Study on how to do wagon maintenance optimization, done by Data Minded together with our client, B-Logistics

1. Project: Maintenance optimization for wagons
23 July 2015

2. Agenda
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 20152
1. About me & my company
2. Project introduction
3. Process raw data
4. Iterative improvement
5. Final results

3. • Bart Maes
• Background in Computer Science (A.I.)
• Data coordinator at SNCB Logistics
• New to data science/big data
• Traveler (on a shoestring)
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 20153
A bit about myself

4. • 200 locs
• 7000 wagons
• 1900 employees
• 30 million transported tons
• 5 billion ton-kilometers
• 2000 trains per week
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 20154
A little more about SNCB Logistics

5. • ..  2011: Ruination
• State owned, part of NMBS
• 2009: +100 million loss/year (EBITDA)
• 2011: private company, NMBS 100% shareholder
• 2011 2015: Transformation
• 2014: 11 million profit (EBITDA)
• 2015: private investor acquires 66,6% of shares
• 2015  ∞: Innovation
• eLearning
• MIA/RITA
• Big Data
• …
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 20155
A brief history of SNCB Logistics

6. • Maintenance optimization for (own) wagons
• 4800 wagons
• Wagon breakdown
• Each wagon checked before each train departure
• Will probably result in lost train
• Hence, expensive
• Current maintenance process
• Time based: every 6 years, simplified or major overhaul
• Cost: €4-16k, independent of wagon condition
• Hypotheses
• Wagon usage varies widely
• Can tolerate less frequent maintenance of less used wagons
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 20156
Project summary

7. B-LOGISTICS WAGON MAINTENANCE - 23 JULY 20157
Project steps
1. Reconstruct each wagon’s exact history to
deduce:
• Travelled distance
• #Loads/unloads (‘cycle’)
• Weight transported
• Damage reports
2. Reconstruct estimated wear from maintenance
reports
3. Correlate wear to wagon history
4. Given usage, estimate time until maintenance
is necessary
Wear
km travelled
Need
Overhaul

8. • Initial rough estimate
• Uneven distribution
• Large number of low-usage
wagons
• Smaller number of high-usage
wagons
• Indicate potential for
maintenance policy improvement
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 20158
Analysis: distance travelled

9. • Source: CargoWeb
• Collected events for wagons
• Potentially 100s events / week
• Generated by operational systems
• Manually inserted
• ~200 types of events: departure, arrival, ready for X, take
over, hand over, passed through, …
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 20159
Raw Data: wagon events

10. B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201510
Raw Data: dirty & complicated
Begin retention - Consignor
RCN accepted
1
Final destination changed
15
First departure
73
Wagon checked (RCN) : ok
74
At disposal
1
Departure
1 41
Wagon assigned to order
39
Physical restitution - Empty wagon
24
Wagon withdrawn from order
3
Assignment of consignee - loaded wagon
10
Assignment of consignee - empty wagon
6
Train composition ok
294
Physical restitution - Loaded wagon
51
Arrival
41 2
Wagon transport manual undone
1Wagon checked (RCN) : rejected
1
Wagon transport manual done
2
42
2
198
1
1
381
End retention - Consignor
1
58
Administrative restitution - Empty wagon
7
10
340
125
32
196 Administrative restitution - Loaded wagon
7
33
10
17
1259
10
5451
Arrival at destination station
505
205
13
4
1
6
1
16
84
2
4
17
4
626
414
1
1
38
259
34
1
469
31
25
273
74
59
14
Ready to pull
5
22
1
37
Status manually changed
1
Physical restitution - Wagon status is unknown
23
Administrative restitution - Wagon status is unknow n
1
12
1159
1
5
1 1 10
1
1
24
1
15 103243 1
434 Last departure
2 1
38
620
136
1
34
3
5
342
120
14
96
6
1
1
34
7
186
17
1
2
11
69
Begin retention - Consignee
2
2
5
6
5
22
1
7
1
1
10
1
5
3
3
23
23
336
149
1
309
11
23
181
7
17
108
47
94
71
11
3
3
End retention - Consignee
1
114
1125
8
2
166119
7
67
9
5
3
1
89
12
4
533
37
1
24
2
312
3
5
2
13
3
5
1
13
1
1
2
1
1 7
4
3
1
187
382 10 2 1
7
6
165
1
1 16 3
3 202
276 13
Wagon pos ition changed (from ...)
1
1
1
1
7
6
1125
12
13
3
2
1821
Begin retention - Own RU
1
10
4
Begin retention - Other RU
1
7
6
1
4
1
3
2
2
8
4
10
1
2
1
End retention - Own RU
1
1
1
11
1
11
8
1
1
1071
181
End retention - Other RU
1
2
1
1
1
1
Wagon pos ition changed (to ...)
1
1
1

11. B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201511
Summarize raw events
Time Place Event
… … …
30-Apr-2012 10:06 Antwerpen Berendrecht Cirkeldijk Physical restitution — empty wagon
30-Apr-2012 10:35 Antwerpen D.S. Berendrecht Train composition OK
30-Apr-2012 12:29 Antwerpen D.S. Berendrecht Train composition OK
30-Apr-2012 13:08 Antwerpen D.S. Berendrecht First Departure
30-Apr-2012 17:00 Antwerpen Waasl-Zuid van Moer Arrival at Destination
30-Apr-2012 17:20 Antwerpen Waasl-Zuid van Moer At Disposal
… … …
From To Action
… 10:06 At rest at ABC
10:06 10:35 Travel ABC – ADSB
10:35 13:08 At rest at ADSB
13:08 17:00 Travel ADSB – AWZvM
17:00 … At rest at AWZvM

12. B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201512
Summarized data: still challenging
arrival
travelling: 231
at rest: 176
other (after arrival)
travelling: 571
at rest: 991
departure
travelling: 33
at rest: 658
travelling: 2
at rest: 29
travelling: 1126
at rest: 8048
travelling: 113
at rest: 1417
travelling: 2126
at rest: 10
travelling: 229
at rest: 217
other (after departure)
travelling: 66
at rest: 30
travelling: 58
at rest: 28
travelling: 2
at rest: 8
travelling: 7
at rest: 19
Even after abstracting and simplifying,
interpreting the data is challenging

13. B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201513
Distance travelled: a first reconstruction
• Room for improvement:
• Noise
• Gap in Business Understanding
• Goal:
• Improve until ‘good enough’

14. • Ignore manually inserted events
• Unreliable
• Often provide little extra information
• Cluster stations into groups
• Maps with business activities
• Eliminates false positive mismatch between arrival & departure
station
• Improve interpretation of event sequences
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201514
Distance travelled: interative improvement

15. • ‘Travel’ action defines movement between 2 points A & B
• No central source for all distances between every 2
points
• Distance matrix constructed using different sources:
• Actual RAILDATA measurements
• Extrapolation (cluster nearby stations)
• Google maps
• Haversine distance
• Manual input
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201515
Distance travelled: another hurdle

16. B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201516
Most wagons travel less than 30k km/year
Distance travelled: results

17. B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201517
Some wagons have very distinct usage patterns
Cycles & average load: results

18. B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201518
Some wagons have very distinct usage patterns
Cycles & average load: results
Genk-Chatelet
Gent-La Louviere
Gent-Clabecq

19. • Business satisfied by presented results
• New maintenance plan uses ‘wagon groups’
• Focus on distance travelled
• Other elements may be added in the future
• #Cycles
• Average load
• Main achievements:
• Cost reduction for maintenance
• Risk/cost reduction for breakdown
B-LOGISTICS WAGON MAINTENANCE - 23 JULY 201519
Final results & business actions

20. SNCB Logistics I Koning Albert II-laan 37 1030 Schaarbeek I Boulevard du Roi Albert II 37 1030 Schaerbeek
T +32 2 432 90 00 I F +32 2 432 90 05 I info@sncblogistics.be I www.sncblogistics.com
Thank you