Modern asset management systems provide tools to help manage the ecosystem, including:
Asset hierarchies. These help processors view assets from both a system and positional perspective so they can understand true costs of assets with the aim to control, plan and avoid capital expenditures.
Inventory control. This provides real-time visibility of inventory to help reduce inventory and material costs while enhancing purchasing control and efficiencies.
Maintenance control and scheduling. Such functionality helps prevent overtime and lag time and creates a more-effective maintenance team and better work scheduling.
Inspection management. These tools help plan and control inspection routes and measurement points, including those that highlight vulnerability of critical assets.
Regulatory and safety requirements. Specific information capture and material labeling requirements by categorization help manage U.S. Environmental Protection Agency inspections, internal self-audits, spill reports and all safety-related matters; tools provide the ability to track and manage key safety and regulatory data related to assets, maintenance and inventory.
Warranty management. This tool keeps track of asset warranty status to reduce maintenance expenditures and prevent unnecessary work and time on assets under warranty.
Asset analysis. Analytics help you understand why assets fail, the costs to operate them and where each asset is located to optimize deployment. Such tools, while seemingly independent, greatly impact each other.
Realizing progress in your asset management strategy isn't something you can just do with a snap of your fingers; you should rely on five best practices to achieve your goal of a strategic predictive maintenance program.
1. Assess existing maintenance strategy. It's difficult to move forward if you don't know where you've been. The stages outlined earlier provide a good indicator of where you are in your asset management program but you first must understand the past and establish a performance baseline. For example, analyze benchmarks such as percentage of work that's planned versus breakdown related/reactive in nature. Further evaluate these indicators by equipment class (e.g., reactors) or type (e.g., 1-gal. fill lines) to determine more accurate baselines and possibly even root causes of failures.
Also, determine your proficiency in capturing and analyzing asset data. The amount of data you can collect and analyze forms the foundation for the entire program. Often the information needed to drive decisions and processes comes from multiple disparate sources, including your asset management and production systems. For example, the Overall Equipment Effectiveness (OEE) metric requires availability information from an asset management system as well as quality and capacity information from a production system. Likewise, production and maintenance requirements and schedules reside in two distinct systems yet apply to the same equipment. You need a holistic view across disparate sources to drive greater efficiencies and better decision-making. With the right data, you can develop a sense for how your asset portfolio is performing and where to invest additional budget to ensure assets align with strategic goals.
In a multi-plant operation, look at how well you're sharing best practices, inventory management and procurement across facilities. Are common performance measures established so you can make comparisons? Can you easily consolidate information across plants and facilities into a single source of truth for analysis? Is cross-plant collaboration taking place? Answering these questions will help you gauge how well your operation leverages best practices.
Maintenance typically is thought of in a silo, one plant at a time, when in fact multi-location economies of scale can offer substantial cost savings.