Edge computing | SAS India


Increasing return on assets

Consider a company operating a wind farm. With cloud computing, you will usually operate these assets until IoT devices and sensors detect a problem such as excessive wind. But there is a long loop between the sensors and the analytical software in the cloud. If the wind quickly rises to dangerous levels, delays in treatment and failure to shut down turbines immediately can result in serious damage, costly downtime and costly repairs.

With hundreds of sensors on each wind turbine, things like output, weather conditions, wear and overall operations relative to target parameters that measure output can be continuously measured. Then real-time analysis and machine learning can use this IoT data to recognize a dangerous state and trigger an immediate shutdown. There are no delays due to sending sensor data to the cloud over expensive WAN links for processing or sending analytic results (or decisions) back to the edge. Filtering IoT data on the periphery reduces the amount of data to be transported over the network, reducing costs even more.

Helps smart manufacturing detect and fix bugs early

Some manufacturers use today computer vision – powered by cameras or edge computer devices integrated into machines – to detect problems faster and earlier. Integrated computer vision is incredibly accurate and detects real-time defects as the factory manufactures products. As a result, it delivers fewer false positives and much earlier detection of product variances than traditional methods. Using IoT data processing on the edge, manufacturers can adjust machines or computer systems before products are out of specification. And they can automatically trigger immediate shutdowns – for example, when edge devices detect significant, unexpected errors.

As a result, factories can expect higher production yields, reduced manual inspection, more active time and lower risk of shipping products beyond customer specifications – all business-critical KPIs.

Differentiating in-store customer customer experiences to maximize sales

retailers uses camcorders as edge devices to monitor the paths customers run in retail environments. These devices use edge computing to merge past purchase and omnichannel stories for each customer and to generate unique, real-time deals based on customer profile and geolocation. (Offers are sent to customers who have chosen to store mobile apps.) Edge devices that capture more IoT data can target those offers even more effectively. Consider that edge devices can track a customer’s proximity to a store, path through the store, and more. If they see a customer looking at diapers for a while, they can immediately send a coupon or incentive to diapers or other baby related products.

Edge computing can also support different product experiences that build loyalty and create retention. For example, automakers build edge computing power in cars that can detect when a customer passes through a service center. By crushing the car’s operation and maintenance history data and combining this information with location-based information, they can alert drivers when their car needs service. Edge computing can detect when certain parts are trending toward failure. Then they can notify the customer or ask a local service center to contact the customer and schedule maintenance. These approaches often lead to higher customer satisfaction, retention and brand loyalty.

Activation of new and innovative business models

Edge analytics can enable new business models that drive new revenue streams. Heating and air conditioning manufacturers build edge calculations for assets, allowing them to self-analyze sensor data and proactively report status to asset owners and maintenance service providers. For example, data processing units may indicate whether the system is operating within expected parameters and to what degree. They can show the potential for potential errors, as well as opportunities to work more efficiently. Manufacturers can offer owners this reporting feature as an optional value-added service (paid).

Edge computing can also help ensure continued service and asset operation despite intermittent cloud connections. Think of an offshore drilling rig losing internet access during a hurricane. With edge computing, it can continue to monitor machine data and take real-time corrective action to keep people and the environment safe.

Similarly, edge computing can transform customer and provision of patient care models. In health care, it can be used to improve the patient experience as well as the clinician’s productivity and efficiency. Connected patients can capture their own vitals (such as blood pressure, blood sugar, heart rate and rhythm data) using IoT-enabled phones or watches and instantly share this data with clinicians through a patient portal. In this way, edge analysis can facilitate continuous patient monitoring, more efficient physician-patient communication, and faster, more accurate clinical decision making and diagnosis.

The results can be happier, healthier and safer customers, patients and workers. Longer active life. Reduced downtime and environmental impact. And higher return on assets.

Can companies afford to delay?

Given the cost of does not processing IoT data on the outskirts, we expect adoption to accelerate rapidly. The manufacturing and transportation industries have been early adopters. Other industries – such as healthcare, agriculture, city government and retail – are expected to come up with faster adoption as part of their digital transformation effort.

Make no mistake: Companies that reveal and automatically trade for new insights at the source gain competitive advantage and will be able to use it to jump from their competitors. From this perspective, late adopters of edge computing and analytics strategies potentially jeopardize profits and market share.



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