  Manufacturers have long benefited from reusing returned products and parts.
This benevolent approach can minimize cost and help the manufacturer to play a
role in sustaining the environment, something which is of utmost importance
these days because of growing environment concerns. Reuse of returned parts and
products aids environment sustainability because doing so helps reduce the use
of raw materials, eliminate energy use to produce new parts, and minimize waste
materials. However, handling returns effectively and efficiently can be
difficult if the processes do not provide the visibility that is necessary to
track, manage, and re-use the returns. This paper applies advanced analytics on
procurement data to increase reutilization in new build by optimizing
Equal-to-New (ETN) parts return. This will reduce 'the spend' on new buy parts
for building new product units. The process involves forecasting and matching
returns supply to demand for new build. Complexity in the process is the
forecasting and matching while making sure a reutilization engineering process
is available. Also, this will identify high demand/value/yield parts for
development engineering to focus. Analytics has been applied on different
levels to enhance the optimization process including forecast of upgraded
parts. Machine Learning algorithms are used to build an automated
infrastructure that can support the transformation of ETN parts utilization in
the procurement parts planning process. This system incorporate returns
forecast in the planning cycle to reduce suppliers liability from 9 weeks to 12
months planning cycle, e.g., reduce 5% of 10 million US dollars liability.

Machine Learning;Artificial Intelligence