Industry Challenges – Quality Control

Lack of confidence in the data leads to poor decisions

If existing QC systems are difficult to use or produce non-standardised outputs, the ability to quantify levels of risk or identify key causes of error in a timely fashion is compromised. The end result is wasted time, money, and missed opportunities.

Assays and other measures act as key parameters for monitoring business performance across mining operations.  Implicit in their use is the assumption that “The quality of data must be known before it can be used in a logical sense”1.

Although implicit, this assumption is often forgotten resulting in data of unknown quality (accuracy and precision) being used for modelling, classification, optimisation, scheduling, metal accounting and for payment of final product. Using data of unknown quality in these key decisions can lead to:

Inconsistent reporting creates inefficiency and confusion

Metrics related to data quality may also be calculated and portrayed differently in different parts of the business.  This impedes rapid decision making and wastes time by inducing confusion.

Further, many companies are required to report the results of their QC programs to demonstrate compliance with mining codes (JORC, SAMREC, P754, NI 43-101) or internal KPI’s.  These reports are often prepared in an ad hoc manner and the data presented inconsistently.  This causes unnecessary complexity for external review parties, which costs time and money to resolve.

“All analytical measurements are wrong: it’s just a question of how large the errors are, and whether they are acceptable.”2

1. J.K. Taylor, Quality Assurance of Chemical measurements, 1987, Lewis Publishers USA

2. G.F. Thompson, Proceedings of the 3rd International Symposium on the Harmonisation of Quality Assurance Systems in Analytical Chemistry, 1989, 183-189