For years, enterprises with significant physical assets — machinery, assembly lines, equipment, vehicles and materials — have taken a reactive approach to the maintenance of those assets. The C-suite viewed them as an expense, to the extent that the C-suite thought about them at all. Responsibility was siloed, ending with functional heads of maintenance or plant managers. The relevant software was project management software. The mindset was tactical.
A new model for enterprise asset management (EAM) has recently emerged — one that empowers enterprises to continuously improve asset reliability, performance and utilization while aligning asset management with key financial indicators and the greater enterprise mission.
Bill Slama, Grant Thornton senior manager, Digital Transformation, explains the origins of the new approach. “Traditionally EAM leading practices focused on work management, but exploration of other phases in the asset lifecycle showed a significant correlation between asset performance, reliability and utilization with capital spend.
“Organizations not leveraging their asset data to optimize capital spend were making significant recurring capital investments on assets they simply didn’t need,” Slama continued. “They had no insight into their asset fleet, what equipment was running end-of-life or what could be repurposed to a different location within the network. These types of uninformed investments were negatively impacting the balance sheet, and we realized very quickly we had an opportunity to help our clients make data-driven decisions related to asset capital planning and spend.”
The new EAM paradigm has eight salient characteristics:
- It fully leverages recent advances in data analytics. These include the ability to process large amounts of data, to process unstructured data, to employ Optical Character Recognition (e.g., the identification of printed characters using photoelectric devices and computer software), and to mine and visualize data. This increasingly robust approach to data allows your team to generate useful hypotheses, spot previously hidden trends and generate actionable insights.
- It views all physical assets and their associated tasks and data as part of a larger integrated system. This system includes all equipment, asset life cycles, warranty requirements, work management, supplies, materials and services, financials and human capital management. On an operational level, this allows better decisions and more efficient deployment of resources.
- It applies both traditional maintenance criteria and financial criteria. Traditional maintenance criteria include mean time to failure, mean time between repairs and total cost of ownership. Financial criteria include capital allocation and portfolio view, time value of money and contribution to revenue.
- It views revenue-generating assets as a part of a portfolio, rather than as isolated equipment. For example, a piece of revenue-generating production — such as a CAT scan in a hospital or an injection-molding machine on a manufacturing line — was once viewed simply as an expense to be written down. Under the new model, it is viewed as a part of a system that produces revenue and, as an asset, should be viewed along with similar revenue-producing assets. As some injection-molding machines age, the revenue they produce allows the purchase of their replacements. Robert Hersh, Grant Thornton principal and EAM practice lead, said, “If you look at the overall deployment of capital in a portfolio and time-based view, you’re going to make better decisions going forward instead of the one-off investment opportunity type of analysis.”
- It ties asset management to fundamental financial metrics. Hersh said, “Everything that we do around asset management should have a direct link to one of the three fundamental financial statements.” These statements include the balance sheet, the income statement and the statement of cash flow.
- It aligns maintenance software capabilities with the enterprise mission. The issue is no longer merely what tasks the software performs – x kinds of data fields, y kinds of reports. The long-term effectiveness of EAM implementations relies on close alignment with company operations and industry leading practices, as well as a determined focus on authoritative performance benchmarks for setting goals and gauging progress. The EAM vision needs to be grounded in a well-considered and peer-reviewed business case for improvement.
- It elevates asset management decisions to the C-suite. EAM may still be the primary responsibility of midlevel managers. But the new approaches tie it to fundamental financial metrics; and its relevance to capital forecasting and budgeting make it a part of larger strategic decisions.
- It acknowledges the importance of incremental change. Implementing and/or optimizing EAM involves capital investments and systemic transformation, so there are good reasons to proceed mindfully leveraging processes and procedures that would sustain this transformation over time.
Grant Thornton Principal
EAM practice lead
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