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Fair Value Estimation in the Industrial World

Roberto Mascherona

Traditionally, Fair Value Estimation is a financial discipline that measures the real value of an asset, considering the purchase value and various depreciations, usury and such; while evaluating costs, generated revenues, obsolescence, etc.

In the industrial field, Aramix considerably extends the potential range of this methodology, applying a different approach: a reliable depreciation of assets instead of a traditional or exclusively financial one.

In fact, industrial assets typically do not depreciate in a standard way, but require a more precise estimate: the life cycle of an asset can be calculated reliably with a certain number of years – its so-called typical period – but if the asset is kept in optimal conditions it can last much longer.

For example, a car lasts many more years if it is used correctly, compared to a neglected one, just as a hydraulic pump system that receives regular maintenance will last much longer than one that does not.

In this sense, the fair value of two identical components can be different, because one is managed well and one badly.


Asset Fair Value: how to calculate it

The first step is to identify all the assets and components to be included in the estimate.

These elements are then accompanied by their accounting information – cost, date of installation or replacement – and by all the data needed to estimate their useful life, coming from various sources, depending on the case:

  • sensors;
  • failure history and usage statistics;
  • the relevant literature or more generally the specific knowledge on the subject;
  • the opinion of experts.

At this point it is necessary to rationalize the information of the assets on the basis of these specific accounting and operational characteristics, in order to obtain an estimate of their Useful Life.

This is the threshold within which a component should be replaced because it is at risk of breaking. In order to calculate it, we also consider the residual value of the asset and the probability of failures (evaluated through the reliability model of the component).

By doing so, we get a fairer estimate of:

  • Residual value of assets on a reliable basis instead of merely  accounting/financial 
  • OPEX: how much you will spend on operating costs, such as ordinary or extraordinary maintenance
  • CAPEX, by discounting the costs of the assets, based not only on the residual value but also by calculating the cost of a new, repurchased asset 
  • REVENUES: how much you can earn on these assets, knowing how much they cost and how much you would need to spend to keep them efficient.


A case-study: Fair Value Estimation for the Energy Industry

A top Italian player in the energy industry commissioned us to carry out a data-driven survey of their production plants.

In fact, our client operates these plants for third-party companies, and this Fair Value Estimation provided a more robust estimate of the value of its assets, as well as a more solid basis for conducting a possible sale.

The starting point is the register of all assets: a very precise and granular database with the list of all items and all related information, such as year of installation, year of purchase, macro-category, etc.

Especially in this context, evaluating all this information is essential to really understand the life expectancy of each asset: both when it comes to single components – such as pipes and valves – and more complex ones, composed by smaller microelements, which have a crucial impact on the estimation of the entire asset.

For example, if a boiler that can count on a standard 40-year useful life, you should consider that a major replacement considerably lengthens the overall life of the whole system.

In this way you get a reliable estimate of the life horizon of an entire register of components, and these can be depreciated on the basis of their life horizon, calculating how much they decrease in value over time.

In this specific case, the assets in question were not particularly sensorized and it was necessary to rely on other sources of information.

By combining proprietary data with statistical data, literature and expert opinion, we were able to provide a reliable estimate of the life span of the components, depreciating them on the basis of their Remaining Useful Life, i.e. predicting how long the component could continue to perform its function correctly.

Potentially, you could even calculate the acceptability threshold of each asset more precisely, to dispose of it when its reliability drops to 50%.

This means you can rely on a much more solid analytical basis to guide business decisions, and this estimate can be integrated into any Decision Support System.

The useful life of the items for sale was found to be much longer than anticipated by the buyer, who relied only on tabulated values ​​of reference standards.

In this way, irrefutable values ​​shared by the counterparty were obtained, for a more equitable gain in the buying and selling phase.


Use cases and other applications for a Fair Value Estimation

The energy industry is typically characterized by large fleets of machines, with great variability but few specimens for each asset. However, this methodology can prove to be a game-changer for any other company that has a large fleet, with fleets of similar machinery.

The crucial question remains the same: how much have these machines depreciated over time and, consequently, at what price to buy them back? And how much would it be worth to resell them as used?

To calculate the Fair Value of these assets, all the data coming from the sensors of the more sophisticated components will be examined: the frequency and intensity of use, breakdowns, energy consumption, etc. With regard to non-sensorized assets, we’ll use the statistics and master data available for the components (as well as the other elements mentioned above: literature and expert knowledge).

A fundamental premise upon which these estimates are based is this: the wear cycle is different on items from different places. For example, for the same years, a machine that has been placed in the most crowded gym of a large city and a machine in the very little frequented gym of a hotel.