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Machine Whispering
Taking control of the ticket vending
machines by understanding and using the
data
Rob Roemers – BI Summ...
Some Figures
• 4 metro lines
• 18 tram lines
• 49 bus lines
• 11 night bus lines
Operates an integrated network +/- 650 km...
And in sales figures…
4
14
Kiosks
16 million
TSC +
yearly &
monthly
passes
270m €
revenue
/year
21 million
transacti
ons
6...
Towards a structured way of
thinking outside the box
Case study « Go 360°»
Click header and footer to change this on all slides 6
Original situation(pre 2016)
>Only few technical services had a mon...
“Data Lab” approach
7
meteorological
« WHAT do we want?»
•Business
requirements
•Hypothesis
•Wishfull thinking
« HOW can w...
Case study “360°GO”
8
meteorological
> Brainstorming sessions:
• WHAT do we want?
• WHOis the end-user
• WHY use the repor...
9
meteorological
• Business requirements
> How can we challenge our suppliers?
• External suppliers:
• Messages on payment...
Case study “360°GO”
10
• Collectingdata:
> What type of data is available all over STIB?
• Sales data
• Technical data
• O...
Case study “360°GO”
11
meteorological
Use the data
Dream of new
analytics
Analyse, explore
and check
technical feasibility...
But everybody can talk about what they’re going to do…
Case study “360°GO”
13
meteorological
Example: general overview (1)
Split:
metro - surface
Number of types of GO KPI’s
Loc...
Case study “360°GO”
14
meteorological
Example: general overview (2)
Transaction status
GO Status
Timeline
Timestamp alarm
...
Case study “360°GO”
15
meteorological
Example: health status of 1 Go in Central Station during 1 year
General info
% Uptim...
Case study “360°GO”
16
meteorological
Example: overview sum of incidentsby type by month
∑ incidents by type by month
% pe...
Case study “360°GO”
17
meteorological
> Used for adhoc reporting
> Special investigations
KPI
Reporting
Webi
The proof is in the tasting…
19
Use the data
Dream of new
analysis
Analyse, check,
explore
Development by
BI and business
Test
Automated SLA compliance check
Turned the tables on the supplier by providing
them with our data
=> Their responsability t...
Getting Smarter with data analytics
21
meteorological
> Understanding & learning from data à BI-data lab approach & sharin...
Thinking beyond the current issues
22
meteorological
> Predictive data à BI-data lab
> Can it be linked with other (extern...
The future
The future of our GO’s
24
>Renewing our 405 GO (between 2018 and 2020)
• New technology: Tactil screen, NFC Bankterminal, ...
High level future
Future landscape validation via POC’s
Rob.Roemers@mivb.brussels
How STIB-MIVB Uses Data to Improve the Brussels Public Transport Experience, particularly at the Vending Machines, by Rob ...
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How STIB-MIVB Uses Data to Improve the Brussels Public Transport Experience, particularly at the Vending Machines, by Rob Roemers, responsible for data & analytics at the Brussels Public Transport Company STIB-MIVB

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Presentation on the "Use of Data to Improve the Customer's Public Transport Experience" by Rob Roemers (Head of Data & Analytics @ STIB-MIVB), at the BI & Data Analytics Summit on June 13th, 2019 in Diegem (Belgium)

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How STIB-MIVB Uses Data to Improve the Brussels Public Transport Experience, particularly at the Vending Machines, by Rob Roemers, responsible for data & analytics at the Brussels Public Transport Company STIB-MIVB

  1. 1. Machine Whispering Taking control of the ticket vending machines by understanding and using the data Rob Roemers – BI Summit 13/06/2019
  2. 2. Some Figures • 4 metro lines • 18 tram lines • 49 bus lines • 11 night bus lines Operates an integrated network +/- 650 km Transports more than one million Brussels residents and commuters every day 2200 stops 1200 vehicles Operating 7/7 and 20h/24h 9000 employees
  3. 3. And in sales figures… 4 14 Kiosks 16 million TSC + yearly & monthly passes 270m € revenue /year 21 million transacti ons 6 Bootik 115 resellers Web Sales 410 GO Generates 43% of the yearly revenue This will be our case study today
  4. 4. Towards a structured way of thinking outside the box Case study « Go 360°»
  5. 5. Click header and footer to change this on all slides 6 Original situation(pre 2016) >Only few technical services had a monitoring tool >Monitoring tool of the supplier >Queries in database >Complaints and problems were signaled by Customer Care, clients etc, -> No response >No view on respecting SLA’s >No challenging of suppliers SALES had no view on the “health status” of their most important sales channel
  6. 6. “Data Lab” approach 7 meteorological « WHAT do we want?» •Business requirements •Hypothesis •Wishfull thinking « HOW can we get it? » •Collecting data •Exploration reports •Data Science CREATE •Develop Cockpits •Recurrent reporting «How to deal with NEW data/ideas» •Continious improvement
  7. 7. Case study “360°GO” 8 meteorological > Brainstorming sessions: • WHAT do we want? • WHOis the end-user • WHY use the reporting • Not HOW to get it ! à BI team « WHAT do we want?» • Business requirements KPI’s Reporting Webi >Analysis of the known incidents/alarms from data exploration or hands-on experience: •Workin progress Schuman - « Always Coca Cola » •How to check actual uptime vs machine uptime? >From highlevel view to detailed view •By zone: BXLAirport ≠ BXLCentre •By station •By GO •To transaction level inside machine
  8. 8. 9 meteorological • Business requirements > How can we challenge our suppliers? • External suppliers: • Messages on payment terminal: • « saldo ontoereikend » / « foute pincode » • Not a problem STIB but problem supplier! • Internal suppliers: • internal service STIB > How can we check if SLA’sare respected, claim penalties, check invoices? > « Are we talking and watching the same KPI’s? » • 1 centralised tool used by all STIB? (support of internal audit) Case study “360°GO” « WHAT do we want?» • Business requirements Reputation damage! Good Business practice!
  9. 9. Case study “360°GO” 10 • Collectingdata: > What type of data is available all over STIB? • Sales data • Technical data • Other external data? • Are these data up-to-date and useful? => Exploration with small datasets > How to interpret? • Complexity to find the correct value and the correct field > Expertise and knowledge of BI Team • Informal discussions and exchange before launching a Business Request • Data exploration in « data science style » to prepare a business case • Same value in disproving a hypothesis as in proving it (and documenting the results) meteorological « HOW can we get it? » • Collecting data
  10. 10. Case study “360°GO” 11 meteorological Use the data Dream of new analytics Analyse, explore and check technical feasibility Developmentby BI Test «How to deal with NEW data/ideas» • Continious improvement • Continiousimprovement: > Iterative process: • Build on what exists • Business can “dream” à reality check and technical feasibility is done by BI Team
  11. 11. But everybody can talk about what they’re going to do…
  12. 12. Case study “360°GO” 13 meteorological Example: general overview (1) Split: metro - surface Number of types of GO KPI’s Location GO by location Link to detail Summary by location Incidents by GO KPI Reporting Webi
  13. 13. Case study “360°GO” 14 meteorological Example: general overview (2) Transaction status GO Status Timeline Timestamp alarm Technical intervention KPI Reporting Webi
  14. 14. Case study “360°GO” 15 meteorological Example: health status of 1 Go in Central Station during 1 year General info % Uptime MAT Filter Number of hours up and running KPI Reporting Webi
  15. 15. Case study “360°GO” 16 meteorological Example: overview sum of incidentsby type by month ∑ incidents by type by month % per incident on MAT # incident by type by month Conditional formatting KPI Reporting Webi
  16. 16. Case study “360°GO” 17 meteorological > Used for adhoc reporting > Special investigations KPI Reporting Webi
  17. 17. The proof is in the tasting…
  18. 18. 19 Use the data Dream of new analysis Analyse, check, explore Development by BI and business Test
  19. 19. Automated SLA compliance check Turned the tables on the supplier by providing them with our data => Their responsability to prove us wrong
  20. 20. Getting Smarter with data analytics 21 meteorological > Understanding & learning from data à BI-data lab approach & sharing within business > Rethinking our SLA demands around AVM maintenance (business case simulations)
  21. 21. Thinking beyond the current issues 22 meteorological > Predictive data à BI-data lab > Can it be linked with other (external) data: • Meteorological data: • Green: sunny weather, no humidity = less incidents • Yellow/orange: not sunny, few humidity = more incidents • Red: bad weather, humidity = a lot of incidents • Direct impact so ask supplier for better waterproofing Dates 1411 Bourrages 1412 Séquestres Semaine 5 du 1 au 7/2 57,71 Semaine 6 du 8 au 14/2 41,57 Semaine 7 du 15 au 21/2 41,57 Semaine 8 du 22 au 28/2 28,43 Semaine 9 du 29 au 6/3 12,71 Semaine 10 du 7 au 13/3 9,43 Semaine 11 du 14 au 20/3 6,43 Semaine 12 du 21 au 27/3 3,29 Semaine 13 du 28 au 3/4 4,86 Semaine 14 du 4 au 10/4 2,86 Semaine 15 du 11 au 17/4 6,00 Semaine 16 du 18 au 24/4 5,14 Semaine 17 du 25 au 1/5 5,86 Semaine 18 du 2 au 8/5 7,86 Semaine 19 du 9 au 15/5 7,00 Semaine 20 du 16 au 22/5 9,71 58,71 Semaine 21 du 23 au 29/5 12,00 76,29 Semaine 22 du 30 au 5/6 16,43 82,43 Semaine 23 du 6 au 12/6 14,29 100,43 Semaine 24 du 13 au 19/6 15,86 102,71 Semaine 25 du 20 au 26/6 18,14 99,86 Semaine 26 du 27 au 3/7 18,14 123,29 Semaine 27 du 4 au 10/7 25,57 125,29 Semaine 28 du 11 au 17/7 23,29 103,57 Semaine 29 du 18 au 24/7 27,43 107,86 Semaine 30 du 25 au 31/7 38,00 124,00 Semaine 31 du 1 au 7/8 54,43 132,00 Semaine 32 du 8 au 14/8 59,57 140,71 Semaine 33 du 15 au 21/8 51,43 130,71 Semaine 34 du 22 au 28/8 38,29 148,00 Semaine 35 du 29 au 4/9 44,71 156,14 Semaine 36 du 5 au 11/9 55,71 179,71 Semaine 37 du 12 au 18/9 16,29 97,29 Semaine 38 du 19 au 25/9 10,43 92,14 Semaine 39 du 26 au 2/10 11,57 92,00 If it is too hot inside the machine the glue of the ticket roll melts and blocks the machine Exceptional in Belgium so pro-active maintenance if this happens
  22. 22. The future
  23. 23. The future of our GO’s 24 >Renewing our 405 GO (between 2018 and 2020) • New technology: Tactil screen, NFC Bankterminal, QRCode >New “services” for our customers: • Sale of transport tickets • Delivering MOBIB-Basic • Traffic info • Marketing Campaignsto promote Metro Station stores meteorological •Access to data included as a minimal requirment during tendering •Better access to supplier data than other public transport players •Data (and access to it) is in the mind of the business
  24. 24. High level future
  25. 25. Future landscape validation via POC’s
  26. 26. Rob.Roemers@mivb.brussels

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