ordina planning & scheduling day - aps - powerful forecasting for a good planning
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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?.TRANSCRIPT
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Powerful forecasting for a good planningOrdina Customer Day 2013
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Contents
Forecasting and planning – a perfect interplay
What to forecast and how to forecast it
Forecasting with Ordina and Quintiq
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Forecasting and planning – a perfect interplay
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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
Trends
Data from the present Certainty of the future?
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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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What to forecast and how to forecast it
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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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What are the main factors that influence the basis of these decisions?
- Historical trends of incidents and B2B agreements.
- Historical trends of visitors, B2B agreements and commercial campaigns,
new product and service launch, …
- Historical trends and customer announcements.
What planning decisions do we want to make?
- E.g. roadside assistance: we want to minimize the use of external parties
needed to maintain our customer service levels.
- E.g. airport services: we want to optimize our time to service and minimize
our personnel cost while maintaining our customer service levels.
- E.g. postal services: we want to optimize machine utilization and minimize
personnel cost while maintaining our target throughput times.
Some examples
Based on the number of incidents on the road.
Based on the number of visitors and passengers at the airport.
Based on the postal volumes received each day.
Information and
knowledge from
other divisions
Historical trends Short term
operational
information
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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 using different techniques are combined to form a final
forecast.
- Typically, a statistical forecast serves as the basis for the forecast. It is
subsequently enriched with information received from other channels to
form a final forecast.
Gartner (september 2012)
Defining the balance between statistical modelling and collaborative forecasting
improves accountability for the forecast, and enables continuous improvement
across the organizationCompanies can benefit from 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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Good forecasting uses the best of all worlds
Advanced statistical
techniques
Relevant forecast information
from all divisions
Last minute operational
informationActuals
Weather forecast
Historical data
B2B agreements
Sales campaigns
Experience
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Forecasting with Ordina and Quintiq
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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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Ordina & Quintiq
- Strong statistical basis
- Enriched with relevant additional
information
- Concurrent what-if scenarios
One methodology versus best of all worlds
Traditional solutions
- Statistical forecasting with limited
override functionality
- Consensus forecasting with limited
statistical foundations
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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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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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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
Graphical representations
Traditional solutions
- Simple spreadsheet with textual
information
- Simple graphical representation with
limited navigation
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Limited refinement versus advanced enrichment andconsumption
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.
Traditional solutions
- Little interaction with operational
information.
- Limited possibility to adjust using last
minute information
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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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Data from the past
Trends
Data from the present
Certainty of the future!
That is, as certain
as we can ever be…
A solid basis for…
17:00 Shift Rostering
by Kris Van Marcke
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Questions?