Demantra Overview

Oracle Demantra
Overview & Introduction

The Oracle APS and Demantra Specialists

Agenda
• After completing this module, you
should be able to:
– Understand Demantra in the context of
APS
– Describe key capabilities of Oracle
Demantra
– Understand what business processes are
supported within Demantra
– Understand Oracle Demantra Concepts
and Terms
Copyright © Inspirage LLC, 2008. Private and confidential. All rights reserved.


Understanding the
Oracle APS &
Demantra Solution

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Oracle Advanced Planning Solution
Trading Partners

Best in class Supply Chain Demand driven Operational Multi-Enterprise
Real-Time
Business Risk Management adaptive planning Excellence Collaboration
S&OP
Processes
Role-based Portals

Network
Design Demand Sensing
Integrated And Shaping
Postponement
Planning Optimization Holistic Preconfigured
Functions Supply Worksheets and
Promise, Workbenches
Planning
Distribute, and
Replenish
Execute to plan

Real-Time Demand Predictive Advanced
Strategic Global
Products Inventory Sales & Management Trade Supply Chain Production Collaborative
Network Order
Optimization Operations & Advanced Planning & Planning Scheduling Planning
Optimization Promising
Planning Forecasting Optimization

Platform Connectors Embedded
WFL Common planning Analytics
data model

LegacyE
Future * E1 * EBS …
* RP


Oracle Demantra Applications

Demand Advanced Forecasting Trade Promotion
Management and Demand Modeling Optimization

Real-time Sales and Deduction and Predictive Trade
Operations Planning Settlement Management Planning


Demantra Integration – The Big Picture
Demantra

Demand Management Trade Promotion Management

Real-Time Sales and
Operations Planning
Demand Management Predictive Trade Planning
Advanced Planning
Command Center

Deduction and
Advanced Forecasting Promotion Optimization
Settlement Management

Supply Chain Planning Oracle EBS JDE E1 Siebel CRM

Strategic Network Optimization
Supply Chain Supply Chain Trade
Inventory Optimization Management Management Management
and Financials and Financials Execution
Adv. Supply Chain Planning

Global Order Promising

Collaborative Planning
Production Scheduling


What is demand planning?
Planning for future demand scenarios
• Estimate future demand based on market conditions
• Collaborative planning process involving internal and external participants
A crucial function for improving operational plans
• Optimal resource allocation
• Reduced inventory levels
• Improved customer satisfaction
Compare and consolidate disparate forecasts
• Sales, Marketing, Manufacturing
• In units or amount
Improve forecast accuracy
• Better statistical forecasts
Manage by exception
• Multi-dimensional OLAP for analysis (product, channel, geography, …)
• User-defined exceptions
• Proactive notifications

Copyright © Inspirage LLC, 2007. Private and confidential. All rights reserved.


High inventory Shorter product
costs lifecycles

Long Frequent new
manufacturing product
lead times introductions
Stock outs & non-
Proper Demand Management and
availability
Customer Switching
Sales and Operations Planning helps
Excessive inventory
companies solve these challenges
Promotion’s negative
and increase profitable revenue
ROI
growth. Loss of revenue
NPI failure
Promotions/Deals Extreme
fluctuate demand product
segmentation

Poor collaboration between Product variations to
sales, marketing, demand meet different
mgmt., operations, and with geographical and legal
customers requirements


Typical Challenges
Shorter Product Lifecycles
• Frequent new product introductions putting increased burden on operations
• Shorter life span of the product making it difficult to forecast
Extreme Product Segmentation
• Extreme segmentation of the product by color and aesthetic attributes to
meet various demographic needs causing stock-outs or excess inventory of
certain products.
• Product variations to meet different geographical and legal requirements.
For example, manuals, labeling and software to meet various language
needs.
Disconnected sales and operations
• Lack of integration between sales/marketing and operations
• Planning and review process is extremely manual and time consuming
Inferior systems
• Manual processes, low visibility in a globalized business climate
• Multiple non integrated local systems


What are the implications?
Impact to customer service levels
• Stock outs due to insufficient forecast can cause customers to switch
suppliers or be dissatisfied
• Reduced pricing power; affecting the margins
Improper capital allocation
• Forecast created for the wrong product could cause your supply chain to be
over-driven resulting in excess inventory
• Impact to resources such as capital, space, production resources, etc.
• Lost opportunity to sell your products and increase revenue and profits
Inability to react to changing market conditions
• If changing market conditions are not turned into advantage, profitability
may suffer
• Competitors may gain
Unmet expectations
• All of the above could result in unmet corporate expectations and decline in
shareholder value


Demand Driven
Supply Chain
An approach to mitigating
challenges



Traditional Supply Chains

Demand Supply
Traditional
Definition of
Supply Chain
Management
supplier
customer

Product

design
Source: AMR Research
partner


Demand-driven Processes

Demand Supply Supply
Demand
Risk
Insights
Management
Drive a
Sense Shape Profitable
Demand Demand Demand
Response

Proactive approach to demand management Product
Makes your supply chain demand driven
Improved profitability due proper prioritization of demands,
Promotes collaboration amongst all stakeholders
Technology
Supports replenishment system with immediate visibility to changing needs
Opportunities
Source: AMR Research


Demand Shaping



Shaping Demands is the focus of a
Demand Driven Supply Network (DDSN)
What is “Shaping Demands”?
• Shaping demands can be defined as aligning your forecast to reality
by taking all possible variables into consideration OR
• Fully exploit market to maximize objectives
Strategic predictive forecasting
• Collaborate with stakeholders
• Historical demand patterns
• Managing new product introductions
• Discounts and promotions
• Trade promotion and promotion optimization
Operational planning to satisfy demands
• Sales and operations planning
• Supply planning based on global forecasts
• Supply positioning


Strategic Predictive Forecasting
Collaboration
• Collaborate to incorporate inputs from different stake-holders in the
organization – such as marketing/sales/Operations
• Forecast at the appropriate level of aggregation. For example, there
may not be enough information for forecast at an individual
item/location combination

Historical demand patterns
• Seasonality and Trends
• Shape Modeling
• Causal Factors

Manage new product introductions
• New item / store introductions
• Lifecycle / supersession / Product phase-in and phase-outs

Discounts and promotion optimization
• Effect of discounts and promotions
• Promotion optimization

Operational Planning
Tolerance along time and organization dimensions for fulfilling
demands
• Global forecasting and consumption
• Automatically sourcing forecasts to appropriate organizations that
fulfill demands

Sales and Operations Planning
• Review current demand fulfillment
• Consider constrained supply
• Re-prioritize demands

Supply Positioning
• Consider forecast accuracy measures to determine inventory levels
• Postponement optimization


Sample Business
processes
supported with
Demantra



Workflow enabled collaboration
Why collaboration capabilities are important?
• It is extremely critical to bring sales, operations, finance and marketing to
agreement on a single forecast number.
• A platform that enables collaboration between these stakeholders will result
in a better aligned corporation marching towards a common goal.
Ability to easily tailor process flows that is custom to each business
• Web based workflow development tool allows creating new workflows and
modifying canned workflows easy.
• For example, you can have a workflow that enables collaboration between
account manager, regional manager and country manager before finalizing
the forecast.
Process to define and handle exceptions
• Create workflows to handle exception events such as over/under forecasting
and remediation plans
Workflows to integrate external systems
• Facilitate integrating external systems’ data through workflows
• For example, allocate around intersections that have sales orders


Workflow enabled collaboration between
stakeholders


Disaggregating higher level entries to
lower level SKU/Location
Why is this important?
• If the forecast quantities end up entered for the wrong products/locations, it
could result in overconsumption and hence result in excess inventory
• Can be mitigated with robust disaggregation mechanisms
Demantra supports multiple disaggregation schemes
• Proportional – where the allocation logic is based on another series
• Proport Mechanism to control disaggregation during analytical engine runs
Proportional
• When a “series” is defined, modelers have the ability to define the allocation
to be based on another series.
• The basis series could be computed based on a business specific logic or
could be collected data series such as order history and backlog.
Custom Methods
• Define custom methods that can be invoked for any special allocation logic.
• For example, allocate around intersections that have sales orders

Disaggregating higher level entries to
lower level SKU/Location


“Expressions” to facilitate numerical
calculations / quick review
Server Expressions
• These are expressions that uses database grouping functions such as
average, sum, etc.
• For example, you could have an expression that computes the average selling
price at the category level and determine profitability.
Client Expressions
• These are expressions that enable using mathematical formulas and if-then-
else statements using different series data.
• For example, you could have an expressions that makes the forecast equal to
or above the sales order quantity for any given combination.
Color expressions
• These are expressions that color code the cell based on conditionality.
• For example, you could color all cells red when the forecast variance is above
a certain percentage.


Expressions / Color Coding


Demantra’s Statistical Forecasting
Capabilities Historical
data
Bayesian
• Bayesian-Markov algorithm uses Estimator

a mixed model that is better than Multiple
causal
Bayesian
Optimizer
best-fit approaches. factors

• Unlimited dimensions and Forecast

hierarchies Combined
model
• Forecast tree uses hierarchy to
determine the best levels to
forecast
– For example, start at the
lowest levels in the hierarchy Promotion
Events
and then move up one step at Seasonality

a time.
Causal
Analysis
• Attribute based forecasting Trend
capabilities
• Nodal tuning capabilities
Cyclical Patterns
• Advanced reporting capabilities Outlier
Detection
such as graphs, charts, various
summarization capabilities.

Shape Modeling
Activity based shape modeling
• Demantra captures the profile of historical demand over a period of time
• Apply shapes scaled for volume and time to future forecasts
• Alignment of future forecasts shape dictated by Quantity Alignment
Duration specified in another series
• Supported in Demand Planning mode and Promotion Effectiveness mode
Promotion Shape Modeling
• Similar to Activity based shape modeling but in addition considers
promotion attributes
• Available only in PE mode

Shape modeling applicable only when there is continuous stream of
demand data


Activity Based Shape Modeling


Causal Factors
• Global Factors
– Model global causal factors
that apply to all products and
locations
– For example, holidays such
as Thanksgiving and
Christmas, seasons, etc
• Local Factors
– Local causal factors apply to
a specific product and
location
– For example, a snow plough
is more likely to sell more
during snowy season in the
North-East than in Arizona.
• Improved forecast accuracy as a
result of better incorporation of
the causals.


Member Management
• New Products Introduction
– All “combinations” are stored at lowest levels
– Most members have “combinations” with
sales data and hence enabled for
forecasting
– For introducing new, use tools such as
Member Management to enable them for
forecasting
– Create a new member at any aggregation
level in the product hierarchy – but typically
lowest level members
• Link new products to Locations
– Link a new product to a location or an
existing product to a new location
– Create combinations of product/locations
• Dummy history creation
– Insert dummy historical records
– Enables viewing combinations in worksheets


Chaining
Forecasting new product / stores
• Chaining is the process of copying series data from one set of combinations to
new set of combinations.
• If you have new product/location combinations, use chaining to create history so
Demantra can generate forecast.
Item Similarity
• For example, you created new store X which sells same items as existing store Y
• Chaining creates all the relevant item combinations for new store X based on Y
Location Similarity
• For example, you created a new item B which is similar to item A and sold in all
locations
• Chaining creates all relevant location combinations for B based on A
Proport Mechanisms
• Multiple proportionality options exist for disaggregating to new combinations –
such as Target, Source, Equal and Similar


Global Forecasting – Publish from Demantra,
Consume and Distribute within ASCP
• Publish forecast from Demantra to ASCP
without the context of an Organization
– Define sourcing rules to distribute the
forecast to appropriate fulfillment centers
based on regions/category/Item and
Instance.
– ASCP utilizes the sourcing rules to distribute
the remaining forecast after consumption.
• Consumption at multiple levels in location
hierarchy
– Zone / Region
– Ship to Location
• Consumption by multiple levels in Item
hierarchy
– Item
– Demand Class


Consumption tolerance along time
dimensions
• Backward Days
– First look for the forecast on the day of the
sales order.
– Then days backwards from sales order date
the forecast should be consumed.
• Forward Days
– After scanning backwards, scan forward to
look for forecast to consume.
• Consume within time bucket
– For non-daily buckets, force the consumption
of the forecast only within that bucket and not
allow spanning backwards and forwards.
• Criticality of consumption
– If the forecasts are not consumed properly,
supply chain will be overdriven causing
excess inventory
– All these different options can be taken
advantage to shape forecast demands to align
with reality.

Respond – Profitably Balance Supply and
Demand
• Visibility to logistics and production
constraints in one number planning
process

• Integrated with Strategic Network
Optimization and Advanced Supply
Chain Planning to evaluate supply
constraints
– Quickly and interactively simulate impact of
demand changes on the supply picture with a
common presentation layer

• Integrated with Inventory
Optimization for safety stock targets
and postponement strategies
– Quickly simulate impact of demand scenarios
and varying budgets on service levels and
hedging strategies


Summary



Path to implement
Start with the corporate objectives
• Identify and prioritize corporate objectives
• For example, improve forecast accuracy by 10% or reduce stock-outs of certain
product line, etc.
Drill down to specifics
• Determine what is needed to accomplish the objectives
• For example, better collaboration among stakeholders or improved statistical
forecasting capability, etc.
Map the software functionality to meet specific requirements
• Demantra is a suite of products within larger APS umbrella with many
components and overlapping features
• Identify the specific features that meet your needs
• Then choose the components that provide those features
Layout an incremental rollout plan
• Start with the quick wins
• Expand the footprint with minimal change management impact


Review of
Demantra Terms



Levels
• Levels control how data is aggregated and organized.
These are used in worksheets, filters, import or export,
and forecasting

Types of levels:
– Items: group data according to characteristics of items
– Locations: group data according to characteristics of
locations
– Combinations: group data according to time-
independent characteristics of item-location combinations
(i.e. Sales Rep)
– Time: group data by sales date
– Promotions: group data according to characteristics of
Promotions including Item, Location and Time
– Settlements: group data according to characteristics of
Settlements including Item, Location and Time


Series
• A set of data that can be displayed in a worksheet table or
graph, at any aggregation level
• 3 data types of Series: Numeric, String or Date
• Types of Series:
– Sales Series: Consists of time-dependent data for each item-
location combination
– Matrix Series: Consists of time-independent data for each
item-location combination (i.e. Store Minimum)
– Promotion Series: Consists of data for each promotion at each
item-location combination, at each time bucket
– Level Series: Stores data associated with a specific member in
a level


Worksheets

• Within Demantra, users work almost entirely within worksheets
• A worksheet is a customized working environment where users can
view and edit data
• When users save changes back to the database, they become
available to other users and to downstream operations
• A worksheet consists of one or more views, usually displayed as
tabs or MDI within the worksheet
• Each view retrieves a set of data that is aggregated in a specific way
and that may also be filtered


Worksheets – UI Components
Table
Menu and
Toolbar

Graph
Member
Browser

Activity
Details
Notes and
Attachments

Embedded
Worksheet

Status Bar


Workflow

• Web-based
• Not accessible to most end users
• Used to define and run workflows


Let us review on the system

• Login to the collaborator workbench using train##/train##
• Open worksheet “Consensus Forecast Product Family”


Demantra Overview

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Demantra Overview