demantra ppt-

Trinamix Technologies

Demantra Forecasting Methods A Look Inside the Black Box

Amit Sharma Trinamix

Accurate forecasting is easy…

2

Here is the formula for the same…..

Why it’s hard to be accurate?

3

Forecasting is an art of predicting the future by looking the past !

4

Why is it important to be accurate ?

Scope of discussion

• General Forecasting – Evolution – General Methods

• Forecasting in Demantra – Setup and consideration

• Forecast Tree• Parameters • Engine Definition, Types and nodes • Forecasting process• Advanced Analytics• Causals

Forecasting Evolution

Naïve forecasting

Moving Average

ExponentialSmoothing

AdvancedMethods

Naïve Models

Advantage: Inexpensive to develop, store data and operate.

Disadvantage: Do not consider any possible causal relationships that underlie the forecasted variable.

Naïve models1. To use actual sales of the current period as the

forecast for the next period; then, Yt+1 = Yt

2. If we consider trends, then, Yt+1 = Yt + (Yt – Yt-1)

Naïve forecasting models are based exclusively on historical observation of salesThey do not explain the underlying casual relationships which produce the variable being forecasted.

Smoothing : Moving Averages

Definition:

Averages that are updated as new information is received. With the moving average a manager simply employs, the

most recent observations, drops the oldest observation, in the earlier calculation and calculates an average which is used as the forecast for the next period.

Limitations:• One has to retain a great deal of data.• All data in the sample are weighed equally.

Uses weighted average of past data as the basis for a forecast.

Y new = a Y old + (1-a) Y’ old, where,

Y new = exponentially smoothed average to be used as the forecastY old = most recent actual dataY’old = most recent smoothed forecasta = smoothing constantSmoothing constant (or weight) has a value between 0 and 1 inclusive.

Smoothing: Exponential

9

Series

-3

-2

-1

0

1

2

3

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Series

Zero Mean White Noise


-3

-2

-1

0

1

2

3

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Series

0.1


-3

-2

-1

0

1

2

3

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Series

0.1

0.3

White Noise with Shifting Mean

-4

-3

-2

-1

0

1

2

3

4

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Series

Mean


-4

-3

-2

-1

0

1

2

3

4

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Series

0.1

Mean


-4

-3

-2

-1

0

1

2

3

4

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96

Series

0.3

Mean

Best Value for Alpha

Actual vs Forecast for Various Alpha

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97

Period

Fo

reca

st

Demand

a=0.1

a=0.3

a=0.9


Series and Forecast using Alpha=0.9

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

Period

Fo

reca

st

Might look good, but is it?


Series and Forecast using Alpha=0.9

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101

Period

Fo

reca

st


Exponential smoothing

The following Rules of Thumb may be given :1. When the magnitude of the random variations is

large, give a lower value to “a” so as to average out the effects of the random variation quickly.

2. When the magnitude of the random variation is moderate, a large value can be assigned to the smoothing constant “a”.

3. It has been found appropriate to have “a” between 0.1 and 0.2 in many systems.

This method has been successfully applied by banks, manufacturing companies, wholesalers and other organizations.

FORECASTING IN DEMANTRA

21

Demantra Forecasting Ideology

Most Solutions

• Best Of Breed– A series of models execute

at a “node”– Model best meeting

selection criteria chosen– Any information not

captured by selected model is lost

• One Forecasting Level– Not flexible for noise or lack

of historical information

Demantra

• Bayesian Estimator• Library of models is executed• Based on goodness of fit

models combined into single result set

• Nuances of models maintained

• Variable Forecast Level• Forecast at most applicable

level

Traditional fit

23

24

Oracle Demantra – Bayesian Model

Estimating techniques based on the assumption that the variable to be forecast (dependent variable) has cause-and-effect relationship with one or more other (independent) variables.


Demantra Engine

Setup and Considerations

4

3

2

1

The forecast tree determines which item/location aggregation combinations the engine will work on.

The highest level (highest number) is ALWAYS all items/all locations and is fictive- not actually used.

The second highest level is the FIRST level actually examined for forecast

Forecast Tree Definition

4

3

2

1

The engine examines each node in the forecast tree, from top to bottom:-

• The nodes are examined to see if a forecast is possible at son of the node and traversal continues

• Once a stopping rule is reached on the downward traversal forecast is executed

Stopping Rules• Reached lowest tree level• Reached desired forecast level• Insufficient data available at lower

levels

Traversing the Forecast Tree

4

3

2

1

• Once a stop has been reached generate a forecast

• Traverse to brothers and continue traversal

• Before going up check if any sons require a forecast if required generate a forecast

• Continue until entire tree traversed

Traversing the Forecast Tree Continued

4

3

2

1

• Higher aggregation of forecast split to lower members who do not have a forecast

• Forecast is split according to defined proportion rule-set

• Forecast always stored at lowest system aggregation to enable view of data at any query level

Forecast Split

Forecast Tree Configuration Continued

• Define forecast tree– Forecast tree combination of item and location levels– Begin with Lowest Item / Lowest Location level– Continue upward in increasing order– Highest level if fictive level Highest Item / Highest

Location– Define minimum and maximum levels in tree

High Level Engine Flow

Pre Engine Processing• Distribute engine

tasks• Create

temporary tables• Clear previous

forecast data• Maintain forecast

columns

Learning Phase• Tree traversal• Per node

process• Fit generation• Bayesian

combination

Forecasting Phase• Generate

forecast• Split

Forecast to lowest level

Post Engine Processing• Drop

temporary tables

• Update forecast history

• Notify of completion

Distributed Engine

• Full data set divided into tasks

• Each task comprised of one or more engine branches

• For recommended Branch ID Multiplier divide planned rows in Sales_data/ (Engines) /250,000

Models

• The Analytical Engine uses a set of theoretical models, each trying to explain history using different methods and algorithms.

– Regression• Regression• Log (log transformation before regression)• CMReg (Markov chain selection of subset of causal

factors)• Elog (uses Markov chain after log transformation)

– Exponential smoothing• Holt• Bwint

– Intermittent Models• CMReg for Intermittent• Regression for Intermittent• Croston

– Time Series Models• ARX and ARIX• Logistic and AR Logistic

– Other Models• BWint (a mixture of regression and exponential

smoothing)• DMULT (Multiplicative)

DP and PE modes

• Depending on installation and settings, you will run the engine in one of two modes:– DP Mode

• Base Only causals• Base Forecast Generated

– PE Mode• Base and Promotional causals• Forecast decomposed to base and

lift

Engine Parameters

• Max Fore Level– The maximum level on the forecast tree at which a forecast

may be produced. Upon failure at this level, the NAIVE model will be used, if enabled.

– The NAIVE model is used only at the highest forecast level, and is used only if all other models have failed. It uses a simple averaging procedure.

• Min Fore Level– Minimum forecast level that the engine will forecast. From that

level down, the engine will split the forecast using the precalculated proportions in the mdp_matrix table.

– The engine does not necessarily create the forecast at this level. If the results are not good at this level (for a portion of the forecast tree), the Analytical Engine moves to a higher node of the forecast tree, creates a forecast there, and splits down to the minimum forecast level.

Engine Parameters (cont.)

• Forecast Generation Horizon– Specifies what historical fit data the engine will write to

the database. If this parameter is 0, the engine writes the forecast only. If this parameter is a positive integer N, the engine writes the last N historical fit values.

• History Length– The number of base time buckets to consider for fit

estimation and for the proport mechanism. Must be a non-negative integer. If equal to 0, the length of the history is set by the start_date parameter instead.


• Detect Outlier - This parameter is used by the preprocessing module of the Analytical Engine. Use one of the following values: – yes: The engine should attempt to detect outliers. If it finds

outliers, it considers them in the analysis. – no: The engine should not attempt to detect outliers.

• Quantity Form– Expression that the Analytical Engine uses to compose the

historical demand from the sales_data table; the result of this expression is the data that the engine receives as input.

– This expression should return 0, null, or a numeric quantity for any date. A date with 0 is treated as if there were no sales. A date with null is treated as a missing date; in this case, the system can interpolate a value or just ignore the date.


• Dying Time– If no sales occurred during the length of time specified by this

parameter, the combination is marked as dead.

• Hist Glob Prop– Maximum number of base time buckets to use in calculating

glob_prop, the running average demand for any given item-location combination. This parameter is used by the proport mechanism.

• Refers to the Analytics window, accessed by clicking here…

Advanced Analytics/Nodal Tuning


• Brings up another window focused on the same combination where you started:


• What you can do here:– Fine-tune which engine models to use on this node (this combination)– Specify how many data points are needed to use each model


• Also:– Fine-tune engine parameter settings for this

node

Causal factors43

Global Causal Factors

Global causal factors apply to all combinations varying only by time

Default Causals• Trend• Month of the year• Constant• Winter• Summer

Typical Causals• Business days in the month• Holidays• Week ending the quarter

Local Causal Factors

Local causal factors varies by item/location/time Vary greatly by customer business

Default Causals• PriceTypical Causals• Number of open stores• Events• Weather

Pre-seeded Causal factor


TrinamixValue Chain Experts

48

Who We Are

Trinamix was founded by former members of the prestigious Oracle Product Development team, who helped create the Value Chain Planning products, and by best in class implementation experts.

Trinamix is a provider of Oracle Value Chain Planning & ERP services. Trinamix

solutions focus on deploying best practices to maximize return on

investment.

Trinamix is a prominent, full service, Value Chain Planning solution company with multiple customer success stories in the San Francisco Bay

area. Trinamix offers pre-built solutions for various industries. Specific industry segments served include high tech, consumer packaged goods,

renewable energy, manufacturing, and semiconductors.

mochakra

49

Click to edit Master title style

On the TRAC

Email: [email protected]

mailto:[email protected]

http://www.bigstockphoto.com/image-4670427/stock-vector-marathon

Documents

demantra ppt-