Thanks to the advances of the Internet-of-Things, companies can continuously record data and monitor their (fleet of) assets in real-time. When used properly, this valuable data can help benchmark the performance of those assets, making it possible to determine if and why certain assets are underperforming.
Asset benchmarking involves comparing the behaviour of assets on the basis of performance indicators such as cost, quality, production... Correct benchmarking requires comparing assets in terms of three dimensions: operational context, fleet and time.
A company operating a fleet of similar assets for a long time - possibly exposed to different operational contexts - has a very valuable dataset that can be used for benchmarking purposes. Even in less than ideal cases, where the data collected is limited to one or more of the dimensions, specific benchmarking methods can still be used to gain meaningful insights into fleet operation and performance.
Want to find out more? The full article on benchmarking asset performance is available on the website of the professional journal T.I.M.! (in French/Dutch)
Want to find out what the options are for your company?
On 29 April, Sirris EluciDATA Lab is organising the fifth edition of its fleet-based analytics seminar, Fleet-based analytics for data-driven operation and maintenance optimisation which will focus on how data analytics can benefit the operation and maintenance of a fleet of machines. Company presentations from AW Europe, Flint Group and Yazzoom will give you an insight into how data from a machinery fleet can be used in a smart way for different challenges and which technological challenges are involved. Sirris also gives you an overview of the current state of the art, where fleet data can be used to assist in the configuration of assets, to profile performance and to benchmark industrial assets. The full programme and the registration form can be found here.
On 15 June, during the second edition of the webinar How to leverage AI to benchmark your industrial assets we will discuss various advanced benchmarking methods to help identify when, in what operational context and for what type of asset underperformance is occurring. This makes it possible to examine the maintenance activities carried out before the change in performance, to define the scenarios leading to underperformance for root cause analysis and to improve fleet performance by eliminating the causes of underperformance of individual assets.
