Jda Demand - Focus 2012 28xt
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It All Starts With a Forecast: An Overview of JDA Demand Paula Natoli
Demand Management The Problem
The Solution
The expansion of product assortments, the shortening of product lifecycles, the proliferation of promotional offerings, and the need for dynamic pricing strategies leads to an increased complexity of being able to accurately predict customer demand.
Understand and predict customer demand by product, location, and time in order to maximize sales effectiveness while minimizing inventory expense.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Management Solution Suite Components
Business Objectives
Demand
Demand Manager
Demand Decomposition
Demand Planner
Demand Classification
Profiler
Dynamic Demand Response Seasonal Profiling
Market Manager
Consolidated view of customer demand
Accurate, efficient time phased forecasting
KPI Driven Planning
Key Capabilities
Automated history classification, algorithm selection and parameter setting
Comprehensive set of statistical & configurable business rules for forecasting
Hierarchical, multi channel forecasting
Robust, Flexible Demand Analysis
Differentiate between seasonal and promotional lift in sales history
Identification of common seasonal profiles
Integrated Promotional planning & analysis
Sense & Respond Order Pattern Recognition for consensus based planning
This document contains forward–looking statements based on current expectations, forecasts and assumptions of the Company that involve risks and uncertainties. Forward looking statements are subject to risks and uncertainties associated with the Company's business that could cause actual results to vary materially from those stated or implied by such forward-looking statements. Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL © 2010 JDA Software
Group, Inc. – CONFIDENTIAL
Demand Management: One View of Synchronized Demand S&OP
Historical Sales Information
Replenishment & Allocation Space & Category Management
Sales History Lost Sales
Demand Management
Consensus Forecast
Workforce Management
Pricing / Promo Effects
Trade Promotions Management
Causal Factors Trend
Merchandise Planning
Pricing & Promotions Management & Optimization
Seasonality Internal / External Collaboration
Copyright 2012 JDA Software 4 Group, Inc. - CONFIDENTIAL
Transportation Management
Demand Key Features and Highlights Comprehensive Algorithm Library • Eight statistical algorithms to properly handle differing demand patterns • Demand Classification classifies historical patterns, assigns appropriate algorithm(s) and tunes parameters.
Multi-Channel, Flexible Hierarchical Demand Management • Flexible hierarchy configuration extending product, channel, and location hierarchies • s bottoms-up, tops-down or middle-out aggregation & reconciliation • Attach Rate Forecasting: Independent + Dependent Demand Forecasting
Lifecycle Management & New Product Introduction • Comprehensive new product/store introduction functionality • Creation and management of lifecycle curves and launch profiles for re-use • Attribute based short lifecycle forecasting
Tight Integration with Complimentary Solutions • Shared data model and UI navigation with Fulfillment, Pricing/Promotions, Master Planning • Common foundation with extended suite: Collaborate, S&OP • Out-of-the-box integration with other solutions needing a demand signal Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Where do I start? • The way you replenish is not necessarily the same way you should forecast.
• Define what entity you will forecast. – SKU/Store – SKU/Warehouse – SKU/Region – Category/Region – SKU/Customer/Warehouse – Etc… Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Forecasting Unit (DFU) At its simplest, a DFU represents an item selling in a market
Product
Market
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Forecasting Unit Markets are often further delineated… Product
Market
Channel
Region
creating the complete DFU: Product
Channel
Region
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
8
Demand Forecasting Unit DFU Examples
Product
Channel
Region
20 oz. Cola
Convenience Stores
New York
Consumer Goods
12 oz. Cola
Superstores
South East
MassMerch Retail Laundry Detergent
Retail
Store 123 Power Drills
Internet
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
West Region
Demand Forecasting Unit + Model Product
DFU Examples
Channel
Consumer Goods
12 oz. Cola
Superstores
South East
Convenience Stores
20 oz. Cola
FourierOrder LewandowskiShipments
New York
MassMerch Retail Laundry Detergent
Retail
Power Drills
Store 123
Internet
MLRPOS
West Region
MLRShipments
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Region
Model
Model represents a combination of history and statistical algorithm
Multi-level Forecasting Process • Organizes your DFUs into a manageable structure • Synchronizes data among multiple levels through aggregation and reconciliation models • Facilitates the inclusion of business knowledge into the forecasting process
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Multi-level Forecasting
Channel Product Category Class Sub Class SKU
Furniture Storage
Chairs
High Back
10001
Filing
Low Back
10002
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Region
Multi-level Forecasting
Region
Product Channel All
All Channels Market/Distribution Channels
Retail
Catalog
Internet
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Multi-level Forecasting
Product
Channel Region All
All Warehouses Ship From Warehouse (e.g. North America)
DC1
Minor Region (e.g. SouthEast United States)
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
DC2
NY
DC3
Bos
Defining the DFU Structure A DFU can be any connection across these three dimensions. Product
Channel
Department
Channel
Region
Country
Category
Store Cluster
Class
Store
Item
Each defined DFU level can generate its own statistical forecast based upon aggregated sales history.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Aggregation Model
Multi Level Structure Aggregation
JDA Demand
Most aggregate level
Granular level
• Aggregate History through the nodes in a hierarchy • Generate independent forecast at each level in hierarchy • Hierarchies can be in other dimensions – Channel & Location Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
16
Reconciliation Model Top Down
Multi Level Structure Reconcile
JDA Demand
Most aggregate level
Granular level
• • •
Enables comparisons of statistically generated forecasts at multiple levels Allocates time phased forecasts across defined hierarchy levels Flexible definition of reconciliation models Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
17
Evaluate and Prepare
Demand Management Process •
Determine which levels of the product and business hierarchy will be forecasted
•
Determine the historical stream: – – – – – – –
•
Consumer Demand (POS) Store Orders Retail DC to Store shipments Retail orders Adjusted orders Shipments Events
History Cleansing – – – –
History
Calculate Model using forecasting method
Evaluate and Fine Tune the Model
Evaluate Forecast Performance
Manage Exceptions
Manage Additional Information
Close the Period
Base vs. Non-base De-spiking Lost Sales Pricing/Promotional Effects
Draft Forecast
•
Amount of history
•
Unit of measure
Send to other processes
Publish Forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Consensus Forecasting
Demand Decomposition Separate Base from Promo History Key Actions
Business Impacts
Separate base history from promotional business and seasonal peaks/valleys
Estimate impacts of promotional history
Provide a cleansed historical data stream for which to apply statistical models
Develop a solid baseline statistical forecast
Properly identify gaps in the future forecast and plan future events accordingly
Understand the impact that promotions play on your business
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Management Process
Evaluate and Prepare History
Calculate Model using forecasting method
• Identify algorithm to use via Demand Classification
• Process to select algorithm is run only 1-4 times a year
Evaluate and Fine Tune the Model
Evaluate Forecast Performance
Manage Exceptions
Manage Additional Information
Close the Period
• Process to tune parameters can be run more frequently • Output is a baseline statistical forecast Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Draft Forecast
Publish Forecast
Send to other processes
– Assigned to each forecasting entity (i.e.: item/channel/loc) – Recommend appropriate settings of the model parameters
Forecasting Methods Regression Based Algorithms • Fourier assumes that business is constant or that it changes at a constant rate •Multiple Linear Regression first develops a profile assuming constant business, then develops the model further through the use of multiple causal variables.
Smoothing Based Algorithms • Lewandowski method assumes that not only is business inconstant, but also that it changes at an inconstant rate. •Croston Used for slow, lumpy items; (i.e.: many zeroes) •Holt Winters Most useful when the seasonal component and the trend component are changing at different paces over time. •Moving Average assumes near term forecast to be similar in nature to recent past •AVS-Graves handles varying intermittent demand patterns with ability to incorporate seasonality
Other • Short Lifecycle utilizes attribute based mapping to extract existing lifecycle curves and apply them to new DFUS Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Sales Patterns Differ Across Products Groups and/or Locations! What type of products do you deal with? • continuous vs. intermittent • seasonal vs. non-seasonal
• trend vs. constant • stable vs. highly variable
A mixture of “all of the above”? When it comes to statistical algorithms, one size does NOT fit all ! Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Classification Assignments by Class
Review Percent of DFUs Per Class
• Classify products in of their historical demand pattern Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Classification Assignments by Algorithm
Review Recommended Algorithms for DFUs
• Automatically assign the recommended algorithm and starting parameters based on history patterns • Reduce planner fine-tuning time Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Generate the Statistical Forecast Statistical forecast is generated based upon the sales history at each defined DFU level
1. Map the historical sales data 2. Fit a model to the history (fitted history) 3. Project the model into the future (forecast) Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Management Process
Evaluate and Prepare History
Calculate Model using forecasting method
• Review system suggestions for parameter settings Evaluate and Fine Tune the Model
– Fine-tune suggested parameter settings where appropriate Evaluate Forecast Performance
Manage Exceptions
• Each algorithm s a unique set of parameters Manage Additional Information
Close the Period
• Each DFU can have parameters adjusted independently
Draft Forecast
Send to other processes
Publish Forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecasting Demand Workbench Configurable s Graphical Hierarchy Navigation
Configurable s
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
New Product Introduction & Lifecycle Management Launch Manager: Allocate forecasts for new launches following previous similar product launch profiles
New Product Introduction: create new
Lifecycle Manager: Create, Manage, Extract and Assign Lifecycle profiles to DFUs
DFUs & generate initial forecasts based upon “like” DFUs.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Short Lifecycle Forecasting • Automated forecast generation for products with short lifecycles • By need to manually select lifecycles to attach to new products • Works upon attribute matching and Bass curve modeling & fitting
Define & Prioritize DFU Attributes Define the DFU attributes that will most likely represent similar traits for new DFUS.
Build Short lifecycle curves Builds and stores curves for selected DFU groupings.
Assign Short Lifecycle Curves Adjust the forecast based on the history and tuning parameters.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Adapt Forecast
Matches new DFUS to stored curves based upon attributes.
Demand Management Process
Evaluate and Prepare History
Calculate Model using forecasting method
• Review modeling exceptions • Tune parameters
Evaluate Forecast Performance
Manage Exceptions
Manage Additional Information
Close the Period
Draft Forecast
Publish Forecast
Send to other processes
• Regenerate forecasts
Evaluate and Fine Tune the Model
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecasting Exception Graphs • •
Plotting of DFUS graphically to understand and manage by exception Multiple graph types various exceptions Immediate Visibility to Changes
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Management Process
Evaluate and Prepare History
Calculate Model using forecasting method
• Adjust baseline forecasts to for market insights – Promotional Information
Evaluate and Fine Tune the Model
– Competitive Information – Forecast Overrides – Cannibalization
Evaluate Forecast Performance
Manage Exceptions
– Business Weather Intelligence
• s integration with Promotions & Pricing Management suite
Manage Additional Information
Close the Period
Draft Forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Publish Forecast
Send to other processes
• Incorporate changes from a consensus forecasting business process
Consensus Forecasting
Incorporate Event Information: A Building Block Approach • Utilize statistical forecast to establish a baseline of current mean, trend, seasonality • Incorporate promotional lifts • Add marketing intelligence, cross-functional information to align the organization to the game plan Consensus Adjustment Total Forecast
Promotional Lift Baseline Statistical Forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecast Overrides Apply incremental overrides to individual forecasts or flexible groupings of forecasts
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Incorporate Promo Lift: Promotions Management
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Market Activities One View of Demand Promotion lift incorporated directly into the forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecast Reconciliation Tops-Down / Bottoms Up • •
Reconcile forecasts via tops/down or bottoms/up ed in both a fixed hierarchy (multiple levels) and a flexible grouping
View Percentage Contribution
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Total of selected DFUS
Evaluate and Prepare History
Demand Management Process Calculate Model using forecasting method
• Transfer forecasts to other required business processes & applications
Evaluate and Fine Tune the Model
– Fulfillment – Enterprise Planning
Evaluate Forecast Performance
Manage Exceptions
– Space & Category Management – Workforce Management Manage Additional Information
Close the Period
Draft Forecast
Publish Forecast
Send to other processes
• Enables all other processes to work from one view of demand
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Evaluate and Prepare History
Demand Management Process
Calculate Model using forecasting method
• Bring in history from posted period
Evaluate and Fine Tune the Model
• Regenerate updated forecast based upon added history
Evaluate Forecast Performance
Manage Exceptions
Manage Additional Information
Close the Period
Draft Forecast
Publish Forecast
Send to other processes
• Review forecast accuracy metrics from completed period
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecast Performance Average Forecast Error
Plan Production
Purchase Materials
Schedule Production
60% 50% 40% 30% Item/ Location
20% 10%
Total Item All Locations
0% DFU Item Family
4
3
2
Months Before Sales
1
0
Sale
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Benefits of Measuring Forecast Performance • Provides a benchmark for continuous improvement of the forecasting process. • Allows forecasters to manage by exceptions. • Measures the accuracy of both the base statistical forecast and the adjustments. Are my adjustments working? • What gets measured gets better!
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecasting Forecast Performance Analysis How is my accuracy tracking? Is it improving over time?
Total History
Total Forecast
Total Error
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Thank you. Please to submit your survey. FAR001
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
It All Starts With a Forecast: An Overview of JDA Demand Paula Natoli
Demand Management The Problem
The Solution
The expansion of product assortments, the shortening of product lifecycles, the proliferation of promotional offerings, and the need for dynamic pricing strategies leads to an increased complexity of being able to accurately predict customer demand.
Understand and predict customer demand by product, location, and time in order to maximize sales effectiveness while minimizing inventory expense.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Management Solution Suite Components
Business Objectives
Demand
Demand Manager
Demand Decomposition
Demand Planner
Demand Classification
Profiler
Dynamic Demand Response Seasonal Profiling
Market Manager
Consolidated view of customer demand
Accurate, efficient time phased forecasting
KPI Driven Planning
Key Capabilities
Automated history classification, algorithm selection and parameter setting
Comprehensive set of statistical & configurable business rules for forecasting
Hierarchical, multi channel forecasting
Robust, Flexible Demand Analysis
Differentiate between seasonal and promotional lift in sales history
Identification of common seasonal profiles
Integrated Promotional planning & analysis
Sense & Respond Order Pattern Recognition for consensus based planning
This document contains forward–looking statements based on current expectations, forecasts and assumptions of the Company that involve risks and uncertainties. Forward looking statements are subject to risks and uncertainties associated with the Company's business that could cause actual results to vary materially from those stated or implied by such forward-looking statements. Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL © 2010 JDA Software
Group, Inc. – CONFIDENTIAL
Demand Management: One View of Synchronized Demand S&OP
Historical Sales Information
Replenishment & Allocation Space & Category Management
Sales History Lost Sales
Demand Management
Consensus Forecast
Workforce Management
Pricing / Promo Effects
Trade Promotions Management
Causal Factors Trend
Merchandise Planning
Pricing & Promotions Management & Optimization
Seasonality Internal / External Collaboration
Copyright 2012 JDA Software 4 Group, Inc. - CONFIDENTIAL
Transportation Management
Demand Key Features and Highlights Comprehensive Algorithm Library • Eight statistical algorithms to properly handle differing demand patterns • Demand Classification classifies historical patterns, assigns appropriate algorithm(s) and tunes parameters.
Multi-Channel, Flexible Hierarchical Demand Management • Flexible hierarchy configuration extending product, channel, and location hierarchies • s bottoms-up, tops-down or middle-out aggregation & reconciliation • Attach Rate Forecasting: Independent + Dependent Demand Forecasting
Lifecycle Management & New Product Introduction • Comprehensive new product/store introduction functionality • Creation and management of lifecycle curves and launch profiles for re-use • Attribute based short lifecycle forecasting
Tight Integration with Complimentary Solutions • Shared data model and UI navigation with Fulfillment, Pricing/Promotions, Master Planning • Common foundation with extended suite: Collaborate, S&OP • Out-of-the-box integration with other solutions needing a demand signal Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Where do I start? • The way you replenish is not necessarily the same way you should forecast.
• Define what entity you will forecast. – SKU/Store – SKU/Warehouse – SKU/Region – Category/Region – SKU/Customer/Warehouse – Etc… Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Forecasting Unit (DFU) At its simplest, a DFU represents an item selling in a market
Product
Market
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Forecasting Unit Markets are often further delineated… Product
Market
Channel
Region
creating the complete DFU: Product
Channel
Region
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
8
Demand Forecasting Unit DFU Examples
Product
Channel
Region
20 oz. Cola
Convenience Stores
New York
Consumer Goods
12 oz. Cola
Superstores
South East
MassMerch Retail Laundry Detergent
Retail
Store 123 Power Drills
Internet
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
West Region
Demand Forecasting Unit + Model Product
DFU Examples
Channel
Consumer Goods
12 oz. Cola
Superstores
South East
Convenience Stores
20 oz. Cola
FourierOrder LewandowskiShipments
New York
MassMerch Retail Laundry Detergent
Retail
Power Drills
Store 123
Internet
MLRPOS
West Region
MLRShipments
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Region
Model
Model represents a combination of history and statistical algorithm
Multi-level Forecasting Process • Organizes your DFUs into a manageable structure • Synchronizes data among multiple levels through aggregation and reconciliation models • Facilitates the inclusion of business knowledge into the forecasting process
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Multi-level Forecasting
Channel Product Category Class Sub Class SKU
Furniture Storage
Chairs
High Back
10001
Filing
Low Back
10002
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Region
Multi-level Forecasting
Region
Product Channel All
All Channels Market/Distribution Channels
Retail
Catalog
Internet
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Multi-level Forecasting
Product
Channel Region All
All Warehouses Ship From Warehouse (e.g. North America)
DC1
Minor Region (e.g. SouthEast United States)
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
DC2
NY
DC3
Bos
Defining the DFU Structure A DFU can be any connection across these three dimensions. Product
Channel
Department
Channel
Region
Country
Category
Store Cluster
Class
Store
Item
Each defined DFU level can generate its own statistical forecast based upon aggregated sales history.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Aggregation Model
Multi Level Structure Aggregation
JDA Demand
Most aggregate level
Granular level
• Aggregate History through the nodes in a hierarchy • Generate independent forecast at each level in hierarchy • Hierarchies can be in other dimensions – Channel & Location Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
16
Reconciliation Model Top Down
Multi Level Structure Reconcile
JDA Demand
Most aggregate level
Granular level
• • •
Enables comparisons of statistically generated forecasts at multiple levels Allocates time phased forecasts across defined hierarchy levels Flexible definition of reconciliation models Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
17
Evaluate and Prepare
Demand Management Process •
Determine which levels of the product and business hierarchy will be forecasted
•
Determine the historical stream: – – – – – – –
•
Consumer Demand (POS) Store Orders Retail DC to Store shipments Retail orders Adjusted orders Shipments Events
History Cleansing – – – –
History
Calculate Model using forecasting method
Evaluate and Fine Tune the Model
Evaluate Forecast Performance
Manage Exceptions
Manage Additional Information
Close the Period
Base vs. Non-base De-spiking Lost Sales Pricing/Promotional Effects
Draft Forecast
•
Amount of history
•
Unit of measure
Send to other processes
Publish Forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Consensus Forecasting
Demand Decomposition Separate Base from Promo History Key Actions
Business Impacts
Separate base history from promotional business and seasonal peaks/valleys
Estimate impacts of promotional history
Provide a cleansed historical data stream for which to apply statistical models
Develop a solid baseline statistical forecast
Properly identify gaps in the future forecast and plan future events accordingly
Understand the impact that promotions play on your business
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Management Process
Evaluate and Prepare History
Calculate Model using forecasting method
• Identify algorithm to use via Demand Classification
• Process to select algorithm is run only 1-4 times a year
Evaluate and Fine Tune the Model
Evaluate Forecast Performance
Manage Exceptions
Manage Additional Information
Close the Period
• Process to tune parameters can be run more frequently • Output is a baseline statistical forecast Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Draft Forecast
Publish Forecast
Send to other processes
– Assigned to each forecasting entity (i.e.: item/channel/loc) – Recommend appropriate settings of the model parameters
Forecasting Methods Regression Based Algorithms • Fourier assumes that business is constant or that it changes at a constant rate •Multiple Linear Regression first develops a profile assuming constant business, then develops the model further through the use of multiple causal variables.
Smoothing Based Algorithms • Lewandowski method assumes that not only is business inconstant, but also that it changes at an inconstant rate. •Croston Used for slow, lumpy items; (i.e.: many zeroes) •Holt Winters Most useful when the seasonal component and the trend component are changing at different paces over time. •Moving Average assumes near term forecast to be similar in nature to recent past •AVS-Graves handles varying intermittent demand patterns with ability to incorporate seasonality
Other • Short Lifecycle utilizes attribute based mapping to extract existing lifecycle curves and apply them to new DFUS Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Sales Patterns Differ Across Products Groups and/or Locations! What type of products do you deal with? • continuous vs. intermittent • seasonal vs. non-seasonal
• trend vs. constant • stable vs. highly variable
A mixture of “all of the above”? When it comes to statistical algorithms, one size does NOT fit all ! Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Classification Assignments by Class
Review Percent of DFUs Per Class
• Classify products in of their historical demand pattern Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Classification Assignments by Algorithm
Review Recommended Algorithms for DFUs
• Automatically assign the recommended algorithm and starting parameters based on history patterns • Reduce planner fine-tuning time Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Generate the Statistical Forecast Statistical forecast is generated based upon the sales history at each defined DFU level
1. Map the historical sales data 2. Fit a model to the history (fitted history) 3. Project the model into the future (forecast) Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Management Process
Evaluate and Prepare History
Calculate Model using forecasting method
• Review system suggestions for parameter settings Evaluate and Fine Tune the Model
– Fine-tune suggested parameter settings where appropriate Evaluate Forecast Performance
Manage Exceptions
• Each algorithm s a unique set of parameters Manage Additional Information
Close the Period
• Each DFU can have parameters adjusted independently
Draft Forecast
Send to other processes
Publish Forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecasting Demand Workbench Configurable s Graphical Hierarchy Navigation
Configurable s
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
New Product Introduction & Lifecycle Management Launch Manager: Allocate forecasts for new launches following previous similar product launch profiles
New Product Introduction: create new
Lifecycle Manager: Create, Manage, Extract and Assign Lifecycle profiles to DFUs
DFUs & generate initial forecasts based upon “like” DFUs.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Short Lifecycle Forecasting • Automated forecast generation for products with short lifecycles • By need to manually select lifecycles to attach to new products • Works upon attribute matching and Bass curve modeling & fitting
Define & Prioritize DFU Attributes Define the DFU attributes that will most likely represent similar traits for new DFUS.
Build Short lifecycle curves Builds and stores curves for selected DFU groupings.
Assign Short Lifecycle Curves Adjust the forecast based on the history and tuning parameters.
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Adapt Forecast
Matches new DFUS to stored curves based upon attributes.
Demand Management Process
Evaluate and Prepare History
Calculate Model using forecasting method
• Review modeling exceptions • Tune parameters
Evaluate Forecast Performance
Manage Exceptions
Manage Additional Information
Close the Period
Draft Forecast
Publish Forecast
Send to other processes
• Regenerate forecasts
Evaluate and Fine Tune the Model
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecasting Exception Graphs • •
Plotting of DFUS graphically to understand and manage by exception Multiple graph types various exceptions Immediate Visibility to Changes
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Demand Management Process
Evaluate and Prepare History
Calculate Model using forecasting method
• Adjust baseline forecasts to for market insights – Promotional Information
Evaluate and Fine Tune the Model
– Competitive Information – Forecast Overrides – Cannibalization
Evaluate Forecast Performance
Manage Exceptions
– Business Weather Intelligence
• s integration with Promotions & Pricing Management suite
Manage Additional Information
Close the Period
Draft Forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Publish Forecast
Send to other processes
• Incorporate changes from a consensus forecasting business process
Consensus Forecasting
Incorporate Event Information: A Building Block Approach • Utilize statistical forecast to establish a baseline of current mean, trend, seasonality • Incorporate promotional lifts • Add marketing intelligence, cross-functional information to align the organization to the game plan Consensus Adjustment Total Forecast
Promotional Lift Baseline Statistical Forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecast Overrides Apply incremental overrides to individual forecasts or flexible groupings of forecasts
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Incorporate Promo Lift: Promotions Management
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Market Activities One View of Demand Promotion lift incorporated directly into the forecast
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecast Reconciliation Tops-Down / Bottoms Up • •
Reconcile forecasts via tops/down or bottoms/up ed in both a fixed hierarchy (multiple levels) and a flexible grouping
View Percentage Contribution
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Total of selected DFUS
Evaluate and Prepare History
Demand Management Process Calculate Model using forecasting method
• Transfer forecasts to other required business processes & applications
Evaluate and Fine Tune the Model
– Fulfillment – Enterprise Planning
Evaluate Forecast Performance
Manage Exceptions
– Space & Category Management – Workforce Management Manage Additional Information
Close the Period
Draft Forecast
Publish Forecast
Send to other processes
• Enables all other processes to work from one view of demand
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Evaluate and Prepare History
Demand Management Process
Calculate Model using forecasting method
• Bring in history from posted period
Evaluate and Fine Tune the Model
• Regenerate updated forecast based upon added history
Evaluate Forecast Performance
Manage Exceptions
Manage Additional Information
Close the Period
Draft Forecast
Publish Forecast
Send to other processes
• Review forecast accuracy metrics from completed period
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecast Performance Average Forecast Error
Plan Production
Purchase Materials
Schedule Production
60% 50% 40% 30% Item/ Location
20% 10%
Total Item All Locations
0% DFU Item Family
4
3
2
Months Before Sales
1
0
Sale
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Benefits of Measuring Forecast Performance • Provides a benchmark for continuous improvement of the forecasting process. • Allows forecasters to manage by exceptions. • Measures the accuracy of both the base statistical forecast and the adjustments. Are my adjustments working? • What gets measured gets better!
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Forecasting Forecast Performance Analysis How is my accuracy tracking? Is it improving over time?
Total History
Total Forecast
Total Error
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
Thank you. Please to submit your survey. FAR001
Copyright 2012 JDA Software Group, Inc. - CONFIDENTIAL
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