In the last couple of years, we have witnessed the emergence of a cottage industry of analysts, thought leaders and consultants with expertise in Industry 4.0. Terms such as disruptive change and paradigm shift are used liberally. At the same time, analysis of industrial internet of things (IIoT) solutions typically overlook issues relating to vendor pricing and the impact on manufacturers’ financial statements. This article will explore a new service and pricing model for original equipment manufacturers (OEM’s) and how this will affect manufacturers and industrial plants.
Since the first industrial revolution, industrial equipment was a physical asset installed in an industrial plant. Without going into the specifics of various taxation jurisdictions, the generally acceptable accounting standards is that asset are capitalised on a balance sheet and depreciated over an extended period of time. This way, the cost of the equipment is spread over the theoretical life time of the asset, even if the payment event is a one-time occurrence when it is originally purchased. In the case of maintenance service agreements, these fees are billed separately and paid during term of the agreement.
Today, equipment manufacturers are largely bystanders witnessing a shifting focus to digitalisation. Almost every industrial plant has a digitalisation and big data strategy with a specific roadmap likely to include machine learning and artificial intelligence (AI).
The common thread between the various IIoT solution types is the data that is generated from sensors embedded in industrial machinery. In its raw form, this data is useless. However, operational insights such machine failure prediction can be uncovered when sophisticated machine learning algorithms are applied to the data.
According to IoT Analytics research, the market for predictive maintenance is expected to grow to US$11 billion in 2022 compared with US$1.5 billion in 2016. How can OEM hardware vendors participate in the new revenue streams?
One way is to bundle the sale of physical machine equipment with a service based on the analysis of the sensor data embedded within the equipment. With Hardware as a Service (HaaS), OEM’s can extract the embedded sensor data and provide machine learning analytics services in the cloud. Of course, traditional OEM’s today lack this capability, but with partnerships and/or investments in machine learning technology companies, this service could be delivered. Based on my experience working with OEM’s, competitive forces and the underlying economics of upselling a service are already driving them to explore this option.
From an OEM perspective, the upside is obvious. If equipment is sold over an extended period, this helps generate long-term sources of predictable money flows. Strategically, OEM’s benefit by moving from a transaction to relationship model. If we look at how software as a service (SaaS) changed the way software has been delivered and monetised since the early 2000s, it’s easy to see why this model is appealing from a vendor perspective and one more benefit to the OEMs. Suddenly, their design engineers can keep in touch with their equipment post-sale, and learn from its ongoing performance, influencing the design of new models. In the past they always lost touch with sold systems, now they can track and learn from their field performance.
What does this mean for manufacturers? From a strategic perspective, if OEM’s are able to offer solutions based on machine learning, this may alleviate some of the pressure on industrial plants to build these competencies inhouse. The caveat is that the end-user experience cannot be burdensome. More importantly, a shift to an OEM HaaS pricing model is likely to result in a corresponding shift from Capex to Opex. Companies do not have to take on debt to finance the cost of the machinery, resulting in a cleaner balance sheet with fewer assets and liabilities.
Both Capex and Opex have advantages and disadvantages and it is beyond the scope of this article to evaluate each. What can be said is that in the new era of IIoT, OEM’s will need to adjust their value proposition and service offerings. It is likely that the hardware as a service model emerges. Manufacturers should recognise the change and plan accordingly.
Deddy Lavid Ben Lulu is the CTO of Presenso
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