Podcasts

Leadership Lyceum

Leadership Lyceum: A CEO’s Virtual Mentor

This podcast series focuses on corporate and industry strategy and trends from the direct vantage point of key industry leaders. Subscribe to the podcast at Apple iTunes. Interviews with Tom Fanning and Bob Flexon are available, as well as one with Joe Rigby, Bob Skaggs and Les Silverman.

See Podcasts

Public Utilities Reports

PUR Guide Fully Updated Version

Available NOW!
PUR Guide

This comprehensive self-study certification course is designed to teach the novice or pro everything they need to understand and succeed in every phase of the public utilities business.

Order Now

Opening the Black Box

A new approach to utility asset management.

Fortnightly Magazine - January 2014

asset strategy .5  This type of dynamic strategy addresses four types of uncertainty: 1) uncertainty regarding an asset’s current condition and how that condition changes over time; 2) uncertainty regarding the accuracy of tests of an asset’s condition; 3) uncertainty regarding an asset’s remaining life; and 4) uncertainty regarding the effects of repairs on an asset’s condition and, therefore, its remaining life. 6

As we discussed previously, determining an optimal asset management strategy requires that we determine how an asset’s condition changes over time, because the condition of an asset at any time t determines the probability of failure thereafter. To do this, we combine condition definitions (e.g., what does it mean for an asset to be in good condition today?) with tests that can evaluate the asset’s condition. These are combined to establish what we call a “condition dynamics model (CDM).” The CDM determines how an asset’s condition is likely to change over time, given its current condition. 7 ( See Figure 3 ).

However, knowing an asset’s condition today – unless it’s already failed – and the forecast of asset condition given by the CDM won’t provide enough information to make asset management (repair, replace, test, do nothing) decisions. That requires a model that estimates the likelihood of asset failure tomorrow, given an asset’s condition today. Such models are called State-Dependent Hazard Rate Models, as shown in Figure 4.

Figure 5 illustrates three hazard rates for a class of assets in different condition today. 8 Although it’s straightforward to determine a repair-replacement strategy along a single hazard function, that strategy won’t be least-cost because we further recognize that repairing an asset can also change its condition and thus change the appropriate hazard rate. Depending on the type of repair made, however, there will also be uncertainty as to what is that new post-repair condition. 9

For example, suppose your car is running poorly and you ask the mechanic to change the car engine’s oil. Changing the oil will improve the engine’s condition because old oil has various contaminants that can increase wear on the various moving parts. However, if the engine has leaking rings or a blown gasket, changing the oil will do little to improve its condition. Thus, a simple repair can still leave a high level of uncertainty as to the engine’s true condition. If, on the other hand, you ask the mechanic to completely rebuild your car’s engine, the engine will be in good condition with no uncertainty (assuming the mechanic has rebuilt it correctly). The optimal engine repair strategy, therefore, depends on the type of repair made and the effect of that repair on the engine’s condition. Moreover, an optimal strategy must evaluate the tradeoffs between the cost of the repair made and the (uncertain) impact on the engine’s post-repair condition. 10

Developing an optimal policy for each class of assets requires additional information, including: 1) the available types of repairs ( e.g., major? minor?); 2) the type of replacement asset ( e.g., the same asset type? an improved asset?); 3) the costs of