WWW.HSO.COM
WHY MANUFACTURERS ARE LOOKING TO INTEGRATE MACHINE LEARNING INTO THEIR OPERATIONS
Manufacturers are already using the
predictive analytics of ERP to gain
competitive advantage, and now
they’re turning to machine learning
to further enhance this function.
This whitepaper explores how machine learning, in
conjunction with ERP, can help manufacturers bring
about business process improvements and greater
productivity as a result.
What is machine learning?
Machine learning refers to a method of data analysis that
enables computer programs to grow and learn by studying
predictive and statistical analytics, rather than by being
explicitly programmed.
This type of artificial intelligence is similar to that of data
mining as it involves the process of searching through
data to look for patterns. However, in the case of machine
learning, the computer program uses the data to adjust its
own actions accordingly, thus reducing the need for human
intervention.
A good example is Facebook’s News Feed, which uses
machine learning to personalise each users’ feed. If a user
frequently stops scrolling to read or ‘like’ a certain friend’s
posts, then the program will adapt so the News Feed starts
to display that friend’s activities earlier in the feed.
Using statistical and predictive analytics, the software is able
to identify patterns in the user’s behaviour and use this
information to populate the News Feed.
If the user then stops reading or ‘liking’ that friend’s posts,
the new data set will pick up on this and the News Feed will
be adjusted accordingly.
Another example of machine learning is the Clutter
functionality in Office 365, which analyses your email habits
to determine which email you’ll want to read and which
messages you’re likely to ignore and delete.
Machine learning in manufacturing
The sophistication of machine learning algorithms is
certainly making the manufacturing industry sit up and
take note. This new technology has the potential to deliver
greater predictive accuracy to each phase of production, as
well as:
•
•
•
•
•
Predictive maintenance or condition monitoring
Warranty reserve estimation
Demand forecasting
Process optimisation
Telematics
It is now within the grasp of every manufacturer to
assimilate machine learning into their operations and
become more competitive by gaining predictive insights into
production.
New insights and intelligence
Machine learning is set to bring new dimensions of insight
and intelligence to manufacturing operations. From supply
chain through to finance, each department will be able to
benefit from access to more relevant data.
A common problem in the past has
been the lack of integration between
departments, making it difficult for
manufacturing companies to achieve
shared goals.
One of the advantages of machine learning is that access
to predictive analytics can help teams optimise production
workflows and inventories to better manage factory needs
and customer demands.
Increasing production capacity
According to a report by General Electric on improving
manufacturing efficiency through predictive analysis:
• Up to 20% of production capacity is recovered as
equipment is proactively tuned for reliability.
• Utility infrastructure is optimised against process
needs, improving efficiency by 2% and lowering material
consumption by 4%.
• Reliable, predictable production capacity allows finished
goods buffers to be reduced by 30% or more.
3 ways machine learning can transform
manufacturing
Machine learning and predictive data analytics have the
potential to improve yield rates for manufacturers at the
machine, production cell, and plant level.
1) Preventative maintenance
The enhanced predictive accuracy of machine learning can
have a big impact on maintenance costs for manufacturers.
With data that drills down to component and part-level,
preventative maintenance is now possible across the factory
floor, enabling time and energy to be spent where it is
needed and before equipment develops faults.
2) Optimised supply chains
The insights generated by machine learning provide exactly
the right information for optimising the supply chain and
creating greater economies of scale. The data produced
allows buyers and suppliers to collaborate more effectively to
improve forecast accuracy and meet delivery dates.
3) Improved product and service quality
Machine learning algorithms can determine the factors that
have the highest and lowest impact on quality. This helps
manufacturers to create workflows and internal processes
that will be most effective in ensuring quality standards in
products and services are met.
• Comprehensive quality data can be shipped alongside
product, reducing rework by 20% and satisfying customer
traceability needs.
Typical Process Improvement
Minimise raw material costs
and protect brand quality
Improve manufacturing
sustainability
Enable profitable and
sustainable packaging
Gain production visibility
and agility
Optimise scheduling and
operator productivity
Machine learning improves ERP
Finally, the combined technologies of ERP and machine
learning can also highlight new opportunities.
Machine learning refers to a method of data analysis that
enables computer programs to grow and learn by studying
predictive and statistical analytics, rather than by being
explicitly programmed.
Machine learning and ERP are not
just complementary technologies.
Manufacturers that integrate the two
gain deeper insights and an improved
ability to forecast.
Patterns that emerge from the data can show product
preferences and customer trends that would otherwise be
unrecognisable. This knowledge helps manufacturers to
capitalise on sales, improve service and even create new
products.
Summary
Machine learning systems can estimate predicted outcomes
accurately based on training data or user experiences.
Although ERP can already provide predictive analytics, with
machine learning you can improve the accuracy of your
analysis over time. As a result, forecasting is improved and
manufacturers can target investments more effectively.
Another advantage of incorporating machine learning into
ERP is the ability to tailor insights. This allows manufacturers
to gain a level of understanding of their processes, customers
and workflows; understanding which becomes more accurate
over time, as machine learning applications adjust to target
specific elements based on the results generated.
By gathering valuable insights for better and more
accurate decision-making, machine learning systems
can help manufacturers improve their operations and
competitiveness.
But while machine learning techniques have found an
increasing level of applicability and relevance to real world
scenarios, they pose a few implementation challenges, and
it goes without saying that you should consult an external
third party expert when investigating how your business
could apply machine learning to your operations.
About HSO
About Microsoft Dynamics
HSO is a Microsoft Gold Partner with over 25 years of
experience in implementing Dynamics solutions.
Microsoft Dynamics makes it easy to operate across
multiple locations and countries by standardising processes,
providing visibility across the organisation, and helping to
simplify compliance.
HSO is an expert in Microsoft Dynamics AX, a comprehensive
ERP solution that enables medium and large companies to work
effectively, manage change, and compete globally.
Learn more at www.hso.com
HSO has the specialist expertise to deliver industry-focused
Microsoft Dynamics AX implementations for customers in
retail, distribution, and manufacturing.
Sources:
http://www.nist.gov/itl/iad/upload/Big-Data-Analytics-for-Smart-Manufacturing-Systems-Report.pdf
1st Floor, 100 Brook Drive,
T: +44 (0) 20 3128 7767
Green Park, Reading. RG2 6UJ
E: [email protected]
HSO_UK
www.hso.com