An automated, systematic approach is required to deliver comprehensive, timely and validated information

By Kate Lothian

Rigorous corporate governance requirements and turbulent economic conditions have put intense pressure on mining organizations to improve their metal accounting practices. Inaccurate estimates of metals inventory and processing plant performance pose huge risks including undetected losses, lack of market responsiveness, and ultimately lost profits.

Times are tough for mining companies. The global economic outlook is bleak, and even China is no longer immune from the slowdown. With several recent reports of profit slumps, the world’s largest mining companies are starting to struggle in this environment. In addition, Sarbanes-Oxley and other similar regulations make senior management personally responsible for accounting figures, while any whiff of potential wrong-doing will result in a severely damaged reputation. Organizations must have efficient business processes to maximize production and exemplary financial accounts that can stand up to intense scrutiny.  

The importance of spotless metal accounting was underlined when a group of six companies including BHP Billiton and Anglo American developed a set of rigorous yet practical metal accounting guidelines (AMIRA P754 code). These guidelines stress the importance of “mine to product” state-of-the-art metal accounting solutions, which improve the credibility and transparency of the reporting process.

Accounting for the transformation of raw materials into concentrates and finished metals is a very unique, complex and time consuming undertaking. Without a “mine to product” solution providing an accurate, single view of metal content, product grade and production data, how can companies truly know the financial health of their business, and be sure they are safe from metal accounting risks? Nevertheless, many mining organizations continue to rely on inadequate manual and rudimentary solutions to manage metal accounting.

By adhering to the following 10 Best Practices of Metallurgical Accounting and implementing a powerful enterprise metal accounting software solution, organizations can reduce risk, maximize profitability and ensure compliance with the AMIRA code.

10 Best Practices of Metallurgical Accounting

1.    Straight-Through Processing: Completeness and Integration
Best practice metal accounting requires Straight-Through Processing (STP)1 of data throughout the entire accounting cycle to ensure a single, accurate and auditable view of production. There should never be any need for re-keying of data or manual intervention; all data should be automatically processed in one metal accounting system and integrated into the business via mine, process, laboratory and enterprise resource planning systems.

2.    Measurement Accuracy
The foundation of any metal accounting strategy is the input of good quality data. Plants must ensure that sampling equipment is fully functional by performing on-going maintenance tests to identify any source of bias.

3.    Data Redundancy and Validation
To increase the accuracy and reliability of data, organizations must ensure sufficient redundancy and adequate reconciliation of their mass balancing data. Without data redundancy mass balances have to be performed on non-validated data, which means inconsistencies cannot be checked and rectified. Once redundancy is established, organizations must be able to reconcile their data by turning inconsistent data into coherent and reliable mass balances. Effective redundancy and reconciliation can only be achieved by a mass balancing engine that is fully integrated into an enterprise metal accounting solution, which can take data from multiple sources to rectify errors and improve data accuracy and reliability.

4.    Target Accuracy
Best practice is considered to be setting targets to define acceptable levels of accuracy for each input and output stream, assessing whether targets are met, and in cases where they are not, identifying and correcting the problem. Attempting to achieve this without a statistical data reconciliation engine is impossible; how can accuracy be measured from non-redundant data?  A data reconciliation engine that is integrated into an enterprise metal accounting solution ensures that all corrections and investigations are recorded and assessable throughout the accounting cycle.

5.    Provisional Data
Reporting deadlines often require the use of provisional data before reconciliation and error detection has been completed. To effectively manage this process, organizations need to rely on a system that outputs provisional production numbers in a consistent and auditable way, showing how and when these numbers become finalized.

6.    In-process Inventory
In-process inventories can represent a significant amount of money; their estimation is essential in order to value an operation. Inventories need to be verified by regular physical stock-takes with clear procedures for stock adjustments and ultimately unaccounted losses or gains. A solution is needed that can reconcile differences in measurement results, ensuring any changes to in-process inventories are both transparent and auditable.

7.    Timeliness
One of the most time-consuming and tedious tasks dedicated to metallurgical engineers is the computation of production numbers. This process must be timely to ensure there are no gaps in financial records. Organizations need to have an automatic system of reporting that delivers timely production information for financial reporting cycles.

8.    Auditability
Any accounts that do not have a record of exactly how numbers were achieved, who provided them, and how particular adjustments were arrived at will not satisfy auditors and lead to serious questions over corporate governance. Effective metal accounting solutions are those that not only transform process data into coherent accounting figures but crucially do so in a fully auditable way.

9.    Transparency
Best practice in this area requires users to understand the entire metal accounting system. What is required is a centralized metal accounting solution that provides full transparency and enables drill down into each accounting figure to see exactly where the data came from, how calculations were made, and conclusions reached.

10.     Documentation
A fully documented solution is required that clearly describes, with words and diagrams, how the metal accounting process works. This should be made available to all current and new users.

Enterprise Metal Accounting Solution
A successful metal accounting strategy must ensure compliance with the 10 Best Practices of Metallurgical Accounting. The only way to do this, as recommended by the AMIRA code, is to have a “mine to product” state-of-the-art metal accounting solution.

Failure to effectively account for the concentration and extraction of metal poses huge risks. Undetected losses, lack of market responsiveness, lost profits, and failed corporate governance will not only be perceived by shareholders and auditors as irresponsible, but could ultimately lead to organizational collapse.

By putting in place an enterprise metal accounting solution, companies will enhance decision making to maximize efficiency, drive profits and significantly reduce the risk of non-compliance with corporate governance policies.  

Forward thinking organizations that want to survive in today’s turbulent conditions should follow in the footsteps of companies like Vale, Xstrata, ArcelorMittal and Rio Tinto, and install enterprise metal accounting solutions.

Lothian is director of product business development at Triple Point Technology, a leading global provider of on-premise and in-cloud commodity management software that delivers advanced analytics for optimizing end-to-end commodity and energy value chains.

1 Straight-Through Processing (STP) is a mechanism that automates the end-to-end processing of metal accounting data without the need for re-keying or manual intervention. It involves use of a single system to process or control all elements of the accounting workflow from initial raw data to final transformation into official production figures. The concept originated in the financial industry but its ability to reduce systemic and operational risk has extended its reach into other sectors including oil, gas and now mining.

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