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Performance Informed Technical Cost Modeling for Novel ManufacturingRobin Joseph Glebes (7443716) 17 October 2019 (has links)
<p>Inaccurate cost
estimates contribute to lost implementation opportunity of novel manufacturing
technologies or lost revenue due to under-bidding or loss of an over-bid
contract. High-volume, long-term orders, such as those the automotive industry
begets, are desired as they lock in revenue streams for months into years.
However, high-rate composite materials and their manufacturing processes are
novel among the industry and traditional costing methods have not advanced at a
proportional rate. This research effort developed a method to reduce the
complex composite manufacturing systems to fungible, upgradable, and linkable
individual processes that derive their manufacturing parameters from the
performance part design process. Employing technical cost modeling, this method
accurately quantifies the value of pursuing composite manufacturing by
integrating impregnation, solidification, heat transfer, kinetics, and
additional technical data from computer-aided part design simulation tools to
deliver an accurate cost estimate. </p>
<p>Cost modeling provides a
quantitative result that weighs heavily in the decision making process for adoption
of a new manufacturing method. In this dissertation, three case studies were
investigated for three different management decision cases: part production
management, in-house manufacturing management, and global manufacturing
management. </p>
<p>Part production management
is the decision making process for selecting a certain manufacturing method. A
case study with a Tier 1 Part Producer was conducted to provide a comparison of
two emerging novel preforming systems versus their in-use, metals based high-rate
manufacturing line in manufacturing a structural automotive part. Determining
material usage was the primary cost driver focus. Equipment Supplier A’s
process operated by seaming single layers of thermoplastic tape into rolls and
then stacking prior to consolidation and resulted in a scrap rate of 23-28%
with a cost of $32.87-36.01 per kilogram saved depending on the input tape
width. Equipment Supplier B’s layup process, essentially a multi-head automatic
tape layup machine, resulted in scrap rate of 20-27% with a cost of
$34.48-36.67 per kilogram saved depending on the input tape width. This
exceeded the Tier 1 Part Producer’s requirement of $6.6-11
per kilogram saved and led to them to abandon this
application as a feasible project and instead look for a different part with a
higher return regarding cost for weight saved.</p>
<p>In-house manufacturing
management is the decision making process governing manufacturing operating
procedures. A case study for the Manufacturing Design Laboratory’s (MDLab)
hybrid molding line was undertaken to determine the manufacturing cost for a
composite test coupon. Processing parameters were obtained from three sources:
performance design computer aided engineering (CAE), common industry transfer
estimation times, and a calculated preform layup time. Compared to a similarly shaped
test coupon made of aluminum, highly-automated manufacturing realizes weight
savings of 46.25% and cost savings of 16.5%. Low-automation manufacturing
captures the same weight savings, but has a cost for weight saved penalty, cost
increase, of $9.89 per kilogram, showing how influential the labor contribution
is to manufacturing cost. </p>
<p>Global manufacturing
management is the decision making process governing manufacturing location.
Various manufacturing cost drivers are location dependent, thus a dataset was
developed to alter these parameters for the U.S. states. Global comparisons are
accomplished through indexing of global cost of living allowances and labor
rates. Within the U.S., high-automation
manufacturing costs in the West Coast/Pacific are 20.1% greater compared to the
Midwest and similarly, low-automation costs are 21.2% greater. Globally,
high-automation manufacturing costs in North America are 52.1% greater compared
to Asia while low-automation costs are 116.5% greater. These variations
highlight why we see geographically clustered manufacturing centers within the
states and major manufacturing relocations due to cost sensitive and labor sensitive
production. </p>
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