Optimization Architecture in
mySAP Supply Chain Management
PD. Dr. Heinrich Braun
Development Manager SCM-Optimization
 SAP AG 2001,
SCM Optimization Infodays, Dr. Heinrich Braun 1
Agenda
Challenge of Supply Chain Planning
Challenge of Generic Optimizer
Optimizer Architecture of mySAP SCM
 SAP AG 2001,
SCM Optimization Infodays, Dr. Heinrich Braun 2
Example of a global Supply Chain
Supplier
Plants
DCs
Customers
Products
Resources
-> Objective: Monetary-based Optimization of Supply Chain
-> Prerequisite: Integrated Planning of Supply Chain
 SAP AG 2001,
SCM Optimization Infodays, Dr. Heinrich Braun 3
Supply Chain Management – mySAP SCM
Measure
Supply Chain Design
Source
Demand and
Supply Planning
Make
Direct
Procurement
Manufacturing
Deliver
Order
Fulfillment
Partner
Customer
Collaborate
Network
Collaborate
Private
Trading
Exchange
Supply Chain Collaboration
Supplier
Plan
Strategize
Supply Chain Collaboration
Supply Chain Performance Management
Private
Trading
Exchange
Network
Partner
Track
Supply Chain Event Management
 SAP AG 2001,
SCM Optimization Infodays, Dr. Heinrich Braun 4
mySAP SCM: Planning Levels
Strategic
Strategize
Supply Chain Design
Tactical
Plan
Demand and
Supply Planning
Operational
Source
Direct
Procurement
 SAP AG 2001,
SCM Optimization Infodays, Dr. Heinrich Braun 5
Make
Manufacturing
Deliver
Order
Fulfillment
Example: Demand and Supply Planning Procedures
Plan
Demand and Supply Planning
Sourcing
LP
MILP
Optimizer
 SAP AG 2001,
SCM Optimization Infodays, Dr. Heinrich Braun 6
Balancing
Lot-Sizing
Propagation CTM
Heuristics
DRP/MRP
mySAP SCM Planning Philosophy
Modeling
Model
...
Version
Model
Version
...
Version
Co
es
,
Ob
j
ec
n
io
tiv
liveCache
t
ra
bo
lla
Co
ns
tra
in
ts
Version
...
Orders
Timeseries
realtime
Scores
Optimization/ Heuristics
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Navigation, Controlling,
Management by Exceptions
Online-Scheduling versus Optimization
Gantt
GanttChart
Chart
Online-Scheduling
" Online
#Insert order
#Check material availability
" Greedy Heuristics
" Response: in seconds
 SAP AG 2001,
SCM Optimization Infodays, Dr. Heinrich Braun 8
Optimization
Optimization
Optimization
" Objective Function
# Weighting
# Goal
several criterias
Programming (phases)
" Evaluating many schedules
" Response: minutes - hours
Hierarchical Planning
Aggregate Planning
Detailed Planning
" Global optimization
" Local optimization
" Maximize Profit
" Disaggregate global plan
" Decide
 SAP AG 2001,
#
Where to produce
#
How much to produce
#
How much to deliver
#
How much capacities
SCM Optimization Infodays, Dr. Heinrich Braun 9
#
Time: When to produce
#
Resource:
On which alternative resource
" Optimize production sequence
Aggregation
Aggregate Planning
Detailed Planning
" Mid term
" short term
" time in buckets (weeks)
" time in seconds
" linear optimization
" scheduling algorithms
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Decision Variables
Supply Network Planning
For each location:
" Production quantity
" Transportation quantity
" Additional capacities
" External supplies
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Detailed Scheduling
" starting time
" resource selection
" given
Set of orders
# Quantities of orders
# Location of production
#
Objective function
Supply Network Planning
" Delay costs
#
Order priorities
" Nondelivery Costs (Maxim. Profit)
" Production costs
" Transportation costs
" Inventory costs
Detailed Scheduling
" Delay costs
#
Order priorities
" Setup
Time
# costs
#
" Makespan
For rolling planning schema
# Compressing in planning periode
#
" Costs for additional capacities
Transportation (Outsourcing)
# Production (over time)
# Product (Outsourcing)
#
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" Production Costs
#
Prioritizing prefered modes
" Inventory Costs (Earliness)
Not recommended: Global optimization with Scheduling
Modeling Supply Planning objectives in Detailed Scheduling
" Transportation
# Use
production resources
# Model transportation time as setup time /costs
" Nondelivery Costs (Maxim. Profit)
# Use
order priorities
# Non deliverable orders are delayed after planning window
" Production costs
# Use
penalties for mode priorities
" Costs for additional capacities
# Model
with dummy resources (available during overtime)
# Penalize use of these using mode priorities
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Constraints
Supply Network Planning
" alternative routings (PPM)
" delivery time
" storage capacities
safety stocks
# shelf life
#
" resource capacities
Production
# transport
# handling
#
" calendar
capacities
# breaks (weekends)
#
" discretization
integer lot sizes / campaigns
# minimal lot sizes
# additional shifts
# Setup time
# piecewise linear cost functions
#
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Detailed Scheduling
" alternative resources
" delivery time
" storage capacities
discrete material flow
# continuous material flow
#
" resource capacities
#
production
" calendar
capacities
# breaks / shifts
# productivity
# block planning
#
" time constraints
minimal (routing)
# maximal (shelf life)
# buffer time
#
" Setup
times
# secondary resources
#
Optimization Performance
Detailed Scheduling
Supply Network Planning
" up to 100 000 activities
" Pure LP
(no hard limitation)
#
Without discrete constraints
# Up to several million decision
variables and about a million
constraints
# Global optimum guaranteed
" First solution
as fast as online heuristics
" More run time
improves solution quality
" For discrete constraints
No global optimum guaranteed
# Quality depends on run time and
approximation by pure LP
#
" First solution
needs solution for pure LP
No „optimize mySCM“ button
" Decompose problem using hierarchical planning
" Global optimization using aggregation
" Feasible plans by local optimization
" Rolling planning schema
 SAP AG 2001,
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Challenge: Generic Optimizer
Generic
"
"
"
"
and
Best of Breed
planning level
vertical Industries
run time requirement
model complexity (size, constraints, objectives)
Generic Model (-> planning level)
" aggregated planning (LP / MILP)
" detailed planning (scheduling)
Customization (-> vertical industries)
" specialization the generic model to customer problem
" scripting the strategies (decomposition, goal programming)
Scalability (-> run time)
" greedy versus complex optimizations strategies
" parallelization
Open Architecture
" internal: adding new special optimizer (software evolution)
" external: integration of optimizer packages
 SAP AG 2001,
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Expectation for Optimization
☺
Optimal Soluton ?
&
☺
Better than 5% below optimum ?
&
$
Best-of-Breed Solution !
#
#
Depends on Problem Complexity (Model, Size)
Computation time
Solution: Scalability ?!
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Challenge: Hardware Scalability
Parallelization
" Multi user
" 3-tier Client Server
Separation LiveCache and Optimizer server
# Several Optimizer server
#
" Multi Processor
parallel optimization runs
# multi optimizer agents in one optimization run
#
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Challenge: Algorithmic Scalability
Tradeoff: generalization versus computation time
" Two Optimization Models
#
#
Linear Optimization
Aggregate Planning
versus
versus
Scheduling
Detailed Planning
" Several optimization algorithms
#
#
e.g. 4 different scheduling optimizer
e.g. 4 different LP optimizer
Tradeoff: algorithmic complexity versus computation time
"
"
'
'
 SAP AG 2001,
Cubic computation time acceptable for small problems
Linear computation time required for large problems
Solution: Metaheuristics / Decomposition
Control: Scripts
SCM Optimization Infodays, Dr. Heinrich Braun 19
Scheduling Optimizer Architecture
LiveCache
GUI
Model Generator
Bottleneck
Reporting
Core Model
Checking
Control
Multi Agent
Meta-Heuristics
Constraint
Programming
Genetic
Algorithm
Sequence
Optimizer
Basic Optimizer
 SAP AG 2001,
Time
Decomposition
SCM Optimization Infodays, Dr. Heinrich Braun 20
Campaign
Optimizer
SNP Optimizer Architecture
LiveCache
GUI
Model Generator
Product
Decomposition
Reporting
Core-Model
Checking
Control
Priority
Decomposition
Meta-Heuristics
SNP
Deployment
Network
Design
Basic Optimizer
 SAP AG 2001,
Time
Decomposition
SCM Optimization Infodays, Dr. Heinrich Braun 21
Vehicle
Allocation
Metaheuristics
Objective
Best quality of solution for given (computation )time frame
(Scalability for problem size
Decomposition
Local Improvement Strategy
" Focus on a Subproblem (planning window)
" Optimize planning window
(script mechanism
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Time Decomposition - Local Improvement
Resources
Current window
Gliding window script
1. Optimize only in current window
2. Move window by a time delta
3. Go to first step
 SAP AG 2001,
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Time
Time Decomposition - First feasible Solution
Planning level
‘Look ahead strategy’ script
" Evaluate several branches with e.g. 50 activities
" Select the best scored branch
" Fix the beginning of this branch
Fixation
Look ahead
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Metaheuristics - Bottleneck
Resources
Bottleneck
Time
Bottleneck Script
1. Determine bottleneck
2. Schedule bottleneck resources only
3. Fix sequence on bottleneck resource
4. Schedule all resources
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
 SAP AG 2001,
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Resource and Time Decomposition
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Multi Agent Optimization
(Genetic Algorithm)
Objective
" Multi Criteria Optimization
250
" user selects out of solutions with
Delay
Setup
Quality= D+S
200
#
#
similar overall quality
different components
" Use power of Pallelization (GA)
150
Multi Agent Strategy
" Different AGENTS focusing on Setup or Delay
or Makespan
100
" New solutions by local improvement
" Integrated in Optimizer Architecture
(independent of basic optimizer)
50
0
Solut. Solut. Solut. Solut.
1
2
3
4
 SAP AG 2001,
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Performance
" Speedup ≈ available processors
Setup
Several Solutions
0
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Delay
Mastering the Challenge with mySAP SCM
Scalability / Flexibility
" generic modeling on each planning level
#
#
Strategic/tactical: LP/MILP
Operational/ Execution: Scheduling
" Specialization to customer problem
#
#
activate constraints
activate objectives
" Scripting the strategies (metaheuristics)
#
#
#
Decomposition techniques
Multiple Phases (goal programming)
Parallelization by Agents
Open Optimization Architecture
" best of breed libraries
#
#
#
Linear Programming (ILOG CPLEX)
Constraint Programming (ILOG SCHEDULER)
Genetic Algorithms (SAP)
" extendible toolbox of
#
#
business oriented basic optimizer
Metaheuristics
" Open to partner solutions: Optimizer extension workbench
 SAP AG 2001,
SCM Optimization Infodays, Dr. Heinrich Braun 48