Effective asset management at any plant involves multiple functions and is a process that begins even before the plant begins operations and continues even through the decommissioning process. In addition to dealing with active plant demands, efficient asset management requires companies to keep future plant requirements in mind, while also addressing historical needs.
Asset-intensive organizations invest billions of dollars in various ERP, EAM, and CMMS systems – such as IBM Maximo, SAP PM, Oracle EAM, Infor EAM, and others – to meet their plant maintenance and reliability goals. Although the tools are expected to make asset management much more efficient, the end goal is often not met, due to inefficiencies in the data backbone that drives these systems. The lack of good quality data often leads to decisions made on the basis of judgment rather than being data driven. This can lead to less effective strategic business decisions, increased costs, customer dissatisfaction, and loss of revenue. In some cases, it could also lead to catastrophic consequences such as a sudden breakdown of critical assets which leads to plant shutdown.
The crux of the issue is that processes, as well as analytics, depend heavily on the data within the system, which includes details of the assets, the MRO materials/spares associated with the assets, as well as the transactional data. When these data points are flawed, it can throw the entire system out of sync, leading to poor decisions making capabilities, wrong analytics, and an overall high-risk high-cost environment. Data needs to be accurate, complete, and completely relevant, otherwise it often does more harm than good.
Accurate data quality impacts various aspects of the business, such as:
Maintenance Strategy: Accurate spare parts data ensures faster retrieval of parts and quick execution of work orders generated, which leads to a considerable reduction in MTTR (Mean Time to Repair). Inaccurate asset data results in incomplete maintenance plans causing extreme overutilization of skilled resources, apart from pushing up costs and increasing downtimes due to maintenance activities. Organizations often do not have good quality asset BOMs available inside the EAM / CMMS systems, which makes it very difficult to ensure availability of critical spares, affecting plant maintenance activities.
Asset Availability & Plant reliability: Accurate asset data helps reduce the number of stock-out related downtimes. An error in parts data can lead to equipment downtime, bringing down the entire production line. Not only would these hamper the efficiency of manufacturing plants, but also cause considerable delays in the entire value creation chain and ultimately affect profitability.
Spare Parts inventory: Optimized spare parts inventory helps reduce maverick purchases, bring down carrying and procurement costs. Surplus, obsolete and duplicates inventory parts need to be cleaned up and eliminated, to achieve optimal inventory levels. Clean spares data is also crucial for good SPIR records (Spare Parts Interchangeability Record).
Engineering & Planning: Analyzing life-cycle costs of equipment is the key to decide which assets to buy maintain or retire, considering total cost of ownership over the asset’s lifetime. When evaluating investment alternatives, the right data about spare parts consumption, asset failure and maintenance schedules help the engineering and maintenance teams to conduct an FMEA with Criticality Analysis & Reliability-centered maintenance (RCM) to improve the reliability of assets and to plan predictive maintenance.
Spend Analytics: The direct cost savings opportunities lie in managing risks and optimizing the organization’s buying power. The accurate data inside EAM / CMMS systems help analyze spend by commodity or category, spend by transactions, total spend on a particular asset, spend by each function, spend on expediting spare parts, etc. This data provides insights into the overall spend and helps identify cost savings opportunities, streamline maintenance and procurement process.
With two decades of experience in Master Data Management for multiple industries, Enventure has seen companies make large investments in CMMS / EAM systems. But they end up loading legacy data into these systems – data that was inaccurate, incomplete, or irrelevant. This often happens due to lack of visibility into how much the data quality affects the ROI on the system investment.
It doesn’t matter at what stage you are – whether you are considering a new CMMS/EAM system or struggling to get value from an implementation you did a while ago – talk to one of our Consultants today to see how you can enhance your organization’s asset management efficiency.
We believe in ‘Efficient Asset Performance’ and not just ‘Asset Management’.