Demantra 7.3: Engine split based on series

Whas is NEW ?:
Demantra 7.3 has come up with quite few features which customers have been wishing to see. One of these is ability to split statistical forecast which generated at aggregated level to split down to lowest level based on user defined/controlled proportions. Prior to 7.3 this split was always based on historical proportions and there was no workaround for it. These historical proportions were calculated based on every product and location combinations historical monthly sales averages using proport procedure. Split by series option allows user to pick a series which dictates weights for dis-aggregation of baseline forecast.

How we USE it?
1. Define a new series with server expression, the server expression gets used in weight calculation.
2. Using Business modeler setup parameter: ENGINE > PROPORT tab > parameter name "ProportionSeries" shall be populated with the internal name of the above series.
3. Select combinations/population to use SPLIT MECHANISM by using series "Engine Proportion Method" in a worksheet. The series has two dropdown values, Matrix Based Proportions which is the default and Series Based Proportions which is the value required for series based split.

Oracle Open World 2010: Sales and Operations Planning for the Food and Beverage Industry

Join us at Oracle Open World 2010 for a session on "Sales and Operations Planning for the Food and Beverage Industry"

http://www.eventreg.com/cc250/sessionDetail.jsp?SID=315911

Oracle E-Business Suite, Virtualization and Cloud Computing

Oracle E-Business Suite, Virtualization and Cloud Computing

Oracle E-Business Suite, Virtualization and Cloud Computing

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New and Valuable Capabilities in this Oracle Release
February 3, 2010,
Volume 144, Issue 1

Oracle E-Business Suite 12.1.1 in a five-part nutshell.

In a five-part series on virtualization and cloud topics, Ivo Dujmovic, an architect in Oracle's Applications Technology Integration group, offers a detailed view of the Oracle E-Business Suite 12.1.1 for prospective and current users.

Read the complete article by clicking on this link "Oracle E-Business Suite, Virtualization and Cloud Computing"

Oracle Cloud Computing - Oracle Wiki

Oracle Cloud Computing - Oracle Wiki

Cloud Computing

Definition:

As defined by NIST(national institute of technology) Cloud computing is essentially on demand access to computing resources.

It has essential characterstics of on demand self service, resource pooling, rapid elasticity to scale out and scale in, the ability to meter who is using what and broad network access.

Three service models for cloud computing are:
1. SaaS software as a service, is a prebuilt, vertically integrated application/solution delivered to customer as a service.

2. IaaS infrastructure as a service, is purely providing computing resources, storage and network, as service to the client.

3. PaaS platform as a service. is a flexible combination of above two service models and thus customer can develop and deploy their own application using best of both the worlds.

Deployment models:

1. Public cloud, is shared across multiple customers or tenants and is hosted and managed by a service provider.

2. Private cloud on the other hand is exclusive for an organization and is controlled and governed by that organization.

3. Hybrid cloud is where an organization with a private cloud model uses public clouds for any excess cloud service requirements occassionally. Eg an overflow or for additional workload needs

4. Community cloud is a semi private cloud with access to only a set of defined tenants who share backgrounds or needs

What is driving clouds ?

AGILITY AGILITY AGILITY
The fact that users can provision resources on demand, acquire more resources when needed and release them when done with them, is the key driver for cloud computing.

Any drop off which may occur in the interaction happening between two clouds, might just mean a complete loss of valuable services. It is like two clouds up in the sky and informatiin flashin like lightening going from cloud to cloud and cloud to ground, the possibiltiess of loss of energy is inevitable and that is where the core problem of cloud computing lies.

Amazon web services are already in this niche market. For a public cloud to become a feasible solution for small businesses, the need to ensure the security of transaction and data.

Google OS is another such initiative where we will see a huge participation and usage of cloud computing capabilities.

Demantra SIG Webinar: "Infrastructure Rationalization for Demantra Environments"

Demantra SIG group is coming up with their March month webinar, you can join in for learning and understanding Demantra better.

----------------------------------------------------------------------------------------

"Demantra SIG Webinar - 03/10/2010

Please join the Demantra SIG's March Webinar. Arup Chatterjee will be presenting "Infrastructure Rationalization for Demantra Environments" Space is limited. Reserve your Webinar seat now at: https://www2.gotomeeting.com/register/607944203 Title: Demantra SIG Webinar - 03/10/2010 Date: Wednesday, March 10, 2010 Time: 8:00 AM - 9:00 AM PST After registering you will receive a confirmation email containing information about joining the Webinar. System Requirements PC-based attendees Required: Windows® 2000, XP Home, XP Pro, 2003 Server, Vista Macintosh®-based attendees Required: Mac OS® X 10.4 (Tiger®) or newer" ---------------------------------------------------------------------------------------

Understanding Forecast Bias

What is bias ?

If you observe forecast error going in one direction or other, then you have a forecast which is possibly biased. In the example below try to observe the bias and see if you are able to find the forecast versions with bias.

As you would have pointed out already following is the observation on above example:

1. Forecast1 has a positive forecast bias
2. Forecast2 has a negative forecast bias
3. Forecast3 has mixed bias and the cumulative bias in such case could be insignificant

Decision on whether a forecast is biased or not can be made by reviewing the forecast error as well, study the example below.

Why it exists?

As final forecast is combination of statistical forecast and manual intelligent updates, the bias can slide into Final forecast through any of the mentioned input gateways. While statistical forecast bias are normally specific to items(local bias), bias building up due of manual intervention usually has impact on all the items(global bias).

Manual updates done by forecasters/demand planners at times lead to building of a biased forecast. These updates are driven by various factors like:

- Increasing forecast to match up with volume targets

- Optimistic or pessimistic approach towards forecasting

- Expecting Promotional incremental sales volume

This kind of bias usually has impact on all the items.

Bias found in statistical forecast is most of the times under-forecasting or over-forecasting situation for a specific item-location combination. Few of the cases which lead to such observation can be list as:

- Under-forecasting for complete future horizon due of recent months sales volume showing a down trend.

- A persistent trend in sales volume

Such forecast bias is normally particular to an item or an item-customer combination.

Getting rid of Bias?

Bias shall be removed from forecast as it can assist in improving your forecast accuracy, which eventually reflects across supply chain health. An overall reduction on forecast across all items can take out the global bias(e.g. 15% decrement on forecast numbers all across). For bias which is specific to item, one needs to identify and fix them for every incident by adjusting the forecast.

Fueling supply chain with retailer's POS data - Oracle DSR

When recession hit the market, the impact on CPG industry was inevitable. With customers trying to save every penny possible, the competition in market for providing best quality product at minimal price possible has surpassed all previous levels. Everyone is trying to get the most of every dollar they spend. Few most common CPG industry specific issues can be easily listed as:

• Lost sales due to out of stock conditions
• High inventory carrying cost due of misplaced stocking
• Promotion expenses going in vain
• Loss of margins due of expedition costs
• Increasing IT Complexity

Circumstances like this call for nothing but best in class, efficient and optimized business processes which are driven by one and only goal, increasing the profitable revenue making.

The road to accomplish this target is anything but smooth, need is to ensure that the supply chain backbone transforms into an efficient value chain. The value shall be visible and transparent to every stakeholder, be it an internal department(e.g. sales, marketing, operations) or an external vendor. With growing capabilities at retailer's end to capture demand at the point of it's origin, need is to build processes to capture this demand data and utilize it to drive the supply chain. Problem in gathering and utilizing this information is variety of retailer's POS capturing systems, standards and granularity.

One of the AMR Research paper clearly outlines this fact by saying "The Bottom Line: Consumer products manufacturers must centrally manage all demand signal data, including POS, to create a Demand-Driven Supply Network (DDSN) that improves demand visibility to reduce stockouts by more than half and increase Perfect Order performance by 17%"

While in today's generation of supply chain, availability of data is lowest of concerns, problem is with managing this high volume and ensuring the accurate required information is made available to upstream for planning. With variety of sources of POS data, the need to capture and cleanse it prior to it's availability for being used in planning is of high importance.

Oracle's Demand Signal Repository product has shown thought leadership in this direction, by providing a collaborative demand signal management platform with best of breed functionality and reporting capabilities.

Oracle's DSR datasheet points out it's three key capabilities:

• Capture and manage large volumes of disparate external demand data
• Analyze, report and take action based on Day/SKU/Store level insights
• Integrate clean, harmonized demand data to external applications

DSR empowers you to:

• Greater visibility of data to better sense, shape and respond to demand
• Reduce manual effort and cost
• Simplify collaboration across teams and applications.

(Read more about DSR@ http://www.oracle.com/ocom/groups/public/@ocompublic/documents/webcontent/018927.pdf

Oracle VCP Product and processes

Exploring Demantra's BPM capabilities

Oracle Demantra's various module releases have a lot to offer for small and medium scale enterprises, who can utilize the out of box processes(best in class) as it is to a lot extent. The standard demand forecasting and S&OP planning process offered with the install benefits the SMBs, but for larger enterprises there is always a need to create a custom process. But, Demantra does have BPM(Business Process Management) capabilities as part of its native platform.

Demantra's platform flexibility allows larger enterprises to build a totally custom tailored process. Some of the already established custom process implementations are:

- Store level forecasting and replenishment planning

- hourly forecasting

- Labor planning

- Safety stock calculations etc.

Basic entities like series, levels, workflows, methods, worksheets etc. are used to create any required custom business processes. The flexibility of designing pretty much any kind of custom process by usage of these entities, makes Demantra's BPM capabilities stronger.

With Demantra being a strong pillar of Oracle's VCP offering, the future road map for its BPM capabilities will be to get upgraded to Oracle's Fusion BPM suite.

(source: Oracle)

Also seeing almost all the Demantra integrations releases already out in market, we expect to see more robust and web-based versions(inching towards Fusion) of Demantra in future.

Model codes used in MDP_MATRIX

Here is a list of the coding convention used by Demantra, when populating MDP_MATRIX with information about which models were used(column MODEL) and which weren't.(column DELMODEL)

H Holt
J Regression FOR Intermittent
K Multiplicative Monte Carlo Regression FOR Intermittent (ICMRegr)
L Transformation Model (log)
R Regression
E Combined Transformation Model (elog)
C Multiplicative Monte Carlo Regression (CMReg)*
B Integrated Causal Exponential Model (BWint)
F Croston For Intermittent
G Logistic*
A ARLogistic*
X Auto and Linear Regression (ARX)
V Integrated Auto and Linear Regression (ARIX)*
D DailyMultiplicativeRegression*

T Naive Holt
N Naive (if all models failed, a moving average forecast is generated)
NULL No forecast attempt made on this combination.
M Modified Ridge Regression*
NONE Combination remains non forecasted yet, not processed by engine.

E.g. MODEL column has value BFR for a combination, this means engine used models B, F and R to generate Bayesian combined model forecast.

Detailed information about models and parameters is available in Demantra implementation guide.
Refer to "My Oracle Support"
Oracle Demantra Documentation Library [ID 443969.1]

News: Oracle Integrates Financial, Operational Planning

Oracle Corp. today introduced a packaged integration between its enterprise performance management application and its sales and operations planning software, making it easier for manufacturers to quickly analyze the financial implications of changing customer demand.

Oracle said the new integration between its Oracle Hyperion Planning EPM product and its Oracle Demantra Real-Time Sales and Operations Planning platform is available now. Based on Oracle’s Application Integration Architecture (AIA), the new EPM-S&OP integration provides manufacturers with a way to more easily tie together operational and financial planning processes

.............................................................read the complete article @

http://www.manufacturing-executive.com/news/read/Oracle_Integrates_Financial_Operational_Planning_33238

Engine enhancements in Demantra 7.3 release

Demantra's 7.3 version has come up with quite a few new enhancements on Analytical engine side. The focus has been on improving the forecast performance, making the AE more robust and faster.

The list of few interesting available features are:
1. Forecast split based on series: Earlier the forecast generated at aggregated level used to get allocated down based on monthly proportions calculated during the proport process. This functionality let's you select a series based from where the proportions shall be picked up for splitting the future forecast.

2. Engine deployment on Linux: Analytical engine is no more restricted to be installed on a windows box, you can install Engine on a Linux box. Engine can be even started and stopped remotely from a browser after doing the required configurations.

3. Forecast output threshold: This functionality provides you more control on engine output, by stating whether or not to write engine output to database, if the forecast numbers are not a lot different from the previous forecast numbers. E.g. if new forecast is 10010, while previous forecast was 10000, then you can mention a threshold so that engine would not try to write the new 10010 output to database.

For complete list of enhancements, refer to metalink's Demantra documentation library.

Bajaj Electrical success story - Oracle ERP, JDE, Demantra & Siebel

A video presentation on Oracle's yet another success story on a big bang implementation of Oracle ERP, JDE, Demantra and Siebel.

Updates: Oracle Rapid planning 1.0

Oracle's latest release in VCP suite, Oracle Rapid Planning(RP)'s version 1.0 can be installed only with VCP version 12.1.1