!– /115087382/fpj-300×250-6 –><!– /115087382/fpj-300×250-7 –> <!– /115087382/button-5 –> <!– /115087382/button-6 –> <!– closes row –>
|<!– /115087382/button-7 –>||<!– /115087382/button-8 –>|
By Kevin P. Vanderslice, Director of Mobile Sales, ifm efector
The last 18 months have been a challenge for everyone, as we deal with supply chain issues, the lingering effects of the COVID shutdown, and the inflationary prices of core commodities. In an ideal world, there are checks and balances to prepare, but these are unprecedented times we are dealing with. The world in fluid power started changing before 2020. Each of us is on a journey to digital transformation, and each of us is in a different phase in that journey.
The term “digital transformation” is certainly buzzy these days. But how should fluid power companies manage this transformation? The manufacturing industry generates more data than any other sector of the economy, and because all this data is generated, companies are making data and analytics a priority. In a recent General Electric survey, 42% of manufacturers have said “big data” is a top priority. In the same survey, another 45% said that it is one of their three highest priorities. Today we talk a lot about the what and the how. What are big data, IIoT, and Industry 4.0? How are smart sensors, software, machine learning, and artificial intelligence connected?
In most of these conversations we do not discuss the why: Why is data important? Why are we using smart sensors?
As an example, let us look at telematics. Today we address a lot of the how and what commodities. Metrics, such as vehicle tracking, are used by 85% of fleet owners. This tells you where the vehicle is always located. However, it does not tell you how efficient the driver may be at planning the route. The car broadcasts data such as machine speed, fuel consumption, and temperature from the engine. It may also record additional inputs such as hydraulic oil, level, pressure, and humidity. This broadcast data is useful, but it does not tell you about impending maintenance issues. Finally, if we look at driver-performance monitoring, it is normally represented in data tracking accelerating or braking styles and how efficiently the driver followed the planned route. It does not explore the possibility of the driver looking down at his or her cell phone.
There is a shift in business models with this digital transformation, and each of us are in a different stage. For the past number of decades, most of us have navigated the waters of Wave 1. This is the classic ownership model, in which you pay a one-time price for a machine or after-sales service. An OEM provides a warranty for a limited time, and any service work beyond that follows the classic model of sending a technician or bringing the machine in for repairs. Wave 2 is the usage-based model, in which a customer pays for the actual usage of the machine but does not own it. In Wave 2, uptime becomes critical. If your machine is not performing at 100%, you may not get paid. Now you need more insights into the machine operation to detect wear and tear. With this information, you can schedule downtime for maintenance and repairs.
Lastly is the final stage, Wave 3, the outcome-based model. The customer only pays for the finished product and does not care which machine achieves it. However, you are responsible for the final product, and having a full understanding of the machine health and product quality is critical. The classic consumer example of Wave 3 is Uber.
As we mitigate these in the times in which we live, the digital transformation movement is ongoing. We need to determine which wave we are in and which one our clients expect from us in the future. This could take a few months or many years. The main point is – we need to prepare ourselves for this change.