This document summarizes a presentation on supply chain planning given by Plan4Demand Solutions. The presentation covered challenges with supply planning, tools for optimization, and questions. It discussed Plan4Demand's consulting services in supply chain planning areas like demand forecasting and inventory optimization. The presentation also covered supply planning engine selection, change management, and challenges with aligning supply network planning results to master data.

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SAP CVN SUPPLY CHAIN MANAGEMENT SUMMIT
APO Supply Network Planning Session
June 19th, 2012 Welcome!

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 Introduction to Plan4Demand
 Supply Planning Business Challenges
 Tools & Accelerators for Optimization
 Q&A

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Supply Chain Planning Consulting Firm specializing in:
• Sales and Operations Planning(S&OP)
•Demand Sensing/Planning/Forecasting
•Supply Replenishment Planning
•Enterprise Inventory Optimization
•Production Planning/Finite Scheduling
•Supply Chain Response Management
•Change Management/ End User Adoption &Sustainability
Services:
•SAP SCM Optimization to Maximize
Benefit of Investment
•Package Fit Validation/Selection Services
•Operational Strategy and Road Mapping
•Business Case Development and Prioritization
•Assessments/Blueprinting
•Process and Technology Implementation
•Customized End User Training and Education
Selected as “Pros-to-Know” for the past 4 years in a row
Why Partner with Plan4Demand? Named to Supply & Demand Chain Executive’s Top 100 List
•Experts in Supply Chain Process SAP and Technology
•Strong References We Celebrate
•Deep Resources: Minimum 10+ years Supply Chain Process
and Technology Experience Business Results
•Business Case Development to Support Investment in SAP
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•Rapid, High Value Optimization Techniques Not “Go-lives”
•Proven Change Management for User Community

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 Positioning for Success
 Supply Planning Engine Selection
 The Black Box Challenge:
SNP results & Master Data

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

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Enterprise Program Office
Process Owners
Users Users Users
User conference and
(Any
knowledge sharing
(ECC) (SCM) application)
Help Desk
Continuous Education
Logistics DP/ SNP BI - report BPC Other
Software configuration and support
Management
Control and
Production
Change
Integration Management
(functional and technical)
Custom Application

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PEOPLE PROCESS TECHNOLOGY
 Managing Change
 Adult Learning Theory – Adults must learn
specifically through repetition in tasks
 Make content specific to the audiences role and
responsibility in the process and how it fits in into the
company strategy
 User adoption and active participation can drive
significant bottom line results – studies have shown a
bottom line increase on average of 19%

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 Positioning for Success
 Supply Planning Engine Selection
 The Black Box Challenge:
SNP results & Master Data

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User Input 9
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

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 Heuristic (unconstrained)
 Capacity leveling (Constraint)
 Capable to Match (CTM) Constraint)
 Optimizer (Constraint)

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 Heuristics
Plans with a Macro approach stepping
through the Network
 Groups all demands for a given product at a location
into one demand bucket
 Processes each planning location sequentially to
determine sourcing requirements
End user Effort
 Infinite capacity or the medium to High Medium Low
long-term horizon High
Set up Effort
Medium X
Low

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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 End user Effort
process models, and external High Medium Low
Set up Effort
High
procurement relationships) Medium X
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
o Linear
o Discrete
o Aggregated Plan – Vertical / Horizontal End user Effort
High Medium Low
o Prioritization
High X
o Incremental Setup / Data
Effort
Medium
o Decomposition Low

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

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

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Strengths Considerations
 Optimizer:  Optimizer:
 Fully Mathematical Model based on LP  Very time consuming and CPU intensive
techniques using cost and time variables for
Production, Storage, Transportation and  Analyzing of results is tedious
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
like) unconstrained modeling  Less precision
 Exceptionally constructive for new  Calculates discrete node and requires Multi
implementation to optimize entire network step planning process
 Need to do subsequent resource leveling
using constraint modeling

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 Positioning for Success
 Supply Planning Engine Selection
 The Black Box Challenge:
SNP results & Master Data

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The Problem - Your business migrated from a
legacy system to a SNP solution and data
settings appear not to be aligned
 Inventories are not in line with targets
 CTM solver works well with alternative
products, however it is not considering
alternate locations
 Resource Balancing is not being leveraged

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Locations Products (Planning Data)
T-Lanes Production Data Structures (PDS)
Quota Arrang. Resources (Capacities)
Forecast
Sales Orders
Inventory
Process Orders
Stock Transfer Ord.
Purchase Orders

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Global Product Planning Data
Product: 200000003885 CKY CLSSC CKN NDL-EZO 18.60 oz 12 CA
Shelf life: 730 Days Min Rem.Shelf life: 90 Days
Transactional Data
Master Data Plant Prod. Planning Data
On Hand Process Stck Trnfr.
Canada Safety Stock
Rounding Min. Lot Inventory Orders Orders
Value Size
Canada 0 0
Canada 14 D 0
Mexico 0 60
Mexico 7D 20 40
US 0 20
US 7D 20 60
DP Forecast
US W1 W2 W3 W4 W5 W6 W7 W8 W9 W10
Canada 8 9 10 12 11 10 11 9 12 11
Mexico 1 2 4 120 110 100 110 90 120 110
US 10 4 30 36 33 30 33 27 36 33
Mexico SNP Plan
W1 W2 W3 W4 W5 W6 W7 W8 W9 W10
Canada 8 9 10 12 11 10 11 9 12 11
Mexico 40 40 80 240 110 100 110 90 120 110
US 60 60 120 720 33 30 33 27 36 33

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T-Lane Minimum Lot size
 Works as a validity field for requirements greater than
or equal to Product Master Minimum Lot Size
 SNP Heuristics internally will create planned orders, applying Product
Master Min.Lot Size (MLS) and Rounding Value (RV).
 Looks for valid T-Lane Sourcing and try to create Stock.Transf.
Requisitions and checks T-lane Min LotSize
 Examples:
 T-Lane Min Lot Size = 300, Product Master: MLS = 100, RV = 50
W2 W3 W4 W5 W6 W7 W8 W9
This line
represents an Demand 50 50 150 50 100 50 170 80 150
internal Plan.Order (internal) 100 0 150 100 100 0 200 100 100
Calculation,
Plnd.Orders will Stck.Tranf.Req 0 0 0 350 0 0 300 0 0
never get created Projected Stock -50 -100 -250 50 -50 -100 30 -50 -200
for this.
350
0,0

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For More Information…
www.Plan4Demand.com
Contact Join our
Supply Planning
 Lisa Kustra, CEO
Leadership Exchange
lisa.kustra@plan4demand.com on LinkedIn
O: 412.733.5050 to continue the discussion
and for more
C: 412.996.7090
learning opportunities!
 Jaime Reints
jaime.reints@plan4demand.com
O: 412.733.5011