by David Almagor, Chairman of Presenso
The market for IIo T Predictive Maintenance was about $2 billion in 2017 and is expected to reach $11 by 2022. For industrial machinery OEMs, the adjustment to the new Io T paradigm cannot simply
be based on incremental product innovation. Extra R&D
budgets to develop more features and functionalities are
insufficient given the magnitude of change. Industry 4.0
is an industrial revolution and revolutions are marked by
the fall of existing institutions and the rise of new elites.
With the writing on the wall, the need for machinery manufacturers to adapt is now well understood, although the
options for many are unclear.
One way to adapt is by embracing Hardware-as-a-Service.
HaaS is not new, but until now it has been applied primarily
to the ICT sector. At Presenso, we have noticed that an
increasing number of customers and partners are now
considering HaaS, even if they are not yet using the term.
Today, if you are pump manufacturer, even if your pump
has the best features, at some point a competitor can
design a better and cheaper version. With HaaS, industrial
machinery is equipped with multiple sensors and connected
to the internet cloud. Now you are no longer selling a product; you provide a service. In an outcome-based scenario,
you track the performance of a single pump or multiple
pumps in an industrial plant. The value that you provide is
to monitor the health of the pump and to proactively alert
the plant to evolving degradation or failure.
From a customer perspective, advanced knowledge
of asset failure means that repairs can occur before the
shutdown of a production line or even an entire facility. If
the root cause of a failure can be isolated in advance, then
production loads can be adjusted, parts ordered, and repairs scheduled with minimal disruption to production. The
result is higher machinery uptime and increased revenue.
Of course, with the new opportunity comes new danger,
especially as we enter this era of disruptive change. Here
are six questions you may have about HaaS:
1.) Will HaaS diSrupt tHe
There is an organizational divide between operations
and technology in the industrial world. The scenario of
hardware OEMs adopting a software operating model
could result in alliances and new areas of competition.
Software and hardware vendors could collaborate on
their product development and go-to-markets, thereby
forcing a re-alignment of the vendor landscape. There are
several IIo T platforms available and the entry of hardware
vendors into the market could create confusion. Will
there be standardized software used by multiple hard-
ware vendors or will individual hardware vendors build
proprietary tools? As customers adjust
to this new reality, demands for single
source solutions may result in industry
consolidation and vertical integrations.
2) Ho W Will induStrial plantS
manage tHeir oem SupplierS?
If hardware vendors start offering their
own IIo T Predictive Maintenance solutions,
there may be resistance on the part of industrial plants who are unwilling to operate
multiple solutions. In the Future of IIo T
Predictive Maintenance Research study of
maintenance and reliability professionals
that was conducted by Emory University
students and sponsored by Presenso,
survey participants were asked to select between one IIo T
Predictive Maintenance solution that fits multiple machines
and one solution installation for each machine. The overwhelming majority of respondents (96.3%) selected solution
installation for multiple types of machines.
3) can Hard Ware Specific SolutionS
provide a HoliStic factory vie W?
When an IIo T Predictive Maintenance solution is limited
to one piece of equipment, the root cause of a failure may
not be found in an adjacent system not directly connected
to the one supplied by the hardware OEM. For instance,
a pump may be displaying abnormal behavior because
adjacent machinery is overheating due to an electric wiring
malfunction. In this case, the pump vendor may detect
evolving failure, but not a root cause that isn’t directly
connected to the pump.
The requirement on the part of industrial plants to limit
the number of Predictive Maintenance solutions is not only
based on the ease of management, but also the accuracy
of results. This is likely to pose a major challenge for
hardware vendors adopting a HaaS model.
4) Ho W Will HaaS impact tHe adoption of
iiot infraStructure arcHitecture to
Support multiple oemS?
In Tom Raftery’s 2018 Io T predictions, he suggests that
although there have been advances in open standards and
architectures, integration will continue to be challenging.
There are at least 10 large vendors with IIo T platform
offerings and lack of common standards. This poses a
challenge to both the hardware OEM and the industrial
plant that needs to integrate multiple Io T applications
while balancing existing legacy systems. According to an
assessment McKinsey & Company, larger customers will
have negotiating leverage and “can demand interoperability
when they write specifications and procure Io T systems.”
5) WHo aSSumeS failure riSk?
When a technology neutral IIo T Predictive Maintenance
solution detects evolving failure, its primary function
is to alert the relevant maintenance and reliability professionals. As vendors shift to providing prescriptive
guidance on asset repair, will the OEM be expected to
assume responsibility for failure? Furthermore, without
Root Cause Failure information about other equipment
in the plant, there is a risk of penalizing the OEM for
breakdowns related to third-party machinery.
The issue of risk also extends to maintenance. If plants
expect OEMs to bundle maintenance as part of the HaaS,
this would require a service model that many OEMs cannot
support effectively. In the software arena, troubleshooting
and patches can be managed via remote. In the case of
hardware, although it can be shipped to geographically
disbursed locations and data can be analyzed anywhere,
the requirement for local repair limits the OEM’s flexibility
to provide an end-to-end service solution.
6) can Hard Ware oemS develop
ne W competencieS?
To provide a Predictive Maintenance solution requires developing deep expertise in the disciplines of Artificial Intelligence and Machine Learning. Traditional hardware vendors
lack this capability. Smaller OEMs may be at a competitive
disadvantage if they cannot afford to invest in R&D. One
likely scenario is for OEMs to integrate third party IIo T Predictive Maintenance applications into their hardware offerings.
This article was contributed by Presenso. To submit articles
and case studies, please email John.Hitch@informa.com.
With IoT-enabled machines and predictive maintenance becoming more commonplace, OEMs must adapt
to a more service-oriented model, making HaaS an intriguing solution to stay ahead.