Final assignment submission for the Coursera Operations Project

1. Railroad Industry-Reducing locomotive
maintenance time
● The Diesel Locomotive Shed at XXX, homes 181 locomotives. These
locomotives visit the shed periodically for maintenance and repair.
● The shed operates on a 24x7, 365 days a year basis. Locomotives enter
and leave the shed round the clock.
● The shed provides about 20 locomotives for working passenger carrying
trains, which are scheduled as per a published timetable. Thus these
locomotives leave and arrive from/to the shed at predetermined times of
the day.
● The remaining locomotives are provided for freight operations which are
more or less random in terms of arrivals/departures to/from the shed.
● The primary objectives of the shed are to ensure adequate Availability and
Reliability of the Locomotives for working Trains.
● The facilities available for this purpose include maintenance bays,
machinery, plant and equipment to carry out the maintenance, materials
(spare parts) required for maintenance and trained manpower to carry out
the maintenance.
Slide No.1

2. My Focus Dimension -Time
I have chosen Time as the key dimension that I want to focus on. The time taken for a
locomotive to be serviced by the shed should be minimized. These could be broken into
●
Flow Time i.e. the time taken to carry out the maintenance/repair work.
●
Wait Timed being the time spent by the locomotive while it waits for maintenance
attention
and
●
Locomotives Turned Away: The instances when a locomotive requiring maintenance is
not admitted into the shed owing to lack of capacity (customers turned away).
The purpose of this project is to help the shed improve upon the availability
metric by minimizing the time taken to carry out the maintenance and repair
activities.
Key Trade Offs- Quality V/s Time
The trade offs could be across multiple pairs of the four dimensions of the efficiency frontier, for
example Time v/s Cost, Time v/s Quality, Quality v/s Cost Time v/s Variety etc.
From the perspective of the Shed management, the key tradeoff is between Quality and Time.
I will be focussing on this pair of dimensions and attempt to strike an optimum balance between
Quality and Time.
Slide No.2

3. Sensing the problem
The key performance measures in this context as related to the four dimensions of performance
are:
1. Time: The time taken to carry out the maintenance/repair work determines the availability of
the Locomotive (flow time). Lesser the time taken, greater the availability for working trains.
Other parameters would be the time spent by the locomotive while it waits for maintenance
attention (wait time), and the instances when a locomotive requiring maintenance is not
admitted into the shed owing to lack of capacity (customers turned away).
2. Quality: The quality of maintenance determines the reliability of the Locomotive. It also affects
the availability since a failed locomotive is not available to work a train. Number of locomotive
failures per million kilometers run measures this parameter (defect rate).
3. Cost: The cost of staff and materials used to carry out the maintenance and repair activities is
an important performance metric for the shed. The relevant parameters would be Maintenance
cost per Locomotive, Staff required per Locomotive, Idle time of staff etc.
4. Variety: The shed turns out single locomotives, Multiple Units (MUs) i.e. two or more
locomotives coupled together. The customer’s requirements are dynamic and the shed is
required to match the customer’s requirement with respect to singles and MUs
Slide No.3

4. Objective- What would Success
Look Like?
Based on my interaction with the users (customers) and the shed
management, the vision of success would be as follows:
1. The shed will improve the Availability of its Locomotives by 1%
(Quantity/Throughput).
2. The shed will be able to utilize its resources optimally to attend to the
maintenance and repair requirements of the Locomotives (Efficiency)
a. Reduce material cost by 10%
b. Reduce Labour Content by 10%
Slide No.4

5. Problem Formulation
How can we reduce the time taken for maintenance?
Alternative#1:How can we reduce the maintenance time for various
activities? (Narrow scope)
A: By reducing time taken for each maintenance activity and reducing
waiting times.
Alternative#2: Why should we reduce the time taken for
maintenance? (Broaden scope)
A: The locomotives can earn more on line
Alternative#3: Q: How can the locomotives earn more on line?
(Narrow scope)
A: By visiting the shed less frequently and by taking minimum time in
each visit.
Alternative#4: Q: Why would locomotives visit the shed less
frequently and why would they require minimum time each visit?
(Broaden scope)
A: If the locomotives are more reliable
Slide No.5

6. KPI Tree: Number of Locomotives in Shed
% Improved
Locomotives
Saved
•I have defined the KPI here as the Avg. No. Of locomotives inside the shed per day. The lower this number, the
better the performance.
•This is a function of the Avg. Time spent by a locomotive and the Avg. No. Of locomotives for each type of
maintenance/repair activities.
•The sliders on the right side are used to know the impact of improvement in each parameter
Slide No.6

7. Explanatory Notes and Inferences
• The Locomotives visit the shed for Monthly (30 days),
90/180/270 Day schedules, Yearly Schedules, Three Yearly
Schedules, Six Yearly Schedules and Out of Course
(Unscheduled) repairs.
• Based on the historical data available and the KPI tree, the “M”
(Monthly) schedule has the biggest impact.
• I have therefore prepared the Process Flow Chart for the “M”
schedule.
• I Have not provided a slide for the customer’s perspective, since
the flow unit here is a locomotive.
• The first four Slides pertain to COP Assignment#1. I have
continued COP Assignment#2 in the same file for continuity.
• Activity#6 in the process flow chart i.e. The Drop Pit appears to
be the bottleneck.
Slide No.7

8. Locomotive Monthly Schedule
Process Flow Diagram
2
5
Locomotive
released
from
Operations
for Monthly
Schedule
1
3
8
6
7
4
S.No.
Activity (time in minutes/%
Attended)
S.No.
Activity (time in minutes/% Attended)
1
Placement in Bay (30/100%)
5
Mechanical Repairs (180/30%)
2
Mechanical Maintenance
(240/100%)
6
Drop Pit (180/40%)
3
Lab Test of Oils and Water
(60/100%)
7
Electrical Repairs (240/10%)
4
Electrical Maintenance
(320/100%)
8
Washing filling of Supplies and despatch
(60/100%)
Slide No.8

9. Peer feedback of COP#2
Based on the feedback from COP#2, I understand that I need to clarify
certain to help my evaluators have a better perspective of the issue (In spite
of having included a slide titled explanatory Notes”!). The points raised by
one of the Peer evaluators are reproduced below:
peer 5 → “The presentation is confusing; the problem formulation is like a cat
running to catch its tail: no consistent answer is given The KPI tree does't
lead to costs, revenues, profits, it's closer to a list of number of locomotives
(why so many decimal digits?) The customer perspective is missing even if
it could be done. EG: what does it mean in terms of waiting times, possible
delays, breakage during the trip, ..... whatever... The process flow diagram
is the only thing closer to the requests, but it's not clear what number
represents and why it was chosen the total process and not only a
significant part. In addition to this, no waiting/triangle is explained ...”
Clarification on my Problem Formulation:
I started with “How can we reduce the time taken for maintenance?”
I have then attempted to alternatively broaden and narrow the scope to help
view the problem from different perspectives.
Slide No.9

10. Peer feedback of COP#2
About the KPI Tree: I have not shown costs here because, in my
organization, there is no “cost” assigned to the locomotive in terms of, say,
$/hour. However, it is intuitive that the lesser the number of locomotives
inside the Shed the better it is. To that end, I have collected data of each
locomotive entering and leaving shed over a period of nine months from 1st
Jan’13 to 30th Sep 13.
The KPI Tree shows the impact of the Number of Locomotives visiting the
shed for each type of Maintenance/Repair and the Time taken to carry out
the maintenance/repair. Thus, more locomotives would be inside the shed if
either more locomotives visit the shed and/or if more time is taken to service
each locomotive. This is what the KPI Tree shows. I have made this Tree in
Excel and built sliders by the side of each parameter. This slider can be
adjusted from 0 to 100%. So if I assume a 10% improvement in service time
for the Six yearly Schedule, I can see the effect of such an impact on the
KPI by merely moving the relevant slider.
I have edited the KPI tree to limit the numbers to two decimal places.
Slide No.10

11. Peer feedback of COP#2
The Process Flow Diagram:
I have mentioned in Slide 8 that I have chosen the M schedule since it
appeared to have the maximum impact on the KPI. The process flow was
thus only for the M schedule. The waiting triangles indicate the locomotives
may have to wait to get attended at the subsequent operation in case the
work station is busy attending to another locomotive. The numbers are
explained in the Legend to the diagram, e.g., 180/30% indicates that the
operation requires 180 minutes and 30% of the locomotives go through this
operation.
My submission for
COP#3 Starts from
the next slide
Slide No.11

12. Collecting Primary Data
In COP#2, I had identified the Monthly Schedule as the potential
activity which would “give the biggest bang-for-the-buck” as far as
the KPI is concerned.
I have studied the process of the monthly schedule being carried out
on the locomotive.
I have however analysed the data and found that:
1. There was little scope for improvement in the M schedule. I could
not find any significant presence of any of the seven sources of
waste.
2. On the other hand, I found that the variability is more in Y and 3Y
schedules.
3. There is evidence of over processing in the Y,3Y and 6Y
schedules. This discovery was serendipitous, i.e. My finding was
unexpected.
Slide No.12

13. Identifying Sources of Waste
Of the seven sources of waste I found that Over processing is a major
contributor to inefficiency with respect to the Y, 3Y and 6Y
schedules.
As per the OEM of the Locomotive, the 6Y Schedule is to be carried
out after the Locomotive produces 23,000 Megawatt hours of power.
The lower schedules (3Y and Y) are also proportionately spaced.
Analysis of data shows that the freight locomotives generate this
amount of power in about 9 years.
So all activities which are presently being carried out in Y6 can be
postponed to Y9.
This would cascade to Y3 and Y schedules also.
I have checked the implication from the technical aspect also and am
satisfied that such an extension is justified.
Slide No.13

14. Primary Data
Time taken for Maintenance/Repair
Schedule Type
Number of Locos
attended over the
period
Average time
Average
spent in shed Locomotives
per loco (days)
in Shed
MONTHLY
1512
1.22
6.74
90/180/270 Days
372
1.84
2.50
Y
61
7.30
1.62
Y3
22
21.68
1.74
Y6
OUT OF
COURSE
24
37.96
3.32
138
3.23
1.63
Grand Total
2129
2.26
17.56
The data pertains to the period from 1st Jan 2013 to 30th Sep 2013.
This was compiled from the available database maintained in the
shed.
Side No.14

15. Process Variability
2
14
1.75
12
10
1.5
Avg Mly Time
1.25
LCL
8
Avg Yly Time
LCL
6
UCL
1
UCL
4
0.75
2
0.5
0
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
9
50
79
69
45
59
40
49
Avg 3 Yly Time
39
LCL
29
UCL
Avg 6 Yly Time
35
LCL
UCL
30
19
25
9
20
-1
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
X Axis: Month (1=Jan) Y Axis: Time taken in Days
8
Slide No.15

16. Demand forecast after review of periodicity of the
Y,3Y and 6Y schedules
Schedule
Existing
Periodicity
Proposed
Periodicity
Existing
Demand
per Year
Demand
per Year
based on
proposed
periodicity
Y
1 year
1.5 years
80
71
locomotives locomotives
3Y
3 years
4.5 years
54
36
locomotives locomotives
6Y
6 years
9 years
27
18
locomotives locomotives
Note: There are 161 freight locomotives. Demand is
calculated as follows:
6Y Existing: 161/6= 27 locomotives
6Y Proposed: 161/9= 18 locomotives
Etc.
Slide No.16

17. Inflexibilities Noticed
I have found evidence of Volume inflexibility: The capacity
which is fixed, is not able to adjust to the demand which
varies over time
Average demand over a 24 hour period
No. of Locomotives entering the shed
1.2
1
0.8
0.6
0.4
0.2
0
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time of Day
Slide No.17

18. Peer feedback of COP#3
“...Regarding your comments on the Monthly Schedule and Seven Wastes : ...
A. Transportation of locomotives back and forth to the repair center is present
here. Is this absolutely necessary for all types of repairs? If there are
category of repairs that are performed by a travelling repair vehicle staffed
appropriately, will that save or improve the capacity of the facility? ...As an
example, electrical maintenance takes 320 minutes and it the longest
operation, can this be done onsite instead? I recognize that the repair vehicle
idea itself promotes transportation. However, if there is evidence that this
movement may free up space at the repair center, then it may be worth
considering.
B. A source of waste seems to be the amount of time a locomotive waits to be
serviced as well as the possibility of it overstaying because of the predetermined schedule to be taken out. This seems to be a possibility for the
passenger locomotives. Is overstay considered as a waste in evaluating a
monthly schedule?
C. Inflexibilities You have identified the inability to respond to volume demand.
Again the bottleneck seems to be the one center or facility that carries out all
forms of repairs. Is there any reason some of these repairs cannot be carried
out on site or where the locomotive is stationed by a visiting repair vehicle? Is
this kind of a strategy that can alleviate the volume demand considered?”
Slide No.18

19. My comments on the feedback
First, thanks to my peers for the constructive feedback. I am to
blame for not adequately clarifying things. So, here is some more
explanation.
Item A of the feedback
• Once the locomotive is berthed (operation 1 in slide#8),
operations 2,3 and 4 take place simultaneously.
• Operations 5 and 7 take place if required (with probabilities
indicated in the table on slide#8). Locomotive is not moved in
these cases.
• Locomotive is required to be moved for Operation 6 (the Drop
Pit) , because this requires the use of a heavy machine which is
installed at a fixed location. This probability that this activity is
required is also shown in the table on slide#8
• Operations 8 is the final operation (required for all locomotives).
The locomotive is moved for this purpose, since the wash house
is located separately.
Slide No.19

20. My comments on the feedback contd...
Item B of the feedback
Yes, I have considered “over stay” of the locomotive beyond the
prescribed schedule time as a waste. The Monthly schedule is
supposed to take 6 hours. The average time taken is 1.25 days.
However, the elements of waste are thinly spread out and
therefore the steps required to tackle these require several inputs
which I feel are beyond the scope of this project.
Item C of the feedback
This is true. I have identified certain “set up” activities which can
be carried out without occupying the bottleneck resource (the
Drop Pit). There are however some organizational/labour union
issues which prevent me from implementing this at present. I
propose to work in this in the future.
My submission for COP#4 Starts from
the next slide
Slide No.20

21. Three ideas for improvement
1.
2.
3.
Extend the periodicity of Y, 3Y and 6Y Schedules to 1.5Y,
4.5Y and 9Y respectively. This will reduce the demand. This
will in-turn release labour to attend to other schedules and
repair activities, in other words, this will increase capacity.
Review the shift timings to better address the peak arrivals at
00:00 hours. Is it possible to schedule the shift timings so that
the locomotives which bunch up are tackled with minimum
delay?
Meet the spikes in arrivals on Wednesdays, Thursdays and
Fridays by deploying an additional gang on these days. There
is already an additional gang, but this gang is available on
Mondays, Tuesdays, and Wednesdays. (Sorry, I did not
include the week day wise variability in COP#3, but here it is
in the next slide...).
Slide No.21

22. Number of Locomotives Arriving
Weekday wise Arrivals of Locomotives
8.13
8.25
7.95
7.63
7.43
7.20
6.65
1
2
3
4
5
6
7
Week Day (1=Sunday)
Slide No.22

23. Expected Benefits of eliminating
Over Processing (per year)
Revising the periodicity of activities hitherto carried out in 1/3/6 year
intervals to 1.5/4.5/9 year intervals is expected to yield in the
following benefits:
Present
Demand per Demand per year Savings in
schedule
year as per
as per proposed Spare Parts
type
existing practice
practice
6Y
47
38
3Y
61
43
Y
338
246
Total
Saving in
Staffing
$727,562 $
$219,187 $
Total Savings
190,865
$ 918,427
25,290
$ 244,477
$
10,774
$ 225,734
$1,161,708 $
226,929
$1,388,638
$214,960
The impact of this idea on the KPI Tree is shown in the next Chart. I
have moved the sliders for “Avg. No. of locomotives Visiting Shed per
Day” to 67% to show the impact of this idea in the next slide.
Apart from the above, the cost of Capital saved (by generating two extra
locos), works out to about $ 8.5 million, with locomotive cost of $ 4.5
million, life of 36 years and interest rate of 6%
Slide No.23

24. Expected returns from these ideas1.Eliminate Over processing
Slide No.24

25. Expected benefits of revised shift
timings to meet the spike in demand
•The demand spikes are at 00:00 hrs, 09:00
hrs, 12:00 hrs, 15:00 hrs and 18:00 hrs (see
Slide#17).
•There are three gangs of staff (A,B and C)
which rotate over the three shifts on a weekly
basis (6 days a week).
•One more gang (D), acts as a “Rest giver” for
the three gangs A,B and C.
•This gang is “Spare” on three days in the
week. The details are as follows:
Slide No.25

26. Present Staffing Plan
Shift
No.
Timings
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
1
06:0014:00
A
A
A
A
A
A
Rest/D
2
14:0022:00
B
B
B
B
B
Rest/D
B
3
22:0006:00
Rest/D
C
C
C
C
C
C
4.
07:3016:30
_
D
D
D
Rest
-
-
Changes Proposed:
1. Shift the Weekly Rest for Gang B to Tuesday
2. Shift Weekly Rest for Gang D to Monday
3. This will result in Gang D available as “Spare” on Wednesdays, Thursdays and Fridays
with revised timings of 08:00 to 16:00 to tackle the peaks at 09:00, 12:00 and 15:00 hrs.
These changes will address idea Nos. 2 and 3. I have indicated these proposals in the chart
on the next slide.
Slide No.26

27. Locomotive Arrivals over 24 hours and Shift timings
0
23
1
22
2
21
3
20
No. Of Locos
4
19
5
18
6
17
7
16
Spare Batch
Shift1
Shift2
8
Shift3
15
9
14
10
13
11
12
Slide No.27

28. Comparison Table
Idea
Ease of
Implementation
Likely Magnitude of
impact
Extend the periodicity of Y,
3Y and 6Y Schedules to 1.5Y,
4.5Y and 9Y respectively.
Easy. The OEM’s
manual supports this.
Next Locomotive falling
due Y/3Y/6Y shall be
attended for M/90 day
schedule.
High. The savings in
terms of Material and
Labour is expected to be
around $1.3 million per
year and a one time
saving of $ 8.5 million.
Review the shift timings to
better address the peak
arrivals at 00:00 hours
Medium. Need to
negotiate issue with
Staff Union.
Low. The peaks in
demand can be
addressed.
Meet the spikes in arrivals on
Wednesdays, Thursdays and
Fridays by deploying an
additional gang on these
days.
Medium. Need to
negotiate issue with
Staff Union.
Low. The peaks in
demand can be
addressed.
Slide No.28

29. COP#5 : Experiments and Results
Slide No.29

30. Extending the Schedule periodicity
(Avoid the “Over processing” type of waste)
The experiment: What will be the benefit of
implementing the extended schedule periodicity to a
locomotive, normally expected to be stopped for a 6Y
schedule?
This experiment shall be quite easy to implement. The
next locomotive programmed for the 6Y schedule would
instead be taken for a 90 Day schedule and released.
This would result in reduced detention of the locomotive
and saving in inputs of materials and labour.
Slide No.30

31. Extending the Schedule periodicity
(Avoid the “Over processing” type of waste)
We implemented the extended schedule periodicity for
6Y schedule for one Locomotive, which was due for 6Y
schedule. The locomotive was instead attended 90 Day
Schedule and released. This resulted in the detention
reducing from an expected 37.96 days (see Slide#14) to
about 1.67 days. Thus the, over the month, this resulted
on an average of 1.17 additional locomotives being
available on line.
The saving as a result of this was about $100,000, i.e.
saving in material cost for this locomotive of about $80,000,
and in Labour cost of about $20,000.
Slide No.31