Transcript Slide 1

Demand Planning
Sam Dimasi, Professional Services Consultant, QAD
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Demand Planning
Safe Harbor Statement
The following is intended to outline QAD’s 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, functional capabilities, and
should not be relied upon in making purchasing
decisions. The development, release, and timing
of any features or functional capabilities
described for QAD’s products remains at the sole
discretion of QAD.
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Demand Planning
Agenda
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Demand Planning
Agenda
 Introduction to Demand Planning
 QAD Demand Planning
 Project Approach
 Summary
 Questions & Answers
 Next Steps
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Demand Planning
Introduction to Demand Planning
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Demand Planning
Definitions
Forecasting
“The business function that attempts to predict sales
and use of products so they can be purchased or
manufactured in appropriate quantities in advance”
APICS Dictionary
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Demand Planning
Definitions
Demand Planning
“Using forecasts and experience to estimate demand
for various items at various points in a supply chain.
Several forecasting techniques may be used during
the planning process. Often, families of items are
aggregated in doing this planning. Aggregation also
may occur by geographical region or by life cycle
stage. Forecast demand is compared to actual
demand in order to measure and increase forecast
accuracy.”
APICS Dictionary
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Demand Planning
Why Forecast?
 To anticipate demand
 Maximize visibility to
− Procure finished goods/ raw materials
− Understand resource constraints/ requirements
− Project and understand inventory levels
− Project manufacturing operational costs
− Assist with the budgeting process to manage
revenue/cost plans and cash flow
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Demand Planning
Elements of a Good Process
 Predefined timeline
− Clearly understood, accepted and supported
 Co-ordination of activity
− A role that keeps things on track
− Part of the TEAM, not the “Lone Ranger”
 Input from those “in the know”
− Promotions, new customers, product deletions
 Measure accuracy
− Key for improvement
− At the appropriate level
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Demand Planning
More on Measurement
 Metric: Forecast accuracy
 Key metric: Stock level
 Key metric: Customer service level
Stock out risk
Probaibility
40%
35%
30%
25%
20%
15%
10%
5%
0%
0%
20%
40%
60%
80%
100%
Relative standard deviation
120%
140%
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Demand Planning
Benefits of a Good Process
 No forecast is perfect but....
 Issues can be identified in advance
 Resource deployment is better controlled
 Inventory is where it needs to be at the right
time and in the right quantity
 Improved ability to meet customer demand
 Improved reputation leading to increased
business
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Demand Planning
Results
up to 50%
Forecast
Accuracy
Improvement
up to 10%
up to 50%
Customer
Service
Improvement
Inventory
Level
Reduction
60%
12%
60%
50%
10%
50%
40%
8%
40%
6%
30%
4%
20%
2%
10%
30%
20%
10%
0%
Guy
Laberyie
Degrenne
Mayoli
Spindler
0%
0%
Cémoi
Janssen
Martell Labeyrie
Cadbury Dorel Laberyie Martell
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Demand Planning
QAD Demand Planning
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Demand Planning
DynaSys Demand & Supply Chain Planning
Our focus today...
Strategic
Collaboration
Web Access
Network & Inventory Optimization
Operational
Procurement
Planning
Production
Planning
Distribution
Planning
Integration
Buy
Manufacture
Demand
Planning
Customer
Supplier
Tactical
Sales & Operations Planning
KPI’s
Deliver
Sell
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Demand Planning
Demand Planning Customers
Food & Beverage/ CPG
Life Sciences
Industries
Luxury Goods
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Demand Planning
QAD Early Adopters
BCM
PCCA
Invacare
Watts Water Technologies
HP Indigo
AMVAC
LEM
ANCA
BAKELS
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Demand Planning
Features
 Process-oriented solution
 Highly configurable
− Flexible integration capability
 Pre-defined parameters and metrics
 Industry leading statistical engine
 Spreadsheet “look and feel” data grids
− Configurable for data and alerts / indicators
− Graphical representation
− Import data directly into grids
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Demand Planning
Process
Clean history
Metrics
Collaborate
Life Cycle
Aggregate
Calculate
Disaggregate
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Demand Planning
Why Clean History?
 Forecasts are built on pattern recognition
 What if your sales history included
− Extraordinary demand caused by a competitor’s
stock outs?
− A weather driven surge or absence of demand?
− A drop in orders caused by your prolonged stock
out?
 Need to deal with outliers where we don’t
want future to reflect past
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Demand Planning
Dealing With Outliers
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Demand Planning
Multi-Level Forecasting
 Aggregate Item
 Update marketing
Events
Bottom Up
Finished
Product
Finished Product
x Customer
Top Down
Import History
Bottom Up
 Aggregate Source
of Demand
 Historical Analysis
 Better Forecast
Top Down
Product
Group
 Disaggregate
 Rebalance
through all levels
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Demand Planning
Statistical Engine
 Calculates initial forecast for review
− Deal with exceptions
 There are over 30 forecasting methods
available including:
− Curve Fitting
− Exponential
− Smoothing
− New Product
− Lifecycle
− ‘Automatic’
 Best fit ‘automatic’ calculation method
recommended initially
 Different methods for each forecast record
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Demand Planning
Life Cycle Management
 New product introduction
 Product substitution
 End of life
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Demand Planning
Collaboration
 Statistical forecast normally forms baseline
 Users contribute business knowledge outside
realm of statistical forecast
 Collaborative users – sales people, regional
marketing managers and demand planners
 Demand Planning allows incorporating
additional business knowledge into forecast
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Demand Planning
Consensus
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Demand Planning
Metrics
 Forecast accuracy
− Archive forecast at the end of each cycle
− A number of reliability measures
− Review poor performance
− Target improvements and overcome issues
 Performance to budget
− Load budgets to compare to actual
− Track gaps to assist with remedial action
 ABC analysis
− Provides focus
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Demand Planning
Reliability Analysis
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Demand Planning
Performance to Budget
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Demand Planning
ABC Analysis
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Demand Planning
Project Approach
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Demand Planning
2 Different Project Approaches
 Dedicated project
− Based on a detailed functional & technical
analysis
− Aims to meet specific customer requirements
 Easy On Boarding
− Based on pre-configured solution with broad
relevance across industries
− Aims to minimize implementation effort and cost
− Represents initial implementation that can be later
enhanced
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Demand Planning
EOB Data Model
Weekly
Monthly
 Statistical
Level 3 x
Product Line
Monthly
 Statistical
 Life cycle
Level 2 x
Item x Site
Monthly
Item x
Site
 Validation
 Export
Item x Site x
Level 1 x Monthly
Customer
Customer
x Item
 Promotions
 History cleaning
 Collaboration
Item x Site x Ship-To
QAD Enterprise
Applications
Forecast
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Demand Planning
EOB Implementation
Project effort: between 30 to 45 days
according to adjustment
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Demand Planning
Summary
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Demand Planning
Summary
 Demand Planning is key for visibility
 Robust, co-ordinated process with supported
timeline
 Leverage experience
− Collaboration is important leading to improved
forecasts
 Measure the outcome
− Forecast accuracy improvements
− Inventory reductions
− Customer Service Level improvements
 QAD Demand Planning is the enabler
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Demand Planning
Questions & Answers
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Demand Planning
Questions & Answers
 Sam Dimasi: [email protected]
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Demand Planning
Next Steps
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Demand Planning
Next Steps
 Attend
− ‘Supply Chain Planning’ following this session
 Ariel Weil
 Rhythms Ballroom (2nd Floor)
 Stop by the Solutions Expo
− See QAD Demand Planning in action
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Demand Planning
www.qad.com
©QAD Inc. 2014
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