Conclusion

Background and overview

A leading industrial company in Mexico, specializing in the storage and distribution of bulk products, primarily fuels, was operating within a highly demanding environment with multiple opportunities for improvement.

From its main facility located in the western region of the country, the company manages the daily receipt, storage, and loading of fuels transported by rail and tanker trucks, which are then distributed to retailers and service stations nationwide.

As operational volumes increased and the need to ensure a consistent and secure flow became more critical, the organization identified an urgent need for a digital system to automate vehicle entry control, registration, and dispatch. The goal was to improve traceability while significantly reducing wait times at the plant.

The challenge

Prior to implementing the solution, all logistics and service processes were fully manual, creating operational risk, inefficiency, and limited visibility across the plant. Key challenges included:

• Driver and vehicle registration completed on paper, with no digital traceability or centralized control
  
  
• No assurance that first in, first out service ordering was consistently enforced
  
  
• Lack of measurable data related to wait times, loading duration, or vehicle departures
  
  
• Heavy reliance on manual supervision, leading to errors, delays, and recurring bottlenecks
  
  
• No reliable data to support logistics planning, productivity analysis, or operational forecasting
  
  

The core challenge was to automate the service and loading process while ensuring fair service order, full control of truck flow, real time operational visibility, and complete traceability from driver registration through plant exit.

The results

Following the implementation of Q-Flow®, the organization achieved significant operational and performance improvements across the entire logistics flow.

Key outcomes included:

• Full automation of the service journey from driver registration through plant exit

• Significant reductions in wait and loading times, eliminating congestion in the maneuvering yard

• Strict enforcement of FIFO service order, improving transparency and fairness across operations

• Real time visibility into every stage of service, enabling better resource and turn allocation

• Increased productivity through data driven identification of patterns, peak days, and peak hours

• Stronger security and traceability through biometric identification and detailed operational reporting

Operational decision making shifted from manual oversight to a data driven model supported by reliable, real-time metrics generated by Q-Flow®. This enabled the organization to improve efficiency, strengthen internal controls, and sustain long term operational optimization.

Conclusión

This project represents a turning point in operational management within Mexico’s industrial sector. With the support of ACF Technologies and the implementation of Q-Flow®, the company transformed a manual, error-prone process into a fully digital, automated, and efficient operational model.

Today, the organization operates with a robust system that enables centralized control, optimized resource utilization, enhanced security, and faster, more reliable, and more transparent service for its customers.