Major companies are investing in machine learning-powered approaches to
improve all aspects of manufacturing. Firms are using this technology to bring
down labour costs, reduce product defects, shorten unplanned downtimes, improve
transition times, and increase production speed.
Artificial intelligence will help drive the fourth industrial revolution – Industry 4.0 – with machine learning and deep learning rapidly becoming mainstream technologies. Recent developments and partnerships have shown how IBM, Microsoft and SAP, among others, are exploring the future of manufacturing.
The mission of the Europe-based Korea
Institute of Science and Technology (KIST) is contributing towards
globalisation of Korean research and innovation by building an open platform
where notable South Korean and European institutions and industrial partners
can collaborate. Manufacturing is a key contributor for both Korean and
European economies and Industry 4.0 is one of the main focus areas.
IBM and KIST Europe are partners in SmartFactory-KL, which is a
manufacturing facility built with modular components which can be reconfigured
for different manufacturing tasks. The components in the facility are equipped
with sensors and connected through the Internet of Things (IoT) to a “digital
twin,” a complete digital replica of the factory’s physical assets, processes
and systems, running in the IBM Cloud.
KIST Europe and IBM’s data scientists used IBM Watson Studio to design,
train, test and use a machine learning model that can predict whether a given
measurement is reliable.
Quartic.ai, meanwhile, has established an Industrial
AI and IoT solution that enables industries to turn their assets into smart,
Industry 4.0 equivalents. Quartic.ai can help manufacturers to release the
stranded intelligence in their plants and enable their subject matter experts
and engineers to turn their experience and knowledge into AI applications
without extensive data science experience.
Rajiv Anand, CEO and co-founder of Quartic.ai, gives an example of how
AI has impacted the business of their customers. He said that increasing
equipment reliability is often seen from the perspective of increasing uptime.
However, in the case of one large chemical processing customer, repeated
failure of a shaft seal on an agitator had led to safety issues due to the leak
of dangerous materials from a sealed, high pressure vessel. This is where AI
came into play. Anand said that the problem could not have been solved without
using machine learning models. Using
historical data from past seal failures, they were able to develop machine
learning models that start alerting operations staff when a seal leak is
developing and is likely to occur.
SAP is specifically
targeting manufacturers with a new set of products aimed at helping
them manage and monitor their systems. The new offerings, part of SAP’s Digital
Manufacturing Cloud, include the SAP Digital Manufacturing Cloud Solution For Execution,
which integrates with existing manufacturing systems on the shop floor in order
to provide visibility into operations at the component and material level.
The SAP Digital Manufacturing Cloud For Insights provides data-driven
performance management capabilities. The company also announced a new ‘predictive
quality’ offering, which allows predictive algorithms to be applied so
manufacturers can reduce the number of defects in their products.
According to TrendForce, smart manufacturing is projected to grow
substantially in the coming five years. It has estimated the global smart
manufacturing market will grow at a projected compound annual growth rate of
12.5% to be well over $200 billion in 2018 and will increase to more than $320
billion by 2020.