866.P4D.INFO | Plan4Demand.com | Info@plan4demand.com 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-Evolution

1. SUPPLY PLANNING LEADERSHIP EXCHANGE
PRESENTS:
The web event will begin momentarily with your host:
August 2nd, 2012 plan4demand

2. 2
 Does your SNP Plan position you for success?
 What is SNP?
 Thinking about SNP Engine Selection
 Summary of Options for Consideration

3. Demand Management S&OP Supply Planning
Capacity
history
Supply
Demand (Constrained
(Unconstrained Inventory
New Product product
market demand) availability)
Transportation
PLM
SAP/APO/DP Supportable SAP/APO/SNP
Demand
SAP/ECC Allocation Supportable Plan
Orders Order Fulfillment Shipments
Logistics & Fulfillment Planning

4. 4
Road Map to Supply Chain Optimization
Business
Master Data Solver Process Plan for Robust Standalone Reporting &
Road Map Governance
to SNP in Place
Strategy Selection Transform Role Technical Training Alerts
Operational Decision ation Changes Environment Environment Strategy
Decisions
Change Management
Many Places to Stumble
Neglect 1 & Progress Slows

5. 5
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
A. Roadmap to SNP
B. Operational Governance Body
C. Operational Master Data Strategy
D. Reporting and Alert Strategy

6. Supply Network Planning
Demand
U Planning C
N Unconstrained Plan O
C N
O Integration
S
N Transfer Transfer T
S Optimize Plan R
T A
R I
A N
Supplier Manufacturing Distribution
I E
N Lead Times Inventory Resource Capacity Storage Capacity D
E
D Supply Network Planning Plan
Plan Planning Horizon for
Review

9. 9
Quality of data in and out of the
SNP Planning Engine
Make your selection on the right side of your screen
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?

10. 10
User Input 10
Simulation Optimizer Output
Lot size, rounding value Process Logic Multidimensional binary Total Cost
Unit Storage cost or golden section search
Material flow Optimal Storage
Run conditions Internal Space or Change in Storage Capacities
Capabilities Return product
Number of time periods location Logic Disposable parts
Number of compatibles
External Storage used Test for improvements Repairable parts
Number of components when necessary & cost New parts
efficient Results used as Starting Finished parts
External/Internal or conditions in next iteration Remanufactured product
both Calculation of total cost Cost breakdown
Demand rates & performance Repeated iterative Internal Storage cost
Yield measures process until improvement Reconfigured cost
External Storage cost
Control We Can Fine Tune Space Optimization

11. 11
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
End user Effort
long-term horizon High Medium Low
High
Set up Effort
Medium X
Low

12. 12
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 End user Effort
High Medium Low
process models, and external Set up Effort
High
procurement relationships) Medium X
Low

13. 13
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 End user Effort
High Medium Low
 Prioritization
High X
 Incremental Setup / Data
Effort
Medium
 Decomposition Low

14. “Smart” logic that creates the requirements plan across the supply
Heuristics
chain model
Cost-based optimizer that takes cross-plant resource and material
SNP Optimizer
availability into account
Optimal Cost A configuration of the optimizer where actual costs are modeled,
Configuration including the penalty for non-delivery cost
Optimal A configuration of the optimizer where the non-delivery cost is set to
Service Level a very large amount, storage costs are modeled and other costs are
Configuration small in comparison
Capable-to-Match:
CTM An order-based planning method that takes production capacity into
account; uses pegging

15. 15
Sprint
Run
Solver Capabilities
Optimizer
• Best in Class
Capable to Match • High customization
Jog • Feasible solution
• Short Product Lifecycles
/ Shelf life
• Matches demand to • Thin Margins
supply via production
Heuristics and transportation • High demand volatility
• Un-constrained capability
• MRP Like
• Capacity leveling

16. 16
Industry Champions
• Hi-Tech
• CPG
Industry Champions
• Process Industry
• Aerospace

17. SNP Solver Comparison

18. 18
Strengths Considerations
 Optimizer:  Optimizer:
 Fully Mathematical Model based on LP  Very time consuming and CPU intensive
techniques using cost and time variables for  Analyzing of results is tedious
Production, Storage, Transportation and
Procurement
 Extremely useful for bottle neck optimization
 CTM:
 CTM:
 Sequential approach to allocation
 Excellent solver resource constrained and
prioritization  Variant configuration products have
challenges
 Heuristic:  Heuristic:
 Excellent solver for entire network (i.e. MRP  Less precision
like) unconstrained modeling  Calculates discrete node and requires
 Exceptionally constructive for new Multi step planning process
implementation to optimize entire network  Need to do subsequent resource leveling
using constraint modeling

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