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New York Air Brake Uses
Crystal Ball to Keep Their Production - Not Their Products
- Rolling
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New York Air Brake (NYAB) of Watertown, NY manufactures
products for the railroad industry that include pneumatic and electro-pneumatic
brake system components for freight cars and locomotives. Gerald
DeFoe, a Six Sigma Black Belt in New York Air Brake's Quality department,
oversees Six Sigma implementation, training, and non-transactional
projects at New York Air Brake. As a Black Belt, DeFoe relies on
Crystal Ball to ensure that part production and repair processes
are as cost and time efficient as possible.
DeFoe was recently asked to examine the company's
replacement process for a multi-assembly locomotive braking product
called CCBII (Computer Controlled Brake). Each of the CCBII subassemblies
is composed of a mechanical portion and an electrical portion. Company
policy states that if a part is returned due to a pure electrical
failure within 120 days of its manufacturing date, then the electrical
part will be repaired or replaced. The mechanical portion is used
"as is" as a replacement unit on another locomotive. The liability
of this practice is that the mechanical part could end up on a locomotive
younger than the replacement, and NYAB wanted DeFoe to determine
the level of risk posed by the cutoff date of the policy.
DeFoe began his analysis by gathering raw data for
locomotive age and part returns for the CCBII product line. In his
spreadsheet model, he applied Crystal Ball's distribution fitting
feature to define probability distributions for the range of expected
days to failure of an electro-pneumatic portion and the range of
ages of the locomotives from which the parts came. Prior to this,
NYAB estimated a single value based on intuition and experience
to represent these uncertain variables. With these new probability
distributions, DeFoe could better address the unknown relationship
between the cutoff criteria for the rebuilding and placement of
the mechanical part and the ages of locomotives that required a
replacement part.
He set the baseline using the 120-day cutoff for rebuilding
the portion and simulated the Excel model for 10,000 trials. His
forecast (output) cell was simply a formula that pulled a random
locomotive from the failed locomotives distribution (age) and subtracted
a random part from the failed part distribution (age). Forecast
results with a negative age indicated that a mechanical part with
some number of days of use had been placed on a newer locomotive
(one with less days of use). The simulation showed that this scenario
occurred for about 1.6% of the mechanical units.
Because the company's target was to have less than
5% older replacement parts on younger locomotives, DeFoe determined
that NYAB could extend the window for rebuilding parts to 210 days.
The analysis also showed that 2.25% of the returned electro-pneumatic
portions came in before 120 days and 7.2% came in before 210 days.
This meant that NYAB could triple the number of units exempt from
rebuild and still meet their goal of <5% of unit age exceeding locomotive
age. The simulation results led to a three-fold reduction in pneumatic
rebuilds and an accompanying cost savings. "But even beyond that,
we now know the risk up front, which no one knew before," said DeFoe.
"Once the data was input into the spreadsheet, the analysis with
Crystal Ball became almost simple."
In addition to the ability to simulate a spreadsheet
model, DeFoe appreciates the Crystal Ball report-generating feature,
which lets him "summarize and send all the information to management
with little to no work. The manager I did the CCBII analysis for
was very pleased with the overall analysis, timeliness, output,
and flexibility. The rest of management was glad to see a circumstance
where they had to expend no money to gain a sizeable cost savings."
Two other features DeFoe finds helpful are the fitting of the distributions
to real data and the graphical interface, which simplifies how he
sets and adjusts cutoff ranges.
Another application for Crystal Ball at NYAB is simulating
part tolerances. When an engineer needs to introduce a design change,
either due to a problem part or for a cost reduction, he first builds
and then tests a number of assemblies with parts whose characteristics
(e.g., spring rates, diameters, depths, squareness) are known. The
test values and measurements are fit to Crystal Ball assumptions
and simulated. Using the results and the desired test range, the
part tolerances are adjusted to meet the target. "We may not be
die-hard users of Crystal Ball, but I believe that when we use it,
the benefits are great, said DeFoe. "If nothing else, we can walk
into a change knowing our risk and probability for success."
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