New pay-per-use equipment business model could end capex for oil, gas producers

Value propositions for predictive maintenance: longer asset up-time, lower maintenance costs, higher productivity, improvements in operational cost/efficiency

Technology advances are powering a new generation of “predictive maintenance solutions” that could lead upstream oil and gas equipment suppliers to pay-per-use business models that change the way producers think of capital expenditures, according to a new study.

Lux Research has released a report arguing that the rapid development of the Industrial Internet of Things – featuring advances in sensors, connectivity, and analytics – that will improve asset up-times and decrease costs, forcing many industrial organizations still living in the maintenance “dark ages” to rapidly embrace change. Oil and gas companies are high on that list of industries.

“Remote diagnostics and maintenance solutions are a key factor in enabling OEMs [original equipment manufacturers] to offer equipment-as-a-service (XaaS) models,” said Isaac Brown, Lux Research Analyst and lead author of the report entitled, “Predictive Maintenance: The Art of Uptime.”

“To fully benefit from the new technology, industrial organizations need to rapidly move away from the current practice of fixing equipment only after failure, or at pre-determined intervals.”

A pay-per-use model would differ from industry to industry, but Brown said in that an email to American Energy News that it would have some common features, including guarantees for some level of uptime (basically a service-level agreement) and charges for use of the product.

“Kaeser Compressor, a German manufacturer of air compressors, has a few beta customers for an experimental model in which it provides equipment to customers for no up-front cost, monitors it remotely, provides predictive maintenance services to uphold an SLA, and charges them for the quantity of air they compress monthly,” he said.

“In these pay-per-use models, it is essential for OEM vendors to keep their products running smoothly to ensure that customers are using them (and paying for usage) as much as possible.”

Under capex models, the vendor is less “financially incentivized” by equipment up-time after the sale, Brown says. The responsibility to maintain equipment lies with the buyer, not the seller.

But in the pay-per-use model, the responsibility to maintain equipment shifts from the customer to the vendor.

“Increasing product lifetime is equally as important for vendors, since customers don’t pay for a new machine in pay-per-use, so new equipment is a pure loss for the vendor,” said Brown.

“This operational expense (OPEX) model is very appealing to both OEMs and their customers, but it will take time to work out the proper pricing structures.”

Brown argues that the emerging pay-per-use models are both an opportunity and a threat for oil and gas equipment suppliers.

“The business models opened up by connectivity and analytics will be extremely disruptive in every industry – oil and gas is no exception. Several progressive providers are experimenting with pay-per-use business models that eliminate capex altogether,” he said.

“Many forward-thinking industrial providers – like GE and Rockwell – are developing connected analytics platforms that they sell on top of / in addition to their core products. This enables recurring revenue (opex) in addition to the up-front product sales (capex).”

Past predictive maintenance models relied on individual inspections of equipment instead of “heavy duty analytics,” says Brown but emerging solutions incorporate “advanced predictive analytics technologies” that forecast maintenance requirements more accurately and further in advance. Some models draw data feeds from existing sensors and data sources, while others integrate complete systems including hardware, connectivity, and data/analytics.

“The value propositions for solutions like these are increased asset uptime, decreased cost of maintenance, increased productivity, and overall improvements in operational cost and efficiency,” said Brown.