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Texas Lube LLC – Predictive Maintenance & Operational Efficiency

Client

Texas Lube LLC, an industrial services provider operating in a high-volume machine services environment.

Context & Challenge

Texas Lube faced recurring operational challenges that threatened service reliability and profitability:

  • Maintenance was performed on fixed schedules regardless of actual machine usage or condition.
  • Sensor data and machine telemetry were siloed; no unified monitoring system existed.
  • Unexpected machine failures resulted in unplanned downtime, delayed service delivery, and increased costs.
  • There was no real-time visibility into machine health or maintenance needs, resulting in reactive rather than proactive maintenance.

These issues resulted in frequent downtime, inefficient resource allocation, elevated maintenance costs, and unreliable service turnaround times.

Engagement

MST Technologies was engaged to deploy its AI-driven maintenance platform. Under the leadership of Ahsan Sharif (Project Transformation lead), the project scope included:

  • Integration of IoT sensor data (temperature, vibration, run-hours, energy draw) via OPC-UA into a unified data pipeline.
  • Mapping and digital reconstruction of maintenance workflows, combining machine data with ERP-derived financial and service-order data.
  • Implementation of predictive maintenance algorithms and scenario-simulation tools to forecast potential failures and maintenance needs.
  • Development of a dynamic scheduling engine that automatically prioritised maintenance orders based on machine load, service deadlines, and resource availability.
  • Deployment of a unified dashboard for operations managers, providing real-time machine-health metrics, downtime risk alerts, and maintenance scheduling.
  • Stakeholder workshops and hands-on training: engineers and operations staff were trained on interpreting alerts, reallocating work, and using the dashboard effectively.

Role Attribution

As a lead, Ahsan Sharif was responsible for:

  • Architectural design of the data pipeline and integration strategy
  • Definition of business requirements, KPIs (downtime, maintenance cost, turnaround time, resource utilisation)
  • Oversight of configuration, deployment, and staff training
  • Alignment of software output with operational and financial reporting standards

The technical team handled coding, data ingestion, dashboard development, and maintenance executing upon the architecture and workflow defined by Mr. Sharif.

Outcome & Significance

The deployment transformed maintenance operations from reactive to predictive. By integrating IoT data with business processes, Texas Lube gained real-time visibility into machine health allowing them to schedule maintenance proactively, reduce downtime, and optimize resource usage.

This case demonstrates the viability of AI-driven operational intelligence in industrial maintenance contexts and the role of data-driven decision-making to improve asset utilization and cost efficiency.

Results (after 6 months of deployment)

  • Machine downtime reduced by > 40%.
  • Operational maintenance costs decreased by approximately 18%.
  • Average service turnaround time improved by 35–40%, reducing delivery windows from ~48 hours to ~28–30 hours.
  • Improved reliability of service scheduling and preventive maintenance planning, increasing client service satisfaction and reducing emergency breakdown interventions.

These results were verified via internal maintenance logs, service order records and financial reporting.

“OptiPro AI fundamentally changed how we run our operations. Downtime fell by 40%, costs dropped, and we gained real-time insight into our service commitments. Ahsan Sharif’s leadership ensured the system wasn’t just installed it was adopted and sustained.”