameras at inspection stations; integration — inspection result posting to SAP QM inspection lot, MES non-conformance record, and automatic reject triggering; and performance monitoring — tracking detection accuracy, false positive rate, and model drift with retraining triggers. Programmes achieve defect detection accuracy above 98 percent for trained defect classes at production line speed.

EMUG Tech’s predictive maintenance programmes deliver 20 to 40 percent reduction in unplanned downtime for the equipment covered — measured against the 12-month pre-deployment baseline. The programmes use vibration, current, temperature, and acoustic sensor fusion to detect: bearing degradation (fatigue and lubrication failure modes); gearbox gear tooth wear and misalignment; pump cavitation and impeller wear; motor winding insulation degradation; and hydraulic system pressure anomalies. Failure prediction lead time is typically 2 to 6 weeks — sufficient to plan maintenance during scheduled downtime windows. All programmes integrate with SAP PM for automatic maintenance notification creation when a degradation threshold is crossed.

EMUG Tech implements MES using DELMIA Apriso, Siemens Opcenter, or custom MES built on modern web architecture. The MES-SAP integration covers: production order receipt from SAP PP — work order download and operation status confirmation upload; material consumption posting — goods issue from SAP MM at each production operation; quality results posting — inspection lot usage decision from MES to SAP QM; and serialised component traceability — production batch linked to SAP material and customer delivery. The PLM-to-MES connection uses the digital thread from Teamcenter or Windchill MBOM and process plan — manufacturing instructions and work order routing sourced from PLM rather than manually entered in MES or SAP.

EMUG Tech delivers lean manufacturing and operational excellence: value stream mapping for current and future state analysis identifying waste and flow improvement opportunities; SMED workshops reducing changeover time by 30 to 60 percent; Kanban and pull system design reducing WIP by 40 to 60 percent; TPM programme design covering planned maintenance, autonomous maintenance, and focused improvement; 5S and visual workplace implementation; DMAIC Six Sigma problem solving for chronic quality defects; design of experiments (DOE) for process parameter optimisation; SPC implementation with control chart automation; and digital lean tools — digital OEE boards, AI-assisted root cause analysis, and digital Gemba walk management. All lean programmes link to ISO 9001 quality management system requirements.

EMUG Tech delivers robot OLP for KUKA, Fanuc, ABB, and YASKAWA robots using DELMIA Process Planner and robot brand-specific simulation tools. OLP programme scope covers: 3D robot cell model build from CAD data; robot reach and collision analysis; weld path programming for MIG, spot, and laser welding with optimised torch angles; painting and sealing path programming with coverage validation; pick-and-place and palletising path programming; programme download and commissioning support on the physical robot; and cycle time validation against simulation predictions. OLP eliminates teach-pendant programming downtime — a typical automotive spot weld robot programme with 500+ welds is programmed offline in days rather than weeks on the production line.

EMUG Tech delivers manufacturing digital twin and simulation across three layers: process simulation — DELMIA and Siemens Plant Simulation (Tecnomatix) for production line throughput, bottleneck analysis, and layout optimisation before physical change; virtual commissioning — PLC code tested in a hardware-in-the-loop simulation environment before site installation, reducing commissioning time by 40 to 60 percent; and equipment digital twin — real-time monitoring platform connecting IIoT sensor data to physics-based equipment models for performance prediction, degradation tracking, and maintenance optimisation. Digital twins are deployed on Azure Digital Twins or AWS IoT TwinMaker, integrated with OSIsoft PI or InfluxDB time-series historians.

EMUG Tech delivers smart factory and manufacturing programmes across 20 countries: Europe — Germany, France, UK, Netherlands, Sweden, Italy, Spain, Czech Republic, Poland; Asia-Pacific — India, Japan, South Korea, China, Malaysia, Thailand; Middle East — UAE, Saudi Arabia, Qatar; Americas — USA, Canada, Mexico, Brazil; Africa — South Africa, Nigeria, Kenya. Manufacturing sector coverage: automotive, aerospace, industrial machinery, electronics and high-tech, pharmaceutical and medical devices, food and beverage, metals and fabrication, and process industries. Engineering delivery from Hyderabad (India), Germany, and Dubai with on-site implementation capability at client manufacturing facilities globally.