The Challenge

Manufacturing facilities operate in demanding environments where uptime, energy efficiency, and asset reliability directly impact productivity and cost. Even minor unplanned disruptions can lead to production delays and increased operating expenses.

Although the organization already used BIM models, enterprise systems, and IoT data, these elements functioned in silos. Design data remained static, operational insights were fragmented, and maintenance activities were largely reactive. The real challenge was not the availability of data, but the lack of connection and context needed to turn information into real-time, actionable operational intelligence.

Client’s Initial Hurdles

The manufacturing facility was facing multiple operational constraints:

• Disconnected data across BIM, ERP, IoT, SCADA, and maintenance platforms
• Reactive maintenance resulting in unplanned downtime
• Limited real-time visibility into equipment health and energy usage
• Manual access to asset documentation and historical records
• Inability to perform predictive analysis or scenario planning

Why This Was Critical

Why wasn’t existing data delivering value?

Because critical information was scattered across BIM, enterprise, and operational systems. Teams could access data, but not in a connected or contextual way making it difficult to understand how assets, systems, and spaces were performing together in real time.

Why was reactive maintenance a business risk?

Reactive maintenance meant issues were addressed only after failures occurred. This led to unplanned downtime, production interruptions, higher repair costs, and increased pressure on maintenance teams to respond urgently rather than strategically.

Why did energy optimization matter?

Energy consumption directly affected operating costs and sustainability targets. Without clear visibility at the asset and system level, inefficiencies went unnoticed, increasing cost per unit of production and limiting opportunities for optimization.

Why was real-time visibility essential?

Delayed or periodic insights meant problems were identified too late often after performance degradation or equipment failure. Real-time visibility was critical to detect anomalies early and act before they impacted production.

Why couldn’t traditional tools solve this?

Traditional dashboards and reports showed isolated data points but lacked spatial, asset-level, and system-wide context. Without a unified digital environment, teams could not analyze cause-and-effect relationships or make confident, data-driven decisions.

Gaps in Existing Information

• No single source of truth linking design, assets, and operations
• Static BIM models disconnected from live facility data
• Historical maintenance data locked in spreadsheets or paper records
• No feedback loop between operational performance and decision-making

Why Specific Requirements Mattered

To truly transform operations, the solution needed to:

• Integrate high-fidelity BIM models with live operational data
• Support both modern and legacy equipment
• Enable real-time monitoring without disrupting production
• Scale incrementally based on ROI

Without these capabilities, digital transformation would remain superficial.

The Desapex Solution

Desapex implemented a Digital Twin platform that acted as a real-time virtual representation of the manufacturing facility.

The solution unified:

• BIM and IFC design models
• Structured asset and equipment data
• Live IoT and operational system inputs
• Analytics, dashboards, and alerts

This created a centralized digital environment where design intelligence and operational data continuously informed each other.

Project Timeline & Milestones

• Model Integration – BIM models integrated to ensure spatial and asset accuracy
• Asset Data Structuring – Standardized information for machines and systems
• System Integration – Sensors and enterprise systems connected
• Analytics Enablement – Real-time dashboards and alerts

Software & Technology Used:

• BIM and IFC models
• IoT sensors (including non-intrusive sensors for legacy equipment)
• Asset information modeling frameworks
• Analytics and visualization dashboards
• Enterprise system integrations

The Real Business Value Delivered

• The Digital Twin enabled measurable operational improvements:
• Reduced unplanned downtime
• Lower maintenance costs through predictive strategies
• Improved energy efficiency per unit of production
• Faster access to asset and operational data
• Organizations typically achieved ROI within 12–18 months, with ongoing value growth over time.

What This Means for Future Projects

For the client, the Digital Twin transformed operations from reactive firefighting to proactive, data-driven control without replacing existing equipment or disrupting production.

For Desapex, this project demonstrated how Digital Twins can bridge the gap between design and operations, even in legacy-heavy manufacturing environments

For the industry, it reinforced a key insight:

Digital transformation is not about replacing assets it’s about making every asset visible, measurable, and intelligent.

By starting with what already exists and scaling strategically, manufacturing organizations can build resilient, efficient, and future-ready operations one insight at a time.