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Five Ways OEMs can Leverage End-User Machine Data

Final user, Industrial Edge Computing, Systems Integrator

Five Ways OEMs can Leverage End-User Machine Data

30 Aug, 2020

This article will discuss:

  • The different ways OEMs can leverage end-user machine data
  • The benefits that come with leveraging end-user machine data
  • Examples of how leveraging machine data can optimize business growth

The decades before Industrie 4.0 were characterized by analog (and occasionally digital) industrial machines, which are now categorized as “legacy assets”. Despite the limited data-capturing abilities of these equipment options, they could still track certain shop-floor parameters such as the operations timeline, the number of goods produced, and oil changes, etc. Facility owners who produced these data sets – end-user machine data – only cared about what was needed to make overall equipment effectiveness (OEE) calculations, while other data sets were actively collected by original equipment manufacturers (OEMs).

With Industrie 4.0, this is no longer the case, but end-user machine data still provides insight and can be used in diverse ways by OEMs.

 

Use cases for leveraging end-user machine data

With Industrie 4.0 and advances in wireless networks came interconnected cyber-physical systems. They ensure every process within industrial facilities can be tracked and measured in near real-time. The interconnectivity means that although individual machines may produce data, communication with other systems on the shop floor paint a picture of the entire facility. For OEMs, the interconnected system brings insight into the actual conditions of factory shop floors and how their machines are used across similar facilities, but with different use cases. The data collected can be leveraged in the following ways:

 

  1. Develop optimized equipment – The obvious option for OEMs seeking to leverage end-user machine data is designing optimized equipment that helps the end-user be more productive. One example is the use of machine maintenance data within harsh environments to improve the durability of equipment components. How machines fare within different facilities, such as one whose operations produce excessive heat or electromagnetic interference, can help OEMs adapt equipment construction to fit Industrie 4.0 initiatives. This means developing equipment with better ingress protection measures and applies to both large OEMs and IoT hardware vendors. The design of shop-floor-friendly equipment will strengthen the data-capturing abilities of equipment while improving their life spans.

  2. Data as a Service (DaaS) – End-user machine data provide some interesting insight into equipment application. Leveraging these data sets can provide enhanced services to the end-user. An example is the inclusion of optimized user packages which provide factory owners with benchmark numbers on the use of particular equipment. These benchmark numbers can be OEE percentages, accurate predictive maintenance figures, or the average production capacity of individual machines for specific applications.
    These insightful packages can then be applied by the end-user to integrate data-driven plant performance models within their facilities.

    For factory owners purchasing an OEM's equipment for the first time, the data package can be used to develop predictive maintenance models without having to spend months aggregating the historical data needed for such a process. Eliminating the need to aggregate data is what data as a service or data as a product aims to achieve. DaaS initiatives also provide SMEs, which do not have the financial capacity of large enterprises, with a pathway to adopting Industrie 4.0 using a limited budget. While these SMEs may have limited resources for extensive data capture and analysis using cloud resources, many OEMs already have cloud platforms for machine monitoring and other activities.

  3. Remote machine monitoring and service provision – Remote machine monitoring involves the use of IoT devices or Wi-Fi connected systems to capture data from the shop floor to centralized storage platforms such as the cloud. The collected data can then be aggregated and processed remotely using advanced tools to provide insight into the equipment’s operations. Monitoring machine use is one aspect of remotely accessing machine operations and another important aspect is providing remote servicing initiatives using the data collected.

    Historically, servicing machinery is a costly initiative. Getting a technician to walk through the doors cost approximately $50 billion in the United States for 2016 alone. Estimates put machine maintenance at roughly 15% of the cost of items sold. Remote servicing provides OEMs and facilities with a pathway to reducing the machine servicing and repairs costs that come with the equipment they manufacture. For factory owners, this means reduced equipment evaluation activities in remote locations while keeping the end-user happy due to reduced servicing fees. Providing remote servicing subscription plans to the end-user also ensures factory owners can access end-user machine data in a decade being defined by individual factory owners hoarding and using shop-floor data to optimize processes.

  4. Improve customer relationship – Delivering optimized services through remote servicing and building durable shop-floor equipment are some of the ways to improve the relationship between the factory owners and OEMs. End-user machine data provides the insight needed to deliver on the above initiatives. One more addition is leveraging end-user machine data to develop physical or virtual training modules and including these training modules as packages that come with manufacturing equipment.

    Virtual training and validation are integral Industrie 4.0 models, which means OEMs can assist more factory owners to get comfortable with using virtual training and virtual remote support to solve specific issues. This initiative will reduce the training costs for new employees for factory owners, while ensuring OEMs focus on the task of building better equipment without having to send out training staff as part of the benefits of purchasing equipment.

  5. Increase revenue generation – Enterprises are always on the lookout for new ways to generate revenue, and leveraging end-user machine data to deliver DaaS or upgrade user packages are revenue-generation options. Providing virtual training and remote services also frees up the capital spent on keeping multiple in-house technicians around the clock. This makes new ways to generate revenue an important by-product of leveraging end-user machine data.

 

 

Conclusion

In a world defined by interconnected cyber-physical systems, data is king. OEMs must create acceptable avenues for capturing the shop-floor data from the equipment they no longer own. Today, the Machine as a Service concept – in which machines are leased to enterprises – has proven to be an excellent option for capturing end-user machine data. The use of IIoT devices across shop floors is another avenue that can be explored to collect data.

After capturing machine data, the next step is deciding what to do with it. Leveraging end-user machine data to improve design, provide remote services or packages, and improve customer use of machines are some examples of what can be achieved. Other future use cases such as developing Industrie 4.0 as a service for SMEs will also apply as data collection and utilization technologies evolve. How OEMs intend to leverage end-user machine data is a process that will continue to evolve with time.

 

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