Module 02: Information Management


Items Firefighting Stablising Preventing Optimising Excellence
Information Requirements Asset management information needs and relevant data sets have not been identified.  Information requirements are based on intuition and what is available:
• Reports are generic and standard, with little customisation.
• Reports requirements are based on day to day operational activities.
• Reports and source data are often duplicated.
Information requirements are based on a formal needs analysis:
• These requirements are linked to the AM Scorecard of selected KPIs.
• Input data and sources are defined.
• The report format, target audience, frequency, responsibilities and development needs have been defined.
• The main focus is on measurement and reporting.
Information requirements have been refined and optimised:
• The information flow is mapped and streamlined to increase data velocity.
• Aligned with AM KPIs on all levels (strategic, tactical and operational).
• Cross-departmental interfaces in information flow are identified.
• The main focus is on analysis and problem solving.
Information requirements have been projected for the next 2 - 3 years:
• AM Strategy determines future information requirements.
• All stakeholders are involved in the requirement analysis.
• Software and hardware purchases are aligned with the AM Strategy.
• The main focus is on real-time information
Information Systems No structured asset management information systems are in place. Information is fragmented and processed manually, limiting its value to individuals. Information systems are informal and spread across different platforms:
• Some managers use spreadsheets for analysis or control.
• The ERP or accounting system is used for financial reporting.
• Manual systems are used for work planning and control.
• Plans exist to implement an EAMS.
A formal CMMS or partial EAMS is used, but it is a stand-alone system:
• It controls maintenance work.
• Maintenance, financial and material management information is available.
• Systems are not integrated and some reports are difficult to generate.
• Access is limited to a few users.
• Users are very dependent on the IT Department for support.
A full EAMS is used, with partial integration with other systems:
• All information needs are satisfied.
• A large % of the system functionality is being used, according to needs.
• Data duplication is eliminated.
• Most people involved in AM have access to the EAMS.
• System security and database back-ups are in place.
The EAMS is streamlined and fully integrated with relevant systems:
• Database structures are optimised for single point data entry.
• Operational staff can develop their own report queries.
• The EAMS is used during daily AM tasks.
• SCADA provides asset performance or condition data directly to the EAMS.
• Cross-system reporting is possible.
Data Quality and Accuracy Data quality is generally very poor, cannot be measured, and is open to interpretation and debate. The accuracy and quality of data vary depending on individuals:
• There are pockets of excellence.
• Data accuracy is only assessed on request, e.g. during external audits.
• Some over inspections are used to check the quality of work feedback and operational data.
Data quality and accuracy are measured and managed reactively:
• Managers do spot checks to emphasise the importance of data quality.
• Evidence exists that data quality and accuracy problem areas are addressed.
• Data quality is still suspect as the systems are new and skill levels are low.
A formal process is in place to ensure data accuracy and quality:
• Statistical samples of data are used to verify and calculate data quality.
• The results are recorded as a KPI and discussed during team meetings.
• The engagement of front line workers  results in more accurate source data. 
The EAMS ensures data quality and accuracy pro-actively:
• Data accuracy is checked at data entry (where possible).
• Data capturing is automated (e.g. Barcode scanners) to avoid human error.
• Data and information verification tests are built into the EAMS.
Data Velocity Data is not  processed electronically. People use manual records for decision making regarding assets. Data is processed in stand-alone systems (e.g. spreadsheets):
• Data processing requires heavy user involvement with little automation.
• Data is only processed when it is required for meetings or at month-end.
• Data velocity is slow and information is out of date.
The CMMS or partial EAMS has improved data velocity:
• Some data processing is automated and less dependent on user interaction.
• A data processing procedure is in place.
• Users have been trained to use the system effectively.
• Data is captured and processed at least once a week.
Information is available from the EAMS  directly after data is captured:
• Artisans enter Work Order feedback directly into the EAMS.
• Material management transactions are captured online.
• Data is processed within 24 hours or in line with requirements.
The EAMS uses technology to provide real-time information:
• The EAMS is integrated with asset performance and/or condition monitoring systems to import data automatically.
• Technology (e.g. barcode scanners) are used to reduce data capturing time.
• Workflow tools are used to automate information flow.
Data Mining Capability Data mining is not possible with the existing information systems. Data mining is difficult and time consuming:
• Information systems are fragmented with separate databases.
• Data is largely used for record keeping and limited reporting.
• High computer literacy is required for data mining.
• Data is captured with limited detail.
The CMMS or partial EAMS enables some data mining, but it is not used often:
• Data mining is difficult because of the use of different systems.
• Only a few experts have the required reporting and analytical skills.
• Pareto analysis and different trend graphs are mostly used for analysis.
• The focus is on reporting, not analysis.
The integrated EAMS allows more rigorous data mining:
• Drill down reports can access data from different information systems.
• Key users are trained to mine data and/or develop user-defined reports.
• Database structures are visible  for drill down or user-defined reports.
• The focus is on root cause analysis and problem solving.
Data mining is a standard practice in asset management:
• Data mining is used to investigate what-if scenarios.
• Data mining is partially automated via a parameter selection on reports.
• Most EAMS users can use the data mining functionality.
• Data mining is being expanded according to user requirements.
Reporting Asset management reports are totally inadequate compared to the stated information requirements. Reporting functionality is limited and there is no analytical capability.
• A few fixed standard reports exist.
• Custom reports can only be generated on request by IT experts. 
A reporting capability exists for trended graphs with limited analysis:
• Reports show general trends and events accurately, but they are limited in depth.
• The system has standard reports and greater flexibility for generating custom reports.
An effective reporting capability exists to satisfy all AM information needs:
• All staff can use the system for data processing and reporting.
• Reports can access data from different systems.
• Reports can be customised as needed.
Web-based dashboards  provide real-time information:
• Key information is displayed in the workplace.
• Reports can be customised by front line users.