Disruptive Physical Asset Risk Transfer

Herman Scheepers

Herman Scheepers,
Senior Technologist and Data Scientist,IS3

In this article I would like to explore new opportunities for value, innovation, and disruption driven by information and AI in the domain of asset performance management, and how this may play out in the context of risk management and how assets are insured.

Claude Shannon, one of the Founding Fathers of Digital, said that ‘information is the resolution of uncertainty’. Uncertainty and risk can be thought of as very closely related. Uncertainty can either refer to uncertainty about some future event (when will some machine in my plant break down again), or the uncertainty in terms of current situational awareness (where in my asset base there are existing problems that, had I known about it, I would be able to address it in order to avoid the risk of an unplanned outage, or the production of bad quality product). Risk, in the context of this discussion, is the estimated financial consequences should one of these risks materialise, under the assumption that both the risk and the consequence can be measured or estimated.

Another interesting aspect of both uncertainty and risk is that both can be mitigated by investment in information, and that higher quality information generally requires more investment. For example, to reduce the uncertainty associated with the power a device or network consumes, we can invest in a higher quality meter, and to reduce the uncertainty associated with a predictive model that addresses some future unobserved event, we can collect more data about the present and the past.

There is a certain degree of risk in the owning an operation of a physical asset, whether it be a vehicle, transformer, or server. In order to mitigate the risk we need to do two things. We need to insure the asset, and we need to maintain it in order to preserve both its capital and functional value. As a simple example, let’s take a motor vehicle. This could be our personal car, or a large mining haulage truck. The insurance in this case is against theft and accidents on the one hand, and against mechanical failure on the other. The former is something we need to explicitly invest in, and the latter degree built into the price of the vehicle in the form of the standard warranty. In the first case the risk is by and large transferred to the insurance company, except in cases where we may violate the contract terms and become the risk taker. An example of the latter would be driving under the influence. In the case of the latter, the risk is by and large transferred by the vehicle manufacturer, since costs resulting from premature mechanical breakdown are ultimately covered by them,

This is pretty much how things worked for a long time, until IIOT and big data analytics came along.

This brings us back to the opening statement relating to the relationship between information at risk. The IOT and big data analytics, including AI, provides an opportunity for not only a new model of risk sharing between the equipment consumer, his insurer, and the equipment manufacturer, but also for an end-to-end lowering of overall risk. This creates new value, and is a concrete instance of information as currency in manufacturing in the context of the fourth industrial revolution. It also creates an innovative, improved, and much richer user experience.

As an example, we can consider Stratus, with their fault tolerant server range and cloud-based asset health monitoring. Clearly end-users appreciate reliability, since Wikipedia states that they have a ‘large customer base’. The comfort of knowing that your mission critical banking application or plant control system runs on a server where the vendor will let you know of any problems and eliminate the root cause before any potentially disastrous downtime occurs, is something that most of us would not mind having.

In future, more and more end-users for various industrial technologies may start creating a similar experience for themselves, or demanding it from their technology suppliers. A few different combinations and permutations of information value chains and business models may emerge to take advantage of this information originated opportunity.

Organisations with the appetite, capacity, and opportunity of economies of scale may invest in either developing or acquiring the means to connect, collect, analyse, and act on information that will enable them to manage their assets in an optimal way. Many would already have some of the dependencies in place. For example, suitable instrumentation, a data historian, a manufacturing execution system, a workflow system, and a maintenance workorder system. For organisations in this bracket only the analytics component would be required in order to start gaining competitive advantage from advanced asset monitoring.

In the case of a supplier of manufacturing equipment or other products or services that want to gain competitive advantage and increased market share, or simply avoid disruption, it may very well be the best option to partner with an existing, leading vendor of a complete, hardware agnostic suite of asset performance software that can be integrated with their hardware, and possibly with that of their customers. This would allow the manufacturer to focus on their own core business, whilst still offering customers a great user experience.

Further, if optimising AI can be used, the vendor would be able to exploit the (big) data collected from its customer base to optimise asset reliability and maintenance costs. This means that, say you manufactured some sort of equipment that needs periodic maintenance, you can build prescriptive analytics (AI domain) models to minimise the overall cost of maintenance needed for a given level of service. As a supplier, you can then decide how you want to best use this new value – do you want to leverage it for bigger market share by passing some of it through to your customer base, or do you want to leverage it for higher margins? Probably a bit of both.

There may also be an opportunity for the emergence of aggregators, that have back-to-back partnerships with asset performance management software vendors, that provide the integration of remote, cloud-based asset information services to technology vendors and manufacturers. These aggregators may have partnerships, or may even be, insurance companies, because with the types of models required to fully optimise the information value and the ultimate benefit gained by the end user, this can be thought of as a type of insurance – asset performance assurance.

As we can see, everybody in the value chain, from the end customer of the manufactured product right through to the OEM and insurer can benefit from this large new information driven opportunity, but companies that realise that the right software partners, with the right technology as well as industry insight, for rapid time to value as well as sustainable service, may emerge as early winners in this new competitive landscape.

Similarly, to me previous article on Steve Jobs, and why he may have been the greatest salesman of all time, this way of applying information and communication technology offers, at both the OEM and information services level, the opportunity of disruption at a global scale.

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