Internet of Things (CBS3008) APPLICATIONS OF IOT IN MANUFACTURING INDUSTRIES

INTRODUCTION TO INTERNET OF
THINGS (CBS3008)
APPLICATIONS OF IOT IN
MANUFACTURING INDUSTRIES.
21/01/2022
APPLICATIONS OF IOT IN MANUFACTURING INDUSTRIES.
The Internet of Things (IoT) is a key component of global industrial transformation, and the
manufacturing sector leads in leveraging this technology.

Analysts expect manufacturing to retain that leading position until at least 2020 for multiple
reasons. IoT has the potential to bring another industrial revolution – Industry 4.0 – with
applications that bring rapid returns while enabling manufacturers to adopt digital
transformation in various perspectives: automation, visibility, customer-centricity and
reduced time to market.
Some of the uses, applications and advantages of IoT in the manufacturing domain are the
following:
1. Quality Control
In a standard reactive quality control process, manufacturers produce an item, their quality
control unit tests it, and they hope to catch and rectify the flaws before the product reaches
the market.
IoT makes this process proactive with thermal and video sensors collecting complete
product data through different stages of a product cycle. The products can also be tested at
each manufacturing step to check if their attributes are within specifications. In addition,
instrumentation and monitoring of manufacturing equipment help quality control personnel
to check if and where equipment calibration diverges from standard settings – such
inaccuracies must be thwarted in time to avoid misalignment of products.
IoT’s support in monitoring both equipment settings and the outcomes of each production
step gives manufacturers a stronger assurance of detecting quality problems at the source.
Measures for improvement can, therefore, be taken in time.
RJ Corp, the largest bottler of Pepsi in India², uses IoT sensors to capture different data
parameters required to gauge quality on a real-time basis. As the material gets prepared,
deviations indicate at a quality concern, and the machine can be stopped for immediate
corrective action.
2. Inventory Management
Together with radio frequency identification (RFID), IoT makes inventory management an
efficient and seamless process. Every item in the inventory gets an RFID tag, and each tag
has a unique identification number (UID) comprising encoded digital information about the
item. RFID readers can scan the tags, and the data extracted gets transmitted to the cloud
for processing.
The role of industrial IoT here involves transforming the data acquired by RFID readers into
useful business insights. It creates a record of the location of inventory items, their statuses

and their movements in the supply chain and gives users comparable results. For instance,
as per the data on inventory quantity and location, IoT-based inventory management
architecture can help calculate the volume of raw materials required for an impending
manufacturing cycle. The outputs of IoT-based inventory management can be used in
diverse ways. The system can send an alert to the users if any individual inventory item is
missing and can notify them when they must replenish the materials.
IoT gives cross-channel visibility to supply chain managers with a realistic estimate of
available materials, the arrival of new materials and work-in-progress, optimising shared
costs in the value chain. By tracking the speed of movement and traffic flow of raw
materials, manufacturers can be better prepared to receive them. This improves handling
times and enables more efficient processing of those materials for production.
3. Predictive Maintenance
Traditionally, manufacturers have employed a time-based approach for planning the
maintenance schedules of their machinery and equipment. However, according to the ARC
group study³, only 18% of equipment fail on account of age, whereas 82% of failures occur
randomly. This proves that a time-based approach is not efficient and may prove costly in
the long run.
Manufacturers can avoid such ineffective maintenance routines by leveraging industrial IoT
and data science for predictive maintenance. By using IoT sensors (on the equipment), they
can monitor its operating environment and perform analytics using related data in the cloud
to evaluate the actual wear and tear. Prompt service and repair enable more efficiency in
the maintenance process, better allocation of work to field technicians and avoidance of
downtime along with more significant cost savings.
As an example, steel plants have several furnaces using water cooling panels for
temperature control. Any leakages in the panels can result in safety issues and production
loss. With IoT-based predictive maintenance, plant managers can strategically identify
anomalies and conduct a root cause analysis to prevent machine failures and delays in
production.
4. More Safety in Operations
In combination with big data analytics, IoT also optimises the safety of workers, equipment
and operations in a manufacturing plant. It can be used to track KPIs like worker absences,
vehicle mishaps, machinery damage and any other mishaps that affect normal activities.