Wazzup Pilipinas!
The public transport industry is a
heavy innovator. Trains and railways are equipped with IoT sensors to measure
performance, delays, and incidents. Bus, subway, and train repair, and
maintenance costs are intensely watched due to budget pressures. In the
meantime, passengers are more critical and demand immediate action
whenever downtimes or delays occur.
In various interactions with public transport industry customers, I’ve
noticed they are generally well informed regarding delays, have good insights
on maintenance and repair, maintain remote visibility on infrastructure
incidents, and are equipped with rolling budgeting models. The thing is that
these insights tend to be isolated. None of the customers I spoke to has
integrated and consolidated insights on them all, so they are like “blind
men in the wood” when I ask which unexpected repair incidents affect which
train delays.
Challenges
The major areas of interest for public transport companies
are:
·
Incidents management: including both
infrastructure incidents and incidents in the actual train, bus, subway,
and so on.
·
Repair costs and maintenance: focusing on supplier
management, unplanned downtime, and cost reduction.
·
Delays management: tied to
narrowing the gaps between planned and actual arrival/departure times,
rankings, and geospatial insights.
As I said above, today’s key challenge for the industry is not measuring
those individual subject areas, rather it’s in consolidating and
interrelating them. The key is the ability to understand how an issue in one
subject area affects an issue in another one, and to what extent.
More Challenges
Are we done with challenges? No, we
aren’t. Today’s competitive public transport market requires more than just
consolidating insights. It also requires agility, meaning
analysts must be able to consolidate insights in real
time, to continuously compare their actuals against budgets and forecasts,
and even to be able to adjust and simulate them while analyzing. We call this
the closed-loop
portfolio of monitoring, budgeting, and forecasting. And last, they need
instant insights from massive data volumes; remember, they all use IoT devices
– and we know what they generate.
So in summary, insights must have:
·
Real-time reporting
·
Predictive-forecast capability
·
The ability to handle massive data
·
Closed-loop capabilities of combining
monitoring, planning, and predicting metrics on the fly while analyzing
·
Simulation functionality
SAP Digital Boardroom Insights
I used the SAP Digital Boardroom on
top of in-memory platforms to analyze over 30 million records of public
transport delays, incidents, and maintenance data. The SAP Digital Boardroom
uses three touch screens to provide insights in real time on the
transactional level. (This
article describes how the SAP Digital Boardroom works.) What impresses me
most is its ability to combine versions of actual, planned, forecasted, and
predicted data; in other words, I can analyze, adjust, predict, simulate, and
re-analyze the end result. Now we’re talking!
It all starts with creating an agenda that lists the various insights
required. The agenda items refer to models and stories with real-time insights.
You can skip between agenda items with a single click, and every
single insight can be further explored with new attributes, filters, or
simulations.
Watch the video below for a full overview of using the SAP Digital
Boardroom to address the challenges I’ve described. Please be aware that I
anonymized the data though the algorithms, structures, and “cadence” of the
data from a real-world example.
This post has been syndicated from
the D!gitalist Magazine and
has been republished with permission.
Ross is known as the Pambansang Blogger ng Pilipinas - An Information and Communication Technology (ICT) Professional by profession and a Social Media Evangelist by heart.
