Fostering efficiency in EAMs through data improvement

⇨ High-quality data is essential for the effectiveness of Enterprise Asset Management (EAM) systems; poor data migration leads to ongoing operational inefficiencies.

⇨ Conducting a thorough data audit before implementing a new EAM system can identify gaps and inconsistencies, allowing companies to improve data quality and enhance business efficiencies.

⇨ AssetOn's work with Glencore Coal demonstrates how targeted data audits and strategic planning can streamline operations, reduce costs, and create accurate maintenance budgets across multiple sites.

Enterprise Asset Management (EAM) Systems are as good as the data they hold. However, many businesses fail to understand this rule and migrate the same old, inaccurate data when they set up a new system. The result? Despite having new tools at hand, they continue to grapple with inaccuracies, incomplete information, and inefficiencies in operations.

Thus, it is necessary to audit the existing data and improve its quality before setting up the new system to foster business efficiencies through better data and systems. Companies like AssetOn, a COSOL company since 2023, bring those skillsets to a project and not only help to fill the gaps in existing data but also understand data structures, systems, and business needs.

For example, AssetOn audited Glencore Coal’s SAP master data to identify gaps and developed a plan to prioritise and fix those issues across 26 sites in Australia.

Glencore Coal Australia deployed SAP as their ERP and asset management system in 2016 and added maintenance tasks, plans, items and bill of material (BOM) site-by-site over the next few years. Despite some good information added to SAP, the data approach was inconsistent. When AssetOn audited the data, they found:

• Some parts were being ordered outside the system because of a gap in cataloguing parts, which resulted in additional administration costs for each order.
• The company was unable to prepare maintenance budgets entirely from SAP data, so the budget was not linked to the maintenance plan, as manual intervention takes time.
• Compromised quality and additional planning time as ad-hoc BOMs were needed for jobs where no BOM existed.

As part of its project that ranged from data audit to the creation of SAP load files to add or extend materials and update the parts catalogue, AssetOn:

• Audited Glencore Coal’s SAP asset master data across 26 sites and reported on the gaps it identified
• Built a plan to prioritise and fix the issues identified during the audit and develop new data to fill the gaps in tasks, plans and BOMs and catalogued parts added to BOMs
• Ensured site-validated BOMs were in place for all major maintenance tasks and services while using any good data created by Glencore wherever possible
• Leveraged any new data that was created for one site or equipment for other sites with similar equipment and created SAP load files to update the system with validated data

AssetOn could accomplish all these tasks using the software tools and processes developed by its master data team to audit, review, and validate Glencore’s SAP asset master data against best practices. These tools guided Glencore to improve its data by highlighting issues and making suggestions about the actions needed to improve data quality.

This resulted in the miner’s getting a consistent execution strategy across multiple sites, ensuring the right parts were ordered for the job, improving planning efficiency, reducing administration costs, and creating a more accurate maintenance budget.