The increasing adoption of the Internet of Things (IoT) is presenting manufacturers with tremendous business opportunities. By using readily available computations of vast amounts of data, a manufacturer can leverage analytics to generate real-time insights to understand the needs of end consumers and product performance under a myriad of operating conditions.
In turn, the automated analytics can apply those insights to a closed loop action on the same device, the network, or other connected devices. Additionally, the data from sensors and other sources can help drive contextual analytics and generate further monetization opportunities for cost-containment or revenue-generation initiatives for different stakeholders in the ecosystem.
IoT analytics can be an important tool for manufacturers as they are pressured to quickly overhaul business and production processes to meet the growing customer demand for higher and customized levels of service, a superior experience, and personalized products. For instance, Harley-Davidson reconfigured and equipped its facility in York, Pennsylvania, with sensors and location awareness to reduce the time it takes to produce customized motorbikes from a 21-day cycle to six hours.
To monetize insights that are worth billions, IoT gives manufacturers growing opportunities both internally and externally to save costs, increase revenue from better product sales, pursue services-oriented business models, and improve internal processes. And manufacturers also have the opportunity for additional revenue generation through the sale of data, insights, and/or advice to benefit the ecosystem stakeholders.
But to fully leverage IoT’s potential, manufacturers need to understand why they need to use IoT analytics and how to build infrastructure and skills, identify the information needs, and start converting data into meaningful insights and foresights.
First, it is important to understand the fundamental difference between edge analytics and traditional analytics. Unlike traditional analytics where data are analyzed after being centralized, data analysis is automated at the point of origination—the connected device, sensor, or network level—as well as at the centralized level, depending on the types of analytics and their applications. The presence of intelligence and computational capability embedded at the lowest levels of the network eliminates the need to always bring data to a central location.
The ability to achieve contextual analytics leads to monetization potential that goes beyond the traditional scope of the organization. For example, data-driven insights on the performance of tires under different conditions can be used to provide an advisory service on tire life enhancement to vehicle owners of the tires, and to provide tire manufacturers with information that might improve tire designs. In the same example, the edge analytics can help real-time correction or adjustment of driving parameters through closed-loop feedback with vehicle systems to control tire wear.
Within the IoT ecosystem, manufacturers need to think about two types of customers: first, the traditional end customers, and second, ecosystem players that serve and support these end customers. It is this second category that is making IoT essential and relevant to manufacturers.
The basic principle of edge analytics is to analyze data at their point of origin, and pass on only the relevant information or insight to other levels of the IoT network hierarchy. This greatly improves the time-to-value of the data, reduces network bottlenecks, reduces service delays, improves response time, and focuses on actionable data.
Edge analytics also allow manufacturers to analyze huge volumes of IoT data in a scalable, efficient way, and are ideal for those who can benefit from automated decisions. For instance, such analytics can alert a manufacturer to switch off a valve once a leak is detected in a piece of equipment. Ideal uses might also include applications that require a great deal of bandwidth—such as smart lighting, parking spaces, and offshore oil rigs. These extensions of edge analytics to a system level can help address issues beyond a single consumer or industrial use. For example, applying edge analytics to manage traffic can encompass multiple vehicles, roads, and roadside infrastructure and can ease the pain of traffic congestion.
One word of caution: edge analytics can create security vulnerabilities since a layer of technology is deployed at the network’s very edge, in isolation from other elements of the information architecture. Manufacturers must make edge analytics part of their holistic data strategy to ensure that data are accessible across the network, with end-to-end security from the device level covering the whole ecosystem.