This document summarizes two forecasting cases developed by Ordina: 1) bForecasting, a volume forecasting tool for Belgium's postal service bpost that forecasts mail volumes across eight dimensions and dynamically adjusts forecasts up until execution. It improves bpost's planning of five mail sorting centers. 2) Pluto Forecasting Tool, which forecasts roadside assistance incident volumes by location, type, and half-hour intervals to optimize patrol staffing levels across Belgium and achieve service-level agreements. It dispelled some common assumptions through data analysis. Both tools use the Quintiq software suite and statistical algorithms in R to generate accurate volume and occupancy forecasts from historical data.

1. Powerful forecasting for a good planning
VisionWorks Seminar 25/02/2014
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2. Contents
 Forecasting and planning – a perfect interplay
 What to forecast and how to forecast it
 Forecasting with Ordina
- The bForecasting case
- The Pluto Forecasting case
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3. Forecasting and planning – a perfect interplay
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4. What is forecasting?
 From businessdictionary.com:
- A planning tool that helps management in its attempts to cope with the
uncertainty of the future, relying mainly on data from the past and present
and analysis of trends.
Data from the past
Data from the present
Certainty of the future?
Trends
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5. Forecasting for planning ends

Forecasting becomes useful in a planning context as soon as the important
planning decisions must be based on
-
Need for a certain product, e.g. the need for certain consumer goods such as beer,
canned goods, …
-
Need for a certain service such as airport security, roadside assistance, shipment
transportation, ...

An accurate forecast leads to a good mid term and long term (capacity)
planning.

A good mid term and long term planning leads to a good short term planning.

This leads to cost reduction as well as customer satisfaction:
-
No external parties need to be used to reach SLA
-
Capacity is available to ensure in time delivery
-
Stocks can be maintained at optimal levels
-
…
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6. What to forecast and how to forecast it
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7. Before deciding to use forecasting.
 When considering forecasting to have a substantiated basis for long term
planning, we need to answer several questions.
1. What planning decisions do we want to make and what do we base these
decisions on?
2. What are the main factors that influence the basis for these decisions?
3. At what level of detail can we make a prediction?
4. Can we refine the prediction as we process in time?
 An answer to these questions will
- not only tell us what to forecast,
- but also what techniques we should use to create this forecast.
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8. Some examples
 What planning decisions do we want to make?
- E.g. roadside assistance:incidents onminimize the use of external parties
Based on the number of we want to the road.
needed to maintain our customer service levels.
- E.g. airportthe number of want to optimize our timeat the airport. minimize
Based on services: we visitors and passengers to service and
our personnel cost while maintaining our customer service levels.
- E.g. postalthe postal we want to optimize machine utilization and minimize
Based on services: volumes received each day.
personnel cost while maintaining our target throughput times.
 What are the main factors that influence the basis of these decisions?
- Historical trends of incidents and B2B agreements.
Historical trends
Information and
Short term
operational
- Historical trends of visitors, knowledge from and commercial campaigns,
B2B agreements
other divisions
information
new product and service launch, …
- Historical trends and customer announcements.
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9. Different forecasting methodologies
 Consensus forecasting
- Several parties each make a separate forecast, based on their experience
and knowledge.
- These separate forecasts are combined together to form a final forecast.
 Statistical forecasting
- Mathematical techniques are used to extrapolate historical data to the
future to form a final forecast.
 Combining forecasts
- Forecasts created usingGartner (september 2012) combined to form a final
different techniques are
forecast.
Defining the balance between statistical modelling and collaborative forecasting
- Typically, a statistical forecast serves as the basis for the forecast. It is
improves accountability for the forecast, and enables continuous improvement
subsequently enriched with information received from other channels to
across the organization
form a final benefit from
Companies canforecast. clearly defining the balance between statistical modelling and
collaborative forecasting methods to improve accountability for the forecast and put in place
continuous improvement plans to improve the forecast. […]
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10. Good forecasting uses the best of all worlds
Sales campaigns
Relevant forecast information
from all divisions
B2B agreements
Experience
Advanced statistical
techniques
Last minute operational
information
Actuals
Weather forecast
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Historical data

11. Forecasting with Ordina: 2 cases
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12. Two Forecasting Cases
 bForecasting: the bpost Volume Forecasting Tool
 Pluto Forecasting Tool: volume forecasting for roadside assistance.
 Both cases were modelled using the Quintiq Software Suite, specifically
designed for modelling Advanced Planning and Scheduling software.
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13. bForecasting – postal Volume Forecasting
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14. The bpost Forecasting Case
SOLUTION
o
Within its Vision 2020 business plan, increasing the efficiency of its
Industrial Mail Centers is an absolute necessity for bpost.
o
For an efficient planning, accurate predictions of future mail volumes
are necessary, which means 8 different dimensions need to be taken
into account
o
Moreover, dynamic corrections of the predicted volumes with newly
received data must guarantee estimate accuracy up to the hour of the
execution of the actual planning.
o
ADVANTAGES
services
Finally, future changes in the bulk of mail volumes received leads to the
necessity of having a dynamic identification of relevant statistical
dimensions and corresponding breakdown layers.
o
bForecasting allows the user to create forecasts with time series of 8
dimensions, where the statistical dimensions can be changed
dynamically over time.
o
User-extendable advanced statistical algorithms allow full flexibility in
statistical forecasts, while dynamic allocation of statistical dimensions
ensures future predictability.
o
A dynamic breakdown management ensures good predictions up to the
most detailed operational level.
o
Advanced enrichments with operational data is possible up to the last
minute, ensuring operational correctness of the predicted volumes.
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15. Bpost: be the strongest and most trusted postal operator
• Bpost is the leading postal operator of the country, with is Mail Service
Operations (MSO) achieving 94% on time delivery.
• The efficient working of its Mail Service Operations (MSO) is crucial for
maintaining its position as strongest and most trusted mail operator in
the rapidly evolving market of postal services.
• An important factor in the bpost delivery process is the sorting which
takes place in its five Industrial Mail Centers: Bruxelles X, Antwerpen X,
Gent X, Charleroi X and Liège.
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16. Bpost: planning the industrial mail centers
 The five industrial mail centers are responsible for sorting mail and
parcels received in their regions.
 The sorted mail and parcels are subsequently transported to the
regional centers for final sorting and distribution.
 To sort the mail and parcels received from the various intake channels,
a large number of sorting machines and their operators need to be
planned.
 For the planning to be efficient, accurate predictions are needed so
as to reserve the necessary resources in time and to ensure optimal
usage of machine and personnel capacity.
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17. Bpost: properties of volumes to be sorted
 The volumes that need sorting depend on eight different dimensions.
5 - Intake channel: through which channel are the volumes collected (from the
red letter boxes, from customer drop offs, from the foreign mail centers, …)
15,000- Customer: the corporate customer, if any, dropped the volume.
12 - Day Plus: how many days after being collected should the volume be
delivered?
550 - Intake location: where was the volume collected?
8 - First Sorting IMC: which industrial mail center executes the first sorting
step?
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- Mechanization level: can the volume be sorted automatically or will it need
manual sorting steps?
8 - Throughput type: the size and type of the volume (normal size envelopes,
large size envelopes, parcels, …)
3 - Sorting level: the extend to which the volume was already sorted.
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18. Bpost: first step towards predictability
 For data to have any chance of being predictable, enough volumes
should be known so as to discern patterns in the data.
 For the dimensions customers and intake location, a large number of
single volumes exist.
 To have any chance at predicting volumes, these single volumes need
to be regrouped.
 For this reason, customer pools and location pools were introduced.
 Complexity is added as these pools depend on other dimensions and
vary through time.
- E.g. the customer pool for D+1 volume might be different from the pool for
D+2 volume.
- E.g. the customer pool for D+1 in September might be different from the
one in November.
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19. Bpost: more steps towards predictability
 Not all dimensions have statistical significance. An important exercise
is to identify those dimensions that have.
 Other dimensions need to be derived from the statistical ones using a
breakdown hierarchy and breakdown factors.
 As with pools, both hierarchy and breakdown factors depend on other
dimension values as well as on certain time periods.
 A large number of breakdown algorithms is available for the
computation.
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20. Bpost: time series for forecasting
 Having correctly regrouped dimensions in statistical and operational
dimensions, bForecasting creates all time series containing historical
data.
 These time series are then used to predict the future volumes using
advanced statistical algorithms.
 bForecasting uses R as an underlying statistical engine, offering all
power and flexibility of the de facto open source standard in statistical
computation.
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21. Bpost: refining the forecast
 From its B2B and B2C customers, bpost typically receives detailed
information on the volumes that will be dropped at the MassPost intake
locations one week in advance.
 This information is processed by bForecasting an replaces – except
when user-overridden – the statistically computed volumes with the
new information
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22. Bpost: operational follow-up
 From operations, bForecasting receives hourly updates on volumes
actually dropped at the IMC.
 These volumes are processes using bpost-defined consumption logic
to adapt predictions for the following hours.
 Different types of logic can be defined and
assigned to sets of dimension values, giving
full flexibility to the user in predicting the
following hours.
 Hourly communication from bForecasting
to the planning tool allows the planning to
be adapted last minute to the volumes
expected in the coming hours.
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23. Pluto Forecasting – Roadside assistance forecasting
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24. Roadside assistance planning

Roadside assistance service providers are highly competitive and need to keep
a competitive edge by increasing their service levels to their members.

Guaranteeing service within 30 minutes, independent of the location of the
incident, can be solved in two ways:
-
Position more than enough patrolmen to ensure coverage of the whole of Belgium
-
Position just enough patrolmen to ensure coverage of the right areas.

Obviously the first solution is expensive as it introduces a lot of idle time for the
individual patrolmen.

The second solution, however, needs an accurate prediction of the number of
incidents and their geographic distribution.

For this purpose, only an advanced forecasting tool will do!
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25. Pluto Forecasting Tool: forecasting for roadside assistance
 For one of the major players on the Belgian roadside assistance
market, Ordina developed the Pluto Forecasting Tool.
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26. Forecasting the volumes – dimensions
 The Pluto forecasting tool allows advanced statistical forecasting and
forecast enrichment
- per incident type
- per geographic location (up to address level)
- up to half hour detail level.
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27. Forecasting the volumes – data cleansing
 Using the de facto open source standard for statistical computing – R –
a user-extendable number of statistical algorithms is provided for data
cleansing.
 Data cleansing in roadside assistance is necessary to eliminate the
inherently unpredictable peaks due to unexpected winter weather or
public holidays.
Season of the
outlier
Percentage
deviation from
historical value
Average deviation:
possible value for the
event’s correction
percentage
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28. Forecasting the volumes – geographic breakdown
 For a forecast to be accurate, enough data needs to be available for a
pattern to emerge.
 For this reason, the end user can select the geographic level and time
granularity for which statistical forecasts should be made.
 Lower level forecasts are
computed using breakdown
factors
- both in time and
- In geographical detail
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29. Forecasting the volumes – statistical forecasting and
enrichment
 Using R algorithms the user computes and compares forecasts to
arrive at the most accurate prediction for the next year.
 Using additional information retrieved from the outlier cleansing, this
forecast can be enriched to model the effects of public holidays and
expected bad weather.
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30. Translating the forecast: occupation requirements
 Having created an accurate forecast, this needs to be converted in a
number of shifts that need to be planned in order to achieve the SLA
towards the members on one hand while maximizing the productive
time of the patrolmen on the other hand.
 This computation is done in the Pluto Forecasting tool using a greedy
heuristic.
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31. Forecasting in production: surprising results
 Roadside assistance incidents prove to be highly predictable on a daily
level:
- Forecast accuracy of over 90%
 Moreover, using the Pluto Forecasting tool, long standing “gut feeling”
common knowledge was shown to be wrong:
- “In the summer, we have significantly less incidents than throughout the
rest of the year”.
This claim was shown to be wrong for the patrolmen and right for the call
center and back office.
- “In the winter, we have significantly more incidents than throughout the rest
of the year”.
This claim was shown to be wrong for most of winter, barring the first
couple of days of a cold spell.
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32. Questions?
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