Quintiq vandaag en morgen Welke trends tekenen zich af in advanced planning & scheduling? Waar gaat Quintiq heen? Welke nieuwe ontwikkelingen en tools zijn er? Zet u Quintiq in als operationele planningtool, of als tactisch en strategisch planningplatform?.

1. Powerful forecasting for a good planning
Ordina Customer Day 2013
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2. Contents
 Forecasting and planning – a perfect interplay
 What to forecast and how to forecast it
 Forecasting with Ordina and Quintiq
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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
- Historical trends of visitors, knowledge from and commercial campaigns,
B2B agreements operational
other divisions
new product and service launch, … information
- 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
Companies canforecast. clearly defining the balance between statistical modelling and
form a final benefit from
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
Advanced statistical
Historical data
Experience techniques
Last minute operational
information
Actuals
Weather forecast
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11. Forecasting with Ordina and Quintiq
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12. Shortcomings of traditional solutions
 Traditional forecasting solutions typically focus on one specific
methodology with little possibilities to interact between methodologies.
 Traditional forecasting solutions serve as a black box. Numbers go in
and numbers come out, with little or no control.
 Traditional solutions have a rigid dimension management, limiting the
correct mix of statistical techniques and enrichments.
 Traditional solutions typically lack dynamical graphical representations
of the forecasts.
 Traditional solutions allow little or no refinement based on operational
data.
Traditional solutions provide shaky foundations to build a planning on!
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13. One methodology versus best of all worlds
 Traditional solutions
- Statistical forecasting with limited
override functionality
- Consensus forecasting with limited
statistical foundations
 Ordina & Quintiq
- Strong statistical basis
- Enriched with relevant additional
information
- Concurrent what-if scenarios
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14. Black box versus open information
 Traditional solutions
- Limited options in algorithm creation
and maintenance
- Limited or no business rules
available
 Ordina & Quintiq, with the power of R
- Statistical algorithms are available
through R.
- Expert users can create their own R
scripts and algorithms in the tool.
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15. Rigid dimensions versus open hierarchies
 Traditional solutions
- Fixed number of dimensions
- Dimensions grouped in fixed
pyramidal hierarchies
 Ordina & Quintiq
- Dimensions can be added without
limitations
- Hierarchies between dimensions can be
created dynamically
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16. Graphical representations
 Traditional solutions
- Simple spreadsheet with textual
information
- Simple graphical representation with
limited navigation
 Ordina & Quintiq
- Colours, formatting and icons can be
used to visualize extra information.
- The graph has advanced
configuration possibilities (bar, line,
dotted) and can easily be navigated
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17. Limited refinement versus advanced enrichment and
consumption
 Traditional solutions
- Little interaction with operational
information.
- Limited possibility to adjust using last
minute information
 Ordina & Quintiq
- Forecasted volumes can be adjusted
based on actuals received.
- A number of consumption logics are
available and can of course be extended
to match any business rule needed.
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18. Summarizing
 Quintiq demand planner: a good basis for developing a forecasting solution
- Clear graphical and textual visualization of forecasts
- Multiple scenarios allow rapid simulations and lead forecast selection
- Standard statistical algorithms available through interface with R
 Ordina forecasting solution: in addition to the demand planner
- Unlimited number of forecasting dimensions available
- Advanced control of breakdown hierarchies and factors (no fixed hierarchy)
- Long and short term forecast enhancement and correction based on external
input.
- Advanced parametrizable consumption logic available
- Full access to R to allow expert users to create and maintain their own
forecasting scripts.
The Ordina and Quintiq forecasting solution provides a solid basis for a
good planning!
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19. That is, as certain
Data from the past as we can ever be…
Certainty of the future!
Data from the present
Trends
A solid basis for…
17:00 Shift Rostering
by Kris Van Marcke
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20. Questions?
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