TLO Load time compliance project

Key Insights

“The challenge of collating and analysing information from multiple sources presented a requirement for data validation.

New techniques were learnt to manipulate and present the data, allowing for easy digestion of information.”

Z

Measure

The Purpose And Desired Outcome Of The TLO Load Time Compliance And DAS System

Plan Necessary Features For Keyosk And How To Incorporate Punchcard

Z

Analyse

TLO Load Time Compliance And Delays Recorded In DAS System

Z

Execute

Identified Improvement Opportunities Of The Load Time Compliance And Delay Accounting System (DAS) Analysis.

Z

Sustain

Alignment Of Reporting Of Delays Across All TLO Operators. Investigative Analysis For Sprint Loading To Help Reduce The Trains Loading Time

Our Goal:

The Train Load Out (TLO) Station is a key process area in the railing of iron ore from the handling plant to the port.   This project was aimed at identifying and selecting the most feasible improvement opportunities to decrease load time.

Our Solution:

To identify the most suitable solution, we defined the problem, collected historical data, identified root causes for under performance, selected improvement opportunities and devised implementation plans.

This included active engagement with key stakeholders and subject matter experts. The data analysis allowed us to:

 

  1. Review of the current TLO load time compliance; designing and proposing an alternative method to accurately calculate it
  2. Deliver a calculation of the actual cycle for trains at the mine (Pre load, Loading, and/or Post Load)
  3. Using DMAIC methodologies, compiled an analysis for the TLO and the reclaimer (added scope), by classification, cause name and standard explanation in order to inform the root causes’ assessment.

Unlocked Potential:

In addition to the benefits of engaging operational personnel to gain their support, we were able to provide a means for consistent data analysis going forward, using inbuilt asset sheet in excel.

Coaching/retraining of TLO operators will improve data analytics, by enhancing the accuracy of the information. This will also align the reporting of delays across all TLO operators.  Implementing the improvement opportunities identified, supports the evolution of the system, minimising the time of manual data entry in the future for the TLO operators.