https://www.perle.com/articles/quality-control-in-the-era-of-iot-and-automation-40189762.shtml
https://www.industryweek.com/operations/quality/article/21965233/six-ways-the-internet-ofthings-can-boost-quality
https://www.cisin.com/coffee-break/Enterprise/how-iot-can-help-control-quality-in-pharmaproduction.html
 Software Quality Assurance Role Redefined:
•
Quality Assurance focuses on preventing defect. Quality Assurance ensures that the
approaches, techniques, methods and processes are designed for the projects are
implemented correctly.
• Quality assurance activities monitor and verify that the processes used to manage and
make the deliverables are followed and are operative, influencing both development
and operational processes using IOT.
 Strategy for effective IOT software testing: Automation rules-Develop
automation systems, through code, that can ensure quality standards at each level and
which assures – Response Time, Data Validation, Real Time Data Accuracy, Secure
Access control, Early detection of issues at design time, Needs more detailed
Acceptance Criteria, Minimal manual end to end testing etc.
 Impact of IOT on Quality Assurance : For manufacturers, IOT technologies
represent the next step in real- time monitoring, process automation and data
analytics. By outfitting production lines with dozens of IOT sensors, plant managers
can keep track of environmental conditions, equipment performance and more.
In today's tech-centric landscape, the ability to monitor and control
production equipment is paramount to the quality of manufactured
goods. Technicians are constantly recalibrating equipment and
optimizing production lines to ensure consistent process parameters
and eliminate time-wasting inefficiencies. The same retooling is also
taking place in the later stages of production, with automated
systems and robotics playing a key role. Historically, manufacturing
plants employed workforces of trained quality assurance specialists
to verify the integrity of finished goods.
How IoT is impacting quality control
For manufacturers, IoT technologies represent the next step in realtime monitoring, process automation and data analytics. By
outfitting production lines with dozens of IoT sensors, plant
managers can keep track of environmental conditions, equipment
performance and more. This added insight can allow manufacturers
to better understand where quality control take action with renewed
confidence. In terms of specific applications, IoT is helping
manufacturing companies.

Perform predictive maintenance: Instead of waiting for a piece of
machinery to fail, manufacturers are using IoT sensors to help
forecast when certain internal components will break down. By
remaining proactive about maintenance and repair, manufacturers
can simultaneously lower their overhead costs and reduce rejection
rates.
 Monitor production remotely: Another benefit of IoT systems is that
they can be monitored and controlled by off-site employees. This
capability is especially important in the current business climate,
where the COVID-19 pandemic has forced companies to curtail their
physical operations. Using vision inspection systems, manufacturers
can continue to perform key quality control tasks without the need
for manual intervention. For example, thanks to recent innovations
from Sightline Process Control Inc., bakeries around the world are able to
integrate high-speed visual measurement tools into their workflows,
AZO Materials reported. These systems are able to analyze up to 100
objects per second, helping identify defective baked goods before they're
sent out to customers.
 Emerging IoT Applications On Quality Assurance
• Once a manufacturing plant is outfitted with IoT sensors, worksite
managers can begin integrating robotic process automation and
other technologies to help make their operations more reliable.
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Information & Analysis
Tracking Behavior : SCM, Trucks with sensors for predictive maintenance
Enhanced Situational Awareness : Examples- Weather Warning Systems
Sensor-driven Decision Analytics : Examples- Oil and gas company optimizing
oilfield production
Automation & and Control Process Optimization : Examples-Manufacturing
Assembly Line
Optimized Resource Consumption : Examples-Energy Distribution Networks
Complex Autonomous Systems : Examples- Collision Avoidance Systems
The massive amount of data these sensors collect can be fed into a
machine-learning algorithm or an AI-powered platform to help drive
real-time insights. In a fully networked manufacturing environment
— one that is equipped with machine-to-machine communications —
automated systems can detect output variations and send that data to
downstream equipment. This can allow the equipment itself to make
minor adjustments on the fly, including those that impact product
quality.
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