supply planning leadership exchange sap apo snp solver engine selection
DESCRIPTION
866.P4D.INFO | Plan4Demand.com | [email protected] Supply Planning Leadership Exchange presents: SAP APO SNP Solver Selection with Sharon Nelson, Managing Director Heuristics vs. CTM vs. Optimizer? This session will focus on the three solver engines available to choose from and help you understand when to use Heuristics, Capable to Match, and Optimizer. We’ll review strengths and considerations of each solver, how to navigate the maturity curve successfully, along with industry specific suggestions and useful migration strategies . Check out this webinar on-demand at http://www.plan4demand.com/Video-SAP-SNP-Engine-Solver-EvolutionTRANSCRIPT
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August 2nd, 2012 plan4demand
SUPPLY PLANNING LEADERSHIP EXCHANGE PRESENTS:
The web event will begin momentarily with your host:
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Does your SNP Plan position you for success?
What is SNP?
Thinking about SNP Engine Selection
Summary of Options for Consideration
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Demand
(Unconstrained
market demand) New Product
his
tory
PL
M
Demand Management
Supply (Constrained
product availability)
Cap
acit
y
Tra
nsp
ort
atio
n
Inventory
Supply Planning
Supportable
Demand
Orders Shipments
Allocation Supportable Plan
Order Fulfillment
S&OP
Logistics & Fulfillment Planning
SAP/APO/DP SAP/APO/SNP
SAP/ECC
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4
Road Map to Supply Chain Optimization
Road Map
to SNP
Standalone
Training
Environment
Plan for
Role
Changes
Master Data
Strategy
Operational
Solver
Selection
Decision
Business
Process
Transform
ation
Decisions
Governance
in Place
Reporting &
Alerts
Strategy
Robust
Technical
Environment
Many Places to Stumble
Neglect 1 & Progress Slows
Change Management
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How far along are you in your Supply Chain
Optimization journey?
Make your selection on the right side of your screen
Select all that apply
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A. Roadmap to SNP
B. Operational Governance Body
C. Operational Master Data Strategy
D. Reporting and Alert Strategy
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Supply Network Planning
Manufacturing Distribution Supplier
Demand
Planning
Unconstrained Plan
Lead Times Inventory Resource Capacity Storage Capacity
Optimize Plan
Integration
U
N
C
O
N
S
T
R
A
I
N
E
D
Plan
C
O
N
S
T
R
A
I
N
E
D
Plan
for
Review
Transfer Transfer
Planning Horizon
Supply Network Planning
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Quality of data in and out of the
SNP Planning Engine
Make your selection on the right side of your screen
9
1. Is the demand data feeding SNP reliable?
2. Do you feed your SNP Plan to a shop floor planning solution?
3. Do you feed your SNP Plan back to Demand for further
analysis?
4. Schedule adherence to the SNP Plan is high?
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Lot size, rounding value Unit Storage cost
Run conditions Number of time periods Number of compatibles
Number of components
External/Internal or both Demand rates Yield
Process Logic
Material flow
Internal Space or location Logic
External Storage used when necessary & cost efficient Calculation of total cost & performance measures
Multidimensional binary or golden section search Change in Storage Capabilities Test for improvements Results used as Starting conditions in next iteration Repeated iterative process until improvement
Total Cost
Optimal Storage Capacities Return product Disposable parts Repairable parts New parts Finished parts Remanufactured product
Cost breakdown Internal Storage cost Reconfigured cost External Storage cost
Space Optimization
User Input Simulation Optimizer Output
We Can Fine Tune Control
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Heuristics
Plans with a Macro approach stepping through the Supply
Network
Groups all demands for a given product at a location
into one demand bucket
Processes each planning location sequentially to
determine sourcing requirements
Infinite capacity over the medium to
long-term horizon
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End user Effort
High Medium Low
Set up E
ffort
High
Medium
Low
X
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Capable to Match (CTM)
Matches demand to available supply via production capacity and
transportation capability checks
Executes a multi-level, finite planning of the demands
in your supply chain using prioritization
Considers constraints on production capacities,
transportation capacities, storage capacities
Takes into account alternative production locations and
sources of supply (locations, production
process models, and external
procurement relationships)
12
X
End user Effort
High Medium Low
Set up E
ffort
High
Medium
Low
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Optimizer
Plans at the lowest level of detail
Optimizer offers cost-based planning
Searches through all feasible plans in an attempt to
find the most total cost-effective solution
Many different methods of optimization
Linear
Discrete
Aggregated Plan – Vertical / Horizontal
Prioritization
Incremental
Decomposition
13
End user Effort
High Medium Low
Setu
p /
Da
ta
Eff
ort
High
Medium
Low
X
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Heuristics “Smart” logic that creates the requirements plan across the supply
chain model
SNP Optimizer Cost-based optimizer that takes cross-plant resource and material
availability into account
Optimal Cost
Configuration
A configuration of the optimizer where actual costs are modeled,
including the penalty for non-delivery cost
Optimal
Service Level
Configuration
A configuration of the optimizer where the non-delivery cost is set to
a very large amount, storage costs are modeled and other costs are
small in comparison
CTM
Capable-to-Match:
An order-based planning method that takes production capacity into
account; uses pegging
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Solv
er
Capabilitie
s
Heuristics
• Un-constrained
• MRP Like
• Capacity leveling
Capable to Match
• Feasible solution
• Matches demand to supply via production and transportation capability
Optimizer
• Best in Class
• High customization
• Short Product Lifecycles / Shelf life
• Thin Margins
• High demand volatility
Jog
Run
Sprint
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Industry Champions
• Hi-Tech
• CPG
Industry Champions
• Process Industry
• Aerospace
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SNP Solver Comparison
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Optimizer:
Fully Mathematical Model based on LP
techniques using cost and time variables for
Production, Storage, Transportation and
Procurement
Extremely useful for bottle neck optimization
CTM:
Excellent solver resource constrained and
prioritization
Heuristic:
Excellent solver for entire network (i.e. MRP
like) unconstrained modeling
Exceptionally constructive for new
implementation to optimize entire network
Optimizer:
Very time consuming and CPU intensive
Analyzing of results is tedious
CTM:
Sequential approach to allocation
Variant configuration products have
challenges
Heuristic:
Less precision
Calculates discrete node and requires
Multi step planning process
Need to do subsequent resource leveling
using constraint modeling
Strengths Considerations
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August 22nd Demand Planning Leadership Exchange:
Developing a Demand Classification Matrix
September 12th S&OP Leadership Exchange:
S&OP Technology A tool, or a strategy?
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