A Capital Spare is a piece of equipment, or a spare part, of significant cost that is maintained in inventory for use in the event that a similar piece of critical equipment fails or must be rebuilt.
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Every organization has some “big problem” that they need to address. New software, sensors, equipment, or analytics can sometimes be the solution to that problem. But sometimes there is a nagging thought that the real issue is more institutional than asset-based. Almost universally, companies that successfully leap into the Internet of Things (IoT) are already mature, efficient, and agile. They don’t need to solve nagging performance issues. They aren’t looking at software as a solution or performing maintenance by capital project. They’re making a step change to maximize already satisfactory performance metrics.
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A considerable amount of effort goes into developing an asset maintenance plan that combines all the technical and administrative measures throughout the life cycle of a piece of equipment aiming to preserve as well as restore its readiness so it can perform its required function. Often, the premise of these plans is that the asset is operating and mostly in a steady state. Strategies based on this premise is an incomplete approach that even the best companies fall victim to with unexpected, and at times, tragic consequences. Engineers and maintenance leaders need to ask themselves what special considerations to address when equipment is in an idle state for an extended period or operating in a reduced or modified state (such as would be experienced following new construction) before being placed into production.
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The Failure Modes and Effects Analysis (FMEA) is a versatile risk management tool that has been around for several decades, first used by the US Armed Forces in 1949. It started gaining popularity in the 1960’s with its use in the aerospace industry during the Apollo missions. FMEA has been called the “swiss army knife” of risk management. FMEA is focused on problem prevention, rather than problem solving. It is used to identify the factors that can lead to failure and recommend mitigation strategies to prevent failures or detect the early onset of failures.
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