The public transport industry heavily innovates: trains and railways are equipped with IoT sensors measuring performance, delays and incidents. Busses, subways and trains are followed up intensely on repair and maintenance costs due to pressure on budgets. In the meantime passengers are more critical and require immediate actioning whenever downtimes or delays occur.
In various touch points with public transport customers, I noticed they all make tremendous progress measuring and monitoring the various aspects: well informed regarding delays, good insights on maintenance and repair, remote visibility on infrastructure incidents and pretty equipped with rolling budgeting models, to name a few. The thing is however, that these insights tend to be isolated. None of the customers I spoke to have integrated and consolidated insights on all of them, being "blind men in the wood" when I ask what unexpected repair incidents affect which train delays.
In general the subject areas of interest for Public Transport companies are:
Incidents-management applies to both infrastructure incidents as incidents in the device (i.e. train, bus, subway etc) itself. The insights needed are on work-order management, and spend analytics and categorization. Repair costs and maintenance focus more on supplier management, unplanned downtimes and cost reduction. The third subject area is on delays focussing on narrowing down the gaps between planned and actual arrival/departure times, rankings and geospatial insights. A said, todays key challenge is not in the individual subject areas, it is in consolidating and interrelating these areas ...... interrelating .. ?? ... yes, that basically comes down on being able to understand how an issue in one subject area affects an issue in another one, and to what extend.
Are we done with challenges? No, we are not. Todays competitive market requires more to public transport than only consolidating insights. Agility comes with it. It means the public transport companies need to be able to have these consolidated insights in real-time. They also require to continuously compare their actuals against budgets and forecasts, or even adjust and simulate them while analyzing. We call this the closed loop portfolio of monitoring, budgeting and forecasting. And lastly, they require instant insights in massive data volumes; remember they all use IoT devices and you and I know what they generate:-).
So in summary:
predictive forecast capabilities
capability to handle mass data
closed loop capabilities of combining monitoring, planning and predicting metrics on the fly while analyzing
I used the concept of the Digital Boardroom on top of in-memory platforms to analyze over 30 million records of delays, incidents and maintenance data. The Digital Boardroom is on the concept of 3 touch-screens allowing insights in real-time onto transactional level. This article describes the working of the Digital Boardroom. What impresses me is the ability to combine both versions of actual, planned, forecasted and predicted data all together: in other words I can analyse, adjust, predict, simulate and analyse-back the end result. Now we are talking !
It all starts with creating an agenda with the various insights required. The agenda points refer to models and stories with real-time insights. Skipping between agenda-points is with a single click. Every single insight can be further explored with new attributes, filters or simulations.
Below video provides a full overview of using the Digital Boardroom covering all the challenges as described here. Enjoy ! Just click below pic to start up the video. Please be aware that I anonymized the data though the algorithms, structures and "cadence" of the data is from a real-world example.
How it was created
In this article I describe some more details on how I created the Public Transport Digital Boardroom.