Hardly a day goes by that I don’t have a conversation with someone about the influence of the Internet of Things (IoT) on our lives. In 2000, Bill Gates saw the future and aptly referred to what we currently call IoT as the digital nervous system.   

"A digital nervous system consists of the digital processes that enable a company to perceive and react to its environment, to sense competitor challenges and customer needs, and to organize timely responses," Gates, explained, in his book "Business at the Speed of Thought."

Today, in response to increasing customer demand, manufacturers, resellers and service providers are bunding hardware and services into comprehensive solutions. If they haven’t already incorporated IoT into product design, they soon will because IoT is a vital component for improving service performance through remote monitoring. It can also open the door to collecting key metric data for competitive pay-per-use programs that brings more affordable payment options to even the smallest companies. In many cases, the data itself will be harvested as an incremental revenue source.

There’s a race to get to a solution before the competition does as it becomes increasingly apparent these programs can boost profit opportunities. In the process of doing that, the entire internal supply chain of the company can be impacted.  

If you’re a leasing executive struggling with lower than desired penetration rates, you might be exuberant that end user monthly payment programs will receive increased visibility at the executive table. Although embedded lease programs bundled with maintenance or other service contracts could be the beginning of the journey, the end game is more apt to be aggressive pay-per-use models that are un-constrained by minimum lease commitments. Looking further into the future it’s likely that the strategy will include the ability to monetize data collected throughout the value stream.  It can be a wild ride getting from one point to the other and require sweeping changes in how products are designed, built, marketed and sold. In almost all cases, this journey will involve incorporating IoT data into most facets of the supply chain solution, including the payment and monetization process.

Market analysis is the first step in this journey. It requires careful consideration of customers, industry market trends and technology. While practical solutions can always be designed within the constraints of current capabilities, there is no time like the present to imagine the possibilities of the future. As we move from monetizing hardware to monetizing the value stream of a solution, there will be necessary intersections between hardware, software and the IoT. Designing the perfect solution doesn’t generally happen as quickly as we want. The short-term race might be in developing the technology to collect data to enable more aggressive pay-per-use structures. The longer-term race is about transforming that data into new opportunities and revenue streams. Along the way, there are obstacles faced with regards to existing budget limitations, unpredictable market/technology shifts or simply because it takes time to gather sufficient data before it can be useful. 

Medical Industry Uses

The medical industry is a good example for us to examine. This is an industry where usage models are gaining favor with hospitals, clinics and physician’s offices in an effort to keep investment costs down. It’s also an industry rich with the potential to apply artificial intelligence to data collected from hundreds of thousands of procedures in order to develop better predictive diagnostics. 

At the most elementary level, traditional leases might be considered the first iteration of a usage model where usage is measured by time or the term of the lease. Managed services programs embedding these hardware leases into routine maintenance and/or break-fix service contracts have existed for several decades.  Although there is a good reason to introduce IoT for service diagnostics or service billings under this model, there isn’t a requirement to incorporate IoT in order to enable billing for the medical equipment itself. Medical equipment payments, under an embedded lease structure, are fixed and have no direct correlation between the payment and the number of patient procedures performed. 

However, if doctors, hospitals or clinics want more cost effective pay-per-use models that provide for better correlation between the cost and profit of a particular procedure, they will look for providers who have the underlying technologies to enable it.

This is where the intersection of hardware, software and IoT will become increasingly important. A few examples of these points of intersection include:  

• IoT enablement: Assuming that the device is IoT enabled for service maintenance, collecting usage meter data for hardware billing is relatively simple.  In the absence of IoT, collecting meter usage data is a manual and error-prone activity. 

• Translating usage data into a billable invoice: Usage data needs to be extracted, either manually or through IoT technology, and integrated into a billing system that can accommodate multiple pricing algorithms. If these tools don’t exist today, they need to be built or outsourced. Many companies initially start with spreadsheet processes to manage this type of billing structure.  Although it’s an acceptable short-term solution when dealing with a handful of customers, it’s still error-prone and labor intensive. 

• Using data to mitigate risk: There is a sliding scale of risk associated with metered usage programs. At the lowest level of risk, a minimum number of procedures at a specified usage rate are required to ensure that the hardware investment is recovered. At the highest level of risk, there is no minimum so investment recovery in the hardware is fully exposed to underutilization of the equipment. As the volume of IoT data grows on patient procedures, it becomes easier to use predictive analytics to determine usage patterns and develop pricing models that can mitigate this risk. In the absence of predictive analytics, it will require an increased appetite for risk.  This risk can be partially mitigated by conducting the required level of due diligence on customer history and by designing transactions that have specific exit strategies in the event economics get out of balance with intention.  Over time, historical data will sufficiently feed the predictive models to mitigate all of these risks.

Hardware usage models can sufficiently monetize the investment in hardware under any of the above models. However, the big leap in design and development is in planning for and harnessing the power of the data. Consider the opportunities that exist in collecting data from thousands of medical diagnostic devices and hundreds of thousands of procedures. In addition to providing the potential for improved individual diagnosis, trend data might be sold to research institutions or pharmaceutical companies.  Revenue streams from these sources can subsidize profitability to further enable access to markets where the medical technology is cost prohibitive today. 

Although there are inevitable iterations in every design process, transforming data into value shouldn’t be accidental. We’re all adapting to this new digital nervous system. Product and software design engineers live in a world where boundaries are broken on a regular basis. It might feel wobbly for the rest of us but it’s time to tap into the potential of this digital nervous system as we build new agile business models.