Assets Connect: Digital Twin CMMS for Predictive Plant Operations

1. Build a live digital twin of equipment, production facilities, and maintenance processes in a single platform.

2. Replace fragmented spreadsheets and paper logs with one CMMS as the source of truth for asset health, history, and cost.

3. Move maintenance from reactive and calendar-based to condition-based and predictive.

4. Forecast failures, spare-part demand, and maintenance workload before they disrupt production.

5. Run “what-if” simulations to optimize maintenance schedules, resource allocation, and facility performance.

6. Reduce downtime and maintenance cost while extending asset life and improving safety and compliance.

7. Create a scalable platform that can be rolled out across plants and industries.

• Results measured estimated six months pilot period against the prior baseline:

  • Unplanned downtime reduced by 22%

  • Maintenance cost reduced by 13%, with preventive maintenance compliance rising from 45% to 72%

  • Mean time to repair reduced by 23%

  • Asset availability up from 86% to 94%

  • 11 potential failures flagged and resolved before they caused stoppages

  • Spare-part inventory optimized, freeing PKR 15 million in working capital

  • Paper-based maintenance processes eliminated at the pilot facility

  • Estimated payback period: 14 months; estimated annual savings: PKR 20 million

Software: Frontend: Angular; Backend: Laravel & Python; Database: MySQL ; mobile app for technicians Flutter; 3D/2D twin visualization library; ML/AI services for anomaly detection and failure forecasting.

Infrastructure: Cloud/on-premise hosting, Hostinger and local data center, containerized deployment, role-based access control, encrypted data transfer, automated backups.

Equipment and data sources: IoT sensors: vibration, temperature, pressure, energy, PLC/SCADA gateways, barcode/QR/RFID asset tags, rugged tablets and handhelds.

Integrated systems: ERP,SAP, inventory and procurement, notifications (email/SMS/WhatsApp), BI dashboards.

• Data quality: Asset records were incomplete and inconsistent. We ran a structured asset census and standardization effort before the twin could be reliable.

• Heterogeneous equipment: Machines of different ages and vendors, many without digital outputs, required retrofit sensors and flexible integration adapters.

• System integration: Connecting the platform with ERP, inventory, and plant data sources demanded careful data mapping and security controls.

• Connectivity and environment: Industrial floors with limited connectivity needed offline-capable mobile workflows and edge buffering.

• Adoption: Moving technicians from paper to digital required training, simplified interfaces, and management sponsorship.

• Live operations: Rollout happened without interrupting production, using phased go-lives line by line.

• Forecasting accuracy: Predictive models needed iterative tuning with maintenance engineers’ domain knowledge.

Assets Connect is a Digital Twin CMMS that gives manufacturers a continuously updated virtual model of their equipment, production facilities, and maintenance processes, and uses it to forecast failures and optimize operations.

The challenge. Industrial operators typically manage maintenance through disconnected spreadsheets, paper logs, and tribal knowledge. Asset history is incomplete, failures are discovered only after production stops, and management has no reliable view of asset health or true cost of ownership. Standalone digital twin tools exist but are expensive, hard to integrate, and disconnected from day to day maintenance execution.

The solution. We built a single platform in which every asset, from a plant-level production line down to an individual motor or valve, has a digital twin. Each twin holds:

• Its place in the facility hierarchy and process flow

• Technical specifications, documents, and spare-part relationships

• Live and periodic condition data from sensors and inspections

• Full maintenance history, downtime events, and cost

Core capabilities.

1. Digital models: A structured, visual representation of equipment, facilities, and processes showing real-time status and dependencies.

2. Maintenance management: Preventive, corrective, and condition-based work orders; mobile execution for technicians; inventory and spare-part control; audit trails.

3. Modeling and forecasting: Analytics on historical and live data predict probable failures, remaining useful life, and spare-part and workload demand.

4. Optimization: Planners simulate scenarios, such as shifting a shutdown window or changing inspection frequency, and see the impact on downtime, cost, and risk before committing.

5. Management intelligence: Dashboards for reliability KPIs (MTBF, MTTR, availability, backlog, cost per asset) at plant and group level.

Delivery approach. We started with an asset census and data cleansing, built the twin data model, then piloted at Plant site before scaling. Training and change management ran alongside the technical work, so maintenance teams adopted the system as their daily tool.

Outcome in brief. The organization now has one trusted view of asset health, shorter response times, and the ability to plan maintenance by data rather than habit. The platform is ready to extend to additional plants and sectors.

The platform is deployed at plant in Faisalabad, Punjab, Pakistan, It was designed and built by Invictus Solutions in Faisalabad. The architecture is multi-site and multi-tenant by design, so additional plants can be onboarded centrally and compared at group level. Cloud hosting and a localized interface make it suitable for rollout across All locations and for other industries such as FMCG, textiles, pharmaceuticals, and process manufacturing.