Dashboards in SCM

Got bored of running into summary details of each and every report to get a high level picture ? Ever thought of having a platform where you can have all the information at your finger tips(literally at finger tips), if yes then you should not ignore the power of Dashboards.

The importance of having dashboards in today's growing supply chain is quite clear, as the need of having visibility of data across the chain is required to move towards being a demand driven network. Dashboards can be utilised to provide an accurate high level picture of how well are the components of supply chain performing. It provides a manager a wonderful tool for reviewing the statuses and for remainging updated with execution statstics. Managers can easily grasp the information by just looking at the various graphs and tables, which can be also utilized in thier meetings with higher management.

A well customized dashboard application inline with the user's requirement and role can serve as a daily cutom newspaper for all the updates and happenings.

Importance of Collaborative Planning (CP)

Most of the industries have their various departments managing their shows on their own and for themselves. Manufacturing crew works for a production plan which utilises the available resources to most, with more number of longer batch runs and try to achieve new production records every month. Working in a motor manufacturing industry I have seen closely how one used to focus on efficiently producing maximum numbers and achieving targets set by manufacturing team, which were not most of the time exactly in line with organisation's targets. In similar fashion we used to see around us other departments like marketing, sales, supply chain working in their own island of comfort, remaining untouched from other

Going solo is not a workable solution in today's work environment, one need to have proper communication channel and portal for sharing information, exchanging point of views and reaching to a consensus plan. All the latest application providers today have this agenda in mind and thus most of them support collaboration.

An online collaborative work environment can be utilised for communication, alerts management and in decision makings. It in one hand where brings visibility to the organization, on the other hand the results of such an environment returns in form of increased profits, sales and service level.

Oracle's Oracle Collaborative planning module which is part of their APS offerings, has capabilities for internal as well as external collaboration. Internal collaboration provides multiple enterprise planning environment, where planning time can be reduced and pro-active actions can be taken during plans execution. External collaboration with suppliers provide information regarding incoming supplies plan, utilising which supply chain plans can be created. Two-way collaboration is beneficial to both the parties, as they with empowering information are in better state of managing operations.

http://www.oracle.com/applications/planning/SCC.html

Oracle's APS product offering R12.1

Oracle's APS product offering R12.1

Oracle unveiled recently set of new products/modules under it's Advanced Planning Suite portfolio. The list now compromises of following modules:
Existing:
1. Advanced Supply Chain Planning (ASCP)
2. Demand Planning- Demantra
3. Collaborative Planning (CP)
4. Global Order Promising (GOP)
5. Inventory Optimization (IO)
New:
6. Manufacturing Operations Centre
7. Advanced Planning Command Center
8. Service Parts Planning (SRP)
9. Demand Signal Repository (DSR)

We will get into details of each of them in later blogs, keep watching this space. :)

Demantra 7.2.1 Release

Oracle released another major Demantra release recently, integrating the Israeli product more with existing acquisitions and new product releases. This release clearly indicates that going forward Demantra will be a core part of APS suite in Fusion, with quite a few major integrations already in market. Demantra's first integration release was with Demantra Demand Management module, Oracle EBS 11i suite and Enterprise One, this was followed up with Demantra's 7.2 release which was Demantra's S&OP module integration with EBS R12.

With the release of 7.2.1 version, Demantra product under Oracle umbrella has been integrated tightly with various best-of-breed technologies. As of today following integrations are available:

Oracle Demantra Release 7.1.1
1. Inbound integration of Demand Management module(+AFDM module) with EBS 11i & Enterprise One
2. Outbound integration of DM (+AFDM) with ASCP, IO, SNO & PS

Oracle Demantra Release 7.2.0
1. Inbound integration of Demand Management module(+AFDM module) with EBS R12
2. Inbound integration of S&OP module(+AFDM module) with EBS R12
3. Outbound integration of S&OP with ASCP, IO & SNO.
4. Integration of PTP module with Enterprise One (JD Edwards)

Oracle Demantra Release 7.2.1
1. Integration of Demantra PTP and TPMO with Siebel TPM

This Oracle release(Demantra 7.2.1) empowers Siebel's Trade Promotion Management offering with Demantra's Promotion intelligence and optimization capabilities. The packaged integration comes with set of seeded hierarchy/levels, interfaces, worksheets and workflows to support the data load from/into Siebel/Demantra.

Besides above, Demantra has been integrated with Demand Signal Repository(DSR), one of the latest release of Oracle's Advanced Planning Suite product. Also Demantra product features for forecasting the demand of spare parts in another new APS release called Service Parts Planning module (SRP).

Demantra 7.2 New Features

Oracle Demantra 7.2 release from Demantra product prospective came up with few new features to empower the application users with more robust demand planning platform. Following features are new in Demantra 7.2 for all the existing customers:
1. Support for multiple forecast streams: With this release Oracle has overcomed the missing ability of support for generating and managing multiple forecast in Demantra product. This has been developed using Engine profile options and system of forecast versioning. You can now create more than one forecast based on different historical data, like Forecast based on Shipment history, Forecast based on Booking history, Forecast based on Customer POS, etc.
Setting up multiple forecast generation is pretty straight forward, one just needs to define separate engine profiles with "Quantity from" parameter pointing to different history data. One can view and analyse these multiple forecasts generated by the engine, in worksheet as usual and can utilise forecasted booking trends to compare with forecasted shipment trends. This feature has for sure added a missing card into the pack of capabilities offered by Demantra product.
2. Enhanced Engine Performance: Demantra's Analytical engine, often referred as heart of the Demantra Suite has been improved by cutting down the processing time. Unlike older version, for combinations which have been forecasted already in previous runs existing branch allocation will be used now on. This cuts down the engine run time, as there are only few new combinations and thus allocation of branch happens only for them. Besides this, with few other changes has been done on engine side, to enhance Engine performance.
3. Enahnced Worksheet usability: You can now create series with "No Summary" option, thus users don't see any irrelevant numbers as Summary for each of the series. This takes off the compulsion of having a summary function even for Series with data, which doesn't have any significance at Summary level, e.g. Date type series, Approval series etc.

Oracle Demantra: Seeded EBS-DM integration data flow

Oracle's seeded Demantra integration with Demantra's Demand Management module can be utilized with ease to empower an organization with a collaborative, web based, integrated demand planning environment.

In EBS-DM integrated environment, one can start the process of demand planning, with responsibility "Demand Management System Administrator" .

The process starts with Collection of master/transactional data (item, location & sales) from EBS and ends with publishing back consensus forecast to Oracle supply chain planning application. Demand Administrator logs into the Demantra responsibility and starts the Collection program, downloads the master and historical data into Demantra application using seeded workflows, kicks off a workflow to execute the analytical engine in batch mode, makes manual adjustments/overrides on forecast with information like causal factors and promotions and finally runs another workflow to upload the forecast back into APS/MRP.

Demand planning using Demantra Demand Management application compromises of following high level process steps:

• Collection of Dimensional, historical, pricing, costing and other reference data
• Validate & Modify data
• Generate Baseline Forecast
• Consolidate Baseline, Simulation and manual forecasts
• Upload Final Forecast to ASCP/SNO/IO/MRP
• Archive Monthly Forecast

Statistical Process Control : Using Demantra

Forecast being the base of any demand planning and further supply scheduling processes, needs a mechanism to have a control on the process of predicting forecast. This can be achieved by utilizing the functionality of your demand planning tool to monitor forecasting process.The need of adjusting tool’s parameters to create quantitatively more accurate forecast is always there, but besides that a Demand Analyst need to capture all those SKUs for which the forecast being generated is not healthy enough.
Statistical Process Control: A tool to monitor forecasting process, identifying those SKUs whose forecast error is going out of set of limits and those whose error are under control.
Benefits: Reduces time required by Analysts for forecast analysis and lets him focus on area of concern instead of wandering through all the SKUs.
Set Performance indicators like health signals for forecast and define alerts/exception raising conditions, say:
Forecast Accuracy <>
95% <> 95% Medium Level AlertAutomated Alerts/
Exceptions: Create setup for invoking alerts/exceptions automatically as and when required during the demand planning cycle. Ex. Raising alerts just after forecasting engine completes batch run, raising alerts after final forecast is approved, and so on.
Notifications: Configure your tool to send automated notifications to concerned business users to keep them updated with what chunk of data they should be analyzing for resolving the raised alerts.
Here is an example of how the same can be achieved by using Oracle’s Demantra application:
Customization Required:
Option1:- Create Exception worksheets with all the SKUs data in it, with Forecast accuracy related series- Put exception condition & Save worksheet definition- Create a workflow with exception step which can be launched by analyst using a Level method from worksheet or directly from Workflow manager page. - Send alerts to Analyst for any exceptions found
Option2:- Create Exception worksheets with all the SKUs data in it, with Forecast accuracy related series- Put required exception conditions- Write custom code to call up run workflow at end of every forecasting cycle.- Send alerts to Analyst for any exceptions found by calling these custom codes

Demantra forecasting models -Part1

Demantra spectrum product has a set of various mathematical models geared up for capturing various demand patterns. It utilizes Bayesian modeling technique to combine the result of forecast generated by individual 15 mathematical models. The final forecast doesn't merely represents results based on prediction done by just selecting a mathematical model which can best fit the historical pattern of demand. Demantra's patented Bayesian modeling forecasting engine, instead captures the qualitative prediction done by multiple models and thus resulting in one of the best forecasting results in industry.

Oracle's Demantra Demand Management module only provides 9 basic mathematical models and 6 configurable Causal Factors, rest of the 6 advanced statistical models and flexibility of creating unlimited Casual Factors are given away with Advanced Forecasting & Demand Modeling (AFDM) module.

Demand Management module's forecast library has following set of models for usage:
1. Regression
2. Transformation Model(log)
3. Regression for Intermittent
4. Holt
5. Croston for Intermittent
6. Combined Transformation Model(elog)
7. Multiplicative Monte Carlo Regression(CMReg)
8. Integrated Causal Exponential Model(BWint)
9. Auto & Linear Regression

Let's try to get a feel of what are these models and how they work ?
1. Regression: These models are statistical models which are capable enough of describing the variation(trend/pattern) one or more variable(s), based on the variation of one or more other variable(s). Inferences based on this kind of methodology are known as Regression analysis.
e.g. Variation of Demand of a product in market based on time variation.

2. Transformation Model(log): Log transformation model utilizes the log function which squeezes the large values of data together and stretches the small values apart, thus leading to correction data issues like skewed data, outliers and unequal variation.
e.g. Normally demand data has various such problems, the log transformation model tries to minimize those.

3. Regression for Intermittent: This model uses regression analysis for intermittent kind of data. There are parameters related to defining intermittent part of data in the application.

4. Holt: An extension of exponential smoothing can be used when time-series data exhibits a linear trend.

5. Croston for Intermittent: Croston’s Intermittent Model is specifically designed to deal with sporadic demand (no seasonality) with a two-step process. Croston’s Intermittent model recognizes both : the demand size and the demand occurrence.

.. to be continued