In the world of bottleneck competition, asset-sensitive industries give top priority to asset management. The main objective of the industries is to achieve the lowest life cycle cost and provide the best services or meet production targets. Asset management is primarily based on the collection of asset information such as asset location, specific maintenance details, inventory, spare parts, etc. But if the data is processed or acted on, it contributes to decision making.
Analysis-based reliability and maintenance
Analytics-driven reliability and maintenance are becoming the key element in the field of asset management. Asset analysis contributes to effective asset maintenance and reliability management and some of the benefits of effective asset management are listed below:
- Facilitates greater control over assets by focusing on asset condition and work processes.
- Helps limit unnecessary costs, lowers cost of ownership, and increases asset productivity.
- Improve maintenance efficiency and cost tracking.
For effective asset management, data generated by various EAM/CMMS is processed, analyzed, and reported. Reports are generated in the form of dashboards, maintenance dashboards, graphical representations, etc. Asset Analytix provides a data analysis and reporting solution for asset reliability and maintenance management. It guides organizations to efficiently acquire information from EAM/CMMS systems and make data-driven decisions. Asset Analytix specializes in providing customized reports regardless of the reporting technology used.
reports for everyone
- As assets are deployed for a particular business objective, multiple reports are generated i.e.
- For Management/CXO
- For the Reliability maintenance team
- Staff for the store floor
Reports for the management of Critical Assets:
In the case of a particular industrial sector, some assets are critical assets. To achieve effective asset optimization, timely analysis and documentation are critical to making informed decisions. This is equally important for end users as well as for high-level management throughout the entire life cycle of an asset.
Advanced statistical models can help predict and analyze:
- residual life of an asset
- repair v/s replace
- Purchase v/s lease
- Plant and equipment risk assessment
- Supplier Performance
- cost of ownership
- Equipment Residual Value
- Equipment warranties
To generate these reports, various statistical techniques are used such as Weibull, Monte Carlo, Crow AMSAA, Poisons, etc. Reports are generated for various audiences such as maintenance professionals, inventory managers, operators, etc. Some of the representative reports are:
- Failure risk forecast
- average residual life
- Supplier Performance
- OEE
- Repair V/s Replace
- These reports act like browsers and lead to:
- Increased availability and reliability of assets
- Reduced unplanned downtime
- Identify underperforming assets
- Reduces the total cost of asset ownership
- Extensive risk analysis of plants and equipment
- predictive analytics
Considering the entire life cycle of an asset, maintenance costs are one of the main concerns in any company’s expense budget. Understanding this fact, companies are implementing strategies to revive the perception that “maintenance is an inevitable evil for one, where maintenance acts as a vital contributor to the profitability of the company.” To take advantage of this, companies have integrated computerized maintenance management system (CMMS), the analysis of the data generated from these systems is essential for decision makers. AssetAnalytix provides analytical reports that provide data-driven asset reliability and maintenance management.