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Boosting Equipment Manufacturing Through Edge Analytics

A challenge with the Industrial Internet of Things is optimizing the data to produce more robust, enterprise-wide value for the organization. Edge analytics can aid efforts.

Andy Howard, Managing Director, Automotive and Industrial Equipment Group, Accenture
Andy Howard, Managing Director, Automotive and Industrial Equipment Group, Accenture

The Industrial Internet of Things (IIoT) is helping industrial equipment manufacturers redesign their entire business. Realizing the full value of IIoT technology has become increasingly critical to their success, as a wealth of new growth opportunities is emerging, driven by the digital revolution. Edge analytics, a new analytics approach, can aid their efforts.

The connected world is expanding ever further at an ever-faster pace. As many as 50 billion devices could be connected to the Internet of Things (IoT) by 2020, according to Cisco, including numerous industrial machines and devices. This will create unprecedented volumes of data. The challenge, however, will be optimizing the data to produce more robust, enterprise-wide value for the organization.

Big Data analytics is a well-established concept that has enabled industrial manufacturers to harness huge datasets to derive insights that support smart decision-making. But, as the IoT makes available ever vaster new datasets, acquiring and transforming them into actionable insights will become more challenging. Large numbers of servers and other forms of infrastructure are already needed to support huge volumes of data, and the advanced analytics and computing power required for machine learning to capture insights. Even the cloud is typically used to augment support.

Edge analytics is a process that can analyze the data of “things” or connected devices, such as agricultural equipment, oil rig generators or any other industrial device, at the edge of the IoT network in support of an organization’s Big Data analysis. It can perform advanced predictive and prescriptive analysis quickly with sufficient granularity to enable meaningful insights to be derived and actions triggered at the edge. In other words, optimization of the network can first occur at the level of individual edge equipment and work inward to spread the wider benefits of edge capabilities enterprise-wide. It is an action that enables analytics to become much more efficient, faster and effective.

More manufacturing benefits

Aligning edge analytics with an organization’s Big Data analytics also can accelerate and increase business benefits that include:

  • Improved equipment uptime—A failure in a subsystem, component or the impact of running a component in a degraded state, for instance, can be predicted in real time, continually refined as more data is analyzed, and be used to enhance operational use and maintenance scheduling.
  • Reduced maintenance costs—Enhanced analysis of needed maintenance also means that more repairs can be completed on first visits by giving mechanics detailed instructions about the causes of a problem, what action is needed, and what parts are required—reducing repair cost.
  • Lower spares inventory—Edge analytics models can be tailored to the requirements of an individual device or system. This might mean reading sensors directly associated with certain components and/or subsystems. Guided by an organization’s desired business value, the edge model can then define how the device or system should be optimally configured to achieve a business goal, making a spares inventory vastly more efficient at minimal cost.
  • Critical failure prevention—By acquiring, monitoring and analyzing data regarding components, edge analytics can identify a cause before its effect materializes, enabling earlier problem detection and prevention.
  • New business models—Perhaps most important, edge analytics can help shape new business models to capture new opportunities. For example, it can improve just-in-time parts management systems using self-monitoring analysis that predicts which components will fail and when—triggering parts replacement notifications throughout the value chain. This enables the creation of an “as needed” maintenance schedule, reduces downtime and parts inventory, and results in a more efficient model.

Gaining an edge

The significant increase in connected devices taking place today will give industrial equipment manufacturers an opportunity to experience major growth. Adopting edge analytics can greatly enhance their operational efficiency and ability to take full advantage of this promising trend.

>>Andy Howard,, is managing director of the Automotive and Industrial Equipment Group at Accenture. Andrew D. Hopkins,, is managing director of Accenture Mobility.


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