Download introduction 1 commercial results typology

Challenge G: An even more competitive and cost efficient railway
TOPIC: Economic efficiency
Performance of Yield Management
ABSTRACT NO:
SESSION:
A. REMY, E-mail: [email protected], Phone: 33-1-53429278, Fax: 33-1-53429254
SNCF Innovation and Research Department, 45 rue de Londres, 75379 Paris Cedex 08, France
INTRODUCTION
A key issue for SNCF Company is to find the best adjustment of on one hand its tariffs and on the
other hand its supply capacity to passenger’s demand. The Yield Management (YM) is an inventory
control system built in order to maximize revenue of perishable services. Adapted to railways issues,
this control system allows an efficient optimisation of the trains occupancy and customer revenues.
In order to manage the YM activity, SNCF needs to measure and analyse the impact of the actions
which have been used in the past. However, it is difficult to quantify the gain due to the YM because
the commercial results are influenced by many market conditions (local effects, seasonal patterns,
general economic environment) and retroactive effects (customers adapt their behaviour).
In that context, this study proposes to develop a new approach for the analysis of commercial results
observed after the train departure and to evaluate performance measurement of the activity of Yield
Management.
We identified two areas of analysis:
- a typology of commercial results obtained on key areas such as bookings volume, customer
revenue and tariff structure
- different types of stress levels applied by the yield management during the booking process.
Then, we defined what we call performance as an incremental result on a level of analysis (putting
together similar trains) compared to a baseline.
Our work is based on intensive management of huge volumes of historical booking and control data
(about several hundreds of millions units). In particular we relied on the implementation of
multidimensional statistical analysis methods and data mining techniques to form and extract relevant
information from various commercial data collected in the past. We also focused on visualisation of
both input and output data adapted to the operational uses of the Yield Management team.
It is noteworthy that our analysis uses the available data after the train departure. This means that
we describe and evaluate the trains results based on the effective booking transactions.
1 COMMERCIAL RESULTS TYPOLOGY
The analysis of commercial data is quite complex due to their spatiotemporal features. Indeed,
customer behaviour can be very different over time for the same Origin-Destination (OD) market.
For instance on the journey from Paris to Marseille, the booking transactions vary greatly between
January and August, depending of the day of week, or during the day …
Similarly, the spatial component plays a decisive role to be taken into account in our analysis. Thus,
each train on a given period is not easily comparable to each others and it does not make sense to
handle all of them together.
1.1 Multidimensional statistical analysis
To address this issue, we implemented multidimensional statistical methods to build a commercial
results typology. In others words, we established homogeneous groups of trains, i.e. close in a
geometric distance to define. It consisted in statistical exploration of historical data to bring up the
Challenge G: An even more competitive and cost efficient railway
different groups of similar trains. In a second step, we defined automatic rules for assigning a train to
its own group based on the same geometric criteria defined above. [1, 2, 3]
We implemented these statistical methods on bookings and revenues data provided by the
commercial database. We collected data on whole French TGV trains (High Speed Train) from 2008
to 2010.
This analysis led optimally to the emergence of seven distinct groups of trains with homogeneous
results. These groups are distinguished according to three mainlines of interpretation: the tariff
structure, the results level achieved at the date of departure and the variance of the results between
the various ODs travelled by a same train. In the following section, we present a more detailed
discussion about these different areas of analysis.
1.2 The tariff structure
SNCF offers an extended price range to meet the varied passengers demand. Thus, each booking
transaction is the result of a customer's choice among the different fare products available. We
proposed to use this information to improve our knowledge and understanding of customer
behaviours.
The Gini indicator is developed in order to measure the income inequality of a population. In our case,
this economic indicator provides a quantitative measure (between 0 and 1 strictly) of the homogeneity
of distribution in each fare class of product between bookings and revenues.
The two extreme theoretical situations are:
- A perfect income equality among the population. In our case, there is the same revenue due to
different tariff products;
- A total inequality, which means that a single tariff product represents almost all the revenue.
Figure 1: Booking tariff structure
It was implemented by a simple and fast iterative calculation method and can be represented by the
graph above (Lorentz curve).
1.3 Indicators in transport sector
Other variables have been selected to characterise the commercial results observed on a train. In
particular, we proposed indicators to evaluate the results level achieved compared to a theoretical
maximum.
Challenge G: An even more competitive and cost efficient railway
Such indicators are more commonly used in the transport sector as they introduce physical
constraints of the material. In our case, we introduced the maximum capacity of passengers that a
commercial vehicle can carry on a given leg of the network, i.e. between two consecutive stations
served by the same train. These indicators evaluate the results level achieved in terms of occupancy
and customer revenue depending on equipment (capacity) and supplement tariff level (concept of
maximum tariff).
2 STRESS LEVEL APLLIED BY THE YIELD MANAGEMENT
The bookings volume in the different fare classes is controlled by the YM activity. Indeed, each fare
product belongs to a bucket class with a booking limit. The availability of every bucket class is
dynamically optimized based on the booking curve at different check points during the booking
period. Those check points are named the relative reading days (RRD). In our case, nested fare
classes are used i.e. the availability of the lowest fare type depends of the offered seats in higher fare
type. [4]
We identified different stress levels applied by the Yield Management to the travel demand based on
this nested structure. We assume that the stress levels have distinct effects on the performance of a
train. For example, the results will not be the same if various fare types or only higher fare classes are
available during the booking period.
2.1 Definition of the stress level applied by the Yield Management
The feedback database keeps the bucket classes quotas for each leg and check point from the
booking opening to the train departure. As the capacity of the rolling stock, quotas are used to
manage inventory capacity on a leg. This information is then deployed in the distribution system for
each OD offered to the customer.
The objective was to return this “control” information on a more aggregated level, for instance a tuple
(train, date of departure). Specifically, our calculations are based on the set of openings and closings
of nested bucket classes during the booking process. The stress levels are defined in ascending
order from the less constraint situation when no classes bucket is closed to the maximum constraints
corresponding to all bucket classes closed
Figure 2: Stress levels applied by the Yield Management
The stress level dynamic is evaluated with respect to the relative bookings made on different ODs. In
particular, bookings on a train are the sum of the booking customers have made on its various ODs.
Challenge G: An even more competitive and cost efficient railway
Then in some cases, significantly different stress levels are expressed in equal part on a reading day.
The stress levels dynamic highlights these specific situations and provides some help to improve the
choice of a coherent set of booking limits on a train.
2.2 Different types of stress level
In our study, we focused on particular moments when the scale of stress level is changing. This is
what we call a “rupture” moment in the stress level dynamic. Following a statistical analysis applied to
these relevant variables, we identified different types of stress level dynamic. To address the
operational requirements, we proposed a set of simple and intuitive rules (boolean conditions) to
assign a train to its type of stress level dynamic.
3 PERFORMANCE MEASUREMENT
We used the tariff structure to establish homogeneous groups of trains in terms of results. Then, we
distinguished the different stress levels applied by the Yield Management during the booking period.
In this part, we propose new indicators to measure performance relative to the actual activity of the
Yield Management team.
3.1 The concept of performance
We wished to give an answer to these questions:
- How to evaluate the incremental results due to a stress level in a homogeneous group of trains?
- What is the stress level with the "best" performance in a homogeneous group of trains?
We defined a performance indicator as a piece of information contributing to the appreciation of a
situation (analysis level) compared to a baseline to define. This information is not necessarily
quantitative (qualitative, graphical ...).
Moreover, an order notion between the different performances observed can be evaluated by relaxing
the degree of freedom of space explored.
Thus, several parameters must be defined for the evaluation of performance:
- The level of analysis is the finest unit
handled. It defines a "homogeneous" group of
units to evaluate (e.g, results / stress level /
Axis TGV).
- The baseline in comparison with the level of
analysis is evaluated. It has at least one
additional degree of freedom compared to the
level of analysis (e.g, result / Axis TGV).
- The degree of freedom defines a dimension
to provide a notion of order between the
performances observed along this axis (e.g,
the spatial axis).
- The variable of interest is the indicator to
analyse (for example, the occupancy rate, the
revenue rate, ...)
Figure 3: Stress levels applied by the Yield
Management
3.2 Applications
In the context of Yield Management, we chose to explore three main dimensions to define the levels
of analysis and the baseline: the homogeneous groups of results, types of stress level and the spatial
axis.
Challenge G: An even more competitive and cost efficient railway
This methodology can be deployed for every choice of parameters and provides a generic and
coherent approach adapted to the specific needs of different operational users (analyst, team leader,
head manager).
In our case, we implemented this methodology for two sets of parameters corresponding respectively
to the operational requirements identified on an Axis TGV and on whole French TGV trains.
4 TECHNICAL IMPLEMENTATION
In this study, we proposed a data conceptual model to ensure the coherence between the various
data and then we constructed a data environment following different topics: the management
structure, the network, the transportation plan, fare product, control (booking limits) and the customer.
The Statistical Analysis Software (SAS)[9] was used to develop and apply our methodology on
different spatiotemporal cases studies. A generic programming has been developed and we
distinguished "macro" programs, which are the key code generated and “start” programs which
execute automatically the macro programs for a set of chosen parameters.
Furthermore, we developed an interactive data visualisation tool. This interface was implemented in
JavaScript language and connected to a Mysql[10] database. An intranet server allows the consultation
by the users on various temporal (hourly, daily, monthly) and spatial levels (OD, relation, axis, global).
This tool easily allows browsing a large volume of data (decade of Gigabits):
- bookings and revenue data on the departure day;
- data between the booking opening and the departure day: the booking revenue curves, tariff
structure and stress level applied by the Yield Management.
Figure 4: Booking and revenue data visualisation
Challenge G: An even more competitive and cost efficient railway
5 CONCLUSION AND PERSPECTIVES
In this work, we managed large volumes of commercial data which had never been explored so
deeply previously. In particular, we identified new operational objects (tariff structure and stress level
applied by the YM) and returned this relevant information at more aggregated levels.
Furthermore, we developed an interactive and intuitive support tool for both data mining and
commercial results monitoring.
Our methodology provides new insight to evaluate the performance in the context of Yield
Management. In the future, these methods should find applications in helping the company in its
strategic choices to manage the YM activity.
A new research perspective would be to explore and adapt to our context new statistical models
(mixed models on longitudinal data for instance). Effectively, those models are currently used in the
medical field to evaluate the performance of several treatments.
In this study, we chose to establish homogeneous groups of trains based on the results observed in
the past. Furthermore we want to identify comparable spatiotemporal “contexts” in order to evaluate
performance measurement. Our research will be exploring statistical method to deal with this
problematic.
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and prediction, 2009
[4] K. T. Talluri , G. J. van Ryzin, The theory and practice of Revenue Management, 2005
[5] P. P. Belobaba, Impacts of yield management in competitive airline markets, Journal of Air
Transport Management, Volume 3, issue 1, 1997
[6] B. Rannou, D. Melli, Measuring the impact of Revenue Management, Journal of Revenue and
Pricing Management, Volume 2, n°3, 2003
[7] W. Lieberman, M. Raskin, Comparable Challenge: A new approach to performance measurement,
Journal of Revenue and Pricing Management, Volume 4, n°2, 2005
[8] K. Talluri, B. Codina, J. Magaz, Proving the performance of a new revenue management system,
2009
[9] Statistical Analysis Software, www.sas.com
[10] Mysql, www.mysql.com