Title: Sense, Shape and Respond to Demand
1(No Transcript)
2Sense, Shape and Respond to Demand
- John Bermudez
- Senor Director, SCM Product Strategy
3The following is intended to outline our general
product direction. It is intended for information
purposes only, and may not be incorporated into
any contract. It is not a commitment to deliver
any material, code, or functionality, and should
not be relied upon in making purchasing
decisions.The development, release, and timing
of any features or functionality described for
Oracles products remains at the sole discretion
of Oracle.
4Demantra Demand Management
Enables real time demand sensing and shaping
- Sense demand real-time
- Improve forecast accuracy
- Shape demand for profitability
- Evolve to real-time sales and operations planning
5Improve Forecast Accuracy
Designed for planners, not programmers (PhD in a
box)
- Engine supports all statistical models -
Complexity is hidden for casual users but can be
fine-tuned by statisticians - Self-tuning engine
- Better than best-fit model with unlimited causal
factors - Use key accuracy metrics
- MPE, MAPE, WMAPE (Weighted MAPE)
Self-Tuning Forecasting Engine
PhD in a box advanced statistical models
Built-in key accuracy metrics
6Improve Forecast Accuracy
Leverage Advanced Forecasting and Demand Modeling
option
Historical data
- Mixed models used in same time series adjust for
multiple causal factors including seasonality,
market trends, and promotions - Each model contributes different forecast
characteristic to the overall model - Automatic model selection provides improved
accuracy of best fit approaches - One forecast based on multiple models instead of
only using best-fit model - Self-tuning engine
- Forecast trees automatically find level with
statistically relevant data - Forecasts stored at lowest level
- Proportion rules applied when necessary
- Can incorporate external information such as
weather, market drivers, forward indicators, and
competitive data
Bayesian Estimator
Multiple causal factors
Bayesian Optimizer
Forecast
Combined model
7Automatic Short/Long Term Forecast Accuracy
Mixed models automatically adapt in single,
precise forecast
8Improve Forecast Accuracy
Built-in accuracy metrics
MAPE Mean Absolute Percent Error
WMAPE Weighted MAPE by revenue, cost, and so on
9The Difference is in the Details
Granular causal factors and coefficients
Product/Group
- Demand history and causal factors maintained at
lowest level - Coefficients calculated and maintained at the
lowest level for which there is demand history - Forecasts and promotion predictions reflect
local, regional, product group customer, time
period, sensitivity, and so on - Demantra approach yields most granular analysis
of demand for more accurate forecasts
Item
Item
Item
Item at a Customer
Item at a Customer/Ship-to A
Demand history Promotion Lift Seasonality Canniba
lization Other Causal Factors
Item at a Customer/Ship-to B
Demand history Promotion Lift Seasonality Canniba
lization Other Causal Factors
Per time period (week/day/month)
10Oracle Demantra Demand Management Products
Summary of product capabilities
- Demand Management
- Statistical forecasting
- Causal factors
- New product introduction (NPI)
- Reporting and analysis
- Collaboration platform - alerts and exceptions
- Unlimited hierarchies, dimensions, levels, and
attributes - Mixed and multiple demand signals
- Advanced Forecasting and
- Demand Modeling
- Computation and display of Individual causal
contribution - Impact analysis of events
- Cross-correlative Analysis
- Attribute based forecasting
- Promotion and sales calendars
- Nodal tuning
- Unlimited causal factors
- NPI - Shape modeling and alignment
- Additional and improved forecasting models
- Advanced reporting (Gantt charts for deals,
trade, promotions, price breaks, and so on)
11Demantra Demand Management
Enables real time demand sensing and shaping
- Sense demand real-time
- Improve forecast accuracy
- Shape demand for profitability
- Evolve to real-time sales and operations planning
12Shape Demand for Profitability
Accurately forecast demand for new products based
on existing data
- New products present forecasting challenges
- Limited or no demand history for a given item
- May combine characteristics of several previous
products - Price points, changing market conditions may be
different - Product demand changes over product life cycle
- Apply shapes, scaled for volume and time
- Re-scale base on initial demand data
Model new item based on past behavior of other
items with similar attributes
New Product C 30 Product A 75 Product B
13Shape Demand for Profitability
Accurately forecast demand for new products based
on existing data
View demand of comparable products based on
characteristics
Derive forecast for new product and adjust
forecast based on actual demand
Automatically detect outliers
14Shape Demand for Profitability
Leverage Advanced Forecasting and Modeling to
understand the real impact of promotions and
sales incentives
- What incremental volume will result from a
marketing program? - How will it impact the sales of other products?
- How does a marketing program at a brand or
product family level impact a specific item? - What were the indirect effects such as
cannibalization and consumer stockpiling? - What is the ROI on my marketing and trade
spending? - What is the predicted impact of future activity?
- How does a promotion impact shipments and DC
replenishments?
15Shape Demand for Profitability
Leverage Advanced Forecasting and Modeling to
understand the real impact of promotions and
sales incentives
Typical
Demantra AFDM
- Baseline versus incremental volume
- Provides decomposition of incremental volume from
advertising, promotions, or sales incentives - Granular lift analytics
- Incremental volume lift coefficients maintained
at lowest level - Localized promotion analysis
- Allows shipments/replenishments to be adjusted by
ship-to location - Cross product and customer effects
- Determines cross-product cannibalization impact
- Adjusts forecasts for product and customer
cannibalization - Configure system for assumption based forecasting
- Structured tracking and categorization of
forecast adjustment reasons, such as market and
geopolitical changes - Evaluation of impact on demand as driver for
future forecasts - Examples
- Semicon forecast based on chip design wins
- High-Tech forecast based on probability of
winning an opportunity - Life Sciences long-term forecast based on drug
approval and patent regulations
Cross product cannibalization
Pre and Post promotion effect
Incremental
Long term growth
Competitive switching
Baseline
Baseline
16Demantra Demand Management
Enables real time demand sensing and shaping
- Sense demand real-time
- Improve forecast accuracy
- Shape demand for profitability
- Evolve to real-time sales and operations planning
17Evolve to Real-Time SOP
Profitably balance supply, demand, and budgets
Develop predictable business plans Shape demand
to meet financial goals Align supply chain to
support plan Monitor performance to plan Shape
demand to close gaps Drive decisions into ERP
18Shape Demand Real-Time SOP
Make demand and supply decisions simultaneously
Marketing
Finance
Strategic
Sales
Tactical Decisions
Product Development
Executives
Phase in and phase out products
Plan accuracy
Manufacturing
- Identify financial and revenue goals
- Analyze demand and develop sales forecast
- Synchronize plan across Finance, Sales,
Marketing, and Supply Chain - Determine potential market variables
- Review supply and demand plans
- Develop constrained plan
- Monitor results and respond to deviations
- Create promotions and incentives to shape demand
and close gaps
19Evolve to Real-Time SOP
Profitably balance supply, demand, and budgets
- Seeded templates for SOP collaboration
- Seeded consensus planning worksheets
- Easily tailored to your business
- Configurable and extensible
- Collaborate at any level
- Manage by exception instantly
- Exceptions and visual cues easily point to
important issues immediately - Document all assumptions with a complete audit
trail of decisions taken - Leverage POS data to alert planners to exceptions
real time (versus batch month by month) - Integrated
- Seeded data streams for data commonly used in the
process
20Evolve to Real-Time SOP
Highly interactive simulation and analysis
- Continuous real-time process
- Simulate demand and supply strategies
- Analyze multiple business scenarios
- Achieve consensus on plans through internal
collaboration - Generate and analyze exceptions
- Use workflow to automate process
- Adaptable
- Custom demand and supply streams
- Multi-dimensional
- Extensible hierarchies and dimensions
- User-defined reports and exceptions
21Evolve to Real-Time SOP
Inputs display from entire collaboration group
Finance, Marketing, Operations, Key Customers and
Suppliers
Each SOP participant has a configurable
role-based view
Integrated approval workflow process
Review historical accuracy for each input
22Achieve Incremental Value
Implement additional Advanced Planning components
quickly by leveraging the same foundation
Advanced Planning
E-Business Suite
- Shipment history
- Booking history
- Sales forecast
- Manufacturing forecast
- Items and categories
- Organizations
- Customers
- Calendars
- UOM and currency conversions
- Price lists
- Hierarchies (product family and product category,
time, ship from, geography, customer, demand
class, sales channel)
Demand Management
Predictive Trade Planning and Optimization
Promotional lift and decomposition
Real-Time Sales and Operations Planning
Range forecast and forecast accuracy
Demand scenarios and priority
Demand variability and forecast error
Demand scenarios
Strategic Network Optimization
Advanced Supply Chain Planning
Inventory Optimization
Production Scheduling
Collaborative Planning
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Customers
24DeRoyal Industries
Live on Demand Management
- Company
- Leading manufacturer of medical devices
- Manufactures more than 25,000 SKUs and operating
from 25 plants and office around the world1
billion in revenues, operating worldwide - Planning problem solved
- Aligned customer demand with supply chain
planning - Unique aspects of implementation
- Integrated with JD Edwards ERP
- Supports the companies many new product
introductions with improved forecast accuracy
- Increased forecast accuracy by 5-10
- Increased customer service levels
- Reduced inventory by 8
- Enabled a comprehensive SOP process
25Welchs
Live on Demand Management, Trade Management
- Company
- Leading producer of juices and jams
- Planning problem solved
- Promotion planning synchronized with demand
planning - Unique aspects of implementation
- Sales reps drive forecasting process from trade
promotion planning process - What-if scenario planning enables sales reps to
test promotion before selecting it
- Increased forecast accuracy at SKU level
- Enables trade promotion planning to be integrated
with RT SOP - Reduced supply chain costs
- Improved HQ and sales planning productivity
26VTech
Live on Demand Management, Real-Time SOP
- Company
- 1 billion in revenues, operating worldwide
- Manufacturing plants in China
- Leading provider of cordless phones and
electronic childrens toys - Planning problem solved
- Real-time SOP process driven by one demand
number - Unique aspects of implementation
- Generates forecasts from customer POS data
- Compares customer and generated forecasts and
routes exceptions to planner, sales
representative, or customer
- Increased order fill rate from 55 to 95
- Increased inventory turns by 100
- Reduced price protection claims by 40
- Reduced logistics costs by 65
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Summary
28Oracle Demantra Demand Management
Real-time demand sensing and collaborative
consensus forecasting
- Sense demand real-time
- Sense demand more frequently, closer to the point
of consumption - Capture demand and forecast at more granular
level (store, shelf, attributes, product
characteristics) - Achieve consensus demand number more quickly by
involving all constituents at the same time,
including customers - Quickly identify and react to demand changes and
exceptions - Improve forecast accuracy
- Leverage advanced statistics for more accurate
demand number - Use any combination of quantitative or
qualitative data to establish your base line
forecast - High precision statistical forecasting, no
statistical background required Superior
Bayesian-Markov forecast analytics - Forecast based on attributes and characteristics
- Leverage Advanced Forecast Modeling for promotion
lift decomposition and causal analysis - Shape demand for profitability
- Plan new product introductions
- Plan promotions and sales incentives
- Identify cross selling opportunities
- Evolve to real-time SOP
Collaboration Workbench
Marketing forecast
Order history
Customer sales
Shipments
Demand Hub and Seeded Worksheets
29Oracle Demantra Demand Management
Evolve at your own pace to a best-in-class
solution
Forecast based on attributes and product
characteristics Compute promotional lifts and
analyze impact of demand drivers Assumption
based forecasting
Start anywhere
Leverage POS and channel data Forecast new
product introductions Collaborate with
customers Use advanced statistics and causal
factors Complex alerts and custom worksheets
Leverage POS and channel data Forecast new
product introductions Collaborate with
customers Use advanced statistics and causal
factors Complex alerts and custom worksheets
Manage rolling forecasts Collaborate with all
constituents on one number Use basic
statistics, alerts, and seeded worksheets Tailor
worksheets for individual users
Manage rolling forecasts Collaborate with all
constituents on one number Use basic
statistics, alerts, and seeded worksheets Tailor
worksheets for individual users
Manage rolling forecasts Collaborate with all
constituents on one number Use basic
statistics, alerts, and seeded worksheets Tailor
worksheets for individual users
Eliminate spreadsheets
From less complex to best in class
30Why Oracle !
Proven, scalable demand management solution
Integrated demand, supply, and sales and
operations planning
Designed for planners, not programmers
Integrated performance management
31Q
A
Q U E S T I O N S
A N S W E R S
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