Predictive Planning is an element of Oracle EPM's focus on Intelligent Performance Management, which is automating as much as possible in order to free up humans to do the real thinking. Predictive Planning is advanced statistical forecasting made easy and tightly integrated into EPBCS. It includes methods such as linear regression, exponential smoothing, and seasonality. For each forecast, it tests many different techniques and creates a forecast using the best one. You might use the results as your primary forecast, you might use them as your forecast seed, or you might use them to compare to and validate human-made forecasts. You don’t need a PhD in statistics. In fact, it’s a good way to learn more about statistical forecasting techniques (aka data science).
So how exactly does it work, and how can you use it to improve your forecasts? This presentation provides a quick overview of the statistical techniques and error measures. It identifies some potential use cases from finance, sales, and HR. Finally, it digs into some examples of how to set up and implement the cube. This presentation is intended for EPBCS admins and developers, as well as Finance, Sales, and HR planners who want to improve their forecasting and analytics.
2. 2
Ron Moore
• Principal Architect at Ranzal
• Over 20 years Essbase consulting and training experience
• Certified in Essbase, Planning and R programming
• Many webcasts and KScope sessions
• 19 Oracle University Quality Awards
Intro
3. 3
Comprehensive Business Solutions
Ranzal’s solutions drive improved business performance
through better decision making, strong customer
engagement and optimized operations
Deep Oracle Partnership Drives Customer Value Adaptable Deployment Models
Diverse Client Portfolio & Industry Expertise
Bio Tech and
Pharma
Medical
Supplies
Team Highlights
Multiple
Oracle ACEs
Seasoned delivery
team with avg ~6
yrs serving Ranzal
clients
Experienced
mgmt team with
avg 12 yrs leading
Ranzal
4. 4
8 Speaker Sessions
Monday, 6/11:
• 10:45am – 11:45am: Baha Mar's All In Bet on Red - The story of integrating data and master data with PBCS, FCCS and ARCS
• 2:30pm - 3:30pm: Visual Approach to Essbase Calcs: 2018
• 4:15pm - 5:15pm: Integrated Planning Using Enterprise Planning and Budgeting Cloud Service at Sims Metal Management
Tuesday, 6/12:
• 9:00am - 10:00am: FDMEE versus Cloud Data Management - The Real Story
• 10:15am - 11:15am: Edgewater Ranzal: Winning Strategies for Oracle Cloud Adoption: Should You Test Drive, Lease, or Buy?
• 2:15pm - 3:15pm: Why Should I Care About DVD? Blu-Ray is the New Thing, Right?
Wednesday, 6/13:
• 11:45am - 12:45pm: Putting Predictive Planning to Work
• 2:15pm - 3:15pm: EPM Automate - Automating Enterprise Performance Management Cloud Solutions
Visit us at Booth # 407
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• Overview
• Potential Predictive Planning objectives
• Walkthrough of a prediction
• Understanding the prediction methods
• Lessons from the field
Agenda
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• Smart View
• Open forms or use ad-hoc views
• Web interface for Planning Cloud
• Tight integration with Planning forms
• Automatically tests different forecasting methods, chooses the
best and creates the forecast
• 12 time series based methods including, moving averages,
exponential smoothing, seasonal and non-seasonal and ARIMA
Features Overview
8. 8
• Automatically handles missing data and outliers
• Flexible control of forecast granularity
• Optionally source history from an alternate plan type
• “Paste” predictions for Most Likely, Best Case and Worst Case
• Comparative views
• Predefined results report
• Extract results to Excel
Features Overview - continued
9. 9
• Crystal Ball
• Additional features such as Monte Carlo, correlation and regression
• Essbase Calc Scripts/Business Rules
• @TREND includes exponential smoothing and regression
• @CORRELATION
• Oracle R Enterprise (ORE)
• Part of Oracle Advanced Analytics
Related functionality
10. 10
• While the brass ring is creating more accurate forecasts, there
is a lot of value in a “second opinion”
• Automatically create “seed” forecasts
• Easily calculate and apply seasonality
• Identify trends human forecasters might miss
• Save time
• Sanity check
Potential Predictive Planning Objectives
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• Forecast revenue and costs for P&L forecasts
• Forecast walk-in traffic
• Forecasts clicks for online sales
• Forecast re-stock requirements for large number or low/medium
costs parts
Examples
12. 12
• Manual forecasts for a large number or low cost parts would
require a lot of manual effort for low to moderate return
• Seasonality is easy to capture statistically and labor-intensive
manually
• Geographic distribution multiples the number of forecasts
required
Example
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• Twice as much data as you want to predict
• 2 full cycles for seasonality
• Predictive Planning will interpolate missing values and
normalize outliers
• Too aggregated will lose definition and you may not have a
place to store it
• Too granular may be too sparse or too volatile
Data Considerations
15. 15
• Period and Year
• Scenarios: e.g. Actual, Forecast and optionally transformations
• Versions for forecast Best, Worst and Middle cases
• “Business Units” : what levels do you want to store
• Accounts : what levels do you want to store
Outline Design
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• Time Axis
• Period, Year or both
• Optionally Scenario and/or Version
• No others
• Series Axis
• e.g. accounts or entities
• Actual and Forecast Scenarios in rows
• Not needed if you copy Actual to Forecast.
Form Design
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• Create Versions for
Prediction and
difference from control
data (actual or
comparison data)
• Create dynamic
difference formula
Form Design – analyze error
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• Lowest level of period determines granularity of prediction
• Prediction end date is independent of the form
• You can predict form members that are read only, but can’t
paste them.
Form Design (continued)
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Set Up Prediction
Menu Purpose
Data Source Select data source plan type and Date
Range
Map Names Select scenarios and versions for
comparison and prediction
destination
Member
Selection
Select which members to predict
Options Select prediction options
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• Optional
connection to an
alternate plan type
with additional
years
• Select date range
Set Up Prediction – Data Source
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• Choose which prediction to
paste
• Current member
• All members
• Filtered members
• Selected members
• Choose the destination to
store the predictions
• Submit Data!!
Paste Results
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Components of a Prediction
Component Parameters
Level Alpha : smoothing parameter
between 0 and 1 not inclusive
Trend Beta : smoothing parameter for the second pass
between 0 and 1 not inclusive
Cycle/Seasonality Gamma: smoothing parameter for seasonality
between 0 and 1 not inclusive
Trend & Seasonality All of the above
Error/Noise
Source: Adapted from Predictive Planning documentation
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Understanding the Prediction Methods
Non-seasonal
Method
(Non-seasonal)
Description Best for Forecast Type Parameters
Single Moving
Average
Simple moving
average
Volatile data with no
trend
Straight flat line Period. 1 to ½ the
number of data points
Double Moving
Average
Applies moving
average twice
Trend but no
seasonality
Straight sloped line Period. 2 to 1/3 the
number of data points
Single Exponential
Smoothing
Weights more recent
data more heavily
Volatile data no trend
No seasonality
Straight flat line Alpha
Double Exponential
Smoothing
Applies SES twice Trend , no seasonality Straight sloped line Alpha and beta
Damped Trend
Smoothing
Trend is damped Trend , no seasonality Trend flattens over
time
Alpha, beta and phi
Source: Adapted from Predictive Planning documentation
46. 46
Method Description Best for Forecast Type Parameters
Seasonal Additive Exponentially smoothed forecast
+ seasonal adjustment
No trend and
seasonality
Seasonal cycle without
trend
Alpha, Gamma
Seasonal Multiplicative Exponentially smoothed level and
seasonal adjustment
* seasonal adjustment
No trend and
seasonality increases
or decreases
Seasonal cycle without
trend
Alpha, Gamma
Holt-Winter’s Additive Exponentially smoothed level, trend
and adjustment
+ seasonal adjustment
Trend and stable
seasonality
Trend and seasonal
cycle
Alpha , beta,
gamma
Holt-Winter’s
Multiplicative
Exponentially smoothed level, trend
and adjustment
* seasonal adjustment
Trend and increasing
seasonality
Trend and seasonal
cycle
Alpha , beta,
gamma
Damped Trend Additive
Seasonal
Projects seasonality, damped trend
and level separately and reassembles
- additive
Trend and seasonality Flattening with
seasonality
Alpha, Beta,
Gamma, Phi
Damped Trend
Multiplicative Seasonal
Projects seasonality, damped trend
and level separately and reassembles
- multiplicative
Trend and seasonality Flattening with
seasonality
Alpha, Beta,
Gamma, Phi
Understanding the Prediction Methods
Seasonal
Source: Adapted from Predictive Planning documentation
47. 47
Non Seasonal
Seasonality
Stable Increasing or Decreasing
No Trend Single Moving Average
Single Exponential Smoothing
Seasonal Additive Seasonal Multiplicative
Trend Double Moving Average
Double Exponential Smoothing
Damped Trend Non-seasonal
Holt-Winter’s Additive
Damped Trend Additive
Damped Trend Multiplicative
Holt-Winter’s Multiplicative
Methods by Trend and Seasonality
Source: Adapted from Predictive Planning documentation
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• Hold out a few months to compare to actuals
• How much data can you afford to hold out before it affects the forecast,
in particular seasonality?
• Hold out some business units
• Do the Business Units behave the same way from a forecast point of
view?
Back-Testing Approaches
50. 50
• Data quality especially at lowest levels
• New company with many new service introductions created a
lot of cannibalization
• Missing data at lowest levels. Need to pick a level with stable
data.
• Logically inconsistent sets of forecasts. e.g. Revenue and costs.
Costs should probably be driven by revenue not forecasted
independently.
• “Events” that affect the actual outcome e.g. advertising, weather
Lessons From the Field
51. 51
• Identifying and quantifying seasonality alone can be a big
improvement and time savings
• Consider Predictive Planning for selected “seed” forecasts
• Predictive Planning may identify trends that aren’t obvious
looking at numbers in a spreadsheet.
• Consider Predictive Planning where you need speed and low
cost
• Consider Predictive Planning as a sanity check or second
opinion
Lesson form the Field - continued
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