Maximo Reporting

Identification of the underlying problem

Whenever a component or product fails in service or if failure occurs in manufacturing or during production processing, failure analysis plays a very important role. In any case, one must determine the cause of failure to prevent future occurrence, and/or to improve the performance of the device, component or structure.

Typical examples of systems/equipment that can be analyzed are electrical generators, heat exchangers, valves, control systems, pumps, components of gas turbines and compressors.

Failure Analysis will disclose;
• Why the event, failure or breakdown occurred
• How future failures can be controlled or eliminated

Analysis to Identify the Causes of Failure / Breakdown

Failure analyses of the repairable systems focus on the model capability to identify, control and eliminate future failures, for a system.
• Root Cause Analysis
• Pareto Analysis for Downtime
• MTBF-MTTR Trending for Bad Assets
• Effect of MTTR on Asset Availability
• Breakdown Analysis
• Identification of Dominant Failure Codes
• Effect of Unplanned Cost on Maintenance Cost
• Analyze Reactive Maintenance
Benefits
• Uses advanced investigative techniques
• Identifies early (unlikely) life failures
• Extends equipment lifetime
• Reduced cost of maintenance
• Improves availability “up-time” and increased production
• Increases safety
• Easy to identify for potential losses where risk is included

This post provides information on improving system performance by using best practices in reporting.
Running reports is a very resource-intensive operation and has the potential to be one of the biggest factors in poor system performance.
To help keep reports from affecting your system performance, you should isolate the reporting function as much as possible.

• Isolate reporting in time.
• Isolate reporting by user.
• Isolate reporting by cluster and application server.
• Isolate reporting by database.
• Manage the report database.
• Configure the report server.

Run Resource-Intensive Reports in Off-Peak Hours
Many reports that consume significant resources are not needed immediately, and do not necessarily need to be run on up-to-the-minute data. You should run such reports in off-peak hours, such as overnight or on the weekend. Time-based reports such as end-of-month and end-of-quarter reports can be run in off-peak hours, on a copied database from a specific date and time. Because you do not need to run these reports on the current database, you can protect the production database from being slowed by these reports.
Limit the Use of Reports
The more users that run reports, and the more reports they run (especially database-intensive reports), the greater the potential effect on system performance. You should establish business practices to help manage the amount
of report use, especially during peak system-use hours. Limit the number of users who can run reports. Limit the number of reports that users can run.
During peak business hours, try to limit report use to reports that users need for their daily work, such as Print Work Orders, Print POs, and so on.

Reporting

Run Reports on a Separate Cluster
If your users do extensive reporting, a good practice is to establish one or more application servers that are dedicated to running reports. You can size the clustering of report application servers based on demand. Establish a separate cluster for running scheduled reports (cron jobs).

Provide a Separate Database for Reporting
Some customers report that providing a separate database to run reports on is the single practice that gives the greatest boost to system performance.
Configure a separate Maximo database that has a copy of the production data, and use that as an off-line database for reporting. Mirror the Maximo production database on a separate database server, and run resource-intensive reports on the mirror database. Create a separate Maximo application that connects to the reporting database and synchronize the production and reporting databases periodically.
For example, you might synchronize the databases at the end of every day or once a week, depending on your needs.
With this setup, reports that require more system resources can be run by just a few administration users. Because they are run on a separate mirror database, these reports do not affect performance of the production system.

Manage Your Reports
By default, executed reports are saved to the Actuate Encyclopedia. Over time, the volume of saved reports can affect report performance. It is a good practice to periodically delete unneeded executed reports from the Encyclopedia. You can delete unneeded reports from the Encyclopedia by enabling the Actuate AutoArchive feature in the Management Console. AutoArchive sweeps the Encyclopedia for documents that are older than a specified age and deletes them.

Configure the Actuate Report Server if necessary
By default, the Actuate report server is configured for typical usage. The basic single-server setup is typically enough to support 100 users. Actuate is a resource-intensive application. Allocate a minimum of two processors and 2 GB of memory to run Actuate. If you are running a large number of reports, consider a load-

For successful “Asset Maintenance & Reliability Management”, companies not only adopt many methodologies like RCM, TPM, CBM, etc. but they also leverage their asset related data to improve processes & performance. To make data driven informed decisions, companies require much more than just investment in the technology infrastructure they also require quality data consistently for trustworthy analysis. There are many challenges in achieving this...

Quality Data Preparation

• Multiple data sources with voluminous data
• Identification of good & bad data
• Need for reliable analysis
• Lack of data uniformity
• Data in non- reportable format
• Cleansing data manually
• Laborious quality data preparation
• Complex data validation

Advance Data Analytics

• Don’t know how to use data Can't leverage on data for analysis
• Asset health assessment
• Less asset availability
• Increased unplanned breakdown
• Lack of domain expertise & experience to analyze
• Unable to analyze & predict events critical to business operation

Managing Analytics Infrastructure

• Lack skilled & trained professionals
• Hiring full time resources
• Huge capital investment
• Lack expertise & experience
• Developing libraries from scratch
• Losing focus from core business functions
• Cost control

Asset Data Quality

• Asset data audit & quality Analysis
• Asset master data development
• Asset data dictionary development
• Asset data cleansing & validation
• Ongoing support

Data Analysis

• Problem Definition
• Data Availability Analysis vis-à-vis Analytics Feasibility Study
• Solution Modeling Using
• Solution Implementation
• Data gathering,
• Report development
• QA
• Result Analysis & Interpretation
• Report Preparation & Delivery
• Ongoing Model Effectiveness & Performance Monitoring


Statistical Modeling Techniques

• Weibull
• Monte Carlo
• Crow-AMSAA
• Pareto
• Croston
• Poisons
• Log Normal
• ARMA
• Exponential Smoothing
• ANOVA
• IR/Control Charts

Business Issues

• Identify underperforming Assets
• Identify equipment to be replaced
• Avoid unnecessary PMs
• Establish PM & failure relationship
• Identify unusual PM costs
• Monitor impact of strategy & process changes
• Determine future Maintenance cost for budgeting
• Finding equipment which have unusual # of failures
• Identifying most impactful failure causes on reliability
• Find optimum spare parts level

Ready to use models for

• Optimum PM Interval
• EOQ Calculator
• Forecast residual life of asset
• Predict TBF & MTTR
• Mean Residual Life of equipment
• Reliability Growth Models & Plots
• Repair Vs Replace Model
• Root Cause Analysis
• Spare Parts estimation Using Crow
• AMSAA Model
• ABC Analysis
• Spare Parts Demand Estimation
• Proportional Hazard Model
• Failure data Analysis
• Competing risks using survival analysis

Maximo collects extensive asset data that can be leveraged to signi­ficantly improve Asset Reliability and Maintenance. This data, if used properly, can provide valuable insights in making informed business decisions related to...

* Asset performance management
* Work management
* Preventive maintenance
* Asset risk analysis
* Planning & scheduling
* EHS compliance
* Inventory optimization
* Predictive maintenance
* Purchase management
* Costing & ­finance
* Failure analysis
* Reliability analysis



Challenges in Maximo Data Analysis

* Lack of time and Specialist technicians
* Combining Maximo data with other apps for reporting
* Keeping up with changing versions of Maximo & other software
* Ad-hoc reporting capabilities & uni­ted data access