Sensai Unveils Top Five Risks Impacting Manufacturing Plant Floor Operations
Discovery, management and mitigation of persistent plant floor issues
is possible through AI technology
MONTERREY, Mexico–(BUSINESS WIRE)–While manufacturers around the world have reached new heights in
technology adoption and equipment innovation, there are still many pain
points that hinder optimization, efficiency and even safety on the plant
floor.
Today, Sensai,
an augmented productivity platform for manufacturing operations,
unveiled a list of the top five plant floor risks that reduce
efficiency, drain productivity and negatively impact business results if
left unattended. These insights are based on the combined manufacturing
industry expertise within SensaiÔÇÖs leadership team, as well as insights
from its current pilot programs at organizations in the manufacturing
industries, including those in automotive, construction materials,
consumer goods and more.
ÔÇ£Industry 4.0 is making the plant floor much smarter than ever before.
But that is not to say that automation, data exchange, IIoT and cloud
computing can manage itself. Companies need to focus their efforts on
identifying and addressing pain points and engaging their workers in the
change process to bridge an understanding of what must be done to
realize the full potential of these innovative technology solutions,ÔÇØ
said Porfirio Lima, CEO of Sensai.
The top five issues impacting manufacturing operations today include:
1. Catastrophic Equipment Failures
When an organization has
to delay or shut down operations due to aging or failed machines, this
can have a serious impact on the safety of employees and bottom line of
the business. Further, in order to continue producing at the pace the
market demands, companies may have to outsource repairs and production
volume, which can be extremely costly. Overall, legacy equipment can
sometimes be a hindrance for manufacturers and the success of digital
transformation due to the complexity of changeovers, and access to data.
2. Data Collection and Mining
For factories to be effective,
information regarding inventory, supply, deliveries, quality,
production, customer support, processing and day-to-day management must
all be analyzed, monitored and updated on a daily basis. Of extreme
importance to the business decisions often needs to be made using a
comprehensive range of data from the production floor, to spreadsheets
and clipboards. Without an efficient system in place, operations
managers and their teams waste a large amount of time searching for the
necessary information vital to making critical business decisions. This
is a hidden waste that most people are not even aware of.
3. Information Reliability
As important as it is to
centralize the data, it is even more important that the data is
accurate. If the data is not reliable, companies may end up choosing the
path of most resistance, resulting in wasted or misused resources and a
complex operational process. Manual data entry is prone to human error,
which can lead to poor business decisions that stem from misleading
information. With facilities that are both robotic and manual,
operations must still pay close attention to the actionable data as it
comes in, which means there is an additional layer of complexity.
Calculating inaccurate KPI data is something that continues to haunt
many production managers today. With the right technology in place using
accurate data, decisions can be made more effectively and efficiently.
4. Slow Onboarding and Knowledge Loss
When new employees are
hired, there is often a steep learning curve, requiring numerous hours
of coaching, training and shadowing veteran employees. However, many
companies do not have the internal resources to properly train and
onboard individuals, increasing the likelihood of operational errors,
unapproved work-arounds and more. Alternatively, when organizations lose
top talent to a competitor or retirement, those years of experience walk
out the door with them. Depending on the existing management protocol,
both can impact the efficiency and productivity level of an entire
company.
5. Process Control
The complex relationship between a
machineÔÇÖs health, the processesÔÇÖ parameters, and the materialÔÇÖs
conditions, all have a tremendous impact on a manufacturerÔÇÖs final
product. When any of these elements are not working correctly, it can be
detrimental to productivity. Having the correct process in place to
analyze and create robust models gives guidance to operators as to where
to act to optimize performance, quality and uptime. Machine Learning
enables smart process controls, so that corrections can be made
automatically and even autonomously considering all the critical and
relevant variables.
ÔÇ£As manufacturers are constantly facing challenges from every angle, it
is imperative that the industry also pivots accordingly, developing and
ultimately implementing powerful predictive and prescriptive
technologies for operations at a much faster rate. SensaiÔÇÖs platform is
designed with a holistic approach so that productivity on the plant
floor can easily be seen, measured and augmented. It is SensaiÔÇÖs mission
to motivate organizations and their employees with a system that is easy
to implement, rewarding and accurate, and most importantly enables
humans unlimited potential to be augmented,ÔÇØ says Lima.
To learn more about Sensai, please visit www.sensai.net.
About Sensai
Founded in 2017, Sensai is headquartered in Monterrey, Mexico and
privately funded by Metalsa, a leading global manufacturer of automotive
structural components. Sensai is an augmented productivity platform for
manufacturing operations that increases throughput and decreases
downtime with an innovative AI technology. Sensai enables manufacturing
operations teams to effectively monitor machinery, accurately diagnose
problems before they happen and quickly implement solutions. The
companyÔÇÖs goal is to augment the potential for humans in the world
through technology. Learn more at: www.sensai.net
Contacts
Sensai PR
Affect
Kennedi Fuller, 212-398-9680
[email protected]
