By: Wolfgang Thieme
As the Internet of Things (IoT) creates a new influx of machine and process data, Original Equipment Manufacturers (OEMs) of industrial assets are finding themselves at a crossroads between
disrupting or being disrupted. Pervasive interconnection powered by IoT technology opens the door to new avenues for value creation that were not previously achievable. For OEMs, this means they
need to rethink their operational models or risk losing ground as digital businesses quickly encroach.
The opportunities of IoT for OEMs are bountiful. They encompass everything from improved maintenance services and aftermarket opportunities to innovative revenue generation, enhanced machine
design and improved quality management. All of these are achieved through a new host of machine telemetry data delivered by embedded IoT sensors. By capitalizing on massive data collection and
deep analytics, OEMs can transform the way they operate while activating new product and service offerings to better serve industrial customers. Here are four ways the IoT can create new business
value for OEMs.
Quality control is fundamental in every industry, but in manufacturing, it’s hyper-critical. Volatile market demand, high material and production costs alongside the mission-critical nature of
end products impel OEM and manufacturers to pursue nothing but first-rate quality and a minimal rejection rate. With the IoT gradually hitting its stride in manufacturing, quality management is
an area with transformational opportunities.
Wireless IoT networks that can capture vast, granular critical data points along the production line give manufacturers unprecedented control over their operations and product outputs. Beyond
reactive, end-of-run quality inspection, IoT data empowers a proactive quality assurance approach to diagnose and prevent defects much earlier in the process for peak production throughput and
repeatability alongside reduced costs and waste. Concurrently, it provides valuable insights for achieving and maintaining storage best practices.
For example, with 24/7 remote monitoring, quality managers can instantly detect off-spec conditions among running equipment and processes that give rise to potential product defects. Following up
with a prompt quality check helps to reaffirm the problem at the source and facilitate troubleshooting to hinder future defects. Once the sources of different quality problems have been diagnosed
and verified, manufacturers can even develop and implement a quality control model to further optimize product properties. Capitalizing on ongoing sensor inputs such a model allows machine
operations to automatically adapt to unwanted fluctuations in variables like environmental conditions, ultimately to achieve the top and consistent product attributes.
Besides improving quality control and reducing waste costs, IoT also allows OEMs to capture an entirely new business model with data-driven product offerings. Rather than selling a piece of
equipment as a one-off, an OEM can provide an option where the customer could rent it and is recurrently charged based on equipment use time and output. This model is called
With its subscription-based pricing strategy, EaaS brings distinct advantages to both OEMs and industrial clients. On one hand, customers can enjoy greater flexibility, reduced risks, and easier
equipment access by replacing high upfront capital costs with a lower monthly pay-per-use expense. Concurrently, maintenance and overhaul activities are fully covered in the Service Level
Agreement (SLA) and better guaranteed, thanks to real-time data on machine operations. On the other hand, OEMs can benefit from stable, recurring revenue streams that span the asset lifetime,
better customer engagement and more upselling opportunities.
In order for EaaS to be successful, it must be implemented with the proper business intelligence to ensure equipment is well-maintained and productive for as long as possible. With IoT sensors
installed on equipment, vendors can remotely gather real-time data from customer sites. This can help them improve their designs to make machinery more robust and better-performing. Insights on
the equipment status,