As part of the upcoming CIBI Innovation Days 2020, I will speak at this year´ s event about how machine data
transforms into innovative, data-based financial products and how IoT and finance can be linked in future. In the following blog post I would like to share a brief summary as it is something that I would like to reiterate for all those who cannot participate in the event.
Before we start, I would like to quickly explain what Pay-per-Use financing is. Pay-per-Use financing is a flexible financing agreement that calculates periodic repayment rates by measuring an equipment’s runtime and the number of units it produced. In other words, you pay off your financing per every unit of inventory your equipment produces.
Why is Pay-per-Use financing necessary?
A good example, why Pay-per-Use financing is necessary is the COVID-19 crisis. During the crisis many manufacturing companies, where struggling due to high fixed costs, which led to cash-flow issues– also caused due to high equipment investments in the past, which were financed with rigid repayment rates. Some companies even had to raise additional loans. With Pay-per-Use financing at least the repayment rates of the financing equipment would have decreased automatically, which would have given the companies additional revenue.
Who benefits from Pay-per-Use financing?
There are several benefits of pay-per-use financing depending on which stakeholder you look at (we discussed the pros and cons in a separate blog post. The most important advantages are:
For the machine users:
Removing investment hurdles due to flexible Pay-per-Use repayment rates
Investment costs can be split and converted into operational costs (CAPEX to OPEX)
Machines can be purchased off-balanced & the investment risk can be (partly) outsourced
For the OEMs:
Increase sales through new sales financing products
Development of new business fields / entering new markets
Possibility of managing the secondary market
For the financial investors:
Strong quantitative framework to measure price and underwrite new contracts based on daily trustable equipment data
Highly efficient underwriting process
Diversification possibility, as investors have the opportunity to invest in different industries
After defining and highlighting the benefits of IoT data-based financing I would like to give an overview about...
What is the impact of Industrial IoT data on equipment financing?
In the past Industry 4.0 data has mainly been used for analysis to increase productivity (OEE) or decrease unplanned downtimes. Mainly only the internal management used this information.
Now, new technologies allow the use of Industry 4.0 data also for financing aspects like superior risk management, invoicing, pricing, portfolio analysis, etc., what also different studies show. For example, PriceWaterhouseCooper has published a study (this year) which states that IoT data will significantly advance predictive underwriting and predictive pricing strategies and make them much more accurate, and that IoT data-based models will therefore bring great added value in the future.
We at linx4 are already using those new technologies, where we are able to predict the future utilization of the machines, based on historical IoT data and other parameters. But not only machine utilization is predictable much better, also risk and portfolio management are finer to forecast, as it is simpler to assess in the ways of how a portfolio could develop in the future due to the IoT data.
Another interesting aspect is the real-time character of the collected IoT-data. Therefore, it is possible to see how a particular machine performs on a daily basis. This allows to react quickly and flexibly when machines perform poorly.
Beside the predictive forecasts about equipment usage, underwriting, etc. the IoT data provides another very interesting insight, especially, when it comes to pay-per-use portfolio financing.
For example the illustration of crises in the performance or the impact on individual industries. The collected data can be used to determine how crises are displayed in different countries, also including the fact that this could happen time delayed.
Here is a practical example:
Below we see three graphs that show the equipment usage from 2018 to 2020, in different industries. In the purple marked area, we see the corona crisis.
If you take a closer look at the first industry, you can see very clearly that the corona effect is visible, but not quite as strong as in the industry shown in Figure 2.
In addition, the middle diagram shows that the corona-related slump in Asia occurs earlier, however leaves a greater production slump in Europe.
All this information, both for individual machines and for entire industries, helps to make risk management more precise and optimizes the underwriting process and pricing.