3. 3
Introduction
Over the summer I had 3 main projects that I was given. The first was how an expeditor track
would help the RIPtrack and howit could increase production.The main program I used to analyze data
and create a model was Simulation with Arena, a simulation and modeling software. The other two
projects wouldbe done over the whole course of the internship and included keeping track of out bound
set outsand howmanycars were released oneach shift.In additionto keeping track of shiftreleases, this
data wouldbe usedto compare ourshop to similar car shopsin the BNSFSystem.
The Denver RIP track has two main repair tracks: 122 and 123.Bad ordered (BO) cars are ideally
spottedinto tothe tracks in the morningbefore0600.Typically,there are thirtyor more badordered cars
throughout the yard that can be spotted, it’s usually a matter of where they are as to if they will get
spotted.120 trackisbothan expeditor andstorage track.The terminal will can pushcarsinto 120 orother
storage tracks duringthe day to be pulled early in the morning.They can also pushsmall repairs onto120
to be repaired so they do not have to go through the actual RIP. Discussed later will be how this would
increase production and how the cars could be spotted more efficiently. There are six to seven carmen
workingthe RIPtrack on a normalday. If 120 isnot used,there are three carmen per track and if it is used
there are two carmen per track. Three is oftentoo many to get workdone efficiently so two is preferred
which is whyhaving 120 track as an expeditor wouldhelp increase efficiency and production.
Out boundset outs are when a car is in an outboundtrain and, uponinspection, there is a repair
that needsto be done onthe RIPtrack. When thishappensthe train has tobe brokenapart sothe car can
be taken out which takes upvaluable time andcan delay the train beyondits schedule.
4. 4
Base Model
Simulation with Arena is a modeling softwarethat is typically used to model assembly lines. The
user can create different modules to model wait times, service times, queues, and entity creations. The
program has a sub-program that can analyze input data and create equations that can be used in the
process modules (service time modules). The InputData Analyzer receives an excel spreadsheet of data,
graphsit, andplotsa curve tothe data. Byusing thistool,I wasable to takeyear-to-datedwell data,which
is a large sample, and create a relatively
accurate model of theshopinits currentstate.
The reason that it is not 100% accurate is
because the equationshave boundsfromzero
to infinity; however, infinity (and other times
past the highest recorded time) has an
extremely low probability of occurring. Also,
there is not data for just repair times so the
“work” processes are actually from the RIP to
release times which can be very high.
` The top picture is a create module (in
this case,the spot);it showsthateighteen (18)
cars will be created per day for seven (7) days
(most amount of entities that can be created
in this version of the program), from the nine
(9) car goal per shift to be released each day in
an ideal situation. The picture below is a
decide module that will pick if a car needs
heavy or light repair based on a percentage.
This module saysthat three (3) percent of cars
will be a heavy repair. The next module is an
assign module with will assign an attribute to
the entity that was created at the spot; for
heavy it is a red ball and for light it is a green
ball. After this, the entity passesthrougha hold
module, pictured on the next page, which
mimics the car sitting on the track outside the
RIP. If a car is the first entity created, it will not
be held at this point; a car is only held if the
resource in theprocessmoduleis being utilized.
The resource is a carman and is based on the
eight (8) hour shift schedule including breaks
and lunchto make this simulation as realistic as
possible. The hold module creates wait time
data that is seen in the report when the
simulation has completed.
5. 5
Because light work is more common than
heavywork,lightcars can be switchedoverto
123 track to be repaired there. This is done in
the “Switch Tracks” decide module which
checks the queue in the hold module and if it
greater than zero, it will switch a car over.
This is tomimic the cars being spottedevenly
or the carmen having to switch a car over
because onetookless time thananother.This
is anideal situationbecause oftenthecars are
spotted only on one track. Spotting
procedures will be discussed later. The
process module is where the actual work on
the car is done. The module pictured is for
heavy work and describes repairs that take
longer than four hours. The probability
distribution used in the base model was
found with the input data analyzer and is in
hours. This module uses a “Seize Delay
Release” action which means that it is held
for a certain amount of time before being
released out of the module. The final module
before the entity is disposed out of the
system is a record module. This module,
pictured below, records how many of each
entity go throughthe system andoff of which
track it came. This datacan also beseen in the
report when the simulation has completed.
A picture of all the modules is on the next
page to visualize what this model is
simulating.
7. 7
Expeditor Model
The expeditor track model was created differently from the base model but with the same
principals. There is no data for an expeditor track for how long a repair will take or how long the dwell
time is for a car repaired because 120 track is not used very often.By looking at shift numberdata that is
sent after every shift, it can be seen when 120 is used and on the few days that it was, production
increased. Shift numberswill be discussed in a differentsection. 120 track is the ideal track to use forthe
expeditor because it is already used to store bad-ordered cars and repairs are already done there
sometimes.Anexpeditor track shouldbe implemented because it can increase productionmeaningmore
cars are repaired per day.
Mostcars thatcome throughthe RIPare light repairs and manyof those are simple repairs. With
the simple repairs that come through the RIP, an air test is still needed which takes a minimum of 20
minutes.The main changes in the simulation are addingthe expeditor track and changing the first decide
module to accommodate that. The
decide module,seen onthe right, says
that three (3) percent of repairs are
heavy, thirty-seven (37) percent are
light, and the remaining sixty (60)
percent are expedited repairs. The
cars in the expedited track are not
inspected fully like the cars going
through the RIP. In this way, they will
not have extra repairs added on to it
and also will not need an air test. The
process modules for the expeditor
model changed from a beta
distributionto a triangular distribution
in order to break out the times better.
This was done because the data
sample fordwell time less thanone (1)
hour was too small and in an effort to
increase accuracy, the triangular
distribution has upper and lower
limits. In addition, this can show an
increase in efficiency because overall
dwell times will decrease when more
cars are able to be released per day.
The expeditor track has a minimum
time of thirty (30) minutes, maximum
time of one (1) hour and a “most
likely” value of thirty-six (36) minutes.
The mostlikely value can weighteither
of the bounds more; for example, in
the “heavy work” shown on the right,
8. 8
ten (10) isclosertofour(4) sorepairtimes of less thanten(10) hourswillbemore likely thantimes greater
than (10) hours.The remaining modules are similar to the base model in that they count and dispose of
the entities out of the systemand record the data in the report when the simulation is complete.
The result of having the expeditor track was that cars released went from 11.5 cars per day to
18.9 carsperday.In themonthof Julythelargest amountof carsthatwere released off theRIPwastwelve
(12); this shows that the base model can be considered accurate in this way. One thing the model does
not take into account is that there are no second shifts on the weekends. This brings the average down
anddoes notreflect theactual average thatshophaswhich makesthe model less ideal. There is noactual
data to compare the expeditor model, however, one day first shift released nine (9) cars while working
120,122, and123 tracks. This proves that using the expeditor track could have productive benefits. This
will also have a more efficient allocation of carmen as there can be two (2) per track instead of three (3)
which is howit currently is. Safetyis the top priority at BNSFRailway,and addingan expeditor track could
compromise safety. However, implementing fall protection on 120 track will mitigate the risk of falling.
The outside fall protection is designed with two hundred and twenty-five (225) feet of cable. There are
two (2) end gallows and two (2) intermediate gallows, bolted to concrete piers that will be buried in the
ground. The gallows are tall enough to allow for a thirteen (13) footfall clearance as required. With this
systemin place, carmen will be able to safely workon 120 track withthe required protection.
In order for an expeditor track to workefficiently, the repair tracks need to be spotted correctly.
Badorder codesappear in theTSSXpressprogramandcan be seenbythe terminal whenthey are spotting
cars; therefore, by specifyingwhich badorder codesto put onwhich track, the terminal can spotthe cars
accordingly. The ideal spot would have very light repairs (safety devices, brake shoe, etc.) on 120 track,
light repairs (wheels,coupler) on 122 track,and heavyrepairs (cushiondevice, truck repair) on 123 track.
Cars that do not take long will then be pushedthrough quickly meaning more cars released per day.The
spot also has to be on time each day. Many days during the summer, the spot was either late, small, or
didn’t happen.This reflects poorly on the carmen because it appears that they are not being productive.
By analyzing the shift number reports and putting a reason to low numbers, it can be seen that, even
thoughonly twocars were released, the carmen have sometimes cleared the entire RIP.
Anexpeditor track would greatly increase the efficiency and productivityof the Denver RIP track.
As long as the spot is accurate and the cars are in the correct tracks corresponding to their bad order
codes, reaching a goal of eighteen (18) carsper day wouldbe attainable. Safety is always a main concern
and installing fall protection above 120 track would greatly reduce the chance of injury when working
outside.
10. 10
Another way to increase efficiency is to spot excess cars onto 123 track. The switch that is by the RIP is
oriented so that it is easier to go from 123 to 122 than it is from 122 to 123. Switching cars from 122 to
123, when done by the carmen, take a lot of time because the cars have to move all the way past the
switchthenbackon to123 pasttheswitch.Thiscanbe visualizedwiththe picture below.Byputtingexcess
cars on 123,movementsare decreased which savestime and allowsthe carmen to workmore cars in the
same time.
123
122
11. 11
Outbound Setouts
The goal for outbound setouts in the Denver yard is 1.09 per day. The spreadsheet on the next
page shows outbound setouts for the month of July, how many each supervisor had, a breakdown of
whether they were preventable or not, and details as to what caused the outbound setout. Most
outboundsetoutshavenotbeen preventable bythe carmen and have happenedbecause of other issues.
Air is not inspected on an inbound train so parts related to air are a non-preventable outbound setout.
One of the main issues that the Denver yard has in terms of outboundsetoutsare bypasses.A bypassis
whentwocarsdonotcoupletogether anddamageeach otherduetothe car beingkicked toohard(travels
toofast) orthe couplers notbeing lined up.This can cause damageto the cushiondevice if the car is going
to fastor damageto safetydevices such as handholdsandwalkingboardsif the couplers are not lined up.
This is not preventable by the carmen but is preventable in the yard. If a train is not inspected on the
inbound and then a flaw is found, that outbound setout is not preventable because no one looked at it
whenit entered the yard.Shiftedloads are also non-preventablebecause that can happenwhenbuilding
a train or if the tie downsbreak after being inbounded.
Red cells in the “Total OS” column indicate when there are more than 1.09 outboundsetoutsfor
that dayto showthat it is more than the goal. In the next column over, the yellow cells indicate whenthe
numberthat are preventable are greater than1.09 because one(1) preventable setout is acceptable. The
green and blue cells showthe average percent preventable, how manyeach supervisor has,the average
outboundsetoutsper day,and a countof how manyare bypasses,air issues,and other reasons.
The main reason to keep track of outbound setouts is to have accountability for how they
occurred. By being able to correctly identify how an outboundsetouthappened, the department whois
having the most issues can review their procedures in order to decrease them. The car department had
the least amount of outbound setouts and for the month of July, it can be seen that seven (7) of the
setoutswere preventable by the carmen andtwenty-seven(27) weredueto air issues,bypasses,or other
issues. However, over the course of July, the overall average was 1.1 per day which is right on target for
the Denver goal. This spreadsheetis expandable sothat outboundsetoutscanbe kept track foras long as
is needed to improve performance. It will also be able to show an improvement as numbers are added
which can show that Denver is reducing costs and increasing productivity by not having to break apart
trains to get a bad ordered car out.
12. 12
Shift Numbers
Shift numbers emails go out after each foreman’s shift to lay out how many cars were released,
howmany are predicted to be released on the next shift,and the total amountbilled on each shift.It will
alsogive reasonsasto whyarelease goal wasn’tmet.Thesereasonsare importanttotake note of because
of accountability; low release numbers can be a result from different situations. Shown in the following
spreadsheet is the predicted releases, actual releases, reasons (if applicable), and graphsto visualize the
numbers.
For first shift, the usual numberpredicted is nine (9) because this is also what the goal is. This is a
feasible goal as seen one day during the monthof June; this happened when all repair tracks were used
(120,122,123) whichisalsoa positivestatistic for using120 trackasan expeditor. Mostreasonsforlower
numbers are short staffed or a small spot. Sometimes repairs can take much longer than expected,
especially heavy repairs, which can slow the RIP down and lower the number of cars released. When a
reason is notlisted, it could be that thecarmen workedslowlythatday in order toinsure second shifthad
cars toworkon whentheystarted. The secondshiftprediction usuallyfluctuates more because it is easier
to predict. The foremen can see down the tracks and count how many cars have been spotted and if
another spotis notanticipated, the goal will be how manyare on the tracks.
The graphs show a visual representation of the predictions and the actual numbers. The second
shift graph lookslike the two lines are similar. This is consistent with what was discussedearlier that it is
easier to predict for second shift. Also, there are no weekend second shifts which appears to make the
actual more accurate. The first shiftoftengets the same goal each day andtherefore, the numbersdonot
line up as nicely as the second shift numbers. The final graph is first and second shift numbers added
together and a trendline to show how it has progressed throughout the month. This trendline shows a
small downward slope meaning that cars released has decreased; however, the slope is only slightly
negative and given more data,the line wouldbe closer to flat meaning constantreleases onaverage. For
the monthof July, the average releases per day was 6.5 cars. This number is falsely weighted downalso
because of the lack of second shiftson weekends.
The spreadsheet breaks downby shifthow many cars were released andthe total amountbilled.
In addition, it gives a percentage of the total that first and second shift did. The first shift does roughly
56%of the repairs with 64%of the billing leaving 44% and 36% respectively for second shift.
This accountability is useful forthe running of the repair shopbecause it showswhenthe carmen
are being productive and working quickly and safely and also shows that low numbers are usually the
cause of small spotsor shortstaff.Thiscan also show where productivityneedsto increase in order toget
more cars out.With reasons listed, it is easier to point out where workcan improve and also where work
has been done very well.
13. 13
Conclusion
The expeditor track model showed that using 120 track for quick repairs would increase
production and would also provide a more efficient allocation of workers. On July 19th
, the first shift
released nine (9) cars while using all repair tracks. Inorder for the three (3) repair tracks to workwell, the
cars needed to be spotted correctly by putting heavy repairs on 123,light repairs on 122,and expedited
repairs on120.
When looking at the data for outboundsetouts in July, just over 13% of them were preventable
by the carmen meaning that the remaining 87% were caused by other issues. This data can be used for
IRPmeetings to showwhichdepartments need to review their policies andprocedures in order to reduce
outboundsetoutsincrease efficiency.
By analyzing shiftnumbers,whichare sent out at the end of each foremanshift,one can see why
the RIPtrackhashigh orlow release numbers.Thespreadsheetgives reasonsasto whytheactual number
is different from the goal which creates accountability for the carmen. This is important because it can
show when low production needs to be improved or that they worked every car they had even if it was
not that many. Both spreadsheets can be used in the future to expand the data and use for future
meetings or performance reviews. Another Spreadsheet that is attached is a comparison of the Denver
RIP track to Alliance, Texas and Amarillo, Texas to see how our numberscompare to those facilities. This
is a goodperformance metric and can be continually expanded.
