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A SURVEY CONDUCTED IN THREE
EUROPEAN COUNTRIES AND THE UNITED STATES
IN 2017 SHOWED THAT 70 % OF 450 IT DECISION-
MAKERS AND ON-SITE SERVICE MANAGERS DO NOT
KNOW EXACTLY WHEN THEIR EQUIPMENT NEEDS TO
BE MAINTAINED OR UPGRADED.
informatics of an artificial intelligence
program called Watson.
Meanwhile, aeronautical subcontractor
Figeac Aero has been deploying predictive
maintenance for the last five years on a
specific area of data received from its manufacturing equipment:
tool vibrations, geometric defects and clamping strength. This has
resulted in the prevention of 40% of malfunctions.
Increasingly accurate predictions
Over time, algorithms can be used to create malfunction flowcharts
based on fault logs. These models help recognise and then predict
potential future malfunctions. Machine learning technologies will
progressively enrich these models to enhance reliability and detect all
types of faults earlier and earlier.
Dominique Le Beuz, Head of Mobile Network Operations at Orange
France, said: “We install sensors at the interfaces of the items of
equipment, which form the mobile network to capture and analyse
traffic data. We can thereby monitor service quality, as real-time
alerts are triggered when network behaviour changes. In particular,
this provides assurance that new equipment or new functionalities
have been correctly integrated. The sensors provide good visibility
over both the network’s service quality and customer experience.”
In addition to service quality, the sensors have other uses. For
example, the data they report can feed into third-party applications
www.intelligentcio.com
and offer customers high-added-value services, an example being
the ‘Welcome’ text messages when a subscriber arrives in another
country and location-based services such as Flux Vision.
Aurélie Piètre-Cambacédès, responsible for the Operational Skill
Centre and Network Operations, Orange France, said: “Sensors enable
network malfunctions to be anticipated; they check whether the
traffic is being handled correctly by comparison with a benchmark
behaviour and they can detect the slightest anomaly. Equipment
operators can then investigate any issues before the customer is
affected. When a customer has a problem, analysing how the call
was handled across the network can sometimes help us anticipate
and prevent much bigger, wider problems.”
What’s more, this ability to anticipate has a clear financial impact
on the allocation of technical resources and on crisis management
costs if a problem arises, all representing costs for corporate clients.
With the advent of automatisation in network operation and the
installation of application programming interfaces (API) on sensor
systems, the operational gains could potentially be even more
significant for Orange than for its customers.
These sensors being deployed on the France mobile network, Orange
will soon be able to correlate data transversally and end-to-end. This
is becoming indispensable to optimise analysis of how it handles
very complex incidents because the network densification sometimes
makes it difficult to pinpoint the incident. All its subsidiaries with a
mobile network can benefit from this system of sensors, and it also
provides support for its installation and operation. n
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